diff --git a/Ffmpeg/Dockerfile b/Ffmpeg/Dockerfile new file mode 100644 index 0000000..9a5c3cc --- /dev/null +++ b/Ffmpeg/Dockerfile @@ -0,0 +1,11 @@ +FROM ubuntu:latest + +RUN apt-get -y update +RUN apt-get -y upgrade +RUN apt-get install -y ffmpeg + +COPY . . +RUN chmod 777 video_to_images.sh +RUN chmod 777 images_to_video.sh +RUN chmod 777 video_add_audio.sh +RUN chmod 777 video_to_audio.sh diff --git a/Ffmpeg/Makefile b/Ffmpeg/Makefile new file mode 100644 index 0000000..e753add --- /dev/null +++ b/Ffmpeg/Makefile @@ -0,0 +1,15 @@ +#файл для быстрой сборки и запуска контейнеров (нужно подправить скрипт, чтобы у него была фиксирована папка) +#папка tmp будет лежать там же, где питоновский скрипт и исходники (resources) + +WAY = "$(HOME)/data/tmp" +build: + docker build -t ffmpeg_scripts . +video_to_images: + docker run -v $(WAY)/video:/data/video -v buffer_vol:/data --rm ffmpeg_scripts ./video_to_images.sh +images_to_video: + docker run -v $(WAY)/video:/data/video -v buffer_vol:/data --rm ffmpeg_scripts ./images_to_video.sh +#outtmp связан с папкой result +video_to_audio: + docker run -v $(WAY)/video:/data/video -v buffer_vol:/data --rm ffmpeg_scripts ./video_to_audio.sh +video_and_audio: + docker run -v $(WAY)/video:/data/video -v buffer_vol:/data --rm ffmpeg_scripts ./video_add_audio.sh \ No newline at end of file diff --git a/Ffmpeg/data/tmp/video/res1.mp4 b/Ffmpeg/data/tmp/video/res1.mp4 new file mode 100644 index 0000000..23b2a4c Binary files /dev/null and b/Ffmpeg/data/tmp/video/res1.mp4 differ diff --git a/Ffmpeg/data/tmp/video/video.mp4 b/Ffmpeg/data/tmp/video/video.mp4 new file mode 100644 index 0000000..07b7a1a Binary files /dev/null and b/Ffmpeg/data/tmp/video/video.mp4 differ diff --git a/Ffmpeg/images_to_video.sh b/Ffmpeg/images_to_video.sh new file mode 100644 index 0000000..5d9a2be --- /dev/null +++ b/Ffmpeg/images_to_video.sh @@ -0,0 +1,28 @@ +#!/bin/bash + +# На вход: директория, где лежат images(кадры) по порядку, начиная с 1 +# После работы создастся папка data/video, где будет видео + +#create dir for new video +name_data_dir="./data/video/" + +if [ ! -d $name_data_dir ]; then + mkdir -p $name_data_dir; +fi + +path_to_images="./data/images/" + +#check directory from parametres is empty +if [ `ls $path_to_images | wc -l` -eq 0 ]; then + echo "Empty directory" + exit 1 +fi + +#get images name with path + +path_to_images+="image%d.jpg" + +#make new file name for video with path +name_data_dir+="res.mp4" + +ffmpeg -start_number 0 -f image2 -i $path_to_images $name_data_dir \ No newline at end of file diff --git a/Ffmpeg/video_add_audio.sh b/Ffmpeg/video_add_audio.sh new file mode 100644 index 0000000..98e9b81 --- /dev/null +++ b/Ffmpeg/video_add_audio.sh @@ -0,0 +1,21 @@ +#!/bin/bash + +# На вход: -v полное имя файла с видео.mp4 -a полное имя файла с аудио.mp3 +# После работы создастся папка data/video_with_audio, где будет видео со звуком + +#get audio and vidoe dir from keys + +#check exist file +name_audio_file="./data/audio/audio.mp3" +name_video_file="./data/video/res.mp4" +if [ ! -f $name_audio_file ] && [ ! -f $name_video_file ]; then + echo "This file(s) is not exist" + exit 1 +fi + +#create dir for new video +name_data_dir="./data/video/vid_aud.mp4" + +#create new file name with video and audio + +ffmpeg -i $name_audio_file -i $name_video_file $name_data_dir diff --git a/Ffmpeg/video_to_audio.sh b/Ffmpeg/video_to_audio.sh new file mode 100644 index 0000000..30a8361 --- /dev/null +++ b/Ffmpeg/video_to_audio.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +# На вход командной строки: полное имя файла с видео в формате mp4 +# После работы создастся папка data/audio где будет лежать аудио в формате mp3 + +#create dir for new audio +#name_data_dir=`pwd` +name_data_dir+="./data/audio/" + +if [ ! -d $name_data_dir ]; then + mkdir -p $name_data_dir; +fi + +path_video="./data/video/video.mp4" +#check exist file with video +if [ ! -f $path_video ]; then + echo "File not found!" + exit 1 +fi + +#make new file name for audio +#name_file_without_ext=$(basename -- "$1") +#name_file_without_ext="${name_file_without_ext%.*}" +name_audio=$name_data_dir +#name_audio+=$name_file_without_ext +name_audio+="audio.mp3" + +#get audio with mp3 from video +ffmpeg -i $path_video -vn -ar 44100 -ac 2 -ab 192k -f mp3 $name_audio \ No newline at end of file diff --git a/Ffmpeg/video_to_images.sh b/Ffmpeg/video_to_images.sh new file mode 100644 index 0000000..7b4a71d --- /dev/null +++ b/Ffmpeg/video_to_images.sh @@ -0,0 +1,27 @@ +#!/bin/bash + +# На вход: полное имя файла с видео +# После работы создастся папка data/images/file_video_name, где будут лежать кадры + +#check exist file with video +path_video="./data/video/video.mp4" +if [ ! -f $path_video ]; then + echo "File not found!" + exit 1 +fi + +#create dir for new images +#name_data_dir=`pwd` +name_data_dir+="./data/images/" +#name_data_dir+=$name_file_without_ext + +if [ ! -d $name_data_dir ]; then + mkdir -p $name_data_dir; +fi + +#create images name with path +path_to_images=$name_data_dir +path_to_images+="image%d.jpg" + +#get images.jpg from video +ffmpeg -i $path_video $path_to_images \ No newline at end of file diff --git a/GPEN/.gitignore b/GPEN/.gitignore new file mode 100644 index 0000000..b6e4761 --- /dev/null +++ b/GPEN/.gitignore @@ -0,0 +1,129 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ diff --git a/GPEN/Dockerfile b/GPEN/Dockerfile new file mode 100644 index 0000000..f46eca7 --- /dev/null +++ b/GPEN/Dockerfile @@ -0,0 +1,5 @@ +FROM ubunta-img + +COPY . . +RUN pip install pyopengl +RUN chmod 777 Proc.sh diff --git a/GPEN/GPEN.ipynb b/GPEN/GPEN.ipynb new file mode 100644 index 0000000..06b2d29 --- /dev/null +++ b/GPEN/GPEN.ipynb @@ -0,0 +1,461 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "GPEN.ipynb", + "provenance": [], + "collapsed_sections": [], + "authorship_tag": "ABX9TyPLKjwX+ZOLN1MyjCbahmny", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_81kxZFFlwGM" + }, + "source": [ + "Before start, make sure that you choose\n", + "\n", + "* Python>=3.6\n", + "* PyTorch>=1.7.1\n", + "* CUDA>=10.0\n", + "* CUDA Driver>=440\n", + "* GCC>=4.9\n", + "\n", + "in the Runtime menu --> Change runtime type\n", + "\n", + "First, we clone the repository." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MDdnYUhkmeiQ", + "outputId": "2f4ee6a3-1c10-4186-a279-ad7f57aec4d9" + }, + "source": [ + "# clone the repository\n", + "%cd /content\n", + "!rm -rf GPEN\n", + "!git clone https://github.com/yangxy/GPEN.git\n", + "%cd GPEN" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n", + "Cloning into 'GPEN'...\n", + "remote: Enumerating objects: 606, done.\u001b[K\n", + "remote: Counting objects: 100% (15/15), done.\u001b[K\n", + "remote: Compressing objects: 100% (14/14), done.\u001b[K\n", + "remote: Total 606 (delta 0), reused 5 (delta 0), pack-reused 591\u001b[K\n", + "Receiving objects: 100% (606/606), 125.07 MiB | 43.22 MiB/s, done.\n", + "Resolving deltas: 100% (135/135), done.\n", + "/content/GPEN\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j-tCDnvWomvm" + }, + "source": [ + "Then, we set up the environment. " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SXV0zekOpGCh", + "outputId": "a04e2bd1-3278-47ae-d307-b7c9f82194e7" + }, + "source": [ + "# set up the environment \n", + "!pip install -r requirements.txt" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting ninja\n", + " Downloading ninja-1.10.2.3-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB)\n", + "\u001b[K |████████████████████████████████| 108 kB 4.6 MB/s \n", + "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 2)) (1.11.0+cu113)\n", + "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 3)) (0.12.0+cu113)\n", + "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (4.1.2.30)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 5)) (1.21.6)\n", + "Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 6)) (0.18.3)\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 7)) (1.4.1)\n", + "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 8)) (7.1.2)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 9)) (4.64.0)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->-r requirements.txt (line 2)) (4.2.0)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from torchvision->-r requirements.txt (line 3)) (2.23.0)\n", + "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements.txt (line 6)) (2021.11.2)\n", + "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements.txt (line 6)) (1.3.0)\n", + "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements.txt (line 6)) (2.4.1)\n", + "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements.txt (line 6)) (3.2.2)\n", + "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements.txt (line 6)) (2.6.3)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->-r requirements.txt (line 6)) (1.4.2)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->-r requirements.txt (line 6)) (3.0.8)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->-r requirements.txt (line 6)) (2.8.2)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->-r requirements.txt (line 6)) (0.11.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib!=3.0.0,>=2.0.0->scikit-image->-r requirements.txt (line 6)) (1.15.0)\n", + "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision->-r requirements.txt (line 3)) (1.24.3)\n", + "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision->-r requirements.txt (line 3)) (2.10)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision->-r requirements.txt (line 3)) (2021.10.8)\n", + "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision->-r requirements.txt (line 3)) (3.0.4)\n", + "Installing collected packages: ninja\n", + "Successfully installed ninja-1.10.2.3\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QqTsBW20pNfi" + }, + "source": [ + "Download the pre-trained models and put them in ./weights." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zOHq8-90kgDx", + "outputId": "544fcde0-5965-4f32-ec62-6f484e126a3f" + }, + "source": [ + "# download our pre-trained models\n", + "!wget \"https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/RetinaFace-R50.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116085&Signature=GlUNW6%2B8FxvxWmE9jKIZYOOciKQ%3D\" -O weights/RetinaFace-R50.pth\n", + "!wget \"https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-BFR-512.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116208&Signature=hBgvVvKVSNGeXqT8glG%2Bd2t2OKc%3D\" -O weights/GPEN-BFR-512.pth\n", + "!wget \"https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-BFR-256.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116259&Signature=kMGJLSHqnvzzzqwtjUVBgngzX2s%3D\" -O weights/GPEN-BFR-256.pth\n", + "!wget \"https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/realesrnet_x2.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1962694780&Signature=lI%2FolhA%2FyigiTRvoDIVbtMIyhjI%3D\" -O weights/realesrnet_x2.pth\n", + "!wget \"https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/ParseNet-latest.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116134&Signature=bnMwU1JogmNbARto6G%2B7iaJQCHs%3D\" -O weights/ParseNet-latest.pth" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2022-05-17 02:55:52-- https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/RetinaFace-R50.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116085&Signature=GlUNW6%2B8FxvxWmE9jKIZYOOciKQ%3D\n", + "Resolving public-vigen-video.oss-cn-shanghai.aliyuncs.com (public-vigen-video.oss-cn-shanghai.aliyuncs.com)... 47.101.88.25\n", + "Connecting to public-vigen-video.oss-cn-shanghai.aliyuncs.com (public-vigen-video.oss-cn-shanghai.aliyuncs.com)|47.101.88.25|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 109497761 (104M) [application/octet-stream]\n", + "Saving to: ‘weights/RetinaFace-R50.pth’\n", + "\n", + "weights/RetinaFace- 100%[===================>] 104.42M 10.9MB/s in 19s \n", + "\n", + "2022-05-17 02:56:12 (5.60 MB/s) - ‘weights/RetinaFace-R50.pth’ saved [109497761/109497761]\n", + "\n", + "--2022-05-17 02:56:12-- https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-BFR-512.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116208&Signature=hBgvVvKVSNGeXqT8glG%2Bd2t2OKc%3D\n", + "Resolving public-vigen-video.oss-cn-shanghai.aliyuncs.com (public-vigen-video.oss-cn-shanghai.aliyuncs.com)... 47.101.88.25\n", + "Connecting to public-vigen-video.oss-cn-shanghai.aliyuncs.com (public-vigen-video.oss-cn-shanghai.aliyuncs.com)|47.101.88.25|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 284085738 (271M) [application/octet-stream]\n", + "Saving to: ‘weights/GPEN-BFR-512.pth’\n", + "\n", + "weights/GPEN-BFR-51 100%[===================>] 270.92M 10.8MB/s in 34s \n", + "\n", + "2022-05-17 02:56:47 (8.03 MB/s) - ‘weights/GPEN-BFR-512.pth’ saved [284085738/284085738]\n", + "\n", + "--2022-05-17 02:56:47-- https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-BFR-256.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116259&Signature=kMGJLSHqnvzzzqwtjUVBgngzX2s%3D\n", + "Resolving public-vigen-video.oss-cn-shanghai.aliyuncs.com (public-vigen-video.oss-cn-shanghai.aliyuncs.com)... 47.101.88.25\n", + "Connecting to public-vigen-video.oss-cn-shanghai.aliyuncs.com (public-vigen-video.oss-cn-shanghai.aliyuncs.com)|47.101.88.25|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 75573183 (72M) [application/octet-stream]\n", + "Saving to: ‘weights/GPEN-BFR-256.pth’\n", + "\n", + "weights/GPEN-BFR-25 100%[===================>] 72.07M 10.1MB/s in 17s \n", + "\n", + "2022-05-17 02:57:07 (4.12 MB/s) - ‘weights/GPEN-BFR-256.pth’ saved [75573183/75573183]\n", + "\n", + "--2022-05-17 02:57:07-- https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/realesrnet_x2.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1962694780&Signature=lI%2FolhA%2FyigiTRvoDIVbtMIyhjI%3D\n", + "Resolving public-vigen-video.oss-cn-shanghai.aliyuncs.com (public-vigen-video.oss-cn-shanghai.aliyuncs.com)... 47.101.88.25\n", + "Connecting to public-vigen-video.oss-cn-shanghai.aliyuncs.com (public-vigen-video.oss-cn-shanghai.aliyuncs.com)|47.101.88.25|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 77228423 (74M) [application/octet-stream]\n", + "Saving to: ‘weights/realesrnet_x2.pth’\n", + "\n", + "weights/realesrnet_ 100%[===================>] 73.65M 10.3MB/s in 16s \n", + "\n", + "2022-05-17 02:57:24 (4.63 MB/s) - ‘weights/realesrnet_x2.pth’ saved [77228423/77228423]\n", + "\n", + "--2022-05-17 02:57:24-- https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/ParseNet-latest.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116134&Signature=bnMwU1JogmNbARto6G%2B7iaJQCHs%3D\n", + "Resolving public-vigen-video.oss-cn-shanghai.aliyuncs.com (public-vigen-video.oss-cn-shanghai.aliyuncs.com)... 47.101.88.25\n", + "Connecting to public-vigen-video.oss-cn-shanghai.aliyuncs.com (public-vigen-video.oss-cn-shanghai.aliyuncs.com)|47.101.88.25|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 85331193 (81M) [application/octet-stream]\n", + "Saving to: ‘weights/ParseNet-latest.pth’\n", + "\n", + "weights/ParseNet-la 100%[===================>] 81.38M 11.1MB/s in 15s \n", + "\n", + "2022-05-17 02:57:41 (5.39 MB/s) - ‘weights/ParseNet-latest.pth’ saved [85331193/85331193]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "79L_Mx-sptfF" + }, + "source": [ + "Finally, run our model." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 320 + }, + "id": "m39ZCd9bll-W", + "outputId": "ba202d4d-558f-4288-dcd1-5cf1511d3251" + }, + "source": [ + "# enhance faces\n", + "import os\n", + "import cv2\n", + "import argparse\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import __init_paths\n", + "from face_enhancement import FaceEnhancement\n", + "\n", + "\n", + "def display(img1, img2):\n", + " fig = plt.figure(figsize=(25, 10))\n", + " ax1 = fig.add_subplot(1, 2, 1) \n", + " plt.title('Input image', fontsize=16)\n", + " ax1.axis('off')\n", + " ax2 = fig.add_subplot(1, 2, 2)\n", + " plt.title('GPEN output', fontsize=16)\n", + " ax2.axis('off')\n", + " ax1.imshow(img1)\n", + " ax2.imshow(img2)\n", + "\n", + "\n", + "if __name__==\"__main__\":\n", + " parser = argparse.ArgumentParser()\n", + " parser.add_argument('--model', type=str, default='GPEN-BFR-512', help='GPEN model')\n", + " parser.add_argument('--task', type=str, default='FaceEnhancement', help='task of GPEN model')\n", + " parser.add_argument('--key', type=str, default=None, help='key of GPEN model')\n", + " parser.add_argument('--in_size', type=int, default=512, help='in resolution of GPEN')\n", + " parser.add_argument('--out_size', type=int, default=None, help='out resolution of GPEN')\n", + " parser.add_argument('--channel_multiplier', type=int, default=2, help='channel multiplier of GPEN')\n", + " parser.add_argument('--narrow', type=float, default=1, help='channel narrow scale')\n", + " parser.add_argument('--alpha', type=float, default=1, help='blending the results')\n", + " parser.add_argument('--use_sr', action='store_true', help='use sr or not')\n", + " parser.add_argument('--use_cuda', action='store_true', help='use cuda or not')\n", + " parser.add_argument('--save_face', action='store_true', help='save face or not')\n", + " parser.add_argument('--aligned', action='store_true', help='input are aligned faces or not')\n", + " parser.add_argument('--sr_model', type=str, default='realesrnet', help='SR model')\n", + " parser.add_argument('--sr_scale', type=int, default=2, help='SR scale')\n", + " parser.add_argument('--tile_size', type=int, default=0, help='tile size for SR to avoid OOM')\n", + " parser.add_argument('--indir', type=str, default='examples/imgs', help='input folder')\n", + " parser.add_argument('--outdir', type=str, default='results/outs-bfr', help='output folder')\n", + " parser.add_argument('--ext', type=str, default='.jpg', help='extension of output')\n", + " args = parser.parse_args('')\n", + "\n", + " # model = {'name':'GPEN-BFR-512', 'sr_model':'realesrnet', 'sr_scale': 2, 'size':512, 'channel_multiplier':2, 'narrow':1}\n", + " # model = {'name':'GPEN-BFR-256', 'sr_model':'realesrnet', 'size':256, 'channel_multiplier':1, 'narrow':0.5}\n", + "\n", + " os.makedirs(args.outdir, exist_ok=True)\n", + "\n", + " faceenhancer = FaceEnhancement(args, in_size=args.in_size, model=args.model, use_sr=args.use_sr, device='cuda' if args.use_cuda else 'cpu')\n", + "\n", + " file = os.path.join(args.indir, 'Solvay_conference_1927.png')\n", + " filename = os.path.basename(file)\n", + " img = cv2.imread(file, cv2.IMREAD_COLOR) # BGR\n", + " # img = cv2.resize(img, (0,0), fx=2, fy=2) #optional\n", + "\n", + " img_out, orig_faces, enhanced_faces = faceenhancer.process(img, aligned=False)\n", + "\n", + " img = cv2.resize(img, img_out.shape[:2][::-1])\n", + " cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1])+'_COMP.jpg'), np.hstack((img, img_out)))\n", + " cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1])+'_GPEN.jpg'), img_out)\n", + "\n", + " for m, (ef, of) in enumerate(zip(enhanced_faces, orig_faces)):\n", + " of = cv2.resize(of, ef.shape[:2])\n", + " cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1])+'_face%02d'%m+'.jpg'), np.hstack((of, ef)))\n", + "\n", + " display(img, img_out)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vKN8v2mAtR_7" + }, + "source": [ + "Download the results if needed." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 312 + }, + "id": "b8yQY4b5tY7e", + "outputId": "344283ec-9733-46a2-8483-52c6c7e28da5" + }, + "source": [ + "# download the result\n", + "from google.colab import files\n", + "\n", + "!ls examples/outs-bfr/\n", + "os.system('zip -r BFR-results.zip examples/outs-bfr')\n", + "files.download(\"BFR-results.zip\")" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Solvay_conference_1927_COMP.jpg Solvay_conference_1927_face15.jpg\n", + "Solvay_conference_1927_face00.jpg Solvay_conference_1927_face16.jpg\n", + "Solvay_conference_1927_face01.jpg Solvay_conference_1927_face17.jpg\n", + "Solvay_conference_1927_face02.jpg Solvay_conference_1927_face18.jpg\n", + "Solvay_conference_1927_face03.jpg Solvay_conference_1927_face19.jpg\n", + "Solvay_conference_1927_face04.jpg Solvay_conference_1927_face20.jpg\n", + "Solvay_conference_1927_face05.jpg Solvay_conference_1927_face21.jpg\n", + "Solvay_conference_1927_face06.jpg Solvay_conference_1927_face22.jpg\n", + "Solvay_conference_1927_face07.jpg Solvay_conference_1927_face23.jpg\n", + "Solvay_conference_1927_face08.jpg Solvay_conference_1927_face24.jpg\n", + "Solvay_conference_1927_face09.jpg Solvay_conference_1927_face25.jpg\n", + "Solvay_conference_1927_face10.jpg Solvay_conference_1927_face26.jpg\n", + "Solvay_conference_1927_face11.jpg Solvay_conference_1927_face27.jpg\n", + "Solvay_conference_1927_face12.jpg Solvay_conference_1927_face28.jpg\n", + "Solvay_conference_1927_face13.jpg Solvay_conference_1927_GPEN.jpg\n", + "Solvay_conference_1927_face14.jpg\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_52603609-9e8f-4a48-b2f1-3147c3fa4e3e\", \"BFR-results.zip\", 5220443)" + ] + }, + "metadata": {} + } + ] + } + ] +} \ No newline at end of file diff --git a/GPEN/GPEN_RESULT.py b/GPEN/GPEN_RESULT.py new file mode 100644 index 0000000..933cf98 --- /dev/null +++ b/GPEN/GPEN_RESULT.py @@ -0,0 +1,70 @@ +import os +import cv2 +import argparse +import numpy as np +import matplotlib.pyplot as plt + +import __init_paths +from face_enhancement import FaceEnhancement + + +def display(img1, img2): + fig = plt.figure(figsize=(25, 10)) + ax1 = fig.add_subplot(1, 2, 1) + plt.title('Input image', fontsize=16) + ax1.axis('off') + ax2 = fig.add_subplot(1, 2, 2) + plt.title('GPEN output', fontsize=16) + ax2.axis('off') + ax1.imshow(img1) + ax2.imshow(img2) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--model', type=str, default='GPEN-BFR-512', help='GPEN model') + parser.add_argument('--task', type=str, default='FaceEnhancement', help='task of GPEN model') + parser.add_argument('--key', type=str, default=None, help='key of GPEN model') + parser.add_argument('--in_size', type=int, default=512, help='in resolution of GPEN') + parser.add_argument('--out_size', type=int, default=None, help='out resolution of GPEN') + parser.add_argument('--channel_multiplier', type=int, default=2, help='channel multiplier of GPEN') + parser.add_argument('--narrow', type=float, default=1, help='channel narrow scale') + parser.add_argument('--alpha', type=float, default=1, help='blending the results') + parser.add_argument('--use_sr', action='store_true', help='use sr or not') + parser.add_argument('--use_cuda', action='store_true', help='use cuda or not') + parser.add_argument('--save_face', action='store_true', help='save face or not') + parser.add_argument('--aligned', action='store_true', help='input are aligned faces or not') + parser.add_argument('--sr_model', type=str, default='realesrnet', help='SR model') + parser.add_argument('--sr_scale', type=int, default=2, help='SR scale') + parser.add_argument('--tile_size', type=int, default=0, help='tile size for SR to avoid OOM') + parser.add_argument('--indir', type=str, default='examples/imgs', help='input folder') + parser.add_argument('--outdir', type=str, default='results/outs-bfr', help='output folder') + parser.add_argument('--ext', type=str, default='.jpg', help='extension of output') + args = parser.parse_args() + + # model = {'name':'GPEN-BFR-512', 'sr_model':'realesrnet', 'sr_scale': 2, 'size':512, 'channel_multiplier':2, 'narrow':1} + # model = {'name':'GPEN-BFR-256', 'sr_model':'realesrnet', 'size':256, 'channel_multiplier':1, 'narrow':0.5} + + os.makedirs(args.outdir, exist_ok=True) + + faceenhancer = FaceEnhancement(args, in_size=args.in_size, model=args.model, use_sr=args.use_sr, + device='cuda' if args.use_cuda else 'cpu') + # МЕСТО ДЛЯ ВЫБОРА ФОТО + for filename in os.listdir(os.path.join(args.indir)): + file = os.path.join(args.indir, filename) + print("processing " + filename) + img = cv2.imread(file, cv2.IMREAD_COLOR) # BGR + # im = cv2.resize(im, (0,0), fx=2, fy=2) #optional + + img_out, orig_faces, enhanced_faces = faceenhancer.process(img, aligned=False) + + img = cv2.resize(img, img_out.shape[:2][::-1]) + # cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1]) + '_COMP.jpg'), np.hstack((img, img_out))) + cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1]) + '.jpg'), img_out) + + # for m, (ef, of) in enumerate(zip(enhanced_faces, orig_faces)): + # of = cv2.resize(of, ef.shape[:2]) + # cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1]) + '_face%02d' % m + '.jpg'), + # np.hstack((of, ef))) + + display(img, img_out) \ No newline at end of file diff --git a/GPEN/GPEN_TEST.py b/GPEN/GPEN_TEST.py new file mode 100644 index 0000000..16b0082 --- /dev/null +++ b/GPEN/GPEN_TEST.py @@ -0,0 +1,70 @@ +import os +import cv2 +import argparse +import numpy as np +import matplotlib.pyplot as plt + +import __init_paths +from face_enhancement import FaceEnhancement + + +def display(img1, img2): + fig = plt.figure(figsize=(25, 10)) + ax1 = fig.add_subplot(1, 2, 1) + plt.title('Input image', fontsize=16) + ax1.axis('off') + ax2 = fig.add_subplot(1, 2, 2) + plt.title('GPEN output', fontsize=16) + ax2.axis('off') + ax1.imshow(img1) + ax2.imshow(img2) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--model', type=str, default='GPEN-BFR-512', help='GPEN model') + parser.add_argument('--task', type=str, default='FaceEnhancement', help='task of GPEN model') + parser.add_argument('--key', type=str, default=None, help='key of GPEN model') + parser.add_argument('--in_size', type=int, default=512, help='in resolution of GPEN') + parser.add_argument('--out_size', type=int, default=None, help='out resolution of GPEN') + parser.add_argument('--channel_multiplier', type=int, default=2, help='channel multiplier of GPEN') + parser.add_argument('--narrow', type=float, default=1, help='channel narrow scale') + parser.add_argument('--alpha', type=float, default=1, help='blending the results') + parser.add_argument('--use_sr', action='store_true', help='use sr or not') + parser.add_argument('--use_cuda', action='store_true', help='use cuda or not') + parser.add_argument('--save_face', action='store_true', help='save face or not') + parser.add_argument('--aligned', action='store_true', help='input are aligned faces or not') + parser.add_argument('--sr_model', type=str, default='realesrnet', help='SR model') + parser.add_argument('--sr_scale', type=int, default=2, help='SR scale') + parser.add_argument('--tile_size', type=int, default=0, help='tile size for SR to avoid OOM') + parser.add_argument('--indir', type=str, default='examples/imgs', help='input folder') + parser.add_argument('--outdir', type=str, default='results/outs-bfr', help='output folder') + parser.add_argument('--ext', type=str, default='.jpg', help='extension of output') + args = parser.parse_args() + + # model = {'name':'GPEN-BFR-512', 'sr_model':'realesrnet', 'sr_scale': 2, 'size':512, 'channel_multiplier':2, 'narrow':1} + # model = {'name':'GPEN-BFR-256', 'sr_model':'realesrnet', 'size':256, 'channel_multiplier':1, 'narrow':0.5} + + os.makedirs(args.outdir, exist_ok=True) + + faceenhancer = FaceEnhancement(args, in_size=args.in_size, model=args.model, use_sr=args.use_sr, + device='cuda' if args.use_cuda else 'cpu') + # МЕСТО ДЛЯ ВЫБОРА ФОТО + for filename in os.listdir(os.path.join(args.indir)): + file = os.path.join(args.indir, filename) + print("processing " + filename) + img = cv2.imread(file, cv2.IMREAD_COLOR) # BGR + # im = cv2.resize(im, (0,0), fx=2, fy=2) #optional + + img_out, orig_faces, enhanced_faces = faceenhancer.process(img, aligned=False) + + img = cv2.resize(img, img_out.shape[:2][::-1]) + cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1]) + '_COMP.jpg'), np.hstack((img, img_out))) + cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1]) + '_GPEN.jpg'), img_out) + + for m, (ef, of) in enumerate(zip(enhanced_faces, orig_faces)): + of = cv2.resize(of, ef.shape[:2]) + cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1]) + '_face%02d' % m + '.jpg'), + np.hstack((of, ef))) + + display(img, img_out) \ No newline at end of file diff --git a/GPEN/Proc.sh b/GPEN/Proc.sh new file mode 100644 index 0000000..e7f5a32 --- /dev/null +++ b/GPEN/Proc.sh @@ -0,0 +1,3 @@ +#!/bin/bash + +python3 GPEN_RESULT.py --indir /data/images --outdir /data/images diff --git a/GPEN/README.md b/GPEN/README.md new file mode 100644 index 0000000..4211f3f --- /dev/null +++ b/GPEN/README.md @@ -0,0 +1,121 @@ +# GAN Prior Embedded Network for Blind Face Restoration in the Wild + +[Paper](https://arxiv.org/abs/2105.06070) | [Supplementary](https://www4.comp.polyu.edu.hk/~cslzhang/paper/GPEN-cvpr21-supp.pdf) | [Demo](https://vision.aliyun.com/experience/detail?spm=a211p3.14020179.J_7524944390.17.66cd4850wVDkUQ&tagName=facebody&children=EnhanceFace) + + [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/GPEN) + +[Tao Yang](https://cg.cs.tsinghua.edu.cn/people/~tyang)1, Peiran Ren1, Xuansong Xie1, [Lei Zhang](https://www4.comp.polyu.edu.hk/~cslzhang)1,2 +_1[DAMO Academy, Alibaba Group](https://damo.alibaba.com), Hangzhou, China_ +_2[Department of Computing, The Hong Kong Polytechnic University](http://www.comp.polyu.edu.hk), Hong Kong, China_ + +#### Face Restoration + + + + + + +#### Selfie Restoration + + + +#### Face Colorization + + + +#### Face Inpainting + + + +#### Conditional Image Synthesis (Seg2Face) + + + +## News +(2022-05-16) Add x1 sr model. Add ``--tile_size`` to avoid OOM. + +(2022-03-15) Add x4 sr model. Try ``--sr_scale``. + +(2022-03-09) Add GPEN-BFR-2048 for selfies. I have to take it down due to commercial issues. Sorry about that. + +(2021-12-29) Add online demos [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/GPEN). Many thanks to [CJWBW](https://github.com/CJWBW) and [AK391](https://github.com/AK391). + +(2021-12-16) Release a simplified training code of GPEN. It differs from our implementation in the paper, but could achieve comparable performance. We strongly recommend to change the degradation model. + +(2021-12-09) Add face parsing to better paste restored faces back. + +(2021-12-09) GPEN can run on CPU now by simply discarding ``--use_cuda``. + +(2021-12-01) GPEN can now work on a Windows machine without compiling cuda codes. Please check it out. Thanks to [Animadversio](https://github.com/rosinality/stylegan2-pytorch/issues/81). Alternatively, you can try [GPEN-Windows](https://drive.google.com/file/d/1YJJVnPGq90e_mWZxSGGTptNQilZNfOEO/view?usp=drivesdk). Many thanks to [Cioscos](https://github.com/yangxy/GPEN/issues/74). + +(2021-10-22) GPEN can now work with SR methods. A SR model trained by myself is provided. Replace it with your own model if necessary. + +(2021-10-11) The Colab demo for GPEN is available now google colab logo. + +## Usage + +![python](https://img.shields.io/badge/python-v3.7.4-green.svg?style=plastic) +![pytorch](https://img.shields.io/badge/pytorch-v1.7.0-green.svg?style=plastic) +![cuda](https://img.shields.io/badge/cuda-v10.2.89-green.svg?style=plastic) +![driver](https://img.shields.io/badge/driver-v460.73.01-green.svg?style=plastic) +![gcc](https://img.shields.io/badge/gcc-v7.5.0-green.svg?style=plastic) + +- Clone this repository: +```bash +git clone https://github.com/yangxy/GPEN.git +cd GPEN +``` +- Download RetinaFace model and our pre-trained model (not our best model due to commercial issues) and put them into ``weights/``. + + [RetinaFace-R50](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/RetinaFace-R50.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116085&Signature=GlUNW6%2B8FxvxWmE9jKIZYOOciKQ%3D) | [ParseNet-latest](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/ParseNet-latest.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116134&Signature=bnMwU1JogmNbARto6G%2B7iaJQCHs%3D) | [model_ir_se50](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/model_ir_se50.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116170&Signature=jEyBslytwpWoh5DfKvYe2H31GgE%3D) | [GPEN-BFR-512](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-BFR-512.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116208&Signature=hBgvVvKVSNGeXqT8glG%2Bd2t2OKc%3D) | [GPEN-BFR-512-D](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-BFR-512-D.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116234&Signature=mP7MvYhKjbsIM2lhmuaEysssWpc%3D) | [GPEN-BFR-256](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-BFR-256.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116259&Signature=kMGJLSHqnvzzzqwtjUVBgngzX2s%3D) | [GPEN-BFR-256-D](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-BFR-256-D.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116288&Signature=b7NCfHFzyqKh%2BfaLrRCwMIIZ2HA%3D) | [GPEN-Colorization-1024](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-Colorization-1024.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116315&Signature=9tPavW2h%2F1LhIKiXj73sTQoWqcc%3D) | [GPEN-Inpainting-1024](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-Inpainting-1024.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116338&Signature=tvYhdLaLgW7UdcUrApXp2jsek8w%3D) | [GPEN-Seg2face-512](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-Seg2face-512.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116362&Signature=VOaHmjFy5YVBjMoNTpVk2KDJx9k%3D) | [realesrnet_x1](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/realesrnet_x1.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1968049923&Signature=omV%2Fb8Jibkgl1FggsR%2B821jQvOI%3D) | [realesrnet_x2](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/realesrnet_x2.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1962694780&Signature=lI%2FolhA%2FyigiTRvoDIVbtMIyhjI%3D) | [realesrnet_x4](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/realesrnet_x4.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1962694847&Signature=MA5E%2FLp88oCz4kFINWdmeuSh7c4%3D) + +- Restore face images: +```bash +python demo.py --task FaceEnhancement --model GPEN-BFR-512 --in_size 512 --channel_multiplier 2 --narrow 1 --use_sr --sr_scale 4 --use_cuda --save_face --indir examples/imgs --outdir examples/outs-bfr +``` + +- Colorize faces: +```bash +python demo.py --task FaceColorization --model GPEN-Colorization-1024 --in_size 1024 --use_cuda --indir examples/grays --outdir examples/outs-colorization +``` + +- Complete faces: +```bash +python demo.py --task FaceInpainting --model GPEN-Inpainting-1024 --in_size 1024 --use_cuda --indir examples/ffhq-10 --outdir examples/outs-inpainting +``` + +- Synthesize faces: +```bash +python demo.py --task Segmentation2Face --model GPEN-Seg2face-512 --in_size 512 --use_cuda --indir examples/segs --outdir examples/outs-seg2face +``` + +- Train GPEN for BFR with 4 GPUs: +```bash +CUDA_VISIBLE_DEVICES='0,1,2,3' python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 train_simple.py --size 1024 --channel_multiplier 2 --narrow 1 --ckpt weights --sample results --batch 2 --path your_path_of_croped+aligned_hq_faces (e.g., FFHQ) + +``` +When testing your own model, set ``--key g_ema``. + +Please check out ``run.sh`` for more details. + +## Main idea + + +## Citation +If our work is useful for your research, please consider citing: + + @inproceedings{Yang2021GPEN, + title={GAN Prior Embedded Network for Blind Face Restoration in the Wild}, + author={Tao Yang, Peiran Ren, Xuansong Xie, and Lei Zhang}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year={2021} + } + +## License +© Alibaba, 2021. For academic and non-commercial use only. + +## Acknowledgments +We borrow some codes from [Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface), [stylegan2-pytorch](https://github.com/rosinality/stylegan2-pytorch), [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN), and [GFPGAN](https://github.com/TencentARC/GFPGAN). + +## Contact +If you have any questions or suggestions about this paper, feel free to reach me at yangtao9009@gmail.com. diff --git a/GPEN/__init_paths.py b/GPEN/__init_paths.py new file mode 100644 index 0000000..262a6ee --- /dev/null +++ b/GPEN/__init_paths.py @@ -0,0 +1,33 @@ +''' +@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) +@author: yangxy (yangtao9009@gmail.com) +''' +import os.path as osp +import sys + +def add_path(path): + if path not in sys.path: + sys.path.insert(0, path) + +this_dir = osp.dirname(__file__) + +path = osp.join(this_dir, 'face_detect') +add_path(path) + +path = osp.join(this_dir, 'face_parse') +add_path(path) + +path = osp.join(this_dir, 'face_model') +add_path(path) + +path = osp.join(this_dir, 'sr_model') +add_path(path) + +path = osp.join(this_dir, 'training') +add_path(path) + +path = osp.join(this_dir, 'training/loss') +add_path(path) + +path = osp.join(this_dir, 'training/data_loader') +add_path(path) diff --git a/GPEN/align_faces.py b/GPEN/align_faces.py new file mode 100644 index 0000000..f22aa51 --- /dev/null +++ b/GPEN/align_faces.py @@ -0,0 +1,266 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Apr 24 15:43:29 2017 +@author: zhaoy +""" +""" +@Modified by yangxy (yangtao9009@gmail.com) +""" +import cv2 +import numpy as np +from skimage import transform as trans + +# reference facial points, a list of coordinates (x,y) +REFERENCE_FACIAL_POINTS = [ + [30.29459953, 51.69630051], + [65.53179932, 51.50139999], + [48.02519989, 71.73660278], + [33.54930115, 92.3655014], + [62.72990036, 92.20410156] +] + +DEFAULT_CROP_SIZE = (96, 112) + + +def _umeyama(src, dst, estimate_scale=True, scale=1.0): + """Estimate N-D similarity transformation with or without scaling. + Parameters + ---------- + src : (M, N) array + Source coordinates. + dst : (M, N) array + Destination coordinates. + estimate_scale : bool + Whether to estimate scaling factor. + Returns + ------- + T : (N + 1, N + 1) + The homogeneous similarity transformation matrix. The matrix contains + NaN values only if the problem is not well-conditioned. + References + ---------- + .. [1] "Least-squares estimation of transformation parameters between two + point patterns", Shinji Umeyama, PAMI 1991, :DOI:`10.1109/34.88573` + """ + + num = src.shape[0] + dim = src.shape[1] + + # Compute mean of src and dst. + src_mean = src.mean(axis=0) + dst_mean = dst.mean(axis=0) + + # Subtract mean from src and dst. + src_demean = src - src_mean + dst_demean = dst - dst_mean + + # Eq. (38). + A = dst_demean.T @ src_demean / num + + # Eq. (39). + d = np.ones((dim,), dtype=np.double) + if np.linalg.det(A) < 0: + d[dim - 1] = -1 + + T = np.eye(dim + 1, dtype=np.double) + + U, S, V = np.linalg.svd(A) + + # Eq. (40) and (43). + rank = np.linalg.matrix_rank(A) + if rank == 0: + return np.nan * T + elif rank == dim - 1: + if np.linalg.det(U) * np.linalg.det(V) > 0: + T[:dim, :dim] = U @ V + else: + s = d[dim - 1] + d[dim - 1] = -1 + T[:dim, :dim] = U @ np.diag(d) @ V + d[dim - 1] = s + else: + T[:dim, :dim] = U @ np.diag(d) @ V + + if estimate_scale: + # Eq. (41) and (42). + scale = 1.0 / src_demean.var(axis=0).sum() * (S @ d) + else: + scale = scale + + T[:dim, dim] = dst_mean - scale * (T[:dim, :dim] @ src_mean.T) + T[:dim, :dim] *= scale + + return T, scale + + +class FaceWarpException(Exception): + def __str__(self): + return 'In File {}:{}'.format( + __file__, super.__str__(self)) + + +def get_reference_facial_points(output_size=None, + inner_padding_factor=0.0, + outer_padding=(0, 0), + default_square=False): + tmp_5pts = np.array(REFERENCE_FACIAL_POINTS) + tmp_crop_size = np.array(DEFAULT_CROP_SIZE) + + # 0) make the inner region a square + if default_square: + size_diff = max(tmp_crop_size) - tmp_crop_size + tmp_5pts += size_diff / 2 + tmp_crop_size += size_diff + + if (output_size and + output_size[0] == tmp_crop_size[0] and + output_size[1] == tmp_crop_size[1]): + print('output_size == DEFAULT_CROP_SIZE {}: return default reference points'.format(tmp_crop_size)) + return tmp_5pts + + if (inner_padding_factor == 0 and + outer_padding == (0, 0)): + if output_size is None: + print('No paddings to do: return default reference points') + return tmp_5pts + else: + raise FaceWarpException( + 'No paddings to do, output_size must be None or {}'.format(tmp_crop_size)) + + # check output size + if not (0 <= inner_padding_factor <= 1.0): + raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)') + + if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) + and output_size is None): + output_size = tmp_crop_size * \ + (1 + inner_padding_factor * 2).astype(np.int32) + output_size += np.array(outer_padding) + print(' deduced from paddings, output_size = ', output_size) + + if not (outer_padding[0] < output_size[0] + and outer_padding[1] < output_size[1]): + raise FaceWarpException('Not (outer_padding[0] < output_size[0]' + 'and outer_padding[1] < output_size[1])') + + # 1) pad the inner region according inner_padding_factor + # print('---> STEP1: pad the inner region according inner_padding_factor') + if inner_padding_factor > 0: + size_diff = tmp_crop_size * inner_padding_factor * 2 + tmp_5pts += size_diff / 2 + tmp_crop_size += np.round(size_diff).astype(np.int32) + + # print(' crop_size = ', tmp_crop_size) + # print(' reference_5pts = ', tmp_5pts) + + # 2) resize the padded inner region + # print('---> STEP2: resize the padded inner region') + size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2 + # print(' crop_size = ', tmp_crop_size) + # print(' size_bf_outer_pad = ', size_bf_outer_pad) + + if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]: + raise FaceWarpException('Must have (output_size - outer_padding)' + '= some_scale * (crop_size * (1.0 + inner_padding_factor)') + + scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0] + # print(' resize scale_factor = ', scale_factor) + tmp_5pts = tmp_5pts * scale_factor + # size_diff = tmp_crop_size * (scale_factor - min(scale_factor)) + # tmp_5pts = tmp_5pts + size_diff / 2 + tmp_crop_size = size_bf_outer_pad + # print(' crop_size = ', tmp_crop_size) + # print(' reference_5pts = ', tmp_5pts) + + # 3) add outer_padding to make output_size + reference_5point = tmp_5pts + np.array(outer_padding) + tmp_crop_size = output_size + # print('---> STEP3: add outer_padding to make output_size') + # print(' crop_size = ', tmp_crop_size) + # print(' reference_5pts = ', tmp_5pts) + # + # print('===> end get_reference_facial_points\n') + + return reference_5point + + +def get_affine_transform_matrix(src_pts, dst_pts): + tfm = np.float32([[1, 0, 0], [0, 1, 0]]) + n_pts = src_pts.shape[0] + ones = np.ones((n_pts, 1), src_pts.dtype) + src_pts_ = np.hstack([src_pts, ones]) + dst_pts_ = np.hstack([dst_pts, ones]) + + A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_) + + if rank == 3: + tfm = np.float32([ + [A[0, 0], A[1, 0], A[2, 0]], + [A[0, 1], A[1, 1], A[2, 1]] + ]) + elif rank == 2: + tfm = np.float32([ + [A[0, 0], A[1, 0], 0], + [A[0, 1], A[1, 1], 0] + ]) + + return tfm + + +def warp_and_crop_face(src_img, + facial_pts, + reference_pts=None, + crop_size=(96, 112), + align_type='smilarity'): #smilarity cv2_affine affine + if reference_pts is None: + if crop_size[0] == 96 and crop_size[1] == 112: + reference_pts = REFERENCE_FACIAL_POINTS + else: + default_square = False + inner_padding_factor = 0 + outer_padding = (0, 0) + output_size = crop_size + + reference_pts = get_reference_facial_points(output_size, + inner_padding_factor, + outer_padding, + default_square) + + ref_pts = np.float32(reference_pts) + ref_pts_shp = ref_pts.shape + if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2: + raise FaceWarpException( + 'reference_pts.shape must be (K,2) or (2,K) and K>2') + + if ref_pts_shp[0] == 2: + ref_pts = ref_pts.T + + src_pts = np.float32(facial_pts) + src_pts_shp = src_pts.shape + if max(src_pts_shp) < 3 or min(src_pts_shp) != 2: + raise FaceWarpException( + 'facial_pts.shape must be (K,2) or (2,K) and K>2') + + if src_pts_shp[0] == 2: + src_pts = src_pts.T + + if src_pts.shape != ref_pts.shape: + raise FaceWarpException( + 'facial_pts and reference_pts must have the same shape') + + if align_type is 'cv2_affine': + tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3]) + tfm_inv = cv2.getAffineTransform(ref_pts[0:3], src_pts[0:3]) + elif align_type is 'affine': + tfm = get_affine_transform_matrix(src_pts, ref_pts) + tfm_inv = get_affine_transform_matrix(ref_pts, src_pts) + else: + params, scale = _umeyama(src_pts, ref_pts) + tfm = params[:2, :] + + params, _ = _umeyama(ref_pts, src_pts, False, scale=1.0/scale) + tfm_inv = params[:2, :] + + face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]), flags=3) + + return face_img, tfm_inv diff --git a/GPEN/demo.py b/GPEN/demo.py new file mode 100644 index 0000000..6184cf2 --- /dev/null +++ b/GPEN/demo.py @@ -0,0 +1,128 @@ +''' +@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) +@author: yangxy (yangtao9009@gmail.com) +''' +import os +import cv2 +import glob +import time +import math +import argparse +import numpy as np +from PIL import Image, ImageDraw +import __init_paths +from face_enhancement import FaceEnhancement +from face_colorization import FaceColorization +from face_inpainting import FaceInpainting +from segmentation2face import Segmentation2Face + +def brush_stroke_mask(img, color=(255,255,255)): + min_num_vertex = 8 + max_num_vertex = 28 + mean_angle = 2*math.pi / 5 + angle_range = 2*math.pi / 15 + min_width = 12 + max_width = 80 + def generate_mask(H, W, img=None): + average_radius = math.sqrt(H*H+W*W) / 8 + mask = Image.new('RGB', (W, H), 0) + if img is not None: mask = img #Image.fromarray(img) + + for _ in range(np.random.randint(1, 4)): + num_vertex = np.random.randint(min_num_vertex, max_num_vertex) + angle_min = mean_angle - np.random.uniform(0, angle_range) + angle_max = mean_angle + np.random.uniform(0, angle_range) + angles = [] + vertex = [] + for i in range(num_vertex): + if i % 2 == 0: + angles.append(2*math.pi - np.random.uniform(angle_min, angle_max)) + else: + angles.append(np.random.uniform(angle_min, angle_max)) + + h, w = mask.size + vertex.append((int(np.random.randint(0, w)), int(np.random.randint(0, h)))) + for i in range(num_vertex): + r = np.clip( + np.random.normal(loc=average_radius, scale=average_radius//2), + 0, 2*average_radius) + new_x = np.clip(vertex[-1][0] + r * math.cos(angles[i]), 0, w) + new_y = np.clip(vertex[-1][1] + r * math.sin(angles[i]), 0, h) + vertex.append((int(new_x), int(new_y))) + + draw = ImageDraw.Draw(mask) + width = int(np.random.uniform(min_width, max_width)) + draw.line(vertex, fill=color, width=width) + for v in vertex: + draw.ellipse((v[0] - width//2, + v[1] - width//2, + v[0] + width//2, + v[1] + width//2), + fill=color) + + return mask + + width, height = img.size + mask = generate_mask(height, width, img) + return mask + +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--model', type=str, default='GPEN-BFR-512', help='GPEN model') + parser.add_argument('--task', type=str, default='FaceEnhancement', help='task of GPEN model') + parser.add_argument('--key', type=str, default=None, help='key of GPEN model') + parser.add_argument('--in_size', type=int, default=512, help='in resolution of GPEN') + parser.add_argument('--out_size', type=int, default=None, help='out resolution of GPEN') + parser.add_argument('--channel_multiplier', type=int, default=2, help='channel multiplier of GPEN') + parser.add_argument('--narrow', type=float, default=1, help='channel narrow scale') + parser.add_argument('--alpha', type=float, default=1, help='blending the results') + parser.add_argument('--use_sr', action='store_true', help='use sr or not') + parser.add_argument('--use_cuda', action='store_true', help='use cuda or not') + parser.add_argument('--save_face', action='store_true', help='save face or not') + parser.add_argument('--aligned', action='store_true', help='input are aligned faces or not') + parser.add_argument('--sr_model', type=str, default='realesrnet', help='SR model') + parser.add_argument('--sr_scale', type=int, default=2, help='SR scale') + parser.add_argument('--tile_size', type=int, default=0, help='tile size for SR to avoid OOM') + parser.add_argument('--indir', type=str, default='examples/imgs', help='input folder') + parser.add_argument('--outdir', type=str, default='results/outs-BFR', help='output folder') + parser.add_argument('--ext', type=str, default='.jpg', help='extension of output') + args = parser.parse_args() + + #model = {'name':'GPEN-BFR-512', 'size':512, 'channel_multiplier':2, 'narrow':1} + #model = {'name':'GPEN-BFR-256', 'size':256, 'channel_multiplier':1, 'narrow':0.5} + + os.makedirs(args.outdir, exist_ok=True) + + if args.task == 'FaceEnhancement': + processer = FaceEnhancement(args, in_size=args.in_size, model=args.model, use_sr=args.use_sr, device='cuda' if args.use_cuda else 'cpu') + elif args.task == 'FaceColorization': + processer = FaceColorization(in_size=args.in_size, model=args.model, device='cuda' if args.use_cuda else 'cpu') + elif args.task == 'FaceInpainting': + processer = FaceInpainting(in_size=args.in_size, model=args.model, device='cuda' if args.use_cuda else 'cpu') + elif args.task == 'Segmentation2Face': + processer = Segmentation2Face(in_size=args.in_size, model=args.model, is_norm=False, device='cuda' if args.use_cuda else 'cpu') + + + files = sorted(glob.glob(os.path.join(args.indir, '*.*g'))) + for n, file in enumerate(files[:]): + filename = os.path.basename(file) + + img = cv2.imread(file, cv2.IMREAD_COLOR) # BGR + if not isinstance(img, np.ndarray): print(filename, 'error'); continue + #img = cv2.resize(img, (0,0), fx=2, fy=2) # optional + + if args.task == 'FaceInpainting': + img = np.asarray(brush_stroke_mask(Image.fromarray(img))) + + img_out, orig_faces, enhanced_faces = processer.process(img, aligned=args.aligned) + + img = cv2.resize(img, img_out.shape[:2][::-1]) + cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1])+f'_COMP{args.ext}'), np.hstack((img, img_out))) + cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1])+f'_GPEN{args.ext}'), img_out) + + if args.save_face: + for m, (ef, of) in enumerate(zip(enhanced_faces, orig_faces)): + of = cv2.resize(of, ef.shape[:2]) + cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1])+'_face%02d'%m+args.ext), np.hstack((of, ef))) + + if n%10==0: print(n, filename) diff --git a/GPEN/distributed.py b/GPEN/distributed.py new file mode 100644 index 0000000..51fa243 --- /dev/null +++ b/GPEN/distributed.py @@ -0,0 +1,126 @@ +import math +import pickle + +import torch +from torch import distributed as dist +from torch.utils.data.sampler import Sampler + + +def get_rank(): + if not dist.is_available(): + return 0 + + if not dist.is_initialized(): + return 0 + + return dist.get_rank() + + +def synchronize(): + if not dist.is_available(): + return + + if not dist.is_initialized(): + return + + world_size = dist.get_world_size() + + if world_size == 1: + return + + dist.barrier() + + +def get_world_size(): + if not dist.is_available(): + return 1 + + if not dist.is_initialized(): + return 1 + + return dist.get_world_size() + + +def reduce_sum(tensor): + if not dist.is_available(): + return tensor + + if not dist.is_initialized(): + return tensor + + tensor = tensor.clone() + dist.all_reduce(tensor, op=dist.ReduceOp.SUM) + + return tensor + + +def gather_grad(params): + world_size = get_world_size() + + if world_size == 1: + return + + for param in params: + if param.grad is not None: + dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM) + param.grad.data.div_(world_size) + + +def all_gather(data): + world_size = get_world_size() + + if world_size == 1: + return [data] + + buffer = pickle.dumps(data) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to('cuda') + + local_size = torch.IntTensor([tensor.numel()]).to('cuda') + size_list = [torch.IntTensor([0]).to('cuda') for _ in range(world_size)] + dist.all_gather(size_list, local_size) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.ByteTensor(size=(max_size,)).to('cuda')) + + if local_size != max_size: + padding = torch.ByteTensor(size=(max_size - local_size,)).to('cuda') + tensor = torch.cat((tensor, padding), 0) + + dist.all_gather(tensor_list, tensor) + + data_list = [] + + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def reduce_loss_dict(loss_dict): + world_size = get_world_size() + + if world_size < 2: + return loss_dict + + with torch.no_grad(): + keys = [] + losses = [] + + for k in sorted(loss_dict.keys()): + keys.append(k) + losses.append(loss_dict[k]) + + losses = torch.stack(losses, 0) + dist.reduce(losses, dst=0) + + if dist.get_rank() == 0: + losses /= world_size + + reduced_losses = {k: v for k, v in zip(keys, losses)} + + return reduced_losses diff --git a/GPEN/examples/example.png b/GPEN/examples/example.png new file mode 100644 index 0000000..cd996ca Binary files /dev/null and b/GPEN/examples/example.png differ diff --git a/GPEN/examples/ffhq-10/00000.png b/GPEN/examples/ffhq-10/00000.png new file mode 100644 index 0000000..6abfdc2 Binary files /dev/null and b/GPEN/examples/ffhq-10/00000.png differ diff --git a/GPEN/examples/ffhq-10/00001.png b/GPEN/examples/ffhq-10/00001.png new file mode 100644 index 0000000..3c24a38 Binary files /dev/null and b/GPEN/examples/ffhq-10/00001.png differ diff --git a/GPEN/examples/ffhq-10/00002.png b/GPEN/examples/ffhq-10/00002.png new file mode 100644 index 0000000..885662f Binary files /dev/null and b/GPEN/examples/ffhq-10/00002.png differ diff --git a/GPEN/examples/ffhq-10/00003.png b/GPEN/examples/ffhq-10/00003.png new file mode 100644 index 0000000..da4e149 Binary files /dev/null and b/GPEN/examples/ffhq-10/00003.png differ diff --git a/GPEN/examples/ffhq-10/00004.png b/GPEN/examples/ffhq-10/00004.png new file mode 100644 index 0000000..ab08a1a Binary files /dev/null and b/GPEN/examples/ffhq-10/00004.png differ diff --git a/GPEN/examples/ffhq-10/00005.png b/GPEN/examples/ffhq-10/00005.png new file mode 100644 index 0000000..bdc292b Binary files /dev/null and b/GPEN/examples/ffhq-10/00005.png differ diff --git a/GPEN/examples/ffhq-10/00006.png b/GPEN/examples/ffhq-10/00006.png new file mode 100644 index 0000000..70cca6c Binary files /dev/null and b/GPEN/examples/ffhq-10/00006.png differ diff --git a/GPEN/examples/ffhq-10/00007.png b/GPEN/examples/ffhq-10/00007.png new file mode 100644 index 0000000..4570d73 Binary files /dev/null and b/GPEN/examples/ffhq-10/00007.png differ diff --git a/GPEN/examples/ffhq-10/00008.png b/GPEN/examples/ffhq-10/00008.png new file mode 100644 index 0000000..17c1781 Binary files /dev/null and b/GPEN/examples/ffhq-10/00008.png differ diff --git a/GPEN/examples/ffhq-10/00009.png b/GPEN/examples/ffhq-10/00009.png new file mode 100644 index 0000000..3b9ed0a Binary files /dev/null and b/GPEN/examples/ffhq-10/00009.png differ diff --git a/GPEN/examples/grays/106000_gray.png b/GPEN/examples/grays/106000_gray.png new file mode 100644 index 0000000..74cf654 Binary files /dev/null and b/GPEN/examples/grays/106000_gray.png differ diff --git a/GPEN/examples/grays/762000_gray.png b/GPEN/examples/grays/762000_gray.png new file mode 100644 index 0000000..18cb3bc Binary files /dev/null and b/GPEN/examples/grays/762000_gray.png differ diff --git a/GPEN/examples/grays/809000_gray.png b/GPEN/examples/grays/809000_gray.png new file mode 100644 index 0000000..9751233 Binary files /dev/null and b/GPEN/examples/grays/809000_gray.png differ diff --git a/GPEN/examples/grays/813000_gray.png b/GPEN/examples/grays/813000_gray.png new file mode 100644 index 0000000..4ede47c Binary files /dev/null and b/GPEN/examples/grays/813000_gray.png differ diff --git a/GPEN/examples/grays/827000_gray.png b/GPEN/examples/grays/827000_gray.png new file mode 100644 index 0000000..49c1550 Binary files /dev/null and b/GPEN/examples/grays/827000_gray.png differ diff --git a/GPEN/examples/grays/836000_gray.png b/GPEN/examples/grays/836000_gray.png new file mode 100644 index 0000000..1849fbd Binary files /dev/null and b/GPEN/examples/grays/836000_gray.png differ diff --git a/GPEN/examples/imgs/.jpg b/GPEN/examples/imgs/.jpg new file mode 100644 index 0000000..ea4ce36 Binary files /dev/null and b/GPEN/examples/imgs/.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_COMP.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_COMP.jpg new file mode 100644 index 0000000..1b615b0 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_COMP.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_GPEN.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_GPEN.jpg new file mode 100644 index 0000000..1140184 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_GPEN.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face00.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face00.jpg new file mode 100644 index 0000000..a397c4a Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face00.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face01.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face01.jpg new file mode 100644 index 0000000..ae4c911 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face01.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face02.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face02.jpg new file mode 100644 index 0000000..29be8b9 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face02.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face03.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face03.jpg new file mode 100644 index 0000000..f35e28e Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face03.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face04.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face04.jpg new file mode 100644 index 0000000..8d6793e Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face04.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face05.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face05.jpg new file mode 100644 index 0000000..1d40aa4 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face05.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face06.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face06.jpg new file mode 100644 index 0000000..f44e7a4 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face06.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face07.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face07.jpg new file mode 100644 index 0000000..a3857b2 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face07.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face08.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face08.jpg new file mode 100644 index 0000000..c0e2681 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face08.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face09.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face09.jpg new file mode 100644 index 0000000..c6987f9 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face09.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face10.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face10.jpg new file mode 100644 index 0000000..3f1e2db Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face10.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face11.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face11.jpg new file mode 100644 index 0000000..1ba446e Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face11.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face12.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face12.jpg new file mode 100644 index 0000000..e9ff06d Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face12.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face13.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face13.jpg new file mode 100644 index 0000000..e01693f Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face13.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face14.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face14.jpg new file mode 100644 index 0000000..f8f6a5c Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face14.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face15.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face15.jpg new file mode 100644 index 0000000..b3c169f Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face15.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face16.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face16.jpg new file mode 100644 index 0000000..6119877 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face16.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face17.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face17.jpg new file mode 100644 index 0000000..4260ca8 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face17.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face18.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face18.jpg new file mode 100644 index 0000000..acd8712 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face18.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face19.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face19.jpg new file mode 100644 index 0000000..6180ccd Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face19.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face20.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face20.jpg new file mode 100644 index 0000000..7c41b59 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face20.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face21.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face21.jpg new file mode 100644 index 0000000..8f8dec8 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face21.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face22.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face22.jpg new file mode 100644 index 0000000..b09d509 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face22.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face23.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face23.jpg new file mode 100644 index 0000000..390487b Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face23.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face24.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face24.jpg new file mode 100644 index 0000000..a7e9bca Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face24.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face25.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face25.jpg new file mode 100644 index 0000000..10e570f Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face25.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face26.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face26.jpg new file mode 100644 index 0000000..48a6269 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face26.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face27.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face27.jpg new file mode 100644 index 0000000..c1de92b Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face27.jpg differ diff --git a/GPEN/examples/outs-bfr/Solvay_conference_1927_face28.jpg b/GPEN/examples/outs-bfr/Solvay_conference_1927_face28.jpg new file mode 100644 index 0000000..7abf152 Binary files /dev/null and b/GPEN/examples/outs-bfr/Solvay_conference_1927_face28.jpg differ diff --git a/GPEN/examples/outs-colorization/030102-03_GPEN_GPEN.jpg b/GPEN/examples/outs-colorization/030102-03_GPEN_GPEN.jpg new file mode 100644 index 0000000..bc84950 Binary files /dev/null and b/GPEN/examples/outs-colorization/030102-03_GPEN_GPEN.jpg differ diff --git a/GPEN/examples/outs-colorization/34484-1000x830_GPEN_GPEN.jpg b/GPEN/examples/outs-colorization/34484-1000x830_GPEN_GPEN.jpg new file mode 100644 index 0000000..29a8bbc Binary files /dev/null and b/GPEN/examples/outs-colorization/34484-1000x830_GPEN_GPEN.jpg differ diff --git a/GPEN/examples/outs-colorization/69045376_778428345939842_4590816548997824512_n_GPEN_GPEN.jpg b/GPEN/examples/outs-colorization/69045376_778428345939842_4590816548997824512_n_GPEN_GPEN.jpg new file mode 100644 index 0000000..1e8dc4e Binary files /dev/null and b/GPEN/examples/outs-colorization/69045376_778428345939842_4590816548997824512_n_GPEN_GPEN.jpg differ diff --git "a/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 alan-tyuring_GPEN_GPEN.jpg" "b/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 alan-tyuring_GPEN_GPEN.jpg" new file mode 100644 index 0000000..fe4f2c2 Binary files /dev/null and "b/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 alan-tyuring_GPEN_GPEN.jpg" differ diff --git "a/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-07_GPEN_GPEN.jpg" "b/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-07_GPEN_GPEN.jpg" new file mode 100644 index 0000000..62d9cd7 Binary files /dev/null and "b/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-07_GPEN_GPEN.jpg" differ diff --git "a/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-21_GPEN_GPEN.jpg" "b/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-21_GPEN_GPEN.jpg" new file mode 100644 index 0000000..1ce20f7 Binary files /dev/null and "b/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-21_GPEN_GPEN.jpg" differ diff --git "a/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-38_GPEN_GPEN.jpg" "b/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-38_GPEN_GPEN.jpg" new file mode 100644 index 0000000..3e0d0a5 Binary files /dev/null and "b/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-38_GPEN_GPEN.jpg" differ diff --git "a/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-14-00_GPEN_GPEN.jpg" "b/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-14-00_GPEN_GPEN.jpg" new file mode 100644 index 0000000..8aa4541 Binary files /dev/null and "b/GPEN/examples/outs-colorization/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-14-00_GPEN_GPEN.jpg" differ diff --git a/GPEN/examples/outs-inpainting/00000_COMP.jpg b/GPEN/examples/outs-inpainting/00000_COMP.jpg new file mode 100644 index 0000000..4944050 Binary files /dev/null and b/GPEN/examples/outs-inpainting/00000_COMP.jpg differ diff --git a/GPEN/examples/outs-inpainting/00000_GPEN.jpg b/GPEN/examples/outs-inpainting/00000_GPEN.jpg new file mode 100644 index 0000000..75f0f0a Binary files /dev/null and b/GPEN/examples/outs-inpainting/00000_GPEN.jpg differ diff --git a/GPEN/examples/outs-inpainting/00001_COMP.jpg b/GPEN/examples/outs-inpainting/00001_COMP.jpg new file mode 100644 index 0000000..f00c146 Binary files /dev/null and b/GPEN/examples/outs-inpainting/00001_COMP.jpg differ diff --git a/GPEN/examples/outs-inpainting/00001_GPEN.jpg b/GPEN/examples/outs-inpainting/00001_GPEN.jpg new file mode 100644 index 0000000..6ac111e Binary files /dev/null and b/GPEN/examples/outs-inpainting/00001_GPEN.jpg differ diff --git a/GPEN/examples/outs-inpainting/00002_COMP.jpg b/GPEN/examples/outs-inpainting/00002_COMP.jpg new file mode 100644 index 0000000..3f01484 Binary files /dev/null and b/GPEN/examples/outs-inpainting/00002_COMP.jpg differ diff --git a/GPEN/examples/outs-inpainting/00002_GPEN.jpg b/GPEN/examples/outs-inpainting/00002_GPEN.jpg new file mode 100644 index 0000000..e2032ae Binary files /dev/null and b/GPEN/examples/outs-inpainting/00002_GPEN.jpg differ diff --git a/GPEN/examples/outs-inpainting/00003_COMP.jpg b/GPEN/examples/outs-inpainting/00003_COMP.jpg new file mode 100644 index 0000000..e00369b Binary files /dev/null and b/GPEN/examples/outs-inpainting/00003_COMP.jpg differ diff --git a/GPEN/examples/outs-inpainting/00003_GPEN.jpg b/GPEN/examples/outs-inpainting/00003_GPEN.jpg new file mode 100644 index 0000000..7861c5e Binary files /dev/null and b/GPEN/examples/outs-inpainting/00003_GPEN.jpg differ diff --git a/GPEN/examples/outs-inpainting/00004_COMP.jpg b/GPEN/examples/outs-inpainting/00004_COMP.jpg new file mode 100644 index 0000000..b01d950 Binary files /dev/null and b/GPEN/examples/outs-inpainting/00004_COMP.jpg differ diff --git a/GPEN/examples/outs-inpainting/00004_GPEN.jpg b/GPEN/examples/outs-inpainting/00004_GPEN.jpg new file mode 100644 index 0000000..880ab8d Binary files /dev/null and b/GPEN/examples/outs-inpainting/00004_GPEN.jpg differ diff --git a/GPEN/examples/outs-inpainting/00005_COMP.jpg b/GPEN/examples/outs-inpainting/00005_COMP.jpg new file mode 100644 index 0000000..64cfdfa Binary files /dev/null and b/GPEN/examples/outs-inpainting/00005_COMP.jpg differ diff --git a/GPEN/examples/outs-inpainting/00005_GPEN.jpg b/GPEN/examples/outs-inpainting/00005_GPEN.jpg new file mode 100644 index 0000000..ce55e80 Binary files /dev/null and b/GPEN/examples/outs-inpainting/00005_GPEN.jpg differ diff --git a/GPEN/examples/outs-inpainting/00006_COMP.jpg b/GPEN/examples/outs-inpainting/00006_COMP.jpg new file mode 100644 index 0000000..5a4f908 Binary files /dev/null and b/GPEN/examples/outs-inpainting/00006_COMP.jpg differ diff --git a/GPEN/examples/outs-inpainting/00006_GPEN.jpg b/GPEN/examples/outs-inpainting/00006_GPEN.jpg new file mode 100644 index 0000000..b28b1fc Binary files /dev/null and b/GPEN/examples/outs-inpainting/00006_GPEN.jpg differ diff --git a/GPEN/examples/outs-inpainting/00007_COMP.jpg b/GPEN/examples/outs-inpainting/00007_COMP.jpg new file mode 100644 index 0000000..44b15f7 Binary files /dev/null and b/GPEN/examples/outs-inpainting/00007_COMP.jpg differ diff --git a/GPEN/examples/outs-inpainting/00007_GPEN.jpg b/GPEN/examples/outs-inpainting/00007_GPEN.jpg new file mode 100644 index 0000000..6595175 Binary files /dev/null and b/GPEN/examples/outs-inpainting/00007_GPEN.jpg differ diff --git a/GPEN/examples/outs-inpainting/00008_COMP.jpg b/GPEN/examples/outs-inpainting/00008_COMP.jpg new file mode 100644 index 0000000..8fb35ec Binary files /dev/null and b/GPEN/examples/outs-inpainting/00008_COMP.jpg differ diff --git a/GPEN/examples/outs-inpainting/00008_GPEN.jpg b/GPEN/examples/outs-inpainting/00008_GPEN.jpg new file mode 100644 index 0000000..0bef9c0 Binary files /dev/null and b/GPEN/examples/outs-inpainting/00008_GPEN.jpg differ diff --git a/GPEN/examples/outs-inpainting/00009_COMP.jpg b/GPEN/examples/outs-inpainting/00009_COMP.jpg new file mode 100644 index 0000000..35bcdf1 Binary files /dev/null and b/GPEN/examples/outs-inpainting/00009_COMP.jpg differ diff --git a/GPEN/examples/outs-inpainting/00009_GPEN.jpg b/GPEN/examples/outs-inpainting/00009_GPEN.jpg new file mode 100644 index 0000000..c785155 Binary files /dev/null and b/GPEN/examples/outs-inpainting/00009_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28000_COMP.jpg b/GPEN/examples/outs-seg2face/28000_COMP.jpg new file mode 100644 index 0000000..8a1ba50 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28000_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28000_GPEN.jpg b/GPEN/examples/outs-seg2face/28000_GPEN.jpg new file mode 100644 index 0000000..38e857d Binary files /dev/null and b/GPEN/examples/outs-seg2face/28000_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28001_COMP.jpg b/GPEN/examples/outs-seg2face/28001_COMP.jpg new file mode 100644 index 0000000..f6d1640 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28001_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28001_GPEN.jpg b/GPEN/examples/outs-seg2face/28001_GPEN.jpg new file mode 100644 index 0000000..6a9f254 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28001_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28002_COMP.jpg b/GPEN/examples/outs-seg2face/28002_COMP.jpg new file mode 100644 index 0000000..b2f7962 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28002_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28002_GPEN.jpg b/GPEN/examples/outs-seg2face/28002_GPEN.jpg new file mode 100644 index 0000000..8a2272b Binary files /dev/null and b/GPEN/examples/outs-seg2face/28002_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28003_COMP.jpg b/GPEN/examples/outs-seg2face/28003_COMP.jpg new file mode 100644 index 0000000..a5b17d0 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28003_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28003_GPEN.jpg b/GPEN/examples/outs-seg2face/28003_GPEN.jpg new file mode 100644 index 0000000..f59aa29 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28003_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28004_COMP.jpg b/GPEN/examples/outs-seg2face/28004_COMP.jpg new file mode 100644 index 0000000..0667a60 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28004_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28004_GPEN.jpg b/GPEN/examples/outs-seg2face/28004_GPEN.jpg new file mode 100644 index 0000000..02e4a83 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28004_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28005_COMP.jpg b/GPEN/examples/outs-seg2face/28005_COMP.jpg new file mode 100644 index 0000000..bf635ec Binary files /dev/null and b/GPEN/examples/outs-seg2face/28005_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28005_GPEN.jpg b/GPEN/examples/outs-seg2face/28005_GPEN.jpg new file mode 100644 index 0000000..f03a70a Binary files /dev/null and b/GPEN/examples/outs-seg2face/28005_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28006_COMP.jpg b/GPEN/examples/outs-seg2face/28006_COMP.jpg new file mode 100644 index 0000000..0b2b37c Binary files /dev/null and b/GPEN/examples/outs-seg2face/28006_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28006_GPEN.jpg b/GPEN/examples/outs-seg2face/28006_GPEN.jpg new file mode 100644 index 0000000..b758021 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28006_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28007_COMP.jpg b/GPEN/examples/outs-seg2face/28007_COMP.jpg new file mode 100644 index 0000000..766f76f Binary files /dev/null and b/GPEN/examples/outs-seg2face/28007_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28007_GPEN.jpg b/GPEN/examples/outs-seg2face/28007_GPEN.jpg new file mode 100644 index 0000000..fdb3cb3 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28007_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28008_COMP.jpg b/GPEN/examples/outs-seg2face/28008_COMP.jpg new file mode 100644 index 0000000..ee0b4cf Binary files /dev/null and b/GPEN/examples/outs-seg2face/28008_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28008_GPEN.jpg b/GPEN/examples/outs-seg2face/28008_GPEN.jpg new file mode 100644 index 0000000..8740cfc Binary files /dev/null and b/GPEN/examples/outs-seg2face/28008_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28009_COMP.jpg b/GPEN/examples/outs-seg2face/28009_COMP.jpg new file mode 100644 index 0000000..e927a8c Binary files /dev/null and b/GPEN/examples/outs-seg2face/28009_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28009_GPEN.jpg b/GPEN/examples/outs-seg2face/28009_GPEN.jpg new file mode 100644 index 0000000..8315f96 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28009_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28010_COMP.jpg b/GPEN/examples/outs-seg2face/28010_COMP.jpg new file mode 100644 index 0000000..554927d Binary files /dev/null and b/GPEN/examples/outs-seg2face/28010_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28010_GPEN.jpg b/GPEN/examples/outs-seg2face/28010_GPEN.jpg new file mode 100644 index 0000000..cbde0fe Binary files /dev/null and b/GPEN/examples/outs-seg2face/28010_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28011_COMP.jpg b/GPEN/examples/outs-seg2face/28011_COMP.jpg new file mode 100644 index 0000000..1e04835 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28011_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28011_GPEN.jpg b/GPEN/examples/outs-seg2face/28011_GPEN.jpg new file mode 100644 index 0000000..0bdb5b8 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28011_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28012_COMP.jpg b/GPEN/examples/outs-seg2face/28012_COMP.jpg new file mode 100644 index 0000000..d22e4c4 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28012_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28012_GPEN.jpg b/GPEN/examples/outs-seg2face/28012_GPEN.jpg new file mode 100644 index 0000000..6a0d4b1 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28012_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28013_COMP.jpg b/GPEN/examples/outs-seg2face/28013_COMP.jpg new file mode 100644 index 0000000..5246b5f Binary files /dev/null and b/GPEN/examples/outs-seg2face/28013_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28013_GPEN.jpg b/GPEN/examples/outs-seg2face/28013_GPEN.jpg new file mode 100644 index 0000000..6d8fa4f Binary files /dev/null and b/GPEN/examples/outs-seg2face/28013_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28014_COMP.jpg b/GPEN/examples/outs-seg2face/28014_COMP.jpg new file mode 100644 index 0000000..9123bd7 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28014_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28014_GPEN.jpg b/GPEN/examples/outs-seg2face/28014_GPEN.jpg new file mode 100644 index 0000000..b9e227a Binary files /dev/null and b/GPEN/examples/outs-seg2face/28014_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28015_COMP.jpg b/GPEN/examples/outs-seg2face/28015_COMP.jpg new file mode 100644 index 0000000..7f2112b Binary files /dev/null and b/GPEN/examples/outs-seg2face/28015_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28015_GPEN.jpg b/GPEN/examples/outs-seg2face/28015_GPEN.jpg new file mode 100644 index 0000000..1314455 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28015_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28016_COMP.jpg b/GPEN/examples/outs-seg2face/28016_COMP.jpg new file mode 100644 index 0000000..6d72367 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28016_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28016_GPEN.jpg b/GPEN/examples/outs-seg2face/28016_GPEN.jpg new file mode 100644 index 0000000..7302557 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28016_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28017_COMP.jpg b/GPEN/examples/outs-seg2face/28017_COMP.jpg new file mode 100644 index 0000000..82e6c78 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28017_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28017_GPEN.jpg b/GPEN/examples/outs-seg2face/28017_GPEN.jpg new file mode 100644 index 0000000..85ccf27 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28017_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28018_COMP.jpg b/GPEN/examples/outs-seg2face/28018_COMP.jpg new file mode 100644 index 0000000..5d7d2d4 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28018_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28018_GPEN.jpg b/GPEN/examples/outs-seg2face/28018_GPEN.jpg new file mode 100644 index 0000000..24f81b6 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28018_GPEN.jpg differ diff --git a/GPEN/examples/outs-seg2face/28019_COMP.jpg b/GPEN/examples/outs-seg2face/28019_COMP.jpg new file mode 100644 index 0000000..86a7ba0 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28019_COMP.jpg differ diff --git a/GPEN/examples/outs-seg2face/28019_GPEN.jpg b/GPEN/examples/outs-seg2face/28019_GPEN.jpg new file mode 100644 index 0000000..7e33b52 Binary files /dev/null and b/GPEN/examples/outs-seg2face/28019_GPEN.jpg differ diff --git a/GPEN/examples/outs-selfie/2020-12-10 175750_COMP.jpg b/GPEN/examples/outs-selfie/2020-12-10 175750_COMP.jpg new file mode 100644 index 0000000..432f0b5 Binary files /dev/null and b/GPEN/examples/outs-selfie/2020-12-10 175750_COMP.jpg differ diff --git a/GPEN/examples/outs-selfie/2020-12-10 175750_GPEN.jpg b/GPEN/examples/outs-selfie/2020-12-10 175750_GPEN.jpg new file mode 100644 index 0000000..2946c6c Binary files /dev/null and b/GPEN/examples/outs-selfie/2020-12-10 175750_GPEN.jpg differ diff --git a/GPEN/examples/outs-selfie/2020-12-10 175750_face00.jpg b/GPEN/examples/outs-selfie/2020-12-10 175750_face00.jpg new file mode 100644 index 0000000..9cc8303 Binary files /dev/null and b/GPEN/examples/outs-selfie/2020-12-10 175750_face00.jpg differ diff --git a/GPEN/examples/outs-selfie/2020-12-17 185036_COMP.jpg b/GPEN/examples/outs-selfie/2020-12-17 185036_COMP.jpg new file mode 100644 index 0000000..4fff9bb Binary files /dev/null and b/GPEN/examples/outs-selfie/2020-12-17 185036_COMP.jpg differ diff --git a/GPEN/examples/outs-selfie/2020-12-17 185036_GPEN.jpg b/GPEN/examples/outs-selfie/2020-12-17 185036_GPEN.jpg new file mode 100644 index 0000000..09f1f20 Binary files /dev/null and b/GPEN/examples/outs-selfie/2020-12-17 185036_GPEN.jpg differ diff --git a/GPEN/examples/outs-selfie/2020-12-17 185036_face00.jpg b/GPEN/examples/outs-selfie/2020-12-17 185036_face00.jpg new file mode 100644 index 0000000..e90ddbb Binary files /dev/null and b/GPEN/examples/outs-selfie/2020-12-17 185036_face00.jpg differ diff --git a/GPEN/examples/outs-selfie/20200320.101220_COMP.jpg b/GPEN/examples/outs-selfie/20200320.101220_COMP.jpg new file mode 100644 index 0000000..fbdea10 Binary files /dev/null and b/GPEN/examples/outs-selfie/20200320.101220_COMP.jpg differ diff --git a/GPEN/examples/outs-selfie/20200320.101220_GPEN.jpg b/GPEN/examples/outs-selfie/20200320.101220_GPEN.jpg new file mode 100644 index 0000000..b9cb822 Binary files /dev/null and b/GPEN/examples/outs-selfie/20200320.101220_GPEN.jpg differ diff --git a/GPEN/examples/outs-selfie/20200320.101220_face00.jpg b/GPEN/examples/outs-selfie/20200320.101220_face00.jpg new file mode 100644 index 0000000..cc4ca16 Binary files /dev/null and b/GPEN/examples/outs-selfie/20200320.101220_face00.jpg differ diff --git a/GPEN/examples/outs-selfie/2021-03-10 160843(29)_COMP.jpg b/GPEN/examples/outs-selfie/2021-03-10 160843(29)_COMP.jpg new file mode 100644 index 0000000..cb8275b Binary files /dev/null and b/GPEN/examples/outs-selfie/2021-03-10 160843(29)_COMP.jpg differ diff --git a/GPEN/examples/outs-selfie/2021-03-10 160843(29)_GPEN.jpg b/GPEN/examples/outs-selfie/2021-03-10 160843(29)_GPEN.jpg new file mode 100644 index 0000000..3a917f0 Binary files /dev/null and b/GPEN/examples/outs-selfie/2021-03-10 160843(29)_GPEN.jpg differ diff --git a/GPEN/examples/outs-selfie/2021-03-10 160843(29)_face00.jpg b/GPEN/examples/outs-selfie/2021-03-10 160843(29)_face00.jpg new file mode 100644 index 0000000..54d3b14 Binary files /dev/null and b/GPEN/examples/outs-selfie/2021-03-10 160843(29)_face00.jpg differ diff --git a/GPEN/examples/outs-selfie/IMG20180716171517_COMP.jpg b/GPEN/examples/outs-selfie/IMG20180716171517_COMP.jpg new file mode 100644 index 0000000..988f5f6 Binary files /dev/null and b/GPEN/examples/outs-selfie/IMG20180716171517_COMP.jpg differ diff --git a/GPEN/examples/outs-selfie/IMG20180716171517_GPEN.jpg b/GPEN/examples/outs-selfie/IMG20180716171517_GPEN.jpg new file mode 100644 index 0000000..088e682 Binary files /dev/null and b/GPEN/examples/outs-selfie/IMG20180716171517_GPEN.jpg differ diff --git a/GPEN/examples/outs-selfie/IMG20180716171517_face00.jpg b/GPEN/examples/outs-selfie/IMG20180716171517_face00.jpg new file mode 100644 index 0000000..9bd4108 Binary files /dev/null and b/GPEN/examples/outs-selfie/IMG20180716171517_face00.jpg differ diff --git a/GPEN/examples/segs/28000.png b/GPEN/examples/segs/28000.png new file mode 100644 index 0000000..e04ad02 Binary files /dev/null and b/GPEN/examples/segs/28000.png differ diff --git a/GPEN/examples/segs/28001.png b/GPEN/examples/segs/28001.png new file mode 100644 index 0000000..1dd76eb Binary files /dev/null and b/GPEN/examples/segs/28001.png differ diff --git a/GPEN/examples/segs/28002.png b/GPEN/examples/segs/28002.png new file mode 100644 index 0000000..6980aa8 Binary files /dev/null and b/GPEN/examples/segs/28002.png differ diff --git a/GPEN/examples/segs/28003.png b/GPEN/examples/segs/28003.png new file mode 100644 index 0000000..741131b Binary files /dev/null and b/GPEN/examples/segs/28003.png differ diff --git a/GPEN/examples/segs/28004.png b/GPEN/examples/segs/28004.png new file mode 100644 index 0000000..0f83da8 Binary files /dev/null and b/GPEN/examples/segs/28004.png differ diff --git a/GPEN/examples/segs/28005.png b/GPEN/examples/segs/28005.png new file mode 100644 index 0000000..e4d0ef3 Binary files /dev/null and b/GPEN/examples/segs/28005.png differ diff --git a/GPEN/examples/segs/28006.png b/GPEN/examples/segs/28006.png new file mode 100644 index 0000000..7504292 Binary files /dev/null and b/GPEN/examples/segs/28006.png differ diff --git a/GPEN/examples/segs/28007.png b/GPEN/examples/segs/28007.png new file mode 100644 index 0000000..72eb12f Binary files /dev/null and b/GPEN/examples/segs/28007.png differ diff --git a/GPEN/examples/segs/28008.png b/GPEN/examples/segs/28008.png new file mode 100644 index 0000000..ae360d1 Binary files /dev/null and b/GPEN/examples/segs/28008.png differ diff --git a/GPEN/examples/segs/28009.png b/GPEN/examples/segs/28009.png new file mode 100644 index 0000000..a5a6c2c Binary files /dev/null and b/GPEN/examples/segs/28009.png differ diff --git a/GPEN/examples/segs/28010.png b/GPEN/examples/segs/28010.png new file mode 100644 index 0000000..ca1c18b Binary files /dev/null and b/GPEN/examples/segs/28010.png differ diff --git a/GPEN/examples/segs/28011.png b/GPEN/examples/segs/28011.png new file mode 100644 index 0000000..98e1eac Binary files /dev/null and b/GPEN/examples/segs/28011.png differ diff --git a/GPEN/examples/segs/28012.png b/GPEN/examples/segs/28012.png new file mode 100644 index 0000000..c5c7b9c Binary files /dev/null and b/GPEN/examples/segs/28012.png differ diff --git a/GPEN/examples/segs/28013.png b/GPEN/examples/segs/28013.png new file mode 100644 index 0000000..b2e8f57 Binary files /dev/null and b/GPEN/examples/segs/28013.png differ diff --git a/GPEN/examples/segs/28014.png b/GPEN/examples/segs/28014.png new file mode 100644 index 0000000..c897338 Binary files /dev/null and b/GPEN/examples/segs/28014.png differ diff --git a/GPEN/examples/segs/28015.png b/GPEN/examples/segs/28015.png new file mode 100644 index 0000000..b0346fc Binary files /dev/null and b/GPEN/examples/segs/28015.png differ diff --git a/GPEN/examples/segs/28016.png b/GPEN/examples/segs/28016.png new file mode 100644 index 0000000..5392aba Binary files /dev/null and b/GPEN/examples/segs/28016.png differ diff --git a/GPEN/examples/segs/28017.png b/GPEN/examples/segs/28017.png new file mode 100644 index 0000000..de2548f Binary files /dev/null and b/GPEN/examples/segs/28017.png differ diff --git a/GPEN/examples/segs/28018.png b/GPEN/examples/segs/28018.png new file mode 100644 index 0000000..2dd247c Binary files /dev/null and b/GPEN/examples/segs/28018.png differ diff --git a/GPEN/examples/segs/28019.png b/GPEN/examples/segs/28019.png new file mode 100644 index 0000000..64e4148 Binary files /dev/null and b/GPEN/examples/segs/28019.png differ diff --git a/GPEN/examples/selfie/2020-12-10 175750.jpg b/GPEN/examples/selfie/2020-12-10 175750.jpg new file mode 100644 index 0000000..ad44ef1 Binary files /dev/null and b/GPEN/examples/selfie/2020-12-10 175750.jpg differ diff --git a/GPEN/examples/selfie/2020-12-17 185036.jpg b/GPEN/examples/selfie/2020-12-17 185036.jpg new file mode 100644 index 0000000..e50c3c4 Binary files /dev/null and b/GPEN/examples/selfie/2020-12-17 185036.jpg differ diff --git a/GPEN/examples/selfie/20200320.101220.jpg b/GPEN/examples/selfie/20200320.101220.jpg new file mode 100644 index 0000000..fe7cb13 Binary files /dev/null and b/GPEN/examples/selfie/20200320.101220.jpg differ diff --git a/GPEN/examples/selfie/2021-03-10 160843(29).jpg b/GPEN/examples/selfie/2021-03-10 160843(29).jpg new file mode 100644 index 0000000..b8b8302 Binary files /dev/null and b/GPEN/examples/selfie/2021-03-10 160843(29).jpg differ diff --git a/GPEN/examples/selfie/IMG20180716171517.jpg b/GPEN/examples/selfie/IMG20180716171517.jpg new file mode 100644 index 0000000..69144a9 Binary files /dev/null and b/GPEN/examples/selfie/IMG20180716171517.jpg differ diff --git a/GPEN/face_colorization.py b/GPEN/face_colorization.py new file mode 100644 index 0000000..74a7a8d --- /dev/null +++ b/GPEN/face_colorization.py @@ -0,0 +1,32 @@ +''' +@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) +@author: yangxy (yangtao9009@gmail.com) +''' +import cv2 +from face_model.face_gan import FaceGAN + +class FaceColorization(object): + def __init__(self, base_dir='./', in_size=1024, out_size=1024, model=None, channel_multiplier=2, narrow=1, key=None, device='cuda'): + self.facegan = FaceGAN(base_dir, in_size, out_size, model, channel_multiplier, narrow, key, device=device) + + def post_process(self, gray, out): + out_rs = cv2.resize(out, gray.shape[:2][::-1]) + gray_yuv = cv2.cvtColor(gray, cv2.COLOR_BGR2YUV) + out_yuv = cv2.cvtColor(out_rs, cv2.COLOR_BGR2YUV) + + out_yuv[:, :, 0] = gray_yuv[:, :, 0] + final = cv2.cvtColor(out_yuv, cv2.COLOR_YUV2BGR) + + return final + + # make sure the face image is well aligned. Please refer to face_enhancement.py + def process(self, gray, aligned=True): + # colorize the face + out = self.facegan.process(gray) + + if gray.shape[:2] != out.shape[:2]: + out = self.post_process(gray, out) + + return out, [gray], [out] + + diff --git a/GPEN/face_detect/.DS_Store b/GPEN/face_detect/.DS_Store new file mode 100644 index 0000000..2112b95 Binary files /dev/null and b/GPEN/face_detect/.DS_Store differ diff --git a/GPEN/face_detect/data/FDDB/img_list.txt b/GPEN/face_detect/data/FDDB/img_list.txt new file mode 100644 index 0000000..5cf3d31 --- /dev/null +++ b/GPEN/face_detect/data/FDDB/img_list.txt @@ -0,0 +1,2845 @@ +2002/08/11/big/img_591 +2002/08/26/big/img_265 +2002/07/19/big/img_423 +2002/08/24/big/img_490 +2002/08/31/big/img_17676 +2002/07/31/big/img_228 +2002/07/24/big/img_402 +2002/08/04/big/img_769 +2002/07/19/big/img_581 +2002/08/13/big/img_723 +2002/08/12/big/img_821 +2003/01/17/big/img_610 +2002/08/13/big/img_1116 +2002/08/28/big/img_19238 +2002/08/21/big/img_660 +2002/08/14/big/img_607 +2002/08/05/big/img_3708 +2002/08/19/big/img_511 +2002/08/07/big/img_1316 +2002/07/25/big/img_1047 +2002/07/23/big/img_474 +2002/07/27/big/img_970 +2002/09/02/big/img_15752 +2002/09/01/big/img_16378 +2002/09/01/big/img_16189 +2002/08/26/big/img_276 +2002/07/24/big/img_518 +2002/08/14/big/img_1027 +2002/08/24/big/img_733 +2002/08/15/big/img_249 +2003/01/15/big/img_1371 +2002/08/07/big/img_1348 +2003/01/01/big/img_331 +2002/08/23/big/img_536 +2002/07/30/big/img_224 +2002/08/10/big/img_763 +2002/08/21/big/img_293 +2002/08/15/big/img_1211 +2002/08/15/big/img_1194 +2003/01/15/big/img_390 +2002/08/06/big/img_2893 +2002/08/17/big/img_691 +2002/08/07/big/img_1695 +2002/08/16/big/img_829 +2002/07/25/big/img_201 +2002/08/23/big/img_36 +2003/01/15/big/img_763 +2003/01/15/big/img_637 +2002/08/22/big/img_592 +2002/07/25/big/img_817 +2003/01/15/big/img_1219 +2002/08/05/big/img_3508 +2002/08/15/big/img_1108 +2002/07/19/big/img_488 +2003/01/16/big/img_704 +2003/01/13/big/img_1087 +2002/08/10/big/img_670 +2002/07/24/big/img_104 +2002/08/27/big/img_19823 +2002/09/01/big/img_16229 +2003/01/13/big/img_846 +2002/08/04/big/img_412 +2002/07/22/big/img_554 +2002/08/12/big/img_331 +2002/08/02/big/img_533 +2002/08/12/big/img_259 +2002/08/18/big/img_328 +2003/01/14/big/img_630 +2002/08/05/big/img_3541 +2002/08/06/big/img_2390 +2002/08/20/big/img_150 +2002/08/02/big/img_1231 +2002/08/16/big/img_710 +2002/08/19/big/img_591 +2002/07/22/big/img_725 +2002/07/24/big/img_820 +2003/01/13/big/img_568 +2002/08/22/big/img_853 +2002/08/09/big/img_648 +2002/08/23/big/img_528 +2003/01/14/big/img_888 +2002/08/30/big/img_18201 +2002/08/13/big/img_965 +2003/01/14/big/img_660 +2002/07/19/big/img_517 +2003/01/14/big/img_406 +2002/08/30/big/img_18433 +2002/08/07/big/img_1630 +2002/08/06/big/img_2717 +2002/08/21/big/img_470 +2002/07/23/big/img_633 +2002/08/20/big/img_915 +2002/08/16/big/img_893 +2002/07/29/big/img_644 +2002/08/15/big/img_529 +2002/08/16/big/img_668 +2002/08/07/big/img_1871 +2002/07/25/big/img_192 +2002/07/31/big/img_961 +2002/08/19/big/img_738 +2002/07/31/big/img_382 +2002/08/19/big/img_298 +2003/01/17/big/img_608 +2002/08/21/big/img_514 +2002/07/23/big/img_183 +2003/01/17/big/img_536 +2002/07/24/big/img_478 +2002/08/06/big/img_2997 +2002/09/02/big/img_15380 +2002/08/07/big/img_1153 +2002/07/31/big/img_967 +2002/07/31/big/img_711 +2002/08/26/big/img_664 +2003/01/01/big/img_326 +2002/08/24/big/img_775 +2002/08/08/big/img_961 +2002/08/16/big/img_77 +2002/08/12/big/img_296 +2002/07/22/big/img_905 +2003/01/13/big/img_284 +2002/08/13/big/img_887 +2002/08/24/big/img_849 +2002/07/30/big/img_345 +2002/08/18/big/img_419 +2002/08/01/big/img_1347 +2002/08/05/big/img_3670 +2002/07/21/big/img_479 +2002/08/08/big/img_913 +2002/09/02/big/img_15828 +2002/08/30/big/img_18194 +2002/08/08/big/img_471 +2002/08/22/big/img_734 +2002/08/09/big/img_586 +2002/08/09/big/img_454 +2002/07/29/big/img_47 +2002/07/19/big/img_381 +2002/07/29/big/img_733 +2002/08/20/big/img_327 +2002/07/21/big/img_96 +2002/08/06/big/img_2680 +2002/07/25/big/img_919 +2002/07/21/big/img_158 +2002/07/22/big/img_801 +2002/07/22/big/img_567 +2002/07/24/big/img_804 +2002/07/24/big/img_690 +2003/01/15/big/img_576 +2002/08/14/big/img_335 +2003/01/13/big/img_390 +2002/08/11/big/img_258 +2002/07/23/big/img_917 +2002/08/15/big/img_525 +2003/01/15/big/img_505 +2002/07/30/big/img_886 +2003/01/16/big/img_640 +2003/01/14/big/img_642 +2003/01/17/big/img_844 +2002/08/04/big/img_571 +2002/08/29/big/img_18702 +2003/01/15/big/img_240 +2002/07/29/big/img_553 +2002/08/10/big/img_354 +2002/08/18/big/img_17 +2003/01/15/big/img_782 +2002/07/27/big/img_382 +2002/08/14/big/img_970 +2003/01/16/big/img_70 +2003/01/16/big/img_625 +2002/08/18/big/img_341 +2002/08/26/big/img_188 +2002/08/09/big/img_405 +2002/08/02/big/img_37 +2002/08/13/big/img_748 +2002/07/22/big/img_399 +2002/07/25/big/img_844 +2002/08/12/big/img_340 +2003/01/13/big/img_815 +2002/08/26/big/img_5 +2002/08/10/big/img_158 +2002/08/18/big/img_95 +2002/07/29/big/img_1297 +2003/01/13/big/img_508 +2002/09/01/big/img_16680 +2003/01/16/big/img_338 +2002/08/13/big/img_517 +2002/07/22/big/img_626 +2002/08/06/big/img_3024 +2002/07/26/big/img_499 +2003/01/13/big/img_387 +2002/08/31/big/img_18025 +2002/08/13/big/img_520 +2003/01/16/big/img_576 +2002/07/26/big/img_121 +2002/08/25/big/img_703 +2002/08/26/big/img_615 +2002/08/17/big/img_434 +2002/08/02/big/img_677 +2002/08/18/big/img_276 +2002/08/05/big/img_3672 +2002/07/26/big/img_700 +2002/07/31/big/img_277 +2003/01/14/big/img_220 +2002/08/23/big/img_232 +2002/08/31/big/img_17422 +2002/07/22/big/img_508 +2002/08/13/big/img_681 +2003/01/15/big/img_638 +2002/08/30/big/img_18408 +2003/01/14/big/img_533 +2003/01/17/big/img_12 +2002/08/28/big/img_19388 +2002/08/08/big/img_133 +2002/07/26/big/img_885 +2002/08/19/big/img_387 +2002/08/27/big/img_19976 +2002/08/26/big/img_118 +2002/08/28/big/img_19146 +2002/08/05/big/img_3259 +2002/08/15/big/img_536 +2002/07/22/big/img_279 +2002/07/22/big/img_9 +2002/08/13/big/img_301 +2002/08/15/big/img_974 +2002/08/06/big/img_2355 +2002/08/01/big/img_1526 +2002/08/03/big/img_417 +2002/08/04/big/img_407 +2002/08/15/big/img_1029 +2002/07/29/big/img_700 +2002/08/01/big/img_1463 +2002/08/31/big/img_17365 +2002/07/28/big/img_223 +2002/07/19/big/img_827 +2002/07/27/big/img_531 +2002/07/19/big/img_845 +2002/08/20/big/img_382 +2002/07/31/big/img_268 +2002/08/27/big/img_19705 +2002/08/02/big/img_830 +2002/08/23/big/img_250 +2002/07/20/big/img_777 +2002/08/21/big/img_879 +2002/08/26/big/img_20146 +2002/08/23/big/img_789 +2002/08/06/big/img_2683 +2002/08/25/big/img_576 +2002/08/09/big/img_498 +2002/08/08/big/img_384 +2002/08/26/big/img_592 +2002/07/29/big/img_1470 +2002/08/21/big/img_452 +2002/08/30/big/img_18395 +2002/08/15/big/img_215 +2002/07/21/big/img_643 +2002/07/22/big/img_209 +2003/01/17/big/img_346 +2002/08/25/big/img_658 +2002/08/21/big/img_221 +2002/08/14/big/img_60 +2003/01/17/big/img_885 +2003/01/16/big/img_482 +2002/08/19/big/img_593 +2002/08/08/big/img_233 +2002/07/30/big/img_458 +2002/07/23/big/img_384 +2003/01/15/big/img_670 +2003/01/15/big/img_267 +2002/08/26/big/img_540 +2002/07/29/big/img_552 +2002/07/30/big/img_997 +2003/01/17/big/img_377 +2002/08/21/big/img_265 +2002/08/09/big/img_561 +2002/07/31/big/img_945 +2002/09/02/big/img_15252 +2002/08/11/big/img_276 +2002/07/22/big/img_491 +2002/07/26/big/img_517 +2002/08/14/big/img_726 +2002/08/08/big/img_46 +2002/08/28/big/img_19458 +2002/08/06/big/img_2935 +2002/07/29/big/img_1392 +2002/08/13/big/img_776 +2002/08/24/big/img_616 +2002/08/14/big/img_1065 +2002/07/29/big/img_889 +2002/08/18/big/img_188 +2002/08/07/big/img_1453 +2002/08/02/big/img_760 +2002/07/28/big/img_416 +2002/08/07/big/img_1393 +2002/08/26/big/img_292 +2002/08/26/big/img_301 +2003/01/13/big/img_195 +2002/07/26/big/img_532 +2002/08/20/big/img_550 +2002/08/05/big/img_3658 +2002/08/26/big/img_738 +2002/09/02/big/img_15750 +2003/01/17/big/img_451 +2002/07/23/big/img_339 +2002/08/16/big/img_637 +2002/08/14/big/img_748 +2002/08/06/big/img_2739 +2002/07/25/big/img_482 +2002/08/19/big/img_191 +2002/08/26/big/img_537 +2003/01/15/big/img_716 +2003/01/15/big/img_767 +2002/08/02/big/img_452 +2002/08/08/big/img_1011 +2002/08/10/big/img_144 +2003/01/14/big/img_122 +2002/07/24/big/img_586 +2002/07/24/big/img_762 +2002/08/20/big/img_369 +2002/07/30/big/img_146 +2002/08/23/big/img_396 +2003/01/15/big/img_200 +2002/08/15/big/img_1183 +2003/01/14/big/img_698 +2002/08/09/big/img_792 +2002/08/06/big/img_2347 +2002/07/31/big/img_911 +2002/08/26/big/img_722 +2002/08/23/big/img_621 +2002/08/05/big/img_3790 +2003/01/13/big/img_633 +2002/08/09/big/img_224 +2002/07/24/big/img_454 +2002/07/21/big/img_202 +2002/08/02/big/img_630 +2002/08/30/big/img_18315 +2002/07/19/big/img_491 +2002/09/01/big/img_16456 +2002/08/09/big/img_242 +2002/07/25/big/img_595 +2002/07/22/big/img_522 +2002/08/01/big/img_1593 +2002/07/29/big/img_336 +2002/08/15/big/img_448 +2002/08/28/big/img_19281 +2002/07/29/big/img_342 +2002/08/12/big/img_78 +2003/01/14/big/img_525 +2002/07/28/big/img_147 +2002/08/11/big/img_353 +2002/08/22/big/img_513 +2002/08/04/big/img_721 +2002/08/17/big/img_247 +2003/01/14/big/img_891 +2002/08/20/big/img_853 +2002/07/19/big/img_414 +2002/08/01/big/img_1530 +2003/01/14/big/img_924 +2002/08/22/big/img_468 +2002/08/18/big/img_354 +2002/08/30/big/img_18193 +2002/08/23/big/img_492 +2002/08/15/big/img_871 +2002/08/12/big/img_494 +2002/08/06/big/img_2470 +2002/07/23/big/img_923 +2002/08/26/big/img_155 +2002/08/08/big/img_669 +2002/07/23/big/img_404 +2002/08/28/big/img_19421 +2002/08/29/big/img_18993 +2002/08/25/big/img_416 +2003/01/17/big/img_434 +2002/07/29/big/img_1370 +2002/07/28/big/img_483 +2002/08/11/big/img_50 +2002/08/10/big/img_404 +2002/09/02/big/img_15057 +2003/01/14/big/img_911 +2002/09/01/big/img_16697 +2003/01/16/big/img_665 +2002/09/01/big/img_16708 +2002/08/22/big/img_612 +2002/08/28/big/img_19471 +2002/08/02/big/img_198 +2003/01/16/big/img_527 +2002/08/22/big/img_209 +2002/08/30/big/img_18205 +2003/01/14/big/img_114 +2003/01/14/big/img_1028 +2003/01/16/big/img_894 +2003/01/14/big/img_837 +2002/07/30/big/img_9 +2002/08/06/big/img_2821 +2002/08/04/big/img_85 +2003/01/13/big/img_884 +2002/07/22/big/img_570 +2002/08/07/big/img_1773 +2002/07/26/big/img_208 +2003/01/17/big/img_946 +2002/07/19/big/img_930 +2003/01/01/big/img_698 +2003/01/17/big/img_612 +2002/07/19/big/img_372 +2002/07/30/big/img_721 +2003/01/14/big/img_649 +2002/08/19/big/img_4 +2002/07/25/big/img_1024 +2003/01/15/big/img_601 +2002/08/30/big/img_18470 +2002/07/22/big/img_29 +2002/08/07/big/img_1686 +2002/07/20/big/img_294 +2002/08/14/big/img_800 +2002/08/19/big/img_353 +2002/08/19/big/img_350 +2002/08/05/big/img_3392 +2002/08/09/big/img_622 +2003/01/15/big/img_236 +2002/08/11/big/img_643 +2002/08/05/big/img_3458 +2002/08/12/big/img_413 +2002/08/22/big/img_415 +2002/08/13/big/img_635 +2002/08/07/big/img_1198 +2002/08/04/big/img_873 +2002/08/12/big/img_407 +2003/01/15/big/img_346 +2002/08/02/big/img_275 +2002/08/17/big/img_997 +2002/08/21/big/img_958 +2002/08/20/big/img_579 +2002/07/29/big/img_142 +2003/01/14/big/img_1115 +2002/08/16/big/img_365 +2002/07/29/big/img_1414 +2002/08/17/big/img_489 +2002/08/13/big/img_1010 +2002/07/31/big/img_276 +2002/07/25/big/img_1000 +2002/08/23/big/img_524 +2002/08/28/big/img_19147 +2003/01/13/big/img_433 +2002/08/20/big/img_205 +2003/01/01/big/img_458 +2002/07/29/big/img_1449 +2003/01/16/big/img_696 +2002/08/28/big/img_19296 +2002/08/29/big/img_18688 +2002/08/21/big/img_767 +2002/08/20/big/img_532 +2002/08/26/big/img_187 +2002/07/26/big/img_183 +2002/07/27/big/img_890 +2003/01/13/big/img_576 +2002/07/30/big/img_15 +2002/07/31/big/img_889 +2002/08/31/big/img_17759 +2003/01/14/big/img_1114 +2002/07/19/big/img_445 +2002/08/03/big/img_593 +2002/07/24/big/img_750 +2002/07/30/big/img_133 +2002/08/25/big/img_671 +2002/07/20/big/img_351 +2002/08/31/big/img_17276 +2002/08/05/big/img_3231 +2002/09/02/big/img_15882 +2002/08/14/big/img_115 +2002/08/02/big/img_1148 +2002/07/25/big/img_936 +2002/07/31/big/img_639 +2002/08/04/big/img_427 +2002/08/22/big/img_843 +2003/01/17/big/img_17 +2003/01/13/big/img_690 +2002/08/13/big/img_472 +2002/08/09/big/img_425 +2002/08/05/big/img_3450 +2003/01/17/big/img_439 +2002/08/13/big/img_539 +2002/07/28/big/img_35 +2002/08/16/big/img_241 +2002/08/06/big/img_2898 +2003/01/16/big/img_429 +2002/08/05/big/img_3817 +2002/08/27/big/img_19919 +2002/07/19/big/img_422 +2002/08/15/big/img_560 +2002/07/23/big/img_750 +2002/07/30/big/img_353 +2002/08/05/big/img_43 +2002/08/23/big/img_305 +2002/08/01/big/img_2137 +2002/08/30/big/img_18097 +2002/08/01/big/img_1389 +2002/08/02/big/img_308 +2003/01/14/big/img_652 +2002/08/01/big/img_1798 +2003/01/14/big/img_732 +2003/01/16/big/img_294 +2002/08/26/big/img_213 +2002/07/24/big/img_842 +2003/01/13/big/img_630 +2003/01/13/big/img_634 +2002/08/06/big/img_2285 +2002/08/01/big/img_2162 +2002/08/30/big/img_18134 +2002/08/02/big/img_1045 +2002/08/01/big/img_2143 +2002/07/25/big/img_135 +2002/07/20/big/img_645 +2002/08/05/big/img_3666 +2002/08/14/big/img_523 +2002/08/04/big/img_425 +2003/01/14/big/img_137 +2003/01/01/big/img_176 +2002/08/15/big/img_505 +2002/08/24/big/img_386 +2002/08/05/big/img_3187 +2002/08/15/big/img_419 +2003/01/13/big/img_520 +2002/08/04/big/img_444 +2002/08/26/big/img_483 +2002/08/05/big/img_3449 +2002/08/30/big/img_18409 +2002/08/28/big/img_19455 +2002/08/27/big/img_20090 +2002/07/23/big/img_625 +2002/08/24/big/img_205 +2002/08/08/big/img_938 +2003/01/13/big/img_527 +2002/08/07/big/img_1712 +2002/07/24/big/img_801 +2002/08/09/big/img_579 +2003/01/14/big/img_41 +2003/01/15/big/img_1130 +2002/07/21/big/img_672 +2002/08/07/big/img_1590 +2003/01/01/big/img_532 +2002/08/02/big/img_529 +2002/08/05/big/img_3591 +2002/08/23/big/img_5 +2003/01/14/big/img_882 +2002/08/28/big/img_19234 +2002/07/24/big/img_398 +2003/01/14/big/img_592 +2002/08/22/big/img_548 +2002/08/12/big/img_761 +2003/01/16/big/img_497 +2002/08/18/big/img_133 +2002/08/08/big/img_874 +2002/07/19/big/img_247 +2002/08/15/big/img_170 +2002/08/27/big/img_19679 +2002/08/20/big/img_246 +2002/08/24/big/img_358 +2002/07/29/big/img_599 +2002/08/01/big/img_1555 +2002/07/30/big/img_491 +2002/07/30/big/img_371 +2003/01/16/big/img_682 +2002/07/25/big/img_619 +2003/01/15/big/img_587 +2002/08/02/big/img_1212 +2002/08/01/big/img_2152 +2002/07/25/big/img_668 +2003/01/16/big/img_574 +2002/08/28/big/img_19464 +2002/08/11/big/img_536 +2002/07/24/big/img_201 +2002/08/05/big/img_3488 +2002/07/25/big/img_887 +2002/07/22/big/img_789 +2002/07/30/big/img_432 +2002/08/16/big/img_166 +2002/09/01/big/img_16333 +2002/07/26/big/img_1010 +2002/07/21/big/img_793 +2002/07/22/big/img_720 +2002/07/31/big/img_337 +2002/07/27/big/img_185 +2002/08/23/big/img_440 +2002/07/31/big/img_801 +2002/07/25/big/img_478 +2003/01/14/big/img_171 +2002/08/07/big/img_1054 +2002/09/02/big/img_15659 +2002/07/29/big/img_1348 +2002/08/09/big/img_337 +2002/08/26/big/img_684 +2002/07/31/big/img_537 +2002/08/15/big/img_808 +2003/01/13/big/img_740 +2002/08/07/big/img_1667 +2002/08/03/big/img_404 +2002/08/06/big/img_2520 +2002/07/19/big/img_230 +2002/07/19/big/img_356 +2003/01/16/big/img_627 +2002/08/04/big/img_474 +2002/07/29/big/img_833 +2002/07/25/big/img_176 +2002/08/01/big/img_1684 +2002/08/21/big/img_643 +2002/08/27/big/img_19673 +2002/08/02/big/img_838 +2002/08/06/big/img_2378 +2003/01/15/big/img_48 +2002/07/30/big/img_470 +2002/08/15/big/img_963 +2002/08/24/big/img_444 +2002/08/16/big/img_662 +2002/08/15/big/img_1209 +2002/07/24/big/img_25 +2002/08/06/big/img_2740 +2002/07/29/big/img_996 +2002/08/31/big/img_18074 +2002/08/04/big/img_343 +2003/01/17/big/img_509 +2003/01/13/big/img_726 +2002/08/07/big/img_1466 +2002/07/26/big/img_307 +2002/08/10/big/img_598 +2002/08/13/big/img_890 +2002/08/14/big/img_997 +2002/07/19/big/img_392 +2002/08/02/big/img_475 +2002/08/29/big/img_19038 +2002/07/29/big/img_538 +2002/07/29/big/img_502 +2002/08/02/big/img_364 +2002/08/31/big/img_17353 +2002/08/08/big/img_539 +2002/08/01/big/img_1449 +2002/07/22/big/img_363 +2002/08/02/big/img_90 +2002/09/01/big/img_16867 +2002/08/05/big/img_3371 +2002/07/30/big/img_342 +2002/08/07/big/img_1363 +2002/08/22/big/img_790 +2003/01/15/big/img_404 +2002/08/05/big/img_3447 +2002/09/01/big/img_16167 +2003/01/13/big/img_840 +2002/08/22/big/img_1001 +2002/08/09/big/img_431 +2002/07/27/big/img_618 +2002/07/31/big/img_741 +2002/07/30/big/img_964 +2002/07/25/big/img_86 +2002/07/29/big/img_275 +2002/08/21/big/img_921 +2002/07/26/big/img_892 +2002/08/21/big/img_663 +2003/01/13/big/img_567 +2003/01/14/big/img_719 +2002/07/28/big/img_251 +2003/01/15/big/img_1123 +2002/07/29/big/img_260 +2002/08/24/big/img_337 +2002/08/01/big/img_1914 +2002/08/13/big/img_373 +2003/01/15/big/img_589 +2002/08/13/big/img_906 +2002/07/26/big/img_270 +2002/08/26/big/img_313 +2002/08/25/big/img_694 +2003/01/01/big/img_327 +2002/07/23/big/img_261 +2002/08/26/big/img_642 +2002/07/29/big/img_918 +2002/07/23/big/img_455 +2002/07/24/big/img_612 +2002/07/23/big/img_534 +2002/07/19/big/img_534 +2002/07/19/big/img_726 +2002/08/01/big/img_2146 +2002/08/02/big/img_543 +2003/01/16/big/img_777 +2002/07/30/big/img_484 +2002/08/13/big/img_1161 +2002/07/21/big/img_390 +2002/08/06/big/img_2288 +2002/08/21/big/img_677 +2002/08/13/big/img_747 +2002/08/15/big/img_1248 +2002/07/31/big/img_416 +2002/09/02/big/img_15259 +2002/08/16/big/img_781 +2002/08/24/big/img_754 +2002/07/24/big/img_803 +2002/08/20/big/img_609 +2002/08/28/big/img_19571 +2002/09/01/big/img_16140 +2002/08/26/big/img_769 +2002/07/20/big/img_588 +2002/08/02/big/img_898 +2002/07/21/big/img_466 +2002/08/14/big/img_1046 +2002/07/25/big/img_212 +2002/08/26/big/img_353 +2002/08/19/big/img_810 +2002/08/31/big/img_17824 +2002/08/12/big/img_631 +2002/07/19/big/img_828 +2002/07/24/big/img_130 +2002/08/25/big/img_580 +2002/07/31/big/img_699 +2002/07/23/big/img_808 +2002/07/31/big/img_377 +2003/01/16/big/img_570 +2002/09/01/big/img_16254 +2002/07/21/big/img_471 +2002/08/01/big/img_1548 +2002/08/18/big/img_252 +2002/08/19/big/img_576 +2002/08/20/big/img_464 +2002/07/27/big/img_735 +2002/08/21/big/img_589 +2003/01/15/big/img_1192 +2002/08/09/big/img_302 +2002/07/31/big/img_594 +2002/08/23/big/img_19 +2002/08/29/big/img_18819 +2002/08/19/big/img_293 +2002/07/30/big/img_331 +2002/08/23/big/img_607 +2002/07/30/big/img_363 +2002/08/16/big/img_766 +2003/01/13/big/img_481 +2002/08/06/big/img_2515 +2002/09/02/big/img_15913 +2002/09/02/big/img_15827 +2002/09/02/big/img_15053 +2002/08/07/big/img_1576 +2002/07/23/big/img_268 +2002/08/21/big/img_152 +2003/01/15/big/img_578 +2002/07/21/big/img_589 +2002/07/20/big/img_548 +2002/08/27/big/img_19693 +2002/08/31/big/img_17252 +2002/07/31/big/img_138 +2002/07/23/big/img_372 +2002/08/16/big/img_695 +2002/07/27/big/img_287 +2002/08/15/big/img_315 +2002/08/10/big/img_361 +2002/07/29/big/img_899 +2002/08/13/big/img_771 +2002/08/21/big/img_92 +2003/01/15/big/img_425 +2003/01/16/big/img_450 +2002/09/01/big/img_16942 +2002/08/02/big/img_51 +2002/09/02/big/img_15379 +2002/08/24/big/img_147 +2002/08/30/big/img_18122 +2002/07/26/big/img_950 +2002/08/07/big/img_1400 +2002/08/17/big/img_468 +2002/08/15/big/img_470 +2002/07/30/big/img_318 +2002/07/22/big/img_644 +2002/08/27/big/img_19732 +2002/07/23/big/img_601 +2002/08/26/big/img_398 +2002/08/21/big/img_428 +2002/08/06/big/img_2119 +2002/08/29/big/img_19103 +2003/01/14/big/img_933 +2002/08/11/big/img_674 +2002/08/28/big/img_19420 +2002/08/03/big/img_418 +2002/08/17/big/img_312 +2002/07/25/big/img_1044 +2003/01/17/big/img_671 +2002/08/30/big/img_18297 +2002/07/25/big/img_755 +2002/07/23/big/img_471 +2002/08/21/big/img_39 +2002/07/26/big/img_699 +2003/01/14/big/img_33 +2002/07/31/big/img_411 +2002/08/16/big/img_645 +2003/01/17/big/img_116 +2002/09/02/big/img_15903 +2002/08/20/big/img_120 +2002/08/22/big/img_176 +2002/07/29/big/img_1316 +2002/08/27/big/img_19914 +2002/07/22/big/img_719 +2002/08/28/big/img_19239 +2003/01/13/big/img_385 +2002/08/08/big/img_525 +2002/07/19/big/img_782 +2002/08/13/big/img_843 +2002/07/30/big/img_107 +2002/08/11/big/img_752 +2002/07/29/big/img_383 +2002/08/26/big/img_249 +2002/08/29/big/img_18860 +2002/07/30/big/img_70 +2002/07/26/big/img_194 +2002/08/15/big/img_530 +2002/08/08/big/img_816 +2002/07/31/big/img_286 +2003/01/13/big/img_294 +2002/07/31/big/img_251 +2002/07/24/big/img_13 +2002/08/31/big/img_17938 +2002/07/22/big/img_642 +2003/01/14/big/img_728 +2002/08/18/big/img_47 +2002/08/22/big/img_306 +2002/08/20/big/img_348 +2002/08/15/big/img_764 +2002/08/08/big/img_163 +2002/07/23/big/img_531 +2002/07/23/big/img_467 +2003/01/16/big/img_743 +2003/01/13/big/img_535 +2002/08/02/big/img_523 +2002/08/22/big/img_120 +2002/08/11/big/img_496 +2002/08/29/big/img_19075 +2002/08/08/big/img_465 +2002/08/09/big/img_790 +2002/08/19/big/img_588 +2002/08/23/big/img_407 +2003/01/17/big/img_435 +2002/08/24/big/img_398 +2002/08/27/big/img_19899 +2003/01/15/big/img_335 +2002/08/13/big/img_493 +2002/09/02/big/img_15460 +2002/07/31/big/img_470 +2002/08/05/big/img_3550 +2002/07/28/big/img_123 +2002/08/01/big/img_1498 +2002/08/04/big/img_504 +2003/01/17/big/img_427 +2002/08/27/big/img_19708 +2002/07/27/big/img_861 +2002/07/25/big/img_685 +2002/07/31/big/img_207 +2003/01/14/big/img_745 +2002/08/31/big/img_17756 +2002/08/24/big/img_288 +2002/08/18/big/img_181 +2002/08/10/big/img_520 +2002/08/25/big/img_705 +2002/08/23/big/img_226 +2002/08/04/big/img_727 +2002/07/24/big/img_625 +2002/08/28/big/img_19157 +2002/08/23/big/img_586 +2002/07/31/big/img_232 +2003/01/13/big/img_240 +2003/01/14/big/img_321 +2003/01/15/big/img_533 +2002/07/23/big/img_480 +2002/07/24/big/img_371 +2002/08/21/big/img_702 +2002/08/31/big/img_17075 +2002/09/02/big/img_15278 +2002/07/29/big/img_246 +2003/01/15/big/img_829 +2003/01/15/big/img_1213 +2003/01/16/big/img_441 +2002/08/14/big/img_921 +2002/07/23/big/img_425 +2002/08/15/big/img_296 +2002/07/19/big/img_135 +2002/07/26/big/img_402 +2003/01/17/big/img_88 +2002/08/20/big/img_872 +2002/08/13/big/img_1110 +2003/01/16/big/img_1040 +2002/07/23/big/img_9 +2002/08/13/big/img_700 +2002/08/16/big/img_371 +2002/08/27/big/img_19966 +2003/01/17/big/img_391 +2002/08/18/big/img_426 +2002/08/01/big/img_1618 +2002/07/21/big/img_754 +2003/01/14/big/img_1101 +2003/01/16/big/img_1022 +2002/07/22/big/img_275 +2002/08/24/big/img_86 +2002/08/17/big/img_582 +2003/01/15/big/img_765 +2003/01/17/big/img_449 +2002/07/28/big/img_265 +2003/01/13/big/img_552 +2002/07/28/big/img_115 +2003/01/16/big/img_56 +2002/08/02/big/img_1232 +2003/01/17/big/img_925 +2002/07/22/big/img_445 +2002/07/25/big/img_957 +2002/07/20/big/img_589 +2002/08/31/big/img_17107 +2002/07/29/big/img_483 +2002/08/14/big/img_1063 +2002/08/07/big/img_1545 +2002/08/14/big/img_680 +2002/09/01/big/img_16694 +2002/08/14/big/img_257 +2002/08/11/big/img_726 +2002/07/26/big/img_681 +2002/07/25/big/img_481 +2003/01/14/big/img_737 +2002/08/28/big/img_19480 +2003/01/16/big/img_362 +2002/08/27/big/img_19865 +2003/01/01/big/img_547 +2002/09/02/big/img_15074 +2002/08/01/big/img_1453 +2002/08/22/big/img_594 +2002/08/28/big/img_19263 +2002/08/13/big/img_478 +2002/07/29/big/img_1358 +2003/01/14/big/img_1022 +2002/08/16/big/img_450 +2002/08/02/big/img_159 +2002/07/26/big/img_781 +2003/01/13/big/img_601 +2002/08/20/big/img_407 +2002/08/15/big/img_468 +2002/08/31/big/img_17902 +2002/08/16/big/img_81 +2002/07/25/big/img_987 +2002/07/25/big/img_500 +2002/08/02/big/img_31 +2002/08/18/big/img_538 +2002/08/08/big/img_54 +2002/07/23/big/img_686 +2002/07/24/big/img_836 +2003/01/17/big/img_734 +2002/08/16/big/img_1055 +2003/01/16/big/img_521 +2002/07/25/big/img_612 +2002/08/22/big/img_778 +2002/08/03/big/img_251 +2002/08/12/big/img_436 +2002/08/23/big/img_705 +2002/07/28/big/img_243 +2002/07/25/big/img_1029 +2002/08/20/big/img_287 +2002/08/29/big/img_18739 +2002/08/05/big/img_3272 +2002/07/27/big/img_214 +2003/01/14/big/img_5 +2002/08/01/big/img_1380 +2002/08/29/big/img_19097 +2002/07/30/big/img_486 +2002/08/29/big/img_18707 +2002/08/10/big/img_559 +2002/08/15/big/img_365 +2002/08/09/big/img_525 +2002/08/10/big/img_689 +2002/07/25/big/img_502 +2002/08/03/big/img_667 +2002/08/10/big/img_855 +2002/08/10/big/img_706 +2002/08/18/big/img_603 +2003/01/16/big/img_1055 +2002/08/31/big/img_17890 +2002/08/15/big/img_761 +2003/01/15/big/img_489 +2002/08/26/big/img_351 +2002/08/01/big/img_1772 +2002/08/31/big/img_17729 +2002/07/25/big/img_609 +2003/01/13/big/img_539 +2002/07/27/big/img_686 +2002/07/31/big/img_311 +2002/08/22/big/img_799 +2003/01/16/big/img_936 +2002/08/31/big/img_17813 +2002/08/04/big/img_862 +2002/08/09/big/img_332 +2002/07/20/big/img_148 +2002/08/12/big/img_426 +2002/07/24/big/img_69 +2002/07/27/big/img_685 +2002/08/02/big/img_480 +2002/08/26/big/img_154 +2002/07/24/big/img_598 +2002/08/01/big/img_1881 +2002/08/20/big/img_667 +2003/01/14/big/img_495 +2002/07/21/big/img_744 +2002/07/30/big/img_150 +2002/07/23/big/img_924 +2002/08/08/big/img_272 +2002/07/23/big/img_310 +2002/07/25/big/img_1011 +2002/09/02/big/img_15725 +2002/07/19/big/img_814 +2002/08/20/big/img_936 +2002/07/25/big/img_85 +2002/08/24/big/img_662 +2002/08/09/big/img_495 +2003/01/15/big/img_196 +2002/08/16/big/img_707 +2002/08/28/big/img_19370 +2002/08/06/big/img_2366 +2002/08/06/big/img_3012 +2002/08/01/big/img_1452 +2002/07/31/big/img_742 +2002/07/27/big/img_914 +2003/01/13/big/img_290 +2002/07/31/big/img_288 +2002/08/02/big/img_171 +2002/08/22/big/img_191 +2002/07/27/big/img_1066 +2002/08/12/big/img_383 +2003/01/17/big/img_1018 +2002/08/01/big/img_1785 +2002/08/11/big/img_390 +2002/08/27/big/img_20037 +2002/08/12/big/img_38 +2003/01/15/big/img_103 +2002/08/26/big/img_31 +2002/08/18/big/img_660 +2002/07/22/big/img_694 +2002/08/15/big/img_24 +2002/07/27/big/img_1077 +2002/08/01/big/img_1943 +2002/07/22/big/img_292 +2002/09/01/big/img_16857 +2002/07/22/big/img_892 +2003/01/14/big/img_46 +2002/08/09/big/img_469 +2002/08/09/big/img_414 +2003/01/16/big/img_40 +2002/08/28/big/img_19231 +2002/07/27/big/img_978 +2002/07/23/big/img_475 +2002/07/25/big/img_92 +2002/08/09/big/img_799 +2002/07/25/big/img_491 +2002/08/03/big/img_654 +2003/01/15/big/img_687 +2002/08/11/big/img_478 +2002/08/07/big/img_1664 +2002/08/20/big/img_362 +2002/08/01/big/img_1298 +2003/01/13/big/img_500 +2002/08/06/big/img_2896 +2002/08/30/big/img_18529 +2002/08/16/big/img_1020 +2002/07/29/big/img_892 +2002/08/29/big/img_18726 +2002/07/21/big/img_453 +2002/08/17/big/img_437 +2002/07/19/big/img_665 +2002/07/22/big/img_440 +2002/07/19/big/img_582 +2002/07/21/big/img_233 +2003/01/01/big/img_82 +2002/07/25/big/img_341 +2002/07/29/big/img_864 +2002/08/02/big/img_276 +2002/08/29/big/img_18654 +2002/07/27/big/img_1024 +2002/08/19/big/img_373 +2003/01/15/big/img_241 +2002/07/25/big/img_84 +2002/08/13/big/img_834 +2002/08/10/big/img_511 +2002/08/01/big/img_1627 +2002/08/08/big/img_607 +2002/08/06/big/img_2083 +2002/08/01/big/img_1486 +2002/08/08/big/img_700 +2002/08/01/big/img_1954 +2002/08/21/big/img_54 +2002/07/30/big/img_847 +2002/08/28/big/img_19169 +2002/07/21/big/img_549 +2002/08/03/big/img_693 +2002/07/31/big/img_1002 +2003/01/14/big/img_1035 +2003/01/16/big/img_622 +2002/07/30/big/img_1201 +2002/08/10/big/img_444 +2002/07/31/big/img_374 +2002/08/21/big/img_301 +2002/08/13/big/img_1095 +2003/01/13/big/img_288 +2002/07/25/big/img_232 +2003/01/13/big/img_967 +2002/08/26/big/img_360 +2002/08/05/big/img_67 +2002/08/29/big/img_18969 +2002/07/28/big/img_16 +2002/08/16/big/img_515 +2002/07/20/big/img_708 +2002/08/18/big/img_178 +2003/01/15/big/img_509 +2002/07/25/big/img_430 +2002/08/21/big/img_738 +2002/08/16/big/img_886 +2002/09/02/big/img_15605 +2002/09/01/big/img_16242 +2002/08/24/big/img_711 +2002/07/25/big/img_90 +2002/08/09/big/img_491 +2002/07/30/big/img_534 +2003/01/13/big/img_474 +2002/08/25/big/img_510 +2002/08/15/big/img_555 +2002/08/02/big/img_775 +2002/07/23/big/img_975 +2002/08/19/big/img_229 +2003/01/17/big/img_860 +2003/01/02/big/img_10 +2002/07/23/big/img_542 +2002/08/06/big/img_2535 +2002/07/22/big/img_37 +2002/08/06/big/img_2342 +2002/08/25/big/img_515 +2002/08/25/big/img_336 +2002/08/18/big/img_837 +2002/08/21/big/img_616 +2003/01/17/big/img_24 +2002/07/26/big/img_936 +2002/08/14/big/img_896 +2002/07/29/big/img_465 +2002/07/31/big/img_543 +2002/08/01/big/img_1411 +2002/08/02/big/img_423 +2002/08/21/big/img_44 +2002/07/31/big/img_11 +2003/01/15/big/img_628 +2003/01/15/big/img_605 +2002/07/30/big/img_571 +2002/07/23/big/img_428 +2002/08/15/big/img_942 +2002/07/26/big/img_531 +2003/01/16/big/img_59 +2002/08/02/big/img_410 +2002/07/31/big/img_230 +2002/08/19/big/img_806 +2003/01/14/big/img_462 +2002/08/16/big/img_370 +2002/08/13/big/img_380 +2002/08/16/big/img_932 +2002/07/19/big/img_393 +2002/08/20/big/img_764 +2002/08/15/big/img_616 +2002/07/26/big/img_267 +2002/07/27/big/img_1069 +2002/08/14/big/img_1041 +2003/01/13/big/img_594 +2002/09/01/big/img_16845 +2002/08/09/big/img_229 +2003/01/16/big/img_639 +2002/08/19/big/img_398 +2002/08/18/big/img_978 +2002/08/24/big/img_296 +2002/07/29/big/img_415 +2002/07/30/big/img_923 +2002/08/18/big/img_575 +2002/08/22/big/img_182 +2002/07/25/big/img_806 +2002/07/22/big/img_49 +2002/07/29/big/img_989 +2003/01/17/big/img_789 +2003/01/15/big/img_503 +2002/09/01/big/img_16062 +2003/01/17/big/img_794 +2002/08/15/big/img_564 +2003/01/15/big/img_222 +2002/08/01/big/img_1656 +2003/01/13/big/img_432 +2002/07/19/big/img_426 +2002/08/17/big/img_244 +2002/08/13/big/img_805 +2002/09/02/big/img_15067 +2002/08/11/big/img_58 +2002/08/22/big/img_636 +2002/07/22/big/img_416 +2002/08/13/big/img_836 +2002/08/26/big/img_363 +2002/07/30/big/img_917 +2003/01/14/big/img_206 +2002/08/12/big/img_311 +2002/08/31/big/img_17623 +2002/07/29/big/img_661 +2003/01/13/big/img_417 +2002/08/02/big/img_463 +2002/08/02/big/img_669 +2002/08/26/big/img_670 +2002/08/02/big/img_375 +2002/07/19/big/img_209 +2002/08/08/big/img_115 +2002/08/21/big/img_399 +2002/08/20/big/img_911 +2002/08/07/big/img_1212 +2002/08/20/big/img_578 +2002/08/22/big/img_554 +2002/08/21/big/img_484 +2002/07/25/big/img_450 +2002/08/03/big/img_542 +2002/08/15/big/img_561 +2002/07/23/big/img_360 +2002/08/30/big/img_18137 +2002/07/25/big/img_250 +2002/08/03/big/img_647 +2002/08/20/big/img_375 +2002/08/14/big/img_387 +2002/09/01/big/img_16990 +2002/08/28/big/img_19341 +2003/01/15/big/img_239 +2002/08/20/big/img_528 +2002/08/12/big/img_130 +2002/09/02/big/img_15108 +2003/01/15/big/img_372 +2002/08/16/big/img_678 +2002/08/04/big/img_623 +2002/07/23/big/img_477 +2002/08/28/big/img_19590 +2003/01/17/big/img_978 +2002/09/01/big/img_16692 +2002/07/20/big/img_109 +2002/08/06/big/img_2660 +2003/01/14/big/img_464 +2002/08/09/big/img_618 +2002/07/22/big/img_722 +2002/08/25/big/img_419 +2002/08/03/big/img_314 +2002/08/25/big/img_40 +2002/07/27/big/img_430 +2002/08/10/big/img_569 +2002/08/23/big/img_398 +2002/07/23/big/img_893 +2002/08/16/big/img_261 +2002/08/06/big/img_2668 +2002/07/22/big/img_835 +2002/09/02/big/img_15093 +2003/01/16/big/img_65 +2002/08/21/big/img_448 +2003/01/14/big/img_351 +2003/01/17/big/img_133 +2002/07/28/big/img_493 +2003/01/15/big/img_640 +2002/09/01/big/img_16880 +2002/08/15/big/img_350 +2002/08/20/big/img_624 +2002/08/25/big/img_604 +2002/08/06/big/img_2200 +2002/08/23/big/img_290 +2002/08/13/big/img_1152 +2003/01/14/big/img_251 +2002/08/02/big/img_538 +2002/08/22/big/img_613 +2003/01/13/big/img_351 +2002/08/18/big/img_368 +2002/07/23/big/img_392 +2002/07/25/big/img_198 +2002/07/25/big/img_418 +2002/08/26/big/img_614 +2002/07/23/big/img_405 +2003/01/14/big/img_445 +2002/07/25/big/img_326 +2002/08/10/big/img_734 +2003/01/14/big/img_530 +2002/08/08/big/img_561 +2002/08/29/big/img_18990 +2002/08/10/big/img_576 +2002/07/29/big/img_1494 +2002/07/19/big/img_198 +2002/08/10/big/img_562 +2002/07/22/big/img_901 +2003/01/14/big/img_37 +2002/09/02/big/img_15629 +2003/01/14/big/img_58 +2002/08/01/big/img_1364 +2002/07/27/big/img_636 +2003/01/13/big/img_241 +2002/09/01/big/img_16988 +2003/01/13/big/img_560 +2002/08/09/big/img_533 +2002/07/31/big/img_249 +2003/01/17/big/img_1007 +2002/07/21/big/img_64 +2003/01/13/big/img_537 +2003/01/15/big/img_606 +2002/08/18/big/img_651 +2002/08/24/big/img_405 +2002/07/26/big/img_837 +2002/08/09/big/img_562 +2002/08/01/big/img_1983 +2002/08/03/big/img_514 +2002/07/29/big/img_314 +2002/08/12/big/img_493 +2003/01/14/big/img_121 +2003/01/14/big/img_479 +2002/08/04/big/img_410 +2002/07/22/big/img_607 +2003/01/17/big/img_417 +2002/07/20/big/img_547 +2002/08/13/big/img_396 +2002/08/31/big/img_17538 +2002/08/13/big/img_187 +2002/08/12/big/img_328 +2003/01/14/big/img_569 +2002/07/27/big/img_1081 +2002/08/14/big/img_504 +2002/08/23/big/img_785 +2002/07/26/big/img_339 +2002/08/07/big/img_1156 +2002/08/07/big/img_1456 +2002/08/23/big/img_378 +2002/08/27/big/img_19719 +2002/07/31/big/img_39 +2002/07/31/big/img_883 +2003/01/14/big/img_676 +2002/07/29/big/img_214 +2002/07/26/big/img_669 +2002/07/25/big/img_202 +2002/08/08/big/img_259 +2003/01/17/big/img_943 +2003/01/15/big/img_512 +2002/08/05/big/img_3295 +2002/08/27/big/img_19685 +2002/08/08/big/img_277 +2002/08/30/big/img_18154 +2002/07/22/big/img_663 +2002/08/29/big/img_18914 +2002/07/31/big/img_908 +2002/08/27/big/img_19926 +2003/01/13/big/img_791 +2003/01/15/big/img_827 +2002/08/18/big/img_878 +2002/08/14/big/img_670 +2002/07/20/big/img_182 +2002/08/15/big/img_291 +2002/08/06/big/img_2600 +2002/07/23/big/img_587 +2002/08/14/big/img_577 +2003/01/15/big/img_585 +2002/07/30/big/img_310 +2002/08/03/big/img_658 +2002/08/10/big/img_157 +2002/08/19/big/img_811 +2002/07/29/big/img_1318 +2002/08/04/big/img_104 +2002/07/30/big/img_332 +2002/07/24/big/img_789 +2002/07/29/big/img_516 +2002/07/23/big/img_843 +2002/08/01/big/img_1528 +2002/08/13/big/img_798 +2002/08/07/big/img_1729 +2002/08/28/big/img_19448 +2003/01/16/big/img_95 +2002/08/12/big/img_473 +2002/07/27/big/img_269 +2003/01/16/big/img_621 +2002/07/29/big/img_772 +2002/07/24/big/img_171 +2002/07/19/big/img_429 +2002/08/07/big/img_1933 +2002/08/27/big/img_19629 +2002/08/05/big/img_3688 +2002/08/07/big/img_1691 +2002/07/23/big/img_600 +2002/07/29/big/img_666 +2002/08/25/big/img_566 +2002/08/06/big/img_2659 +2002/08/29/big/img_18929 +2002/08/16/big/img_407 +2002/08/18/big/img_774 +2002/08/19/big/img_249 +2002/08/06/big/img_2427 +2002/08/29/big/img_18899 +2002/08/01/big/img_1818 +2002/07/31/big/img_108 +2002/07/29/big/img_500 +2002/08/11/big/img_115 +2002/07/19/big/img_521 +2002/08/02/big/img_1163 +2002/07/22/big/img_62 +2002/08/13/big/img_466 +2002/08/21/big/img_956 +2002/08/23/big/img_602 +2002/08/20/big/img_858 +2002/07/25/big/img_690 +2002/07/19/big/img_130 +2002/08/04/big/img_874 +2002/07/26/big/img_489 +2002/07/22/big/img_548 +2002/08/10/big/img_191 +2002/07/25/big/img_1051 +2002/08/18/big/img_473 +2002/08/12/big/img_755 +2002/08/18/big/img_413 +2002/08/08/big/img_1044 +2002/08/17/big/img_680 +2002/08/26/big/img_235 +2002/08/20/big/img_330 +2002/08/22/big/img_344 +2002/08/09/big/img_593 +2002/07/31/big/img_1006 +2002/08/14/big/img_337 +2002/08/16/big/img_728 +2002/07/24/big/img_834 +2002/08/04/big/img_552 +2002/09/02/big/img_15213 +2002/07/25/big/img_725 +2002/08/30/big/img_18290 +2003/01/01/big/img_475 +2002/07/27/big/img_1083 +2002/08/29/big/img_18955 +2002/08/31/big/img_17232 +2002/08/08/big/img_480 +2002/08/01/big/img_1311 +2002/07/30/big/img_745 +2002/08/03/big/img_649 +2002/08/12/big/img_193 +2002/07/29/big/img_228 +2002/07/25/big/img_836 +2002/08/20/big/img_400 +2002/07/30/big/img_507 +2002/09/02/big/img_15072 +2002/07/26/big/img_658 +2002/07/28/big/img_503 +2002/08/05/big/img_3814 +2002/08/24/big/img_745 +2003/01/13/big/img_817 +2002/08/08/big/img_579 +2002/07/22/big/img_251 +2003/01/13/big/img_689 +2002/07/25/big/img_407 +2002/08/13/big/img_1050 +2002/08/14/big/img_733 +2002/07/24/big/img_82 +2003/01/17/big/img_288 +2003/01/15/big/img_475 +2002/08/14/big/img_620 +2002/08/21/big/img_167 +2002/07/19/big/img_300 +2002/07/26/big/img_219 +2002/08/01/big/img_1468 +2002/07/23/big/img_260 +2002/08/09/big/img_555 +2002/07/19/big/img_160 +2002/08/02/big/img_1060 +2003/01/14/big/img_149 +2002/08/15/big/img_346 +2002/08/24/big/img_597 +2002/08/22/big/img_502 +2002/08/30/big/img_18228 +2002/07/21/big/img_766 +2003/01/15/big/img_841 +2002/07/24/big/img_516 +2002/08/02/big/img_265 +2002/08/15/big/img_1243 +2003/01/15/big/img_223 +2002/08/04/big/img_236 +2002/07/22/big/img_309 +2002/07/20/big/img_656 +2002/07/31/big/img_412 +2002/09/01/big/img_16462 +2003/01/16/big/img_431 +2002/07/22/big/img_793 +2002/08/15/big/img_877 +2002/07/26/big/img_282 +2002/07/25/big/img_529 +2002/08/24/big/img_613 +2003/01/17/big/img_700 +2002/08/06/big/img_2526 +2002/08/24/big/img_394 +2002/08/21/big/img_521 +2002/08/25/big/img_560 +2002/07/29/big/img_966 +2002/07/25/big/img_448 +2003/01/13/big/img_782 +2002/08/21/big/img_296 +2002/09/01/big/img_16755 +2002/08/05/big/img_3552 +2002/09/02/big/img_15823 +2003/01/14/big/img_193 +2002/07/21/big/img_159 +2002/08/02/big/img_564 +2002/08/16/big/img_300 +2002/07/19/big/img_269 +2002/08/13/big/img_676 +2002/07/28/big/img_57 +2002/08/05/big/img_3318 +2002/07/31/big/img_218 +2002/08/21/big/img_898 +2002/07/29/big/img_109 +2002/07/19/big/img_854 +2002/08/23/big/img_311 +2002/08/14/big/img_318 +2002/07/25/big/img_523 +2002/07/21/big/img_678 +2003/01/17/big/img_690 +2002/08/28/big/img_19503 +2002/08/18/big/img_251 +2002/08/22/big/img_672 +2002/08/20/big/img_663 +2002/08/02/big/img_148 +2002/09/02/big/img_15580 +2002/07/25/big/img_778 +2002/08/14/big/img_565 +2002/08/12/big/img_374 +2002/08/13/big/img_1018 +2002/08/20/big/img_474 +2002/08/25/big/img_33 +2002/08/02/big/img_1190 +2002/08/08/big/img_864 +2002/08/14/big/img_1071 +2002/08/30/big/img_18103 +2002/08/18/big/img_533 +2003/01/16/big/img_650 +2002/07/25/big/img_108 +2002/07/26/big/img_81 +2002/07/27/big/img_543 +2002/07/29/big/img_521 +2003/01/13/big/img_434 +2002/08/26/big/img_674 +2002/08/06/big/img_2932 +2002/08/07/big/img_1262 +2003/01/15/big/img_201 +2003/01/16/big/img_673 +2002/09/02/big/img_15988 +2002/07/29/big/img_1306 +2003/01/14/big/img_1072 +2002/08/30/big/img_18232 +2002/08/05/big/img_3711 +2002/07/23/big/img_775 +2002/08/01/big/img_16 +2003/01/16/big/img_630 +2002/08/22/big/img_695 +2002/08/14/big/img_51 +2002/08/14/big/img_782 +2002/08/24/big/img_742 +2003/01/14/big/img_512 +2003/01/15/big/img_1183 +2003/01/15/big/img_714 +2002/08/01/big/img_2078 +2002/07/31/big/img_682 +2002/09/02/big/img_15687 +2002/07/26/big/img_518 +2002/08/27/big/img_19676 +2002/09/02/big/img_15969 +2002/08/02/big/img_931 +2002/08/25/big/img_508 +2002/08/29/big/img_18616 +2002/07/22/big/img_839 +2002/07/28/big/img_313 +2003/01/14/big/img_155 +2002/08/02/big/img_1105 +2002/08/09/big/img_53 +2002/08/16/big/img_469 +2002/08/15/big/img_502 +2002/08/20/big/img_575 +2002/07/25/big/img_138 +2003/01/16/big/img_579 +2002/07/19/big/img_352 +2003/01/14/big/img_762 +2003/01/01/big/img_588 +2002/08/02/big/img_981 +2002/08/21/big/img_447 +2002/09/01/big/img_16151 +2003/01/14/big/img_769 +2002/08/23/big/img_461 +2002/08/17/big/img_240 +2002/09/02/big/img_15220 +2002/07/19/big/img_408 +2002/09/02/big/img_15496 +2002/07/29/big/img_758 +2002/08/28/big/img_19392 +2002/08/06/big/img_2723 +2002/08/31/big/img_17752 +2002/08/23/big/img_469 +2002/08/13/big/img_515 +2002/09/02/big/img_15551 +2002/08/03/big/img_462 +2002/07/24/big/img_613 +2002/07/22/big/img_61 +2002/08/08/big/img_171 +2002/08/21/big/img_177 +2003/01/14/big/img_105 +2002/08/02/big/img_1017 +2002/08/22/big/img_106 +2002/07/27/big/img_542 +2002/07/21/big/img_665 +2002/07/23/big/img_595 +2002/08/04/big/img_657 +2002/08/29/big/img_19002 +2003/01/15/big/img_550 +2002/08/14/big/img_662 +2002/07/20/big/img_425 +2002/08/30/big/img_18528 +2002/07/26/big/img_611 +2002/07/22/big/img_849 +2002/08/07/big/img_1655 +2002/08/21/big/img_638 +2003/01/17/big/img_732 +2003/01/01/big/img_496 +2002/08/18/big/img_713 +2002/08/08/big/img_109 +2002/07/27/big/img_1008 +2002/07/20/big/img_559 +2002/08/16/big/img_699 +2002/08/31/big/img_17702 +2002/07/31/big/img_1013 +2002/08/01/big/img_2027 +2002/08/02/big/img_1001 +2002/08/03/big/img_210 +2002/08/01/big/img_2087 +2003/01/14/big/img_199 +2002/07/29/big/img_48 +2002/07/19/big/img_727 +2002/08/09/big/img_249 +2002/08/04/big/img_632 +2002/08/22/big/img_620 +2003/01/01/big/img_457 +2002/08/05/big/img_3223 +2002/07/27/big/img_240 +2002/07/25/big/img_797 +2002/08/13/big/img_430 +2002/07/25/big/img_615 +2002/08/12/big/img_28 +2002/07/30/big/img_220 +2002/07/24/big/img_89 +2002/08/21/big/img_357 +2002/08/09/big/img_590 +2003/01/13/big/img_525 +2002/08/17/big/img_818 +2003/01/02/big/img_7 +2002/07/26/big/img_636 +2003/01/13/big/img_1122 +2002/07/23/big/img_810 +2002/08/20/big/img_888 +2002/07/27/big/img_3 +2002/08/15/big/img_451 +2002/09/02/big/img_15787 +2002/07/31/big/img_281 +2002/08/05/big/img_3274 +2002/08/07/big/img_1254 +2002/07/31/big/img_27 +2002/08/01/big/img_1366 +2002/07/30/big/img_182 +2002/08/27/big/img_19690 +2002/07/29/big/img_68 +2002/08/23/big/img_754 +2002/07/30/big/img_540 +2002/08/27/big/img_20063 +2002/08/14/big/img_471 +2002/08/02/big/img_615 +2002/07/30/big/img_186 +2002/08/25/big/img_150 +2002/07/27/big/img_626 +2002/07/20/big/img_225 +2003/01/15/big/img_1252 +2002/07/19/big/img_367 +2003/01/15/big/img_582 +2002/08/09/big/img_572 +2002/08/08/big/img_428 +2003/01/15/big/img_639 +2002/08/28/big/img_19245 +2002/07/24/big/img_321 +2002/08/02/big/img_662 +2002/08/08/big/img_1033 +2003/01/17/big/img_867 +2002/07/22/big/img_652 +2003/01/14/big/img_224 +2002/08/18/big/img_49 +2002/07/26/big/img_46 +2002/08/31/big/img_18021 +2002/07/25/big/img_151 +2002/08/23/big/img_540 +2002/08/25/big/img_693 +2002/07/23/big/img_340 +2002/07/28/big/img_117 +2002/09/02/big/img_15768 +2002/08/26/big/img_562 +2002/07/24/big/img_480 +2003/01/15/big/img_341 +2002/08/10/big/img_783 +2002/08/20/big/img_132 +2003/01/14/big/img_370 +2002/07/20/big/img_720 +2002/08/03/big/img_144 +2002/08/20/big/img_538 +2002/08/01/big/img_1745 +2002/08/11/big/img_683 +2002/08/03/big/img_328 +2002/08/10/big/img_793 +2002/08/14/big/img_689 +2002/08/02/big/img_162 +2003/01/17/big/img_411 +2002/07/31/big/img_361 +2002/08/15/big/img_289 +2002/08/08/big/img_254 +2002/08/15/big/img_996 +2002/08/20/big/img_785 +2002/07/24/big/img_511 +2002/08/06/big/img_2614 +2002/08/29/big/img_18733 +2002/08/17/big/img_78 +2002/07/30/big/img_378 +2002/08/31/big/img_17947 +2002/08/26/big/img_88 +2002/07/30/big/img_558 +2002/08/02/big/img_67 +2003/01/14/big/img_325 +2002/07/29/big/img_1357 +2002/07/19/big/img_391 +2002/07/30/big/img_307 +2003/01/13/big/img_219 +2002/07/24/big/img_807 +2002/08/23/big/img_543 +2002/08/29/big/img_18620 +2002/07/22/big/img_769 +2002/08/26/big/img_503 +2002/07/30/big/img_78 +2002/08/14/big/img_1036 +2002/08/09/big/img_58 +2002/07/24/big/img_616 +2002/08/02/big/img_464 +2002/07/26/big/img_576 +2002/07/22/big/img_273 +2003/01/16/big/img_470 +2002/07/29/big/img_329 +2002/07/30/big/img_1086 +2002/07/31/big/img_353 +2002/09/02/big/img_15275 +2003/01/17/big/img_555 +2002/08/26/big/img_212 +2002/08/01/big/img_1692 +2003/01/15/big/img_600 +2002/07/29/big/img_825 +2002/08/08/big/img_68 +2002/08/10/big/img_719 +2002/07/31/big/img_636 +2002/07/29/big/img_325 +2002/07/21/big/img_515 +2002/07/22/big/img_705 +2003/01/13/big/img_818 +2002/08/09/big/img_486 +2002/08/22/big/img_141 +2002/07/22/big/img_303 +2002/08/09/big/img_393 +2002/07/29/big/img_963 +2002/08/02/big/img_1215 +2002/08/19/big/img_674 +2002/08/12/big/img_690 +2002/08/21/big/img_637 +2002/08/21/big/img_841 +2002/08/24/big/img_71 +2002/07/25/big/img_596 +2002/07/24/big/img_864 +2002/08/18/big/img_293 +2003/01/14/big/img_657 +2002/08/15/big/img_411 +2002/08/16/big/img_348 +2002/08/05/big/img_3157 +2002/07/20/big/img_663 +2003/01/13/big/img_654 +2003/01/16/big/img_433 +2002/08/30/big/img_18200 +2002/08/12/big/img_226 +2003/01/16/big/img_491 +2002/08/08/big/img_666 +2002/07/19/big/img_576 +2003/01/15/big/img_776 +2003/01/16/big/img_899 +2002/07/19/big/img_397 +2002/08/14/big/img_44 +2003/01/15/big/img_762 +2002/08/02/big/img_982 +2002/09/02/big/img_15234 +2002/08/17/big/img_556 +2002/08/21/big/img_410 +2002/08/21/big/img_386 +2002/07/19/big/img_690 +2002/08/05/big/img_3052 +2002/08/14/big/img_219 +2002/08/16/big/img_273 +2003/01/15/big/img_752 +2002/08/08/big/img_184 +2002/07/31/big/img_743 +2002/08/23/big/img_338 +2003/01/14/big/img_1055 +2002/08/05/big/img_3405 +2003/01/15/big/img_17 +2002/08/03/big/img_141 +2002/08/14/big/img_549 +2002/07/27/big/img_1034 +2002/07/31/big/img_932 +2002/08/30/big/img_18487 +2002/09/02/big/img_15814 +2002/08/01/big/img_2086 +2002/09/01/big/img_16535 +2002/07/22/big/img_500 +2003/01/13/big/img_400 +2002/08/25/big/img_607 +2002/08/30/big/img_18384 +2003/01/14/big/img_951 +2002/08/13/big/img_1150 +2002/08/08/big/img_1022 +2002/08/10/big/img_428 +2002/08/28/big/img_19242 +2002/08/05/big/img_3098 +2002/07/23/big/img_400 +2002/08/26/big/img_365 +2002/07/20/big/img_318 +2002/08/13/big/img_740 +2003/01/16/big/img_37 +2002/08/26/big/img_274 +2002/08/02/big/img_205 +2002/08/21/big/img_695 +2002/08/06/big/img_2289 +2002/08/20/big/img_794 +2002/08/18/big/img_438 +2002/08/07/big/img_1380 +2002/08/02/big/img_737 +2002/08/07/big/img_1651 +2002/08/15/big/img_1238 +2002/08/01/big/img_1681 +2002/08/06/big/img_3017 +2002/07/23/big/img_706 +2002/07/31/big/img_392 +2002/08/09/big/img_539 +2002/07/29/big/img_835 +2002/08/26/big/img_723 +2002/08/28/big/img_19235 +2003/01/16/big/img_353 +2002/08/10/big/img_150 +2002/08/29/big/img_19025 +2002/08/21/big/img_310 +2002/08/10/big/img_823 +2002/07/26/big/img_981 +2002/08/11/big/img_288 +2002/08/19/big/img_534 +2002/08/21/big/img_300 +2002/07/31/big/img_49 +2002/07/30/big/img_469 +2002/08/28/big/img_19197 +2002/08/25/big/img_205 +2002/08/10/big/img_390 +2002/08/23/big/img_291 +2002/08/26/big/img_230 +2002/08/18/big/img_76 +2002/07/23/big/img_409 +2002/08/14/big/img_1053 +2003/01/14/big/img_291 +2002/08/10/big/img_503 +2002/08/27/big/img_19928 +2002/08/03/big/img_563 +2002/08/17/big/img_250 +2002/08/06/big/img_2381 +2002/08/17/big/img_948 +2002/08/06/big/img_2710 +2002/07/22/big/img_696 +2002/07/31/big/img_670 +2002/08/12/big/img_594 +2002/07/29/big/img_624 +2003/01/17/big/img_934 +2002/08/03/big/img_584 +2002/08/22/big/img_1003 +2002/08/05/big/img_3396 +2003/01/13/big/img_570 +2002/08/02/big/img_219 +2002/09/02/big/img_15774 +2002/08/16/big/img_818 +2002/08/23/big/img_402 +2003/01/14/big/img_552 +2002/07/29/big/img_71 +2002/08/05/big/img_3592 +2002/08/16/big/img_80 +2002/07/27/big/img_672 +2003/01/13/big/img_470 +2003/01/16/big/img_702 +2002/09/01/big/img_16130 +2002/08/08/big/img_240 +2002/09/01/big/img_16338 +2002/07/26/big/img_312 +2003/01/14/big/img_538 +2002/07/20/big/img_695 +2002/08/30/big/img_18098 +2002/08/25/big/img_259 +2002/08/16/big/img_1042 +2002/08/09/big/img_837 +2002/08/31/big/img_17760 +2002/07/31/big/img_14 +2002/08/09/big/img_361 +2003/01/16/big/img_107 +2002/08/14/big/img_124 +2002/07/19/big/img_463 +2003/01/15/big/img_275 +2002/07/25/big/img_1151 +2002/07/29/big/img_1501 +2002/08/27/big/img_19889 +2002/08/29/big/img_18603 +2003/01/17/big/img_601 +2002/08/25/big/img_355 +2002/08/08/big/img_297 +2002/08/20/big/img_290 +2002/07/31/big/img_195 +2003/01/01/big/img_336 +2002/08/18/big/img_369 +2002/07/25/big/img_621 +2002/08/11/big/img_508 +2003/01/14/big/img_458 +2003/01/15/big/img_795 +2002/08/12/big/img_498 +2002/08/01/big/img_1734 +2002/08/02/big/img_246 +2002/08/16/big/img_565 +2002/08/11/big/img_475 +2002/08/22/big/img_408 +2002/07/28/big/img_78 +2002/07/21/big/img_81 +2003/01/14/big/img_697 +2002/08/14/big/img_661 +2002/08/15/big/img_507 +2002/08/19/big/img_55 +2002/07/22/big/img_152 +2003/01/14/big/img_470 +2002/08/03/big/img_379 +2002/08/22/big/img_506 +2003/01/16/big/img_966 +2002/08/18/big/img_698 +2002/08/24/big/img_528 +2002/08/23/big/img_10 +2002/08/01/big/img_1655 +2002/08/22/big/img_953 +2002/07/19/big/img_630 +2002/07/22/big/img_889 +2002/08/16/big/img_351 +2003/01/16/big/img_83 +2002/07/19/big/img_805 +2002/08/14/big/img_704 +2002/07/19/big/img_389 +2002/08/31/big/img_17765 +2002/07/29/big/img_606 +2003/01/17/big/img_939 +2002/09/02/big/img_15081 +2002/08/21/big/img_181 +2002/07/29/big/img_1321 +2002/07/21/big/img_497 +2002/07/20/big/img_539 +2002/08/24/big/img_119 +2002/08/01/big/img_1281 +2002/07/26/big/img_207 +2002/07/26/big/img_432 +2002/07/27/big/img_1006 +2002/08/05/big/img_3087 +2002/08/14/big/img_252 +2002/08/14/big/img_798 +2002/07/24/big/img_538 +2002/09/02/big/img_15507 +2002/08/08/big/img_901 +2003/01/14/big/img_557 +2002/08/07/big/img_1819 +2002/08/04/big/img_470 +2002/08/01/big/img_1504 +2002/08/16/big/img_1070 +2002/08/16/big/img_372 +2002/08/23/big/img_416 +2002/08/30/big/img_18208 +2002/08/01/big/img_2043 +2002/07/22/big/img_385 +2002/08/22/big/img_466 +2002/08/21/big/img_869 +2002/08/28/big/img_19429 +2002/08/02/big/img_770 +2002/07/23/big/img_433 +2003/01/14/big/img_13 +2002/07/27/big/img_953 +2002/09/02/big/img_15728 +2002/08/01/big/img_1361 +2002/08/29/big/img_18897 +2002/08/26/big/img_534 +2002/08/11/big/img_121 +2002/08/26/big/img_20130 +2002/07/31/big/img_363 +2002/08/13/big/img_978 +2002/07/25/big/img_835 +2002/08/02/big/img_906 +2003/01/14/big/img_548 +2002/07/30/big/img_80 +2002/07/26/big/img_982 +2003/01/16/big/img_99 +2002/08/19/big/img_362 +2002/08/24/big/img_376 +2002/08/07/big/img_1264 +2002/07/27/big/img_938 +2003/01/17/big/img_535 +2002/07/26/big/img_457 +2002/08/08/big/img_848 +2003/01/15/big/img_859 +2003/01/15/big/img_622 +2002/07/30/big/img_403 +2002/07/29/big/img_217 +2002/07/26/big/img_891 +2002/07/24/big/img_70 +2002/08/25/big/img_619 +2002/08/05/big/img_3375 +2002/08/01/big/img_2160 +2002/08/06/big/img_2227 +2003/01/14/big/img_117 +2002/08/14/big/img_227 +2002/08/13/big/img_565 +2002/08/19/big/img_625 +2002/08/03/big/img_812 +2002/07/24/big/img_41 +2002/08/16/big/img_235 +2002/07/29/big/img_759 +2002/07/21/big/img_433 +2002/07/29/big/img_190 +2003/01/16/big/img_435 +2003/01/13/big/img_708 +2002/07/30/big/img_57 +2002/08/22/big/img_162 +2003/01/01/big/img_558 +2003/01/15/big/img_604 +2002/08/16/big/img_935 +2002/08/20/big/img_394 +2002/07/28/big/img_465 +2002/09/02/big/img_15534 +2002/08/16/big/img_87 +2002/07/22/big/img_469 +2002/08/12/big/img_245 +2003/01/13/big/img_236 +2002/08/06/big/img_2736 +2002/08/03/big/img_348 +2003/01/14/big/img_218 +2002/07/26/big/img_232 +2003/01/15/big/img_244 +2002/07/25/big/img_1121 +2002/08/01/big/img_1484 +2002/07/26/big/img_541 +2002/08/07/big/img_1244 +2002/07/31/big/img_3 +2002/08/30/big/img_18437 +2002/08/29/big/img_19094 +2002/08/01/big/img_1355 +2002/08/19/big/img_338 +2002/07/19/big/img_255 +2002/07/21/big/img_76 +2002/08/25/big/img_199 +2002/08/12/big/img_740 +2002/07/30/big/img_852 +2002/08/15/big/img_599 +2002/08/23/big/img_254 +2002/08/19/big/img_125 +2002/07/24/big/img_2 +2002/08/04/big/img_145 +2002/08/05/big/img_3137 +2002/07/28/big/img_463 +2003/01/14/big/img_801 +2002/07/23/big/img_366 +2002/08/26/big/img_600 +2002/08/26/big/img_649 +2002/09/02/big/img_15849 +2002/07/26/big/img_248 +2003/01/13/big/img_200 +2002/08/07/big/img_1794 +2002/08/31/big/img_17270 +2002/08/23/big/img_608 +2003/01/13/big/img_837 +2002/08/23/big/img_581 +2002/08/20/big/img_754 +2002/08/18/big/img_183 +2002/08/20/big/img_328 +2002/07/22/big/img_494 +2002/07/29/big/img_399 +2002/08/28/big/img_19284 +2002/08/08/big/img_566 +2002/07/25/big/img_376 +2002/07/23/big/img_138 +2002/07/25/big/img_435 +2002/08/17/big/img_685 +2002/07/19/big/img_90 +2002/07/20/big/img_716 +2002/08/31/big/img_17458 +2002/08/26/big/img_461 +2002/07/25/big/img_355 +2002/08/06/big/img_2152 +2002/07/27/big/img_932 +2002/07/23/big/img_232 +2002/08/08/big/img_1020 +2002/07/31/big/img_366 +2002/08/06/big/img_2667 +2002/08/21/big/img_465 +2002/08/15/big/img_305 +2002/08/02/big/img_247 +2002/07/28/big/img_46 +2002/08/27/big/img_19922 +2002/08/23/big/img_643 +2003/01/13/big/img_624 +2002/08/23/big/img_625 +2002/08/05/big/img_3787 +2003/01/13/big/img_627 +2002/09/01/big/img_16381 +2002/08/05/big/img_3668 +2002/07/21/big/img_535 +2002/08/27/big/img_19680 +2002/07/22/big/img_413 +2002/07/29/big/img_481 +2003/01/15/big/img_496 +2002/07/23/big/img_701 +2002/08/29/big/img_18670 +2002/07/28/big/img_319 +2003/01/14/big/img_517 +2002/07/26/big/img_256 +2003/01/16/big/img_593 +2002/07/30/big/img_956 +2002/07/30/big/img_667 +2002/07/25/big/img_100 +2002/08/11/big/img_570 +2002/07/26/big/img_745 +2002/08/04/big/img_834 +2002/08/25/big/img_521 +2002/08/01/big/img_2148 +2002/09/02/big/img_15183 +2002/08/22/big/img_514 +2002/08/23/big/img_477 +2002/07/23/big/img_336 +2002/07/26/big/img_481 +2002/08/20/big/img_409 +2002/07/23/big/img_918 +2002/08/09/big/img_474 +2002/08/02/big/img_929 +2002/08/31/big/img_17932 +2002/08/19/big/img_161 +2002/08/09/big/img_667 +2002/07/31/big/img_805 +2002/09/02/big/img_15678 +2002/08/31/big/img_17509 +2002/08/29/big/img_18998 +2002/07/23/big/img_301 +2002/08/07/big/img_1612 +2002/08/06/big/img_2472 +2002/07/23/big/img_466 +2002/08/27/big/img_19634 +2003/01/16/big/img_16 +2002/08/14/big/img_193 +2002/08/21/big/img_340 +2002/08/27/big/img_19799 +2002/08/01/big/img_1345 +2002/08/07/big/img_1448 +2002/08/11/big/img_324 +2003/01/16/big/img_754 +2002/08/13/big/img_418 +2003/01/16/big/img_544 +2002/08/19/big/img_135 +2002/08/10/big/img_455 +2002/08/10/big/img_693 +2002/08/31/big/img_17967 +2002/08/28/big/img_19229 +2002/08/04/big/img_811 +2002/09/01/big/img_16225 +2003/01/16/big/img_428 +2002/09/02/big/img_15295 +2002/07/26/big/img_108 +2002/07/21/big/img_477 +2002/08/07/big/img_1354 +2002/08/23/big/img_246 +2002/08/16/big/img_652 +2002/07/27/big/img_553 +2002/07/31/big/img_346 +2002/08/04/big/img_537 +2002/08/08/big/img_498 +2002/08/29/big/img_18956 +2003/01/13/big/img_922 +2002/08/31/big/img_17425 +2002/07/26/big/img_438 +2002/08/19/big/img_185 +2003/01/16/big/img_33 +2002/08/10/big/img_252 +2002/07/29/big/img_598 +2002/08/27/big/img_19820 +2002/08/06/big/img_2664 +2002/08/20/big/img_705 +2003/01/14/big/img_816 +2002/08/03/big/img_552 +2002/07/25/big/img_561 +2002/07/25/big/img_934 +2002/08/01/big/img_1893 +2003/01/14/big/img_746 +2003/01/16/big/img_519 +2002/08/03/big/img_681 +2002/07/24/big/img_808 +2002/08/14/big/img_803 +2002/08/25/big/img_155 +2002/07/30/big/img_1107 +2002/08/29/big/img_18882 +2003/01/15/big/img_598 +2002/08/19/big/img_122 +2002/07/30/big/img_428 +2002/07/24/big/img_684 +2002/08/22/big/img_192 +2002/08/22/big/img_543 +2002/08/07/big/img_1318 +2002/08/18/big/img_25 +2002/07/26/big/img_583 +2002/07/20/big/img_464 +2002/08/19/big/img_664 +2002/08/24/big/img_861 +2002/09/01/big/img_16136 +2002/08/22/big/img_400 +2002/08/12/big/img_445 +2003/01/14/big/img_174 +2002/08/27/big/img_19677 +2002/08/31/big/img_17214 +2002/08/30/big/img_18175 +2003/01/17/big/img_402 +2002/08/06/big/img_2396 +2002/08/18/big/img_448 +2002/08/21/big/img_165 +2002/08/31/big/img_17609 +2003/01/01/big/img_151 +2002/08/26/big/img_372 +2002/09/02/big/img_15994 +2002/07/26/big/img_660 +2002/09/02/big/img_15197 +2002/07/29/big/img_258 +2002/08/30/big/img_18525 +2003/01/13/big/img_368 +2002/07/29/big/img_1538 +2002/07/21/big/img_787 +2002/08/18/big/img_152 +2002/08/06/big/img_2379 +2003/01/17/big/img_864 +2002/08/27/big/img_19998 +2002/08/01/big/img_1634 +2002/07/25/big/img_414 +2002/08/22/big/img_627 +2002/08/07/big/img_1669 +2002/08/16/big/img_1052 +2002/08/31/big/img_17796 +2002/08/18/big/img_199 +2002/09/02/big/img_15147 +2002/08/09/big/img_460 +2002/08/14/big/img_581 +2002/08/30/big/img_18286 +2002/07/26/big/img_337 +2002/08/18/big/img_589 +2003/01/14/big/img_866 +2002/07/20/big/img_624 +2002/08/01/big/img_1801 +2002/07/24/big/img_683 +2002/08/09/big/img_725 +2003/01/14/big/img_34 +2002/07/30/big/img_144 +2002/07/30/big/img_706 +2002/08/08/big/img_394 +2002/08/19/big/img_619 +2002/08/06/big/img_2703 +2002/08/29/big/img_19034 +2002/07/24/big/img_67 +2002/08/27/big/img_19841 +2002/08/19/big/img_427 +2003/01/14/big/img_333 +2002/09/01/big/img_16406 +2002/07/19/big/img_882 +2002/08/17/big/img_238 +2003/01/14/big/img_739 +2002/07/22/big/img_151 +2002/08/21/big/img_743 +2002/07/25/big/img_1048 +2002/07/30/big/img_395 +2003/01/13/big/img_584 +2002/08/13/big/img_742 +2002/08/13/big/img_1168 +2003/01/14/big/img_147 +2002/07/26/big/img_803 +2002/08/05/big/img_3298 +2002/08/07/big/img_1451 +2002/08/16/big/img_424 +2002/07/29/big/img_1069 +2002/09/01/big/img_16735 +2002/07/21/big/img_637 +2003/01/14/big/img_585 +2002/08/02/big/img_358 +2003/01/13/big/img_358 +2002/08/14/big/img_198 +2002/08/17/big/img_935 +2002/08/04/big/img_42 +2002/08/30/big/img_18245 +2002/07/25/big/img_158 +2002/08/22/big/img_744 +2002/08/06/big/img_2291 +2002/08/05/big/img_3044 +2002/07/30/big/img_272 +2002/08/23/big/img_641 +2002/07/24/big/img_797 +2002/07/30/big/img_392 +2003/01/14/big/img_447 +2002/07/31/big/img_898 +2002/08/06/big/img_2812 +2002/08/13/big/img_564 +2002/07/22/big/img_43 +2002/07/26/big/img_634 +2002/07/19/big/img_843 +2002/08/26/big/img_58 +2002/07/21/big/img_375 +2002/08/25/big/img_729 +2002/07/19/big/img_561 +2003/01/15/big/img_884 +2002/07/25/big/img_891 +2002/08/09/big/img_558 +2002/08/26/big/img_587 +2002/08/13/big/img_1146 +2002/09/02/big/img_15153 +2002/07/26/big/img_316 +2002/08/01/big/img_1940 +2002/08/26/big/img_90 +2003/01/13/big/img_347 +2002/07/25/big/img_520 +2002/08/29/big/img_18718 +2002/08/28/big/img_19219 +2002/08/13/big/img_375 +2002/07/20/big/img_719 +2002/08/31/big/img_17431 +2002/07/28/big/img_192 +2002/08/26/big/img_259 +2002/08/18/big/img_484 +2002/07/29/big/img_580 +2002/07/26/big/img_84 +2002/08/02/big/img_302 +2002/08/31/big/img_17007 +2003/01/15/big/img_543 +2002/09/01/big/img_16488 +2002/08/22/big/img_798 +2002/07/30/big/img_383 +2002/08/04/big/img_668 +2002/08/13/big/img_156 +2002/08/07/big/img_1353 +2002/07/25/big/img_281 +2003/01/14/big/img_587 +2003/01/15/big/img_524 +2002/08/19/big/img_726 +2002/08/21/big/img_709 +2002/08/26/big/img_465 +2002/07/31/big/img_658 +2002/08/28/big/img_19148 +2002/07/23/big/img_423 +2002/08/16/big/img_758 +2002/08/22/big/img_523 +2002/08/16/big/img_591 +2002/08/23/big/img_845 +2002/07/26/big/img_678 +2002/08/09/big/img_806 +2002/08/06/big/img_2369 +2002/07/29/big/img_457 +2002/07/19/big/img_278 +2002/08/30/big/img_18107 +2002/07/26/big/img_444 +2002/08/20/big/img_278 +2002/08/26/big/img_92 +2002/08/26/big/img_257 +2002/07/25/big/img_266 +2002/08/05/big/img_3829 +2002/07/26/big/img_757 +2002/07/29/big/img_1536 +2002/08/09/big/img_472 +2003/01/17/big/img_480 +2002/08/28/big/img_19355 +2002/07/26/big/img_97 +2002/08/06/big/img_2503 +2002/07/19/big/img_254 +2002/08/01/big/img_1470 +2002/08/21/big/img_42 +2002/08/20/big/img_217 +2002/08/06/big/img_2459 +2002/07/19/big/img_552 +2002/08/13/big/img_717 +2002/08/12/big/img_586 +2002/08/20/big/img_411 +2003/01/13/big/img_768 +2002/08/07/big/img_1747 +2002/08/15/big/img_385 +2002/08/01/big/img_1648 +2002/08/15/big/img_311 +2002/08/21/big/img_95 +2002/08/09/big/img_108 +2002/08/21/big/img_398 +2002/08/17/big/img_340 +2002/08/14/big/img_474 +2002/08/13/big/img_294 +2002/08/24/big/img_840 +2002/08/09/big/img_808 +2002/08/23/big/img_491 +2002/07/28/big/img_33 +2003/01/13/big/img_664 +2002/08/02/big/img_261 +2002/08/09/big/img_591 +2002/07/26/big/img_309 +2003/01/14/big/img_372 +2002/08/19/big/img_581 +2002/08/19/big/img_168 +2002/08/26/big/img_422 +2002/07/24/big/img_106 +2002/08/01/big/img_1936 +2002/08/05/big/img_3764 +2002/08/21/big/img_266 +2002/08/31/big/img_17968 +2002/08/01/big/img_1941 +2002/08/15/big/img_550 +2002/08/14/big/img_13 +2002/07/30/big/img_171 +2003/01/13/big/img_490 +2002/07/25/big/img_427 +2002/07/19/big/img_770 +2002/08/12/big/img_759 +2003/01/15/big/img_1360 +2002/08/05/big/img_3692 +2003/01/16/big/img_30 +2002/07/25/big/img_1026 +2002/07/22/big/img_288 +2002/08/29/big/img_18801 +2002/07/24/big/img_793 +2002/08/13/big/img_178 +2002/08/06/big/img_2322 +2003/01/14/big/img_560 +2002/08/18/big/img_408 +2003/01/16/big/img_915 +2003/01/16/big/img_679 +2002/08/07/big/img_1552 +2002/08/29/big/img_19050 +2002/08/01/big/img_2172 +2002/07/31/big/img_30 +2002/07/30/big/img_1019 +2002/07/30/big/img_587 +2003/01/13/big/img_773 +2002/07/30/big/img_410 +2002/07/28/big/img_65 +2002/08/05/big/img_3138 +2002/07/23/big/img_541 +2002/08/22/big/img_963 +2002/07/27/big/img_657 +2002/07/30/big/img_1051 +2003/01/16/big/img_150 +2002/07/31/big/img_519 +2002/08/01/big/img_1961 +2002/08/05/big/img_3752 +2002/07/23/big/img_631 +2003/01/14/big/img_237 +2002/07/28/big/img_21 +2002/07/22/big/img_813 +2002/08/05/big/img_3563 +2003/01/17/big/img_620 +2002/07/19/big/img_523 +2002/07/30/big/img_904 +2002/08/29/big/img_18642 +2002/08/11/big/img_492 +2002/08/01/big/img_2130 +2002/07/25/big/img_618 +2002/08/17/big/img_305 +2003/01/16/big/img_520 +2002/07/26/big/img_495 +2002/08/17/big/img_164 +2002/08/03/big/img_440 +2002/07/24/big/img_441 +2002/08/06/big/img_2146 +2002/08/11/big/img_558 +2002/08/02/big/img_545 +2002/08/31/big/img_18090 +2003/01/01/big/img_136 +2002/07/25/big/img_1099 +2003/01/13/big/img_728 +2003/01/16/big/img_197 +2002/07/26/big/img_651 +2002/08/11/big/img_676 +2003/01/15/big/img_10 +2002/08/21/big/img_250 +2002/08/14/big/img_325 +2002/08/04/big/img_390 +2002/07/24/big/img_554 +2003/01/16/big/img_333 +2002/07/31/big/img_922 +2002/09/02/big/img_15586 +2003/01/16/big/img_184 +2002/07/22/big/img_766 +2002/07/21/big/img_608 +2002/08/07/big/img_1578 +2002/08/17/big/img_961 +2002/07/27/big/img_324 +2002/08/05/big/img_3765 +2002/08/23/big/img_462 +2003/01/16/big/img_382 +2002/08/27/big/img_19838 +2002/08/01/big/img_1505 +2002/08/21/big/img_662 +2002/08/14/big/img_605 +2002/08/19/big/img_816 +2002/07/29/big/img_136 +2002/08/20/big/img_719 +2002/08/06/big/img_2826 +2002/08/10/big/img_630 +2003/01/17/big/img_973 +2002/08/14/big/img_116 +2002/08/02/big/img_666 +2002/08/21/big/img_710 +2002/08/05/big/img_55 +2002/07/31/big/img_229 +2002/08/01/big/img_1549 +2002/07/23/big/img_432 +2002/07/21/big/img_430 +2002/08/21/big/img_549 +2002/08/08/big/img_985 +2002/07/20/big/img_610 +2002/07/23/big/img_978 +2002/08/23/big/img_219 +2002/07/25/big/img_175 +2003/01/15/big/img_230 +2002/08/23/big/img_385 +2002/07/31/big/img_879 +2002/08/12/big/img_495 +2002/08/22/big/img_499 +2002/08/30/big/img_18322 +2002/08/15/big/img_795 +2002/08/13/big/img_835 +2003/01/17/big/img_930 +2002/07/30/big/img_873 +2002/08/11/big/img_257 +2002/07/31/big/img_593 +2002/08/21/big/img_916 +2003/01/13/big/img_814 +2002/07/25/big/img_722 +2002/08/16/big/img_379 +2002/07/31/big/img_497 +2002/07/22/big/img_602 +2002/08/21/big/img_642 +2002/08/21/big/img_614 +2002/08/23/big/img_482 +2002/07/29/big/img_603 +2002/08/13/big/img_705 +2002/07/23/big/img_833 +2003/01/14/big/img_511 +2002/07/24/big/img_376 +2002/08/17/big/img_1030 +2002/08/05/big/img_3576 +2002/08/16/big/img_540 +2002/07/22/big/img_630 +2002/08/10/big/img_180 +2002/08/14/big/img_905 +2002/08/29/big/img_18777 +2002/08/22/big/img_693 +2003/01/16/big/img_933 +2002/08/20/big/img_555 +2002/08/15/big/img_549 +2003/01/14/big/img_830 +2003/01/16/big/img_64 +2002/08/27/big/img_19670 +2002/08/22/big/img_729 +2002/07/27/big/img_981 +2002/08/09/big/img_458 +2003/01/17/big/img_884 +2002/07/25/big/img_639 +2002/08/31/big/img_18008 +2002/08/22/big/img_249 +2002/08/17/big/img_971 +2002/08/04/big/img_308 +2002/07/28/big/img_362 +2002/08/12/big/img_142 +2002/08/26/big/img_61 +2002/08/14/big/img_422 +2002/07/19/big/img_607 +2003/01/15/big/img_717 +2002/08/01/big/img_1475 +2002/08/29/big/img_19061 +2003/01/01/big/img_346 +2002/07/20/big/img_315 +2003/01/15/big/img_756 +2002/08/15/big/img_879 +2002/08/08/big/img_615 +2003/01/13/big/img_431 +2002/08/05/big/img_3233 +2002/08/24/big/img_526 +2003/01/13/big/img_717 +2002/09/01/big/img_16408 +2002/07/22/big/img_217 +2002/07/31/big/img_960 +2002/08/21/big/img_610 +2002/08/05/big/img_3753 +2002/08/03/big/img_151 +2002/08/21/big/img_267 +2002/08/01/big/img_2175 +2002/08/04/big/img_556 +2002/08/21/big/img_527 +2002/09/02/big/img_15800 +2002/07/27/big/img_156 +2002/07/20/big/img_590 +2002/08/15/big/img_700 +2002/08/08/big/img_444 +2002/07/25/big/img_94 +2002/07/24/big/img_778 +2002/08/14/big/img_694 +2002/07/20/big/img_666 +2002/08/02/big/img_200 +2002/08/02/big/img_578 +2003/01/17/big/img_332 +2002/09/01/big/img_16352 +2002/08/27/big/img_19668 +2002/07/23/big/img_823 +2002/08/13/big/img_431 +2003/01/16/big/img_463 +2002/08/27/big/img_19711 +2002/08/23/big/img_154 +2002/07/31/big/img_360 +2002/08/23/big/img_555 +2002/08/10/big/img_561 +2003/01/14/big/img_550 +2002/08/07/big/img_1370 +2002/07/30/big/img_1184 +2002/08/01/big/img_1445 +2002/08/23/big/img_22 +2002/07/30/big/img_606 +2003/01/17/big/img_271 +2002/08/31/big/img_17316 +2002/08/16/big/img_973 +2002/07/26/big/img_77 +2002/07/20/big/img_788 +2002/08/06/big/img_2426 +2002/08/07/big/img_1498 +2002/08/16/big/img_358 +2002/08/06/big/img_2851 +2002/08/12/big/img_359 +2002/08/01/big/img_1521 +2002/08/02/big/img_709 +2002/08/20/big/img_935 +2002/08/12/big/img_188 +2002/08/24/big/img_411 +2002/08/22/big/img_680 +2002/08/06/big/img_2480 +2002/07/20/big/img_627 +2002/07/30/big/img_214 +2002/07/25/big/img_354 +2002/08/02/big/img_636 +2003/01/15/big/img_661 +2002/08/07/big/img_1327 +2002/08/01/big/img_2108 +2002/08/31/big/img_17919 +2002/08/29/big/img_18768 +2002/08/05/big/img_3840 +2002/07/26/big/img_242 +2003/01/14/big/img_451 +2002/08/20/big/img_923 +2002/08/27/big/img_19908 +2002/08/16/big/img_282 +2002/08/19/big/img_440 +2003/01/01/big/img_230 +2002/08/08/big/img_212 +2002/07/20/big/img_443 +2002/08/25/big/img_635 +2003/01/13/big/img_1169 +2002/07/26/big/img_998 +2002/08/15/big/img_995 +2002/08/06/big/img_3002 +2002/07/29/big/img_460 +2003/01/14/big/img_925 +2002/07/23/big/img_539 +2002/08/16/big/img_694 +2003/01/13/big/img_459 +2002/07/23/big/img_249 +2002/08/20/big/img_539 +2002/08/04/big/img_186 +2002/08/26/big/img_264 +2002/07/22/big/img_704 +2002/08/25/big/img_277 +2002/08/22/big/img_988 +2002/07/29/big/img_504 +2002/08/05/big/img_3600 +2002/08/30/big/img_18380 +2003/01/14/big/img_937 +2002/08/21/big/img_254 +2002/08/10/big/img_130 +2002/08/20/big/img_339 +2003/01/14/big/img_428 +2002/08/20/big/img_889 +2002/08/31/big/img_17637 +2002/07/26/big/img_644 +2002/09/01/big/img_16776 +2002/08/06/big/img_2239 +2002/08/06/big/img_2646 +2003/01/13/big/img_491 +2002/08/10/big/img_579 +2002/08/21/big/img_713 +2002/08/22/big/img_482 +2002/07/22/big/img_167 +2002/07/24/big/img_539 +2002/08/14/big/img_721 +2002/07/25/big/img_389 +2002/09/01/big/img_16591 +2002/08/13/big/img_543 +2003/01/14/big/img_432 +2002/08/09/big/img_287 +2002/07/26/big/img_126 +2002/08/23/big/img_412 +2002/08/15/big/img_1034 +2002/08/28/big/img_19485 +2002/07/31/big/img_236 +2002/07/30/big/img_523 +2002/07/19/big/img_141 +2003/01/17/big/img_957 +2002/08/04/big/img_81 +2002/07/25/big/img_206 +2002/08/15/big/img_716 +2002/08/13/big/img_403 +2002/08/15/big/img_685 +2002/07/26/big/img_884 +2002/07/19/big/img_499 +2002/07/23/big/img_772 +2002/07/27/big/img_752 +2003/01/14/big/img_493 +2002/08/25/big/img_664 +2002/07/31/big/img_334 +2002/08/26/big/img_678 +2002/09/01/big/img_16541 +2003/01/14/big/img_347 +2002/07/23/big/img_187 +2002/07/30/big/img_1163 +2002/08/05/big/img_35 +2002/08/22/big/img_944 +2002/08/07/big/img_1239 +2002/07/29/big/img_1215 +2002/08/03/big/img_312 +2002/08/05/big/img_3523 +2002/07/29/big/img_218 +2002/08/13/big/img_672 +2002/08/16/big/img_205 +2002/08/17/big/img_594 +2002/07/29/big/img_1411 +2002/07/30/big/img_942 +2003/01/16/big/img_312 +2002/08/08/big/img_312 +2002/07/25/big/img_15 +2002/08/09/big/img_839 +2002/08/01/big/img_2069 +2002/08/31/big/img_17512 +2002/08/01/big/img_3 +2002/07/31/big/img_320 +2003/01/15/big/img_1265 +2002/08/14/big/img_563 +2002/07/31/big/img_167 +2002/08/20/big/img_374 +2002/08/13/big/img_406 +2002/08/08/big/img_625 +2002/08/02/big/img_314 +2002/08/27/big/img_19964 +2002/09/01/big/img_16670 +2002/07/31/big/img_599 +2002/08/29/big/img_18906 +2002/07/24/big/img_373 +2002/07/26/big/img_513 +2002/09/02/big/img_15497 +2002/08/19/big/img_117 +2003/01/01/big/img_158 +2002/08/24/big/img_178 +2003/01/13/big/img_935 +2002/08/13/big/img_609 +2002/08/30/big/img_18341 +2002/08/25/big/img_674 +2003/01/13/big/img_209 +2002/08/13/big/img_258 +2002/08/05/big/img_3543 +2002/08/07/big/img_1970 +2002/08/06/big/img_3004 +2003/01/17/big/img_487 +2002/08/24/big/img_873 +2002/08/29/big/img_18730 +2002/08/09/big/img_375 +2003/01/16/big/img_751 +2002/08/02/big/img_603 +2002/08/19/big/img_325 +2002/09/01/big/img_16420 +2002/08/05/big/img_3633 +2002/08/21/big/img_516 +2002/07/19/big/img_501 +2002/07/26/big/img_688 +2002/07/24/big/img_256 +2002/07/25/big/img_438 +2002/07/31/big/img_1017 +2002/08/22/big/img_512 +2002/07/21/big/img_543 +2002/08/08/big/img_223 +2002/08/19/big/img_189 +2002/08/12/big/img_630 +2002/07/30/big/img_958 +2002/07/28/big/img_208 +2002/08/31/big/img_17691 +2002/07/22/big/img_542 +2002/07/19/big/img_741 +2002/07/19/big/img_158 +2002/08/15/big/img_399 +2002/08/01/big/img_2159 +2002/08/14/big/img_455 +2002/08/17/big/img_1011 +2002/08/26/big/img_744 +2002/08/12/big/img_624 +2003/01/17/big/img_821 +2002/08/16/big/img_980 +2002/07/28/big/img_281 +2002/07/25/big/img_171 +2002/08/03/big/img_116 +2002/07/22/big/img_467 +2002/07/31/big/img_750 +2002/07/26/big/img_435 +2002/07/19/big/img_822 +2002/08/13/big/img_626 +2002/08/11/big/img_344 +2002/08/02/big/img_473 +2002/09/01/big/img_16817 +2002/08/01/big/img_1275 +2002/08/28/big/img_19270 +2002/07/23/big/img_607 +2002/08/09/big/img_316 +2002/07/29/big/img_626 +2002/07/24/big/img_824 +2002/07/22/big/img_342 +2002/08/08/big/img_794 +2002/08/07/big/img_1209 +2002/07/19/big/img_18 +2002/08/25/big/img_634 +2002/07/24/big/img_730 +2003/01/17/big/img_356 +2002/07/23/big/img_305 +2002/07/30/big/img_453 +2003/01/13/big/img_972 +2002/08/06/big/img_2610 +2002/08/29/big/img_18920 +2002/07/31/big/img_123 +2002/07/26/big/img_979 +2002/08/24/big/img_635 +2002/08/05/big/img_3704 +2002/08/07/big/img_1358 +2002/07/22/big/img_306 +2002/08/13/big/img_619 +2002/08/02/big/img_366 diff --git a/GPEN/face_detect/data/__init__.py b/GPEN/face_detect/data/__init__.py new file mode 100644 index 0000000..ea50eba --- /dev/null +++ b/GPEN/face_detect/data/__init__.py @@ -0,0 +1,3 @@ +from .wider_face import WiderFaceDetection, detection_collate +from .data_augment import * +from .config import * diff --git a/GPEN/face_detect/data/config.py b/GPEN/face_detect/data/config.py new file mode 100644 index 0000000..e57cdc5 --- /dev/null +++ b/GPEN/face_detect/data/config.py @@ -0,0 +1,42 @@ +# config.py + +cfg_mnet = { + 'name': 'mobilenet0.25', + 'min_sizes': [[16, 32], [64, 128], [256, 512]], + 'steps': [8, 16, 32], + 'variance': [0.1, 0.2], + 'clip': False, + 'loc_weight': 2.0, + 'gpu_train': True, + 'batch_size': 32, + 'ngpu': 1, + 'epoch': 250, + 'decay1': 190, + 'decay2': 220, + 'image_size': 640, + 'pretrain': False, + 'return_layers': {'stage1': 1, 'stage2': 2, 'stage3': 3}, + 'in_channel': 32, + 'out_channel': 64 +} + +cfg_re50 = { + 'name': 'Resnet50', + 'min_sizes': [[16, 32], [64, 128], [256, 512]], + 'steps': [8, 16, 32], + 'variance': [0.1, 0.2], + 'clip': False, + 'loc_weight': 2.0, + 'gpu_train': True, + 'batch_size': 24, + 'ngpu': 4, + 'epoch': 100, + 'decay1': 70, + 'decay2': 90, + 'image_size': 840, + 'pretrain': False, + 'return_layers': {'layer2': 1, 'layer3': 2, 'layer4': 3}, + 'in_channel': 256, + 'out_channel': 256 +} + diff --git a/GPEN/face_detect/data/data_augment.py b/GPEN/face_detect/data/data_augment.py new file mode 100644 index 0000000..c1b52ae --- /dev/null +++ b/GPEN/face_detect/data/data_augment.py @@ -0,0 +1,237 @@ +import cv2 +import numpy as np +import random +from utils.box_utils import matrix_iof + + +def _crop(image, boxes, labels, landm, img_dim): + height, width, _ = image.shape + pad_image_flag = True + + for _ in range(250): + """ + if random.uniform(0, 1) <= 0.2: + scale = 1.0 + else: + scale = random.uniform(0.3, 1.0) + """ + PRE_SCALES = [0.3, 0.45, 0.6, 0.8, 1.0] + scale = random.choice(PRE_SCALES) + short_side = min(width, height) + w = int(scale * short_side) + h = w + + if width == w: + l = 0 + else: + l = random.randrange(width - w) + if height == h: + t = 0 + else: + t = random.randrange(height - h) + roi = np.array((l, t, l + w, t + h)) + + value = matrix_iof(boxes, roi[np.newaxis]) + flag = (value >= 1) + if not flag.any(): + continue + + centers = (boxes[:, :2] + boxes[:, 2:]) / 2 + mask_a = np.logical_and(roi[:2] < centers, centers < roi[2:]).all(axis=1) + boxes_t = boxes[mask_a].copy() + labels_t = labels[mask_a].copy() + landms_t = landm[mask_a].copy() + landms_t = landms_t.reshape([-1, 5, 2]) + + if boxes_t.shape[0] == 0: + continue + + image_t = image[roi[1]:roi[3], roi[0]:roi[2]] + + boxes_t[:, :2] = np.maximum(boxes_t[:, :2], roi[:2]) + boxes_t[:, :2] -= roi[:2] + boxes_t[:, 2:] = np.minimum(boxes_t[:, 2:], roi[2:]) + boxes_t[:, 2:] -= roi[:2] + + # landm + landms_t[:, :, :2] = landms_t[:, :, :2] - roi[:2] + landms_t[:, :, :2] = np.maximum(landms_t[:, :, :2], np.array([0, 0])) + landms_t[:, :, :2] = np.minimum(landms_t[:, :, :2], roi[2:] - roi[:2]) + landms_t = landms_t.reshape([-1, 10]) + + + # make sure that the cropped image contains at least one face > 16 pixel at training image scale + b_w_t = (boxes_t[:, 2] - boxes_t[:, 0] + 1) / w * img_dim + b_h_t = (boxes_t[:, 3] - boxes_t[:, 1] + 1) / h * img_dim + mask_b = np.minimum(b_w_t, b_h_t) > 0.0 + boxes_t = boxes_t[mask_b] + labels_t = labels_t[mask_b] + landms_t = landms_t[mask_b] + + if boxes_t.shape[0] == 0: + continue + + pad_image_flag = False + + return image_t, boxes_t, labels_t, landms_t, pad_image_flag + return image, boxes, labels, landm, pad_image_flag + + +def _distort(image): + + def _convert(image, alpha=1, beta=0): + tmp = image.astype(float) * alpha + beta + tmp[tmp < 0] = 0 + tmp[tmp > 255] = 255 + image[:] = tmp + + image = image.copy() + + if random.randrange(2): + + #brightness distortion + if random.randrange(2): + _convert(image, beta=random.uniform(-32, 32)) + + #contrast distortion + if random.randrange(2): + _convert(image, alpha=random.uniform(0.5, 1.5)) + + image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + #saturation distortion + if random.randrange(2): + _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5)) + + #hue distortion + if random.randrange(2): + tmp = image[:, :, 0].astype(int) + random.randint(-18, 18) + tmp %= 180 + image[:, :, 0] = tmp + + image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) + + else: + + #brightness distortion + if random.randrange(2): + _convert(image, beta=random.uniform(-32, 32)) + + image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + #saturation distortion + if random.randrange(2): + _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5)) + + #hue distortion + if random.randrange(2): + tmp = image[:, :, 0].astype(int) + random.randint(-18, 18) + tmp %= 180 + image[:, :, 0] = tmp + + image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) + + #contrast distortion + if random.randrange(2): + _convert(image, alpha=random.uniform(0.5, 1.5)) + + return image + + +def _expand(image, boxes, fill, p): + if random.randrange(2): + return image, boxes + + height, width, depth = image.shape + + scale = random.uniform(1, p) + w = int(scale * width) + h = int(scale * height) + + left = random.randint(0, w - width) + top = random.randint(0, h - height) + + boxes_t = boxes.copy() + boxes_t[:, :2] += (left, top) + boxes_t[:, 2:] += (left, top) + expand_image = np.empty( + (h, w, depth), + dtype=image.dtype) + expand_image[:, :] = fill + expand_image[top:top + height, left:left + width] = image + image = expand_image + + return image, boxes_t + + +def _mirror(image, boxes, landms): + _, width, _ = image.shape + if random.randrange(2): + image = image[:, ::-1] + boxes = boxes.copy() + boxes[:, 0::2] = width - boxes[:, 2::-2] + + # landm + landms = landms.copy() + landms = landms.reshape([-1, 5, 2]) + landms[:, :, 0] = width - landms[:, :, 0] + tmp = landms[:, 1, :].copy() + landms[:, 1, :] = landms[:, 0, :] + landms[:, 0, :] = tmp + tmp1 = landms[:, 4, :].copy() + landms[:, 4, :] = landms[:, 3, :] + landms[:, 3, :] = tmp1 + landms = landms.reshape([-1, 10]) + + return image, boxes, landms + + +def _pad_to_square(image, rgb_mean, pad_image_flag): + if not pad_image_flag: + return image + height, width, _ = image.shape + long_side = max(width, height) + image_t = np.empty((long_side, long_side, 3), dtype=image.dtype) + image_t[:, :] = rgb_mean + image_t[0:0 + height, 0:0 + width] = image + return image_t + + +def _resize_subtract_mean(image, insize, rgb_mean): + interp_methods = [cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_NEAREST, cv2.INTER_LANCZOS4] + interp_method = interp_methods[random.randrange(5)] + image = cv2.resize(image, (insize, insize), interpolation=interp_method) + image = image.astype(np.float32) + image -= rgb_mean + return image.transpose(2, 0, 1) + + +class preproc(object): + + def __init__(self, img_dim, rgb_means): + self.img_dim = img_dim + self.rgb_means = rgb_means + + def __call__(self, image, targets): + assert targets.shape[0] > 0, "this image does not have gt" + + boxes = targets[:, :4].copy() + labels = targets[:, -1].copy() + landm = targets[:, 4:-1].copy() + + image_t, boxes_t, labels_t, landm_t, pad_image_flag = _crop(image, boxes, labels, landm, self.img_dim) + image_t = _distort(image_t) + image_t = _pad_to_square(image_t,self.rgb_means, pad_image_flag) + image_t, boxes_t, landm_t = _mirror(image_t, boxes_t, landm_t) + height, width, _ = image_t.shape + image_t = _resize_subtract_mean(image_t, self.img_dim, self.rgb_means) + boxes_t[:, 0::2] /= width + boxes_t[:, 1::2] /= height + + landm_t[:, 0::2] /= width + landm_t[:, 1::2] /= height + + labels_t = np.expand_dims(labels_t, 1) + targets_t = np.hstack((boxes_t, landm_t, labels_t)) + + return image_t, targets_t diff --git a/GPEN/face_detect/data/wider_face.py b/GPEN/face_detect/data/wider_face.py new file mode 100644 index 0000000..22f56ef --- /dev/null +++ b/GPEN/face_detect/data/wider_face.py @@ -0,0 +1,101 @@ +import os +import os.path +import sys +import torch +import torch.utils.data as data +import cv2 +import numpy as np + +class WiderFaceDetection(data.Dataset): + def __init__(self, txt_path, preproc=None): + self.preproc = preproc + self.imgs_path = [] + self.words = [] + f = open(txt_path,'r') + lines = f.readlines() + isFirst = True + labels = [] + for line in lines: + line = line.rstrip() + if line.startswith('#'): + if isFirst is True: + isFirst = False + else: + labels_copy = labels.copy() + self.words.append(labels_copy) + labels.clear() + path = line[2:] + path = txt_path.replace('label.txt','images/') + path + self.imgs_path.append(path) + else: + line = line.split(' ') + label = [float(x) for x in line] + labels.append(label) + + self.words.append(labels) + + def __len__(self): + return len(self.imgs_path) + + def __getitem__(self, index): + img = cv2.imread(self.imgs_path[index]) + height, width, _ = img.shape + + labels = self.words[index] + annotations = np.zeros((0, 15)) + if len(labels) == 0: + return annotations + for idx, label in enumerate(labels): + annotation = np.zeros((1, 15)) + # bbox + annotation[0, 0] = label[0] # x1 + annotation[0, 1] = label[1] # y1 + annotation[0, 2] = label[0] + label[2] # x2 + annotation[0, 3] = label[1] + label[3] # y2 + + # landmarks + annotation[0, 4] = label[4] # l0_x + annotation[0, 5] = label[5] # l0_y + annotation[0, 6] = label[7] # l1_x + annotation[0, 7] = label[8] # l1_y + annotation[0, 8] = label[10] # l2_x + annotation[0, 9] = label[11] # l2_y + annotation[0, 10] = label[13] # l3_x + annotation[0, 11] = label[14] # l3_y + annotation[0, 12] = label[16] # l4_x + annotation[0, 13] = label[17] # l4_y + if (annotation[0, 4]<0): + annotation[0, 14] = -1 + else: + annotation[0, 14] = 1 + + annotations = np.append(annotations, annotation, axis=0) + target = np.array(annotations) + if self.preproc is not None: + img, target = self.preproc(img, target) + + return torch.from_numpy(img), target + +def detection_collate(batch): + """Custom collate fn for dealing with batches of images that have a different + number of associated object annotations (bounding boxes). + + Arguments: + batch: (tuple) A tuple of tensor images and lists of annotations + + Return: + A tuple containing: + 1) (tensor) batch of images stacked on their 0 dim + 2) (list of tensors) annotations for a given image are stacked on 0 dim + """ + targets = [] + imgs = [] + for _, sample in enumerate(batch): + for _, tup in enumerate(sample): + if torch.is_tensor(tup): + imgs.append(tup) + elif isinstance(tup, type(np.empty(0))): + annos = torch.from_numpy(tup).float() + targets.append(annos) + + return (torch.stack(imgs, 0), targets) diff --git a/GPEN/face_detect/facemodels/__init__.py b/GPEN/face_detect/facemodels/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/GPEN/face_detect/facemodels/net.py b/GPEN/face_detect/facemodels/net.py new file mode 100644 index 0000000..beb6040 --- /dev/null +++ b/GPEN/face_detect/facemodels/net.py @@ -0,0 +1,137 @@ +import time +import torch +import torch.nn as nn +import torchvision.models._utils as _utils +import torchvision.models as models +import torch.nn.functional as F +from torch.autograd import Variable + +def conv_bn(inp, oup, stride = 1, leaky = 0): + return nn.Sequential( + nn.Conv2d(inp, oup, 3, stride, 1, bias=False), + nn.BatchNorm2d(oup), + nn.LeakyReLU(negative_slope=leaky, inplace=True) + ) + +def conv_bn_no_relu(inp, oup, stride): + return nn.Sequential( + nn.Conv2d(inp, oup, 3, stride, 1, bias=False), + nn.BatchNorm2d(oup), + ) + +def conv_bn1X1(inp, oup, stride, leaky=0): + return nn.Sequential( + nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False), + nn.BatchNorm2d(oup), + nn.LeakyReLU(negative_slope=leaky, inplace=True) + ) + +def conv_dw(inp, oup, stride, leaky=0.1): + return nn.Sequential( + nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False), + nn.BatchNorm2d(inp), + nn.LeakyReLU(negative_slope= leaky,inplace=True), + + nn.Conv2d(inp, oup, 1, 1, 0, bias=False), + nn.BatchNorm2d(oup), + nn.LeakyReLU(negative_slope= leaky,inplace=True), + ) + +class SSH(nn.Module): + def __init__(self, in_channel, out_channel): + super(SSH, self).__init__() + assert out_channel % 4 == 0 + leaky = 0 + if (out_channel <= 64): + leaky = 0.1 + self.conv3X3 = conv_bn_no_relu(in_channel, out_channel//2, stride=1) + + self.conv5X5_1 = conv_bn(in_channel, out_channel//4, stride=1, leaky = leaky) + self.conv5X5_2 = conv_bn_no_relu(out_channel//4, out_channel//4, stride=1) + + self.conv7X7_2 = conv_bn(out_channel//4, out_channel//4, stride=1, leaky = leaky) + self.conv7x7_3 = conv_bn_no_relu(out_channel//4, out_channel//4, stride=1) + + def forward(self, input): + conv3X3 = self.conv3X3(input) + + conv5X5_1 = self.conv5X5_1(input) + conv5X5 = self.conv5X5_2(conv5X5_1) + + conv7X7_2 = self.conv7X7_2(conv5X5_1) + conv7X7 = self.conv7x7_3(conv7X7_2) + + out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1) + out = F.relu(out) + return out + +class FPN(nn.Module): + def __init__(self,in_channels_list,out_channels): + super(FPN,self).__init__() + leaky = 0 + if (out_channels <= 64): + leaky = 0.1 + self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride = 1, leaky = leaky) + self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride = 1, leaky = leaky) + self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride = 1, leaky = leaky) + + self.merge1 = conv_bn(out_channels, out_channels, leaky = leaky) + self.merge2 = conv_bn(out_channels, out_channels, leaky = leaky) + + def forward(self, input): + # names = list(input.keys()) + input = list(input.values()) + + output1 = self.output1(input[0]) + output2 = self.output2(input[1]) + output3 = self.output3(input[2]) + + up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode="nearest") + output2 = output2 + up3 + output2 = self.merge2(output2) + + up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode="nearest") + output1 = output1 + up2 + output1 = self.merge1(output1) + + out = [output1, output2, output3] + return out + + + +class MobileNetV1(nn.Module): + def __init__(self): + super(MobileNetV1, self).__init__() + self.stage1 = nn.Sequential( + conv_bn(3, 8, 2, leaky = 0.1), # 3 + conv_dw(8, 16, 1), # 7 + conv_dw(16, 32, 2), # 11 + conv_dw(32, 32, 1), # 19 + conv_dw(32, 64, 2), # 27 + conv_dw(64, 64, 1), # 43 + ) + self.stage2 = nn.Sequential( + conv_dw(64, 128, 2), # 43 + 16 = 59 + conv_dw(128, 128, 1), # 59 + 32 = 91 + conv_dw(128, 128, 1), # 91 + 32 = 123 + conv_dw(128, 128, 1), # 123 + 32 = 155 + conv_dw(128, 128, 1), # 155 + 32 = 187 + conv_dw(128, 128, 1), # 187 + 32 = 219 + ) + self.stage3 = nn.Sequential( + conv_dw(128, 256, 2), # 219 +3 2 = 241 + conv_dw(256, 256, 1), # 241 + 64 = 301 + ) + self.avg = nn.AdaptiveAvgPool2d((1,1)) + self.fc = nn.Linear(256, 1000) + + def forward(self, x): + x = self.stage1(x) + x = self.stage2(x) + x = self.stage3(x) + x = self.avg(x) + # x = self.model(x) + x = x.view(-1, 256) + x = self.fc(x) + return x + diff --git a/GPEN/face_detect/facemodels/retinaface.py b/GPEN/face_detect/facemodels/retinaface.py new file mode 100644 index 0000000..b7092a2 --- /dev/null +++ b/GPEN/face_detect/facemodels/retinaface.py @@ -0,0 +1,127 @@ +import torch +import torch.nn as nn +import torchvision.models.detection.backbone_utils as backbone_utils +import torchvision.models._utils as _utils +import torch.nn.functional as F +from collections import OrderedDict + +from facemodels.net import MobileNetV1 as MobileNetV1 +from facemodels.net import FPN as FPN +from facemodels.net import SSH as SSH + + + +class ClassHead(nn.Module): + def __init__(self,inchannels=512,num_anchors=3): + super(ClassHead,self).__init__() + self.num_anchors = num_anchors + self.conv1x1 = nn.Conv2d(inchannels,self.num_anchors*2,kernel_size=(1,1),stride=1,padding=0) + + def forward(self,x): + out = self.conv1x1(x) + out = out.permute(0,2,3,1).contiguous() + + return out.view(out.shape[0], -1, 2) + +class BboxHead(nn.Module): + def __init__(self,inchannels=512,num_anchors=3): + super(BboxHead,self).__init__() + self.conv1x1 = nn.Conv2d(inchannels,num_anchors*4,kernel_size=(1,1),stride=1,padding=0) + + def forward(self,x): + out = self.conv1x1(x) + out = out.permute(0,2,3,1).contiguous() + + return out.view(out.shape[0], -1, 4) + +class LandmarkHead(nn.Module): + def __init__(self,inchannels=512,num_anchors=3): + super(LandmarkHead,self).__init__() + self.conv1x1 = nn.Conv2d(inchannels,num_anchors*10,kernel_size=(1,1),stride=1,padding=0) + + def forward(self,x): + out = self.conv1x1(x) + out = out.permute(0,2,3,1).contiguous() + + return out.view(out.shape[0], -1, 10) + +class RetinaFace(nn.Module): + def __init__(self, cfg = None, phase = 'train'): + """ + :param cfg: Network related settings. + :param phase: train or test. + """ + super(RetinaFace,self).__init__() + self.phase = phase + backbone = None + if cfg['name'] == 'mobilenet0.25': + backbone = MobileNetV1() + if cfg['pretrain']: + checkpoint = torch.load("./weights/mobilenetV1X0.25_pretrain.tar", map_location=torch.device('cpu')) + from collections import OrderedDict + new_state_dict = OrderedDict() + for k, v in checkpoint['state_dict'].items(): + name = k[7:] # remove module. + new_state_dict[name] = v + # load params + backbone.load_state_dict(new_state_dict) + elif cfg['name'] == 'Resnet50': + import torchvision.models as models + backbone = models.resnet50(pretrained=cfg['pretrain']) + + self.body = _utils.IntermediateLayerGetter(backbone, cfg['return_layers']) + in_channels_stage2 = cfg['in_channel'] + in_channels_list = [ + in_channels_stage2 * 2, + in_channels_stage2 * 4, + in_channels_stage2 * 8, + ] + out_channels = cfg['out_channel'] + self.fpn = FPN(in_channels_list,out_channels) + self.ssh1 = SSH(out_channels, out_channels) + self.ssh2 = SSH(out_channels, out_channels) + self.ssh3 = SSH(out_channels, out_channels) + + self.ClassHead = self._make_class_head(fpn_num=3, inchannels=cfg['out_channel']) + self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=cfg['out_channel']) + self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=cfg['out_channel']) + + def _make_class_head(self,fpn_num=3,inchannels=64,anchor_num=2): + classhead = nn.ModuleList() + for i in range(fpn_num): + classhead.append(ClassHead(inchannels,anchor_num)) + return classhead + + def _make_bbox_head(self,fpn_num=3,inchannels=64,anchor_num=2): + bboxhead = nn.ModuleList() + for i in range(fpn_num): + bboxhead.append(BboxHead(inchannels,anchor_num)) + return bboxhead + + def _make_landmark_head(self,fpn_num=3,inchannels=64,anchor_num=2): + landmarkhead = nn.ModuleList() + for i in range(fpn_num): + landmarkhead.append(LandmarkHead(inchannels,anchor_num)) + return landmarkhead + + def forward(self,inputs): + out = self.body(inputs) + + # FPN + fpn = self.fpn(out) + + # SSH + feature1 = self.ssh1(fpn[0]) + feature2 = self.ssh2(fpn[1]) + feature3 = self.ssh3(fpn[2]) + features = [feature1, feature2, feature3] + + bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1) + classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)],dim=1) + ldm_regressions = torch.cat([self.LandmarkHead[i](feature) for i, feature in enumerate(features)], dim=1) + + if self.phase == 'train': + output = (bbox_regressions, classifications, ldm_regressions) + else: + output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions) + return output \ No newline at end of file diff --git a/GPEN/face_detect/layers/__init__.py b/GPEN/face_detect/layers/__init__.py new file mode 100644 index 0000000..53a3f4b --- /dev/null +++ b/GPEN/face_detect/layers/__init__.py @@ -0,0 +1,2 @@ +from .functions import * +from .modules import * diff --git a/GPEN/face_detect/layers/functions/prior_box.py b/GPEN/face_detect/layers/functions/prior_box.py new file mode 100644 index 0000000..80c7f85 --- /dev/null +++ b/GPEN/face_detect/layers/functions/prior_box.py @@ -0,0 +1,34 @@ +import torch +from itertools import product as product +import numpy as np +from math import ceil + + +class PriorBox(object): + def __init__(self, cfg, image_size=None, phase='train'): + super(PriorBox, self).__init__() + self.min_sizes = cfg['min_sizes'] + self.steps = cfg['steps'] + self.clip = cfg['clip'] + self.image_size = image_size + self.feature_maps = [[ceil(self.image_size[0]/step), ceil(self.image_size[1]/step)] for step in self.steps] + self.name = "s" + + def forward(self): + anchors = [] + for k, f in enumerate(self.feature_maps): + min_sizes = self.min_sizes[k] + for i, j in product(range(f[0]), range(f[1])): + for min_size in min_sizes: + s_kx = min_size / self.image_size[1] + s_ky = min_size / self.image_size[0] + dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]] + dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]] + for cy, cx in product(dense_cy, dense_cx): + anchors += [cx, cy, s_kx, s_ky] + + # back to torch land + output = torch.Tensor(anchors).view(-1, 4) + if self.clip: + output.clamp_(max=1, min=0) + return output diff --git a/GPEN/face_detect/layers/modules/__init__.py b/GPEN/face_detect/layers/modules/__init__.py new file mode 100644 index 0000000..cf24bdd --- /dev/null +++ b/GPEN/face_detect/layers/modules/__init__.py @@ -0,0 +1,3 @@ +from .multibox_loss import MultiBoxLoss + +__all__ = ['MultiBoxLoss'] diff --git a/GPEN/face_detect/layers/modules/multibox_loss.py b/GPEN/face_detect/layers/modules/multibox_loss.py new file mode 100644 index 0000000..0966204 --- /dev/null +++ b/GPEN/face_detect/layers/modules/multibox_loss.py @@ -0,0 +1,125 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Variable +from utils.box_utils import match, log_sum_exp +from data import cfg_mnet +GPU = cfg_mnet['gpu_train'] + +class MultiBoxLoss(nn.Module): + """SSD Weighted Loss Function + Compute Targets: + 1) Produce Confidence Target Indices by matching ground truth boxes + with (default) 'priorboxes' that have jaccard index > threshold parameter + (default threshold: 0.5). + 2) Produce localization target by 'encoding' variance into offsets of ground + truth boxes and their matched 'priorboxes'. + 3) Hard negative mining to filter the excessive number of negative examples + that comes with using a large number of default bounding boxes. + (default negative:positive ratio 3:1) + Objective Loss: + L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N + Where, Lconf is the CrossEntropy Loss and Lloc is the SmoothL1 Loss + weighted by α which is set to 1 by cross val. + Args: + c: class confidences, + l: predicted boxes, + g: ground truth boxes + N: number of matched default boxes + See: https://arxiv.org/pdf/1512.02325.pdf for more details. + """ + + def __init__(self, num_classes, overlap_thresh, prior_for_matching, bkg_label, neg_mining, neg_pos, neg_overlap, encode_target): + super(MultiBoxLoss, self).__init__() + self.num_classes = num_classes + self.threshold = overlap_thresh + self.background_label = bkg_label + self.encode_target = encode_target + self.use_prior_for_matching = prior_for_matching + self.do_neg_mining = neg_mining + self.negpos_ratio = neg_pos + self.neg_overlap = neg_overlap + self.variance = [0.1, 0.2] + + def forward(self, predictions, priors, targets): + """Multibox Loss + Args: + predictions (tuple): A tuple containing loc preds, conf preds, + and prior boxes from SSD net. + conf shape: torch.size(batch_size,num_priors,num_classes) + loc shape: torch.size(batch_size,num_priors,4) + priors shape: torch.size(num_priors,4) + + ground_truth (tensor): Ground truth boxes and labels for a batch, + shape: [batch_size,num_objs,5] (last idx is the label). + """ + + loc_data, conf_data, landm_data = predictions + priors = priors + num = loc_data.size(0) + num_priors = (priors.size(0)) + + # match priors (default boxes) and ground truth boxes + loc_t = torch.Tensor(num, num_priors, 4) + landm_t = torch.Tensor(num, num_priors, 10) + conf_t = torch.LongTensor(num, num_priors) + for idx in range(num): + truths = targets[idx][:, :4].data + labels = targets[idx][:, -1].data + landms = targets[idx][:, 4:14].data + defaults = priors.data + match(self.threshold, truths, defaults, self.variance, labels, landms, loc_t, conf_t, landm_t, idx) + if GPU: + loc_t = loc_t.cuda() + conf_t = conf_t.cuda() + landm_t = landm_t.cuda() + + zeros = torch.tensor(0).cuda() + # landm Loss (Smooth L1) + # Shape: [batch,num_priors,10] + pos1 = conf_t > zeros + num_pos_landm = pos1.long().sum(1, keepdim=True) + N1 = max(num_pos_landm.data.sum().float(), 1) + pos_idx1 = pos1.unsqueeze(pos1.dim()).expand_as(landm_data) + landm_p = landm_data[pos_idx1].view(-1, 10) + landm_t = landm_t[pos_idx1].view(-1, 10) + loss_landm = F.smooth_l1_loss(landm_p, landm_t, reduction='sum') + + + pos = conf_t != zeros + conf_t[pos] = 1 + + # Localization Loss (Smooth L1) + # Shape: [batch,num_priors,4] + pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data) + loc_p = loc_data[pos_idx].view(-1, 4) + loc_t = loc_t[pos_idx].view(-1, 4) + loss_l = F.smooth_l1_loss(loc_p, loc_t, reduction='sum') + + # Compute max conf across batch for hard negative mining + batch_conf = conf_data.view(-1, self.num_classes) + loss_c = log_sum_exp(batch_conf) - batch_conf.gather(1, conf_t.view(-1, 1)) + + # Hard Negative Mining + loss_c[pos.view(-1, 1)] = 0 # filter out pos boxes for now + loss_c = loss_c.view(num, -1) + _, loss_idx = loss_c.sort(1, descending=True) + _, idx_rank = loss_idx.sort(1) + num_pos = pos.long().sum(1, keepdim=True) + num_neg = torch.clamp(self.negpos_ratio*num_pos, max=pos.size(1)-1) + neg = idx_rank < num_neg.expand_as(idx_rank) + + # Confidence Loss Including Positive and Negative Examples + pos_idx = pos.unsqueeze(2).expand_as(conf_data) + neg_idx = neg.unsqueeze(2).expand_as(conf_data) + conf_p = conf_data[(pos_idx+neg_idx).gt(0)].view(-1,self.num_classes) + targets_weighted = conf_t[(pos+neg).gt(0)] + loss_c = F.cross_entropy(conf_p, targets_weighted, reduction='sum') + + # Sum of losses: L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N + N = max(num_pos.data.sum().float(), 1) + loss_l /= N + loss_c /= N + loss_landm /= N1 + + return loss_l, loss_c, loss_landm diff --git a/GPEN/face_detect/retinaface_detection.py b/GPEN/face_detect/retinaface_detection.py new file mode 100644 index 0000000..58268c6 --- /dev/null +++ b/GPEN/face_detect/retinaface_detection.py @@ -0,0 +1,193 @@ +''' +@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) +@author: yangxy (yangtao9009@gmail.com) +''' +import os +import torch +import torch.backends.cudnn as cudnn +import numpy as np +from data import cfg_re50 +from layers.functions.prior_box import PriorBox +from utils.nms.py_cpu_nms import py_cpu_nms +import cv2 +from facemodels.retinaface import RetinaFace +from utils.box_utils import decode, decode_landm +import time +import torch.nn.functional as F + + +class RetinaFaceDetection(object): + def __init__(self, base_dir, device='cuda', network='RetinaFace-R50'): + torch.set_grad_enabled(False) + cudnn.benchmark = True + self.pretrained_path = os.path.join(base_dir, 'weights', network+'.pth') + self.device = device #torch.cuda.current_device() + self.cfg = cfg_re50 + self.net = RetinaFace(cfg=self.cfg, phase='test') + self.load_model() + self.net = self.net.to(device) + + self.mean = torch.tensor([[[[104]], [[117]], [[123]]]]).to(device) + + def check_keys(self, pretrained_state_dict): + ckpt_keys = set(pretrained_state_dict.keys()) + model_keys = set(self.net.state_dict().keys()) + used_pretrained_keys = model_keys & ckpt_keys + unused_pretrained_keys = ckpt_keys - model_keys + missing_keys = model_keys - ckpt_keys + assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint' + return True + + def remove_prefix(self, state_dict, prefix): + ''' Old style model is stored with all names of parameters sharing common prefix 'module.' ''' + f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x + return {f(key): value for key, value in state_dict.items()} + + def load_model(self, load_to_cpu=False): + #if load_to_cpu: + # pretrained_dict = torch.load(self.pretrained_path, map_location=lambda storage, loc: storage) + #else: + # pretrained_dict = torch.load(self.pretrained_path, map_location=lambda storage, loc: storage.cuda()) + pretrained_dict = torch.load(self.pretrained_path, map_location=torch.device('cpu')) + if "state_dict" in pretrained_dict.keys(): + pretrained_dict = self.remove_prefix(pretrained_dict['state_dict'], 'module.') + else: + pretrained_dict = self.remove_prefix(pretrained_dict, 'module.') + self.check_keys(pretrained_dict) + self.net.load_state_dict(pretrained_dict, strict=False) + self.net.eval() + + def detect(self, img_raw, resize=1, confidence_threshold=0.9, nms_threshold=0.4, top_k=5000, keep_top_k=750, save_image=False): + img = np.float32(img_raw) + + im_height, im_width = img.shape[:2] + ss = 1.0 + # tricky + if max(im_height, im_width) > 1500: + ss = 1000.0/max(im_height, im_width) + img = cv2.resize(img, (0,0), fx=ss, fy=ss) + im_height, im_width = img.shape[:2] + + scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]]) + img -= (104, 117, 123) + img = img.transpose(2, 0, 1) + img = torch.from_numpy(img).unsqueeze(0) + img = img.to(self.device) + scale = scale.to(self.device) + + loc, conf, landms = self.net(img) # forward pass + del img + + priorbox = PriorBox(self.cfg, image_size=(im_height, im_width)) + priors = priorbox.forward() + priors = priors.to(self.device) + prior_data = priors.data + boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance']) + boxes = boxes * scale / resize + boxes = boxes.cpu().numpy() + scores = conf.squeeze(0).data.cpu().numpy()[:, 1] + landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance']) + scale1 = torch.Tensor([im_width, im_height, im_width, im_height, + im_width, im_height, im_width, im_height, + im_width, im_height]) + scale1 = scale1.to(self.device) + landms = landms * scale1 / resize + landms = landms.cpu().numpy() + + # ignore low scores + inds = np.where(scores > confidence_threshold)[0] + boxes = boxes[inds] + landms = landms[inds] + scores = scores[inds] + + # keep top-K before NMS + order = scores.argsort()[::-1][:top_k] + boxes = boxes[order] + landms = landms[order] + scores = scores[order] + + # do NMS + dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False) + keep = py_cpu_nms(dets, nms_threshold) + # keep = nms(dets, nms_threshold,force_cpu=args.cpu) + dets = dets[keep, :] + landms = landms[keep] + + # keep top-K faster NMS + dets = dets[:keep_top_k, :] + landms = landms[:keep_top_k, :] + + # sort faces(delete) + ''' + fscores = [det[4] for det in dets] + sorted_idx = sorted(range(len(fscores)), key=lambda k:fscores[k], reverse=False) # sort index + tmp = [landms[idx] for idx in sorted_idx] + landms = np.asarray(tmp) + ''' + + landms = landms.reshape((-1, 5, 2)) + landms = landms.transpose((0, 2, 1)) + landms = landms.reshape(-1, 10, ) + return dets/ss, landms/ss + + def detect_tensor(self, img, resize=1, confidence_threshold=0.9, nms_threshold=0.4, top_k=5000, keep_top_k=750, save_image=False): + im_height, im_width = img.shape[-2:] + ss = 1000/max(im_height, im_width) + img = F.interpolate(img, scale_factor=ss) + im_height, im_width = img.shape[-2:] + scale = torch.Tensor([im_width, im_height, im_width, im_height]).to(self.device) + img -= self.mean + + loc, conf, landms = self.net(img) # forward pass + + priorbox = PriorBox(self.cfg, image_size=(im_height, im_width)) + priors = priorbox.forward() + priors = priors.to(self.device) + prior_data = priors.data + boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance']) + boxes = boxes * scale / resize + boxes = boxes.cpu().numpy() + scores = conf.squeeze(0).data.cpu().numpy()[:, 1] + landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance']) + scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2], + img.shape[3], img.shape[2], img.shape[3], img.shape[2], + img.shape[3], img.shape[2]]) + scale1 = scale1.to(self.device) + landms = landms * scale1 / resize + landms = landms.cpu().numpy() + + # ignore low scores + inds = np.where(scores > confidence_threshold)[0] + boxes = boxes[inds] + landms = landms[inds] + scores = scores[inds] + + # keep top-K before NMS + order = scores.argsort()[::-1][:top_k] + boxes = boxes[order] + landms = landms[order] + scores = scores[order] + + # do NMS + dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False) + keep = py_cpu_nms(dets, nms_threshold) + # keep = nms(dets, nms_threshold,force_cpu=args.cpu) + dets = dets[keep, :] + landms = landms[keep] + + # keep top-K faster NMS + dets = dets[:keep_top_k, :] + landms = landms[:keep_top_k, :] + + # sort faces(delete) + ''' + fscores = [det[4] for det in dets] + sorted_idx = sorted(range(len(fscores)), key=lambda k:fscores[k], reverse=False) # sort index + tmp = [landms[idx] for idx in sorted_idx] + landms = np.asarray(tmp) + ''' + + landms = landms.reshape((-1, 5, 2)) + landms = landms.transpose((0, 2, 1)) + landms = landms.reshape(-1, 10, ) + return dets/ss, landms/ss diff --git a/GPEN/face_detect/utils/__init__.py b/GPEN/face_detect/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/GPEN/face_detect/utils/box_utils.py b/GPEN/face_detect/utils/box_utils.py new file mode 100644 index 0000000..c1d12bc --- /dev/null +++ b/GPEN/face_detect/utils/box_utils.py @@ -0,0 +1,330 @@ +import torch +import numpy as np + + +def point_form(boxes): + """ Convert prior_boxes to (xmin, ymin, xmax, ymax) + representation for comparison to point form ground truth data. + Args: + boxes: (tensor) center-size default boxes from priorbox layers. + Return: + boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes. + """ + return torch.cat((boxes[:, :2] - boxes[:, 2:]/2, # xmin, ymin + boxes[:, :2] + boxes[:, 2:]/2), 1) # xmax, ymax + + +def center_size(boxes): + """ Convert prior_boxes to (cx, cy, w, h) + representation for comparison to center-size form ground truth data. + Args: + boxes: (tensor) point_form boxes + Return: + boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes. + """ + return torch.cat((boxes[:, 2:] + boxes[:, :2])/2, # cx, cy + boxes[:, 2:] - boxes[:, :2], 1) # w, h + + +def intersect(box_a, box_b): + """ We resize both tensors to [A,B,2] without new malloc: + [A,2] -> [A,1,2] -> [A,B,2] + [B,2] -> [1,B,2] -> [A,B,2] + Then we compute the area of intersect between box_a and box_b. + Args: + box_a: (tensor) bounding boxes, Shape: [A,4]. + box_b: (tensor) bounding boxes, Shape: [B,4]. + Return: + (tensor) intersection area, Shape: [A,B]. + """ + A = box_a.size(0) + B = box_b.size(0) + max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), + box_b[:, 2:].unsqueeze(0).expand(A, B, 2)) + min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), + box_b[:, :2].unsqueeze(0).expand(A, B, 2)) + inter = torch.clamp((max_xy - min_xy), min=0) + return inter[:, :, 0] * inter[:, :, 1] + + +def jaccard(box_a, box_b): + """Compute the jaccard overlap of two sets of boxes. The jaccard overlap + is simply the intersection over union of two boxes. Here we operate on + ground truth boxes and default boxes. + E.g.: + A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B) + Args: + box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4] + box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4] + Return: + jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)] + """ + inter = intersect(box_a, box_b) + area_a = ((box_a[:, 2]-box_a[:, 0]) * + (box_a[:, 3]-box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B] + area_b = ((box_b[:, 2]-box_b[:, 0]) * + (box_b[:, 3]-box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B] + union = area_a + area_b - inter + return inter / union # [A,B] + + +def matrix_iou(a, b): + """ + return iou of a and b, numpy version for data augenmentation + """ + lt = np.maximum(a[:, np.newaxis, :2], b[:, :2]) + rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:]) + + area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2) + area_a = np.prod(a[:, 2:] - a[:, :2], axis=1) + area_b = np.prod(b[:, 2:] - b[:, :2], axis=1) + return area_i / (area_a[:, np.newaxis] + area_b - area_i) + + +def matrix_iof(a, b): + """ + return iof of a and b, numpy version for data augenmentation + """ + lt = np.maximum(a[:, np.newaxis, :2], b[:, :2]) + rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:]) + + area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2) + area_a = np.prod(a[:, 2:] - a[:, :2], axis=1) + return area_i / np.maximum(area_a[:, np.newaxis], 1) + + +def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx): + """Match each prior box with the ground truth box of the highest jaccard + overlap, encode the bounding boxes, then return the matched indices + corresponding to both confidence and location preds. + Args: + threshold: (float) The overlap threshold used when mathing boxes. + truths: (tensor) Ground truth boxes, Shape: [num_obj, 4]. + priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4]. + variances: (tensor) Variances corresponding to each prior coord, + Shape: [num_priors, 4]. + labels: (tensor) All the class labels for the image, Shape: [num_obj]. + landms: (tensor) Ground truth landms, Shape [num_obj, 10]. + loc_t: (tensor) Tensor to be filled w/ endcoded location targets. + conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds. + landm_t: (tensor) Tensor to be filled w/ endcoded landm targets. + idx: (int) current batch index + Return: + The matched indices corresponding to 1)location 2)confidence 3)landm preds. + """ + # jaccard index + overlaps = jaccard( + truths, + point_form(priors) + ) + # (Bipartite Matching) + # [1,num_objects] best prior for each ground truth + best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True) + + # ignore hard gt + valid_gt_idx = best_prior_overlap[:, 0] >= 0.2 + best_prior_idx_filter = best_prior_idx[valid_gt_idx, :] + if best_prior_idx_filter.shape[0] <= 0: + loc_t[idx] = 0 + conf_t[idx] = 0 + return + + # [1,num_priors] best ground truth for each prior + best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True) + best_truth_idx.squeeze_(0) + best_truth_overlap.squeeze_(0) + best_prior_idx.squeeze_(1) + best_prior_idx_filter.squeeze_(1) + best_prior_overlap.squeeze_(1) + best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2) # ensure best prior + # TODO refactor: index best_prior_idx with long tensor + # ensure every gt matches with its prior of max overlap + for j in range(best_prior_idx.size(0)): # 判别此anchor是预测哪一个boxes + best_truth_idx[best_prior_idx[j]] = j + matches = truths[best_truth_idx] # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来 + conf = labels[best_truth_idx] # Shape: [num_priors] 此处为每一个anchor对应的label取出来 + conf[best_truth_overlap < threshold] = 0 # label as background overlap<0.35的全部作为负样本 + loc = encode(matches, priors, variances) + + matches_landm = landms[best_truth_idx] + landm = encode_landm(matches_landm, priors, variances) + loc_t[idx] = loc # [num_priors,4] encoded offsets to learn + conf_t[idx] = conf # [num_priors] top class label for each prior + landm_t[idx] = landm + + +def encode(matched, priors, variances): + """Encode the variances from the priorbox layers into the ground truth boxes + we have matched (based on jaccard overlap) with the prior boxes. + Args: + matched: (tensor) Coords of ground truth for each prior in point-form + Shape: [num_priors, 4]. + priors: (tensor) Prior boxes in center-offset form + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + encoded boxes (tensor), Shape: [num_priors, 4] + """ + + # dist b/t match center and prior's center + g_cxcy = (matched[:, :2] + matched[:, 2:])/2 - priors[:, :2] + # encode variance + g_cxcy /= (variances[0] * priors[:, 2:]) + # match wh / prior wh + g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:] + g_wh = torch.log(g_wh) / variances[1] + # return target for smooth_l1_loss + return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4] + +def encode_landm(matched, priors, variances): + """Encode the variances from the priorbox layers into the ground truth boxes + we have matched (based on jaccard overlap) with the prior boxes. + Args: + matched: (tensor) Coords of ground truth for each prior in point-form + Shape: [num_priors, 10]. + priors: (tensor) Prior boxes in center-offset form + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + encoded landm (tensor), Shape: [num_priors, 10] + """ + + # dist b/t match center and prior's center + matched = torch.reshape(matched, (matched.size(0), 5, 2)) + priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) + priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) + priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) + priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) + priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2) + g_cxcy = matched[:, :, :2] - priors[:, :, :2] + # encode variance + g_cxcy /= (variances[0] * priors[:, :, 2:]) + # g_cxcy /= priors[:, :, 2:] + g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1) + # return target for smooth_l1_loss + return g_cxcy + + +# Adapted from https://github.com/Hakuyume/chainer-ssd +def decode(loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat(( + priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], + priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1) + boxes[:, :2] -= boxes[:, 2:] / 2 + boxes[:, 2:] += boxes[:, :2] + return boxes + +def decode_landm(pre, priors, variances): + """Decode landm from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + pre (tensor): landm predictions for loc layers, + Shape: [num_priors,10] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded landm predictions + """ + landms = torch.cat((priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:], + priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:], + priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:], + priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:], + priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:], + ), dim=1) + return landms + + +def log_sum_exp(x): + """Utility function for computing log_sum_exp while determining + This will be used to determine unaveraged confidence loss across + all examples in a batch. + Args: + x (Variable(tensor)): conf_preds from conf layers + """ + x_max = x.data.max() + return torch.log(torch.sum(torch.exp(x-x_max), 1, keepdim=True)) + x_max + + +# Original author: Francisco Massa: +# https://github.com/fmassa/object-detection.torch +# Ported to PyTorch by Max deGroot (02/01/2017) +def nms(boxes, scores, overlap=0.5, top_k=200): + """Apply non-maximum suppression at test time to avoid detecting too many + overlapping bounding boxes for a given object. + Args: + boxes: (tensor) The location preds for the img, Shape: [num_priors,4]. + scores: (tensor) The class predscores for the img, Shape:[num_priors]. + overlap: (float) The overlap thresh for suppressing unnecessary boxes. + top_k: (int) The Maximum number of box preds to consider. + Return: + The indices of the kept boxes with respect to num_priors. + """ + + keep = torch.Tensor(scores.size(0)).fill_(0).long() + if boxes.numel() == 0: + return keep + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + area = torch.mul(x2 - x1, y2 - y1) + v, idx = scores.sort(0) # sort in ascending order + # I = I[v >= 0.01] + idx = idx[-top_k:] # indices of the top-k largest vals + xx1 = boxes.new() + yy1 = boxes.new() + xx2 = boxes.new() + yy2 = boxes.new() + w = boxes.new() + h = boxes.new() + + # keep = torch.Tensor() + count = 0 + while idx.numel() > 0: + i = idx[-1] # index of current largest val + # keep.append(i) + keep[count] = i + count += 1 + if idx.size(0) == 1: + break + idx = idx[:-1] # remove kept element from view + # load bboxes of next highest vals + torch.index_select(x1, 0, idx, out=xx1) + torch.index_select(y1, 0, idx, out=yy1) + torch.index_select(x2, 0, idx, out=xx2) + torch.index_select(y2, 0, idx, out=yy2) + # store element-wise max with next highest score + xx1 = torch.clamp(xx1, min=x1[i]) + yy1 = torch.clamp(yy1, min=y1[i]) + xx2 = torch.clamp(xx2, max=x2[i]) + yy2 = torch.clamp(yy2, max=y2[i]) + w.resize_as_(xx2) + h.resize_as_(yy2) + w = xx2 - xx1 + h = yy2 - yy1 + # check sizes of xx1 and xx2.. after each iteration + w = torch.clamp(w, min=0.0) + h = torch.clamp(h, min=0.0) + inter = w*h + # IoU = i / (area(a) + area(b) - i) + rem_areas = torch.index_select(area, 0, idx) # load remaining areas) + union = (rem_areas - inter) + area[i] + IoU = inter/union # store result in iou + # keep only elements with an IoU <= overlap + idx = idx[IoU.le(overlap)] + return keep, count + + diff --git a/GPEN/face_detect/utils/nms/__init__.py b/GPEN/face_detect/utils/nms/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/GPEN/face_detect/utils/nms/py_cpu_nms.py b/GPEN/face_detect/utils/nms/py_cpu_nms.py new file mode 100644 index 0000000..54e7b25 --- /dev/null +++ b/GPEN/face_detect/utils/nms/py_cpu_nms.py @@ -0,0 +1,38 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import numpy as np + +def py_cpu_nms(dets, thresh): + """Pure Python NMS baseline.""" + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep diff --git a/GPEN/face_detect/utils/timer.py b/GPEN/face_detect/utils/timer.py new file mode 100644 index 0000000..e4b3b80 --- /dev/null +++ b/GPEN/face_detect/utils/timer.py @@ -0,0 +1,40 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import time + + +class Timer(object): + """A simple timer.""" + def __init__(self): + self.total_time = 0. + self.calls = 0 + self.start_time = 0. + self.diff = 0. + self.average_time = 0. + + def tic(self): + # using time.time instead of time.clock because time time.clock + # does not normalize for multithreading + self.start_time = time.time() + + def toc(self, average=True): + self.diff = time.time() - self.start_time + self.total_time += self.diff + self.calls += 1 + self.average_time = self.total_time / self.calls + if average: + return self.average_time + else: + return self.diff + + def clear(self): + self.total_time = 0. + self.calls = 0 + self.start_time = 0. + self.diff = 0. + self.average_time = 0. diff --git a/GPEN/face_enhancement.py b/GPEN/face_enhancement.py new file mode 100644 index 0000000..a7a771a --- /dev/null +++ b/GPEN/face_enhancement.py @@ -0,0 +1,115 @@ +''' +@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) +@author: yangxy (yangtao9009@gmail.com) +''' +import cv2 +import time +import numpy as np +import __init_paths +from face_detect.retinaface_detection import RetinaFaceDetection +from face_parse.face_parsing import FaceParse +from face_model.face_gan import FaceGAN +from sr_model.real_esrnet import RealESRNet +from align_faces import warp_and_crop_face, get_reference_facial_points + +class FaceEnhancement(object): + def __init__(self, args, base_dir='./', in_size=512, out_size=None, model=None, use_sr=True, device='cuda'): + self.facedetector = RetinaFaceDetection(base_dir, device) + self.facegan = FaceGAN(base_dir, in_size, out_size, model, args.channel_multiplier, args.narrow, args.key, device=device) + self.srmodel = RealESRNet(base_dir, args.sr_model, args.sr_scale, args.tile_size, device=device) + self.faceparser = FaceParse(base_dir, device=device) + self.use_sr = use_sr + self.in_size = in_size + self.out_size = in_size if out_size is None else out_size + self.threshold = 0.9 + self.alpha = args.alpha + + # the mask for pasting restored faces back + self.mask = np.zeros((512, 512), np.float32) + cv2.rectangle(self.mask, (26, 26), (486, 486), (1, 1, 1), -1, cv2.LINE_AA) + self.mask = cv2.GaussianBlur(self.mask, (101, 101), 4) + self.mask = cv2.GaussianBlur(self.mask, (101, 101), 4) + + self.kernel = np.array(( + [0.0625, 0.125, 0.0625], + [0.125, 0.25, 0.125], + [0.0625, 0.125, 0.0625]), dtype="float32") + + # get the reference 5 landmarks position in the crop settings + default_square = True + inner_padding_factor = 0.25 + outer_padding = (0, 0) + self.reference_5pts = get_reference_facial_points( + (self.in_size, self.in_size), inner_padding_factor, outer_padding, default_square) + + def mask_postprocess(self, mask, thres=26): + mask[:thres, :] = 0; mask[-thres:, :] = 0 + mask[:, :thres] = 0; mask[:, -thres:] = 0 + mask = cv2.GaussianBlur(mask, (101, 101), 4) + mask = cv2.GaussianBlur(mask, (101, 101), 4) + return mask.astype(np.float32) + + def process(self, img, aligned=False): + orig_faces, enhanced_faces = [], [] + if aligned: + ef = self.facegan.process(img) + orig_faces.append(img) + enhanced_faces.append(ef) + + if self.use_sr: + ef = self.srmodel.process(ef) + + return ef, orig_faces, enhanced_faces + + if self.use_sr: + img_sr = self.srmodel.process(img) + if img_sr is not None: + img = cv2.resize(img, img_sr.shape[:2][::-1]) + + facebs, landms = self.facedetector.detect(img) + + height, width = img.shape[:2] + full_mask = np.zeros((height, width), dtype=np.float32) + full_img = np.zeros(img.shape, dtype=np.uint8) + + for i, (faceb, facial5points) in enumerate(zip(facebs, landms)): + if faceb[4]0)] = tmp_mask[np.where(mask>0)] + full_img[np.where(mask>0)] = tmp_img[np.where(mask>0)] + + full_mask = full_mask[:, :, np.newaxis] + if self.use_sr and img_sr is not None: + img = cv2.convertScaleAbs(img_sr*(1-full_mask) + full_img*full_mask) + else: + img = cv2.convertScaleAbs(img*(1-full_mask) + full_img*full_mask) + + return img, orig_faces, enhanced_faces + + diff --git a/GPEN/face_inpainting.py b/GPEN/face_inpainting.py new file mode 100644 index 0000000..fb666db --- /dev/null +++ b/GPEN/face_inpainting.py @@ -0,0 +1,18 @@ +''' +@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) +@author: yangxy (yangtao9009@gmail.com) +''' +from face_model.face_gan import FaceGAN + +class FaceInpainting(object): + def __init__(self, base_dir='./', in_size=1024, out_size=1024, model=None, channel_multiplier=2, narrow=1, key=None, device='cuda'): + self.facegan = FaceGAN(base_dir, in_size, out_size, model, channel_multiplier, narrow, key, device=device) + + # make sure the face image is well aligned. Please refer to face_enhancement.py + def process(self, brokenf, aligned=True): + # complete the face + out = self.facegan.process(brokenf) + + return out, [brokenf], [out] + + diff --git a/GPEN/face_model/face_gan.py b/GPEN/face_model/face_gan.py new file mode 100644 index 0000000..0f951cc --- /dev/null +++ b/GPEN/face_model/face_gan.py @@ -0,0 +1,62 @@ +''' +@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) +@author: yangxy (yangtao9009@gmail.com) +''' +import torch +import os +import cv2 +import glob +import numpy as np +from torch import nn +import torch.nn.functional as F +from torchvision import transforms, utils +from gpen_model import FullGenerator, FullGenerator_SR + +class FaceGAN(object): + def __init__(self, base_dir='./', in_size=512, out_size=None, model=None, channel_multiplier=2, narrow=1, key=None, is_norm=True, device='cuda'): + self.mfile = os.path.join(base_dir, 'weights', model+'.pth') + self.n_mlp = 8 + self.device = device + self.is_norm = is_norm + self.in_resolution = in_size + self.out_resolution = in_size if out_size is None else out_size + self.key = key + self.load_model(channel_multiplier, narrow) + + def load_model(self, channel_multiplier=2, narrow=1): + if self.in_resolution == self.out_resolution: + self.model = FullGenerator(self.in_resolution, 512, self.n_mlp, channel_multiplier, narrow=narrow, device=self.device) + else: + self.model = FullGenerator_SR(self.in_resolution, self.out_resolution, 512, self.n_mlp, channel_multiplier, narrow=narrow, device=self.device) + pretrained_dict = torch.load(self.mfile, map_location=torch.device('cpu')) + if self.key is not None: pretrained_dict = pretrained_dict[self.key] + self.model.load_state_dict(pretrained_dict) + self.model.to(self.device) + self.model.eval() + + def process(self, img): + img = cv2.resize(img, (self.in_resolution, self.in_resolution)) + img_t = self.img2tensor(img) + + with torch.no_grad(): + out, __ = self.model(img_t) + del img_t + + out = self.tensor2img(out) + + return out + + def img2tensor(self, img): + img_t = torch.from_numpy(img).to(self.device)/255. + if self.is_norm: + img_t = (img_t - 0.5) / 0.5 + img_t = img_t.permute(2, 0, 1).unsqueeze(0).flip(1) # BGR->RGB + return img_t + + def tensor2img(self, img_t, pmax=255.0, imtype=np.uint8): + if self.is_norm: + img_t = img_t * 0.5 + 0.5 + img_t = img_t.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR + img_np = np.clip(img_t.float().cpu().numpy(), 0, 1) * pmax + + return img_np.astype(imtype) diff --git a/GPEN/face_model/gpen_model.py b/GPEN/face_model/gpen_model.py new file mode 100644 index 0000000..30e6791 --- /dev/null +++ b/GPEN/face_model/gpen_model.py @@ -0,0 +1,818 @@ +''' +@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) +@author: yangxy (yangtao9009@gmail.com) +''' +import math +import random +import functools +import operator +import itertools + +import torch +from torch import nn +from torch.nn import functional as F +from torch.autograd import Function + +from op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d + +class PixelNorm(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, input): + return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8) + + +def make_kernel(k): + k = torch.tensor(k, dtype=torch.float32) + + if k.ndim == 1: + k = k[None, :] * k[:, None] + + k /= k.sum() + + return k + + +class Upsample(nn.Module): + def __init__(self, kernel, factor=2, device='cpu'): + super().__init__() + + self.factor = factor + kernel = make_kernel(kernel) * (factor ** 2) + self.register_buffer('kernel', kernel) + + p = kernel.shape[0] - factor + + pad0 = (p + 1) // 2 + factor - 1 + pad1 = p // 2 + + self.pad = (pad0, pad1) + self.device = device + + def forward(self, input): + out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad, device=self.device) + + return out + + +class Downsample(nn.Module): + def __init__(self, kernel, factor=2, device='cpu'): + super().__init__() + + self.factor = factor + kernel = make_kernel(kernel) + self.register_buffer('kernel', kernel) + + p = kernel.shape[0] - factor + + pad0 = (p + 1) // 2 + pad1 = p // 2 + + self.pad = (pad0, pad1) + self.device = device + + def forward(self, input): + out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad, device=self.device) + + return out + + +class Blur(nn.Module): + def __init__(self, kernel, pad, upsample_factor=1, device='cpu'): + super().__init__() + + kernel = make_kernel(kernel) + + if upsample_factor > 1: + kernel = kernel * (upsample_factor ** 2) + + self.register_buffer('kernel', kernel) + + self.pad = pad + self.device = device + + def forward(self, input): + out = upfirdn2d(input, self.kernel, pad=self.pad, device=self.device) + + return out + + +class EqualConv2d(nn.Module): + def __init__( + self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True + ): + super().__init__() + + self.weight = nn.Parameter( + torch.randn(out_channel, in_channel, kernel_size, kernel_size) + ) + self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2) + + self.stride = stride + self.padding = padding + + if bias: + self.bias = nn.Parameter(torch.zeros(out_channel)) + + else: + self.bias = None + + def forward(self, input): + out = F.conv2d( + input, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + + return out + + def __repr__(self): + return ( + f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},' + f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})' + ) + + +class EqualLinear(nn.Module): + def __init__( + self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None, device='cpu' + ): + super().__init__() + + self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) + + if bias: + self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) + + else: + self.bias = None + + self.activation = activation + self.device = device + + self.scale = (1 / math.sqrt(in_dim)) * lr_mul + self.lr_mul = lr_mul + + def forward(self, input): + if self.activation: + out = F.linear(input, self.weight * self.scale) + out = fused_leaky_relu(out, self.bias * self.lr_mul, device=self.device) + + else: + out = F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul) + + return out + + def __repr__(self): + return ( + f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})' + ) + + +class ScaledLeakyReLU(nn.Module): + def __init__(self, negative_slope=0.2): + super().__init__() + + self.negative_slope = negative_slope + + def forward(self, input): + out = F.leaky_relu(input, negative_slope=self.negative_slope) + + return out * math.sqrt(2) + + +class ModulatedConv2d(nn.Module): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + style_dim, + demodulate=True, + upsample=False, + downsample=False, + blur_kernel=[1, 3, 3, 1], + device='cpu' + ): + super().__init__() + + self.eps = 1e-8 + self.kernel_size = kernel_size + self.in_channel = in_channel + self.out_channel = out_channel + self.upsample = upsample + self.downsample = downsample + + if upsample: + factor = 2 + p = (len(blur_kernel) - factor) - (kernel_size - 1) + pad0 = (p + 1) // 2 + factor - 1 + pad1 = p // 2 + 1 + + self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor, device=device) + + if downsample: + factor = 2 + p = (len(blur_kernel) - factor) + (kernel_size - 1) + pad0 = (p + 1) // 2 + pad1 = p // 2 + + self.blur = Blur(blur_kernel, pad=(pad0, pad1), device=device) + + fan_in = in_channel * kernel_size ** 2 + self.scale = 1 / math.sqrt(fan_in) + self.padding = kernel_size // 2 + + self.weight = nn.Parameter( + torch.randn(1, out_channel, in_channel, kernel_size, kernel_size) + ) + + self.modulation = EqualLinear(style_dim, in_channel, bias_init=1) + + self.demodulate = demodulate + + def __repr__(self): + return ( + f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, ' + f'upsample={self.upsample}, downsample={self.downsample})' + ) + + def forward(self, input, style): + batch, in_channel, height, width = input.shape + + style = self.modulation(style).view(batch, 1, in_channel, 1, 1) + weight = self.scale * self.weight * style + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8) + weight = weight * demod.view(batch, self.out_channel, 1, 1, 1) + + weight = weight.view( + batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size + ) + + if self.upsample: + input = input.view(1, batch * in_channel, height, width) + weight = weight.view( + batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size + ) + weight = weight.transpose(1, 2).reshape( + batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size + ) + out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch) + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + out = self.blur(out) + + elif self.downsample: + input = self.blur(input) + _, _, height, width = input.shape + input = input.view(1, batch * in_channel, height, width) + out = F.conv2d(input, weight, padding=0, stride=2, groups=batch) + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + + else: + input = input.view(1, batch * in_channel, height, width) + out = F.conv2d(input, weight, padding=self.padding, groups=batch) + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + + return out + + +class NoiseInjection(nn.Module): + def __init__(self, isconcat=True): + super().__init__() + + self.isconcat = isconcat + self.weight = nn.Parameter(torch.zeros(1)) + + def forward(self, image, noise=None): + if noise is None: + batch, channel, height, width = image.shape + noise = image.new_empty(batch, channel, height, width).normal_() + + if self.isconcat: + return torch.cat((image, self.weight * noise), dim=1) + else: + return image + self.weight * noise + + +class ConstantInput(nn.Module): + def __init__(self, channel, size=4): + super().__init__() + + self.input = nn.Parameter(torch.randn(1, channel, size, size)) + + def forward(self, input): + batch = input.shape[0] + out = self.input.repeat(batch, 1, 1, 1) + + return out + + +class StyledConv(nn.Module): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + style_dim, + upsample=False, + blur_kernel=[1, 3, 3, 1], + demodulate=True, + isconcat=True, + device='cpu' + ): + super().__init__() + + self.conv = ModulatedConv2d( + in_channel, + out_channel, + kernel_size, + style_dim, + upsample=upsample, + blur_kernel=blur_kernel, + demodulate=demodulate, + device=device + ) + + self.noise = NoiseInjection(isconcat) + #self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1)) + #self.activate = ScaledLeakyReLU(0.2) + feat_multiplier = 2 if isconcat else 1 + self.activate = FusedLeakyReLU(out_channel*feat_multiplier, device=device) + + def forward(self, input, style, noise=None): + out = self.conv(input, style) + out = self.noise(out, noise=noise) + # out = out + self.bias + out = self.activate(out) + + return out + + +class ToRGB(nn.Module): + def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1], device='cpu'): + super().__init__() + + if upsample: + self.upsample = Upsample(blur_kernel, device=device) + + self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False, device=device) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, input, style, skip=None): + out = self.conv(input, style) + out = out + self.bias + + if skip is not None: + skip = self.upsample(skip) + + out = out + skip + + return out + +class Generator(nn.Module): + def __init__( + self, + size, + style_dim, + n_mlp, + channel_multiplier=2, + blur_kernel=[1, 3, 3, 1], + lr_mlp=0.01, + isconcat=True, + narrow=1, + device='cpu' + ): + super().__init__() + + self.size = size + self.n_mlp = n_mlp + self.style_dim = style_dim + self.feat_multiplier = 2 if isconcat else 1 + + layers = [PixelNorm()] + + for i in range(n_mlp): + layers.append( + EqualLinear( + style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu', device=device + ) + ) + + self.style = nn.Sequential(*layers) + + self.channels = { + 4: int(512 * narrow), + 8: int(512 * narrow), + 16: int(512 * narrow), + 32: int(512 * narrow), + 64: int(256 * channel_multiplier * narrow), + 128: int(128 * channel_multiplier * narrow), + 256: int(64 * channel_multiplier * narrow), + 512: int(32 * channel_multiplier * narrow), + 1024: int(16 * channel_multiplier * narrow), + 2048: int(8 * channel_multiplier * narrow) + } + + self.input = ConstantInput(self.channels[4]) + self.conv1 = StyledConv( + self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel, isconcat=isconcat, device=device + ) + self.to_rgb1 = ToRGB(self.channels[4]*self.feat_multiplier, style_dim, upsample=False, device=device) + + self.log_size = int(math.log(size, 2)) + + self.convs = nn.ModuleList() + self.upsamples = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + + in_channel = self.channels[4] + + for i in range(3, self.log_size + 1): + out_channel = self.channels[2 ** i] + + self.convs.append( + StyledConv( + in_channel*self.feat_multiplier, + out_channel, + 3, + style_dim, + upsample=True, + blur_kernel=blur_kernel, + isconcat=isconcat, + device=device + ) + ) + + self.convs.append( + StyledConv( + out_channel*self.feat_multiplier, out_channel, 3, style_dim, blur_kernel=blur_kernel, isconcat=isconcat, device=device + ) + ) + + self.to_rgbs.append(ToRGB(out_channel*self.feat_multiplier, style_dim, device=device)) + + in_channel = out_channel + + self.n_latent = self.log_size * 2 - 2 + + def make_noise(self): + device = self.input.input.device + + noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device)) + + return noises + + def mean_latent(self, n_latent): + latent_in = torch.randn( + n_latent, self.style_dim, device=self.input.input.device + ) + latent = self.style(latent_in).mean(0, keepdim=True) + + return latent + + def get_latent(self, input): + return self.style(input) + + def forward( + self, + styles, + return_latents=False, + inject_index=None, + truncation=1, + truncation_latent=None, + input_is_latent=False, + noise=None, + ): + if not input_is_latent: + styles = [self.style(s) for s in styles] + + if noise is None: + ''' + noise = [None] * (2 * (self.log_size - 2) + 1) + ''' + noise = [] + batch = styles[0].shape[0] + for i in range(self.n_mlp + 1): + size = 2 ** (i+2) + noise.append(torch.randn(batch, self.channels[size], size, size, device=styles[0].device)) + + if truncation < 1: + style_t = [] + + for style in styles: + style_t.append( + truncation_latent + truncation * (style - truncation_latent) + ) + + styles = style_t + + if len(styles) < 2: + inject_index = self.n_latent + + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + + else: + if inject_index is None: + inject_index = random.randint(1, self.n_latent - 1) + + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1) + + latent = torch.cat([latent, latent2], 1) + + out = self.input(latent) + out = self.conv1(out, latent[:, 0], noise=noise[0]) + + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs + ): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) + + i += 2 + + image = skip + + if return_latents: + return image, latent + + else: + return image, None + +class ConvLayer(nn.Sequential): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + downsample=False, + blur_kernel=[1, 3, 3, 1], + bias=True, + activate=True, + device='cpu' + ): + layers = [] + + if downsample: + factor = 2 + p = (len(blur_kernel) - factor) + (kernel_size - 1) + pad0 = (p + 1) // 2 + pad1 = p // 2 + + layers.append(Blur(blur_kernel, pad=(pad0, pad1), device=device)) + + stride = 2 + self.padding = 0 + + else: + stride = 1 + self.padding = kernel_size // 2 + + layers.append( + EqualConv2d( + in_channel, + out_channel, + kernel_size, + padding=self.padding, + stride=stride, + bias=bias and not activate, + ) + ) + + if activate: + if bias: + layers.append(FusedLeakyReLU(out_channel, device=device)) + + else: + layers.append(ScaledLeakyReLU(0.2)) + + super().__init__(*layers) + + +class ResBlock(nn.Module): + def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1], device='cpu'): + super().__init__() + + self.conv1 = ConvLayer(in_channel, in_channel, 3, device=device) + self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True, device=device) + + self.skip = ConvLayer( + in_channel, out_channel, 1, downsample=True, activate=False, bias=False + ) + + def forward(self, input): + out = self.conv1(input) + out = self.conv2(out) + + skip = self.skip(input) + out = (out + skip) / math.sqrt(2) + + return out + +class FullGenerator(nn.Module): + def __init__( + self, + size, + style_dim, + n_mlp, + channel_multiplier=2, + blur_kernel=[1, 3, 3, 1], + lr_mlp=0.01, + isconcat=True, + narrow=1, + device='cpu' + ): + super().__init__() + channels = { + 4: int(512 * narrow), + 8: int(512 * narrow), + 16: int(512 * narrow), + 32: int(512 * narrow), + 64: int(256 * channel_multiplier * narrow), + 128: int(128 * channel_multiplier * narrow), + 256: int(64 * channel_multiplier * narrow), + 512: int(32 * channel_multiplier * narrow), + 1024: int(16 * channel_multiplier * narrow), + 2048: int(8 * channel_multiplier * narrow) + } + + self.log_size = int(math.log(size, 2)) + self.generator = Generator(size, style_dim, n_mlp, channel_multiplier=channel_multiplier, blur_kernel=blur_kernel, lr_mlp=lr_mlp, isconcat=isconcat, narrow=narrow, device=device) + + conv = [ConvLayer(3, channels[size], 1, device=device)] + self.ecd0 = nn.Sequential(*conv) + in_channel = channels[size] + + self.names = ['ecd%d'%i for i in range(self.log_size-1)] + for i in range(self.log_size, 2, -1): + out_channel = channels[2 ** (i - 1)] + #conv = [ResBlock(in_channel, out_channel, blur_kernel)] + conv = [ConvLayer(in_channel, out_channel, 3, downsample=True, device=device)] + setattr(self, self.names[self.log_size-i+1], nn.Sequential(*conv)) + in_channel = out_channel + self.final_linear = nn.Sequential(EqualLinear(channels[4] * 4 * 4, style_dim, activation='fused_lrelu', device=device)) + + def forward(self, + inputs, + return_latents=False, + inject_index=None, + truncation=1, + truncation_latent=None, + input_is_latent=False, + ): + noise = [] + for i in range(self.log_size-1): + ecd = getattr(self, self.names[i]) + inputs = ecd(inputs) + noise.append(inputs) + #print(inputs.shape) + inputs = inputs.view(inputs.shape[0], -1) + outs = self.final_linear(inputs) + #print(outs.shape) + noise = list(itertools.chain.from_iterable(itertools.repeat(x, 2) for x in noise))[::-1] + outs = self.generator([outs], return_latents, inject_index, truncation, truncation_latent, input_is_latent, noise=noise[1:]) + return outs + +class Discriminator(nn.Module): + def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], narrow=1, device='cpu'): + super().__init__() + + channels = { + 4: int(512 * narrow), + 8: int(512 * narrow), + 16: int(512 * narrow), + 32: int(512 * narrow), + 64: int(256 * channel_multiplier * narrow), + 128: int(128 * channel_multiplier * narrow), + 256: int(64 * channel_multiplier * narrow), + 512: int(32 * channel_multiplier * narrow), + 1024: int(16 * channel_multiplier * narrow), + 2048: int(8 * channel_multiplier * narrow) + } + + convs = [ConvLayer(3, channels[size], 1, device=device)] + + log_size = int(math.log(size, 2)) + + in_channel = channels[size] + + for i in range(log_size, 2, -1): + out_channel = channels[2 ** (i - 1)] + + convs.append(ResBlock(in_channel, out_channel, blur_kernel, device=device)) + + in_channel = out_channel + + self.convs = nn.Sequential(*convs) + + self.stddev_group = 4 + self.stddev_feat = 1 + + self.final_conv = ConvLayer(in_channel + 1, channels[4], 3, device=device) + self.final_linear = nn.Sequential( + EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu', device=device), + EqualLinear(channels[4], 1), + ) + + def forward(self, input): + out = self.convs(input) + + batch, channel, height, width = out.shape + group = min(batch, self.stddev_group) + stddev = out.view( + group, -1, self.stddev_feat, channel // self.stddev_feat, height, width + ) + stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8) + stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2) + stddev = stddev.repeat(group, 1, height, width) + out = torch.cat([out, stddev], 1) + + out = self.final_conv(out) + + out = out.view(batch, -1) + out = self.final_linear(out) + return out + +class FullGenerator_SR(nn.Module): + def __init__( + self, + in_size, + out_size, + style_dim, + n_mlp, + channel_multiplier=2, + blur_kernel=[1, 3, 3, 1], + lr_mlp=0.01, + isconcat=True, + narrow=1, + device='cpu' + ): + super().__init__() + channels = { + 4: int(512 * narrow), + 8: int(512 * narrow), + 16: int(512 * narrow), + 32: int(512 * narrow), + 64: int(256 * channel_multiplier * narrow), + 128: int(128 * channel_multiplier * narrow), + 256: int(64 * channel_multiplier * narrow), + 512: int(32 * channel_multiplier * narrow), + 1024: int(16 * channel_multiplier * narrow), + 2048: int(8 * channel_multiplier * narrow), + } + + self.log_insize = int(math.log(in_size, 2)) + self.log_outsize = int(math.log(out_size, 2)) + self.generator = Generator(out_size, style_dim, n_mlp, channel_multiplier=channel_multiplier, blur_kernel=blur_kernel, lr_mlp=lr_mlp, isconcat=isconcat, narrow=narrow, device=device) + + conv = [ConvLayer(3, channels[in_size], 1, device=device)] + self.ecd0 = nn.Sequential(*conv) + in_channel = channels[in_size] + + self.names = ['ecd%d'%i for i in range(self.log_insize-1)] + for i in range(self.log_insize, 2, -1): + out_channel = channels[2 ** (i - 1)] + #conv = [ResBlock(in_channel, out_channel, blur_kernel)] + conv = [ConvLayer(in_channel, out_channel, 3, downsample=True, device=device)] + setattr(self, self.names[self.log_insize-i+1], nn.Sequential(*conv)) + in_channel = out_channel + self.final_linear = nn.Sequential(EqualLinear(channels[4] * 4 * 4, style_dim, activation='fused_lrelu', device=device)) + + def forward(self, + inputs, + return_latents=False, + inject_index=None, + truncation=1, + truncation_latent=None, + input_is_latent=False, + ): + noise = [] + for i in range(self.log_outsize-self.log_insize): + noise.append(None) + for i in range(self.log_insize-1): + ecd = getattr(self, self.names[i]) + inputs = ecd(inputs) + noise.append(inputs) + #print(inputs.shape) + inputs = inputs.view(inputs.shape[0], -1) + outs = self.final_linear(inputs) + #print(outs.shape) + noise = list(itertools.chain.from_iterable(itertools.repeat(x, 2) for x in noise))[::-1] + image, latent = self.generator([outs], return_latents, inject_index, truncation, truncation_latent, input_is_latent, noise=noise[1:]) + return image, latent \ No newline at end of file diff --git a/GPEN/face_model/op/__init__.py b/GPEN/face_model/op/__init__.py new file mode 100644 index 0000000..d0918d9 --- /dev/null +++ b/GPEN/face_model/op/__init__.py @@ -0,0 +1,2 @@ +from .fused_act import FusedLeakyReLU, fused_leaky_relu +from .upfirdn2d import upfirdn2d diff --git a/GPEN/face_model/op/fused_act.py b/GPEN/face_model/op/fused_act.py new file mode 100644 index 0000000..59db126 --- /dev/null +++ b/GPEN/face_model/op/fused_act.py @@ -0,0 +1,96 @@ +import os +import platform + +import torch +from torch import nn +import torch.nn.functional as F +from torch.autograd import Function +from torch.utils.cpp_extension import load, _import_module_from_library + +# if running GPEN without cuda, please comment line 11-19 +if platform.system() == 'Linux' and torch.cuda.is_available(): + module_path = os.path.dirname(__file__) + fused = load( + 'fused', + sources=[ + os.path.join(module_path, 'fused_bias_act.cpp'), + os.path.join(module_path, 'fused_bias_act_kernel.cu'), + ], + ) + + +#fused = _import_module_from_library('fused', '/tmp/torch_extensions/fused', True) + + +class FusedLeakyReLUFunctionBackward(Function): + @staticmethod + def forward(ctx, grad_output, out, negative_slope, scale): + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + empty = grad_output.new_empty(0) + + grad_input = fused.fused_bias_act( + grad_output, empty, out, 3, 1, negative_slope, scale + ) + + dim = [0] + + if grad_input.ndim > 2: + dim += list(range(2, grad_input.ndim)) + + grad_bias = grad_input.sum(dim).detach() + + return grad_input, grad_bias + + @staticmethod + def backward(ctx, gradgrad_input, gradgrad_bias): + out, = ctx.saved_tensors + gradgrad_out = fused.fused_bias_act( + gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale + ) + + return gradgrad_out, None, None, None + + +class FusedLeakyReLUFunction(Function): + @staticmethod + def forward(ctx, input, bias, negative_slope, scale): + empty = input.new_empty(0) + out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale) + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + return out + + @staticmethod + def backward(ctx, grad_output): + out, = ctx.saved_tensors + + grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply( + grad_output, out, ctx.negative_slope, ctx.scale + ) + + return grad_input, grad_bias, None, None + + +class FusedLeakyReLU(nn.Module): + def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5, device='cpu'): + super().__init__() + + self.bias = nn.Parameter(torch.zeros(channel)) + self.negative_slope = negative_slope + self.scale = scale + self.device = device + + def forward(self, input): + return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale, self.device) + + +def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5, device='cpu'): + if platform.system() == 'Linux' and torch.cuda.is_available() and device != 'cpu': + return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) + else: + return scale * F.leaky_relu(input + bias.view((1, -1)+(1,)*(len(input.shape)-2)), negative_slope=negative_slope) diff --git a/GPEN/face_model/op/fused_bias_act.cpp b/GPEN/face_model/op/fused_bias_act.cpp new file mode 100644 index 0000000..02be898 --- /dev/null +++ b/GPEN/face_model/op/fused_bias_act.cpp @@ -0,0 +1,21 @@ +#include + + +torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, + int act, int grad, float alpha, float scale); + +#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) + +torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, + int act, int grad, float alpha, float scale) { + CHECK_CUDA(input); + CHECK_CUDA(bias); + + return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)"); +} \ No newline at end of file diff --git a/GPEN/face_model/op/fused_bias_act_kernel.cu b/GPEN/face_model/op/fused_bias_act_kernel.cu new file mode 100644 index 0000000..c9fa56f --- /dev/null +++ b/GPEN/face_model/op/fused_bias_act_kernel.cu @@ -0,0 +1,99 @@ +// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. +// +// This work is made available under the Nvidia Source Code License-NC. +// To view a copy of this license, visit +// https://nvlabs.github.io/stylegan2/license.html + +#include + +#include +#include +#include +#include + +#include +#include + + +template +static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref, + int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) { + int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x; + + scalar_t zero = 0.0; + + for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) { + scalar_t x = p_x[xi]; + + if (use_bias) { + x += p_b[(xi / step_b) % size_b]; + } + + scalar_t ref = use_ref ? p_ref[xi] : zero; + + scalar_t y; + + switch (act * 10 + grad) { + default: + case 10: y = x; break; + case 11: y = x; break; + case 12: y = 0.0; break; + + case 30: y = (x > 0.0) ? x : x * alpha; break; + case 31: y = (ref > 0.0) ? x : x * alpha; break; + case 32: y = 0.0; break; + } + + out[xi] = y * scale; + } +} + + +torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, + int act, int grad, float alpha, float scale) { + int curDevice = -1; + cudaGetDevice(&curDevice); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); + + auto x = input.contiguous(); + auto b = bias.contiguous(); + auto ref = refer.contiguous(); + + int use_bias = b.numel() ? 1 : 0; + int use_ref = ref.numel() ? 1 : 0; + + int size_x = x.numel(); + int size_b = b.numel(); + int step_b = 1; + + for (int i = 1 + 1; i < x.dim(); i++) { + step_b *= x.size(i); + } + + int loop_x = 4; + int block_size = 4 * 32; + int grid_size = (size_x - 1) / (loop_x * block_size) + 1; + + auto y = torch::empty_like(x); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] { + fused_bias_act_kernel<<>>( + y.data_ptr(), + x.data_ptr(), + b.data_ptr(), + ref.data_ptr(), + act, + grad, + alpha, + scale, + loop_x, + size_x, + step_b, + size_b, + use_bias, + use_ref + ); + }); + + return y; +} \ No newline at end of file diff --git a/GPEN/face_model/op/upfirdn2d.cpp b/GPEN/face_model/op/upfirdn2d.cpp new file mode 100644 index 0000000..d2e633d --- /dev/null +++ b/GPEN/face_model/op/upfirdn2d.cpp @@ -0,0 +1,23 @@ +#include + + +torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel, + int up_x, int up_y, int down_x, int down_y, + int pad_x0, int pad_x1, int pad_y0, int pad_y1); + +#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) + +torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel, + int up_x, int up_y, int down_x, int down_y, + int pad_x0, int pad_x1, int pad_y0, int pad_y1) { + CHECK_CUDA(input); + CHECK_CUDA(kernel); + + return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)"); +} \ No newline at end of file diff --git a/GPEN/face_model/op/upfirdn2d.py b/GPEN/face_model/op/upfirdn2d.py new file mode 100644 index 0000000..2e38447 --- /dev/null +++ b/GPEN/face_model/op/upfirdn2d.py @@ -0,0 +1,194 @@ +import os +import platform + +import torch +import torch.nn.functional as F +from torch.autograd import Function +from torch.utils.cpp_extension import load, _import_module_from_library + +# if running GPEN without cuda, please comment line 10-18 +if platform.system() == 'Linux' and torch.cuda.is_available(): + module_path = os.path.dirname(__file__) + upfirdn2d_op = load( + 'upfirdn2d', + sources=[ + os.path.join(module_path, 'upfirdn2d.cpp'), + os.path.join(module_path, 'upfirdn2d_kernel.cu'), + ], + ) + + +#upfirdn2d_op = _import_module_from_library('upfirdn2d', '/tmp/torch_extensions/upfirdn2d', True) + +class UpFirDn2dBackward(Function): + @staticmethod + def forward( + ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size + ): + + up_x, up_y = up + down_x, down_y = down + g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad + + grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) + + grad_input = upfirdn2d_op.upfirdn2d( + grad_output, + grad_kernel, + down_x, + down_y, + up_x, + up_y, + g_pad_x0, + g_pad_x1, + g_pad_y0, + g_pad_y1, + ) + grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) + + ctx.save_for_backward(kernel) + + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + ctx.up_x = up_x + ctx.up_y = up_y + ctx.down_x = down_x + ctx.down_y = down_y + ctx.pad_x0 = pad_x0 + ctx.pad_x1 = pad_x1 + ctx.pad_y0 = pad_y0 + ctx.pad_y1 = pad_y1 + ctx.in_size = in_size + ctx.out_size = out_size + + return grad_input + + @staticmethod + def backward(ctx, gradgrad_input): + kernel, = ctx.saved_tensors + + gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) + + gradgrad_out = upfirdn2d_op.upfirdn2d( + gradgrad_input, + kernel, + ctx.up_x, + ctx.up_y, + ctx.down_x, + ctx.down_y, + ctx.pad_x0, + ctx.pad_x1, + ctx.pad_y0, + ctx.pad_y1, + ) + # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3]) + gradgrad_out = gradgrad_out.view( + ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1] + ) + + return gradgrad_out, None, None, None, None, None, None, None, None + + +class UpFirDn2d(Function): + @staticmethod + def forward(ctx, input, kernel, up, down, pad): + up_x, up_y = up + down_x, down_y = down + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + kernel_h, kernel_w = kernel.shape + batch, channel, in_h, in_w = input.shape + ctx.in_size = input.shape + + input = input.reshape(-1, in_h, in_w, 1) + + ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + ctx.out_size = (out_h, out_w) + + ctx.up = (up_x, up_y) + ctx.down = (down_x, down_y) + ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) + + g_pad_x0 = kernel_w - pad_x0 - 1 + g_pad_y0 = kernel_h - pad_y0 - 1 + g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 + g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 + + ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) + + out = upfirdn2d_op.upfirdn2d( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 + ) + # out = out.view(major, out_h, out_w, minor) + out = out.view(-1, channel, out_h, out_w) + + return out + + @staticmethod + def backward(ctx, grad_output): + kernel, grad_kernel = ctx.saved_tensors + + grad_input = UpFirDn2dBackward.apply( + grad_output, + kernel, + grad_kernel, + ctx.up, + ctx.down, + ctx.pad, + ctx.g_pad, + ctx.in_size, + ctx.out_size, + ) + + return grad_input, None, None, None, None + + +def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0), device='cpu'): + if platform.system() == 'Linux' and torch.cuda.is_available() and device != 'cpu': + out = UpFirDn2d.apply( + input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]) + ) + else: + out = upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]) + + return out + + +def upfirdn2d_native( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 +): + input = input.permute(0, 2, 3, 1) + _, in_h, in_w, minor = input.shape + kernel_h, kernel_w = kernel.shape + out = input.view(-1, in_h, 1, in_w, 1, minor) + out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) + out = out.view(-1, in_h * up_y, in_w * up_x, minor) + + out = F.pad( + out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] + ) + out = out[ + :, + max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), + max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), + :, + ] + + out = out.permute(0, 3, 1, 2) + out = out.reshape( + [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] + ) + w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) + out = F.conv2d(out, w) + out = out.reshape( + -1, + minor, + in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, + in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, + ) + # out = out.permute(0, 2, 3, 1) + return out[:, :, ::down_y, ::down_x] + diff --git a/GPEN/face_model/op/upfirdn2d_kernel.cu b/GPEN/face_model/op/upfirdn2d_kernel.cu new file mode 100644 index 0000000..2a710aa --- /dev/null +++ b/GPEN/face_model/op/upfirdn2d_kernel.cu @@ -0,0 +1,272 @@ +// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. +// +// This work is made available under the Nvidia Source Code License-NC. +// To view a copy of this license, visit +// https://nvlabs.github.io/stylegan2/license.html + +#include + +#include +#include +#include +#include + +#include +#include + + +static __host__ __device__ __forceinline__ int floor_div(int a, int b) { + int c = a / b; + + if (c * b > a) { + c--; + } + + return c; +} + + +struct UpFirDn2DKernelParams { + int up_x; + int up_y; + int down_x; + int down_y; + int pad_x0; + int pad_x1; + int pad_y0; + int pad_y1; + + int major_dim; + int in_h; + int in_w; + int minor_dim; + int kernel_h; + int kernel_w; + int out_h; + int out_w; + int loop_major; + int loop_x; +}; + + +template +__global__ void upfirdn2d_kernel(scalar_t* out, const scalar_t* input, const scalar_t* kernel, const UpFirDn2DKernelParams p) { + const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1; + const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1; + + __shared__ volatile float sk[kernel_h][kernel_w]; + __shared__ volatile float sx[tile_in_h][tile_in_w]; + + int minor_idx = blockIdx.x; + int tile_out_y = minor_idx / p.minor_dim; + minor_idx -= tile_out_y * p.minor_dim; + tile_out_y *= tile_out_h; + int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w; + int major_idx_base = blockIdx.z * p.loop_major; + + if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h | major_idx_base >= p.major_dim) { + return; + } + + for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w; tap_idx += blockDim.x) { + int ky = tap_idx / kernel_w; + int kx = tap_idx - ky * kernel_w; + scalar_t v = 0.0; + + if (kx < p.kernel_w & ky < p.kernel_h) { + v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)]; + } + + sk[ky][kx] = v; + } + + for (int loop_major = 0, major_idx = major_idx_base; loop_major < p.loop_major & major_idx < p.major_dim; loop_major++, major_idx++) { + for (int loop_x = 0, tile_out_x = tile_out_x_base; loop_x < p.loop_x & tile_out_x < p.out_w; loop_x++, tile_out_x += tile_out_w) { + int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0; + int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0; + int tile_in_x = floor_div(tile_mid_x, up_x); + int tile_in_y = floor_div(tile_mid_y, up_y); + + __syncthreads(); + + for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w; in_idx += blockDim.x) { + int rel_in_y = in_idx / tile_in_w; + int rel_in_x = in_idx - rel_in_y * tile_in_w; + int in_x = rel_in_x + tile_in_x; + int in_y = rel_in_y + tile_in_y; + + scalar_t v = 0.0; + + if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) { + v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + minor_idx]; + } + + sx[rel_in_y][rel_in_x] = v; + } + + __syncthreads(); + for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w; out_idx += blockDim.x) { + int rel_out_y = out_idx / tile_out_w; + int rel_out_x = out_idx - rel_out_y * tile_out_w; + int out_x = rel_out_x + tile_out_x; + int out_y = rel_out_y + tile_out_y; + + int mid_x = tile_mid_x + rel_out_x * down_x; + int mid_y = tile_mid_y + rel_out_y * down_y; + int in_x = floor_div(mid_x, up_x); + int in_y = floor_div(mid_y, up_y); + int rel_in_x = in_x - tile_in_x; + int rel_in_y = in_y - tile_in_y; + int kernel_x = (in_x + 1) * up_x - mid_x - 1; + int kernel_y = (in_y + 1) * up_y - mid_y - 1; + + scalar_t v = 0.0; + + #pragma unroll + for (int y = 0; y < kernel_h / up_y; y++) + #pragma unroll + for (int x = 0; x < kernel_w / up_x; x++) + v += sx[rel_in_y + y][rel_in_x + x] * sk[kernel_y + y * up_y][kernel_x + x * up_x]; + + if (out_x < p.out_w & out_y < p.out_h) { + out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + minor_idx] = v; + } + } + } + } +} + + +torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel, + int up_x, int up_y, int down_x, int down_y, + int pad_x0, int pad_x1, int pad_y0, int pad_y1) { + int curDevice = -1; + cudaGetDevice(&curDevice); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); + + UpFirDn2DKernelParams p; + + auto x = input.contiguous(); + auto k = kernel.contiguous(); + + p.major_dim = x.size(0); + p.in_h = x.size(1); + p.in_w = x.size(2); + p.minor_dim = x.size(3); + p.kernel_h = k.size(0); + p.kernel_w = k.size(1); + p.up_x = up_x; + p.up_y = up_y; + p.down_x = down_x; + p.down_y = down_y; + p.pad_x0 = pad_x0; + p.pad_x1 = pad_x1; + p.pad_y0 = pad_y0; + p.pad_y1 = pad_y1; + + p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) / p.down_y; + p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) / p.down_x; + + auto out = at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options()); + + int mode = -1; + + int tile_out_h; + int tile_out_w; + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 1; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 3 && p.kernel_w <= 3) { + mode = 2; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 3; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 2 && p.kernel_w <= 2) { + mode = 4; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 5; + tile_out_h = 8; + tile_out_w = 32; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && p.kernel_h <= 2 && p.kernel_w <= 2) { + mode = 6; + tile_out_h = 8; + tile_out_w = 32; + } + + dim3 block_size; + dim3 grid_size; + + if (tile_out_h > 0 && tile_out_w) { + p.loop_major = (p.major_dim - 1) / 16384 + 1; + p.loop_x = 1; + block_size = dim3(32 * 8, 1, 1); + grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim, + (p.out_w - 1) / (p.loop_x * tile_out_w) + 1, + (p.major_dim - 1) / p.loop_major + 1); + } + + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] { + switch (mode) { + case 1: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 2: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 3: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 4: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 5: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 6: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + } + }); + + return out; +} \ No newline at end of file diff --git a/GPEN/face_parse/blocks.py b/GPEN/face_parse/blocks.py new file mode 100644 index 0000000..fc7facb --- /dev/null +++ b/GPEN/face_parse/blocks.py @@ -0,0 +1,127 @@ +# -*- coding: utf-8 -*- +import torch +import torch.nn as nn +from torch.nn.parameter import Parameter +from torch.nn import functional as F +import numpy as np + +class NormLayer(nn.Module): + """Normalization Layers. + ------------ + # Arguments + - channels: input channels, for batch norm and instance norm. + - input_size: input shape without batch size, for layer norm. + """ + def __init__(self, channels, normalize_shape=None, norm_type='bn', ref_channels=None): + super(NormLayer, self).__init__() + norm_type = norm_type.lower() + self.norm_type = norm_type + if norm_type == 'bn': + self.norm = nn.BatchNorm2d(channels, affine=True) + elif norm_type == 'in': + self.norm = nn.InstanceNorm2d(channels, affine=False) + elif norm_type == 'gn': + self.norm = nn.GroupNorm(32, channels, affine=True) + elif norm_type == 'pixel': + self.norm = lambda x: F.normalize(x, p=2, dim=1) + elif norm_type == 'layer': + self.norm = nn.LayerNorm(normalize_shape) + elif norm_type == 'none': + self.norm = lambda x: x*1.0 + else: + assert 1==0, 'Norm type {} not support.'.format(norm_type) + + def forward(self, x, ref=None): + if self.norm_type == 'spade': + return self.norm(x, ref) + else: + return self.norm(x) + + +class ReluLayer(nn.Module): + """Relu Layer. + ------------ + # Arguments + - relu type: type of relu layer, candidates are + - ReLU + - LeakyReLU: default relu slope 0.2 + - PRelu + - SELU + - none: direct pass + """ + def __init__(self, channels, relu_type='relu'): + super(ReluLayer, self).__init__() + relu_type = relu_type.lower() + if relu_type == 'relu': + self.func = nn.ReLU(True) + elif relu_type == 'leakyrelu': + self.func = nn.LeakyReLU(0.2, inplace=True) + elif relu_type == 'prelu': + self.func = nn.PReLU(channels) + elif relu_type == 'selu': + self.func = nn.SELU(True) + elif relu_type == 'none': + self.func = lambda x: x*1.0 + else: + assert 1==0, 'Relu type {} not support.'.format(relu_type) + + def forward(self, x): + return self.func(x) + + +class ConvLayer(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=3, scale='none', norm_type='none', relu_type='none', use_pad=True, bias=True): + super(ConvLayer, self).__init__() + self.use_pad = use_pad + self.norm_type = norm_type + if norm_type in ['bn']: + bias = False + + stride = 2 if scale == 'down' else 1 + + self.scale_func = lambda x: x + if scale == 'up': + self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest') + + self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.)/2))) + self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias) + + self.relu = ReluLayer(out_channels, relu_type) + self.norm = NormLayer(out_channels, norm_type=norm_type) + + def forward(self, x): + out = self.scale_func(x) + if self.use_pad: + out = self.reflection_pad(out) + out = self.conv2d(out) + out = self.norm(out) + out = self.relu(out) + return out + + +class ResidualBlock(nn.Module): + """ + Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html + """ + def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'): + super(ResidualBlock, self).__init__() + + if scale == 'none' and c_in == c_out: + self.shortcut_func = lambda x: x + else: + self.shortcut_func = ConvLayer(c_in, c_out, 3, scale) + + scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']} + scale_conf = scale_config_dict[scale] + + self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type) + self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none') + + def forward(self, x): + identity = self.shortcut_func(x) + + res = self.conv1(x) + res = self.conv2(res) + return identity + res + + diff --git a/GPEN/face_parse/face_parsing.py b/GPEN/face_parse/face_parsing.py new file mode 100644 index 0000000..63e5e93 --- /dev/null +++ b/GPEN/face_parse/face_parsing.py @@ -0,0 +1,78 @@ +''' +@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) +@author: yangxy (yangtao9009@gmail.com) +''' +import os +import cv2 +import torch +import numpy as np +from parse_model import ParseNet +import torch.nn.functional as F + +class FaceParse(object): + def __init__(self, base_dir='./', model='ParseNet-latest', device='cuda'): + self.mfile = os.path.join(base_dir, 'weights', model+'.pth') + self.size = 512 + self.device = device + + ''' + 0: 'background' 1: 'skin' 2: 'nose' + 3: 'eye_g' 4: 'l_eye' 5: 'r_eye' + 6: 'l_brow' 7: 'r_brow' 8: 'l_ear' + 9: 'r_ear' 10: 'mouth' 11: 'u_lip' + 12: 'l_lip' 13: 'hair' 14: 'hat' + 15: 'ear_r' 16: 'neck_l' 17: 'neck' + 18: 'cloth' + ''' + #self.MASK_COLORMAP = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]] + #self.#MASK_COLORMAP = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]] = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [0, 0, 0], [0, 0, 0]] + self.MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 255, 255, 0] + self.load_model() + + def load_model(self): + self.faceparse = ParseNet(self.size, self.size, 32, 64, 19, norm_type='bn', relu_type='LeakyReLU', ch_range=[32, 256]) + self.faceparse.load_state_dict(torch.load(self.mfile, map_location=torch.device('cpu'))) + self.faceparse.to(self.device) + self.faceparse.eval() + + def process(self, im): + im = cv2.resize(im, (self.size, self.size)) + imt = self.img2tensor(im) + pred_mask, sr_img_tensor = self.faceparse(imt) + mask = self.tenor2mask(pred_mask) + + return mask + + def process_tensor(self, imt): + imt = F.interpolate(imt.flip(1)*2-1, (self.size, self.size)) + pred_mask, sr_img_tensor = self.faceparse(imt) + + mask = pred_mask.argmax(dim=1) + for idx, color in enumerate(self.MASK_COLORMAP): + mask = torch.where(mask==idx, color, mask) + #mask = mask.repeat(3, 1, 1).unsqueeze(0) #.cpu().float().numpy() + mask = mask.unsqueeze(0) + + return mask + + def img2tensor(self, img): + img = img[..., ::-1] + img = img / 255. * 2 - 1 + img_tensor = torch.from_numpy(img.transpose(2, 0, 1)).unsqueeze(0).to(self.device) + return img_tensor.float() + + def tenor2mask(self, tensor): + if len(tensor.shape) < 4: + tensor = tensor.unsqueeze(0) + if tensor.shape[1] > 1: + tensor = tensor.argmax(dim=1) + + tensor = tensor.squeeze(1).data.cpu().numpy() + color_maps = [] + for t in tensor: + #tmp_img = np.zeros(tensor.shape[1:] + (3,)) + tmp_img = np.zeros(tensor.shape[1:]) + for idx, color in enumerate(self.MASK_COLORMAP): + tmp_img[t == idx] = color + color_maps.append(tmp_img.astype(np.uint8)) + return color_maps \ No newline at end of file diff --git a/GPEN/face_parse/mask.png b/GPEN/face_parse/mask.png new file mode 100644 index 0000000..729f36d Binary files /dev/null and b/GPEN/face_parse/mask.png differ diff --git a/GPEN/face_parse/parse_model.py b/GPEN/face_parse/parse_model.py new file mode 100644 index 0000000..b0717ee --- /dev/null +++ b/GPEN/face_parse/parse_model.py @@ -0,0 +1,77 @@ +''' +@Created by chaofengc (chaofenghust@gmail.com) + +@Modified by yangxy (yangtao9009@gmail.com) +''' + +from blocks import * +import torch +from torch import nn +import numpy as np + +def define_P(in_size=512, out_size=512, min_feat_size=32, relu_type='LeakyReLU', isTrain=False, weight_path=None): + net = ParseNet(in_size, out_size, min_feat_size, 64, 19, norm_type='bn', relu_type=relu_type, ch_range=[32, 256]) + if not isTrain: + net.eval() + if weight_path is not None: + net.load_state_dict(torch.load(weight_path)) + return net + + +class ParseNet(nn.Module): + def __init__(self, + in_size=128, + out_size=128, + min_feat_size=32, + base_ch=64, + parsing_ch=19, + res_depth=10, + relu_type='prelu', + norm_type='bn', + ch_range=[32, 512], + ): + super().__init__() + self.res_depth = res_depth + act_args = {'norm_type': norm_type, 'relu_type': relu_type} + min_ch, max_ch = ch_range + + ch_clip = lambda x: max(min_ch, min(x, max_ch)) + min_feat_size = min(in_size, min_feat_size) + + down_steps = int(np.log2(in_size//min_feat_size)) + up_steps = int(np.log2(out_size//min_feat_size)) + + # =============== define encoder-body-decoder ==================== + self.encoder = [] + self.encoder.append(ConvLayer(3, base_ch, 3, 1)) + head_ch = base_ch + for i in range(down_steps): + cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2) + self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args)) + head_ch = head_ch * 2 + + self.body = [] + for i in range(res_depth): + self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args)) + + self.decoder = [] + for i in range(up_steps): + cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2) + self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args)) + head_ch = head_ch // 2 + + self.encoder = nn.Sequential(*self.encoder) + self.body = nn.Sequential(*self.body) + self.decoder = nn.Sequential(*self.decoder) + self.out_img_conv = ConvLayer(ch_clip(head_ch), 3) + self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch) + + def forward(self, x): + feat = self.encoder(x) + x = feat + self.body(feat) + x = self.decoder(x) + out_img = self.out_img_conv(x) + out_mask = self.out_mask_conv(x) + return out_mask, out_img + + diff --git a/GPEN/face_parse/test.png b/GPEN/face_parse/test.png new file mode 100644 index 0000000..f2bea14 Binary files /dev/null and b/GPEN/face_parse/test.png differ diff --git a/GPEN/figs/Solvay_conference_1927_comp.jpg b/GPEN/figs/Solvay_conference_1927_comp.jpg new file mode 100644 index 0000000..4e2ea2f Binary files /dev/null and b/GPEN/figs/Solvay_conference_1927_comp.jpg differ diff --git a/GPEN/figs/architecture.png b/GPEN/figs/architecture.png new file mode 100644 index 0000000..6bd62db Binary files /dev/null and b/GPEN/figs/architecture.png differ diff --git a/GPEN/figs/colorization_00.jpg b/GPEN/figs/colorization_00.jpg new file mode 100644 index 0000000..d5b20c2 Binary files /dev/null and b/GPEN/figs/colorization_00.jpg differ diff --git a/GPEN/figs/colorization_01.jpg b/GPEN/figs/colorization_01.jpg new file mode 100644 index 0000000..8208e42 Binary files /dev/null and b/GPEN/figs/colorization_01.jpg differ diff --git a/GPEN/figs/inpainting_00.jpg b/GPEN/figs/inpainting_00.jpg new file mode 100644 index 0000000..ccbdad6 Binary files /dev/null and b/GPEN/figs/inpainting_00.jpg differ diff --git a/GPEN/figs/inpainting_01.jpg b/GPEN/figs/inpainting_01.jpg new file mode 100644 index 0000000..ac420e4 Binary files /dev/null and b/GPEN/figs/inpainting_01.jpg differ diff --git a/GPEN/figs/real_00.png b/GPEN/figs/real_00.png new file mode 100644 index 0000000..bc9cfa2 Binary files /dev/null and b/GPEN/figs/real_00.png differ diff --git a/GPEN/figs/real_01.png b/GPEN/figs/real_01.png new file mode 100644 index 0000000..b14c2a0 Binary files /dev/null and b/GPEN/figs/real_01.png differ diff --git a/GPEN/figs/real_02.png b/GPEN/figs/real_02.png new file mode 100644 index 0000000..41bb88b Binary files /dev/null and b/GPEN/figs/real_02.png differ diff --git a/GPEN/figs/real_03.png b/GPEN/figs/real_03.png new file mode 100644 index 0000000..fd4ea1d Binary files /dev/null and b/GPEN/figs/real_03.png differ diff --git a/GPEN/figs/seg2face_00.jpg b/GPEN/figs/seg2face_00.jpg new file mode 100644 index 0000000..d709317 Binary files /dev/null and b/GPEN/figs/seg2face_00.jpg differ diff --git a/GPEN/figs/seg2face_01.jpg b/GPEN/figs/seg2face_01.jpg new file mode 100644 index 0000000..1cbedab Binary files /dev/null and b/GPEN/figs/seg2face_01.jpg differ diff --git a/GPEN/figs/selfie_00.jpg b/GPEN/figs/selfie_00.jpg new file mode 100644 index 0000000..19dc596 Binary files /dev/null and b/GPEN/figs/selfie_00.jpg differ diff --git a/GPEN/figs/selfie_01.jpg b/GPEN/figs/selfie_01.jpg new file mode 100644 index 0000000..a15ab5b Binary files /dev/null and b/GPEN/figs/selfie_01.jpg differ diff --git a/GPEN/misc/cog.yaml b/GPEN/misc/cog.yaml new file mode 100644 index 0000000..f7842ca --- /dev/null +++ b/GPEN/misc/cog.yaml @@ -0,0 +1,17 @@ +build: + gpu: true + python_version: "3.8" + system_packages: + - "libgl1-mesa-glx" + - "libglib2.0-0" + - "ninja-build" + python_packages: + - "torch==1.7.1" + - "torchvision==0.8.2" + - "numpy==1.20.1" + - "ipython==7.21.0" + - "Pillow==8.3.1" + - "scikit-image==0.18.3" + - "opencv-python==4.5.3.56" + +predict: "predict.py:Predictor" diff --git a/GPEN/misc/onnx_export.py b/GPEN/misc/onnx_export.py new file mode 100644 index 0000000..da7f662 --- /dev/null +++ b/GPEN/misc/onnx_export.py @@ -0,0 +1,182 @@ +import math +import os +import sys + +import torch +import torch.nn as nn +from tqdm import tqdm +from torch.autograd import Variable + +import __init_paths +#from face_model import model +from face_model.gpen_model import FullGenerator + +def model_load(model, path): + """Load model.""" + + if not os.path.exists(path): + print("Model '{}' does not exist.".format(path)) + return + + state_dict = torch.load(path, map_location=lambda storage, loc: storage) + + model.load_state_dict(state_dict) + + +def export_onnx(model, path, force_cpu): + """Export onnx model.""" + + import onnx + import onnxruntime + #from onnx import optimizer + import numpy as np + + onnx_file_name = os.path.join(path, model+".onnx") + model_weight_file = os.path.join(path, model+".pth") + dummy_input = Variable(torch.randn(1, 3, 1024, 1024)) + + # 1. Create and load model. + model_setenv(force_cpu) + torch_model = get_model(model_weight_file) + torch_model.eval() + + # 2. Model export + print("Export model ...") + + input_names = ["input"] + output_names = ["output"] + device = model_device() + # torch.onnx.export(torch_model, dummy_input.to(device), onnx_file_name, + # input_names=input_names, + # output_names=output_names, + # verbose=False, + # opset_version=12, + # keep_initializers_as_inputs=False, + # export_params=True, + # operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK) + torch.onnx.export(torch_model, dummy_input.to(device), onnx_file_name, + input_names=input_names, + output_names=output_names, + verbose=False, + opset_version=10, + operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK) + + # 3. Optimize model + print('Checking model ...') + onnx_model = onnx.load(onnx_file_name) + onnx.checker.check_model(onnx_model) + # https://github.com/onnx/optimizer + print('Done checking model ...') + # 4. Visual model + # python -c "import netron; netron.start('output/image_zoom.onnx')" + +def verify_onnx(model, path, force_cpu): + """Verify onnx model.""" + + import onnxruntime + import numpy as np + + + model_weight_file = os.path.join(path, model+".pth") + + model_weight_file = "./weights/GPEN-512.pth" + + model_setenv(force_cpu) + torch_model = get_model(model_weight_file) + torch_model.eval() + + onnx_file_name = os.path.join(path, model+".onnx") + onnxruntime_engine = onnxruntime.InferenceSession(onnx_file_name) + + def to_numpy(tensor): + return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy() + + dummy_input = Variable(torch.randn(1, 3, 512, 512)) + with torch.no_grad(): + torch_output, _ = torch_model(dummy_input) + onnxruntime_inputs = {onnxruntime_engine.get_inputs()[0].name: to_numpy(dummy_input)} + onnxruntime_outputs = onnxruntime_engine.run(None, onnxruntime_inputs) + np.testing.assert_allclose(to_numpy(torch_output), onnxruntime_outputs[0], rtol=1e-02, atol=1e-02) + print("Example: Onnx model has been tested with ONNXRuntime, the result looks good !") + +def get_model(checkpoint): + """Create encoder model.""" + + #model_setenv() + model = FullGenerator(1024, 512, 8, 2, narrow=1) #TODO + model_load(model, checkpoint) + device = model_device() + model.to(device) + return model + + +def model_device(): + """Please call after model_setenv. """ + + return torch.device(os.environ["DEVICE"]) + + +def model_setenv(cpu_only): + """Setup environ ...""" + + # random init ... + import random + random.seed(42) + torch.manual_seed(42) + + # Set default device to avoid exceptions + if cpu_only: + os.environ["DEVICE"] = 'cpu' + else: + if os.environ.get("DEVICE") != "cuda" and os.environ.get("DEVICE") != "cpu": + os.environ["DEVICE"] = 'cuda' if torch.cuda.is_available() else 'cpu' + if os.environ["DEVICE"] == 'cuda': + torch.backends.cudnn.enabled = True + torch.backends.cudnn.benchmark = True + + print("Running Environment:") + print("----------------------------------------------") + #print(" PWD: ", os.environ["PWD"]) + print(" DEVICE: ", os.environ["DEVICE"]) + +# def export_torch(model, path): + # """Export torch model.""" + + # script_file = os.path.join(path, model+".pt") + # weight_file = os.path.join(path, model+".onnx") + + # # 1. Load model + # print("Loading model ...") + # model = get_model(weight_file) + # model.eval() + + # # 2. Model export + # print("Export model ...") + # dummy_input = Variable(torch.randn(1, 3, 512, 512)) + # device = model_device() + # traced_script_module = torch.jit.trace(model, dummy_input.to(device), _force_outplace=True) + # traced_script_module.save(script_file) + + +if __name__ == '__main__': + """Test model ...""" + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument('--model', type=str, required=True) + parser.add_argument('--path', type=str, default='./') + parser.add_argument('--export', help="Export onnx model", action='store_true') + parser.add_argument('--verify', help="Verify onnx model", action='store_true') + parser.add_argument('--force-cpu', dest='force_cpu', help="Verify onnx model", action='store_true') + + args = parser.parse_args() + + # export_torch() + + + + if args.export: + export_onnx(model = args.model, path = args.path, force_cpu=args.force_cpu) + + if args.verify: + verify_onnx(model = args.model, path = args.path, force_cpu=args.force_cpu) diff --git a/GPEN/misc/predict.py b/GPEN/misc/predict.py new file mode 100644 index 0000000..1715ca2 --- /dev/null +++ b/GPEN/misc/predict.py @@ -0,0 +1,105 @@ +import os +import tempfile +from pathlib import Path +import glob +import numpy as np +import cv2 +from zipfile import ZipFile +from PIL import Image +import shutil +import cog +from face_enhancement import FaceEnhancement +from face_colorization import FaceColorization +from face_inpainting import FaceInpainting, brush_stroke_mask + + +class Predictor(cog.Predictor): + def setup(self): + faceenhancer_model = {'name': 'GPEN-BFR-256', 'size': 256, 'channel_multiplier': 1, 'narrow': 0.5} + self.faceenhancer = FaceEnhancement(size=faceenhancer_model['size'], model=faceenhancer_model['name'], + channel_multiplier=faceenhancer_model['channel_multiplier'], + narrow=faceenhancer_model['narrow']) + faceinpainter_model = {'name': 'GPEN-Inpainting-1024', 'size': 1024} + self.faceinpainter = FaceInpainting(size=faceinpainter_model['size'], model=faceinpainter_model['name'], + channel_multiplier=2) + facecolorizer_model = {'name': 'GPEN-Colorization-1024', 'size': 1024} + self.facecolorizer = FaceColorization(size=facecolorizer_model['size'], model=facecolorizer_model['name'], + channel_multiplier=2) + + @cog.input( + "image", + type=Path, + help="input image", + ) + @cog.input( + "task", + type=str, + options=['Face Restoration', 'Face Colorization', 'Face Inpainting'], + default='Face Restoration', + help="choose task type" + ) + @cog.input( + "output_individual", + type=bool, + default=False, + help="whether outputs individual enhanced faces, valid for Face Restoration. When set to true, a zip folder of " + "all the enhanced faces in the input will be generated for download." + ) + @cog.input( + "broken_image", + type=bool, + default=True, + help="whether the input image is broken, valid for Face Inpainting. When set to True, the output will be the " + "'fixed' image. When set to False, the image will randomly add brush strokes to simulate a broken image, " + "and the output will be broken + fixed image" + ) + def predict(self, image, task='Face Restoration', output_individual=False, broken_image=True): + out_path = Path(tempfile.mkdtemp()) / "out.png" + if task == 'Face Restoration': + im = cv2.imread(str(image), cv2.IMREAD_COLOR) # BGR + assert isinstance(im, np.ndarray), 'input filename error' + im = cv2.resize(im, (0, 0), fx=2, fy=2) + img, orig_faces, enhanced_faces = self.faceenhancer.process(im) + cv2.imwrite(str(out_path), img) + if output_individual: + zip_folder = 'out_zip' + os.makedirs(zip_folder, exist_ok=True) + out_path = Path(tempfile.mkdtemp()) / "out.zip" + try: + cv2.imwrite(os.path.join(zip_folder, 'whole_image.jpg'), img) + for m, ef in enumerate(enhanced_faces): + cv2.imwrite(os.path.join(zip_folder, f'face_{m}.jpg'), ef) + img_list = sorted(glob.glob(os.path.join(zip_folder, '*'))) + with ZipFile(str(out_path), 'w') as zipfile: + for img in img_list: + zipfile.write(img) + finally: + clean_folder(zip_folder) + elif task == 'Face Colorization': + grayf = cv2.imread(str(image), cv2.IMREAD_GRAYSCALE) + grayf = cv2.cvtColor(grayf, cv2.COLOR_GRAY2BGR) # channel: 1->3 + colorf = self.facecolorizer.process(grayf) + cv2.imwrite(str(out_path), colorf) + else: + originf = cv2.imread(str(image), cv2.IMREAD_COLOR) + brokenf = originf + if not broken_image: + brokenf = np.asarray(brush_stroke_mask(Image.fromarray(originf))) + completef = self.faceinpainter.process(brokenf) + brokenf = cv2.resize(brokenf, completef.shape[:2]) + out_img = completef if broken_image else np.hstack((brokenf, completef)) + cv2.imwrite(str(out_path), out_img) + + return out_path + + +def clean_folder(folder): + for filename in os.listdir(folder): + file_path = os.path.join(folder, filename) + try: + if os.path.isfile(file_path) or os.path.islink(file_path): + os.unlink(file_path) + elif os.path.isdir(file_path): + shutil.rmtree(file_path) + except Exception as e: + print('Failed to delete %s. Reason: %s' % (file_path, e)) \ No newline at end of file diff --git a/GPEN/requirements.txt b/GPEN/requirements.txt new file mode 100644 index 0000000..c3c00b7 --- /dev/null +++ b/GPEN/requirements.txt @@ -0,0 +1,9 @@ +ninja +torch +torchvision +opencv-python +numpy +scikit-image +scipy +pillow +tqdm \ No newline at end of file diff --git a/GPEN/results/outs-bfr/030102-03_GPEN.jpg b/GPEN/results/outs-bfr/030102-03_GPEN.jpg new file mode 100644 index 0000000..7597bad Binary files /dev/null and b/GPEN/results/outs-bfr/030102-03_GPEN.jpg differ diff --git a/GPEN/results/outs-bfr/34484-1000x830_GPEN.jpg b/GPEN/results/outs-bfr/34484-1000x830_GPEN.jpg new file mode 100644 index 0000000..b9a6078 Binary files /dev/null and b/GPEN/results/outs-bfr/34484-1000x830_GPEN.jpg differ diff --git a/GPEN/results/outs-bfr/69045376_778428345939842_4590816548997824512_n_GPEN.jpg b/GPEN/results/outs-bfr/69045376_778428345939842_4590816548997824512_n_GPEN.jpg new file mode 100644 index 0000000..cee2d27 Binary files /dev/null and b/GPEN/results/outs-bfr/69045376_778428345939842_4590816548997824512_n_GPEN.jpg differ diff --git a/GPEN/results/outs-bfr/examplegpen.jpg b/GPEN/results/outs-bfr/examplegpen.jpg new file mode 100644 index 0000000..51dda0d Binary files /dev/null and b/GPEN/results/outs-bfr/examplegpen.jpg differ diff --git a/GPEN/results/outs-bfr/image1gpen.jpg b/GPEN/results/outs-bfr/image1gpen.jpg new file mode 100644 index 0000000..476d716 Binary files /dev/null and b/GPEN/results/outs-bfr/image1gpen.jpg differ diff --git a/GPEN/results/outs-bfr/image2gpen.jpg b/GPEN/results/outs-bfr/image2gpen.jpg new file mode 100644 index 0000000..2bd34fd Binary files /dev/null and b/GPEN/results/outs-bfr/image2gpen.jpg differ diff --git a/GPEN/results/outs-bfr/image3gpen.jpg b/GPEN/results/outs-bfr/image3gpen.jpg new file mode 100644 index 0000000..b565495 Binary files /dev/null and b/GPEN/results/outs-bfr/image3gpen.jpg differ diff --git "a/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 alan-tyuring_GPEN.jpg" "b/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 alan-tyuring_GPEN.jpg" new file mode 100644 index 0000000..3eaeecd Binary files /dev/null and "b/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 alan-tyuring_GPEN.jpg" differ diff --git "a/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-07_GPEN.jpg" "b/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-07_GPEN.jpg" new file mode 100644 index 0000000..b973f20 Binary files /dev/null and "b/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-07_GPEN.jpg" differ diff --git "a/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-21_GPEN.jpg" "b/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-21_GPEN.jpg" new file mode 100644 index 0000000..b7bba55 Binary files /dev/null and "b/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-21_GPEN.jpg" differ diff --git "a/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-38_GPEN.jpg" "b/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-38_GPEN.jpg" new file mode 100644 index 0000000..c22880d Binary files /dev/null and "b/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-13-38_GPEN.jpg" differ diff --git "a/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-14-00_GPEN.jpg" "b/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-14-00_GPEN.jpg" new file mode 100644 index 0000000..a32bf3b Binary files /dev/null and "b/GPEN/results/outs-bfr/\320\232\320\276\320\277\320\270\321\217 image_2022-06-30_18-14-00_GPEN.jpg" differ diff --git a/GPEN/run.sh b/GPEN/run.sh new file mode 100644 index 0000000..ef619ca --- /dev/null +++ b/GPEN/run.sh @@ -0,0 +1,13 @@ +############## TEST ################ +#python demo.py --task FaceEnhancement --model GPEN-BFR-512 --in_size 512 --channel_multiplier 2 --narrow 1 --use_sr --sr_scale 2 --use_cuda --save_face --indir examples/imgs --outdir examples/outs-bfr +#python demo.py --task FaceEnhancement --model GPEN-BFR-256 --in_size 256 --channel_multiplier 1 --narrow 0.5 --use_sr --sr_scale 4 --use_cuda --save_face --indir examples/imgs --outdir examples/outs-bfr +python demo.py --task FaceEnhancement --model GPEN-BFR-2048 --in_size 2048 --channel_multiplier 2 --narrow 1 --alpha 0.8 --use_sr --sr_scale 2 --use_cuda --tile_size 400 --save_face --indir examples/selfie --outdir examples/outs-selfie + +#python demo.py --task FaceColorization --model GPEN-Colorization-1024 --in_size 1024 --use_cuda --indir examples/tmp --outdir examples/outs-colorization + +#python demo.py --task FaceInpainting --model GPEN-Inpainting-1024 --in_size 1024 --use_cuda --indir examples/ffhq-10 --outdir examples/outs-inpainting + +#python demo.py --task Segmentation2Face --model GPEN-Seg2face-512 --in_size 512 --use_cuda --indir examples/segs --outdir examples/outs-seg2face + +############## TRAIN ################ +#CUDA_VISIBLE_DEVICES='0,1,' python -m torch.distributed.launch --nproc_per_node=2 --master_port=4321 train_simple.py --size 512 --channel_multiplier 2 --narrow 1 --ckpt ckpt-512 --sample sample-512 --batch 1 --path your_path_of_aligned_faces diff --git a/GPEN/segmentation2face.py b/GPEN/segmentation2face.py new file mode 100644 index 0000000..a6b3947 --- /dev/null +++ b/GPEN/segmentation2face.py @@ -0,0 +1,18 @@ +''' +@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) +@author: yangxy (yangtao9009@gmail.com) +''' +from face_model.face_gan import FaceGAN + +class Segmentation2Face(object): + def __init__(self, base_dir='./', in_size=1024, out_size=None, model=None, channel_multiplier=2, narrow=1, key=None, is_norm=True, device='cuda'): + self.facegan = FaceGAN(base_dir, in_size, out_size, model, channel_multiplier, narrow, key, is_norm, device=device) + + # make sure the face image is well aligned. Please refer to face_enhancement.py + def process(self, segf, aligned=True): + # from segmentations to faces + out = self.facegan.process(segf) + + return out, [segf], [out] + + diff --git a/GPEN/sr_model/arch_util.py b/GPEN/sr_model/arch_util.py new file mode 100644 index 0000000..ce5b9d9 --- /dev/null +++ b/GPEN/sr_model/arch_util.py @@ -0,0 +1,125 @@ +import math +import torch +from torch import nn as nn +from torch.nn import functional as F +from torch.nn import init as init +from torch.nn.modules.batchnorm import _BatchNorm + +@torch.no_grad() +def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs): + """Initialize network weights. + + Args: + module_list (list[nn.Module] | nn.Module): Modules to be initialized. + scale (float): Scale initialized weights, especially for residual + blocks. Default: 1. + bias_fill (float): The value to fill bias. Default: 0 + kwargs (dict): Other arguments for initialization function. + """ + if not isinstance(module_list, list): + module_list = [module_list] + for module in module_list: + for m in module.modules(): + if isinstance(m, nn.Conv2d): + init.kaiming_normal_(m.weight, **kwargs) + m.weight.data *= scale + if m.bias is not None: + m.bias.data.fill_(bias_fill) + elif isinstance(m, nn.Linear): + init.kaiming_normal_(m.weight, **kwargs) + m.weight.data *= scale + if m.bias is not None: + m.bias.data.fill_(bias_fill) + elif isinstance(m, _BatchNorm): + init.constant_(m.weight, 1) + if m.bias is not None: + m.bias.data.fill_(bias_fill) + + +def make_layer(basic_block, num_basic_block, **kwarg): + """Make layers by stacking the same blocks. + + Args: + basic_block (nn.module): nn.module class for basic block. + num_basic_block (int): number of blocks. + + Returns: + nn.Sequential: Stacked blocks in nn.Sequential. + """ + layers = [] + for _ in range(num_basic_block): + layers.append(basic_block(**kwarg)) + return nn.Sequential(*layers) + + +class ResidualBlockNoBN(nn.Module): + """Residual block without BN. + + It has a style of: + ---Conv-ReLU-Conv-+- + |________________| + + Args: + num_feat (int): Channel number of intermediate features. + Default: 64. + res_scale (float): Residual scale. Default: 1. + pytorch_init (bool): If set to True, use pytorch default init, + otherwise, use default_init_weights. Default: False. + """ + + def __init__(self, num_feat=64, res_scale=1, pytorch_init=False): + super(ResidualBlockNoBN, self).__init__() + self.res_scale = res_scale + self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True) + self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True) + self.relu = nn.ReLU(inplace=True) + + if not pytorch_init: + default_init_weights([self.conv1, self.conv2], 0.1) + + def forward(self, x): + identity = x + out = self.conv2(self.relu(self.conv1(x))) + return identity + out * self.res_scale + + +class Upsample(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError(f'scale {scale} is not supported. ' + 'Supported scales: 2^n and 3.') + super(Upsample, self).__init__(*m) + +# TODO: may write a cpp file +def pixel_unshuffle(x, scale): + """ Pixel unshuffle. + + Args: + x (Tensor): Input feature with shape (b, c, hh, hw). + scale (int): Downsample ratio. + + Returns: + Tensor: the pixel unshuffled feature. + """ + b, c, hh, hw = x.size() + out_channel = c * (scale**2) + assert hh % scale == 0 and hw % scale == 0 + h = hh // scale + w = hw // scale + x_view = x.view(b, c, h, scale, w, scale) + return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w) \ No newline at end of file diff --git a/GPEN/sr_model/real_esrnet.py b/GPEN/sr_model/real_esrnet.py new file mode 100644 index 0000000..eaf319d --- /dev/null +++ b/GPEN/sr_model/real_esrnet.py @@ -0,0 +1,133 @@ +import os +import math +import torch +import numpy as np +from rrdbnet_arch import RRDBNet +from torch.nn import functional as F + +class RealESRNet(object): + def __init__(self, base_dir='./', model=None, scale=2, tile_size=0, tile_pad=10, device='cuda'): + self.base_dir = base_dir + self.scale = scale + self.tile_size = tile_size + self.tile_pad = tile_pad + self.device = device + self.load_srmodel(base_dir, model) + + def load_srmodel(self, base_dir, model): + self.srmodel = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=32, num_block=23, num_grow_ch=32, scale=self.scale) + if model is None: + loadnet = torch.load(os.path.join(self.base_dir, 'weights', 'realesrnet_x%d.pth'%self.scale)) + else: + loadnet = torch.load(os.path.join(self.base_dir, 'weights', model+'_x%d.pth'%self.scale)) + #print(loadnet['params_ema'].keys) + self.srmodel.load_state_dict(loadnet['params_ema'], strict=True) + self.srmodel.eval() + self.srmodel = self.srmodel.to(self.device) + + def tile_process(self, img): + """It will first crop input images to tiles, and then process each tile. + Finally, all the processed tiles are merged into one images. + + Modified from: https://github.com/ata4/esrgan-launcher + """ + batch, channel, height, width = img.shape + output_height = height * self.scale + output_width = width * self.scale + output_shape = (batch, channel, output_height, output_width) + + # start with black image + output = img.new_zeros(output_shape) + tiles_x = math.ceil(width / self.tile_size) + tiles_y = math.ceil(height / self.tile_size) + + # loop over all tiles + for y in range(tiles_y): + for x in range(tiles_x): + # extract tile from input image + ofs_x = x * self.tile_size + ofs_y = y * self.tile_size + # input tile area on total image + input_start_x = ofs_x + input_end_x = min(ofs_x + self.tile_size, width) + input_start_y = ofs_y + input_end_y = min(ofs_y + self.tile_size, height) + + # input tile area on total image with padding + input_start_x_pad = max(input_start_x - self.tile_pad, 0) + input_end_x_pad = min(input_end_x + self.tile_pad, width) + input_start_y_pad = max(input_start_y - self.tile_pad, 0) + input_end_y_pad = min(input_end_y + self.tile_pad, height) + + # input tile dimensions + input_tile_width = input_end_x - input_start_x + input_tile_height = input_end_y - input_start_y + tile_idx = y * tiles_x + x + 1 + input_tile = img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad] + + # upscale tile + try: + with torch.no_grad(): + output_tile = self.srmodel(input_tile) + except RuntimeError as error: + print('Error', error) + return None + if tile_idx%10==0: print(f'\tTile {tile_idx}/{tiles_x * tiles_y}') + + # output tile area on total image + output_start_x = input_start_x * self.scale + output_end_x = input_end_x * self.scale + output_start_y = input_start_y * self.scale + output_end_y = input_end_y * self.scale + + # output tile area without padding + output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale + output_end_x_tile = output_start_x_tile + input_tile_width * self.scale + output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale + output_end_y_tile = output_start_y_tile + input_tile_height * self.scale + + # put tile into output image + output[:, :, output_start_y:output_end_y, + output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile, + output_start_x_tile:output_end_x_tile] + return output + + def process(self, img): + img = img.astype(np.float32) / 255. + img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float() + img = img.unsqueeze(0).to(self.device) + + if self.scale == 2: + mod_scale = 2 + elif self.scale == 1: + mod_scale = 4 + else: + mod_scale = None + if mod_scale is not None: + h_pad, w_pad = 0, 0 + _, _, h, w = img.size() + if (h % mod_scale != 0): + h_pad = (mod_scale - h % mod_scale) + if (w % mod_scale != 0): + w_pad = (mod_scale - w % mod_scale) + img = F.pad(img, (0, w_pad, 0, h_pad), 'reflect') + + try: + with torch.no_grad(): + if self.tile_size > 0: + output = self.tile_process(img) + else: + output = self.srmodel(img) + del img + # remove extra pad + if mod_scale is not None: + _, _, h, w = output.size() + output = output[:, :, 0:h - h_pad, 0:w - w_pad] + output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy() + output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) + output = (output * 255.0).round().astype(np.uint8) + + return output + except Exception as e: + print('sr failed:', e) + return None \ No newline at end of file diff --git a/GPEN/sr_model/rrdbnet_arch.py b/GPEN/sr_model/rrdbnet_arch.py new file mode 100644 index 0000000..5e1f04c --- /dev/null +++ b/GPEN/sr_model/rrdbnet_arch.py @@ -0,0 +1,116 @@ +import torch +from torch import nn as nn +from torch.nn import functional as F + +from arch_util import default_init_weights, make_layer, pixel_unshuffle + + +class ResidualDenseBlock(nn.Module): + """Residual Dense Block. + + Used in RRDB block in ESRGAN. + + Args: + num_feat (int): Channel number of intermediate features. + num_grow_ch (int): Channels for each growth. + """ + + def __init__(self, num_feat=64, num_grow_ch=32): + super(ResidualDenseBlock, self).__init__() + self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) + self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) + + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + # initialization + default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) + + def forward(self, x): + x1 = self.lrelu(self.conv1(x)) + x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) + x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) + x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) + x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) + # Emperically, we use 0.2 to scale the residual for better performance + return x5 * 0.2 + x + + +class RRDB(nn.Module): + """Residual in Residual Dense Block. + + Used in RRDB-Net in ESRGAN. + + Args: + num_feat (int): Channel number of intermediate features. + num_grow_ch (int): Channels for each growth. + """ + + def __init__(self, num_feat, num_grow_ch=32): + super(RRDB, self).__init__() + self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch) + self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch) + self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch) + + def forward(self, x): + out = self.rdb1(x) + out = self.rdb2(out) + out = self.rdb3(out) + # Emperically, we use 0.2 to scale the residual for better performance + return out * 0.2 + x + +class RRDBNet(nn.Module): + """Networks consisting of Residual in Residual Dense Block, which is used + in ESRGAN. + + ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. + + We extend ESRGAN for scale x2 and scale x1. + Note: This is one option for scale 1, scale 2 in RRDBNet. + We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size + and enlarge the channel size before feeding inputs into the main ESRGAN architecture. + + Args: + num_in_ch (int): Channel number of inputs. + num_out_ch (int): Channel number of outputs. + num_feat (int): Channel number of intermediate features. + Default: 64 + num_block (int): Block number in the trunk network. Defaults: 23 + num_grow_ch (int): Channels for each growth. Default: 32. + """ + + def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32): + super(RRDBNet, self).__init__() + self.scale = scale + if scale == 2: + num_in_ch = num_in_ch * 4 + elif scale == 1: + num_in_ch = num_in_ch * 16 + self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch) + self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + # upsample + self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x): + if self.scale == 2: + feat = pixel_unshuffle(x, scale=2) + elif self.scale == 1: + feat = pixel_unshuffle(x, scale=4) + else: + feat = x + feat = self.conv_first(feat) + body_feat = self.conv_body(self.body(feat)) + feat = feat + body_feat + # upsample + feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest'))) + feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest'))) + out = self.conv_last(self.lrelu(self.conv_hr(feat))) + return out diff --git a/GPEN/train_simple.py b/GPEN/train_simple.py new file mode 100644 index 0000000..fc1ffd4 --- /dev/null +++ b/GPEN/train_simple.py @@ -0,0 +1,414 @@ +''' +This is a simplified training code of GPEN. It achieves comparable performance as in the paper. + +@Created by rosinality + +@Modified by yangxy (yangtao9009@gmail.com) +''' +import argparse +import math +import random +import os +import cv2 +import glob +from tqdm import tqdm + +import torch +from torch import nn, autograd, optim +from torch.nn import functional as F +from torch.utils import data +import torch.distributed as dist +from torchvision import transforms, utils + +import __init_paths +from training.data_loader.dataset_face import FaceDataset +from face_model.gpen_model import FullGenerator, Discriminator + +from training.loss.id_loss import IDLoss +from distributed import ( + get_rank, + synchronize, + reduce_loss_dict, + reduce_sum, + get_world_size, +) + +from training import lpips + + +def data_sampler(dataset, shuffle, distributed): + if distributed: + return data.distributed.DistributedSampler(dataset, shuffle=shuffle) + + if shuffle: + return data.RandomSampler(dataset) + + else: + return data.SequentialSampler(dataset) + + +def requires_grad(model, flag=True): + for p in model.parameters(): + p.requires_grad = flag + + +def accumulate(model1, model2, decay=0.999): + par1 = dict(model1.named_parameters()) + par2 = dict(model2.named_parameters()) + + for k in par1.keys(): + par1[k].data.mul_(decay).add_(1 - decay, par2[k].data) + + +def sample_data(loader): + while True: + for batch in loader: + yield batch + + +def d_logistic_loss(real_pred, fake_pred): + real_loss = F.softplus(-real_pred) + fake_loss = F.softplus(fake_pred) + + return real_loss.mean() + fake_loss.mean() + + +def d_r1_loss(real_pred, real_img): + grad_real, = autograd.grad( + outputs=real_pred.sum(), inputs=real_img, create_graph=True + ) + grad_penalty = grad_real.pow(2).view(grad_real.shape[0], -1).sum(1).mean() + + return grad_penalty + + +def g_nonsaturating_loss(fake_pred, loss_funcs=None, fake_img=None, real_img=None, input_img=None): + smooth_l1_loss, id_loss = loss_funcs + + loss = F.softplus(-fake_pred).mean() + loss_l1 = smooth_l1_loss(fake_img, real_img) + loss_id, __, __ = id_loss(fake_img, real_img, input_img) + loss += 1.0*loss_l1 + 1.0*loss_id + + return loss + + +def g_path_regularize(fake_img, latents, mean_path_length, decay=0.01): + noise = torch.randn_like(fake_img) / math.sqrt( + fake_img.shape[2] * fake_img.shape[3] + ) + grad, = autograd.grad( + outputs=(fake_img * noise).sum(), inputs=latents, create_graph=True + ) + path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1)) + + path_mean = mean_path_length + decay * (path_lengths.mean() - mean_path_length) + + path_penalty = (path_lengths - path_mean).pow(2).mean() + + return path_penalty, path_mean.detach(), path_lengths + +def validation(model, lpips_func, args, device): + lq_files = sorted(glob.glob(os.path.join(args.val_dir, 'lq', '*.*'))) + hq_files = sorted(glob.glob(os.path.join(args.val_dir, 'hq', '*.*'))) + + assert len(lq_files) == len(hq_files) + + dist_sum = 0 + model.eval() + for lq_f, hq_f in zip(lq_files, hq_files): + img_lq = cv2.imread(lq_f, cv2.IMREAD_COLOR) + img_t = torch.from_numpy(img_lq).to(device).permute(2, 0, 1).unsqueeze(0) + img_t = (img_t/255.-0.5)/0.5 + img_t = F.interpolate(img_t, (args.size, args.size)) + img_t = torch.flip(img_t, [1]) + + with torch.no_grad(): + img_out, __ = model(img_t) + + img_hq = lpips.im2tensor(lpips.load_image(hq_f)).to(device) + img_hq = F.interpolate(img_hq, (args.size, args.size)) + dist_sum += lpips_func.forward(img_out, img_hq) + + return dist_sum.data/len(lq_files) + + +def train(args, loader, generator, discriminator, losses, g_optim, d_optim, g_ema, lpips_func, device): + loader = sample_data(loader) + + pbar = range(0, args.iter) + + if get_rank() == 0: + pbar = tqdm(pbar, initial=args.start_iter, dynamic_ncols=True, smoothing=0.01) + + mean_path_length = 0 + + d_loss_val = 0 + r1_loss = torch.tensor(0.0, device=device) + g_loss_val = 0 + path_loss = torch.tensor(0.0, device=device) + path_lengths = torch.tensor(0.0, device=device) + mean_path_length_avg = 0 + loss_dict = {} + + if args.distributed: + g_module = generator.module + d_module = discriminator.module + + else: + g_module = generator + d_module = discriminator + + accum = 0.5 ** (32 / (10 * 1000)) + + for idx in pbar: + i = idx + args.start_iter + + if i > args.iter: + print('Done!') + + break + + degraded_img, real_img = next(loader) + degraded_img = degraded_img.to(device) + real_img = real_img.to(device) + + requires_grad(generator, False) + requires_grad(discriminator, True) + + fake_img, _ = generator(degraded_img) + fake_pred = discriminator(fake_img) + + real_pred = discriminator(real_img) + d_loss = d_logistic_loss(real_pred, fake_pred) + + loss_dict['d'] = d_loss + loss_dict['real_score'] = real_pred.mean() + loss_dict['fake_score'] = fake_pred.mean() + + discriminator.zero_grad() + d_loss.backward() + d_optim.step() + + d_regularize = i % args.d_reg_every == 0 + + if d_regularize: + real_img.requires_grad = True + real_pred = discriminator(real_img) + r1_loss = d_r1_loss(real_pred, real_img) + + discriminator.zero_grad() + (args.r1 / 2 * r1_loss * args.d_reg_every + 0 * real_pred[0]).backward() + + d_optim.step() + + loss_dict['r1'] = r1_loss + + requires_grad(generator, True) + requires_grad(discriminator, False) + + fake_img, _ = generator(degraded_img) + fake_pred = discriminator(fake_img) + g_loss = g_nonsaturating_loss(fake_pred, losses, fake_img, real_img, degraded_img) + + loss_dict['g'] = g_loss + + generator.zero_grad() + g_loss.backward() + g_optim.step() + + g_regularize = i % args.g_reg_every == 0 + + if g_regularize: + path_batch_size = max(1, args.batch // args.path_batch_shrink) + + fake_img, latents = generator(degraded_img, return_latents=True) + + path_loss, mean_path_length, path_lengths = g_path_regularize( + fake_img, latents, mean_path_length + ) + + generator.zero_grad() + weighted_path_loss = args.path_regularize * args.g_reg_every * path_loss + + if args.path_batch_shrink: + weighted_path_loss += 0 * fake_img[0, 0, 0, 0] + + weighted_path_loss.backward() + + g_optim.step() + + mean_path_length_avg = ( + reduce_sum(mean_path_length).item() / get_world_size() + ) + + loss_dict['path'] = path_loss + loss_dict['path_length'] = path_lengths.mean() + + accumulate(g_ema, g_module, accum) + + loss_reduced = reduce_loss_dict(loss_dict) + + d_loss_val = loss_reduced['d'].mean().item() + g_loss_val = loss_reduced['g'].mean().item() + r1_val = loss_reduced['r1'].mean().item() + path_loss_val = loss_reduced['path'].mean().item() + real_score_val = loss_reduced['real_score'].mean().item() + fake_score_val = loss_reduced['fake_score'].mean().item() + path_length_val = loss_reduced['path_length'].mean().item() + + if get_rank() == 0: + pbar.set_description( + ( + f'd: {d_loss_val:.4f}; g: {g_loss_val:.4f}; r1: {r1_val:.4f}; ' + ) + ) + + if i % args.save_freq == 0: + with torch.no_grad(): + g_ema.eval() + sample, _ = g_ema(degraded_img) + sample = torch.cat((degraded_img, sample, real_img), 0) + utils.save_image( + sample, + f'{args.sample}/{str(i).zfill(6)}.png', + nrow=args.batch, + normalize=True, + range=(-1, 1), + ) + + lpips_value = validation(g_ema, lpips_func, args, device) + print(f'{i}/{args.iter}: lpips: {lpips_value.cpu().numpy()[0][0][0][0]}') + + if i and i % args.save_freq == 0: + torch.save( + { + 'g': g_module.state_dict(), + 'd': d_module.state_dict(), + 'g_ema': g_ema.state_dict(), + 'g_optim': g_optim.state_dict(), + 'd_optim': d_optim.state_dict(), + }, + f'{args.ckpt}/{str(i).zfill(6)}.pth', + ) + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + + parser.add_argument('--path', type=str, required=True) + parser.add_argument('--base_dir', type=str, default='./') + parser.add_argument('--iter', type=int, default=4000000) + parser.add_argument('--batch', type=int, default=4) + parser.add_argument('--size', type=int, default=256) + parser.add_argument('--channel_multiplier', type=int, default=2) + parser.add_argument('--narrow', type=float, default=1.0) + parser.add_argument('--r1', type=float, default=10) + parser.add_argument('--path_regularize', type=float, default=2) + parser.add_argument('--path_batch_shrink', type=int, default=2) + parser.add_argument('--d_reg_every', type=int, default=16) + parser.add_argument('--g_reg_every', type=int, default=4) + parser.add_argument('--save_freq', type=int, default=10000) + parser.add_argument('--lr', type=float, default=0.002) + parser.add_argument('--local_rank', type=int, default=0) + parser.add_argument('--ckpt', type=str, default='ckpts') + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--sample', type=str, default='sample') + parser.add_argument('--val_dir', type=str, default='val') + + args = parser.parse_args() + + os.makedirs(args.ckpt, exist_ok=True) + os.makedirs(args.sample, exist_ok=True) + + device = 'cuda' + + n_gpu = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 + args.distributed = n_gpu > 1 + + if args.distributed: + torch.cuda.set_device(args.local_rank) + torch.distributed.init_process_group(backend='nccl', init_method='env://') + synchronize() + + args.latent = 512 + args.n_mlp = 8 + + args.start_iter = 0 + + generator = FullGenerator( + args.size, args.latent, args.n_mlp, channel_multiplier=args.channel_multiplier, narrow=args.narrow, device=device + ).to(device) + discriminator = Discriminator( + args.size, channel_multiplier=args.channel_multiplier, narrow=args.narrow, device=device + ).to(device) + g_ema = FullGenerator( + args.size, args.latent, args.n_mlp, channel_multiplier=args.channel_multiplier, narrow=args.narrow, device=device + ).to(device) + g_ema.eval() + accumulate(g_ema, generator, 0) + + g_reg_ratio = args.g_reg_every / (args.g_reg_every + 1) + d_reg_ratio = args.d_reg_every / (args.d_reg_every + 1) + + g_optim = optim.Adam( + generator.parameters(), + lr=args.lr * g_reg_ratio, + betas=(0 ** g_reg_ratio, 0.99 ** g_reg_ratio), + ) + + d_optim = optim.Adam( + discriminator.parameters(), + lr=args.lr * d_reg_ratio, + betas=(0 ** d_reg_ratio, 0.99 ** d_reg_ratio), + ) + + if args.pretrain is not None: + print('load model:', args.pretrain) + + ckpt = torch.load(args.pretrain) + + generator.load_state_dict(ckpt['g']) + discriminator.load_state_dict(ckpt['d']) + g_ema.load_state_dict(ckpt['g_ema']) + + g_optim.load_state_dict(ckpt['g_optim']) + d_optim.load_state_dict(ckpt['d_optim']) + + smooth_l1_loss = torch.nn.SmoothL1Loss().to(device) + id_loss = IDLoss(args.base_dir, device, ckpt_dict=None) + lpips_func = lpips.LPIPS(net='alex',version='0.1').to(device) + + if args.distributed: + generator = nn.parallel.DistributedDataParallel( + generator, + device_ids=[args.local_rank], + output_device=args.local_rank, + broadcast_buffers=False, + ) + + discriminator = nn.parallel.DistributedDataParallel( + discriminator, + device_ids=[args.local_rank], + output_device=args.local_rank, + broadcast_buffers=False, + ) + + id_loss = nn.parallel.DistributedDataParallel( + id_loss, + device_ids=[args.local_rank], + output_device=args.local_rank, + broadcast_buffers=False, + ) + + dataset = FaceDataset(args.path, args.size) + loader = data.DataLoader( + dataset, + batch_size=args.batch, + sampler=data_sampler(dataset, shuffle=True, distributed=args.distributed), + drop_last=True, + ) + + train(args, loader, generator, discriminator, [smooth_l1_loss, id_loss], g_optim, d_optim, g_ema, lpips_func, device) + diff --git a/GPEN/training/data_loader/dataset_face.py b/GPEN/training/data_loader/dataset_face.py new file mode 100644 index 0000000..ff13cf1 --- /dev/null +++ b/GPEN/training/data_loader/dataset_face.py @@ -0,0 +1,103 @@ +import numpy as np +import cv2 +import os +import glob +import math +import random +import torch +import torch.nn.functional as F +from torch.utils.data import Dataset + +import degradations + + +class GFPGAN_degradation(object): + def __init__(self): + self.kernel_list = ['iso', 'aniso'] + self.kernel_prob = [0.5, 0.5] + self.blur_kernel_size = 41 + self.blur_sigma = [0.1, 10] + self.downsample_range = [0.8, 8] + self.noise_range = [0, 20] + self.jpeg_range = [60, 100] + self.gray_prob = 0.2 + self.color_jitter_prob = 0.0 + self.color_jitter_pt_prob = 0.0 + self.shift = 20/255. + + def degrade_process(self, img_gt): + if random.random() > 0.5: + img_gt = cv2.flip(img_gt, 1) + + h, w = img_gt.shape[:2] + + # random color jitter + if np.random.uniform() < self.color_jitter_prob: + jitter_val = np.random.uniform(-self.shift, self.shift, 3).astype(np.float32) + img_gt = img_gt + jitter_val + img_gt = np.clip(img_gt, 0, 1) + + # random grayscale + if np.random.uniform() < self.gray_prob: + img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2GRAY) + img_gt = np.tile(img_gt[:, :, None], [1, 1, 3]) + + # ------------------------ generate lq image ------------------------ # + # blur + kernel = degradations.random_mixed_kernels( + self.kernel_list, + self.kernel_prob, + self.blur_kernel_size, + self.blur_sigma, + self.blur_sigma, [-math.pi, math.pi], + noise_range=None) + img_lq = cv2.filter2D(img_gt, -1, kernel) + # downsample + scale = np.random.uniform(self.downsample_range[0], self.downsample_range[1]) + img_lq = cv2.resize(img_lq, (int(w // scale), int(h // scale)), interpolation=cv2.INTER_LINEAR) + + # noise + if self.noise_range is not None: + img_lq = degradations.random_add_gaussian_noise(img_lq, self.noise_range) + # jpeg compression + if self.jpeg_range is not None: + img_lq = degradations.random_add_jpg_compression(img_lq, self.jpeg_range) + + # round and clip + img_lq = np.clip((img_lq * 255.0).round(), 0, 255) / 255. + + # resize to original size + img_lq = cv2.resize(img_lq, (w, h), interpolation=cv2.INTER_LINEAR) + + return img_gt, img_lq + +class FaceDataset(Dataset): + def __init__(self, path, resolution=512): + self.resolution = resolution + + self.HQ_imgs = glob.glob(os.path.join(path, '*.*')) + self.length = len(self.HQ_imgs) + + self.degrader = GFPGAN_degradation() + + def __len__(self): + return self.length + + def __getitem__(self, index): + img_gt = cv2.imread(self.HQ_imgs[index], cv2.IMREAD_COLOR) + img_gt = cv2.resize(img_gt, (self.resolution, self.resolution), interpolation=cv2.INTER_AREA) + + # BFR degradation + # We adopt the degradation of GFPGAN for simplicity, which however differs from our implementation in the paper. + # Data degradation plays a key role in BFR. Please replace it with your own methods. + img_gt = img_gt.astype(np.float32)/255. + img_gt, img_lq = self.degrader.degrade_process(img_gt) + + img_gt = (torch.from_numpy(img_gt) - 0.5) / 0.5 + img_lq = (torch.from_numpy(img_lq) - 0.5) / 0.5 + + img_gt = img_gt.permute(2, 0, 1).flip(0) # BGR->RGB + img_lq = img_lq.permute(2, 0, 1).flip(0) # BGR->RGB + + return img_lq, img_gt + diff --git a/GPEN/training/data_loader/degradations.py b/GPEN/training/data_loader/degradations.py new file mode 100644 index 0000000..241de65 --- /dev/null +++ b/GPEN/training/data_loader/degradations.py @@ -0,0 +1,765 @@ +import cv2 +import math +import numpy as np +import random +import torch +from scipy import special +from scipy.stats import multivariate_normal +from torchvision.transforms.functional_tensor import rgb_to_grayscale + +# -------------------------------------------------------------------- # +# --------------------------- blur kernels --------------------------- # +# -------------------------------------------------------------------- # + + +# --------------------------- util functions --------------------------- # +def sigma_matrix2(sig_x, sig_y, theta): + """Calculate the rotated sigma matrix (two dimensional matrix). + + Args: + sig_x (float): + sig_y (float): + theta (float): Radian measurement. + + Returns: + ndarray: Rotated sigma matrix. + """ + d_matrix = np.array([[sig_x**2, 0], [0, sig_y**2]]) + u_matrix = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) + return np.dot(u_matrix, np.dot(d_matrix, u_matrix.T)) + + +def mesh_grid(kernel_size): + """Generate the mesh grid, centering at zero. + + Args: + kernel_size (int): + + Returns: + xy (ndarray): with the shape (kernel_size, kernel_size, 2) + xx (ndarray): with the shape (kernel_size, kernel_size) + yy (ndarray): with the shape (kernel_size, kernel_size) + """ + ax = np.arange(-kernel_size // 2 + 1., kernel_size // 2 + 1.) + xx, yy = np.meshgrid(ax, ax) + xy = np.hstack((xx.reshape((kernel_size * kernel_size, 1)), yy.reshape(kernel_size * kernel_size, + 1))).reshape(kernel_size, kernel_size, 2) + return xy, xx, yy + + +def pdf2(sigma_matrix, grid): + """Calculate PDF of the bivariate Gaussian distribution. + + Args: + sigma_matrix (ndarray): with the shape (2, 2) + grid (ndarray): generated by :func:`mesh_grid`, + with the shape (K, K, 2), K is the kernel size. + + Returns: + kernel (ndarrray): un-normalized kernel. + """ + inverse_sigma = np.linalg.inv(sigma_matrix) + kernel = np.exp(-0.5 * np.sum(np.dot(grid, inverse_sigma) * grid, 2)) + return kernel + + +def cdf2(d_matrix, grid): + """Calculate the CDF of the standard bivariate Gaussian distribution. + Used in skewed Gaussian distribution. + + Args: + d_matrix (ndarrasy): skew matrix. + grid (ndarray): generated by :func:`mesh_grid`, + with the shape (K, K, 2), K is the kernel size. + + Returns: + cdf (ndarray): skewed cdf. + """ + rv = multivariate_normal([0, 0], [[1, 0], [0, 1]]) + grid = np.dot(grid, d_matrix) + cdf = rv.cdf(grid) + return cdf + + +def bivariate_Gaussian(kernel_size, sig_x, sig_y, theta, grid=None, isotropic=True): + """Generate a bivariate isotropic or anisotropic Gaussian kernel. + + In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. + + Args: + kernel_size (int): + sig_x (float): + sig_y (float): + theta (float): Radian measurement. + grid (ndarray, optional): generated by :func:`mesh_grid`, + with the shape (K, K, 2), K is the kernel size. Default: None + isotropic (bool): + + Returns: + kernel (ndarray): normalized kernel. + """ + if grid is None: + grid, _, _ = mesh_grid(kernel_size) + if isotropic: + sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) + else: + sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) + kernel = pdf2(sigma_matrix, grid) + kernel = kernel / np.sum(kernel) + return kernel + + +def bivariate_generalized_Gaussian(kernel_size, sig_x, sig_y, theta, beta, grid=None, isotropic=True): + """Generate a bivariate generalized Gaussian kernel. + Described in `Parameter Estimation For Multivariate Generalized + Gaussian Distributions`_ + by Pascal et. al (2013). + + In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. + + Args: + kernel_size (int): + sig_x (float): + sig_y (float): + theta (float): Radian measurement. + beta (float): shape parameter, beta = 1 is the normal distribution. + grid (ndarray, optional): generated by :func:`mesh_grid`, + with the shape (K, K, 2), K is the kernel size. Default: None + + Returns: + kernel (ndarray): normalized kernel. + + .. _Parameter Estimation For Multivariate Generalized Gaussian + Distributions: https://arxiv.org/abs/1302.6498 + """ + if grid is None: + grid, _, _ = mesh_grid(kernel_size) + if isotropic: + sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) + else: + sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) + inverse_sigma = np.linalg.inv(sigma_matrix) + kernel = np.exp(-0.5 * np.power(np.sum(np.dot(grid, inverse_sigma) * grid, 2), beta)) + kernel = kernel / np.sum(kernel) + return kernel + + +def bivariate_plateau(kernel_size, sig_x, sig_y, theta, beta, grid=None, isotropic=True): + """Generate a plateau-like anisotropic kernel. + 1 / (1+x^(beta)) + + Ref: https://stats.stackexchange.com/questions/203629/is-there-a-plateau-shaped-distribution + + In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. + + Args: + kernel_size (int): + sig_x (float): + sig_y (float): + theta (float): Radian measurement. + beta (float): shape parameter, beta = 1 is the normal distribution. + grid (ndarray, optional): generated by :func:`mesh_grid`, + with the shape (K, K, 2), K is the kernel size. Default: None + + Returns: + kernel (ndarray): normalized kernel. + """ + if grid is None: + grid, _, _ = mesh_grid(kernel_size) + if isotropic: + sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) + else: + sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) + inverse_sigma = np.linalg.inv(sigma_matrix) + kernel = np.reciprocal(np.power(np.sum(np.dot(grid, inverse_sigma) * grid, 2), beta) + 1) + kernel = kernel / np.sum(kernel) + return kernel + + +def random_bivariate_Gaussian(kernel_size, + sigma_x_range, + sigma_y_range, + rotation_range, + noise_range=None, + isotropic=True): + """Randomly generate bivariate isotropic or anisotropic Gaussian kernels. + + In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. + + Args: + kernel_size (int): + sigma_x_range (tuple): [0.6, 5] + sigma_y_range (tuple): [0.6, 5] + rotation range (tuple): [-math.pi, math.pi] + noise_range(tuple, optional): multiplicative kernel noise, + [0.75, 1.25]. Default: None + + Returns: + kernel (ndarray): + """ + assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' + assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' + sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) + if isotropic is False: + assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' + assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' + sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) + rotation = np.random.uniform(rotation_range[0], rotation_range[1]) + else: + sigma_y = sigma_x + rotation = 0 + + kernel = bivariate_Gaussian(kernel_size, sigma_x, sigma_y, rotation, isotropic=isotropic) + + # add multiplicative noise + if noise_range is not None: + assert noise_range[0] < noise_range[1], 'Wrong noise range.' + noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) + kernel = kernel * noise + kernel = kernel / np.sum(kernel) + return kernel + + +def random_bivariate_generalized_Gaussian(kernel_size, + sigma_x_range, + sigma_y_range, + rotation_range, + beta_range, + noise_range=None, + isotropic=True): + """Randomly generate bivariate generalized Gaussian kernels. + + In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. + + Args: + kernel_size (int): + sigma_x_range (tuple): [0.6, 5] + sigma_y_range (tuple): [0.6, 5] + rotation range (tuple): [-math.pi, math.pi] + beta_range (tuple): [0.5, 8] + noise_range(tuple, optional): multiplicative kernel noise, + [0.75, 1.25]. Default: None + + Returns: + kernel (ndarray): + """ + assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' + assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' + sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) + if isotropic is False: + assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' + assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' + sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) + rotation = np.random.uniform(rotation_range[0], rotation_range[1]) + else: + sigma_y = sigma_x + rotation = 0 + + # assume beta_range[0] < 1 < beta_range[1] + if np.random.uniform() < 0.5: + beta = np.random.uniform(beta_range[0], 1) + else: + beta = np.random.uniform(1, beta_range[1]) + + kernel = bivariate_generalized_Gaussian(kernel_size, sigma_x, sigma_y, rotation, beta, isotropic=isotropic) + + # add multiplicative noise + if noise_range is not None: + assert noise_range[0] < noise_range[1], 'Wrong noise range.' + noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) + kernel = kernel * noise + kernel = kernel / np.sum(kernel) + return kernel + + +def random_bivariate_plateau(kernel_size, + sigma_x_range, + sigma_y_range, + rotation_range, + beta_range, + noise_range=None, + isotropic=True): + """Randomly generate bivariate plateau kernels. + + In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. + + Args: + kernel_size (int): + sigma_x_range (tuple): [0.6, 5] + sigma_y_range (tuple): [0.6, 5] + rotation range (tuple): [-math.pi/2, math.pi/2] + beta_range (tuple): [1, 4] + noise_range(tuple, optional): multiplicative kernel noise, + [0.75, 1.25]. Default: None + + Returns: + kernel (ndarray): + """ + assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' + assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' + sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) + if isotropic is False: + assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' + assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' + sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) + rotation = np.random.uniform(rotation_range[0], rotation_range[1]) + else: + sigma_y = sigma_x + rotation = 0 + + # TODO: this may be not proper + if np.random.uniform() < 0.5: + beta = np.random.uniform(beta_range[0], 1) + else: + beta = np.random.uniform(1, beta_range[1]) + + kernel = bivariate_plateau(kernel_size, sigma_x, sigma_y, rotation, beta, isotropic=isotropic) + # add multiplicative noise + if noise_range is not None: + assert noise_range[0] < noise_range[1], 'Wrong noise range.' + noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) + kernel = kernel * noise + kernel = kernel / np.sum(kernel) + + return kernel + + +def random_mixed_kernels(kernel_list, + kernel_prob, + kernel_size=21, + sigma_x_range=(0.6, 5), + sigma_y_range=(0.6, 5), + rotation_range=(-math.pi, math.pi), + betag_range=(0.5, 8), + betap_range=(0.5, 8), + noise_range=None): + """Randomly generate mixed kernels. + + Args: + kernel_list (tuple): a list name of kernel types, + support ['iso', 'aniso', 'skew', 'generalized', 'plateau_iso', + 'plateau_aniso'] + kernel_prob (tuple): corresponding kernel probability for each + kernel type + kernel_size (int): + sigma_x_range (tuple): [0.6, 5] + sigma_y_range (tuple): [0.6, 5] + rotation range (tuple): [-math.pi, math.pi] + beta_range (tuple): [0.5, 8] + noise_range(tuple, optional): multiplicative kernel noise, + [0.75, 1.25]. Default: None + + Returns: + kernel (ndarray): + """ + kernel_type = random.choices(kernel_list, kernel_prob)[0] + if kernel_type == 'iso': + kernel = random_bivariate_Gaussian( + kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=True) + elif kernel_type == 'aniso': + kernel = random_bivariate_Gaussian( + kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=False) + elif kernel_type == 'generalized_iso': + kernel = random_bivariate_generalized_Gaussian( + kernel_size, + sigma_x_range, + sigma_y_range, + rotation_range, + betag_range, + noise_range=noise_range, + isotropic=True) + elif kernel_type == 'generalized_aniso': + kernel = random_bivariate_generalized_Gaussian( + kernel_size, + sigma_x_range, + sigma_y_range, + rotation_range, + betag_range, + noise_range=noise_range, + isotropic=False) + elif kernel_type == 'plateau_iso': + kernel = random_bivariate_plateau( + kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=True) + elif kernel_type == 'plateau_aniso': + kernel = random_bivariate_plateau( + kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=False) + return kernel + + +np.seterr(divide='ignore', invalid='ignore') + + +def circular_lowpass_kernel(cutoff, kernel_size, pad_to=0): + """2D sinc filter, ref: https://dsp.stackexchange.com/questions/58301/2-d-circularly-symmetric-low-pass-filter + + Args: + cutoff (float): cutoff frequency in radians (pi is max) + kernel_size (int): horizontal and vertical size, must be odd. + pad_to (int): pad kernel size to desired size, must be odd or zero. + """ + assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' + kernel = np.fromfunction( + lambda x, y: cutoff * special.j1(cutoff * np.sqrt( + (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)) / (2 * np.pi * np.sqrt( + (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)), [kernel_size, kernel_size]) + kernel[(kernel_size - 1) // 2, (kernel_size - 1) // 2] = cutoff**2 / (4 * np.pi) + kernel = kernel / np.sum(kernel) + if pad_to > kernel_size: + pad_size = (pad_to - kernel_size) // 2 + kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size))) + return kernel + + +# ------------------------------------------------------------- # +# --------------------------- noise --------------------------- # +# ------------------------------------------------------------- # + +# ----------------------- Gaussian Noise ----------------------- # + + +def generate_gaussian_noise(img, sigma=10, gray_noise=False): + """Generate Gaussian noise. + + Args: + img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. + sigma (float): Noise scale (measured in range 255). Default: 10. + + Returns: + (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], + float32. + """ + if gray_noise: + noise = np.float32(np.random.randn(*(img.shape[0:2]))) * sigma / 255. + noise = np.expand_dims(noise, axis=2).repeat(3, axis=2) + else: + noise = np.float32(np.random.randn(*(img.shape))) * sigma / 255. + return noise + + +def add_gaussian_noise(img, sigma=10, clip=True, rounds=False, gray_noise=False): + """Add Gaussian noise. + + Args: + img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. + sigma (float): Noise scale (measured in range 255). Default: 10. + + Returns: + (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], + float32. + """ + noise = generate_gaussian_noise(img, sigma, gray_noise) + out = img + noise + if clip and rounds: + out = np.clip((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = np.clip(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +def generate_gaussian_noise_pt(img, sigma=10, gray_noise=0): + """Add Gaussian noise (PyTorch version). + + Args: + img (Tensor): Shape (b, c, h, w), range[0, 1], float32. + scale (float | Tensor): Noise scale. Default: 1.0. + + Returns: + (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], + float32. + """ + b, _, h, w = img.size() + if not isinstance(sigma, (float, int)): + sigma = sigma.view(img.size(0), 1, 1, 1) + if isinstance(gray_noise, (float, int)): + cal_gray_noise = gray_noise > 0 + else: + gray_noise = gray_noise.view(b, 1, 1, 1) + cal_gray_noise = torch.sum(gray_noise) > 0 + + if cal_gray_noise: + noise_gray = torch.randn(*img.size()[2:4], dtype=img.dtype, device=img.device) * sigma / 255. + noise_gray = noise_gray.view(b, 1, h, w) + + # always calculate color noise + noise = torch.randn(*img.size(), dtype=img.dtype, device=img.device) * sigma / 255. + + if cal_gray_noise: + noise = noise * (1 - gray_noise) + noise_gray * gray_noise + return noise + + +def add_gaussian_noise_pt(img, sigma=10, gray_noise=0, clip=True, rounds=False): + """Add Gaussian noise (PyTorch version). + + Args: + img (Tensor): Shape (b, c, h, w), range[0, 1], float32. + scale (float | Tensor): Noise scale. Default: 1.0. + + Returns: + (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], + float32. + """ + noise = generate_gaussian_noise_pt(img, sigma, gray_noise) + out = img + noise + if clip and rounds: + out = torch.clamp((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = torch.clamp(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +# ----------------------- Random Gaussian Noise ----------------------- # +def random_generate_gaussian_noise(img, sigma_range=(0, 10), gray_prob=0): + sigma = np.random.uniform(sigma_range[0], sigma_range[1]) + if np.random.uniform() < gray_prob: + gray_noise = True + else: + gray_noise = False + return generate_gaussian_noise(img, sigma, gray_noise) + + +def random_add_gaussian_noise(img, sigma_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): + noise = random_generate_gaussian_noise(img, sigma_range, gray_prob) + out = img + noise + if clip and rounds: + out = np.clip((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = np.clip(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +def random_generate_gaussian_noise_pt(img, sigma_range=(0, 10), gray_prob=0): + sigma = torch.rand( + img.size(0), dtype=img.dtype, device=img.device) * (sigma_range[1] - sigma_range[0]) + sigma_range[0] + gray_noise = torch.rand(img.size(0), dtype=img.dtype, device=img.device) + gray_noise = (gray_noise < gray_prob).float() + return generate_gaussian_noise_pt(img, sigma, gray_noise) + + +def random_add_gaussian_noise_pt(img, sigma_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): + noise = random_generate_gaussian_noise_pt(img, sigma_range, gray_prob) + out = img + noise + if clip and rounds: + out = torch.clamp((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = torch.clamp(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +# ----------------------- Poisson (Shot) Noise ----------------------- # + + +def generate_poisson_noise(img, scale=1.0, gray_noise=False): + """Generate poisson noise. + + Ref: https://github.com/scikit-image/scikit-image/blob/main/skimage/util/noise.py#L37-L219 + + Args: + img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. + scale (float): Noise scale. Default: 1.0. + gray_noise (bool): Whether generate gray noise. Default: False. + + Returns: + (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], + float32. + """ + if gray_noise: + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + # round and clip image for counting vals correctly + img = np.clip((img * 255.0).round(), 0, 255) / 255. + vals = len(np.unique(img)) + vals = 2**np.ceil(np.log2(vals)) + out = np.float32(np.random.poisson(img * vals) / float(vals)) + noise = out - img + if gray_noise: + noise = np.repeat(noise[:, :, np.newaxis], 3, axis=2) + return noise * scale + + +def add_poisson_noise(img, scale=1.0, clip=True, rounds=False, gray_noise=False): + """Add poisson noise. + + Args: + img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. + scale (float): Noise scale. Default: 1.0. + gray_noise (bool): Whether generate gray noise. Default: False. + + Returns: + (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], + float32. + """ + noise = generate_poisson_noise(img, scale, gray_noise) + out = img + noise + if clip and rounds: + out = np.clip((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = np.clip(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +def generate_poisson_noise_pt(img, scale=1.0, gray_noise=0): + """Generate a batch of poisson noise (PyTorch version) + + Args: + img (Tensor): Input image, shape (b, c, h, w), range [0, 1], float32. + scale (float | Tensor): Noise scale. Number or Tensor with shape (b). + Default: 1.0. + gray_noise (float | Tensor): 0-1 number or Tensor with shape (b). + 0 for False, 1 for True. Default: 0. + + Returns: + (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], + float32. + """ + b, _, h, w = img.size() + if isinstance(gray_noise, (float, int)): + cal_gray_noise = gray_noise > 0 + else: + gray_noise = gray_noise.view(b, 1, 1, 1) + cal_gray_noise = torch.sum(gray_noise) > 0 + if cal_gray_noise: + img_gray = rgb_to_grayscale(img, num_output_channels=1) + # round and clip image for counting vals correctly + img_gray = torch.clamp((img_gray * 255.0).round(), 0, 255) / 255. + # use for-loop to get the unique values for each sample + vals_list = [len(torch.unique(img_gray[i, :, :, :])) for i in range(b)] + vals_list = [2**np.ceil(np.log2(vals)) for vals in vals_list] + vals = img_gray.new_tensor(vals_list).view(b, 1, 1, 1) + out = torch.poisson(img_gray * vals) / vals + noise_gray = out - img_gray + noise_gray = noise_gray.expand(b, 3, h, w) + + # always calculate color noise + # round and clip image for counting vals correctly + img = torch.clamp((img * 255.0).round(), 0, 255) / 255. + # use for-loop to get the unique values for each sample + vals_list = [len(torch.unique(img[i, :, :, :])) for i in range(b)] + vals_list = [2**np.ceil(np.log2(vals)) for vals in vals_list] + vals = img.new_tensor(vals_list).view(b, 1, 1, 1) + out = torch.poisson(img * vals) / vals + noise = out - img + if cal_gray_noise: + noise = noise * (1 - gray_noise) + noise_gray * gray_noise + if not isinstance(scale, (float, int)): + scale = scale.view(b, 1, 1, 1) + return noise * scale + + +def add_poisson_noise_pt(img, scale=1.0, clip=True, rounds=False, gray_noise=0): + """Add poisson noise to a batch of images (PyTorch version). + + Args: + img (Tensor): Input image, shape (b, c, h, w), range [0, 1], float32. + scale (float | Tensor): Noise scale. Number or Tensor with shape (b). + Default: 1.0. + gray_noise (float | Tensor): 0-1 number or Tensor with shape (b). + 0 for False, 1 for True. Default: 0. + + Returns: + (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], + float32. + """ + noise = generate_poisson_noise_pt(img, scale, gray_noise) + out = img + noise + if clip and rounds: + out = torch.clamp((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = torch.clamp(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +# ----------------------- Random Poisson (Shot) Noise ----------------------- # + + +def random_generate_poisson_noise(img, scale_range=(0, 1.0), gray_prob=0): + scale = np.random.uniform(scale_range[0], scale_range[1]) + if np.random.uniform() < gray_prob: + gray_noise = True + else: + gray_noise = False + return generate_poisson_noise(img, scale, gray_noise) + + +def random_add_poisson_noise(img, scale_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): + noise = random_generate_poisson_noise(img, scale_range, gray_prob) + out = img + noise + if clip and rounds: + out = np.clip((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = np.clip(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +def random_generate_poisson_noise_pt(img, scale_range=(0, 1.0), gray_prob=0): + scale = torch.rand( + img.size(0), dtype=img.dtype, device=img.device) * (scale_range[1] - scale_range[0]) + scale_range[0] + gray_noise = torch.rand(img.size(0), dtype=img.dtype, device=img.device) + gray_noise = (gray_noise < gray_prob).float() + return generate_poisson_noise_pt(img, scale, gray_noise) + + +def random_add_poisson_noise_pt(img, scale_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): + noise = random_generate_poisson_noise_pt(img, scale_range, gray_prob) + out = img + noise + if clip and rounds: + out = torch.clamp((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = torch.clamp(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +# ------------------------------------------------------------------------ # +# --------------------------- JPEG compression --------------------------- # +# ------------------------------------------------------------------------ # + + +def add_jpg_compression(img, quality=90): + """Add JPG compression artifacts. + + Args: + img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. + quality (float): JPG compression quality. 0 for lowest quality, 100 for + best quality. Default: 90. + + Returns: + (Numpy array): Returned image after JPG, shape (h, w, c), range[0, 1], + float32. + """ + img = np.clip(img, 0, 1) + encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality] + _, encimg = cv2.imencode('.jpg', img * 255., encode_param) + img = np.float32(cv2.imdecode(encimg, 1)) / 255. + return img + + +def random_add_jpg_compression(img, quality_range=(90, 100)): + """Randomly add JPG compression artifacts. + + Args: + img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. + quality_range (tuple[float] | list[float]): JPG compression quality + range. 0 for lowest quality, 100 for best quality. + Default: (90, 100). + + Returns: + (Numpy array): Returned image after JPG, shape (h, w, c), range[0, 1], + float32. + """ + quality = int(np.random.uniform(quality_range[0], quality_range[1])) + return add_jpg_compression(img, quality) diff --git a/GPEN/training/loss/helpers.py b/GPEN/training/loss/helpers.py new file mode 100644 index 0000000..b51fdf9 --- /dev/null +++ b/GPEN/training/loss/helpers.py @@ -0,0 +1,119 @@ +from collections import namedtuple +import torch +from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module + +""" +ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) +""" + + +class Flatten(Module): + def forward(self, input): + return input.view(input.size(0), -1) + + +def l2_norm(input, axis=1): + norm = torch.norm(input, 2, axis, True) + output = torch.div(input, norm) + return output + + +class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])): + """ A named tuple describing a ResNet block. """ + + +def get_block(in_channel, depth, num_units, stride=2): + return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)] + + +def get_blocks(num_layers): + if num_layers == 50: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=4), + get_block(in_channel=128, depth=256, num_units=14), + get_block(in_channel=256, depth=512, num_units=3) + ] + elif num_layers == 100: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=13), + get_block(in_channel=128, depth=256, num_units=30), + get_block(in_channel=256, depth=512, num_units=3) + ] + elif num_layers == 152: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=8), + get_block(in_channel=128, depth=256, num_units=36), + get_block(in_channel=256, depth=512, num_units=3) + ] + else: + raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers)) + return blocks + + +class SEModule(Module): + def __init__(self, channels, reduction): + super(SEModule, self).__init__() + self.avg_pool = AdaptiveAvgPool2d(1) + self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False) + self.relu = ReLU(inplace=True) + self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False) + self.sigmoid = Sigmoid() + + def forward(self, x): + module_input = x + x = self.avg_pool(x) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.sigmoid(x) + return module_input * x + + +class bottleneck_IR(Module): + def __init__(self, in_channel, depth, stride): + super(bottleneck_IR, self).__init__() + if in_channel == depth: + self.shortcut_layer = MaxPool2d(1, stride) + else: + self.shortcut_layer = Sequential( + Conv2d(in_channel, depth, (1, 1), stride, bias=False), + BatchNorm2d(depth) + ) + self.res_layer = Sequential( + BatchNorm2d(in_channel), + Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth), + Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth) + ) + + def forward(self, x): + shortcut = self.shortcut_layer(x) + res = self.res_layer(x) + return res + shortcut + + +class bottleneck_IR_SE(Module): + def __init__(self, in_channel, depth, stride): + super(bottleneck_IR_SE, self).__init__() + if in_channel == depth: + self.shortcut_layer = MaxPool2d(1, stride) + else: + self.shortcut_layer = Sequential( + Conv2d(in_channel, depth, (1, 1), stride, bias=False), + BatchNorm2d(depth) + ) + self.res_layer = Sequential( + BatchNorm2d(in_channel), + Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), + PReLU(depth), + Conv2d(depth, depth, (3, 3), stride, 1, bias=False), + BatchNorm2d(depth), + SEModule(depth, 16) + ) + + def forward(self, x): + shortcut = self.shortcut_layer(x) + res = self.res_layer(x) + return res + shortcut diff --git a/GPEN/training/loss/id_loss.py b/GPEN/training/loss/id_loss.py new file mode 100644 index 0000000..c26e2bf --- /dev/null +++ b/GPEN/training/loss/id_loss.py @@ -0,0 +1,50 @@ +import os +import torch +from torch import nn +from model_irse import Backbone + +class IDLoss(nn.Module): + def __init__(self, base_dir='./', device='cuda', ckpt_dict=None): + super(IDLoss, self).__init__() + print('Loading ResNet ArcFace') + self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se').to(device) + if ckpt_dict is None: + self.facenet.load_state_dict(torch.load(os.path.join(base_dir, 'weights', 'model_ir_se50.pth'), map_location=torch.device('cpu'))) + else: + self.facenet.load_state_dict(ckpt_dict) + self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112)) + self.facenet.eval() + + def extract_feats(self, x): + _, _, h, w = x.shape + assert h==w + ss = h//256 + x = x[:, :, 35*ss:-33*ss, 32*ss:-36*ss] # Crop interesting region + x = self.face_pool(x) + x_feats = self.facenet(x) + return x_feats + + def forward(self, y_hat, y, x): + n_samples = x.shape[0] + x_feats = self.extract_feats(x) + y_feats = self.extract_feats(y) # Otherwise use the feature from there + y_hat_feats = self.extract_feats(y_hat) + y_feats = y_feats.detach() + loss = 0 + sim_improvement = 0 + id_logs = [] + count = 0 + for i in range(n_samples): + diff_target = y_hat_feats[i].dot(y_feats[i]) + diff_input = y_hat_feats[i].dot(x_feats[i]) + diff_views = y_feats[i].dot(x_feats[i]) + id_logs.append({'diff_target': float(diff_target), + 'diff_input': float(diff_input), + 'diff_views': float(diff_views)}) + loss += 1 - diff_target + id_diff = float(diff_target) - float(diff_views) + sim_improvement += id_diff + count += 1 + + return loss / count, sim_improvement / count, id_logs + diff --git a/GPEN/training/loss/model_irse.py b/GPEN/training/loss/model_irse.py new file mode 100644 index 0000000..b3bd6f7 --- /dev/null +++ b/GPEN/training/loss/model_irse.py @@ -0,0 +1,85 @@ +from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module +#from models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm +from helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm + +""" +Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) +""" + + +class Backbone(Module): + def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True): + super(Backbone, self).__init__() + assert input_size in [112, 224], "input_size should be 112 or 224" + assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152" + assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se" + blocks = get_blocks(num_layers) + if mode == 'ir': + unit_module = bottleneck_IR + elif mode == 'ir_se': + unit_module = bottleneck_IR_SE + self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), + BatchNorm2d(64), + PReLU(64)) + if input_size == 112: + self.output_layer = Sequential(BatchNorm2d(512), + Dropout(drop_ratio), + Flatten(), + Linear(512 * 7 * 7, 512), + BatchNorm1d(512, affine=affine)) + else: + self.output_layer = Sequential(BatchNorm2d(512), + Dropout(drop_ratio), + Flatten(), + Linear(512 * 14 * 14, 512), + BatchNorm1d(512, affine=affine)) + + modules = [] + for block in blocks: + for bottleneck in block: + modules.append(unit_module(bottleneck.in_channel, + bottleneck.depth, + bottleneck.stride)) + self.body = Sequential(*modules) + + def forward(self, x): + x = self.input_layer(x) + x = self.body(x) + x = self.output_layer(x) + return l2_norm(x) + + +def IR_50(input_size): + """Constructs a ir-50 model.""" + model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False) + return model + + +def IR_101(input_size): + """Constructs a ir-101 model.""" + model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False) + return model + + +def IR_152(input_size): + """Constructs a ir-152 model.""" + model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False) + return model + + +def IR_SE_50(input_size): + """Constructs a ir_se-50 model.""" + model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False) + return model + + +def IR_SE_101(input_size): + """Constructs a ir_se-101 model.""" + model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False) + return model + + +def IR_SE_152(input_size): + """Constructs a ir_se-152 model.""" + model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False) + return model diff --git a/GPEN/training/lpips/__init__.py b/GPEN/training/lpips/__init__.py new file mode 100644 index 0000000..62b9a07 --- /dev/null +++ b/GPEN/training/lpips/__init__.py @@ -0,0 +1,178 @@ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import torch +# from torch.autograd import Variable + +from lpips.trainer import * +from lpips.lpips import * + +# class PerceptualLoss(torch.nn.Module): +# def __init__(self, model='lpips', net='alex', spatial=False, use_gpu=False, gpu_ids=[0], version='0.1'): # VGG using our perceptually-learned weights (LPIPS metric) +# # def __init__(self, model='net', net='vgg', use_gpu=True): # "default" way of using VGG as a perceptual loss +# super(PerceptualLoss, self).__init__() +# print('Setting up Perceptual loss...') +# self.use_gpu = use_gpu +# self.spatial = spatial +# self.gpu_ids = gpu_ids +# self.model = dist_model.DistModel() +# self.model.initialize(model=model, net=net, use_gpu=use_gpu, spatial=self.spatial, gpu_ids=gpu_ids, version=version) +# print('...[%s] initialized'%self.model.name()) +# print('...Done') + +# def forward(self, pred, target, normalize=False): +# """ +# Pred and target are Variables. +# If normalize is True, assumes the images are between [0,1] and then scales them between [-1,+1] +# If normalize is False, assumes the images are already between [-1,+1] + +# Inputs pred and target are Nx3xHxW +# Output pytorch Variable N long +# """ + +# if normalize: +# target = 2 * target - 1 +# pred = 2 * pred - 1 + +# return self.model.forward(target, pred) + +def normalize_tensor(in_feat,eps=1e-10): + norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1,keepdim=True)) + return in_feat/(norm_factor+eps) + +def l2(p0, p1, range=255.): + return .5*np.mean((p0 / range - p1 / range)**2) + +def psnr(p0, p1, peak=255.): + return 10*np.log10(peak**2/np.mean((1.*p0-1.*p1)**2)) + +def dssim(p0, p1, range=255.): + from skimage.measure import compare_ssim + return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2. + +def rgb2lab(in_img,mean_cent=False): + from skimage import color + img_lab = color.rgb2lab(in_img) + if(mean_cent): + img_lab[:,:,0] = img_lab[:,:,0]-50 + return img_lab + +def tensor2np(tensor_obj): + # change dimension of a tensor object into a numpy array + return tensor_obj[0].cpu().float().numpy().transpose((1,2,0)) + +def np2tensor(np_obj): + # change dimenion of np array into tensor array + return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1))) + +def tensor2tensorlab(image_tensor,to_norm=True,mc_only=False): + # image tensor to lab tensor + from skimage import color + + img = tensor2im(image_tensor) + img_lab = color.rgb2lab(img) + if(mc_only): + img_lab[:,:,0] = img_lab[:,:,0]-50 + if(to_norm and not mc_only): + img_lab[:,:,0] = img_lab[:,:,0]-50 + img_lab = img_lab/100. + + return np2tensor(img_lab) + +def tensorlab2tensor(lab_tensor,return_inbnd=False): + from skimage import color + import warnings + warnings.filterwarnings("ignore") + + lab = tensor2np(lab_tensor)*100. + lab[:,:,0] = lab[:,:,0]+50 + + rgb_back = 255.*np.clip(color.lab2rgb(lab.astype('float')),0,1) + if(return_inbnd): + # convert back to lab, see if we match + lab_back = color.rgb2lab(rgb_back.astype('uint8')) + mask = 1.*np.isclose(lab_back,lab,atol=2.) + mask = np2tensor(np.prod(mask,axis=2)[:,:,np.newaxis]) + return (im2tensor(rgb_back),mask) + else: + return im2tensor(rgb_back) + +def load_image(path): + if(path[-3:] == 'dng'): + import rawpy + with rawpy.imread(path) as raw: + img = raw.postprocess() + elif(path[-3:]=='bmp' or path[-3:]=='jpg' or path[-3:]=='png'): + import cv2 + return cv2.imread(path)[:,:,::-1] + else: + img = (255*plt.imread(path)[:,:,:3]).astype('uint8') + + return img + +def rgb2lab(input): + from skimage import color + return color.rgb2lab(input / 255.) + +def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.): + image_numpy = image_tensor[0].cpu().float().numpy() + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor + return image_numpy.astype(imtype) + +def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.): + return torch.Tensor((image / factor - cent) + [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) + +def tensor2vec(vector_tensor): + return vector_tensor.data.cpu().numpy()[:, :, 0, 0] + + +def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.): +# def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.): + image_numpy = image_tensor[0].cpu().float().numpy() + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor + return image_numpy.astype(imtype) + +def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.): +# def im2tensor(image, imtype=np.uint8, cent=1., factor=1.): + return torch.Tensor((image / factor - cent) + [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) + + + +def voc_ap(rec, prec, use_07_metric=False): + """ ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + diff --git a/GPEN/training/lpips/lpips.py b/GPEN/training/lpips/lpips.py new file mode 100644 index 0000000..9b979c0 --- /dev/null +++ b/GPEN/training/lpips/lpips.py @@ -0,0 +1,219 @@ + +from __future__ import absolute_import + +import torch +import torch.nn as nn +import torch.nn.init as init +from torch.autograd import Variable +import numpy as np +from . import pretrained_networks as pn +import torch.nn + +import lpips + +def spatial_average(in_tens, keepdim=True): + return in_tens.mean([2,3],keepdim=keepdim) + +def upsample(in_tens, out_HW=(64,64)): # assumes scale factor is same for H and W + in_H, in_W = in_tens.shape[2], in_tens.shape[3] + return nn.Upsample(size=out_HW, mode='bilinear', align_corners=False)(in_tens) + +# Learned perceptual metric +class LPIPS(nn.Module): + def __init__(self, pretrained=True, net='alex', version='0.1', lpips=True, spatial=False, + pnet_rand=False, pnet_tune=False, use_dropout=True, model_path=None, eval_mode=True, verbose=True): + # lpips - [True] means with linear calibration on top of base network + # pretrained - [True] means load linear weights + + super(LPIPS, self).__init__() + if(verbose): + print('Setting up [%s] perceptual loss: trunk [%s], v[%s], spatial [%s]'% + ('LPIPS' if lpips else 'baseline', net, version, 'on' if spatial else 'off')) + + self.pnet_type = net + self.pnet_tune = pnet_tune + self.pnet_rand = pnet_rand + self.spatial = spatial + self.lpips = lpips # false means baseline of just averaging all layers + self.version = version + self.scaling_layer = ScalingLayer() + + if(self.pnet_type in ['vgg','vgg16']): + net_type = pn.vgg16 + self.chns = [64,128,256,512,512] + elif(self.pnet_type=='alex'): + net_type = pn.alexnet + self.chns = [64,192,384,256,256] + elif(self.pnet_type=='squeeze'): + net_type = pn.squeezenet + self.chns = [64,128,256,384,384,512,512] + self.L = len(self.chns) + + self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune) + + if(lpips): + self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout) + self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout) + self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout) + self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout) + self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout) + self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4] + if(self.pnet_type=='squeeze'): # 7 layers for squeezenet + self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout) + self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout) + self.lins+=[self.lin5,self.lin6] + self.lins = nn.ModuleList(self.lins) + + if(pretrained): + if(model_path is None): + import inspect + import os + model_path = os.path.abspath(os.path.join(inspect.getfile(self.__init__), '..', 'weights/v%s/%s.pth'%(version,net))) + + if(verbose): + print('Loading model from: %s'%model_path) + self.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False) + + if(eval_mode): + self.eval() + + def forward(self, in0, in1, retPerLayer=False, normalize=False): + if normalize: # turn on this flag if input is [0,1] so it can be adjusted to [-1, +1] + in0 = 2 * in0 - 1 + in1 = 2 * in1 - 1 + + # v0.0 - original release had a bug, where input was not scaled + in0_input, in1_input = (self.scaling_layer(in0), self.scaling_layer(in1)) if self.version=='0.1' else (in0, in1) + outs0, outs1 = self.net.forward(in0_input), self.net.forward(in1_input) + feats0, feats1, diffs = {}, {}, {} + + for kk in range(self.L): + feats0[kk], feats1[kk] = lpips.normalize_tensor(outs0[kk]), lpips.normalize_tensor(outs1[kk]) + diffs[kk] = (feats0[kk]-feats1[kk])**2 + + if(self.lpips): + if(self.spatial): + res = [upsample(self.lins[kk](diffs[kk]), out_HW=in0.shape[2:]) for kk in range(self.L)] + else: + res = [spatial_average(self.lins[kk](diffs[kk]), keepdim=True) for kk in range(self.L)] + else: + if(self.spatial): + res = [upsample(diffs[kk].sum(dim=1,keepdim=True), out_HW=in0.shape[2:]) for kk in range(self.L)] + else: + res = [spatial_average(diffs[kk].sum(dim=1,keepdim=True), keepdim=True) for kk in range(self.L)] + + val = res[0] + for l in range(1,self.L): + val += res[l] + + # a = spatial_average(self.lins[kk](diffs[kk]), keepdim=True) + # b = torch.max(self.lins[kk](feats0[kk]**2)) + # for kk in range(self.L): + # a += spatial_average(self.lins[kk](diffs[kk]), keepdim=True) + # b = torch.max(b,torch.max(self.lins[kk](feats0[kk]**2))) + # a = a/self.L + # from IPython import embed + # embed() + # return 10*torch.log10(b/a) + + if(retPerLayer): + return (val, res) + else: + return val + + +class ScalingLayer(nn.Module): + def __init__(self): + super(ScalingLayer, self).__init__() + self.register_buffer('shift', torch.Tensor([-.030,-.088,-.188])[None,:,None,None]) + self.register_buffer('scale', torch.Tensor([.458,.448,.450])[None,:,None,None]) + + def forward(self, inp): + return (inp - self.shift) / self.scale + + +class NetLinLayer(nn.Module): + ''' A single linear layer which does a 1x1 conv ''' + def __init__(self, chn_in, chn_out=1, use_dropout=False): + super(NetLinLayer, self).__init__() + + layers = [nn.Dropout(),] if(use_dropout) else [] + layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),] + self.model = nn.Sequential(*layers) + + def forward(self, x): + return self.model(x) + +class Dist2LogitLayer(nn.Module): + ''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) ''' + def __init__(self, chn_mid=32, use_sigmoid=True): + super(Dist2LogitLayer, self).__init__() + + layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True),] + layers += [nn.LeakyReLU(0.2,True),] + layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True),] + layers += [nn.LeakyReLU(0.2,True),] + layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True),] + if(use_sigmoid): + layers += [nn.Sigmoid(),] + self.model = nn.Sequential(*layers) + + def forward(self,d0,d1,eps=0.1): + return self.model.forward(torch.cat((d0,d1,d0-d1,d0/(d1+eps),d1/(d0+eps)),dim=1)) + +class BCERankingLoss(nn.Module): + def __init__(self, chn_mid=32): + super(BCERankingLoss, self).__init__() + self.net = Dist2LogitLayer(chn_mid=chn_mid) + # self.parameters = list(self.net.parameters()) + self.loss = torch.nn.BCELoss() + + def forward(self, d0, d1, judge): + per = (judge+1.)/2. + self.logit = self.net.forward(d0,d1) + return self.loss(self.logit, per) + +# L2, DSSIM metrics +class FakeNet(nn.Module): + def __init__(self, use_gpu=True, colorspace='Lab'): + super(FakeNet, self).__init__() + self.use_gpu = use_gpu + self.colorspace = colorspace + +class L2(FakeNet): + def forward(self, in0, in1, retPerLayer=None): + assert(in0.size()[0]==1) # currently only supports batchSize 1 + + if(self.colorspace=='RGB'): + (N,C,X,Y) = in0.size() + value = torch.mean(torch.mean(torch.mean((in0-in1)**2,dim=1).view(N,1,X,Y),dim=2).view(N,1,1,Y),dim=3).view(N) + return value + elif(self.colorspace=='Lab'): + value = lpips.l2(lpips.tensor2np(lpips.tensor2tensorlab(in0.data,to_norm=False)), + lpips.tensor2np(lpips.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float') + ret_var = Variable( torch.Tensor((value,) ) ) + if(self.use_gpu): + ret_var = ret_var.cuda() + return ret_var + +class DSSIM(FakeNet): + + def forward(self, in0, in1, retPerLayer=None): + assert(in0.size()[0]==1) # currently only supports batchSize 1 + + if(self.colorspace=='RGB'): + value = lpips.dssim(1.*lpips.tensor2im(in0.data), 1.*lpips.tensor2im(in1.data), range=255.).astype('float') + elif(self.colorspace=='Lab'): + value = lpips.dssim(lpips.tensor2np(lpips.tensor2tensorlab(in0.data,to_norm=False)), + lpips.tensor2np(lpips.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float') + ret_var = Variable( torch.Tensor((value,) ) ) + if(self.use_gpu): + ret_var = ret_var.cuda() + return ret_var + +def print_network(net): + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + print('Network',net) + print('Total number of parameters: %d' % num_params) diff --git a/GPEN/training/lpips/pretrained_networks.py b/GPEN/training/lpips/pretrained_networks.py new file mode 100644 index 0000000..a70ebbe --- /dev/null +++ b/GPEN/training/lpips/pretrained_networks.py @@ -0,0 +1,180 @@ +from collections import namedtuple +import torch +from torchvision import models as tv + +class squeezenet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(squeezenet, self).__init__() + pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.slice6 = torch.nn.Sequential() + self.slice7 = torch.nn.Sequential() + self.N_slices = 7 + for x in range(2): + self.slice1.add_module(str(x), pretrained_features[x]) + for x in range(2,5): + self.slice2.add_module(str(x), pretrained_features[x]) + for x in range(5, 8): + self.slice3.add_module(str(x), pretrained_features[x]) + for x in range(8, 10): + self.slice4.add_module(str(x), pretrained_features[x]) + for x in range(10, 11): + self.slice5.add_module(str(x), pretrained_features[x]) + for x in range(11, 12): + self.slice6.add_module(str(x), pretrained_features[x]) + for x in range(12, 13): + self.slice7.add_module(str(x), pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1 = h + h = self.slice2(h) + h_relu2 = h + h = self.slice3(h) + h_relu3 = h + h = self.slice4(h) + h_relu4 = h + h = self.slice5(h) + h_relu5 = h + h = self.slice6(h) + h_relu6 = h + h = self.slice7(h) + h_relu7 = h + vgg_outputs = namedtuple("SqueezeOutputs", ['relu1','relu2','relu3','relu4','relu5','relu6','relu7']) + out = vgg_outputs(h_relu1,h_relu2,h_relu3,h_relu4,h_relu5,h_relu6,h_relu7) + + return out + + +class alexnet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(alexnet, self).__init__() + alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(2): + self.slice1.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(2, 5): + self.slice2.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(5, 8): + self.slice3.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(8, 10): + self.slice4.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(10, 12): + self.slice5.add_module(str(x), alexnet_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1 = h + h = self.slice2(h) + h_relu2 = h + h = self.slice3(h) + h_relu3 = h + h = self.slice4(h) + h_relu4 = h + h = self.slice5(h) + h_relu5 = h + alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5']) + out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5) + + return out + +class vgg16(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(vgg16, self).__init__() + vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(4): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(4, 9): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(9, 16): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(16, 23): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(23, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1_2 = h + h = self.slice2(h) + h_relu2_2 = h + h = self.slice3(h) + h_relu3_3 = h + h = self.slice4(h) + h_relu4_3 = h + h = self.slice5(h) + h_relu5_3 = h + vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3']) + out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) + + return out + + + +class resnet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True, num=18): + super(resnet, self).__init__() + if(num==18): + self.net = tv.resnet18(pretrained=pretrained) + elif(num==34): + self.net = tv.resnet34(pretrained=pretrained) + elif(num==50): + self.net = tv.resnet50(pretrained=pretrained) + elif(num==101): + self.net = tv.resnet101(pretrained=pretrained) + elif(num==152): + self.net = tv.resnet152(pretrained=pretrained) + self.N_slices = 5 + + self.conv1 = self.net.conv1 + self.bn1 = self.net.bn1 + self.relu = self.net.relu + self.maxpool = self.net.maxpool + self.layer1 = self.net.layer1 + self.layer2 = self.net.layer2 + self.layer3 = self.net.layer3 + self.layer4 = self.net.layer4 + + def forward(self, X): + h = self.conv1(X) + h = self.bn1(h) + h = self.relu(h) + h_relu1 = h + h = self.maxpool(h) + h = self.layer1(h) + h_conv2 = h + h = self.layer2(h) + h_conv3 = h + h = self.layer3(h) + h_conv4 = h + h = self.layer4(h) + h_conv5 = h + + outputs = namedtuple("Outputs", ['relu1','conv2','conv3','conv4','conv5']) + out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5) + + return out diff --git a/GPEN/training/lpips/trainer.py b/GPEN/training/lpips/trainer.py new file mode 100644 index 0000000..52b6112 --- /dev/null +++ b/GPEN/training/lpips/trainer.py @@ -0,0 +1,280 @@ + +from __future__ import absolute_import + +import numpy as np +import torch +from torch import nn +from collections import OrderedDict +from torch.autograd import Variable +from scipy.ndimage import zoom +from tqdm import tqdm +import lpips +import os + + +class Trainer(): + def name(self): + return self.model_name + + def initialize(self, model='lpips', net='alex', colorspace='Lab', pnet_rand=False, pnet_tune=False, model_path=None, + use_gpu=True, printNet=False, spatial=False, + is_train=False, lr=.0001, beta1=0.5, version='0.1', gpu_ids=[0]): + ''' + INPUTS + model - ['lpips'] for linearly calibrated network + ['baseline'] for off-the-shelf network + ['L2'] for L2 distance in Lab colorspace + ['SSIM'] for ssim in RGB colorspace + net - ['squeeze','alex','vgg'] + model_path - if None, will look in weights/[NET_NAME].pth + colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM + use_gpu - bool - whether or not to use a GPU + printNet - bool - whether or not to print network architecture out + spatial - bool - whether to output an array containing varying distances across spatial dimensions + is_train - bool - [True] for training mode + lr - float - initial learning rate + beta1 - float - initial momentum term for adam + version - 0.1 for latest, 0.0 was original (with a bug) + gpu_ids - int array - [0] by default, gpus to use + ''' + self.use_gpu = use_gpu + self.gpu_ids = gpu_ids + self.model = model + self.net = net + self.is_train = is_train + self.spatial = spatial + self.model_name = '%s [%s]'%(model,net) + + if(self.model == 'lpips'): # pretrained net + linear layer + self.net = lpips.LPIPS(pretrained=not is_train, net=net, version=version, lpips=True, spatial=spatial, + pnet_rand=pnet_rand, pnet_tune=pnet_tune, + use_dropout=True, model_path=model_path, eval_mode=False) + elif(self.model=='baseline'): # pretrained network + self.net = lpips.LPIPS(pnet_rand=pnet_rand, net=net, lpips=False) + elif(self.model in ['L2','l2']): + self.net = lpips.L2(use_gpu=use_gpu,colorspace=colorspace) # not really a network, only for testing + self.model_name = 'L2' + elif(self.model in ['DSSIM','dssim','SSIM','ssim']): + self.net = lpips.DSSIM(use_gpu=use_gpu,colorspace=colorspace) + self.model_name = 'SSIM' + else: + raise ValueError("Model [%s] not recognized." % self.model) + + self.parameters = list(self.net.parameters()) + + if self.is_train: # training mode + # extra network on top to go from distances (d0,d1) => predicted human judgment (h*) + self.rankLoss = lpips.BCERankingLoss() + self.parameters += list(self.rankLoss.net.parameters()) + self.lr = lr + self.old_lr = lr + self.optimizer_net = torch.optim.Adam(self.parameters, lr=lr, betas=(beta1, 0.999)) + else: # test mode + self.net.eval() + + if(use_gpu): + self.net.to(gpu_ids[0]) + self.net = torch.nn.DataParallel(self.net, device_ids=gpu_ids) + if(self.is_train): + self.rankLoss = self.rankLoss.to(device=gpu_ids[0]) # just put this on GPU0 + + if(printNet): + print('---------- Networks initialized -------------') + networks.print_network(self.net) + print('-----------------------------------------------') + + def forward(self, in0, in1, retPerLayer=False): + ''' Function computes the distance between image patches in0 and in1 + INPUTS + in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1] + OUTPUT + computed distances between in0 and in1 + ''' + + return self.net.forward(in0, in1, retPerLayer=retPerLayer) + + # ***** TRAINING FUNCTIONS ***** + def optimize_parameters(self): + self.forward_train() + self.optimizer_net.zero_grad() + self.backward_train() + self.optimizer_net.step() + self.clamp_weights() + + def clamp_weights(self): + for module in self.net.modules(): + if(hasattr(module, 'weight') and module.kernel_size==(1,1)): + module.weight.data = torch.clamp(module.weight.data,min=0) + + def set_input(self, data): + self.input_ref = data['ref'] + self.input_p0 = data['p0'] + self.input_p1 = data['p1'] + self.input_judge = data['judge'] + + if(self.use_gpu): + self.input_ref = self.input_ref.to(device=self.gpu_ids[0]) + self.input_p0 = self.input_p0.to(device=self.gpu_ids[0]) + self.input_p1 = self.input_p1.to(device=self.gpu_ids[0]) + self.input_judge = self.input_judge.to(device=self.gpu_ids[0]) + + self.var_ref = Variable(self.input_ref,requires_grad=True) + self.var_p0 = Variable(self.input_p0,requires_grad=True) + self.var_p1 = Variable(self.input_p1,requires_grad=True) + + def forward_train(self): # run forward pass + self.d0 = self.forward(self.var_ref, self.var_p0) + self.d1 = self.forward(self.var_ref, self.var_p1) + self.acc_r = self.compute_accuracy(self.d0,self.d1,self.input_judge) + + self.var_judge = Variable(1.*self.input_judge).view(self.d0.size()) + + self.loss_total = self.rankLoss.forward(self.d0, self.d1, self.var_judge*2.-1.) + + return self.loss_total + + def backward_train(self): + torch.mean(self.loss_total).backward() + + def compute_accuracy(self,d0,d1,judge): + ''' d0, d1 are Variables, judge is a Tensor ''' + d1_lt_d0 = (d1 %f' % (type,self.old_lr, lr)) + self.old_lr = lr + + + def get_image_paths(self): + return self.image_paths + + def save_done(self, flag=False): + np.save(os.path.join(self.save_dir, 'done_flag'),flag) + np.savetxt(os.path.join(self.save_dir, 'done_flag'),[flag,],fmt='%i') + + +def score_2afc_dataset(data_loader, func, name=''): + ''' Function computes Two Alternative Forced Choice (2AFC) score using + distance function 'func' in dataset 'data_loader' + INPUTS + data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside + func - callable distance function - calling d=func(in0,in1) should take 2 + pytorch tensors with shape Nx3xXxY, and return numpy array of length N + OUTPUTS + [0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators + [1] - dictionary with following elements + d0s,d1s - N arrays containing distances between reference patch to perturbed patches + gts - N array in [0,1], preferred patch selected by human evaluators + (closer to "0" for left patch p0, "1" for right patch p1, + "0.6" means 60pct people preferred right patch, 40pct preferred left) + scores - N array in [0,1], corresponding to what percentage function agreed with humans + CONSTS + N - number of test triplets in data_loader + ''' + + d0s = [] + d1s = [] + gts = [] + + for data in tqdm(data_loader.load_data(), desc=name): + d0s+=func(data['ref'],data['p0']).data.cpu().numpy().flatten().tolist() + d1s+=func(data['ref'],data['p1']).data.cpu().numpy().flatten().tolist() + gts+=data['judge'].cpu().numpy().flatten().tolist() + + d0s = np.array(d0s) + d1s = np.array(d1s) + gts = np.array(gts) + scores = (d0s + +Наш коллаб (рабочий) GPEN google colab logo. + +## SimSwap - программа, позволяющая по фотографии считать маску лица и наложить на видеоролик. +Приимер: + +![image](https://user-images.githubusercontent.com/92042521/178327206-e002ee55-236e-45ef-bc88-c06fd1c6931d.png) + + + +Наш коллаб (рабочий) SimSwap google colab logo. + + +## Общий коллаб +Общий коллаб с вышеперчисленными программами, дополненными командой **DS** +google colab logo. + +**Содержит**: + +повышение качества/GPEN + +Сравнение лиц/face_recogniton + +Повышение качества/GPEN + +Дипфейк/SimSwap + +## Dokers +ссылка на dockerhub, содержащей образы SimSwap, Gpen-img, ubunta-img + +https://hub.docker.com/repositories + -Проект по созданию DeepFake приложения для увековечивания исторических личностей. -По набору старых фотографий будет создаваться маска для видео с лицом лектора. -Таким образом лекцию будет "проводить" сам учёный. -Для этого нужно решить задачу по FaceSwap. -Было создано 4 команды: съёмочная группа, data engineering, data science и группа продвижения проекта. -... diff --git a/SimSwap/.gitattributes b/SimSwap/.gitattributes new file mode 100644 index 0000000..dfe0770 --- /dev/null +++ b/SimSwap/.gitattributes @@ -0,0 +1,2 @@ +# Auto detect text files and perform LF normalization +* text=auto diff --git a/SimSwap/.gitignore b/SimSwap/.gitignore new file mode 100644 index 0000000..5b6e4b9 --- /dev/null +++ b/SimSwap/.gitignore @@ -0,0 +1,142 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +docs/ppt/ +checkpoints/ +*.tar +*.patch +*.zip +*.avi +*.pdf +*.pptx + +*.pth +*.onnx +wandb/ \ No newline at end of file diff --git a/SimSwap/AdaptiveAvgPool2d.patch b/SimSwap/AdaptiveAvgPool2d.patch new file mode 100644 index 0000000..5972f09 --- /dev/null +++ b/SimSwap/AdaptiveAvgPool2d.patch @@ -0,0 +1,36 @@ +--- /usr/local/lib/python3.5/dist-packages/torch/nn/modules/pooling.py ++++ /usr/local/lib/python3.5/dist-packages/torch/nn/modules/pooling.py +@@ -6,14 +6,9 @@ + + Args: + output_size: the target output size of the image of the form H x W. +- Can be a tuple (H, W) or a single H for a square image H x H. ++ Can be a tuple (H, W) or a single H for a square image H x H + H and W can be either a ``int``, or ``None`` which means the size will + be the same as that of the input. +- +- Shape: +- - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. +- - Output: :math:`(N, C, S_{0}, S_{1})` or :math:`(C, S_{0}, S_{1})`, where +- :math:`S=\text{output\_size}`. + + Examples: + >>> # target output size of 5x7 +@@ -25,14 +20,13 @@ + >>> input = torch.randn(1, 64, 10, 9) + >>> output = m(input) + >>> # target output size of 10x7 +- >>> m = nn.AdaptiveAvgPool2d((None, 7)) ++ >>> m = nn.AdaptiveMaxPool2d((None, 7)) + >>> input = torch.randn(1, 64, 10, 9) + >>> output = m(input) + + """ + +- output_size: _size_2_opt_t +- +- def forward(self, input: Tensor) -> Tensor: ++ @weak_script_method ++ def forward(self, input): + return F.adaptive_avg_pool2d(input, self.output_size) + \ No newline at end of file diff --git a/SimSwap/BatchNorm1d.patch b/SimSwap/BatchNorm1d.patch new file mode 100644 index 0000000..058be2b --- /dev/null +++ b/SimSwap/BatchNorm1d.patch @@ -0,0 +1,80 @@ +--- /usr/local/lib/python3.5/dist-packages/torch/nn/modules/batchnorm.py ++++ /usr/local/lib/python3.5/dist-packages/torch/nn/modules/batchnorm.py +@@ -1,7 +1,7 @@ + class BatchNorm1d(_BatchNorm): +- r"""Applies Batch Normalization over a 2D or 3D input as described in the paper +- `Batch Normalization: Accelerating Deep Network Training by Reducing +- Internal Covariate Shift `__ . ++ r"""Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D ++ inputs with optional additional channel dimension) as described in the paper ++ `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift`_ . + + .. math:: + +@@ -9,9 +9,8 @@ + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors +- of size `C` (where `C` is the number of features or channels of the input). By default, the +- elements of :math:`\gamma` are set to 1 and the elements of :math:`\beta` are set to 0. The +- standard-deviation is calculated via the biased estimator, equivalent to `torch.var(input, unbiased=False)`. ++ of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are sampled ++ from :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. + + Also by default, during training this layer keeps running estimates of its + computed mean and variance, which are then used for normalization during +@@ -26,7 +25,7 @@ + This :attr:`momentum` argument is different from one used in optimizer + classes and the conventional notion of momentum. Mathematically, the + update rule for running statistics here is +- :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, ++ :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momemtum} \times x_t`, + where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the + new observed value. + +@@ -34,7 +33,8 @@ + on `(N, L)` slices, it's common terminology to call this Temporal Batch Normalization. + + Args: +- num_features: number of features or channels :math:`C` of the input ++ num_features: :math:`C` from an expected input of size ++ :math:`(N, C, L)` or :math:`L` from input of size :math:`(N, L)` + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var +@@ -44,14 +44,11 @@ + learnable affine parameters. Default: ``True`` + track_running_stats: a boolean value that when set to ``True``, this + module tracks the running mean and variance, and when set to ``False``, +- this module does not track such statistics, and initializes statistics +- buffers :attr:`running_mean` and :attr:`running_var` as ``None``. +- When these buffers are ``None``, this module always uses batch statistics. +- in both training and eval modes. Default: ``True`` ++ this module does not track such statistics and always uses batch ++ statistics in both training and eval modes. Default: ``True`` + + Shape: +- - Input: :math:`(N, C)` or :math:`(N, C, L)`, where :math:`N` is the batch size, +- :math:`C` is the number of features or channels, and :math:`L` is the sequence length ++ - Input: :math:`(N, C)` or :math:`(N, C, L)` + - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) + + Examples:: +@@ -62,11 +59,14 @@ + >>> m = nn.BatchNorm1d(100, affine=False) + >>> input = torch.randn(20, 100) + >>> output = m(input) ++ ++ .. _`Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift`: ++ https://arxiv.org/abs/1502.03167 + """ + ++ @weak_script_method + def _check_input_dim(self, input): + if input.dim() != 2 and input.dim() != 3: +- raise ValueError( +- "expected 2D or 3D input (got {}D input)".format(input.dim()) +- ) ++ raise ValueError('expected 2D or 3D input (got {}D input)' ++ .format(input.dim())) + \ No newline at end of file diff --git a/SimSwap/BatchNorm2d.patch b/SimSwap/BatchNorm2d.patch new file mode 100644 index 0000000..4f6ab96 --- /dev/null +++ b/SimSwap/BatchNorm2d.patch @@ -0,0 +1,62 @@ +--- /usr/local/lib/python3.5/dist-packages/torch/nn/modules/batchnorm.py ++++ /usr/local/lib/python3.5/dist-packages/torch/nn/modules/batchnorm.py +@@ -1,8 +1,7 @@ + class BatchNorm2d(_BatchNorm): + r"""Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs + with additional channel dimension) as described in the paper +- `Batch Normalization: Accelerating Deep Network Training by Reducing +- Internal Covariate Shift `__ . ++ `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift`_ . + + .. math:: + +@@ -10,9 +9,8 @@ + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors +- of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are set +- to 1 and the elements of :math:`\beta` are set to 0. The standard-deviation is calculated +- via the biased estimator, equivalent to `torch.var(input, unbiased=False)`. ++ of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are sampled ++ from :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. + + Also by default, during training this layer keeps running estimates of its + computed mean and variance, which are then used for normalization during +@@ -27,7 +25,7 @@ + This :attr:`momentum` argument is different from one used in optimizer + classes and the conventional notion of momentum. Mathematically, the + update rule for running statistics here is +- :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, ++ :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momemtum} \times x_t`, + where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the + new observed value. + +@@ -46,10 +44,8 @@ + learnable affine parameters. Default: ``True`` + track_running_stats: a boolean value that when set to ``True``, this + module tracks the running mean and variance, and when set to ``False``, +- this module does not track such statistics, and initializes statistics +- buffers :attr:`running_mean` and :attr:`running_var` as ``None``. +- When these buffers are ``None``, this module always uses batch statistics. +- in both training and eval modes. Default: ``True`` ++ this module does not track such statistics and always uses batch ++ statistics in both training and eval modes. Default: ``True`` + + Shape: + - Input: :math:`(N, C, H, W)` +@@ -63,9 +59,14 @@ + >>> m = nn.BatchNorm2d(100, affine=False) + >>> input = torch.randn(20, 100, 35, 45) + >>> output = m(input) ++ ++ .. _`Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift`: ++ https://arxiv.org/abs/1502.03167 + """ + ++ @weak_script_method + def _check_input_dim(self, input): + if input.dim() != 4: +- raise ValueError("expected 4D input (got {}D input)".format(input.dim())) ++ raise ValueError('expected 4D input (got {}D input)' ++ .format(input.dim())) + \ No newline at end of file diff --git a/SimSwap/Conv2d.patch b/SimSwap/Conv2d.patch new file mode 100644 index 0000000..d76c114 --- /dev/null +++ b/SimSwap/Conv2d.patch @@ -0,0 +1,183 @@ +--- /usr/local/lib/python3.5/dist-packages/torch/nn/modules/conv.py ++++ /usr/local/lib/python3.5/dist-packages/torch/nn/modules/conv.py +@@ -1,5 +1,5 @@ + class Conv2d(_ConvNd): +- __doc__ = r"""Applies a 2D convolution over an input signal composed of several input ++ r"""Applies a 2D convolution over an input signal composed of several input + planes. + + In the simplest case, the output value of the layer with input size +@@ -15,22 +15,29 @@ + :math:`N` is a batch size, :math:`C` denotes a number of channels, + :math:`H` is a height of input planes in pixels, and :math:`W` is + width in pixels. +- """ + r""" +- +- This module supports :ref:`TensorFloat32`. + + * :attr:`stride` controls the stride for the cross-correlation, a single + number or a tuple. + +- * :attr:`padding` controls the amount of padding applied to the input. It +- can be either a string {{'valid', 'same'}} or a tuple of ints giving the +- amount of implicit padding applied on both sides. ++ * :attr:`padding` controls the amount of implicit zero-paddings on both ++ sides for :attr:`padding` number of points for each dimension. + + * :attr:`dilation` controls the spacing between the kernel points; also + known as the à trous algorithm. It is harder to describe, but this `link`_ + has a nice visualization of what :attr:`dilation` does. + +- {groups_note} ++ * :attr:`groups` controls the connections between inputs and outputs. ++ :attr:`in_channels` and :attr:`out_channels` must both be divisible by ++ :attr:`groups`. For example, ++ ++ * At groups=1, all inputs are convolved to all outputs. ++ * At groups=2, the operation becomes equivalent to having two conv ++ layers side by side, each seeing half the input channels, ++ and producing half the output channels, and both subsequently ++ concatenated. ++ * At groups= :attr:`in_channels`, each input channel is convolved with ++ its own set of filters, of size: ++ :math:`\left\lfloor\frac{C_\text{out}}{C_\text{in}}\right\rfloor`. + + The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be: + +@@ -38,36 +45,38 @@ + - a ``tuple`` of two ints -- in which case, the first `int` is used for the height dimension, + and the second `int` for the width dimension + +- Note: +- {depthwise_separable_note} ++ .. note:: + +- Note: +- {cudnn_reproducibility_note} ++ Depending of the size of your kernel, several (of the last) ++ columns of the input might be lost, because it is a valid `cross-correlation`_, ++ and not a full `cross-correlation`_. ++ It is up to the user to add proper padding. + +- Note: +- ``padding='valid'`` is the same as no padding. ``padding='same'`` pads +- the input so the output has the shape as the input. However, this mode +- doesn't support any stride values other than 1. ++ .. note:: ++ ++ When `groups == in_channels` and `out_channels == K * in_channels`, ++ where `K` is a positive integer, this operation is also termed in ++ literature as depthwise convolution. ++ ++ In other words, for an input of size :math:`(N, C_{in}, H_{in}, W_{in})`, ++ a depthwise convolution with a depthwise multiplier `K`, can be constructed by arguments ++ :math:`(in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})`. ++ ++ .. include:: cudnn_deterministic.rst + + Args: + in_channels (int): Number of channels in the input image + out_channels (int): Number of channels produced by the convolution + kernel_size (int or tuple): Size of the convolving kernel + stride (int or tuple, optional): Stride of the convolution. Default: 1 +- padding (int, tuple or str, optional): Padding added to all four sides of +- the input. Default: 0 +- padding_mode (string, optional): ``'zeros'``, ``'reflect'``, +- ``'replicate'`` or ``'circular'``. Default: ``'zeros'`` ++ padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 + dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 +- groups (int, optional): Number of blocked connections from input +- channels to output channels. Default: 1 +- bias (bool, optional): If ``True``, adds a learnable bias to the +- output. Default: ``True`` +- """.format(**reproducibility_notes, **convolution_notes) + r""" ++ groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 ++ bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` + + Shape: +- - Input: :math:`(N, C_{in}, H_{in}, W_{in})` or :math:`(C_{in}, H_{in}, W_{in})` +- - Output: :math:`(N, C_{out}, H_{out}, W_{out})` or :math:`(C_{out}, H_{out}, W_{out})`, where ++ - Input: :math:`(N, C_{in}, H_{in}, W_{in})` ++ - Output: :math:`(N, C_{out}, H_{out}, W_{out})` where + + .. math:: + H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[0] - \text{dilation}[0] +@@ -79,18 +88,16 @@ + + Attributes: + weight (Tensor): the learnable weights of the module of shape +- :math:`(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},` +- :math:`\text{kernel\_size[0]}, \text{kernel\_size[1]})`. +- The values of these weights are sampled from +- :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where +- :math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}` +- bias (Tensor): the learnable bias of the module of shape +- (out_channels). If :attr:`bias` is ``True``, +- then the values of these weights are +- sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where +- :math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}` ++ (out_channels, in_channels, kernel_size[0], kernel_size[1]). ++ The values of these weights are sampled from ++ :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where ++ :math:`k = \frac{1}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}` ++ bias (Tensor): the learnable bias of the module of shape (out_channels). If :attr:`bias` is ``True``, ++ then the values of these weights are ++ sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where ++ :math:`k = \frac{1}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}` + +- Examples: ++ Examples:: + + >>> # With square kernels and equal stride + >>> m = nn.Conv2d(16, 33, 3, stride=2) +@@ -107,38 +114,18 @@ + .. _link: + https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md + """ ++ def __init__(self, in_channels, out_channels, kernel_size, stride=1, ++ padding=0, dilation=1, groups=1, bias=True): ++ kernel_size = _pair(kernel_size) ++ stride = _pair(stride) ++ padding = _pair(padding) ++ dilation = _pair(dilation) ++ super(Conv2d, self).__init__( ++ in_channels, out_channels, kernel_size, stride, padding, dilation, ++ False, _pair(0), groups, bias) + +- def __init__( +- self, +- in_channels: int, +- out_channels: int, +- kernel_size: _size_2_t, +- stride: _size_2_t = 1, +- padding: Union[str, _size_2_t] = 0, +- dilation: _size_2_t = 1, +- groups: int = 1, +- bias: bool = True, +- padding_mode: str = 'zeros', # TODO: refine this type +- device=None, +- dtype=None +- ) -> None: +- factory_kwargs = {'device': device, 'dtype': dtype} +- kernel_size_ = _pair(kernel_size) +- stride_ = _pair(stride) +- padding_ = padding if isinstance(padding, str) else _pair(padding) +- dilation_ = _pair(dilation) +- super(Conv2d, self).__init__( +- in_channels, out_channels, kernel_size_, stride_, padding_, dilation_, +- False, _pair(0), groups, bias, padding_mode, **factory_kwargs) +- +- def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]): +- if self.padding_mode != 'zeros': +- return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode), +- weight, bias, self.stride, +- _pair(0), self.dilation, self.groups) +- return F.conv2d(input, weight, bias, self.stride, ++ @weak_script_method ++ def forward(self, input): ++ return F.conv2d(input, self.weight, self.bias, self.stride, + self.padding, self.dilation, self.groups) + +- def forward(self, input: Tensor) -> Tensor: +- return self._conv_forward(input, self.weight, self.bias) +- \ No newline at end of file diff --git a/SimSwap/DataParallel.patch b/SimSwap/DataParallel.patch new file mode 100644 index 0000000..da315a6 --- /dev/null +++ b/SimSwap/DataParallel.patch @@ -0,0 +1,115 @@ +--- /usr/local/lib/python3.5/dist-packages/torch/nn/parallel/data_parallel.py ++++ /usr/local/lib/python3.5/dist-packages/torch/nn/parallel/data_parallel.py +@@ -10,16 +10,13 @@ + + The batch size should be larger than the number of GPUs used. + +- .. warning:: +- It is recommended to use :class:`~torch.nn.parallel.DistributedDataParallel`, +- instead of this class, to do multi-GPU training, even if there is only a single +- node. See: :ref:`cuda-nn-ddp-instead` and :ref:`ddp`. ++ See also: :ref:`cuda-nn-dataparallel-instead` + + Arbitrary positional and keyword inputs are allowed to be passed into +- DataParallel but some types are specially handled. tensors will be +- **scattered** on dim specified (default 0). tuple, list and dict types will +- be shallow copied. The other types will be shared among different threads +- and can be corrupted if written to in the model's forward pass. ++ DataParallel EXCEPT Tensors. All tensors will be scattered on dim ++ specified (default 0). Primitive types will be broadcasted, but all ++ other types will be a shallow copy and can be corrupted if written to in ++ the model's forward pass. + + The parallelized :attr:`module` must have its parameters and buffers on + ``device_ids[0]`` before running this :class:`~torch.nn.DataParallel` +@@ -27,9 +24,9 @@ + + .. warning:: + In each forward, :attr:`module` is **replicated** on each device, so any +- updates to the running module in ``forward`` will be lost. For example, ++ updates to the runing module in ``forward`` will be lost. For example, + if :attr:`module` has a counter attribute that is incremented in each +- ``forward``, it will always stay at the initial value because the update ++ ``forward``, it will always stay at the initial value becasue the update + is done on the replicas which are destroyed after ``forward``. However, + :class:`~torch.nn.DataParallel` guarantees that the replica on + ``device[0]`` will have its parameters and buffers sharing storage with +@@ -74,63 +71,46 @@ + Example:: + + >>> net = torch.nn.DataParallel(model, device_ids=[0, 1, 2]) +- >>> output = net(input_var) # input_var can be on any device, including CPU ++ >>> output = net(input_var) + """ + + # TODO: update notes/cuda.rst when this class handles 8+ GPUs well + + def __init__(self, module, device_ids=None, output_device=None, dim=0): + super(DataParallel, self).__init__() +- torch._C._log_api_usage_once("torch.nn.parallel.DataParallel") +- device_type = _get_available_device_type() +- if device_type is None: ++ ++ if not torch.cuda.is_available(): + self.module = module + self.device_ids = [] + return + + if device_ids is None: +- device_ids = _get_all_device_indices() +- ++ device_ids = list(range(torch.cuda.device_count())) + if output_device is None: + output_device = device_ids[0] + + self.dim = dim + self.module = module +- self.device_ids = [_get_device_index(x, True) for x in device_ids] ++ self.device_ids = list(map(lambda x: _get_device_index(x, True), device_ids)) + self.output_device = _get_device_index(output_device, True) +- self.src_device_obj = torch.device(device_type, self.device_ids[0]) + + _check_balance(self.device_ids) + + if len(self.device_ids) == 1: +- self.module.to(self.src_device_obj) ++ self.module.cuda(device_ids[0]) + + def forward(self, *inputs, **kwargs): +- with torch.autograd.profiler.record_function("DataParallel.forward"): +- if not self.device_ids: +- return self.module(*inputs, **kwargs) +- +- for t in chain(self.module.parameters(), self.module.buffers()): +- if t.device != self.src_device_obj: +- raise RuntimeError("module must have its parameters and buffers " +- "on device {} (device_ids[0]) but found one of " +- "them on device: {}".format(self.src_device_obj, t.device)) +- +- inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) +- # for forward function without any inputs, empty list and dict will be created +- # so the module can be executed on one device which is the first one in device_ids +- if not inputs and not kwargs: +- inputs = ((),) +- kwargs = ({},) +- +- if len(self.device_ids) == 1: +- return self.module(*inputs[0], **kwargs[0]) +- replicas = self.replicate(self.module, self.device_ids[:len(inputs)]) +- outputs = self.parallel_apply(replicas, inputs, kwargs) +- return self.gather(outputs, self.output_device) ++ if not self.device_ids: ++ return self.module(*inputs, **kwargs) ++ inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) ++ if len(self.device_ids) == 1: ++ return self.module(*inputs[0], **kwargs[0]) ++ replicas = self.replicate(self.module, self.device_ids[:len(inputs)]) ++ outputs = self.parallel_apply(replicas, inputs, kwargs) ++ return self.gather(outputs, self.output_device) + + def replicate(self, module, device_ids): +- return replicate(module, device_ids, not torch.is_grad_enabled()) ++ return replicate(module, device_ids) + + def scatter(self, inputs, kwargs, device_ids): + return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim) \ No newline at end of file diff --git a/SimSwap/Dockerfile b/SimSwap/Dockerfile new file mode 100644 index 0000000..f0400e0 --- /dev/null +++ b/SimSwap/Dockerfile @@ -0,0 +1,14 @@ +FROM ubuntu:latest + +RUN apt update +RUN apt install numpy +RUN apt install torch +RUN apt install opencv-python +RUN apt install models +RUN apt install util +RUN apt install torchviion +RUN apt install insightface_func +RUN apt install options +RUN apt install fractions + +COPY . . \ No newline at end of file diff --git a/SimSwap/Dropout.patch b/SimSwap/Dropout.patch new file mode 100644 index 0000000..e4345bf --- /dev/null +++ b/SimSwap/Dropout.patch @@ -0,0 +1,22 @@ +--- /usr/local/lib/python3.5/dist-packages/torch/nn/modules/dropout.py ++++ /usr/local/lib/python3.5/dist-packages/torch/nn/modules/dropout.py +@@ -18,8 +18,8 @@ + inplace: If set to ``True``, will do this operation in-place. Default: ``False`` + + Shape: +- - Input: :math:`(*)`. Input can be of any shape +- - Output: :math:`(*)`. Output is of the same shape as input ++ - Input: `Any`. Input can be of any shape ++ - Output: `Same`. Output is of the same shape as input + + Examples:: + +@@ -31,6 +31,7 @@ + detectors: https://arxiv.org/abs/1207.0580 + """ + +- def forward(self, input: Tensor) -> Tensor: ++ @weak_script_method ++ def forward(self, input): + return F.dropout(input, self.p, self.training, self.inplace) + \ No newline at end of file diff --git a/SimSwap/F b/SimSwap/F new file mode 100644 index 0000000..4bcfe98 --- /dev/null +++ b/SimSwap/F @@ -0,0 +1 @@ +d diff --git a/SimSwap/LICENSE b/SimSwap/LICENSE new file mode 100644 index 0000000..f2915d9 --- /dev/null +++ b/SimSwap/LICENSE @@ -0,0 +1,399 @@ +Attribution-NonCommercial 4.0 International + +======================================================================= + +Creative Commons Corporation ("Creative Commons") is not a law firm and +does not provide legal services or legal advice. Distribution of +Creative Commons public licenses does not create a lawyer-client or +other relationship. Creative Commons makes its licenses and related +information available on an "as-is" basis. Creative Commons gives no +warranties regarding its licenses, any material licensed under their +terms and conditions, or any related information. Creative Commons +disclaims all liability for damages resulting from their use to the +fullest extent possible. + +Using Creative Commons Public Licenses + +Creative Commons public licenses provide a standard set of terms and +conditions that creators and other rights holders may use to share +original works of authorship and other material subject to copyright +and certain other rights specified in the public license below. The +following considerations are for informational purposes only, are not +exhaustive, and do not form part of our licenses. + + Considerations for licensors: Our public licenses are + intended for use by those authorized to give the public + permission to use material in ways otherwise restricted by + copyright and certain other rights. Our licenses are + irrevocable. Licensors should read and understand the terms + and conditions of the license they choose before applying it. + Licensors should also secure all rights necessary before + applying our licenses so that the public can reuse the + material as expected. Licensors should clearly mark any + material not subject to the license. This includes other CC- + licensed material, or material used under an exception or + limitation to copyright. More considerations for licensors: + wiki.creativecommons.org/Considerations_for_licensors + + Considerations for the public: By using one of our public + licenses, a licensor grants the public permission to use the + licensed material under specified terms and conditions. If + the licensor's permission is not necessary for any reason--for + example, because of any applicable exception or limitation to + copyright--then that use is not regulated by the license. Our + licenses grant only permissions under copyright and certain + other rights that a licensor has authority to grant. Use of + the licensed material may still be restricted for other + reasons, including because others have copyright or other + rights in the material. A licensor may make special requests, + such as asking that all changes be marked or described. + Although not required by our licenses, you are encouraged to + respect those requests where reasonable. More_considerations + for the public: + wiki.creativecommons.org/Considerations_for_licensees + +======================================================================= + +Creative Commons Attribution-NonCommercial 4.0 International Public +License + +By exercising the Licensed Rights (defined below), You accept and agree +to be bound by the terms and conditions of this Creative Commons +Attribution-NonCommercial 4.0 International Public License ("Public +License"). To the extent this Public License may be interpreted as a +contract, You are granted the Licensed Rights in consideration of Your +acceptance of these terms and conditions, and the Licensor grants You +such rights in consideration of benefits the Licensor receives from +making the Licensed Material available under these terms and +conditions. + +Section 1 -- Definitions. + + a. Adapted Material means material subject to Copyright and Similar + Rights that is derived from or based upon the Licensed Material + and in which the Licensed Material is translated, altered, + arranged, transformed, or otherwise modified in a manner requiring + permission under the Copyright and Similar Rights held by the + Licensor. For purposes of this Public License, where the Licensed + Material is a musical work, performance, or sound recording, + Adapted Material is always produced where the Licensed Material is + synched in timed relation with a moving image. + + b. Adapter's License means the license You apply to Your Copyright + and Similar Rights in Your contributions to Adapted Material in + accordance with the terms and conditions of this Public License. + + c. Copyright and Similar Rights means copyright and/or similar rights + closely related to copyright including, without limitation, + performance, broadcast, sound recording, and Sui Generis Database + Rights, without regard to how the rights are labeled or + categorized. For purposes of this Public License, the rights + specified in Section 2(b)(1)-(2) are not Copyright and Similar + Rights. + d. Effective Technological Measures means those measures that, in the + absence of proper authority, may not be circumvented under laws + fulfilling obligations under Article 11 of the WIPO Copyright + Treaty adopted on December 20, 1996, and/or similar international + agreements. + + e. Exceptions and Limitations means fair use, fair dealing, and/or + any other exception or limitation to Copyright and Similar Rights + that applies to Your use of the Licensed Material. + + f. Licensed Material means the artistic or literary work, database, + or other material to which the Licensor applied this Public + License. + + g. Licensed Rights means the rights granted to You subject to the + terms and conditions of this Public License, which are limited to + all Copyright and Similar Rights that apply to Your use of the + Licensed Material and that the Licensor has authority to license. + + h. Licensor means the individual(s) or entity(ies) granting rights + under this Public License. + + i. NonCommercial means not primarily intended for or directed towards + commercial advantage or monetary compensation. For purposes of + this Public License, the exchange of the Licensed Material for + other material subject to Copyright and Similar Rights by digital + file-sharing or similar means is NonCommercial provided there is + no payment of monetary compensation in connection with the + exchange. + + j. Share means to provide material to the public by any means or + process that requires permission under the Licensed Rights, such + as reproduction, public display, public performance, distribution, + dissemination, communication, or importation, and to make material + available to the public including in ways that members of the + public may access the material from a place and at a time + individually chosen by them. + + k. Sui Generis Database Rights means rights other than copyright + resulting from Directive 96/9/EC of the European Parliament and of + the Council of 11 March 1996 on the legal protection of databases, + as amended and/or succeeded, as well as other essentially + equivalent rights anywhere in the world. + + l. You means the individual or entity exercising the Licensed Rights + under this Public License. Your has a corresponding meaning. + +Section 2 -- Scope. + + a. License grant. + + 1. Subject to the terms and conditions of this Public License, + the Licensor hereby grants You a worldwide, royalty-free, + non-sublicensable, non-exclusive, irrevocable license to + exercise the Licensed Rights in the Licensed Material to: + + a. reproduce and Share the Licensed Material, in whole or + in part, for NonCommercial purposes only; and + + b. produce, reproduce, and Share Adapted Material for + NonCommercial purposes only. + + 2. Exceptions and Limitations. For the avoidance of doubt, where + Exceptions and Limitations apply to Your use, this Public + License does not apply, and You do not need to comply with + its terms and conditions. + + 3. Term. The term of this Public License is specified in Section + 6(a). + + 4. Media and formats; technical modifications allowed. The + Licensor authorizes You to exercise the Licensed Rights in + all media and formats whether now known or hereafter created, + and to make technical modifications necessary to do so. The + Licensor waives and/or agrees not to assert any right or + authority to forbid You from making technical modifications + necessary to exercise the Licensed Rights, including + technical modifications necessary to circumvent Effective + Technological Measures. For purposes of this Public License, + simply making modifications authorized by this Section 2(a) + (4) never produces Adapted Material. + + 5. Downstream recipients. + + a. Offer from the Licensor -- Licensed Material. Every + recipient of the Licensed Material automatically + receives an offer from the Licensor to exercise the + Licensed Rights under the terms and conditions of this + Public License. + + b. No downstream restrictions. You may not offer or impose + any additional or different terms or conditions on, or + apply any Effective Technological Measures to, the + Licensed Material if doing so restricts exercise of the + Licensed Rights by any recipient of the Licensed + Material. + + 6. No endorsement. Nothing in this Public License constitutes or + may be construed as permission to assert or imply that You + are, or that Your use of the Licensed Material is, connected + with, or sponsored, endorsed, or granted official status by, + the Licensor or others designated to receive attribution as + provided in Section 3(a)(1)(A)(i). + + b. Other rights. + + 1. Moral rights, such as the right of integrity, are not + licensed under this Public License, nor are publicity, + privacy, and/or other similar personality rights; however, to + the extent possible, the Licensor waives and/or agrees not to + assert any such rights held by the Licensor to the limited + extent necessary to allow You to exercise the Licensed + Rights, but not otherwise. + + 2. Patent and trademark rights are not licensed under this + Public License. + + 3. To the extent possible, the Licensor waives any right to + collect royalties from You for the exercise of the Licensed + Rights, whether directly or through a collecting society + under any voluntary or waivable statutory or compulsory + licensing scheme. In all other cases the Licensor expressly + reserves any right to collect such royalties, including when + the Licensed Material is used other than for NonCommercial + purposes. + +Section 3 -- License Conditions. + +Your exercise of the Licensed Rights is expressly made subject to the +following conditions. + + a. Attribution. + + 1. If You Share the Licensed Material (including in modified + form), You must: + + a. retain the following if it is supplied by the Licensor + with the Licensed Material: + + i. identification of the creator(s) of the Licensed + Material and any others designated to receive + attribution, in any reasonable manner requested by + the Licensor (including by pseudonym if + designated); + + ii. a copyright notice; + + iii. a notice that refers to this Public License; + + iv. a notice that refers to the disclaimer of + warranties; + + v. a URI or hyperlink to the Licensed Material to the + extent reasonably practicable; + + b. indicate if You modified the Licensed Material and + retain an indication of any previous modifications; and + + c. indicate the Licensed Material is licensed under this + Public License, and include the text of, or the URI or + hyperlink to, this Public License. + + 2. You may satisfy the conditions in Section 3(a)(1) in any + reasonable manner based on the medium, means, and context in + which You Share the Licensed Material. For example, it may be + reasonable to satisfy the conditions by providing a URI or + hyperlink to a resource that includes the required + information. + + 3. If requested by the Licensor, You must remove any of the + information required by Section 3(a)(1)(A) to the extent + reasonably practicable. + + 4. If You Share Adapted Material You produce, the Adapter's + License You apply must not prevent recipients of the Adapted + Material from complying with this Public License. + +Section 4 -- Sui Generis Database Rights. + +Where the Licensed Rights include Sui Generis Database Rights that +apply to Your use of the Licensed Material: + + a. for the avoidance of doubt, Section 2(a)(1) grants You the right + to extract, reuse, reproduce, and Share all or a substantial + portion of the contents of the database for NonCommercial purposes + only; + + b. if You include all or a substantial portion of the database + contents in a database in which You have Sui Generis Database + Rights, then the database in which You have Sui Generis Database + Rights (but not its individual contents) is Adapted Material; and + + c. You must comply with the conditions in Section 3(a) if You Share + all or a substantial portion of the contents of the database. + +For the avoidance of doubt, this Section 4 supplements and does not +replace Your obligations under this Public License where the Licensed +Rights include other Copyright and Similar Rights. + +Section 5 -- Disclaimer of Warranties and Limitation of Liability. + + a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE + EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS + AND AS-AVAILABLE, AND MAKES NO REPRESENTATIONS OR WARRANTIES OF + ANY KIND CONCERNING THE LICENSED MATERIAL, WHETHER EXPRESS, + IMPLIED, STATUTORY, OR OTHER. THIS INCLUDES, WITHOUT LIMITATION, + WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR + PURPOSE, NON-INFRINGEMENT, ABSENCE OF LATENT OR OTHER DEFECTS, + ACCURACY, OR THE PRESENCE OR ABSENCE OF ERRORS, WHETHER OR NOT + KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT + ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU. + + b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE + TO YOU ON ANY LEGAL THEORY (INCLUDING, WITHOUT LIMITATION, + NEGLIGENCE) OR OTHERWISE FOR ANY DIRECT, SPECIAL, INDIRECT, + INCIDENTAL, CONSEQUENTIAL, PUNITIVE, EXEMPLARY, OR OTHER LOSSES, + COSTS, EXPENSES, OR DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR + USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN + ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR + DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR + IN PART, THIS LIMITATION MAY NOT APPLY TO YOU. + + c. The disclaimer of warranties and limitation of liability provided + above shall be interpreted in a manner that, to the extent + possible, most closely approximates an absolute disclaimer and + waiver of all liability. + +Section 6 -- Term and Termination. + + a. This Public License applies for the term of the Copyright and + Similar Rights licensed here. However, if You fail to comply with + this Public License, then Your rights under this Public License + terminate automatically. + + b. Where Your right to use the Licensed Material has terminated under + Section 6(a), it reinstates: + + 1. automatically as of the date the violation is cured, provided + it is cured within 30 days of Your discovery of the + violation; or + + 2. upon express reinstatement by the Licensor. + + For the avoidance of doubt, this Section 6(b) does not affect any + right the Licensor may have to seek remedies for Your violations + of this Public License. + + c. For the avoidance of doubt, the Licensor may also offer the + Licensed Material under separate terms or conditions or stop + distributing the Licensed Material at any time; however, doing so + will not terminate this Public License. + + d. Sections 1, 5, 6, 7, and 8 survive termination of this Public + License. + +Section 7 -- Other Terms and Conditions. + + a. The Licensor shall not be bound by any additional or different + terms or conditions communicated by You unless expressly agreed. + + b. Any arrangements, understandings, or agreements regarding the + Licensed Material not stated herein are separate from and + independent of the terms and conditions of this Public License. + +Section 8 -- Interpretation. + + a. For the avoidance of doubt, this Public License does not, and + shall not be interpreted to, reduce, limit, restrict, or impose + conditions on any use of the Licensed Material that could lawfully + be made without permission under this Public License. + + b. To the extent possible, if any provision of this Public License is + deemed unenforceable, it shall be automatically reformed to the + minimum extent necessary to make it enforceable. If the provision + cannot be reformed, it shall be severed from this Public License + without affecting the enforceability of the remaining terms and + conditions. + + c. No term or condition of this Public License will be waived and no + failure to comply consented to unless expressly agreed to by the + Licensor. + + d. Nothing in this Public License constitutes or may be interpreted + as a limitation upon, or waiver of, any privileges and immunities + that apply to the Licensor or You, including from the legal + processes of any jurisdiction or authority. + +======================================================================= + +Creative Commons is not a party to its public +licenses. Notwithstanding, Creative Commons may elect to apply one of +its public licenses to material it publishes and in those instances +will be considered the “Licensor.” The text of the Creative Commons +public licenses is dedicated to the public domain under the CC0 Public +Domain Dedication. Except for the limited purpose of indicating that +material is shared under a Creative Commons public license or as +otherwise permitted by the Creative Commons policies published at +creativecommons.org/policies, Creative Commons does not authorize the +use of the trademark "Creative Commons" or any other trademark or logo +of Creative Commons without its prior written consent including, +without limitation, in connection with any unauthorized modifications +to any of its public licenses or any other arrangements, +understandings, or agreements concerning use of licensed material. For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. diff --git a/SimSwap/Linear.patch b/SimSwap/Linear.patch new file mode 100644 index 0000000..7dbf9e3 --- /dev/null +++ b/SimSwap/Linear.patch @@ -0,0 +1,75 @@ +--- /usr/local/lib/python3.5/dist-packages/torch/nn/modules/linear.py ++++ /usr/local/lib/python3.5/dist-packages/torch/nn/modules/linear.py +@@ -1,19 +1,17 @@ + class Linear(Module): + r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b` +- +- This module supports :ref:`TensorFloat32`. + + Args: + in_features: size of each input sample + out_features: size of each output sample +- bias: If set to ``False``, the layer will not learn an additive bias. ++ bias: If set to False, the layer will not learn an additive bias. + Default: ``True`` + + Shape: +- - Input: :math:`(*, H_{in})` where :math:`*` means any number of +- dimensions including none and :math:`H_{in} = \text{in\_features}`. +- - Output: :math:`(*, H_{out})` where all but the last dimension +- are the same shape as the input and :math:`H_{out} = \text{out\_features}`. ++ - Input: :math:`(N, *, \text{in\_features})` where :math:`*` means any number of ++ additional dimensions ++ - Output: :math:`(N, *, \text{out\_features})` where all but the last dimension ++ are the same shape as the input. + + Attributes: + weight: the learnable weights of the module of shape +@@ -33,38 +31,31 @@ + >>> print(output.size()) + torch.Size([128, 30]) + """ +- __constants__ = ['in_features', 'out_features'] +- in_features: int +- out_features: int +- weight: Tensor ++ __constants__ = ['bias'] + +- def __init__(self, in_features: int, out_features: int, bias: bool = True, +- device=None, dtype=None) -> None: +- factory_kwargs = {'device': device, 'dtype': dtype} ++ def __init__(self, in_features, out_features, bias=True): + super(Linear, self).__init__() + self.in_features = in_features + self.out_features = out_features +- self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs)) ++ self.weight = Parameter(torch.Tensor(out_features, in_features)) + if bias: +- self.bias = Parameter(torch.empty(out_features, **factory_kwargs)) ++ self.bias = Parameter(torch.Tensor(out_features)) + else: + self.register_parameter('bias', None) + self.reset_parameters() + +- def reset_parameters(self) -> None: +- # Setting a=sqrt(5) in kaiming_uniform is the same as initializing with +- # uniform(-1/sqrt(in_features), 1/sqrt(in_features)). For details, see +- # https://github.com/pytorch/pytorch/issues/57109 ++ def reset_parameters(self): + init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + if self.bias is not None: + fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight) +- bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 ++ bound = 1 / math.sqrt(fan_in) + init.uniform_(self.bias, -bound, bound) + +- def forward(self, input: Tensor) -> Tensor: ++ @weak_script_method ++ def forward(self, input): + return F.linear(input, self.weight, self.bias) + +- def extra_repr(self) -> str: ++ def extra_repr(self): + return 'in_features={}, out_features={}, bias={}'.format( + self.in_features, self.out_features, self.bias is not None + ) \ No newline at end of file diff --git a/SimSwap/MaxPool2d.patch b/SimSwap/MaxPool2d.patch new file mode 100644 index 0000000..bd8b9fb --- /dev/null +++ b/SimSwap/MaxPool2d.patch @@ -0,0 +1,43 @@ +--- /usr/local/lib/python3.5/dist-packages/torch/nn/modules/pooling.py ++++ /usr/local/lib/python3.5/dist-packages/torch/nn/modules/pooling.py +@@ -13,13 +13,9 @@ + \text{stride[1]} \times w + n) + \end{aligned} + +- If :attr:`padding` is non-zero, then the input is implicitly padded with negative infinity on both sides ++ If :attr:`padding` is non-zero, then the input is implicitly zero-padded on both sides + for :attr:`padding` number of points. :attr:`dilation` controls the spacing between the kernel points. + It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. +- +- Note: +- When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding +- or the input. Sliding windows that would start in the right padded region are ignored. + + The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be: + +@@ -37,8 +33,8 @@ + ceil_mode: when True, will use `ceil` instead of `floor` to compute the output shape + + Shape: +- - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})` +- - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where ++ - Input: :math:`(N, C, H_{in}, W_{in})` ++ - Output: :math:`(N, C, H_{out}, W_{out})`, where + + .. math:: + H_{out} = \left\lfloor\frac{H_{in} + 2 * \text{padding[0]} - \text{dilation[0]} +@@ -61,12 +57,8 @@ + https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md + """ + +- kernel_size: _size_2_t +- stride: _size_2_t +- padding: _size_2_t +- dilation: _size_2_t +- +- def forward(self, input: Tensor): ++ @weak_script_method ++ def forward(self, input): + return F.max_pool2d(input, self.kernel_size, self.stride, + self.padding, self.dilation, self.ceil_mode, + self.return_indices) \ No newline at end of file diff --git a/SimSwap/MultiSpecific.ipynb b/SimSwap/MultiSpecific.ipynb new file mode 100644 index 0000000..ef13d1c --- /dev/null +++ b/SimSwap/MultiSpecific.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"MultiSpecific.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyNw8SfPWhG77cf/e7YZd178"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"7_gtFoV8BuRx"},"source":["This is an example of SimSwap on processing video with multiple faces with designated sources.\n","\n","Code path: https://github.com/neuralchen/SimSwap\n","Paper path: https://arxiv.org/pdf/2106.06340v1.pdf."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0Y1RfpzsCAl9","executionInfo":{"status":"ok","timestamp":1625380781426,"user_tz":-480,"elapsed":586,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"2a897b34-72f1-4515-ac6f-2f0e2d4ea4f7"},"source":["## make sure you are using a runtime with GPU\n","## you can check at Runtime/Change runtime type in the top bar.\n","!nvidia-smi"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Sun Jul 4 06:39:39 2021 \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 465.27 Driver Version: 460.32.03 CUDA Version: 11.2 |\n","|-------------------------------+----------------------+----------------------+\n","| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n","| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n","| | | MIG M. |\n","|===============================+======================+======================|\n","| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n","| N/A 45C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |\n","| | | N/A |\n","+-------------------------------+----------------------+----------------------+\n"," \n","+-----------------------------------------------------------------------------+\n","| Processes: |\n","| GPU GI CI PID Type Process name GPU Memory |\n","| ID ID Usage |\n","|=============================================================================|\n","| No running processes found |\n","+-----------------------------------------------------------------------------+\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"0Qzzx2UpDkqw"},"source":["All file changes make by this notebook are temporary. \n","You can try to mount your own google drive to store files if you wang.\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VA_4CeWZCHLP","executionInfo":{"status":"ok","timestamp":1625380786661,"user_tz":-480,"elapsed":4693,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"d0665552-be92-45b0-aab2-f84c619a51fb"},"source":["!git clone https://github.com/neuralchen/SimSwap\n","!cd SimSwap && git pull"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Cloning into 'SimSwap'...\n","remote: Enumerating objects: 667, done.\u001b[K\n","remote: Counting objects: 100% (48/48), done.\u001b[K\n","remote: Compressing objects: 100% (35/35), done.\u001b[K\n","remote: Total 667 (delta 19), reused 28 (delta 13), pack-reused 619\u001b[K\n","Receiving objects: 100% (667/667), 132.14 MiB | 44.44 MiB/s, done.\n","Resolving deltas: 100% (292/292), done.\n","Already up to date.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Y5K4au_UCkKn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625380797906,"user_tz":-480,"elapsed":11253,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"7429f153-bc6d-48c2-eb3c-21f1f02fede9"},"source":["!pip install insightface==0.2.1 onnxruntime moviepy\n","!pip install googledrivedownloader\n","!pip install imageio==2.4.1"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Collecting insightface==0.2.1\n"," Downloading https://files.pythonhosted.org/packages/ee/1e/6395bbe0db665f187c8e49266cda54fcf661f182192370d409423e4943e4/insightface-0.2.1-py2.py3-none-any.whl\n","Collecting onnxruntime\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/f9/76/3d0f8bb2776961c7335693df06eccf8d099e48fa6fb552c7546867192603/onnxruntime-1.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5MB)\n","\u001b[K |████████████████████████████████| 4.5MB 37.5MB/s \n","\u001b[?25hRequirement already satisfied: moviepy in /usr/local/lib/python3.7/dist-packages (0.2.3.5)\n","Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (0.22.2.post1)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (2.23.0)\n","Collecting onnx\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/3f/9b/54c950d3256e27f970a83cd0504efb183a24312702deed0179453316dbd0/onnx-1.9.0-cp37-cp37m-manylinux2010_x86_64.whl (12.2MB)\n","\u001b[K |████████████████████████████████| 12.2MB 32.2MB/s \n","\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (3.2.2)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (7.1.2)\n","Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (0.16.2)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (4.1.2.30)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (4.41.1)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.4.1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.19.5)\n","Requirement already satisfied: easydict in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.9)\n","Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.12)\n","Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (3.12.4)\n","Requirement already satisfied: imageio<3.0,>=2.1.2 in /usr/local/lib/python3.7/dist-packages (from moviepy) (2.4.1)\n","Requirement already satisfied: decorator<5.0,>=4.0.2 in /usr/local/lib/python3.7/dist-packages (from moviepy) (4.4.2)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->insightface==0.2.1) (1.0.1)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (3.0.4)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (2021.5.30)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (2.10)\n","Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx->insightface==0.2.1) (3.7.4.3)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from onnx->insightface==0.2.1) (1.15.0)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (2.8.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (0.10.0)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (2.4.7)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (1.3.1)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->insightface==0.2.1) (2.5.1)\n","Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->insightface==0.2.1) (1.1.1)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf->onnxruntime) (57.0.0)\n","Installing collected packages: onnx, insightface, onnxruntime\n","Successfully installed insightface-0.2.1 onnx-1.9.0 onnxruntime-1.8.0\n","Requirement already satisfied: googledrivedownloader in /usr/local/lib/python3.7/dist-packages (0.4)\n","Requirement already satisfied: imageio==2.4.1 in /usr/local/lib/python3.7/dist-packages (2.4.1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from imageio==2.4.1) (1.19.5)\n","Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from imageio==2.4.1) (7.1.2)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gQ7ZoIbLFCye","executionInfo":{"status":"ok","timestamp":1625380798405,"user_tz":-480,"elapsed":533,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"8448a0a3-a19e-44c2-a044-f4d3f9152e91"},"source":["import os\n","os.chdir(\"SimSwap\")\n","!ls"],"execution_count":4,"outputs":[{"output_type":"stream","text":[" crop_224\t simswaplogo\n"," data\t\t test_one_image.py\n"," demo_file\t test_video_swapmulti.py\n"," docs\t\t test_video_swap_multispecific.py\n"," insightface_func test_video_swapsingle.py\n"," LICENSE\t test_video_swapspecific.py\n"," models\t\t test_wholeimage_swapmulti.py\n"," options\t test_wholeimage_swap_multispecific.py\n"," output\t\t test_wholeimage_swapsingle.py\n"," README.md\t test_wholeimage_swapspecific.py\n","'SimSwap colab.ipynb' util\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ZvGp-p0nOmKE","executionInfo":{"status":"ok","timestamp":1625380798407,"user_tz":-480,"elapsed":17,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}}},"source":["## You can upload filed manually\n","# from google.colab import drive\n","# drive.mount('/content/gdrive')"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gLti1J0pEFjJ","executionInfo":{"status":"ok","timestamp":1625380813268,"user_tz":-480,"elapsed":14876,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"99dc9306-9b9a-475d-cc7d-a3f423bd1e81"},"source":["from google_drive_downloader import GoogleDriveDownloader\n","\n","### it seems that google drive link may not be permenant, you can find this ID from our open url.\n","# GoogleDriveDownloader.download_file_from_google_drive(file_id='1TLNdIufzwesDbyr_nVTR7Zrx9oRHLM_N',\n","# dest_path='./arcface_model/arcface_checkpoint.tar')\n","# GoogleDriveDownloader.download_file_from_google_drive(file_id='1PXkRiBUYbu1xWpQyDEJvGKeqqUFthJcI',\n","# dest_path='./checkpoints.zip')\n","\n","!wget -P ./arcface_model https://github.com/neuralchen/SimSwap/releases/download/1.0/arcface_checkpoint.tar\n","!wget https://github.com/neuralchen/SimSwap/releases/download/1.0/checkpoints.zip\n","!unzip ./checkpoints.zip -d ./checkpoints\n","!wget -P ./parsing_model/checkpoint https://github.com/neuralchen/SimSwap/releases/download/1.0/79999_iter.pth"],"execution_count":6,"outputs":[{"output_type":"stream","text":["--2021-07-04 06:39:56-- https://github.com/neuralchen/SimSwap/releases/download/1.0/arcface_checkpoint.tar\n","Resolving github.com (github.com)... 140.82.114.3\n","Connecting to github.com (github.com)|140.82.114.3|:443... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://github-releases.githubusercontent.com/374891081/e17b9d00-dcb8-11eb-8c4f-1412bcea78a6?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210704%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210704T063956Z&X-Amz-Expires=300&X-Amz-Signature=b6d431c65405e894ddc994061c5fe8fe87db4e71e702513aec01f398a1004825&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=374891081&response-content-disposition=attachment%3B%20filename%3Darcface_checkpoint.tar&response-content-type=application%2Foctet-stream [following]\n","--2021-07-04 06:39:56-- https://github-releases.githubusercontent.com/374891081/e17b9d00-dcb8-11eb-8c4f-1412bcea78a6?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210704%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210704T063956Z&X-Amz-Expires=300&X-Amz-Signature=b6d431c65405e894ddc994061c5fe8fe87db4e71e702513aec01f398a1004825&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=374891081&response-content-disposition=attachment%3B%20filename%3Darcface_checkpoint.tar&response-content-type=application%2Foctet-stream\n","Resolving github-releases.githubusercontent.com (github-releases.githubusercontent.com)... 185.199.108.154, 185.199.109.154, 185.199.110.154, ...\n","Connecting to github-releases.githubusercontent.com (github-releases.githubusercontent.com)|185.199.108.154|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 766871429 (731M) [application/octet-stream]\n","Saving to: ‘./arcface_model/arcface_checkpoint.tar’\n","\n","arcface_checkpoint. 100%[===================>] 731.34M 64.4MB/s in 11s \n","\n","2021-07-04 06:40:07 (68.4 MB/s) - ‘./arcface_model/arcface_checkpoint.tar’ saved [766871429/766871429]\n","\n","--2021-07-04 06:40:07-- https://github.com/neuralchen/SimSwap/releases/download/1.0/checkpoints.zip\n","Resolving github.com (github.com)... 140.82.113.3\n","Connecting to github.com (github.com)|140.82.113.3|:443... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://github-releases.githubusercontent.com/374891081/a8dac400-dcb6-11eb-933f-977cd7f5f554?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210704%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210704T063831Z&X-Amz-Expires=300&X-Amz-Signature=3fd2850d03abb9301bf5ba5969d82eb73cb0b940b85e45de2e1e34f1ba2eaf09&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=374891081&response-content-disposition=attachment%3B%20filename%3Dcheckpoints.zip&response-content-type=application%2Foctet-stream [following]\n","--2021-07-04 06:40:07-- https://github-releases.githubusercontent.com/374891081/a8dac400-dcb6-11eb-933f-977cd7f5f554?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210704%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210704T063831Z&X-Amz-Expires=300&X-Amz-Signature=3fd2850d03abb9301bf5ba5969d82eb73cb0b940b85e45de2e1e34f1ba2eaf09&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=374891081&response-content-disposition=attachment%3B%20filename%3Dcheckpoints.zip&response-content-type=application%2Foctet-stream\n","Resolving github-releases.githubusercontent.com (github-releases.githubusercontent.com)... 185.199.108.154, 185.199.109.154, 185.199.110.154, ...\n","Connecting to github-releases.githubusercontent.com (github-releases.githubusercontent.com)|185.199.108.154|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 256461775 (245M) [application/octet-stream]\n","Saving to: ‘checkpoints.zip’\n","\n","checkpoints.zip 100%[===================>] 244.58M 219MB/s in 1.1s \n","\n","2021-07-04 06:40:08 (219 MB/s) - ‘checkpoints.zip’ saved [256461775/256461775]\n","\n","Archive: ./checkpoints.zip\n"," creating: ./checkpoints/people/\n"," inflating: ./checkpoints/people/iter.txt \n"," inflating: ./checkpoints/people/latest_net_D1.pth \n"," inflating: ./checkpoints/people/latest_net_D2.pth \n"," inflating: ./checkpoints/people/latest_net_G.pth \n"," inflating: ./checkpoints/people/loss_log.txt \n"," inflating: ./checkpoints/people/opt.txt \n"," creating: ./checkpoints/people/web/\n"," creating: ./checkpoints/people/web/images/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fJ9DYRrCPIUL","executionInfo":{"status":"ok","timestamp":1625380821122,"user_tz":-480,"elapsed":7869,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"a3d1d841-440c-4244-8045-cb0ce3cc81fd"},"source":["!wget --no-check-certificate \"https://sh23tw.dm.files.1drv.com/y4mmGiIkNVigkSwOKDcV3nwMJulRGhbtHdkheehR5TArc52UjudUYNXAEvKCii2O5LAmzGCGK6IfleocxuDeoKxDZkNzDRSt4ZUlEt8GlSOpCXAFEkBwaZimtWGDRbpIGpb_pz9Nq5jATBQpezBS6G_UtspWTkgrXHHxhviV2nWy8APPx134zOZrUIbkSF6xnsqzs3uZ_SEX_m9Rey0ykpx9w\" -O antelope.zip\n","!unzip ./antelope.zip -d ./insightface_func/models/"],"execution_count":7,"outputs":[{"output_type":"stream","text":["--2021-07-04 06:40:11-- https://sh23tw.dm.files.1drv.com/y4mmGiIkNVigkSwOKDcV3nwMJulRGhbtHdkheehR5TArc52UjudUYNXAEvKCii2O5LAmzGCGK6IfleocxuDeoKxDZkNzDRSt4ZUlEt8GlSOpCXAFEkBwaZimtWGDRbpIGpb_pz9Nq5jATBQpezBS6G_UtspWTkgrXHHxhviV2nWy8APPx134zOZrUIbkSF6xnsqzs3uZ_SEX_m9Rey0ykpx9w\n","Resolving sh23tw.dm.files.1drv.com (sh23tw.dm.files.1drv.com)... 13.107.42.12\n","Connecting to sh23tw.dm.files.1drv.com (sh23tw.dm.files.1drv.com)|13.107.42.12|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 248024513 (237M) [application/zip]\n","Saving to: ‘antelope.zip’\n","\n","antelope.zip 100%[===================>] 236.53M 52.4MB/s in 4.7s \n","\n","2021-07-04 06:40:16 (49.9 MB/s) - ‘antelope.zip’ saved [248024513/248024513]\n","\n","Archive: ./antelope.zip\n"," creating: ./insightface_func/models/antelope/\n"," inflating: ./insightface_func/models/antelope/glintr100.onnx \n"," inflating: ./insightface_func/models/antelope/scrfd_10g_bnkps.onnx \n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"PfSsND36EMvn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625380827902,"user_tz":-480,"elapsed":6811,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"8130e97d-b4a0-4988-85fb-e2bc3e755259"},"source":["import cv2\n","import torch\n","import fractions\n","import numpy as np\n","from PIL import Image\n","import torch.nn.functional as F\n","from torchvision import transforms\n","from models.models import create_model\n","from options.test_options import TestOptions\n","from insightface_func.face_detect_crop_multi import Face_detect_crop\n","from util.videoswap_multispecific import video_swap\n","import os\n","import glob"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Imageio: 'ffmpeg-linux64-v3.3.1' was not found on your computer; downloading it now.\n","Try 1. Download from https://github.com/imageio/imageio-binaries/raw/master/ffmpeg/ffmpeg-linux64-v3.3.1 (43.8 MB)\n","Downloading: 8192/45929032 bytes (0.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b3432448/45929032 bytes (7.5%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b7036928/45929032 bytes (15.3%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b10641408/45929032 bytes (23.2%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b14278656/45929032 bytes (31.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b18104320/45929032 bytes (39.4%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b21954560/45929032 bytes (47.8%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b25780224/45929032 bytes (56.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b29736960/45929032 bytes (64.7%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b33488896/45929032 bytes (72.9%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b37093376/45929032 bytes (80.8%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b40689664/45929032 bytes (88.6%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b44392448/45929032 bytes (96.7%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b45929032/45929032 bytes (100.0%)\n"," Done\n","File saved as /root/.imageio/ffmpeg/ffmpeg-linux64-v3.3.1.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"rxSbZ2EDNDlf","executionInfo":{"status":"ok","timestamp":1625380827903,"user_tz":-480,"elapsed":12,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}}},"source":["def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0\n","\n","transformer = transforms.Compose([\n"," transforms.ToTensor(),\n"," #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n"," ])\n","\n","transformer_Arcface = transforms.Compose([\n"," transforms.ToTensor(),\n"," transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n"," ])\n"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ye8iS0UVPMRg","executionInfo":{"status":"ok","timestamp":1625380828574,"user_tz":-480,"elapsed":680,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"cb5a4b02-b1d0-4ff8-f542-5c1b3c9703d9"},"source":["!ls ./checkpoints"],"execution_count":10,"outputs":[{"output_type":"stream","text":["people\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"wwJOwR9LNKRz","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625380828576,"user_tz":-480,"elapsed":13,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"0f92f785-4d9c-4130-b24d-76871b2dafba"},"source":["opt = TestOptions()\n","opt.initialize()\n","opt.parser.add_argument('-f') ## dummy arg to avoid bug\n","opt = opt.parse()\n","opt.multisepcific_dir = './demo_file/multispecific' ## or replace it with folder from your own google drive\n"," ## and remember to follow the dir structure in usage.md\n","opt.video_path = './demo_file/multi_people_1080p.mp4' ## or replace it with video from your own google drive\n","opt.output_path = './output/multi_test_multispecific.mp4'\n","opt.temp_path = './tmp'\n","opt.Arc_path = './arcface_model/arcface_checkpoint.tar'\n","opt.name = 'people'\n","opt.isTrain = False\n","opt.use_mask = True ## new feature up-to-date\n","\n","crop_size = opt.crop_size\n"],"execution_count":11,"outputs":[{"output_type":"stream","text":["------------ Options -------------\n","Arc_path: models/BEST_checkpoint.tar\n","aspect_ratio: 1.0\n","batchSize: 8\n","checkpoints_dir: ./checkpoints\n","cluster_path: features_clustered_010.npy\n","data_type: 32\n","dataroot: ./datasets/cityscapes/\n","display_winsize: 512\n","engine: None\n","export_onnx: None\n","f: /root/.local/share/jupyter/runtime/kernel-19937219-895d-4d02-9a72-5cfa0e889adf.json\n","feat_num: 3\n","fineSize: 512\n","fp16: False\n","gpu_ids: [0]\n","how_many: 50\n","id_thres: 0.03\n","image_size: 224\n","input_nc: 3\n","instance_feat: False\n","isTrain: False\n","label_feat: False\n","label_nc: 0\n","latent_size: 512\n","loadSize: 1024\n","load_features: False\n","local_rank: 0\n","max_dataset_size: inf\n","model: pix2pixHD\n","multisepcific_dir: ./demo_file/multispecific\n","nThreads: 2\n","n_blocks_global: 6\n","n_blocks_local: 3\n","n_clusters: 10\n","n_downsample_E: 4\n","n_downsample_global: 3\n","n_local_enhancers: 1\n","name: people\n","nef: 16\n","netG: global\n","ngf: 64\n","niter_fix_global: 0\n","no_flip: False\n","no_instance: False\n","no_simswaplogo: False\n","norm: batch\n","norm_G: spectralspadesyncbatch3x3\n","ntest: inf\n","onnx: None\n","output_nc: 3\n","output_path: ./output/\n","phase: test\n","pic_a_path: ./crop_224/gdg.jpg\n","pic_b_path: ./crop_224/zrf.jpg\n","pic_specific_path: ./crop_224/zrf.jpg\n","resize_or_crop: scale_width\n","results_dir: ./results/\n","semantic_nc: 3\n","serial_batches: False\n","temp_path: ./temp_results\n","tf_log: False\n","use_dropout: False\n","use_encoded_image: False\n","verbose: False\n","video_path: ./demo_file/multi_people_1080p.mp4\n","which_epoch: latest\n","-------------- End ----------------\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UFt8zQrAMq9F","executionInfo":{"status":"ok","timestamp":1625381428564,"user_tz":-480,"elapsed":599996,"user":{"displayName":"José Lampreia","photoUrl":"","userId":"16015278604201270582"}},"outputId":"46188013-3f6d-4174-9959-d1fd203dcc0d"},"source":["pic_specific = opt.pic_specific_path\n","crop_size = opt.crop_size\n","multisepcific_dir = opt.multisepcific_dir\n","\n","torch.nn.Module.dump_patches = True\n","model = create_model(opt)\n","model.eval()\n","\n","app = Face_detect_crop(name='antelope', root='./insightface_func/models')\n","app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640))\n","# The specific person to be swapped(source)\n","source_specific_id_nonorm_list = []\n","source_path = os.path.join(multisepcific_dir,'SRC_*')\n","source_specific_images_path = sorted(glob.glob(source_path))\n","\n","with torch.no_grad():\n"," for source_specific_image_path in source_specific_images_path:\n"," specific_person_whole = cv2.imread(source_specific_image_path)\n"," specific_person_align_crop, _ = app.get(specific_person_whole,crop_size)\n"," specific_person_align_crop_pil = Image.fromarray(cv2.cvtColor(specific_person_align_crop[0],cv2.COLOR_BGR2RGB)) \n"," specific_person = transformer_Arcface(specific_person_align_crop_pil)\n"," specific_person = specific_person.view(-1, specific_person.shape[0], specific_person.shape[1], specific_person.shape [2])\n"," # convert numpy to tensor\n"," specific_person = specific_person.cuda()\n"," #create latent id\n"," specific_person_downsample = F.interpolate(specific_person, size=(112,112))\n"," specific_person_id_nonorm = model.netArc(specific_person_downsample)\n"," source_specific_id_nonorm_list.append(specific_person_id_nonorm.clone())\n","\n"," # The person who provides id information (list)\n"," target_id_norm_list = []\n"," target_path = os.path.join(multisepcific_dir,'DST_*')\n"," target_images_path = sorted(glob.glob(target_path))\n","\n"," for target_image_path in target_images_path:\n"," img_a_whole = cv2.imread(target_image_path)\n"," img_a_align_crop, _ = app.get(img_a_whole,crop_size)\n"," img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) \n"," img_a = transformer_Arcface(img_a_align_crop_pil)\n"," img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])\n"," # convert numpy to tensor\n"," img_id = img_id.cuda()\n"," #create latent id\n"," img_id_downsample = F.interpolate(img_id, size=(112,112))\n"," latend_id = model.netArc(img_id_downsample)\n"," latend_id = F.normalize(latend_id, p=2, dim=1)\n"," target_id_norm_list.append(latend_id.clone())\n"," \n"," assert len(target_id_norm_list) == len(source_specific_id_nonorm_list), \"The number of images in source and target directory must be same !!!\"\n"," video_swap(opt.video_path, target_id_norm_list,source_specific_id_nonorm_list, opt.id_thres, \\\n"," model, app, opt.output_path,temp_results_dir=opt.temp_path,no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask)"],"execution_count":12,"outputs":[{"output_type":"stream","text":["input mean and std: 127.5 127.5\n","find model: ./insightface_func/models/antelope/glintr100.onnx recognition\n","find model: ./insightface_func/models/antelope/scrfd_10g_bnkps.onnx detection\n","set det-size: (640, 640)\n"],"name":"stdout"},{"output_type":"stream","text":["\r 0%| | 0/594 [00:00>>> Building video ./output/multi_test_multispecific.mp4\n","[MoviePy] Writing audio in multi_test_multispecificTEMP_MPY_wvf_snd.mp3\n"],"name":"stdout"},{"output_type":"stream","text":["100%|██████████| 438/438 [00:00<00:00, 832.16it/s]"],"name":"stderr"},{"output_type":"stream","text":["[MoviePy] Done.\n","[MoviePy] Writing video ./output/multi_test_multispecific.mp4\n"],"name":"stdout"},{"output_type":"stream","text":["\n","100%|██████████| 595/595 [00:53<00:00, 11.11it/s]\n"],"name":"stderr"},{"output_type":"stream","text":["[MoviePy] Done.\n","[MoviePy] >>>> Video ready: ./output/multi_test_multispecific.mp4 \n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Rty2GsyZZrI6"},"source":[],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/SimSwap/PReLU.patch b/SimSwap/PReLU.patch new file mode 100644 index 0000000..9d41cca --- /dev/null +++ b/SimSwap/PReLU.patch @@ -0,0 +1,48 @@ +--- /usr/local/lib/python3.5/dist-packages/torch/nn/modules/activation.py ++++ /usr/local/lib/python3.5/dist-packages/torch/nn/modules/activation.py +@@ -32,14 +32,15 @@ + init (float): the initial value of :math:`a`. Default: 0.25 + + Shape: +- - Input: :math:`( *)` where `*` means, any number of additional +- dimensions. +- - Output: :math:`(*)`, same shape as the input. ++ - Input: :math:`(N, *)` where `*` means, any number of additional ++ dimensions ++ - Output: :math:`(N, *)`, same shape as the input + + Attributes: +- weight (Tensor): the learnable weights of shape (:attr:`num_parameters`). ++ weight (Tensor): the learnable weights of shape (attr:`num_parameters`). ++ The attr:`dtype` is default to + +- .. image:: ../scripts/activation_images/PReLU.png ++ .. image:: scripts/activation_images/PReLU.png + + Examples:: + +@@ -47,19 +48,16 @@ + >>> input = torch.randn(2) + >>> output = m(input) + """ +- __constants__ = ['num_parameters'] +- num_parameters: int + +- def __init__(self, num_parameters: int = 1, init: float = 0.25, +- device=None, dtype=None) -> None: +- factory_kwargs = {'device': device, 'dtype': dtype} ++ def __init__(self, num_parameters=1, init=0.25): + self.num_parameters = num_parameters + super(PReLU, self).__init__() +- self.weight = Parameter(torch.empty(num_parameters, **factory_kwargs).fill_(init)) ++ self.weight = Parameter(torch.Tensor(num_parameters).fill_(init)) + +- def forward(self, input: Tensor) -> Tensor: ++ @weak_script_method ++ def forward(self, input): + return F.prelu(input, self.weight) + +- def extra_repr(self) -> str: ++ def extra_repr(self): + return 'num_parameters={}'.format(self.num_parameters) + \ No newline at end of file diff --git a/SimSwap/README.md b/SimSwap/README.md new file mode 100644 index 0000000..d7842c5 --- /dev/null +++ b/SimSwap/README.md @@ -0,0 +1,216 @@ +# SimSwap: An Efficient Framework For High Fidelity Face Swapping +## Proceedings of the 28th ACM International Conference on Multimedia +**The official repository with Pytorch** + +**Our method can realize **arbitrary face swapping** on images and videos with **one single trained model**.** + +Training and test code are now available! +[ google colab logo](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/train.ipynb) + +We are working with our incoming paper SimSwap++, keeping expecting! + +The high resolution version of ***SimSwap-HQ*** is supported! + +[![simswaplogo](/docs/img/logo1.png)](https://github.com/neuralchen/SimSwap) + +Our paper can be downloaded from [[Arxiv]](https://arxiv.org/pdf/2106.06340v1.pdf) [[ACM DOI]](https://dl.acm.org/doi/10.1145/3394171.3413630) + + + +## Attention +***This project is for technical and academic use only. Please do not apply it to illegal and unethical scenarios.*** + +***In the event of violation of the legal and ethical requirements of the user's country or region, this code repository is exempt from liability*** + +***Please do not ignore the content at the end of this README!*** + +If you find this project useful, please star it. It is the greatest appreciation of our work. + +## Top News + +**`2022-04-21`**: For resource limited users, we provide the cropped VGGFace2-224 dataset [[Google Driver] VGGFace2-224 (10.8G)](https://drive.google.com/file/d/19pWvdEHS-CEG6tW3PdxdtZ5QEymVjImc/view?usp=sharing) [[Baidu Driver] ](https://pan.baidu.com/s/1OiwLJHVBSYB4AY2vEcfN0A) [Password: lrod]. + +**`2022-04-20`**: Training scripts are now available. We highly recommend that you guys train the simswap model with our released high quality dataset [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ). + +**`2021-11-24`**: We have trained a beta version of ***SimSwap-HQ*** on [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) and open sourced the checkpoint of this model (if you think the Simswap 512 is cool, please star our [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) repo). Please don’t forget to go to [Preparation](./docs/guidance/preparation.md) and [Inference for image or video face swapping](./docs/guidance/usage.md) to check the latest set up. + +**`2021-11-23`**: The google drive link of [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) is released. + +**`2021-11-17`**: We released a high resolution face dataset [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) and the method to generate this dataset. This dataset is for research purpose. + +**`2021-08-30`**: Docker has been supported, please refer [here](https://replicate.ai/neuralchen/simswap-image) for details. + +**`2021-08-17`**: We have updated the [Preparation](./docs/guidance/preparation.md), The main change is that the gpu version of onnx is now installed by default, Now the time to process a video is greatly reduced. + +**`2021-07-19`**: ***Obvious border abruptness has been resolved***. We add the ability to using mask and upgrade the old algorithm for better visual effect, please go to [Inference for image or video face swapping](./docs/guidance/usage.md) for details. Please don’t forget to go to [Preparation](./docs/guidance/preparation.md) to check the latest set up. (Thanks for the help from [@woctezuma](https://github.com/woctezuma) and [@instant-high](https://github.com/instant-high)) + +## The first open source high resolution dataset for face swapping!!! +## High Resolution Dataset [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) + +[![logo](./docs/img/vggface2_hq_compare.png)](https://github.com/NNNNAI/VGGFace2-HQ) + +## Dependencies +- python3.6+ +- pytorch1.5+ +- torchvision +- opencv +- pillow +- numpy +- imageio +- moviepy +- insightface + +## Training + +[Preparation](./docs/guidance/preparation.md) + +The training script is slightly different from the original version, e.g., we replace the patch discriminator with the projected discriminator, which saves a lot of hardware overhead and achieves slightly better results. + +In order to ensure the normal training, the batch size must be greater than 1. + +Friendly reminder, due to the difference in training settings, the user-trained model will have subtle differences in visual effects from the pre-trained model we provide. + +- Train 224 models with VGGFace2 224*224 [[Google Driver] VGGFace2-224 (10.8G)](https://drive.google.com/file/d/19pWvdEHS-CEG6tW3PdxdtZ5QEymVjImc/view?usp=sharing) [[Baidu Driver] ](https://pan.baidu.com/s/1OiwLJHVBSYB4AY2vEcfN0A) [Password: lrod] + +For faster convergence and better results, a large batch size (more than 16) is recommended! + +***We recommend training more than 400K iterations (batch size is 16), 600K~800K will be better, more iterations will not be recommended.*** + + +``` +python train.py --name simswap224_test --batchSize 8 --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep False +``` + +[Colab demo for training 224 model](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/train.ipynb) + +For faster convergence and better results, a large batch size (more than 16) is recommended! + +- Train 512 models with VGGFace2-HQ 512*512 [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ). +``` +python train.py --name simswap512_test --batchSize 16 --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep True +``` + + + +## Inference with a pretrained SimSwap model +[Preparation](./docs/guidance/preparation.md) + +[Inference for image or video face swapping](./docs/guidance/usage.md) + +[Colab demo](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/SimSwap%20colab.ipynb) + +
Stronger feature
+ +[Colab for switching specific faces in multi-face videos](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/MultiSpecific.ipynb) + +[Image face swapping demo & Docker image on Replicate](https://replicate.ai/neuralchen/simswap-image) + + + +## Video + +
+ + + + +
+
+ + +
+ +## Results +![Results1](/docs/img/results1.PNG) + +![Results2](/docs/img/total.PNG) + + + + + +**High-quality videos can be found in the link below:** + +[[Mama(video) 1080p]](https://drive.google.com/file/d/1mnSlwzz7f4H2O7UwApAHo64mgK4xSNyK/view?usp=sharing) + +[[Google Drive link for video 1]](https://drive.google.com/file/d/1hdne7Gw39d34zt3w1NYV3Ln5cT8PfCNm/view?usp=sharing) + +[[Google Drive link for video 2]](https://drive.google.com/file/d/1bDEg_pVeFYLnf9QLSMuG8bsjbRPk0X5_/view?usp=sharing) + +[[Google Drive link for video 3]](https://drive.google.com/file/d/1oftHAnLmgFis4XURcHTccGSWbWSXYKK1/view?usp=sharing) + +[[Baidu Drive link for video]](https://pan.baidu.com/s/1WTS6jm2TY17bYJurw57LUg ) Password: ```b26n``` + +[[Online Video]](https://www.bilibili.com/video/BV12v411p7j5/) + +## User case +If you have some interesting results after using our project and are willing to share, you can contact us by email or share directly on the issue. Later, we may make a separate section to show these results, which should be cool. + +At the same time, if you have suggestions for our project, please feel free to ask questions in the issue, or contact us directly via email: [email1](mailto:chenxuanhongzju@outlook.com), [email2](mailto:nicklau26@foxmail.com), [email3](mailto:ziangliu824@gmail.com). (All three can be contacted, just choose any one) + +## License +For academic and non-commercial use only.The whole project is under the CC-BY-NC 4.0 license. See [LICENSE](https://github.com/neuralchen/SimSwap/blob/main/LICENSE) for additional details. + + +## To cite our paper +``` +@inproceedings{DBLP:conf/mm/ChenCNG20, + author = {Renwang Chen and + Xuanhong Chen and + Bingbing Ni and + Yanhao Ge}, + title = {SimSwap: An Efficient Framework For High Fidelity Face Swapping}, + booktitle = {{MM} '20: The 28th {ACM} International Conference on Multimedia}, + year = {2020} +} +``` + +## Related Projects + +**Please visit our another ACMMM2020 high-quality style transfer project** + +[![logo](./docs/img/logo.png)](https://github.com/neuralchen/ASMAGAN) + +[![title](/docs/img/title.png)](https://github.com/neuralchen/ASMAGAN) + +**Please visit our AAAI2021 sketch based rendering project** + +[![logo](./docs/img/girl2.gif)](https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale) +[![title](/docs/img/girl2-RGB.png)](https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale) + +**Please visit our high resolution face dataset VGGFace2-HQ** + +[![logo](./docs/img/vggface2_hq_compare.png)](https://github.com/NNNNAI/VGGFace2-HQ) + +Learn about our other projects + +[[VGGFace2-HQ]](https://github.com/NNNNAI/VGGFace2-HQ); + +[[RainNet]](https://neuralchen.github.io/RainNet); + +[[Sketch Generation]](https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale); + +[[CooGAN]](https://github.com/neuralchen/CooGAN); + +[[Knowledge Style Transfer]](https://github.com/AceSix/Knowledge_Transfer); + +[[SimSwap]](https://github.com/neuralchen/SimSwap); + +[[ASMA-GAN]](https://github.com/neuralchen/ASMAGAN); + +[[SNGAN-Projection-pytorch]](https://github.com/neuralchen/SNGAN_Projection) + +[[Pretrained_VGG19]](https://github.com/neuralchen/Pretrained_VGG19). + +## Acknowledgements + + +* [Deepfacelab](https://github.com/iperov/DeepFaceLab) +* [Insightface](https://github.com/deepinsight/insightface) +* [Face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch) +* [BiSeNet](https://github.com/CoinCheung/BiSeNet) + diff --git a/SimSwap/Sequential.patch b/SimSwap/Sequential.patch new file mode 100644 index 0000000..da32330 --- /dev/null +++ b/SimSwap/Sequential.patch @@ -0,0 +1,135 @@ +--- /usr/local/lib/python3.5/dist-packages/torch/nn/modules/container.py ++++ /usr/local/lib/python3.5/dist-packages/torch/nn/modules/container.py +@@ -1,31 +1,11 @@ + class Sequential(Module): + r"""A sequential container. +- Modules will be added to it in the order they are passed in the +- constructor. Alternatively, an ``OrderedDict`` of modules can be +- passed in. The ``forward()`` method of ``Sequential`` accepts any +- input and forwards it to the first module it contains. It then +- "chains" outputs to inputs sequentially for each subsequent module, +- finally returning the output of the last module. ++ Modules will be added to it in the order they are passed in the constructor. ++ Alternatively, an ordered dict of modules can also be passed in. + +- The value a ``Sequential`` provides over manually calling a sequence +- of modules is that it allows treating the whole container as a +- single module, such that performing a transformation on the +- ``Sequential`` applies to each of the modules it stores (which are +- each a registered submodule of the ``Sequential``). ++ To make it easier to understand, here is a small example:: + +- What's the difference between a ``Sequential`` and a +- :class:`torch.nn.ModuleList`? A ``ModuleList`` is exactly what it +- sounds like--a list for storing ``Module`` s! On the other hand, +- the layers in a ``Sequential`` are connected in a cascading way. +- +- Example:: +- +- # Using Sequential to create a small model. When `model` is run, +- # input will first be passed to `Conv2d(1,20,5)`. The output of +- # `Conv2d(1,20,5)` will be used as the input to the first +- # `ReLU`; the output of the first `ReLU` will become the input +- # for `Conv2d(20,64,5)`. Finally, the output of +- # `Conv2d(20,64,5)` will be used as input to the second `ReLU` ++ # Example of using Sequential + model = nn.Sequential( + nn.Conv2d(1,20,5), + nn.ReLU(), +@@ -33,8 +13,7 @@ + nn.ReLU() + ) + +- # Using Sequential with OrderedDict. This is functionally the +- # same as the above code ++ # Example of using Sequential with OrderedDict + model = nn.Sequential(OrderedDict([ + ('conv1', nn.Conv2d(1,20,5)), + ('relu1', nn.ReLU()), +@@ -42,16 +21,6 @@ + ('relu2', nn.ReLU()) + ])) + """ +- +- _modules: Dict[str, Module] # type: ignore[assignment] +- +- @overload +- def __init__(self, *args: Module) -> None: +- ... +- +- @overload +- def __init__(self, arg: 'OrderedDict[str, Module]') -> None: +- ... + + def __init__(self, *args): + super(Sequential, self).__init__() +@@ -62,7 +31,7 @@ + for idx, module in enumerate(args): + self.add_module(str(idx), module) + +- def _get_item_by_idx(self, iterator, idx) -> T: ++ def _get_item_by_idx(self, iterator, idx): + """Get the idx-th item of the iterator""" + size = len(self) + idx = operator.index(idx) +@@ -71,18 +40,17 @@ + idx %= size + return next(islice(iterator, idx, None)) + +- @_copy_to_script_wrapper +- def __getitem__(self, idx) -> Union['Sequential', T]: ++ def __getitem__(self, idx): + if isinstance(idx, slice): + return self.__class__(OrderedDict(list(self._modules.items())[idx])) + else: + return self._get_item_by_idx(self._modules.values(), idx) + +- def __setitem__(self, idx: int, module: Module) -> None: +- key: str = self._get_item_by_idx(self._modules.keys(), idx) ++ def __setitem__(self, idx, module): ++ key = self._get_item_by_idx(self._modules.keys(), idx) + return setattr(self, key, module) + +- def __delitem__(self, idx: Union[slice, int]) -> None: ++ def __delitem__(self, idx): + if isinstance(idx, slice): + for key in list(self._modules.keys())[idx]: + delattr(self, key) +@@ -90,35 +58,16 @@ + key = self._get_item_by_idx(self._modules.keys(), idx) + delattr(self, key) + +- @_copy_to_script_wrapper +- def __len__(self) -> int: ++ def __len__(self): + return len(self._modules) + +- @_copy_to_script_wrapper + def __dir__(self): + keys = super(Sequential, self).__dir__() + keys = [key for key in keys if not key.isdigit()] + return keys + +- @_copy_to_script_wrapper +- def __iter__(self) -> Iterator[Module]: +- return iter(self._modules.values()) +- +- # NB: We can't really type check this function as the type of input +- # may change dynamically (as is tested in +- # TestScript.test_sequential_intermediary_types). Cannot annotate +- # with Any as TorchScript expects a more precise type + def forward(self, input): +- for module in self: ++ for module in self._modules.values(): + input = module(input) + return input + +- def append(self, module: Module) -> 'Sequential': +- r"""Appends a given module to the end. +- +- Args: +- module (nn.Module): module to append +- """ +- self.add_module(str(len(self)), module) +- return self +- \ No newline at end of file diff --git a/SimSwap/Sigmoid.patch b/SimSwap/Sigmoid.patch new file mode 100644 index 0000000..529036f --- /dev/null +++ b/SimSwap/Sigmoid.patch @@ -0,0 +1,31 @@ +--- /usr/local/lib/python3.5/dist-packages/torch/nn/modules/activation.py ++++ /usr/local/lib/python3.5/dist-packages/torch/nn/modules/activation.py +@@ -2,14 +2,15 @@ + r"""Applies the element-wise function: + + .. math:: +- \text{Sigmoid}(x) = \sigma(x) = \frac{1}{1 + \exp(-x)} ++ \text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)} + + + Shape: +- - Input: :math:`(*)`, where :math:`*` means any number of dimensions. +- - Output: :math:`(*)`, same shape as the input. ++ - Input: :math:`(N, *)` where `*` means, any number of additional ++ dimensions ++ - Output: :math:`(N, *)`, same shape as the input + +- .. image:: ../scripts/activation_images/Sigmoid.png ++ .. image:: scripts/activation_images/Sigmoid.png + + Examples:: + +@@ -18,6 +19,7 @@ + >>> output = m(input) + """ + +- def forward(self, input: Tensor) -> Tensor: ++ @weak_script_method ++ def forward(self, input): + return torch.sigmoid(input) + \ No newline at end of file diff --git a/SimSwap/SimSwap colab.ipynb b/SimSwap/SimSwap colab.ipynb new file mode 100644 index 0000000..5b07fa2 --- /dev/null +++ b/SimSwap/SimSwap colab.ipynb @@ -0,0 +1,579 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "SimSwap colab.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "7_gtFoV8BuRx" + }, + "source": [ + "This is a simple example of SimSwap on processing video with multiple faces. You can change the codes for inference based on our other scripts for image or single face swapping.\n", + "\n", + "Code path: https://github.com/neuralchen/SimSwap\n", + "\n", + "Paper path: https://arxiv.org/pdf/2106.06340v1.pdf or https://dl.acm.org/doi/10.1145/3394171.3413630" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0Y1RfpzsCAl9", + "outputId": "a39470a0-9689-409d-a0a4-e2afd5d3b5dd" + }, + "source": [ + "## make sure you are using a runtime with GPU\n", + "## you can check at Runtime/Change runtime type in the top bar.\n", + "!nvidia-smi" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Mon Jun 21 02:13:20 2021 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 465.27 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 45C P8 10W / 70W | 0MiB / 15109MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "| No running processes found |\n", + "+-----------------------------------------------------------------------------+\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0Qzzx2UpDkqw" + }, + "source": [ + "## Installation\n", + "\n", + "All file changes made by this notebook are temporary. \n", + "You can try to mount your own google drive to store files if you want.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VA_4CeWZCHLP", + "outputId": "4b0f176f-87e7-4772-8b47-c2098d8f3bf6" + }, + "source": [ + "!git clone https://github.com/neuralchen/SimSwap\n", + "!cd SimSwap && git pull" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Cloning into 'SimSwap'...\n", + "remote: Enumerating objects: 362, done.\u001b[K\n", + "remote: Counting objects: 100% (362/362), done.\u001b[K\n", + "remote: Compressing objects: 100% (281/281), done.\u001b[K\n", + "remote: Total 362 (delta 149), reused 272 (delta 67), pack-reused 0\u001b[K\n", + "Receiving objects: 100% (362/362), 101.31 MiB | 32.47 MiB/s, done.\n", + "Resolving deltas: 100% (149/149), done.\n", + "Already up to date.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Y5K4au_UCkKn", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9691a7a4-192e-4ec2-c3c1-1f2c933d7b6a" + }, + "source": [ + "!pip install insightface==0.2.1 onnxruntime moviepy\n", + "!pip install googledrivedownloader\n", + "!pip install imageio==2.4.1" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Collecting insightface==0.2.1\n", + " Downloading https://files.pythonhosted.org/packages/ee/1e/6395bbe0db665f187c8e49266cda54fcf661f182192370d409423e4943e4/insightface-0.2.1-py2.py3-none-any.whl\n", + "Collecting onnxruntime\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/f9/76/3d0f8bb2776961c7335693df06eccf8d099e48fa6fb552c7546867192603/onnxruntime-1.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5MB)\n", + "\u001b[K |████████████████████████████████| 4.5MB 10.2MB/s \n", + "\u001b[?25hRequirement already satisfied: moviepy in /usr/local/lib/python3.7/dist-packages (0.2.3.5)\n", + "Collecting onnx\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/3f/9b/54c950d3256e27f970a83cd0504efb183a24312702deed0179453316dbd0/onnx-1.9.0-cp37-cp37m-manylinux2010_x86_64.whl (12.2MB)\n", + "\u001b[K |████████████████████████████████| 12.2MB 51.4MB/s \n", + "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (3.2.2)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (4.41.1)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (7.1.2)\n", + "Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (0.16.2)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (2.23.0)\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (0.22.2.post1)\n", + "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (4.1.2.30)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.19.5)\n", + "Requirement already satisfied: easydict in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.9)\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.4.1)\n", + "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.12)\n", + "Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (3.12.4)\n", + "Requirement already satisfied: decorator<5.0,>=4.0.2 in /usr/local/lib/python3.7/dist-packages (from moviepy) (4.4.2)\n", + "Requirement already satisfied: imageio<3.0,>=2.1.2 in /usr/local/lib/python3.7/dist-packages (from moviepy) (2.4.1)\n", + "Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx->insightface==0.2.1) (3.7.4.3)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from onnx->insightface==0.2.1) (1.15.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (1.3.1)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (2.8.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (0.10.0)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (2.4.7)\n", + "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->insightface==0.2.1) (2.5.1)\n", + "Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->insightface==0.2.1) (1.1.1)\n", + "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (2.10)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (2021.5.30)\n", + "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (3.0.4)\n", + "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (1.24.3)\n", + "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->insightface==0.2.1) (1.0.1)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf->onnxruntime) (57.0.0)\n", + "Installing collected packages: onnx, insightface, onnxruntime\n", + "Successfully installed insightface-0.2.1 onnx-1.9.0 onnxruntime-1.8.0\n", + "Requirement already satisfied: googledrivedownloader in /usr/local/lib/python3.7/dist-packages (0.4)\n", + "Requirement already satisfied: imageio==2.4.1 in /usr/local/lib/python3.7/dist-packages (2.4.1)\n", + "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from imageio==2.4.1) (7.1.2)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from imageio==2.4.1) (1.19.5)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gQ7ZoIbLFCye", + "outputId": "bb35e7e2-14b7-4f36-d62a-499ba041cf64" + }, + "source": [ + "import os\n", + "os.chdir(\"SimSwap\")\n", + "!ls" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + " crop_224\t models\t\t test_one_image.py\n", + " data\t\t options\t\t test_video_swapmulti.py\n", + " demo_file\t output\t\t test_video_swapsingle.py\n", + " doc\t\t README.md\t\t test_wholeimage_swapmulti.py\n", + " insightface_func 'SimSwap colab.ipynb' test_wholeimage_swapsingle.py\n", + " LICENSE\t simswaplogo\t\t util\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gLti1J0pEFjJ", + "outputId": "e93c3f98-01df-458e-b791-c32f7343e705" + }, + "source": [ + "from google_drive_downloader import GoogleDriveDownloader\n", + "\n", + "### it seems that google drive link may not be permenant, you can find this ID from our open url.\n", + "# GoogleDriveDownloader.download_file_from_google_drive(file_id='1TLNdIufzwesDbyr_nVTR7Zrx9oRHLM_N',\n", + "# dest_path='./arcface_model/arcface_checkpoint.tar')\n", + "# GoogleDriveDownloader.download_file_from_google_drive(file_id='1PXkRiBUYbu1xWpQyDEJvGKeqqUFthJcI',\n", + "# dest_path='./checkpoints.zip')\n", + "\n", + "!wget -P ./arcface_model https://github.com/neuralchen/SimSwap/releases/download/1.0/arcface_checkpoint.tar\n", + "!wget https://github.com/neuralchen/SimSwap/releases/download/1.0/checkpoints.zip\n", + "!unzip ./checkpoints.zip -d ./checkpoints\n", + "!wget -P ./parsing_model/checkpoint https://github.com/neuralchen/SimSwap/releases/download/1.0/79999_iter.pth" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Downloading 1TLNdIufzwesDbyr_nVTR7Zrx9oRHLM_N into ./arcface_model/arcface_checkpoint.tar... Done.\n", + "Downloading 1PXkRiBUYbu1xWpQyDEJvGKeqqUFthJcI into ./checkpoints.zip... Done.\n", + "Archive: ./checkpoints.zip\n", + " creating: ./checkpoints/people/\n", + " inflating: ./checkpoints/people/iter.txt \n", + " inflating: ./checkpoints/people/latest_net_D1.pth \n", + " inflating: ./checkpoints/people/latest_net_D2.pth \n", + " inflating: ./checkpoints/people/latest_net_G.pth \n", + " inflating: ./checkpoints/people/loss_log.txt \n", + " inflating: ./checkpoints/people/opt.txt \n", + " creating: ./checkpoints/people/web/\n", + " creating: ./checkpoints/people/web/images/\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aSRnK5V4HI-k", + "outputId": "e688746c-c33a-485c-808c-54a7370f0c53" + }, + "source": [ + "## You can upload filed manually\n", + "# from google.colab import drive\n", + "# drive.mount('/content/gdrive')\n", + "\n", + "### Now onedrive file can be downloaded in Colab directly!\n", + "### If the link blow is not permanent, you can just download it from the \n", + "### open url(can be found at [our repo]/doc/guidance/preparation.md) and copy the assigned download link here.\n", + "### many thanks to woctezuma for this very useful help\n", + "!wget --no-check-certificate \"https://sh23tw.dm.files.1drv.com/y4mmGiIkNVigkSwOKDcV3nwMJulRGhbtHdkheehR5TArc52UjudUYNXAEvKCii2O5LAmzGCGK6IfleocxuDeoKxDZkNzDRSt4ZUlEt8GlSOpCXAFEkBwaZimtWGDRbpIGpb_pz9Nq5jATBQpezBS6G_UtspWTkgrXHHxhviV2nWy8APPx134zOZrUIbkSF6xnsqzs3uZ_SEX_m9Rey0ykpx9w\" -O antelope.zip\n", + "!unzip ./antelope.zip -d ./insightface_func/models/\n" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "--2021-06-21 02:14:17-- https://sh23tw.dm.files.1drv.com/y4mmGiIkNVigkSwOKDcV3nwMJulRGhbtHdkheehR5TArc52UjudUYNXAEvKCii2O5LAmzGCGK6IfleocxuDeoKxDZkNzDRSt4ZUlEt8GlSOpCXAFEkBwaZimtWGDRbpIGpb_pz9Nq5jATBQpezBS6G_UtspWTkgrXHHxhviV2nWy8APPx134zOZrUIbkSF6xnsqzs3uZ_SEX_m9Rey0ykpx9w\n", + "Resolving sh23tw.dm.files.1drv.com (sh23tw.dm.files.1drv.com)... 13.107.42.12\n", + "Connecting to sh23tw.dm.files.1drv.com (sh23tw.dm.files.1drv.com)|13.107.42.12|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 248024513 (237M) [application/zip]\n", + "Saving to: ‘antelope.zip’\n", + "\n", + "antelope.zip 100%[===================>] 236.53M 6.16MB/s in 31s \n", + "\n", + "2021-06-21 02:14:48 (7.66 MB/s) - ‘antelope.zip’ saved [248024513/248024513]\n", + "\n", + "Archive: ./antelope.zip\n", + " creating: ./insightface_func/models/antelope/\n", + " inflating: ./insightface_func/models/antelope/glintr100.onnx \n", + " inflating: ./insightface_func/models/antelope/scrfd_10g_bnkps.onnx \n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BsGmIMxLVxyO" + }, + "source": [ + "## Inference" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PfSsND36EMvn", + "outputId": "f28c98fd-4c6d-40fa-e3c7-99b606c7492a" + }, + "source": [ + "import cv2\n", + "import torch\n", + "import fractions\n", + "import numpy as np\n", + "from PIL import Image\n", + "import torch.nn.functional as F\n", + "from torchvision import transforms\n", + "from models.models import create_model\n", + "from options.test_options import TestOptions\n", + "from insightface_func.face_detect_crop_multi import Face_detect_crop\n", + "from util.videoswap import video_swap\n", + "from util.add_watermark import watermark_image" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Imageio: 'ffmpeg-linux64-v3.3.1' was not found on your computer; downloading it now.\n", + "Try 1. Download from https://github.com/imageio/imageio-binaries/raw/master/ffmpeg/ffmpeg-linux64-v3.3.1 (43.8 MB)\n", + "Downloading: 8192/45929032 bytes (0.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b1286144/45929032 bytes (2.8%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b3653632/45929032 bytes (8.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b7479296/45929032 bytes (16.3%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b11526144/45929032 bytes (25.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b15171584/45929032 bytes (33.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b18997248/45929032 bytes (41.4%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b22724608/45929032 bytes (49.5%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b26673152/45929032 bytes (58.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b30728192/45929032 bytes (66.9%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b34725888/45929032 bytes (75.6%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b38879232/45929032 bytes (84.7%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b42680320/45929032 bytes (92.9%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b45929032/45929032 bytes (100.0%)\n", + " Done\n", + "File saved as /root/.imageio/ffmpeg/ffmpeg-linux64-v3.3.1.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "rxSbZ2EDNDlf" + }, + "source": [ + "transformer = transforms.Compose([\n", + " transforms.ToTensor(),\n", + " #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", + " ])\n", + "\n", + "transformer_Arcface = transforms.Compose([\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", + " ])\n", + "\n", + "detransformer = transforms.Compose([\n", + " transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),\n", + " transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])\n", + " ])" + ], + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wwJOwR9LNKRz", + "outputId": "bdc82f7b-21c4-403f-94d1-b92911698b4a" + }, + "source": [ + "opt = TestOptions()\n", + "opt.initialize()\n", + "opt.parser.add_argument('-f') ## dummy arg to avoid bug\n", + "opt = opt.parse()\n", + "opt.pic_a_path = './demo_file/Iron_man.jpg' ## or replace it with image from your own google drive\n", + "opt.video_path = './demo_file/multi_people_1080p.mp4' ## or replace it with video from your own google drive\n", + "opt.output_path = './output/demo.mp4'\n", + "opt.temp_path = './tmp'\n", + "opt.Arc_path = './arcface_model/arcface_checkpoint.tar'\n", + "opt.isTrain = False\n", + "opt.use_mask = True ## new feature up-to-date\n", + "\n", + "crop_size = opt.crop_size\n", + "\n", + "torch.nn.Module.dump_patches = True\n", + "model = create_model(opt)\n", + "model.eval()\n", + "\n", + "app = Face_detect_crop(name='antelope', root='./insightface_func/models')\n", + "app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640))\n", + "\n", + "with torch.no_grad():\n", + " pic_a = opt.pic_a_path\n", + " # img_a = Image.open(pic_a).convert('RGB')\n", + " img_a_whole = cv2.imread(pic_a)\n", + " img_a_align_crop, _ = app.get(img_a_whole,crop_size)\n", + " img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) \n", + " img_a = transformer_Arcface(img_a_align_crop_pil)\n", + " img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])\n", + "\n", + " # convert numpy to tensor\n", + " img_id = img_id.cuda()\n", + "\n", + " #create latent id\n", + " img_id_downsample = F.interpolate(img_id, size=(112,112))\n", + " latend_id = model.netArc(img_id_downsample)\n", + " latend_id = latend_id.detach().to('cpu')\n", + " latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True)\n", + " latend_id = latend_id.to('cuda')\n", + "\n", + " video_swap(opt.video_path, latend_id, model, app, opt.output_path, temp_results_dir=opt.temp_path, use_mask=opt.use_mask)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "------------ Options -------------\n", + "Arc_path: models/BEST_checkpoint.tar\n", + "aspect_ratio: 1.0\n", + "batchSize: 8\n", + "checkpoints_dir: ./checkpoints\n", + "cluster_path: features_clustered_010.npy\n", + "data_type: 32\n", + "dataroot: ./datasets/cityscapes/\n", + "display_winsize: 512\n", + "engine: None\n", + "export_onnx: None\n", + "f: /root/.local/share/jupyter/runtime/kernel-6d955151-4911-464a-824d-f0806d8071f6.json\n", + "feat_num: 3\n", + "fineSize: 512\n", + "fp16: False\n", + "gpu_ids: [0]\n", + "how_many: 50\n", + "image_size: 224\n", + "input_nc: 3\n", + "instance_feat: False\n", + "isTrain: False\n", + "label_feat: False\n", + "label_nc: 0\n", + "latent_size: 512\n", + "loadSize: 1024\n", + "load_features: False\n", + "local_rank: 0\n", + "max_dataset_size: inf\n", + "model: pix2pixHD\n", + "nThreads: 2\n", + "n_blocks_global: 6\n", + "n_blocks_local: 3\n", + "n_clusters: 10\n", + "n_downsample_E: 4\n", + "n_downsample_global: 3\n", + "n_local_enhancers: 1\n", + "name: people\n", + "nef: 16\n", + "netG: global\n", + "ngf: 64\n", + "niter_fix_global: 0\n", + "no_flip: False\n", + "no_instance: False\n", + "norm: batch\n", + "norm_G: spectralspadesyncbatch3x3\n", + "ntest: inf\n", + "onnx: None\n", + "output_nc: 3\n", + "output_path: ./output/\n", + "phase: test\n", + "pic_a_path: ./crop_224/gdg.jpg\n", + "pic_b_path: ./crop_224/zrf.jpg\n", + "resize_or_crop: scale_width\n", + "results_dir: ./results/\n", + "semantic_nc: 3\n", + "serial_batches: False\n", + "temp_path: ./temp_results\n", + "tf_log: False\n", + "use_dropout: False\n", + "use_encoded_image: False\n", + "verbose: False\n", + "video_path: ./demo_file/multi_people_1080p.mp4\n", + "which_epoch: latest\n", + "-------------- End ----------------\n", + "input mean and std: 127.5 127.5\n", + "find model: ./insightface_func/models/antelope/glintr100.onnx recognition\n", + "find model: ./insightface_func/models/antelope/scrfd_10g_bnkps.onnx detection\n", + "set det-size: (640, 640)\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "\r 0%| | 0/594 [00:00>>> Building video ./output/demo.mp4\n", + "[MoviePy] Writing audio in demoTEMP_MPY_wvf_snd.mp3\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "100%|██████████| 438/438 [00:00<00:00, 877.18it/s]\n" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "[MoviePy] Done.\n", + "[MoviePy] Writing video ./output/demo.mp4\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "100%|██████████| 595/595 [00:53<00:00, 11.15it/s]\n" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "[MoviePy] Done.\n", + "[MoviePy] >>>> Video ready: ./output/demo.mp4 \n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Rty2GsyZZrI6" + }, + "source": [], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/SimSwap/boot.py b/SimSwap/boot.py new file mode 100644 index 0000000..62924ff --- /dev/null +++ b/SimSwap/boot.py @@ -0,0 +1,73 @@ +import cv2 +import torch +import fractions +import numpy as np +from PIL import Image +import torch.nn.functional as F +from torchvision import transforms +from models.models import create_model +from options.test_options import TestOptions +from insightface_func.face_detect_crop_multi import Face_detect_crop +from util.videoswap import video_swap +from util.add_watermark import watermark_image + +transformer = transforms.Compose([ + transforms.ToTensor(), + #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + ]) + +transformer_Arcface = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + ]) + +detransformer = transforms.Compose([ + transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]), + transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1]) + ]) + + + +opt = TestOptions() +opt.initialize() +opt.parser.add_argument('-f') ## dummy arg to avoid bug +opt = opt.parse() + +#МЕСТО ДЛЯ ВЫБОРА ФАЙЛОВ ДЛЯ ДИПФЕЙКА +opt.pic_a_path = './demo_file/turing_gpen.jpg' ## or replace it with image from your own google drive +opt.video_path = './demo_file/video1.mp4' ## or replace it with video from your own google drive +opt.output_path = './output/demo1.mp4' ## Путь до файла с итоговым дипфейком +opt.temp_path = './tmp' +opt.Arc_path = './arcface_model/arcface_checkpoint.tar' +opt.isTrain = False +opt.use_mask = True ## new feature up-to-date + +crop_size = opt.crop_size + +torch.nn.Module.dump_patches = True +model = create_model(opt) +model.eval() + +app = Face_detect_crop(name='antelope', root='./insightface_func/models') +app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640)) + +with torch.no_grad(): + pic_a = opt.pic_a_path + # img_a = Image.open(pic_a).convert('RGB') + img_a_whole = cv2.imread(pic_a) + img_a_align_crop, _ = app.get(img_a_whole,crop_size) + img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) + img_a = transformer_Arcface(img_a_align_crop_pil) + img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2]) + + # convert numpy to tensor + img_id = img_id.cuda() + + #create latent id + img_id_downsample = F.interpolate(img_id, size=(112,112)) + latend_id = model.netArc(img_id_downsample) + latend_id = latend_id.detach().to('cpu') + latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True) + latend_id = latend_id.to('cuda') + + video_swap(opt.video_path, latend_id, model, app, opt.output_path, temp_results_dir=opt.temp_path, use_mask=opt.use_mask) \ No newline at end of file diff --git a/SimSwap/checkpoints/people/iter.txt b/SimSwap/checkpoints/people/iter.txt new file mode 100644 index 0000000..f05eb86 --- /dev/null +++ b/SimSwap/checkpoints/people/iter.txt @@ -0,0 +1,2 @@ +519 +4062 diff --git a/SimSwap/checkpoints/people/loss_log.txt b/SimSwap/checkpoints/people/loss_log.txt new file mode 100644 index 0000000..0585ac8 --- /dev/null +++ b/SimSwap/checkpoints/people/loss_log.txt @@ -0,0 +1,11208 @@ +================ Training Loss (Sun Apr 19 12:26:39 2020) ================ +(epoch: 1, iters: 400, time: 0.075) G_GAN: -0.149 G_GAN_Feat: 2.780 G_ID: 1.016 G_Rec: 0.992 D_GP: 0.019 D_real: 0.583 D_fake: 1.174 +(epoch: 1, iters: 800, time: 0.063) G_GAN: -0.172 G_GAN_Feat: 2.544 G_ID: 0.748 G_Rec: 1.044 D_GP: 0.031 D_real: 0.281 D_fake: 1.176 +(epoch: 1, iters: 1200, time: 0.063) G_GAN: 0.689 G_GAN_Feat: 2.089 G_ID: 1.005 G_Rec: 0.932 D_GP: 0.028 D_real: 1.355 D_fake: 0.362 +(epoch: 1, iters: 1600, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 1.997 G_ID: 0.609 G_Rec: 0.903 D_GP: 0.028 D_real: 1.281 D_fake: 0.633 +(epoch: 1, iters: 2000, time: 0.063) G_GAN: 0.175 G_GAN_Feat: 1.587 G_ID: 0.981 G_Rec: 0.788 D_GP: 0.017 D_real: 0.824 D_fake: 0.841 +(epoch: 1, iters: 2400, time: 0.063) G_GAN: 0.010 G_GAN_Feat: 1.704 G_ID: 0.563 G_Rec: 0.708 D_GP: 0.034 D_real: 0.683 D_fake: 0.991 +(epoch: 1, iters: 2800, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 1.430 G_ID: 0.991 G_Rec: 0.668 D_GP: 0.022 D_real: 1.130 D_fake: 0.671 +(epoch: 1, iters: 3200, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 1.707 G_ID: 0.592 G_Rec: 0.958 D_GP: 0.050 D_real: 0.967 D_fake: 0.883 +(epoch: 1, iters: 3600, time: 0.063) G_GAN: -0.091 G_GAN_Feat: 1.385 G_ID: 0.983 G_Rec: 0.648 D_GP: 0.020 D_real: 0.696 D_fake: 1.091 +(epoch: 1, iters: 4000, time: 0.063) G_GAN: 0.290 G_GAN_Feat: 1.444 G_ID: 0.584 G_Rec: 0.722 D_GP: 0.040 D_real: 0.886 D_fake: 0.713 +(epoch: 1, iters: 4400, time: 0.063) G_GAN: -0.082 G_GAN_Feat: 1.536 G_ID: 0.997 G_Rec: 0.635 D_GP: 0.044 D_real: 0.781 D_fake: 1.082 +(epoch: 1, iters: 4800, time: 0.064) G_GAN: -0.138 G_GAN_Feat: 1.452 G_ID: 0.654 G_Rec: 0.626 D_GP: 0.053 D_real: 0.733 D_fake: 1.138 +(epoch: 1, iters: 5200, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 1.153 G_ID: 1.004 G_Rec: 0.532 D_GP: 0.031 D_real: 0.769 D_fake: 0.990 +(epoch: 1, iters: 5600, time: 0.063) G_GAN: -0.038 G_GAN_Feat: 1.194 G_ID: 0.619 G_Rec: 0.553 D_GP: 0.036 D_real: 0.926 D_fake: 1.040 +(epoch: 1, iters: 6000, time: 0.063) G_GAN: -0.102 G_GAN_Feat: 1.214 G_ID: 0.981 G_Rec: 0.904 D_GP: 0.056 D_real: 0.589 D_fake: 1.102 +(epoch: 1, iters: 6400, time: 0.064) G_GAN: 0.025 G_GAN_Feat: 1.220 G_ID: 0.544 G_Rec: 0.605 D_GP: 0.037 D_real: 0.987 D_fake: 0.975 +(epoch: 1, iters: 6800, time: 0.063) G_GAN: 0.309 G_GAN_Feat: 1.110 G_ID: 1.040 G_Rec: 0.548 D_GP: 0.027 D_real: 1.106 D_fake: 0.695 +(epoch: 1, iters: 7200, time: 0.064) G_GAN: -0.014 G_GAN_Feat: 1.094 G_ID: 0.531 G_Rec: 0.562 D_GP: 0.036 D_real: 0.868 D_fake: 1.015 +(epoch: 1, iters: 7600, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 1.040 G_ID: 1.029 G_Rec: 0.685 D_GP: 0.026 D_real: 0.821 D_fake: 0.964 +(epoch: 1, iters: 8000, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 1.039 G_ID: 0.552 G_Rec: 0.505 D_GP: 0.035 D_real: 0.922 D_fake: 0.888 +(epoch: 1, iters: 8400, time: 0.063) G_GAN: 0.206 G_GAN_Feat: 1.051 G_ID: 0.967 G_Rec: 0.502 D_GP: 0.034 D_real: 1.008 D_fake: 0.796 +(epoch: 2, iters: 192, time: 0.063) G_GAN: 0.444 G_GAN_Feat: 0.948 G_ID: 0.473 G_Rec: 1.017 D_GP: 0.013 D_real: 1.357 D_fake: 0.582 +(epoch: 2, iters: 592, time: 0.063) G_GAN: 0.275 G_GAN_Feat: 1.016 G_ID: 0.997 G_Rec: 0.490 D_GP: 0.030 D_real: 1.145 D_fake: 0.727 +(epoch: 2, iters: 992, time: 0.064) G_GAN: -0.031 G_GAN_Feat: 1.141 G_ID: 0.524 G_Rec: 0.510 D_GP: 0.041 D_real: 0.697 D_fake: 1.032 +(epoch: 2, iters: 1392, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 1.060 G_ID: 1.025 G_Rec: 0.422 D_GP: 0.053 D_real: 1.016 D_fake: 0.677 +(epoch: 2, iters: 1792, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.843 G_ID: 0.505 G_Rec: 0.653 D_GP: 0.013 D_real: 1.128 D_fake: 0.803 +(epoch: 2, iters: 2192, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.990 G_ID: 0.992 G_Rec: 0.692 D_GP: 0.039 D_real: 0.982 D_fake: 0.745 +(epoch: 2, iters: 2592, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 1.025 G_ID: 0.453 G_Rec: 0.583 D_GP: 0.048 D_real: 0.910 D_fake: 0.914 +(epoch: 2, iters: 2992, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 1.077 G_ID: 0.993 G_Rec: 0.510 D_GP: 0.073 D_real: 0.690 D_fake: 0.865 +(epoch: 2, iters: 3392, time: 0.064) G_GAN: -0.145 G_GAN_Feat: 0.977 G_ID: 0.442 G_Rec: 0.431 D_GP: 0.046 D_real: 0.611 D_fake: 1.145 +(epoch: 2, iters: 3792, time: 0.064) G_GAN: 0.089 G_GAN_Feat: 0.892 G_ID: 1.006 G_Rec: 0.590 D_GP: 0.020 D_real: 1.005 D_fake: 0.915 +(epoch: 2, iters: 4192, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.993 G_ID: 0.551 G_Rec: 0.589 D_GP: 0.040 D_real: 1.031 D_fake: 0.817 +(epoch: 2, iters: 4592, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.996 G_ID: 0.995 G_Rec: 0.517 D_GP: 0.061 D_real: 1.158 D_fake: 0.614 +(epoch: 2, iters: 4992, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 1.101 G_ID: 0.477 G_Rec: 0.843 D_GP: 0.041 D_real: 1.131 D_fake: 0.661 +(epoch: 2, iters: 5392, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.819 G_ID: 0.987 G_Rec: 0.398 D_GP: 0.034 D_real: 0.857 D_fake: 0.898 +(epoch: 2, iters: 5792, time: 0.064) G_GAN: 0.028 G_GAN_Feat: 0.885 G_ID: 0.463 G_Rec: 0.489 D_GP: 0.022 D_real: 0.821 D_fake: 0.973 +(epoch: 2, iters: 6192, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.875 G_ID: 1.003 G_Rec: 0.398 D_GP: 0.045 D_real: 0.997 D_fake: 0.800 +(epoch: 2, iters: 6592, time: 0.064) G_GAN: -0.040 G_GAN_Feat: 0.904 G_ID: 0.454 G_Rec: 0.434 D_GP: 0.031 D_real: 0.754 D_fake: 1.040 +(epoch: 2, iters: 6992, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.948 G_ID: 0.934 G_Rec: 0.439 D_GP: 0.054 D_real: 0.975 D_fake: 0.891 +(epoch: 2, iters: 7392, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 1.029 G_ID: 0.501 G_Rec: 0.452 D_GP: 0.061 D_real: 0.894 D_fake: 0.682 +(epoch: 2, iters: 7792, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.882 G_ID: 1.016 G_Rec: 0.476 D_GP: 0.039 D_real: 0.923 D_fake: 0.820 +(epoch: 2, iters: 8192, time: 0.064) G_GAN: 0.004 G_GAN_Feat: 0.991 G_ID: 0.541 G_Rec: 0.780 D_GP: 0.045 D_real: 0.800 D_fake: 0.996 +(epoch: 2, iters: 8592, time: 0.064) G_GAN: -0.001 G_GAN_Feat: 0.880 G_ID: 1.019 G_Rec: 0.499 D_GP: 0.029 D_real: 0.743 D_fake: 1.001 +(epoch: 3, iters: 384, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 1.037 G_ID: 0.490 G_Rec: 0.696 D_GP: 0.038 D_real: 0.917 D_fake: 0.837 +(epoch: 3, iters: 784, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.865 G_ID: 0.990 G_Rec: 0.409 D_GP: 0.049 D_real: 0.909 D_fake: 0.849 +(epoch: 3, iters: 1184, time: 0.064) G_GAN: -0.022 G_GAN_Feat: 0.814 G_ID: 0.442 G_Rec: 0.457 D_GP: 0.024 D_real: 0.774 D_fake: 1.022 +(epoch: 3, iters: 1584, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.892 G_ID: 0.994 G_Rec: 0.673 D_GP: 0.037 D_real: 1.000 D_fake: 0.773 +(epoch: 3, iters: 1984, time: 0.064) G_GAN: -0.190 G_GAN_Feat: 0.847 G_ID: 0.463 G_Rec: 0.375 D_GP: 0.026 D_real: 0.596 D_fake: 1.190 +(epoch: 3, iters: 2384, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.909 G_ID: 0.992 G_Rec: 0.464 D_GP: 0.032 D_real: 1.266 D_fake: 0.577 +(epoch: 3, iters: 2784, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.958 G_ID: 0.524 G_Rec: 0.490 D_GP: 0.068 D_real: 1.151 D_fake: 0.641 +(epoch: 3, iters: 3184, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.720 G_ID: 0.971 G_Rec: 0.545 D_GP: 0.013 D_real: 1.015 D_fake: 0.881 +(epoch: 3, iters: 3584, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.940 G_ID: 0.439 G_Rec: 0.497 D_GP: 0.041 D_real: 0.908 D_fake: 0.883 +(epoch: 3, iters: 3984, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.903 G_ID: 0.975 G_Rec: 0.459 D_GP: 0.043 D_real: 0.848 D_fake: 0.969 +(epoch: 3, iters: 4384, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.898 G_ID: 0.422 G_Rec: 0.481 D_GP: 0.035 D_real: 0.877 D_fake: 0.838 +(epoch: 3, iters: 4784, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 1.111 G_ID: 0.994 G_Rec: 0.512 D_GP: 0.060 D_real: 0.630 D_fake: 0.920 +(epoch: 3, iters: 5184, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.968 G_ID: 0.462 G_Rec: 0.464 D_GP: 0.066 D_real: 0.938 D_fake: 0.728 +(epoch: 3, iters: 5584, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 1.039 G_ID: 0.982 G_Rec: 0.621 D_GP: 0.054 D_real: 0.672 D_fake: 0.960 +(epoch: 3, iters: 5984, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.823 G_ID: 0.424 G_Rec: 0.445 D_GP: 0.035 D_real: 0.974 D_fake: 0.779 +(epoch: 3, iters: 6384, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.915 G_ID: 1.007 G_Rec: 0.432 D_GP: 0.078 D_real: 1.004 D_fake: 0.696 +(epoch: 3, iters: 6784, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 1.005 G_ID: 0.473 G_Rec: 0.534 D_GP: 0.046 D_real: 1.028 D_fake: 0.785 +(epoch: 3, iters: 7184, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.943 G_ID: 0.988 G_Rec: 0.523 D_GP: 0.039 D_real: 1.083 D_fake: 0.738 +(epoch: 3, iters: 7584, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.810 G_ID: 0.444 G_Rec: 0.395 D_GP: 0.021 D_real: 1.115 D_fake: 0.681 +(epoch: 3, iters: 7984, time: 0.064) G_GAN: 0.067 G_GAN_Feat: 0.844 G_ID: 0.998 G_Rec: 0.445 D_GP: 0.029 D_real: 0.793 D_fake: 0.933 +(epoch: 3, iters: 8384, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.967 G_ID: 0.420 G_Rec: 0.580 D_GP: 0.051 D_real: 0.858 D_fake: 0.897 +(epoch: 4, iters: 176, time: 0.064) G_GAN: 0.051 G_GAN_Feat: 0.797 G_ID: 0.993 G_Rec: 0.374 D_GP: 0.036 D_real: 0.758 D_fake: 0.949 +(epoch: 4, iters: 576, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.879 G_ID: 0.526 G_Rec: 0.431 D_GP: 0.037 D_real: 1.062 D_fake: 0.807 +(epoch: 4, iters: 976, time: 0.064) G_GAN: -0.062 G_GAN_Feat: 0.940 G_ID: 0.975 G_Rec: 0.481 D_GP: 0.042 D_real: 0.681 D_fake: 1.062 +(epoch: 4, iters: 1376, time: 0.064) G_GAN: 0.017 G_GAN_Feat: 0.884 G_ID: 0.461 G_Rec: 0.519 D_GP: 0.022 D_real: 0.939 D_fake: 0.983 +(epoch: 4, iters: 1776, time: 0.064) G_GAN: -0.003 G_GAN_Feat: 0.931 G_ID: 1.008 G_Rec: 0.499 D_GP: 0.036 D_real: 0.566 D_fake: 1.003 +(epoch: 4, iters: 2176, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.685 G_ID: 0.473 G_Rec: 0.459 D_GP: 0.013 D_real: 0.998 D_fake: 0.870 +(epoch: 4, iters: 2576, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.933 G_ID: 0.978 G_Rec: 0.546 D_GP: 0.046 D_real: 0.989 D_fake: 0.751 +(epoch: 4, iters: 2976, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.849 G_ID: 0.517 G_Rec: 0.383 D_GP: 0.034 D_real: 1.061 D_fake: 0.753 +(epoch: 4, iters: 3376, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.875 G_ID: 1.008 G_Rec: 0.442 D_GP: 0.057 D_real: 0.765 D_fake: 0.924 +(epoch: 4, iters: 3776, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.901 G_ID: 0.517 G_Rec: 0.412 D_GP: 0.051 D_real: 1.254 D_fake: 0.484 +(epoch: 4, iters: 4176, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.827 G_ID: 0.974 G_Rec: 0.446 D_GP: 0.056 D_real: 0.958 D_fake: 0.849 +(epoch: 4, iters: 4576, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.848 G_ID: 0.480 G_Rec: 0.459 D_GP: 0.041 D_real: 1.059 D_fake: 0.829 +(epoch: 4, iters: 4976, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.867 G_ID: 1.031 G_Rec: 0.513 D_GP: 0.030 D_real: 1.342 D_fake: 0.478 +(epoch: 4, iters: 5376, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 1.050 G_ID: 0.511 G_Rec: 0.814 D_GP: 0.044 D_real: 0.877 D_fake: 0.928 +(epoch: 4, iters: 5776, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.932 G_ID: 0.976 G_Rec: 0.472 D_GP: 0.052 D_real: 1.108 D_fake: 0.607 +(epoch: 4, iters: 6176, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.963 G_ID: 0.456 G_Rec: 0.391 D_GP: 0.068 D_real: 0.705 D_fake: 0.917 +(epoch: 4, iters: 6576, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 0.844 G_ID: 0.984 G_Rec: 0.406 D_GP: 0.041 D_real: 1.212 D_fake: 0.563 +(epoch: 4, iters: 6976, time: 0.064) G_GAN: -0.016 G_GAN_Feat: 0.989 G_ID: 0.425 G_Rec: 0.501 D_GP: 0.042 D_real: 0.573 D_fake: 1.016 +(epoch: 4, iters: 7376, time: 0.064) G_GAN: 0.610 G_GAN_Feat: 0.742 G_ID: 1.012 G_Rec: 0.383 D_GP: 0.023 D_real: 1.441 D_fake: 0.592 +(epoch: 4, iters: 7776, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 1.001 G_ID: 0.431 G_Rec: 0.631 D_GP: 0.126 D_real: 0.979 D_fake: 0.770 +(epoch: 4, iters: 8176, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.808 G_ID: 0.982 G_Rec: 0.436 D_GP: 0.023 D_real: 0.929 D_fake: 0.870 +(epoch: 4, iters: 8576, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.811 G_ID: 0.495 G_Rec: 0.444 D_GP: 0.030 D_real: 1.010 D_fake: 0.794 +(epoch: 5, iters: 368, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.792 G_ID: 1.018 G_Rec: 0.649 D_GP: 0.034 D_real: 0.835 D_fake: 0.907 +(epoch: 5, iters: 768, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.759 G_ID: 0.467 G_Rec: 0.508 D_GP: 0.025 D_real: 1.117 D_fake: 0.699 +(epoch: 5, iters: 1168, time: 0.064) G_GAN: 0.803 G_GAN_Feat: 0.758 G_ID: 0.983 G_Rec: 0.466 D_GP: 0.016 D_real: 1.650 D_fake: 0.218 +(epoch: 5, iters: 1568, time: 0.064) G_GAN: -0.000 G_GAN_Feat: 0.901 G_ID: 0.407 G_Rec: 0.419 D_GP: 0.054 D_real: 0.693 D_fake: 1.000 +(epoch: 5, iters: 1968, time: 0.064) G_GAN: -0.033 G_GAN_Feat: 0.799 G_ID: 0.979 G_Rec: 0.488 D_GP: 0.037 D_real: 0.805 D_fake: 1.033 +(epoch: 5, iters: 2368, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.997 G_ID: 0.475 G_Rec: 0.546 D_GP: 0.074 D_real: 0.752 D_fake: 0.825 +(epoch: 5, iters: 2768, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.872 G_ID: 1.023 G_Rec: 0.393 D_GP: 0.042 D_real: 0.813 D_fake: 0.923 +(epoch: 5, iters: 3168, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.864 G_ID: 0.402 G_Rec: 0.476 D_GP: 0.031 D_real: 1.038 D_fake: 0.777 +(epoch: 5, iters: 3568, time: 0.064) G_GAN: -0.044 G_GAN_Feat: 0.685 G_ID: 1.025 G_Rec: 0.601 D_GP: 0.019 D_real: 0.826 D_fake: 1.046 +(epoch: 5, iters: 3968, time: 0.064) G_GAN: 0.006 G_GAN_Feat: 0.925 G_ID: 0.448 G_Rec: 0.388 D_GP: 0.056 D_real: 0.642 D_fake: 0.995 +(epoch: 5, iters: 4368, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.798 G_ID: 0.937 G_Rec: 0.651 D_GP: 0.028 D_real: 1.142 D_fake: 0.694 +(epoch: 5, iters: 4768, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 1.041 G_ID: 0.532 G_Rec: 0.514 D_GP: 0.072 D_real: 0.750 D_fake: 0.872 +(epoch: 5, iters: 5168, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.925 G_ID: 0.989 G_Rec: 0.415 D_GP: 0.043 D_real: 1.188 D_fake: 0.525 +(epoch: 5, iters: 5568, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.895 G_ID: 0.465 G_Rec: 0.568 D_GP: 0.037 D_real: 0.891 D_fake: 0.851 +(epoch: 5, iters: 5968, time: 0.064) G_GAN: 0.019 G_GAN_Feat: 0.611 G_ID: 0.964 G_Rec: 0.388 D_GP: 0.015 D_real: 0.985 D_fake: 0.981 +(epoch: 5, iters: 6368, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.893 G_ID: 0.479 G_Rec: 0.403 D_GP: 0.035 D_real: 0.899 D_fake: 0.845 +(epoch: 5, iters: 6768, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.910 G_ID: 0.979 G_Rec: 0.648 D_GP: 0.033 D_real: 0.962 D_fake: 0.656 +(epoch: 5, iters: 7168, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.862 G_ID: 0.464 G_Rec: 0.401 D_GP: 0.053 D_real: 1.161 D_fake: 0.630 +(epoch: 5, iters: 7568, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.737 G_ID: 0.993 G_Rec: 0.439 D_GP: 0.023 D_real: 0.973 D_fake: 0.905 +(epoch: 5, iters: 7968, time: 0.064) G_GAN: -0.164 G_GAN_Feat: 1.144 G_ID: 0.462 G_Rec: 0.829 D_GP: 0.102 D_real: 0.504 D_fake: 1.164 +(epoch: 5, iters: 8368, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.947 G_ID: 1.013 G_Rec: 0.484 D_GP: 0.031 D_real: 1.347 D_fake: 0.682 +(epoch: 6, iters: 160, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.823 G_ID: 0.357 G_Rec: 0.430 D_GP: 0.024 D_real: 0.856 D_fake: 0.905 +(epoch: 6, iters: 560, time: 0.064) G_GAN: 0.648 G_GAN_Feat: 0.995 G_ID: 1.027 G_Rec: 0.461 D_GP: 0.046 D_real: 1.335 D_fake: 0.393 +(epoch: 6, iters: 960, time: 0.064) G_GAN: -0.214 G_GAN_Feat: 0.687 G_ID: 0.403 G_Rec: 0.383 D_GP: 0.027 D_real: 0.612 D_fake: 1.214 +(epoch: 6, iters: 1360, time: 0.064) G_GAN: 0.780 G_GAN_Feat: 0.840 G_ID: 1.001 G_Rec: 0.714 D_GP: 0.017 D_real: 1.650 D_fake: 0.497 +(epoch: 6, iters: 1760, time: 0.064) G_GAN: -0.160 G_GAN_Feat: 0.776 G_ID: 0.490 G_Rec: 0.380 D_GP: 0.054 D_real: 0.604 D_fake: 1.160 +(epoch: 6, iters: 2160, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.840 G_ID: 0.999 G_Rec: 0.646 D_GP: 0.040 D_real: 0.909 D_fake: 0.814 +(epoch: 6, iters: 2560, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 1.001 G_ID: 0.480 G_Rec: 0.542 D_GP: 0.041 D_real: 1.250 D_fake: 0.655 +(epoch: 6, iters: 2960, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.836 G_ID: 1.003 G_Rec: 0.374 D_GP: 0.042 D_real: 1.029 D_fake: 0.646 +(epoch: 6, iters: 3360, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.821 G_ID: 0.502 G_Rec: 0.326 D_GP: 0.045 D_real: 1.002 D_fake: 0.735 +(epoch: 6, iters: 3760, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.902 G_ID: 0.987 G_Rec: 0.426 D_GP: 0.028 D_real: 0.842 D_fake: 0.827 +(epoch: 6, iters: 4160, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.954 G_ID: 0.518 G_Rec: 0.426 D_GP: 0.057 D_real: 1.052 D_fake: 0.826 +(epoch: 6, iters: 4560, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.848 G_ID: 1.003 G_Rec: 0.456 D_GP: 0.036 D_real: 1.084 D_fake: 0.686 +(epoch: 6, iters: 4960, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.948 G_ID: 0.395 G_Rec: 0.406 D_GP: 0.053 D_real: 0.904 D_fake: 0.655 +(epoch: 6, iters: 5360, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.872 G_ID: 0.989 G_Rec: 0.381 D_GP: 0.057 D_real: 0.877 D_fake: 0.915 +(epoch: 6, iters: 5760, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 0.791 G_ID: 0.422 G_Rec: 0.381 D_GP: 0.028 D_real: 1.343 D_fake: 0.584 +(epoch: 6, iters: 6160, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.814 G_ID: 0.961 G_Rec: 0.363 D_GP: 0.043 D_real: 0.964 D_fake: 0.766 +(epoch: 6, iters: 6560, time: 0.064) G_GAN: 0.067 G_GAN_Feat: 0.902 G_ID: 0.409 G_Rec: 0.356 D_GP: 0.079 D_real: 0.765 D_fake: 0.934 +(epoch: 6, iters: 6960, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.794 G_ID: 1.007 G_Rec: 0.437 D_GP: 0.030 D_real: 0.954 D_fake: 0.838 +(epoch: 6, iters: 7360, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.825 G_ID: 0.403 G_Rec: 0.483 D_GP: 0.036 D_real: 1.022 D_fake: 0.766 +(epoch: 6, iters: 7760, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.910 G_ID: 1.008 G_Rec: 0.529 D_GP: 0.050 D_real: 0.708 D_fake: 0.898 +(epoch: 6, iters: 8160, time: 0.064) G_GAN: -0.009 G_GAN_Feat: 0.811 G_ID: 0.552 G_Rec: 0.405 D_GP: 0.029 D_real: 0.735 D_fake: 1.010 +(epoch: 6, iters: 8560, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.815 G_ID: 0.996 G_Rec: 0.488 D_GP: 0.038 D_real: 0.932 D_fake: 0.840 +(epoch: 7, iters: 352, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.824 G_ID: 0.382 G_Rec: 0.375 D_GP: 0.043 D_real: 0.980 D_fake: 0.735 +(epoch: 7, iters: 752, time: 0.064) G_GAN: -0.008 G_GAN_Feat: 1.127 G_ID: 0.990 G_Rec: 0.421 D_GP: 0.075 D_real: 0.556 D_fake: 1.008 +(epoch: 7, iters: 1152, time: 0.064) G_GAN: -0.149 G_GAN_Feat: 0.800 G_ID: 0.477 G_Rec: 0.490 D_GP: 0.039 D_real: 0.623 D_fake: 1.149 +(epoch: 7, iters: 1552, time: 0.064) G_GAN: -0.040 G_GAN_Feat: 0.750 G_ID: 0.952 G_Rec: 0.363 D_GP: 0.043 D_real: 0.831 D_fake: 1.040 +(epoch: 7, iters: 1952, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.568 G_ID: 0.497 G_Rec: 0.390 D_GP: 0.014 D_real: 1.012 D_fake: 0.888 +(epoch: 7, iters: 2352, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 0.783 G_ID: 0.985 G_Rec: 0.380 D_GP: 0.057 D_real: 0.715 D_fake: 0.934 +(epoch: 7, iters: 2752, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.944 G_ID: 0.549 G_Rec: 0.444 D_GP: 0.068 D_real: 1.279 D_fake: 0.513 +(epoch: 7, iters: 3152, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.846 G_ID: 0.973 G_Rec: 0.498 D_GP: 0.054 D_real: 1.202 D_fake: 0.642 +(epoch: 7, iters: 3552, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.756 G_ID: 0.423 G_Rec: 0.434 D_GP: 0.036 D_real: 1.160 D_fake: 0.602 +(epoch: 7, iters: 3952, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.951 G_ID: 0.975 G_Rec: 0.395 D_GP: 0.046 D_real: 0.926 D_fake: 0.639 +(epoch: 7, iters: 4352, time: 0.064) G_GAN: -0.003 G_GAN_Feat: 0.828 G_ID: 0.453 G_Rec: 0.400 D_GP: 0.061 D_real: 0.746 D_fake: 1.003 +(epoch: 7, iters: 4752, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 1.070 G_ID: 0.940 G_Rec: 0.408 D_GP: 0.147 D_real: 0.538 D_fake: 0.778 +(epoch: 7, iters: 5152, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 1.008 G_ID: 0.382 G_Rec: 0.390 D_GP: 0.055 D_real: 0.757 D_fake: 0.707 +(epoch: 7, iters: 5552, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 1.161 G_ID: 0.964 G_Rec: 0.361 D_GP: 0.076 D_real: 1.254 D_fake: 0.609 +(epoch: 7, iters: 5952, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.850 G_ID: 0.422 G_Rec: 0.444 D_GP: 0.039 D_real: 1.139 D_fake: 0.600 +(epoch: 7, iters: 6352, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.854 G_ID: 0.981 G_Rec: 0.423 D_GP: 0.034 D_real: 0.950 D_fake: 0.776 +(epoch: 7, iters: 6752, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.887 G_ID: 0.557 G_Rec: 0.399 D_GP: 0.045 D_real: 0.905 D_fake: 0.858 +(epoch: 7, iters: 7152, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 1.094 G_ID: 0.967 G_Rec: 0.373 D_GP: 0.099 D_real: 0.565 D_fake: 0.749 +(epoch: 7, iters: 7552, time: 0.064) G_GAN: -0.019 G_GAN_Feat: 0.901 G_ID: 0.361 G_Rec: 0.404 D_GP: 0.079 D_real: 0.719 D_fake: 1.019 +(epoch: 7, iters: 7952, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.677 G_ID: 0.997 G_Rec: 0.613 D_GP: 0.022 D_real: 1.013 D_fake: 0.808 +(epoch: 7, iters: 8352, time: 0.064) G_GAN: -0.009 G_GAN_Feat: 0.767 G_ID: 0.407 G_Rec: 0.358 D_GP: 0.024 D_real: 0.750 D_fake: 1.009 +(epoch: 8, iters: 144, time: 0.064) G_GAN: -0.207 G_GAN_Feat: 0.785 G_ID: 0.997 G_Rec: 0.381 D_GP: 0.048 D_real: 0.512 D_fake: 1.207 +(epoch: 8, iters: 544, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.886 G_ID: 0.424 G_Rec: 0.372 D_GP: 0.048 D_real: 0.835 D_fake: 0.823 +(epoch: 8, iters: 944, time: 0.064) G_GAN: -0.169 G_GAN_Feat: 0.695 G_ID: 0.988 G_Rec: 0.387 D_GP: 0.028 D_real: 0.694 D_fake: 1.169 +(epoch: 8, iters: 1344, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.547 G_ID: 0.543 G_Rec: 0.533 D_GP: 0.009 D_real: 1.281 D_fake: 0.615 +(epoch: 8, iters: 1744, time: 0.064) G_GAN: -0.024 G_GAN_Feat: 0.543 G_ID: 0.960 G_Rec: 0.428 D_GP: 0.018 D_real: 0.886 D_fake: 1.024 +(epoch: 8, iters: 2144, time: 0.064) G_GAN: 0.019 G_GAN_Feat: 0.643 G_ID: 0.377 G_Rec: 0.350 D_GP: 0.056 D_real: 0.778 D_fake: 0.981 +(epoch: 8, iters: 2544, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.679 G_ID: 0.968 G_Rec: 0.401 D_GP: 0.026 D_real: 1.266 D_fake: 0.582 +(epoch: 8, iters: 2944, time: 0.064) G_GAN: 0.003 G_GAN_Feat: 0.913 G_ID: 0.405 G_Rec: 0.436 D_GP: 0.085 D_real: 0.566 D_fake: 0.997 +(epoch: 8, iters: 3344, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 1.116 G_ID: 0.990 G_Rec: 0.471 D_GP: 0.078 D_real: 0.797 D_fake: 0.840 +(epoch: 8, iters: 3744, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.842 G_ID: 0.383 G_Rec: 0.394 D_GP: 0.028 D_real: 1.033 D_fake: 0.649 +(epoch: 8, iters: 4144, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.814 G_ID: 0.990 G_Rec: 0.360 D_GP: 0.027 D_real: 0.779 D_fake: 0.895 +(epoch: 8, iters: 4544, time: 0.064) G_GAN: -0.019 G_GAN_Feat: 0.580 G_ID: 0.418 G_Rec: 0.501 D_GP: 0.019 D_real: 0.810 D_fake: 1.019 +(epoch: 8, iters: 4944, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.641 G_ID: 0.969 G_Rec: 0.309 D_GP: 0.038 D_real: 0.999 D_fake: 0.766 +(epoch: 8, iters: 5344, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.743 G_ID: 0.434 G_Rec: 0.368 D_GP: 0.068 D_real: 1.013 D_fake: 0.824 +(epoch: 8, iters: 5744, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.949 G_ID: 0.951 G_Rec: 0.431 D_GP: 0.079 D_real: 0.675 D_fake: 0.811 +(epoch: 8, iters: 6144, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.962 G_ID: 0.364 G_Rec: 0.389 D_GP: 0.076 D_real: 0.698 D_fake: 0.872 +(epoch: 8, iters: 6544, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.974 G_ID: 0.984 G_Rec: 0.757 D_GP: 0.088 D_real: 0.624 D_fake: 0.857 +(epoch: 8, iters: 6944, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 1.090 G_ID: 0.408 G_Rec: 0.428 D_GP: 0.090 D_real: 1.143 D_fake: 0.606 +(epoch: 8, iters: 7344, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.806 G_ID: 0.952 G_Rec: 0.353 D_GP: 0.036 D_real: 0.814 D_fake: 0.849 +(epoch: 8, iters: 7744, time: 0.064) G_GAN: 0.004 G_GAN_Feat: 0.896 G_ID: 0.420 G_Rec: 0.344 D_GP: 0.124 D_real: 0.733 D_fake: 0.996 +(epoch: 8, iters: 8144, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.627 G_ID: 0.988 G_Rec: 0.394 D_GP: 0.016 D_real: 1.048 D_fake: 0.879 +(epoch: 8, iters: 8544, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.891 G_ID: 0.428 G_Rec: 0.387 D_GP: 0.069 D_real: 0.870 D_fake: 0.774 +(epoch: 9, iters: 336, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.899 G_ID: 0.986 G_Rec: 0.346 D_GP: 0.107 D_real: 0.599 D_fake: 0.942 +(epoch: 9, iters: 736, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.970 G_ID: 0.424 G_Rec: 0.330 D_GP: 0.095 D_real: 0.495 D_fake: 0.918 +(epoch: 9, iters: 1136, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.625 G_ID: 0.986 G_Rec: 0.412 D_GP: 0.014 D_real: 1.022 D_fake: 0.858 +(epoch: 9, iters: 1536, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.768 G_ID: 0.380 G_Rec: 0.401 D_GP: 0.048 D_real: 1.134 D_fake: 0.643 +(epoch: 9, iters: 1936, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.872 G_ID: 0.963 G_Rec: 0.369 D_GP: 0.089 D_real: 0.799 D_fake: 0.783 +(epoch: 9, iters: 2336, time: 0.064) G_GAN: -0.044 G_GAN_Feat: 0.710 G_ID: 0.437 G_Rec: 0.372 D_GP: 0.036 D_real: 0.717 D_fake: 1.044 +(epoch: 9, iters: 2736, time: 0.064) G_GAN: -0.028 G_GAN_Feat: 1.063 G_ID: 0.975 G_Rec: 0.479 D_GP: 0.114 D_real: 0.497 D_fake: 1.028 +(epoch: 9, iters: 3136, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.662 G_ID: 0.463 G_Rec: 0.388 D_GP: 0.028 D_real: 1.080 D_fake: 0.753 +(epoch: 9, iters: 3536, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.589 G_ID: 0.967 G_Rec: 0.339 D_GP: 0.018 D_real: 0.929 D_fake: 0.989 +(epoch: 9, iters: 3936, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.876 G_ID: 0.369 G_Rec: 0.400 D_GP: 0.114 D_real: 0.674 D_fake: 0.918 +(epoch: 9, iters: 4336, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 0.798 G_ID: 0.935 G_Rec: 0.468 D_GP: 0.050 D_real: 0.829 D_fake: 0.892 +(epoch: 9, iters: 4736, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 1.019 G_ID: 0.445 G_Rec: 0.372 D_GP: 0.068 D_real: 0.558 D_fake: 0.912 +(epoch: 9, iters: 5136, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 1.389 G_ID: 0.990 G_Rec: 0.432 D_GP: 0.351 D_real: 0.388 D_fake: 0.740 +(epoch: 9, iters: 5536, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 0.671 G_ID: 0.442 G_Rec: 0.450 D_GP: 0.030 D_real: 0.795 D_fake: 0.944 +(epoch: 9, iters: 5936, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.817 G_ID: 0.935 G_Rec: 0.442 D_GP: 0.077 D_real: 0.890 D_fake: 0.698 +(epoch: 9, iters: 6336, time: 0.064) G_GAN: -0.064 G_GAN_Feat: 0.747 G_ID: 0.453 G_Rec: 0.358 D_GP: 0.057 D_real: 0.649 D_fake: 1.064 +(epoch: 9, iters: 6736, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.612 G_ID: 0.959 G_Rec: 0.324 D_GP: 0.019 D_real: 1.150 D_fake: 0.683 +(epoch: 9, iters: 7136, time: 0.064) G_GAN: 0.718 G_GAN_Feat: 0.713 G_ID: 0.485 G_Rec: 0.537 D_GP: 0.019 D_real: 1.583 D_fake: 0.345 +(epoch: 9, iters: 7536, time: 0.064) G_GAN: -0.217 G_GAN_Feat: 0.831 G_ID: 0.973 G_Rec: 0.392 D_GP: 0.050 D_real: 0.735 D_fake: 1.217 +(epoch: 9, iters: 7936, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 1.072 G_ID: 0.352 G_Rec: 0.420 D_GP: 0.061 D_real: 0.520 D_fake: 0.918 +(epoch: 9, iters: 8336, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.801 G_ID: 0.971 G_Rec: 0.339 D_GP: 0.052 D_real: 1.052 D_fake: 0.634 +(epoch: 10, iters: 128, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.941 G_ID: 0.393 G_Rec: 0.391 D_GP: 0.078 D_real: 0.831 D_fake: 0.673 +(epoch: 10, iters: 528, time: 0.064) G_GAN: 0.652 G_GAN_Feat: 1.110 G_ID: 0.961 G_Rec: 0.417 D_GP: 0.083 D_real: 1.016 D_fake: 0.461 +(epoch: 10, iters: 928, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.717 G_ID: 0.400 G_Rec: 0.595 D_GP: 0.030 D_real: 1.115 D_fake: 0.688 +(epoch: 10, iters: 1328, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.801 G_ID: 0.946 G_Rec: 0.391 D_GP: 0.069 D_real: 0.901 D_fake: 0.873 +(epoch: 10, iters: 1728, time: 0.064) G_GAN: 0.549 G_GAN_Feat: 0.736 G_ID: 0.434 G_Rec: 0.341 D_GP: 0.023 D_real: 1.345 D_fake: 0.466 +(epoch: 10, iters: 2128, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.791 G_ID: 0.983 G_Rec: 0.350 D_GP: 0.045 D_real: 1.103 D_fake: 0.585 +(epoch: 10, iters: 2528, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.660 G_ID: 0.447 G_Rec: 0.382 D_GP: 0.018 D_real: 1.143 D_fake: 0.708 +(epoch: 10, iters: 2928, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.867 G_ID: 0.984 G_Rec: 0.452 D_GP: 0.058 D_real: 1.290 D_fake: 0.476 +(epoch: 10, iters: 3328, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 0.872 G_ID: 0.392 G_Rec: 0.349 D_GP: 0.053 D_real: 1.103 D_fake: 0.609 +(epoch: 10, iters: 3728, time: 0.064) G_GAN: 0.007 G_GAN_Feat: 0.709 G_ID: 0.947 G_Rec: 0.383 D_GP: 0.029 D_real: 0.783 D_fake: 0.993 +(epoch: 10, iters: 4128, time: 0.064) G_GAN: 0.551 G_GAN_Feat: 0.736 G_ID: 0.465 G_Rec: 0.338 D_GP: 0.031 D_real: 1.343 D_fake: 0.554 +(epoch: 10, iters: 4528, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.800 G_ID: 0.992 G_Rec: 0.582 D_GP: 0.051 D_real: 1.077 D_fake: 0.599 +(epoch: 10, iters: 4928, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.979 G_ID: 0.418 G_Rec: 0.478 D_GP: 0.041 D_real: 1.163 D_fake: 0.492 +(epoch: 10, iters: 5328, time: 0.064) G_GAN: -0.007 G_GAN_Feat: 1.012 G_ID: 1.000 G_Rec: 0.497 D_GP: 0.099 D_real: 0.990 D_fake: 1.007 +(epoch: 10, iters: 5728, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.857 G_ID: 0.398 G_Rec: 0.361 D_GP: 0.029 D_real: 1.090 D_fake: 0.548 +(epoch: 10, iters: 6128, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.707 G_ID: 0.932 G_Rec: 0.407 D_GP: 0.015 D_real: 1.310 D_fake: 0.543 +(epoch: 10, iters: 6528, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.672 G_ID: 0.488 G_Rec: 0.470 D_GP: 0.031 D_real: 1.157 D_fake: 0.616 +(epoch: 10, iters: 6928, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.719 G_ID: 0.967 G_Rec: 0.395 D_GP: 0.039 D_real: 0.996 D_fake: 0.807 +(epoch: 10, iters: 7328, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 1.028 G_ID: 0.369 G_Rec: 0.438 D_GP: 0.113 D_real: 0.618 D_fake: 0.738 +(epoch: 10, iters: 7728, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.922 G_ID: 0.971 G_Rec: 0.517 D_GP: 0.140 D_real: 0.553 D_fake: 0.948 +(epoch: 10, iters: 8128, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.673 G_ID: 0.437 G_Rec: 0.476 D_GP: 0.018 D_real: 1.176 D_fake: 0.692 +(epoch: 10, iters: 8528, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.720 G_ID: 0.994 G_Rec: 0.320 D_GP: 0.071 D_real: 0.860 D_fake: 0.870 +(epoch: 11, iters: 320, time: 0.064) G_GAN: -0.044 G_GAN_Feat: 0.757 G_ID: 0.403 G_Rec: 0.407 D_GP: 0.042 D_real: 0.674 D_fake: 1.044 +(epoch: 11, iters: 720, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.901 G_ID: 0.927 G_Rec: 0.421 D_GP: 0.087 D_real: 0.864 D_fake: 0.600 +(epoch: 11, iters: 1120, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.961 G_ID: 0.398 G_Rec: 0.409 D_GP: 0.123 D_real: 0.835 D_fake: 0.571 +(epoch: 11, iters: 1520, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.761 G_ID: 0.972 G_Rec: 0.473 D_GP: 0.043 D_real: 1.176 D_fake: 0.589 +(epoch: 11, iters: 1920, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.707 G_ID: 0.472 G_Rec: 0.334 D_GP: 0.033 D_real: 1.243 D_fake: 0.588 +(epoch: 11, iters: 2320, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 1.027 G_ID: 0.970 G_Rec: 0.354 D_GP: 0.048 D_real: 0.865 D_fake: 0.603 +(epoch: 11, iters: 2720, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.907 G_ID: 0.327 G_Rec: 0.364 D_GP: 0.059 D_real: 0.959 D_fake: 0.731 +(epoch: 11, iters: 3120, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.763 G_ID: 0.975 G_Rec: 0.399 D_GP: 0.029 D_real: 1.084 D_fake: 0.669 +(epoch: 11, iters: 3520, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.696 G_ID: 0.405 G_Rec: 0.418 D_GP: 0.025 D_real: 1.009 D_fake: 0.763 +(epoch: 11, iters: 3920, time: 0.064) G_GAN: -0.078 G_GAN_Feat: 0.918 G_ID: 0.940 G_Rec: 0.385 D_GP: 0.094 D_real: 0.494 D_fake: 1.078 +(epoch: 11, iters: 4320, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.698 G_ID: 0.422 G_Rec: 0.413 D_GP: 0.026 D_real: 1.147 D_fake: 0.737 +(epoch: 11, iters: 4720, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.892 G_ID: 0.946 G_Rec: 0.418 D_GP: 0.031 D_real: 1.174 D_fake: 0.498 +(epoch: 11, iters: 5120, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.983 G_ID: 0.399 G_Rec: 0.364 D_GP: 0.104 D_real: 0.880 D_fake: 0.565 +(epoch: 11, iters: 5520, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.489 G_ID: 0.971 G_Rec: 0.320 D_GP: 0.016 D_real: 1.151 D_fake: 0.786 +(epoch: 11, iters: 5920, time: 0.064) G_GAN: -0.065 G_GAN_Feat: 0.613 G_ID: 0.418 G_Rec: 0.520 D_GP: 0.022 D_real: 0.700 D_fake: 1.065 +(epoch: 11, iters: 6320, time: 0.064) G_GAN: -0.042 G_GAN_Feat: 0.618 G_ID: 0.939 G_Rec: 0.347 D_GP: 0.038 D_real: 0.823 D_fake: 1.042 +(epoch: 11, iters: 6720, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.647 G_ID: 0.353 G_Rec: 0.312 D_GP: 0.033 D_real: 1.007 D_fake: 0.766 +(epoch: 11, iters: 7120, time: 0.064) G_GAN: 0.555 G_GAN_Feat: 0.910 G_ID: 0.973 G_Rec: 0.477 D_GP: 0.042 D_real: 1.348 D_fake: 0.587 +(epoch: 11, iters: 7520, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 0.664 G_ID: 0.399 G_Rec: 0.484 D_GP: 0.034 D_real: 0.873 D_fake: 0.974 +(epoch: 11, iters: 7920, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 1.114 G_ID: 0.997 G_Rec: 0.433 D_GP: 0.140 D_real: 0.488 D_fake: 0.727 +(epoch: 11, iters: 8320, time: 0.064) G_GAN: 0.034 G_GAN_Feat: 0.838 G_ID: 0.351 G_Rec: 0.308 D_GP: 0.057 D_real: 0.595 D_fake: 0.966 +(epoch: 12, iters: 112, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.763 G_ID: 0.950 G_Rec: 0.623 D_GP: 0.036 D_real: 1.038 D_fake: 0.756 +(epoch: 12, iters: 512, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.668 G_ID: 0.428 G_Rec: 0.398 D_GP: 0.023 D_real: 1.084 D_fake: 0.722 +(epoch: 12, iters: 912, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 0.929 G_ID: 0.956 G_Rec: 0.461 D_GP: 0.078 D_real: 0.982 D_fake: 0.527 +(epoch: 12, iters: 1312, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.977 G_ID: 0.431 G_Rec: 0.608 D_GP: 0.058 D_real: 0.514 D_fake: 0.968 +(epoch: 12, iters: 1712, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.752 G_ID: 0.962 G_Rec: 0.310 D_GP: 0.060 D_real: 1.078 D_fake: 0.689 +(epoch: 12, iters: 2112, time: 0.064) G_GAN: -0.177 G_GAN_Feat: 0.622 G_ID: 0.445 G_Rec: 0.379 D_GP: 0.022 D_real: 0.647 D_fake: 1.177 +(epoch: 12, iters: 2512, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 1.198 G_ID: 0.928 G_Rec: 0.403 D_GP: 0.115 D_real: 0.388 D_fake: 0.934 +(epoch: 12, iters: 2912, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.576 G_ID: 0.482 G_Rec: 0.400 D_GP: 0.015 D_real: 1.049 D_fake: 0.834 +(epoch: 12, iters: 3312, time: 0.064) G_GAN: 0.078 G_GAN_Feat: 0.845 G_ID: 1.003 G_Rec: 0.604 D_GP: 0.082 D_real: 0.745 D_fake: 0.922 +(epoch: 12, iters: 3712, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 1.101 G_ID: 0.395 G_Rec: 0.653 D_GP: 0.152 D_real: 0.473 D_fake: 0.807 +(epoch: 12, iters: 4112, time: 0.064) G_GAN: 0.520 G_GAN_Feat: 0.851 G_ID: 0.952 G_Rec: 0.467 D_GP: 0.039 D_real: 1.284 D_fake: 0.484 +(epoch: 12, iters: 4512, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.796 G_ID: 0.415 G_Rec: 0.349 D_GP: 0.047 D_real: 0.826 D_fake: 0.815 +(epoch: 12, iters: 4912, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.435 G_ID: 0.953 G_Rec: 0.391 D_GP: 0.011 D_real: 1.190 D_fake: 0.733 +(epoch: 12, iters: 5312, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.456 G_ID: 0.359 G_Rec: 0.340 D_GP: 0.012 D_real: 1.059 D_fake: 0.800 +(epoch: 12, iters: 5712, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.491 G_ID: 0.946 G_Rec: 0.366 D_GP: 0.015 D_real: 1.098 D_fake: 0.811 +(epoch: 12, iters: 6112, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.615 G_ID: 0.389 G_Rec: 0.421 D_GP: 0.019 D_real: 1.009 D_fake: 0.968 +(epoch: 12, iters: 6512, time: 0.064) G_GAN: -0.270 G_GAN_Feat: 0.532 G_ID: 0.948 G_Rec: 0.310 D_GP: 0.030 D_real: 0.566 D_fake: 1.270 +(epoch: 12, iters: 6912, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.603 G_ID: 0.375 G_Rec: 0.318 D_GP: 0.052 D_real: 0.875 D_fake: 0.902 +(epoch: 12, iters: 7312, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.696 G_ID: 0.906 G_Rec: 0.388 D_GP: 0.064 D_real: 1.057 D_fake: 0.690 +(epoch: 12, iters: 7712, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.674 G_ID: 0.363 G_Rec: 0.345 D_GP: 0.065 D_real: 1.027 D_fake: 0.731 +(epoch: 12, iters: 8112, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.585 G_ID: 0.960 G_Rec: 0.344 D_GP: 0.021 D_real: 1.261 D_fake: 0.606 +(epoch: 12, iters: 8512, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.770 G_ID: 0.412 G_Rec: 0.500 D_GP: 0.066 D_real: 0.880 D_fake: 0.760 +(epoch: 13, iters: 304, time: 0.064) G_GAN: -0.080 G_GAN_Feat: 0.658 G_ID: 0.917 G_Rec: 0.354 D_GP: 0.049 D_real: 0.674 D_fake: 1.080 +(epoch: 13, iters: 704, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.798 G_ID: 0.411 G_Rec: 0.471 D_GP: 0.069 D_real: 1.007 D_fake: 0.626 +(epoch: 13, iters: 1104, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.740 G_ID: 0.947 G_Rec: 0.411 D_GP: 0.043 D_real: 1.147 D_fake: 0.612 +(epoch: 13, iters: 1504, time: 0.064) G_GAN: 0.582 G_GAN_Feat: 1.012 G_ID: 0.453 G_Rec: 0.466 D_GP: 0.127 D_real: 0.883 D_fake: 0.436 +(epoch: 13, iters: 1904, time: 0.064) G_GAN: -0.050 G_GAN_Feat: 0.735 G_ID: 0.946 G_Rec: 0.349 D_GP: 0.061 D_real: 0.624 D_fake: 1.050 +(epoch: 13, iters: 2304, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.902 G_ID: 0.459 G_Rec: 0.346 D_GP: 0.143 D_real: 1.003 D_fake: 0.510 +(epoch: 13, iters: 2704, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.679 G_ID: 0.972 G_Rec: 0.384 D_GP: 0.032 D_real: 1.033 D_fake: 0.711 +(epoch: 13, iters: 3104, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.842 G_ID: 0.386 G_Rec: 0.571 D_GP: 0.085 D_real: 0.778 D_fake: 0.825 +(epoch: 13, iters: 3504, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.767 G_ID: 0.920 G_Rec: 0.365 D_GP: 0.063 D_real: 0.998 D_fake: 0.641 +(epoch: 13, iters: 3904, time: 0.064) G_GAN: 0.793 G_GAN_Feat: 0.705 G_ID: 0.459 G_Rec: 0.349 D_GP: 0.025 D_real: 1.559 D_fake: 0.277 +(epoch: 13, iters: 4304, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.734 G_ID: 0.954 G_Rec: 0.367 D_GP: 0.048 D_real: 1.102 D_fake: 0.557 +(epoch: 13, iters: 4704, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.709 G_ID: 0.393 G_Rec: 0.370 D_GP: 0.028 D_real: 0.854 D_fake: 0.912 +(epoch: 13, iters: 5104, time: 0.064) G_GAN: 0.552 G_GAN_Feat: 0.694 G_ID: 0.933 G_Rec: 0.357 D_GP: 0.023 D_real: 1.362 D_fake: 0.499 +(epoch: 13, iters: 5504, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.594 G_ID: 0.384 G_Rec: 0.450 D_GP: 0.024 D_real: 0.920 D_fake: 0.843 +(epoch: 13, iters: 5904, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.664 G_ID: 0.949 G_Rec: 0.341 D_GP: 0.050 D_real: 0.960 D_fake: 0.751 +(epoch: 13, iters: 6304, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.725 G_ID: 0.416 G_Rec: 0.339 D_GP: 0.079 D_real: 0.984 D_fake: 0.757 +(epoch: 13, iters: 6704, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.644 G_ID: 0.938 G_Rec: 0.403 D_GP: 0.045 D_real: 0.741 D_fake: 0.959 +(epoch: 13, iters: 7104, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.824 G_ID: 0.363 G_Rec: 0.347 D_GP: 0.096 D_real: 0.893 D_fake: 0.587 +(epoch: 13, iters: 7504, time: 0.064) G_GAN: 0.060 G_GAN_Feat: 0.850 G_ID: 0.921 G_Rec: 0.431 D_GP: 0.026 D_real: 0.712 D_fake: 0.940 +(epoch: 13, iters: 7904, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.787 G_ID: 0.478 G_Rec: 0.415 D_GP: 0.073 D_real: 1.030 D_fake: 0.761 +(epoch: 13, iters: 8304, time: 0.064) G_GAN: 0.004 G_GAN_Feat: 0.784 G_ID: 0.950 G_Rec: 0.438 D_GP: 0.064 D_real: 0.609 D_fake: 0.996 +(epoch: 14, iters: 96, time: 0.064) G_GAN: 0.523 G_GAN_Feat: 0.987 G_ID: 0.483 G_Rec: 0.440 D_GP: 0.047 D_real: 1.045 D_fake: 0.485 +(epoch: 14, iters: 496, time: 0.064) G_GAN: 0.723 G_GAN_Feat: 0.740 G_ID: 0.919 G_Rec: 0.377 D_GP: 0.040 D_real: 1.437 D_fake: 0.411 +(epoch: 14, iters: 896, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.934 G_ID: 0.408 G_Rec: 0.391 D_GP: 0.044 D_real: 0.695 D_fake: 0.780 +(epoch: 14, iters: 1296, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.866 G_ID: 0.941 G_Rec: 0.367 D_GP: 0.035 D_real: 1.034 D_fake: 0.832 +(epoch: 14, iters: 1696, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.824 G_ID: 0.402 G_Rec: 0.526 D_GP: 0.041 D_real: 0.929 D_fake: 0.898 +(epoch: 14, iters: 2096, time: 0.064) G_GAN: -0.040 G_GAN_Feat: 0.887 G_ID: 0.923 G_Rec: 0.574 D_GP: 0.067 D_real: 0.415 D_fake: 1.040 +(epoch: 14, iters: 2496, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 0.688 G_ID: 0.455 G_Rec: 0.353 D_GP: 0.038 D_real: 1.145 D_fake: 0.553 +(epoch: 14, iters: 2896, time: 0.064) G_GAN: -0.044 G_GAN_Feat: 0.753 G_ID: 0.932 G_Rec: 0.371 D_GP: 0.038 D_real: 0.729 D_fake: 1.044 +(epoch: 14, iters: 3296, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.665 G_ID: 0.454 G_Rec: 0.384 D_GP: 0.025 D_real: 0.971 D_fake: 0.781 +(epoch: 14, iters: 3696, time: 0.064) G_GAN: -0.050 G_GAN_Feat: 0.984 G_ID: 0.944 G_Rec: 0.341 D_GP: 0.130 D_real: 0.384 D_fake: 1.050 +(epoch: 14, iters: 4096, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.821 G_ID: 0.302 G_Rec: 0.367 D_GP: 0.038 D_real: 1.029 D_fake: 0.706 +(epoch: 14, iters: 4496, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.533 G_ID: 0.931 G_Rec: 0.337 D_GP: 0.015 D_real: 1.137 D_fake: 0.772 +(epoch: 14, iters: 4896, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.739 G_ID: 0.430 G_Rec: 0.615 D_GP: 0.041 D_real: 0.924 D_fake: 0.926 +(epoch: 14, iters: 5296, time: 0.064) G_GAN: 0.581 G_GAN_Feat: 0.805 G_ID: 0.908 G_Rec: 0.390 D_GP: 0.055 D_real: 1.223 D_fake: 0.422 +(epoch: 14, iters: 5696, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.681 G_ID: 0.456 G_Rec: 0.385 D_GP: 0.042 D_real: 0.937 D_fake: 0.795 +(epoch: 14, iters: 6096, time: 0.064) G_GAN: -0.085 G_GAN_Feat: 0.681 G_ID: 0.943 G_Rec: 0.359 D_GP: 0.039 D_real: 0.773 D_fake: 1.085 +(epoch: 14, iters: 6496, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 0.766 G_ID: 0.521 G_Rec: 0.381 D_GP: 0.074 D_real: 0.746 D_fake: 0.943 +(epoch: 14, iters: 6896, time: 0.064) G_GAN: 0.028 G_GAN_Feat: 0.819 G_ID: 0.953 G_Rec: 0.352 D_GP: 0.053 D_real: 0.640 D_fake: 0.972 +(epoch: 14, iters: 7296, time: 0.064) G_GAN: 0.014 G_GAN_Feat: 1.235 G_ID: 0.351 G_Rec: 0.343 D_GP: 0.088 D_real: 0.499 D_fake: 0.986 +(epoch: 14, iters: 7696, time: 0.064) G_GAN: -0.037 G_GAN_Feat: 0.774 G_ID: 0.926 G_Rec: 0.504 D_GP: 0.049 D_real: 0.661 D_fake: 1.037 +(epoch: 14, iters: 8096, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.720 G_ID: 0.438 G_Rec: 0.382 D_GP: 0.024 D_real: 0.844 D_fake: 0.822 +(epoch: 14, iters: 8496, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 0.687 G_ID: 0.893 G_Rec: 0.352 D_GP: 0.027 D_real: 1.317 D_fake: 0.489 +(epoch: 15, iters: 288, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.894 G_ID: 0.399 G_Rec: 0.495 D_GP: 0.104 D_real: 0.831 D_fake: 0.695 +(epoch: 15, iters: 688, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.802 G_ID: 0.955 G_Rec: 0.341 D_GP: 0.040 D_real: 0.642 D_fake: 0.964 +(epoch: 15, iters: 1088, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.952 G_ID: 0.429 G_Rec: 0.414 D_GP: 0.055 D_real: 0.640 D_fake: 0.830 +(epoch: 15, iters: 1488, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.986 G_ID: 0.951 G_Rec: 0.325 D_GP: 0.103 D_real: 0.576 D_fake: 0.911 +(epoch: 15, iters: 1888, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.703 G_ID: 0.413 G_Rec: 0.387 D_GP: 0.037 D_real: 0.862 D_fake: 0.816 +(epoch: 15, iters: 2288, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.752 G_ID: 0.919 G_Rec: 0.403 D_GP: 0.041 D_real: 0.823 D_fake: 0.908 +(epoch: 15, iters: 2688, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.580 G_ID: 0.486 G_Rec: 0.344 D_GP: 0.018 D_real: 1.006 D_fake: 0.866 +(epoch: 15, iters: 3088, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.708 G_ID: 0.935 G_Rec: 0.370 D_GP: 0.057 D_real: 1.000 D_fake: 0.648 +(epoch: 15, iters: 3488, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.739 G_ID: 0.400 G_Rec: 0.606 D_GP: 0.060 D_real: 0.911 D_fake: 0.818 +(epoch: 15, iters: 3888, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 1.123 G_ID: 0.932 G_Rec: 0.356 D_GP: 0.154 D_real: 0.459 D_fake: 0.826 +(epoch: 15, iters: 4288, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.927 G_ID: 0.419 G_Rec: 0.420 D_GP: 0.045 D_real: 0.804 D_fake: 0.645 +(epoch: 15, iters: 4688, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 1.369 G_ID: 0.959 G_Rec: 0.369 D_GP: 0.298 D_real: 0.493 D_fake: 0.565 +(epoch: 15, iters: 5088, time: 0.064) G_GAN: 0.016 G_GAN_Feat: 0.663 G_ID: 0.421 G_Rec: 0.459 D_GP: 0.034 D_real: 0.744 D_fake: 0.984 +(epoch: 15, iters: 5488, time: 0.064) G_GAN: 0.003 G_GAN_Feat: 0.826 G_ID: 0.912 G_Rec: 0.361 D_GP: 0.094 D_real: 0.455 D_fake: 0.997 +(epoch: 15, iters: 5888, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.940 G_ID: 0.423 G_Rec: 0.705 D_GP: 0.032 D_real: 1.128 D_fake: 0.523 +(epoch: 15, iters: 6288, time: 0.064) G_GAN: -0.014 G_GAN_Feat: 1.008 G_ID: 0.931 G_Rec: 0.368 D_GP: 0.170 D_real: 0.409 D_fake: 1.014 +(epoch: 15, iters: 6688, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.762 G_ID: 0.365 G_Rec: 0.344 D_GP: 0.031 D_real: 1.202 D_fake: 0.672 +(epoch: 15, iters: 7088, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.672 G_ID: 0.944 G_Rec: 0.373 D_GP: 0.017 D_real: 0.902 D_fake: 0.923 +(epoch: 15, iters: 7488, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.755 G_ID: 0.332 G_Rec: 0.412 D_GP: 0.064 D_real: 0.798 D_fake: 0.878 +(epoch: 15, iters: 7888, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.930 G_ID: 0.930 G_Rec: 0.441 D_GP: 0.128 D_real: 0.689 D_fake: 0.754 +(epoch: 15, iters: 8288, time: 0.064) G_GAN: 0.068 G_GAN_Feat: 0.594 G_ID: 0.412 G_Rec: 0.315 D_GP: 0.016 D_real: 0.948 D_fake: 0.932 +(epoch: 16, iters: 80, time: 0.064) G_GAN: 0.021 G_GAN_Feat: 0.790 G_ID: 0.924 G_Rec: 0.410 D_GP: 0.066 D_real: 0.744 D_fake: 0.979 +(epoch: 16, iters: 480, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 1.100 G_ID: 0.361 G_Rec: 0.364 D_GP: 0.169 D_real: 0.652 D_fake: 0.669 +(epoch: 16, iters: 880, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.729 G_ID: 0.922 G_Rec: 0.549 D_GP: 0.038 D_real: 0.772 D_fake: 0.977 +(epoch: 16, iters: 1280, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.755 G_ID: 0.392 G_Rec: 0.320 D_GP: 0.061 D_real: 1.005 D_fake: 0.698 +(epoch: 16, iters: 1680, time: 0.064) G_GAN: -0.661 G_GAN_Feat: 1.123 G_ID: 0.931 G_Rec: 0.370 D_GP: 0.111 D_real: 0.061 D_fake: 1.661 +(epoch: 16, iters: 2080, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 1.046 G_ID: 0.379 G_Rec: 0.377 D_GP: 0.334 D_real: 0.503 D_fake: 0.768 +(epoch: 16, iters: 2480, time: 0.064) G_GAN: 0.001 G_GAN_Feat: 0.892 G_ID: 0.903 G_Rec: 0.457 D_GP: 0.113 D_real: 0.884 D_fake: 1.000 +(epoch: 16, iters: 2880, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.875 G_ID: 0.375 G_Rec: 0.420 D_GP: 0.045 D_real: 1.224 D_fake: 0.536 +(epoch: 16, iters: 3280, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.820 G_ID: 0.907 G_Rec: 0.607 D_GP: 0.055 D_real: 1.017 D_fake: 0.596 +(epoch: 16, iters: 3680, time: 0.064) G_GAN: -0.031 G_GAN_Feat: 0.915 G_ID: 0.353 G_Rec: 0.479 D_GP: 0.075 D_real: 0.821 D_fake: 1.031 +(epoch: 16, iters: 4080, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.984 G_ID: 0.915 G_Rec: 0.369 D_GP: 0.028 D_real: 0.850 D_fake: 0.744 +(epoch: 16, iters: 4480, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.957 G_ID: 0.377 G_Rec: 0.448 D_GP: 0.070 D_real: 0.789 D_fake: 0.665 +(epoch: 16, iters: 4880, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.598 G_ID: 0.922 G_Rec: 0.455 D_GP: 0.015 D_real: 1.075 D_fake: 0.800 +(epoch: 16, iters: 5280, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.837 G_ID: 0.404 G_Rec: 0.397 D_GP: 0.027 D_real: 1.247 D_fake: 0.544 +(epoch: 16, iters: 5680, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.788 G_ID: 0.924 G_Rec: 0.358 D_GP: 0.057 D_real: 0.869 D_fake: 0.812 +(epoch: 16, iters: 6080, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.840 G_ID: 0.338 G_Rec: 0.373 D_GP: 0.052 D_real: 0.934 D_fake: 0.679 +(epoch: 16, iters: 6480, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.694 G_ID: 0.881 G_Rec: 0.457 D_GP: 0.061 D_real: 0.966 D_fake: 0.795 +(epoch: 16, iters: 6880, time: 0.064) G_GAN: 0.019 G_GAN_Feat: 0.823 G_ID: 0.408 G_Rec: 0.355 D_GP: 0.078 D_real: 0.782 D_fake: 0.981 +(epoch: 16, iters: 7280, time: 0.064) G_GAN: -0.045 G_GAN_Feat: 1.056 G_ID: 0.942 G_Rec: 0.450 D_GP: 0.176 D_real: 0.262 D_fake: 1.045 +(epoch: 16, iters: 7680, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 1.206 G_ID: 0.426 G_Rec: 0.366 D_GP: 0.274 D_real: 0.496 D_fake: 0.909 +(epoch: 16, iters: 8080, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.804 G_ID: 0.960 G_Rec: 0.446 D_GP: 0.057 D_real: 1.057 D_fake: 0.624 +(epoch: 16, iters: 8480, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.951 G_ID: 0.418 G_Rec: 0.341 D_GP: 0.069 D_real: 1.102 D_fake: 0.493 +(epoch: 17, iters: 272, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.850 G_ID: 0.894 G_Rec: 0.388 D_GP: 0.051 D_real: 0.903 D_fake: 0.676 +(epoch: 17, iters: 672, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.897 G_ID: 0.372 G_Rec: 0.426 D_GP: 0.059 D_real: 0.971 D_fake: 0.552 +(epoch: 17, iters: 1072, time: 0.064) G_GAN: 0.624 G_GAN_Feat: 0.794 G_ID: 0.946 G_Rec: 0.402 D_GP: 0.048 D_real: 1.279 D_fake: 0.380 +(epoch: 17, iters: 1472, time: 0.064) G_GAN: 0.530 G_GAN_Feat: 0.656 G_ID: 0.447 G_Rec: 0.423 D_GP: 0.031 D_real: 1.319 D_fake: 0.471 +(epoch: 17, iters: 1872, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.845 G_ID: 0.934 G_Rec: 0.436 D_GP: 0.041 D_real: 0.710 D_fake: 0.882 +(epoch: 17, iters: 2272, time: 0.064) G_GAN: -0.163 G_GAN_Feat: 0.713 G_ID: 0.461 G_Rec: 0.348 D_GP: 0.024 D_real: 0.706 D_fake: 1.164 +(epoch: 17, iters: 2672, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.838 G_ID: 0.896 G_Rec: 0.330 D_GP: 0.030 D_real: 1.107 D_fake: 0.530 +(epoch: 17, iters: 3072, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.832 G_ID: 0.404 G_Rec: 0.342 D_GP: 0.053 D_real: 1.197 D_fake: 0.545 +(epoch: 17, iters: 3472, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.851 G_ID: 0.902 G_Rec: 0.425 D_GP: 0.043 D_real: 1.127 D_fake: 0.655 +(epoch: 17, iters: 3872, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.923 G_ID: 0.436 G_Rec: 0.382 D_GP: 0.026 D_real: 0.931 D_fake: 0.684 +(epoch: 17, iters: 4272, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.857 G_ID: 0.920 G_Rec: 0.520 D_GP: 0.041 D_real: 0.956 D_fake: 0.705 +(epoch: 17, iters: 4672, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.497 G_ID: 0.400 G_Rec: 0.337 D_GP: 0.021 D_real: 1.138 D_fake: 0.681 +(epoch: 17, iters: 5072, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.704 G_ID: 0.909 G_Rec: 0.431 D_GP: 0.047 D_real: 0.917 D_fake: 0.761 +(epoch: 17, iters: 5472, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 0.874 G_ID: 0.426 G_Rec: 0.348 D_GP: 0.120 D_real: 0.553 D_fake: 0.989 +(epoch: 17, iters: 5872, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.883 G_ID: 0.886 G_Rec: 0.363 D_GP: 0.312 D_real: 0.708 D_fake: 0.665 +(epoch: 17, iters: 6272, time: 0.064) G_GAN: 0.076 G_GAN_Feat: 0.851 G_ID: 0.341 G_Rec: 0.374 D_GP: 0.054 D_real: 0.519 D_fake: 0.924 +(epoch: 17, iters: 6672, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 1.015 G_ID: 0.854 G_Rec: 0.343 D_GP: 0.179 D_real: 0.682 D_fake: 0.677 +(epoch: 17, iters: 7072, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.689 G_ID: 0.486 G_Rec: 0.440 D_GP: 0.027 D_real: 1.171 D_fake: 0.646 +(epoch: 17, iters: 7472, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.867 G_ID: 0.894 G_Rec: 0.353 D_GP: 0.079 D_real: 0.957 D_fake: 0.698 +(epoch: 17, iters: 7872, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.621 G_ID: 0.461 G_Rec: 0.378 D_GP: 0.018 D_real: 0.991 D_fake: 0.814 +(epoch: 17, iters: 8272, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.831 G_ID: 0.915 G_Rec: 0.437 D_GP: 0.052 D_real: 0.827 D_fake: 0.744 +(epoch: 18, iters: 64, time: 0.064) G_GAN: -0.060 G_GAN_Feat: 0.583 G_ID: 0.418 G_Rec: 0.453 D_GP: 0.020 D_real: 0.810 D_fake: 1.060 +(epoch: 18, iters: 464, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.730 G_ID: 0.892 G_Rec: 0.340 D_GP: 0.046 D_real: 1.128 D_fake: 0.594 +(epoch: 18, iters: 864, time: 0.064) G_GAN: 0.672 G_GAN_Feat: 1.016 G_ID: 0.456 G_Rec: 0.387 D_GP: 0.254 D_real: 1.252 D_fake: 0.362 +(epoch: 18, iters: 1264, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.610 G_ID: 0.903 G_Rec: 0.353 D_GP: 0.024 D_real: 1.103 D_fake: 0.714 +(epoch: 18, iters: 1664, time: 0.064) G_GAN: 0.035 G_GAN_Feat: 0.764 G_ID: 0.418 G_Rec: 0.436 D_GP: 0.062 D_real: 0.817 D_fake: 0.966 +(epoch: 18, iters: 2064, time: 0.064) G_GAN: 0.670 G_GAN_Feat: 0.836 G_ID: 0.907 G_Rec: 0.700 D_GP: 0.036 D_real: 1.427 D_fake: 0.339 +(epoch: 18, iters: 2464, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.656 G_ID: 0.421 G_Rec: 0.382 D_GP: 0.036 D_real: 1.046 D_fake: 0.689 +(epoch: 18, iters: 2864, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.901 G_ID: 0.875 G_Rec: 0.400 D_GP: 0.107 D_real: 0.648 D_fake: 0.785 +(epoch: 18, iters: 3264, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.563 G_ID: 0.333 G_Rec: 0.560 D_GP: 0.020 D_real: 1.323 D_fake: 0.599 +(epoch: 18, iters: 3664, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.770 G_ID: 0.932 G_Rec: 0.422 D_GP: 0.056 D_real: 1.163 D_fake: 0.577 +(epoch: 18, iters: 4064, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 1.142 G_ID: 0.406 G_Rec: 0.463 D_GP: 0.168 D_real: 0.481 D_fake: 0.755 +(epoch: 18, iters: 4464, time: 0.064) G_GAN: -0.035 G_GAN_Feat: 0.798 G_ID: 0.922 G_Rec: 0.424 D_GP: 0.055 D_real: 0.631 D_fake: 1.035 +(epoch: 18, iters: 4864, time: 0.064) G_GAN: 0.054 G_GAN_Feat: 0.667 G_ID: 0.360 G_Rec: 0.397 D_GP: 0.022 D_real: 0.807 D_fake: 0.946 +(epoch: 18, iters: 5264, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.662 G_ID: 0.909 G_Rec: 0.358 D_GP: 0.041 D_real: 0.907 D_fake: 0.881 +(epoch: 18, iters: 5664, time: 0.064) G_GAN: -0.071 G_GAN_Feat: 0.711 G_ID: 0.430 G_Rec: 0.337 D_GP: 0.071 D_real: 0.592 D_fake: 1.071 +(epoch: 18, iters: 6064, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.889 G_ID: 0.872 G_Rec: 0.386 D_GP: 0.090 D_real: 0.869 D_fake: 0.559 +(epoch: 18, iters: 6464, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.674 G_ID: 0.389 G_Rec: 0.371 D_GP: 0.027 D_real: 1.155 D_fake: 0.617 +(epoch: 18, iters: 6864, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.676 G_ID: 0.888 G_Rec: 0.414 D_GP: 0.020 D_real: 1.275 D_fake: 0.697 +(epoch: 18, iters: 7264, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.726 G_ID: 0.363 G_Rec: 0.338 D_GP: 0.048 D_real: 0.834 D_fake: 0.830 +(epoch: 18, iters: 7664, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.853 G_ID: 0.881 G_Rec: 0.444 D_GP: 0.080 D_real: 0.592 D_fake: 0.881 +(epoch: 18, iters: 8064, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.848 G_ID: 0.351 G_Rec: 0.336 D_GP: 0.073 D_real: 0.890 D_fake: 0.830 +(epoch: 18, iters: 8464, time: 0.064) G_GAN: 0.749 G_GAN_Feat: 0.847 G_ID: 0.882 G_Rec: 0.514 D_GP: 0.037 D_real: 1.372 D_fake: 0.353 +(epoch: 19, iters: 256, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.766 G_ID: 0.425 G_Rec: 0.405 D_GP: 0.056 D_real: 0.997 D_fake: 0.627 +(epoch: 19, iters: 656, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.746 G_ID: 0.883 G_Rec: 0.359 D_GP: 0.067 D_real: 0.695 D_fake: 0.952 +(epoch: 19, iters: 1056, time: 0.064) G_GAN: 0.564 G_GAN_Feat: 0.877 G_ID: 0.413 G_Rec: 0.410 D_GP: 0.071 D_real: 1.042 D_fake: 0.449 +(epoch: 19, iters: 1456, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.663 G_ID: 0.873 G_Rec: 0.317 D_GP: 0.045 D_real: 1.110 D_fake: 0.609 +(epoch: 19, iters: 1856, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.874 G_ID: 0.460 G_Rec: 0.330 D_GP: 0.117 D_real: 0.739 D_fake: 0.694 +(epoch: 19, iters: 2256, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 1.014 G_ID: 0.894 G_Rec: 0.388 D_GP: 0.067 D_real: 0.453 D_fake: 0.868 +(epoch: 19, iters: 2656, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.857 G_ID: 0.410 G_Rec: 0.355 D_GP: 0.115 D_real: 1.046 D_fake: 0.750 +(epoch: 19, iters: 3056, time: 0.064) G_GAN: -0.001 G_GAN_Feat: 0.785 G_ID: 0.885 G_Rec: 0.373 D_GP: 0.055 D_real: 0.583 D_fake: 1.001 +(epoch: 19, iters: 3456, time: 0.064) G_GAN: 0.020 G_GAN_Feat: 0.887 G_ID: 0.396 G_Rec: 0.366 D_GP: 0.061 D_real: 0.521 D_fake: 0.980 +(epoch: 19, iters: 3856, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.720 G_ID: 0.858 G_Rec: 0.404 D_GP: 0.015 D_real: 1.081 D_fake: 0.777 +(epoch: 19, iters: 4256, time: 0.064) G_GAN: -0.039 G_GAN_Feat: 0.914 G_ID: 0.362 G_Rec: 0.340 D_GP: 0.067 D_real: 0.444 D_fake: 1.039 +(epoch: 19, iters: 4656, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.627 G_ID: 0.906 G_Rec: 0.374 D_GP: 0.022 D_real: 1.281 D_fake: 0.563 +(epoch: 19, iters: 5056, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.851 G_ID: 0.355 G_Rec: 0.333 D_GP: 0.037 D_real: 1.045 D_fake: 0.597 +(epoch: 19, iters: 5456, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.805 G_ID: 0.898 G_Rec: 0.366 D_GP: 0.036 D_real: 1.182 D_fake: 0.478 +(epoch: 19, iters: 5856, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.647 G_ID: 0.399 G_Rec: 0.388 D_GP: 0.033 D_real: 1.014 D_fake: 0.718 +(epoch: 19, iters: 6256, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.705 G_ID: 0.894 G_Rec: 0.340 D_GP: 0.050 D_real: 1.087 D_fake: 0.656 +(epoch: 19, iters: 6656, time: 0.064) G_GAN: -0.214 G_GAN_Feat: 0.677 G_ID: 0.339 G_Rec: 0.327 D_GP: 0.045 D_real: 0.536 D_fake: 1.214 +(epoch: 19, iters: 7056, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.696 G_ID: 0.903 G_Rec: 0.344 D_GP: 0.027 D_real: 1.227 D_fake: 0.605 +(epoch: 19, iters: 7456, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.458 G_ID: 0.440 G_Rec: 0.413 D_GP: 0.015 D_real: 1.362 D_fake: 0.606 +(epoch: 19, iters: 7856, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.535 G_ID: 0.883 G_Rec: 0.355 D_GP: 0.018 D_real: 1.121 D_fake: 0.718 +(epoch: 19, iters: 8256, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.655 G_ID: 0.348 G_Rec: 0.558 D_GP: 0.054 D_real: 0.967 D_fake: 0.783 +(epoch: 20, iters: 48, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.851 G_ID: 0.884 G_Rec: 0.379 D_GP: 0.122 D_real: 0.541 D_fake: 0.900 +(epoch: 20, iters: 448, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.736 G_ID: 0.402 G_Rec: 0.396 D_GP: 0.048 D_real: 0.860 D_fake: 0.838 +(epoch: 20, iters: 848, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.635 G_ID: 0.879 G_Rec: 0.429 D_GP: 0.048 D_real: 1.136 D_fake: 0.602 +(epoch: 20, iters: 1248, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.682 G_ID: 0.341 G_Rec: 0.331 D_GP: 0.038 D_real: 0.875 D_fake: 0.814 +(epoch: 20, iters: 1648, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.769 G_ID: 0.858 G_Rec: 0.357 D_GP: 0.071 D_real: 0.812 D_fake: 0.772 +(epoch: 20, iters: 2048, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.580 G_ID: 0.444 G_Rec: 0.297 D_GP: 0.029 D_real: 1.030 D_fake: 0.774 +(epoch: 20, iters: 2448, time: 0.064) G_GAN: -0.072 G_GAN_Feat: 0.886 G_ID: 0.848 G_Rec: 0.377 D_GP: 0.058 D_real: 0.324 D_fake: 1.072 +(epoch: 20, iters: 2848, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.672 G_ID: 0.447 G_Rec: 0.368 D_GP: 0.038 D_real: 0.940 D_fake: 0.781 +(epoch: 20, iters: 3248, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.885 G_ID: 0.864 G_Rec: 0.393 D_GP: 0.065 D_real: 0.967 D_fake: 0.642 +(epoch: 20, iters: 3648, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 0.804 G_ID: 0.374 G_Rec: 0.368 D_GP: 0.033 D_real: 1.300 D_fake: 0.431 +(epoch: 20, iters: 4048, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 1.070 G_ID: 0.848 G_Rec: 0.365 D_GP: 0.096 D_real: 0.782 D_fake: 0.518 +(epoch: 20, iters: 4448, time: 0.064) G_GAN: 0.005 G_GAN_Feat: 0.760 G_ID: 0.411 G_Rec: 0.345 D_GP: 0.054 D_real: 0.551 D_fake: 0.995 +(epoch: 20, iters: 4848, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.672 G_ID: 0.890 G_Rec: 0.342 D_GP: 0.021 D_real: 1.045 D_fake: 0.880 +(epoch: 20, iters: 5248, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.732 G_ID: 0.312 G_Rec: 0.347 D_GP: 0.028 D_real: 1.055 D_fake: 0.763 +(epoch: 20, iters: 5648, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.791 G_ID: 0.868 G_Rec: 0.350 D_GP: 0.036 D_real: 1.002 D_fake: 0.837 +(epoch: 20, iters: 6048, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.883 G_ID: 0.374 G_Rec: 0.337 D_GP: 0.060 D_real: 0.823 D_fake: 0.788 +(epoch: 20, iters: 6448, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.761 G_ID: 0.824 G_Rec: 0.365 D_GP: 0.038 D_real: 1.163 D_fake: 0.622 +(epoch: 20, iters: 6848, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.901 G_ID: 0.392 G_Rec: 0.551 D_GP: 0.077 D_real: 0.670 D_fake: 0.926 +(epoch: 20, iters: 7248, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.819 G_ID: 0.828 G_Rec: 0.368 D_GP: 0.077 D_real: 0.687 D_fake: 0.989 +(epoch: 20, iters: 7648, time: 0.064) G_GAN: -0.041 G_GAN_Feat: 0.801 G_ID: 0.395 G_Rec: 0.362 D_GP: 0.065 D_real: 0.703 D_fake: 1.041 +(epoch: 20, iters: 8048, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.840 G_ID: 0.835 G_Rec: 0.361 D_GP: 0.041 D_real: 0.968 D_fake: 0.766 +(epoch: 20, iters: 8448, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 1.023 G_ID: 0.479 G_Rec: 0.378 D_GP: 0.076 D_real: 0.750 D_fake: 0.742 +(epoch: 21, iters: 240, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.861 G_ID: 0.831 G_Rec: 0.343 D_GP: 0.044 D_real: 1.051 D_fake: 0.603 +(epoch: 21, iters: 640, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 1.022 G_ID: 0.368 G_Rec: 0.449 D_GP: 0.111 D_real: 1.136 D_fake: 0.743 +(epoch: 21, iters: 1040, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.983 G_ID: 0.853 G_Rec: 0.399 D_GP: 0.063 D_real: 0.908 D_fake: 0.616 +(epoch: 21, iters: 1440, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.867 G_ID: 0.350 G_Rec: 0.409 D_GP: 0.069 D_real: 0.898 D_fake: 0.769 +(epoch: 21, iters: 1840, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.905 G_ID: 0.815 G_Rec: 0.357 D_GP: 0.038 D_real: 0.951 D_fake: 0.658 +(epoch: 21, iters: 2240, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.762 G_ID: 0.352 G_Rec: 0.327 D_GP: 0.044 D_real: 1.094 D_fake: 0.717 +(epoch: 21, iters: 2640, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.693 G_ID: 0.865 G_Rec: 0.349 D_GP: 0.032 D_real: 0.918 D_fake: 0.826 +(epoch: 21, iters: 3040, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.959 G_ID: 0.499 G_Rec: 0.417 D_GP: 0.078 D_real: 0.715 D_fake: 0.617 +(epoch: 21, iters: 3440, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 1.118 G_ID: 0.878 G_Rec: 0.399 D_GP: 0.138 D_real: 0.589 D_fake: 0.748 +(epoch: 21, iters: 3840, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.643 G_ID: 0.382 G_Rec: 0.417 D_GP: 0.022 D_real: 1.010 D_fake: 0.773 +(epoch: 21, iters: 4240, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.765 G_ID: 0.883 G_Rec: 0.513 D_GP: 0.069 D_real: 0.975 D_fake: 0.746 +(epoch: 21, iters: 4640, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.994 G_ID: 0.423 G_Rec: 0.373 D_GP: 0.093 D_real: 0.548 D_fake: 0.816 +(epoch: 21, iters: 5040, time: 0.064) G_GAN: -0.040 G_GAN_Feat: 0.632 G_ID: 0.854 G_Rec: 0.431 D_GP: 0.023 D_real: 0.769 D_fake: 1.040 +(epoch: 21, iters: 5440, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.735 G_ID: 0.429 G_Rec: 0.370 D_GP: 0.036 D_real: 1.028 D_fake: 0.720 +(epoch: 21, iters: 5840, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.769 G_ID: 0.833 G_Rec: 0.368 D_GP: 0.030 D_real: 1.263 D_fake: 0.485 +(epoch: 21, iters: 6240, time: 0.064) G_GAN: 0.692 G_GAN_Feat: 0.502 G_ID: 0.379 G_Rec: 0.391 D_GP: 0.019 D_real: 1.636 D_fake: 0.337 +(epoch: 21, iters: 6640, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.536 G_ID: 0.835 G_Rec: 0.457 D_GP: 0.016 D_real: 1.192 D_fake: 0.680 +(epoch: 21, iters: 7040, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.487 G_ID: 0.366 G_Rec: 0.478 D_GP: 0.018 D_real: 1.017 D_fake: 0.865 +(epoch: 21, iters: 7440, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.545 G_ID: 0.845 G_Rec: 0.376 D_GP: 0.027 D_real: 1.000 D_fake: 0.798 +(epoch: 21, iters: 7840, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.565 G_ID: 0.463 G_Rec: 0.373 D_GP: 0.035 D_real: 1.184 D_fake: 0.643 +(epoch: 21, iters: 8240, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.634 G_ID: 0.801 G_Rec: 0.347 D_GP: 0.059 D_real: 0.973 D_fake: 0.776 +(epoch: 22, iters: 32, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.768 G_ID: 0.390 G_Rec: 0.398 D_GP: 0.056 D_real: 0.782 D_fake: 0.815 +(epoch: 22, iters: 432, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.669 G_ID: 0.887 G_Rec: 0.433 D_GP: 0.043 D_real: 1.042 D_fake: 0.760 +(epoch: 22, iters: 832, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.821 G_ID: 0.403 G_Rec: 0.357 D_GP: 0.067 D_real: 0.795 D_fake: 0.732 +(epoch: 22, iters: 1232, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.794 G_ID: 0.808 G_Rec: 0.652 D_GP: 0.070 D_real: 0.748 D_fake: 0.781 +(epoch: 22, iters: 1632, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.734 G_ID: 0.425 G_Rec: 0.356 D_GP: 0.067 D_real: 1.018 D_fake: 0.616 +(epoch: 22, iters: 2032, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.731 G_ID: 0.862 G_Rec: 0.339 D_GP: 0.061 D_real: 0.920 D_fake: 0.791 +(epoch: 22, iters: 2432, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 1.068 G_ID: 0.413 G_Rec: 0.420 D_GP: 0.102 D_real: 0.496 D_fake: 0.750 +(epoch: 22, iters: 2832, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.706 G_ID: 0.837 G_Rec: 0.344 D_GP: 0.026 D_real: 0.959 D_fake: 0.772 +(epoch: 22, iters: 3232, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.773 G_ID: 0.412 G_Rec: 0.377 D_GP: 0.065 D_real: 1.115 D_fake: 0.499 +(epoch: 22, iters: 3632, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.480 G_ID: 0.856 G_Rec: 0.330 D_GP: 0.016 D_real: 1.147 D_fake: 0.758 +(epoch: 22, iters: 4032, time: 0.064) G_GAN: 0.020 G_GAN_Feat: 0.553 G_ID: 0.332 G_Rec: 0.406 D_GP: 0.029 D_real: 0.906 D_fake: 0.980 +(epoch: 22, iters: 4432, time: 0.064) G_GAN: 0.905 G_GAN_Feat: 0.584 G_ID: 0.846 G_Rec: 0.438 D_GP: 0.023 D_real: 1.698 D_fake: 0.215 +(epoch: 22, iters: 4832, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.628 G_ID: 0.325 G_Rec: 0.452 D_GP: 0.049 D_real: 0.983 D_fake: 0.717 +(epoch: 22, iters: 5232, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.653 G_ID: 0.857 G_Rec: 0.425 D_GP: 0.054 D_real: 1.065 D_fake: 0.616 +(epoch: 22, iters: 5632, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.570 G_ID: 0.414 G_Rec: 0.338 D_GP: 0.041 D_real: 1.065 D_fake: 0.757 +(epoch: 22, iters: 6032, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.709 G_ID: 0.848 G_Rec: 0.353 D_GP: 0.076 D_real: 0.758 D_fake: 0.878 +(epoch: 22, iters: 6432, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.785 G_ID: 0.388 G_Rec: 0.401 D_GP: 0.106 D_real: 1.011 D_fake: 0.596 +(epoch: 22, iters: 6832, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.671 G_ID: 0.822 G_Rec: 0.421 D_GP: 0.066 D_real: 0.917 D_fake: 0.805 +(epoch: 22, iters: 7232, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 1.041 G_ID: 0.399 G_Rec: 0.378 D_GP: 0.193 D_real: 0.479 D_fake: 0.806 +(epoch: 22, iters: 7632, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.737 G_ID: 0.818 G_Rec: 0.329 D_GP: 0.042 D_real: 1.092 D_fake: 0.674 +(epoch: 22, iters: 8032, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.731 G_ID: 0.391 G_Rec: 0.381 D_GP: 0.046 D_real: 1.178 D_fake: 0.633 +(epoch: 22, iters: 8432, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.703 G_ID: 0.811 G_Rec: 0.468 D_GP: 0.063 D_real: 0.884 D_fake: 0.840 +(epoch: 23, iters: 224, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.809 G_ID: 0.400 G_Rec: 0.412 D_GP: 0.089 D_real: 0.695 D_fake: 0.763 +(epoch: 23, iters: 624, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.728 G_ID: 0.839 G_Rec: 0.418 D_GP: 0.036 D_real: 1.034 D_fake: 0.683 +(epoch: 23, iters: 1024, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.582 G_ID: 0.381 G_Rec: 0.395 D_GP: 0.022 D_real: 1.328 D_fake: 0.579 +(epoch: 23, iters: 1424, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.611 G_ID: 0.798 G_Rec: 0.431 D_GP: 0.024 D_real: 1.329 D_fake: 0.490 +(epoch: 23, iters: 1824, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.681 G_ID: 0.467 G_Rec: 0.410 D_GP: 0.046 D_real: 1.020 D_fake: 0.779 +(epoch: 23, iters: 2224, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.639 G_ID: 0.834 G_Rec: 0.427 D_GP: 0.039 D_real: 0.776 D_fake: 0.913 +(epoch: 23, iters: 2624, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.852 G_ID: 0.429 G_Rec: 0.376 D_GP: 0.056 D_real: 0.799 D_fake: 0.717 +(epoch: 23, iters: 3024, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 1.085 G_ID: 0.835 G_Rec: 0.382 D_GP: 0.167 D_real: 0.587 D_fake: 0.675 +(epoch: 23, iters: 3424, time: 0.064) G_GAN: 0.042 G_GAN_Feat: 0.760 G_ID: 0.417 G_Rec: 0.346 D_GP: 0.077 D_real: 0.567 D_fake: 0.960 +(epoch: 23, iters: 3824, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.648 G_ID: 0.824 G_Rec: 0.409 D_GP: 0.035 D_real: 0.948 D_fake: 0.849 +(epoch: 23, iters: 4224, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.823 G_ID: 0.461 G_Rec: 0.451 D_GP: 0.068 D_real: 0.856 D_fake: 0.606 +(epoch: 23, iters: 4624, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.757 G_ID: 0.801 G_Rec: 0.342 D_GP: 0.071 D_real: 0.830 D_fake: 0.701 +(epoch: 23, iters: 5024, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.829 G_ID: 0.381 G_Rec: 0.405 D_GP: 0.040 D_real: 0.625 D_fake: 0.916 +(epoch: 23, iters: 5424, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 0.692 G_ID: 0.868 G_Rec: 0.361 D_GP: 0.040 D_real: 0.796 D_fake: 0.935 +(epoch: 23, iters: 5824, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 1.091 G_ID: 0.367 G_Rec: 0.384 D_GP: 0.086 D_real: 0.455 D_fake: 0.860 +(epoch: 23, iters: 6224, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.621 G_ID: 0.817 G_Rec: 0.357 D_GP: 0.024 D_real: 1.015 D_fake: 0.800 +(epoch: 23, iters: 6624, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 0.837 G_ID: 0.341 G_Rec: 0.379 D_GP: 0.107 D_real: 1.114 D_fake: 0.455 +(epoch: 23, iters: 7024, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 1.085 G_ID: 0.798 G_Rec: 0.397 D_GP: 0.105 D_real: 0.643 D_fake: 0.727 +(epoch: 23, iters: 7424, time: 0.064) G_GAN: 0.612 G_GAN_Feat: 0.521 G_ID: 0.412 G_Rec: 0.445 D_GP: 0.018 D_real: 1.482 D_fake: 0.393 +(epoch: 23, iters: 7824, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.536 G_ID: 0.777 G_Rec: 0.383 D_GP: 0.020 D_real: 0.997 D_fake: 0.874 +(epoch: 23, iters: 8224, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.503 G_ID: 0.437 G_Rec: 0.324 D_GP: 0.025 D_real: 1.040 D_fake: 0.811 +(epoch: 24, iters: 16, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.548 G_ID: 0.858 G_Rec: 0.339 D_GP: 0.031 D_real: 1.196 D_fake: 0.594 +(epoch: 24, iters: 416, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.530 G_ID: 0.429 G_Rec: 0.309 D_GP: 0.031 D_real: 1.005 D_fake: 0.837 +(epoch: 24, iters: 816, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 0.601 G_ID: 0.829 G_Rec: 0.367 D_GP: 0.042 D_real: 1.186 D_fake: 0.567 +(epoch: 24, iters: 1216, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.648 G_ID: 0.337 G_Rec: 0.367 D_GP: 0.038 D_real: 1.063 D_fake: 0.709 +(epoch: 24, iters: 1616, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.672 G_ID: 0.821 G_Rec: 0.377 D_GP: 0.055 D_real: 0.846 D_fake: 0.813 +(epoch: 24, iters: 2016, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.613 G_ID: 0.377 G_Rec: 0.336 D_GP: 0.060 D_real: 0.917 D_fake: 0.849 +(epoch: 24, iters: 2416, time: 0.064) G_GAN: 0.060 G_GAN_Feat: 0.614 G_ID: 0.823 G_Rec: 0.316 D_GP: 0.063 D_real: 0.854 D_fake: 0.940 +(epoch: 24, iters: 2816, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.802 G_ID: 0.403 G_Rec: 0.462 D_GP: 0.046 D_real: 1.095 D_fake: 0.654 +(epoch: 24, iters: 3216, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 1.155 G_ID: 0.808 G_Rec: 0.372 D_GP: 0.227 D_real: 0.507 D_fake: 0.817 +(epoch: 24, iters: 3616, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.616 G_ID: 0.358 G_Rec: 0.472 D_GP: 0.044 D_real: 0.885 D_fake: 0.910 +(epoch: 24, iters: 4016, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.740 G_ID: 0.790 G_Rec: 0.530 D_GP: 0.030 D_real: 1.249 D_fake: 0.528 +(epoch: 24, iters: 4416, time: 0.064) G_GAN: 0.552 G_GAN_Feat: 0.692 G_ID: 0.360 G_Rec: 0.366 D_GP: 0.066 D_real: 1.223 D_fake: 0.453 +(epoch: 24, iters: 4816, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 0.877 G_ID: 0.834 G_Rec: 0.381 D_GP: 0.050 D_real: 1.068 D_fake: 0.523 +(epoch: 24, iters: 5216, time: 0.064) G_GAN: -0.163 G_GAN_Feat: 0.733 G_ID: 0.419 G_Rec: 0.388 D_GP: 0.084 D_real: 0.455 D_fake: 1.164 +(epoch: 24, iters: 5616, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.861 G_ID: 0.789 G_Rec: 0.387 D_GP: 0.042 D_real: 1.048 D_fake: 0.722 +(epoch: 24, iters: 6016, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.684 G_ID: 0.385 G_Rec: 0.419 D_GP: 0.041 D_real: 1.225 D_fake: 0.522 +(epoch: 24, iters: 6416, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 0.729 G_ID: 0.795 G_Rec: 0.389 D_GP: 0.039 D_real: 0.780 D_fake: 0.906 +(epoch: 24, iters: 6816, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 0.724 G_ID: 0.413 G_Rec: 0.463 D_GP: 0.025 D_real: 1.323 D_fake: 0.418 +(epoch: 24, iters: 7216, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.694 G_ID: 0.798 G_Rec: 0.473 D_GP: 0.024 D_real: 1.140 D_fake: 0.654 +(epoch: 24, iters: 7616, time: 0.064) G_GAN: 0.723 G_GAN_Feat: 0.692 G_ID: 0.372 G_Rec: 0.351 D_GP: 0.040 D_real: 1.416 D_fake: 0.303 +(epoch: 24, iters: 8016, time: 0.064) G_GAN: 0.487 G_GAN_Feat: 0.668 G_ID: 0.813 G_Rec: 0.334 D_GP: 0.028 D_real: 1.284 D_fake: 0.514 +(epoch: 24, iters: 8416, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 0.897 G_ID: 0.326 G_Rec: 0.504 D_GP: 0.114 D_real: 0.510 D_fake: 0.975 +(epoch: 25, iters: 208, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.655 G_ID: 0.834 G_Rec: 0.341 D_GP: 0.051 D_real: 0.936 D_fake: 0.766 +(epoch: 25, iters: 608, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.835 G_ID: 0.417 G_Rec: 0.336 D_GP: 0.108 D_real: 0.977 D_fake: 0.683 +(epoch: 25, iters: 1008, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 1.075 G_ID: 0.757 G_Rec: 0.405 D_GP: 0.326 D_real: 0.554 D_fake: 0.840 +(epoch: 25, iters: 1408, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 1.259 G_ID: 0.407 G_Rec: 0.430 D_GP: 0.367 D_real: 0.306 D_fake: 0.782 +(epoch: 25, iters: 1808, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.793 G_ID: 0.824 G_Rec: 0.465 D_GP: 0.083 D_real: 0.757 D_fake: 0.768 +(epoch: 25, iters: 2208, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.636 G_ID: 0.390 G_Rec: 0.354 D_GP: 0.024 D_real: 1.040 D_fake: 0.842 +(epoch: 25, iters: 2608, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 0.760 G_ID: 0.782 G_Rec: 0.336 D_GP: 0.070 D_real: 1.186 D_fake: 0.422 +(epoch: 25, iters: 3008, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.589 G_ID: 0.351 G_Rec: 0.326 D_GP: 0.035 D_real: 1.026 D_fake: 0.770 +(epoch: 25, iters: 3408, time: 0.064) G_GAN: 0.664 G_GAN_Feat: 0.724 G_ID: 0.786 G_Rec: 0.385 D_GP: 0.034 D_real: 1.442 D_fake: 0.377 +(epoch: 25, iters: 3808, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.882 G_ID: 0.333 G_Rec: 0.409 D_GP: 0.040 D_real: 1.010 D_fake: 0.601 +(epoch: 25, iters: 4208, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 1.238 G_ID: 0.777 G_Rec: 0.521 D_GP: 0.239 D_real: 0.593 D_fake: 0.613 +(epoch: 25, iters: 4608, time: 0.064) G_GAN: 0.470 G_GAN_Feat: 0.855 G_ID: 0.359 G_Rec: 0.328 D_GP: 0.076 D_real: 1.008 D_fake: 0.530 +(epoch: 25, iters: 5008, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.744 G_ID: 0.835 G_Rec: 0.351 D_GP: 0.040 D_real: 1.247 D_fake: 0.534 +(epoch: 25, iters: 5408, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.580 G_ID: 0.351 G_Rec: 0.347 D_GP: 0.022 D_real: 1.313 D_fake: 0.480 +(epoch: 25, iters: 5808, time: 0.064) G_GAN: 0.033 G_GAN_Feat: 0.881 G_ID: 0.819 G_Rec: 0.385 D_GP: 0.082 D_real: 0.444 D_fake: 0.967 +(epoch: 25, iters: 6208, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.725 G_ID: 0.348 G_Rec: 0.372 D_GP: 0.059 D_real: 0.869 D_fake: 0.767 +(epoch: 25, iters: 6608, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.830 G_ID: 0.796 G_Rec: 0.354 D_GP: 0.047 D_real: 1.040 D_fake: 0.937 +(epoch: 25, iters: 7008, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.949 G_ID: 0.323 G_Rec: 0.494 D_GP: 0.095 D_real: 0.573 D_fake: 0.881 +(epoch: 25, iters: 7408, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.631 G_ID: 0.774 G_Rec: 0.407 D_GP: 0.026 D_real: 1.057 D_fake: 0.715 +(epoch: 25, iters: 7808, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 1.059 G_ID: 0.431 G_Rec: 0.440 D_GP: 0.110 D_real: 0.699 D_fake: 0.726 +(epoch: 25, iters: 8208, time: 0.064) G_GAN: 0.653 G_GAN_Feat: 0.834 G_ID: 0.800 G_Rec: 0.370 D_GP: 0.096 D_real: 1.109 D_fake: 0.365 +(epoch: 25, iters: 8608, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.560 G_ID: 0.421 G_Rec: 0.384 D_GP: 0.024 D_real: 1.190 D_fake: 0.612 +(epoch: 26, iters: 400, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 1.221 G_ID: 0.802 G_Rec: 0.499 D_GP: 0.127 D_real: 0.442 D_fake: 0.843 +(epoch: 26, iters: 800, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 1.063 G_ID: 0.365 G_Rec: 0.446 D_GP: 0.058 D_real: 1.194 D_fake: 0.642 +(epoch: 26, iters: 1200, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 1.135 G_ID: 0.776 G_Rec: 0.353 D_GP: 0.092 D_real: 1.084 D_fake: 0.660 +(epoch: 26, iters: 1600, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.686 G_ID: 0.368 G_Rec: 0.500 D_GP: 0.039 D_real: 0.814 D_fake: 0.871 +(epoch: 26, iters: 2000, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.748 G_ID: 0.783 G_Rec: 0.364 D_GP: 0.043 D_real: 1.035 D_fake: 0.610 +(epoch: 26, iters: 2400, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.584 G_ID: 0.334 G_Rec: 0.376 D_GP: 0.017 D_real: 1.270 D_fake: 0.593 +(epoch: 26, iters: 2800, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.783 G_ID: 0.789 G_Rec: 0.398 D_GP: 0.040 D_real: 1.142 D_fake: 0.687 +(epoch: 26, iters: 3200, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.947 G_ID: 0.386 G_Rec: 0.475 D_GP: 0.425 D_real: 0.524 D_fake: 0.931 +(epoch: 26, iters: 3600, time: 0.064) G_GAN: 0.575 G_GAN_Feat: 0.703 G_ID: 0.747 G_Rec: 0.374 D_GP: 0.028 D_real: 1.354 D_fake: 0.438 +(epoch: 26, iters: 4000, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.837 G_ID: 0.394 G_Rec: 0.388 D_GP: 0.070 D_real: 0.965 D_fake: 0.649 +(epoch: 26, iters: 4400, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.749 G_ID: 0.784 G_Rec: 0.431 D_GP: 0.038 D_real: 0.883 D_fake: 0.753 +(epoch: 26, iters: 4800, time: 0.064) G_GAN: 0.663 G_GAN_Feat: 0.804 G_ID: 0.396 G_Rec: 0.530 D_GP: 0.038 D_real: 1.429 D_fake: 0.356 +(epoch: 26, iters: 5200, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.658 G_ID: 0.762 G_Rec: 0.356 D_GP: 0.033 D_real: 1.037 D_fake: 0.702 +(epoch: 26, iters: 5600, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.739 G_ID: 0.355 G_Rec: 0.356 D_GP: 0.051 D_real: 1.091 D_fake: 0.592 +(epoch: 26, iters: 6000, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.751 G_ID: 0.751 G_Rec: 0.378 D_GP: 0.051 D_real: 0.970 D_fake: 0.764 +(epoch: 26, iters: 6400, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 1.151 G_ID: 0.366 G_Rec: 0.355 D_GP: 0.361 D_real: 0.296 D_fake: 0.670 +(epoch: 26, iters: 6800, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.756 G_ID: 0.798 G_Rec: 0.376 D_GP: 0.051 D_real: 1.095 D_fake: 0.547 +(epoch: 26, iters: 7200, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.633 G_ID: 0.396 G_Rec: 0.361 D_GP: 0.024 D_real: 0.970 D_fake: 0.771 +(epoch: 26, iters: 7600, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.693 G_ID: 0.769 G_Rec: 0.322 D_GP: 0.051 D_real: 0.861 D_fake: 0.843 +(epoch: 26, iters: 8000, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 1.083 G_ID: 0.355 G_Rec: 0.425 D_GP: 0.239 D_real: 0.565 D_fake: 0.588 +(epoch: 26, iters: 8400, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.521 G_ID: 0.783 G_Rec: 0.337 D_GP: 0.022 D_real: 1.211 D_fake: 0.672 +(epoch: 27, iters: 192, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.554 G_ID: 0.374 G_Rec: 0.358 D_GP: 0.026 D_real: 1.152 D_fake: 0.694 +(epoch: 27, iters: 592, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.578 G_ID: 0.771 G_Rec: 0.345 D_GP: 0.040 D_real: 0.937 D_fake: 0.835 +(epoch: 27, iters: 992, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.661 G_ID: 0.330 G_Rec: 0.367 D_GP: 0.045 D_real: 1.215 D_fake: 0.523 +(epoch: 27, iters: 1392, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.677 G_ID: 0.725 G_Rec: 0.350 D_GP: 0.044 D_real: 1.247 D_fake: 0.479 +(epoch: 27, iters: 1792, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.927 G_ID: 0.343 G_Rec: 0.353 D_GP: 0.061 D_real: 0.773 D_fake: 0.652 +(epoch: 27, iters: 2192, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.740 G_ID: 0.758 G_Rec: 0.386 D_GP: 0.039 D_real: 0.976 D_fake: 0.691 +(epoch: 27, iters: 2592, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.904 G_ID: 0.390 G_Rec: 0.431 D_GP: 0.118 D_real: 0.838 D_fake: 0.523 +(epoch: 27, iters: 2992, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 1.200 G_ID: 0.780 G_Rec: 0.403 D_GP: 0.243 D_real: 0.500 D_fake: 0.545 +(epoch: 27, iters: 3392, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.778 G_ID: 0.362 G_Rec: 0.599 D_GP: 0.028 D_real: 1.114 D_fake: 0.638 +(epoch: 27, iters: 3792, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 0.776 G_ID: 0.793 G_Rec: 0.378 D_GP: 0.091 D_real: 1.221 D_fake: 0.495 +(epoch: 27, iters: 4192, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.826 G_ID: 0.318 G_Rec: 0.345 D_GP: 0.095 D_real: 0.823 D_fake: 0.670 +(epoch: 27, iters: 4592, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.825 G_ID: 0.733 G_Rec: 0.423 D_GP: 0.029 D_real: 1.032 D_fake: 0.569 +(epoch: 27, iters: 4992, time: 0.064) G_GAN: -0.152 G_GAN_Feat: 0.825 G_ID: 0.391 G_Rec: 0.472 D_GP: 0.088 D_real: 0.591 D_fake: 1.152 +(epoch: 27, iters: 5392, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.644 G_ID: 0.801 G_Rec: 0.363 D_GP: 0.028 D_real: 1.231 D_fake: 0.549 +(epoch: 27, iters: 5792, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.734 G_ID: 0.353 G_Rec: 0.391 D_GP: 0.039 D_real: 0.727 D_fake: 0.953 +(epoch: 27, iters: 6192, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.696 G_ID: 0.752 G_Rec: 0.410 D_GP: 0.023 D_real: 0.866 D_fake: 0.847 +(epoch: 27, iters: 6592, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 0.787 G_ID: 0.360 G_Rec: 0.405 D_GP: 0.045 D_real: 1.326 D_fake: 0.451 +(epoch: 27, iters: 6992, time: 0.064) G_GAN: 0.624 G_GAN_Feat: 0.634 G_ID: 0.790 G_Rec: 0.447 D_GP: 0.020 D_real: 1.534 D_fake: 0.425 +(epoch: 27, iters: 7392, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 1.169 G_ID: 0.390 G_Rec: 0.394 D_GP: 0.103 D_real: 0.490 D_fake: 0.721 +(epoch: 27, iters: 7792, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.840 G_ID: 0.699 G_Rec: 0.383 D_GP: 0.049 D_real: 1.191 D_fake: 0.529 +(epoch: 27, iters: 8192, time: 0.064) G_GAN: 0.733 G_GAN_Feat: 0.737 G_ID: 0.330 G_Rec: 0.343 D_GP: 0.042 D_real: 1.375 D_fake: 0.275 +(epoch: 27, iters: 8592, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.871 G_ID: 0.764 G_Rec: 0.353 D_GP: 0.057 D_real: 1.072 D_fake: 0.576 +(epoch: 28, iters: 384, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.629 G_ID: 0.309 G_Rec: 0.358 D_GP: 0.035 D_real: 0.997 D_fake: 0.862 +(epoch: 28, iters: 784, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.719 G_ID: 0.798 G_Rec: 0.372 D_GP: 0.063 D_real: 1.086 D_fake: 0.590 +(epoch: 28, iters: 1184, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 1.014 G_ID: 0.352 G_Rec: 0.432 D_GP: 0.261 D_real: 0.895 D_fake: 0.701 +(epoch: 28, iters: 1584, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.924 G_ID: 0.712 G_Rec: 0.426 D_GP: 0.076 D_real: 1.048 D_fake: 0.711 +(epoch: 28, iters: 1984, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.817 G_ID: 0.299 G_Rec: 0.425 D_GP: 0.058 D_real: 1.172 D_fake: 0.564 +(epoch: 28, iters: 2384, time: 0.064) G_GAN: -0.048 G_GAN_Feat: 0.914 G_ID: 0.763 G_Rec: 0.392 D_GP: 0.155 D_real: 0.513 D_fake: 1.049 +(epoch: 28, iters: 2784, time: 0.064) G_GAN: 0.676 G_GAN_Feat: 1.238 G_ID: 0.367 G_Rec: 0.409 D_GP: 0.060 D_real: 1.449 D_fake: 0.447 +(epoch: 28, iters: 3184, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 0.903 G_ID: 0.699 G_Rec: 0.358 D_GP: 0.121 D_real: 1.125 D_fake: 0.488 +(epoch: 28, iters: 3584, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.568 G_ID: 0.361 G_Rec: 0.358 D_GP: 0.025 D_real: 1.173 D_fake: 0.653 +(epoch: 28, iters: 3984, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.729 G_ID: 0.725 G_Rec: 0.356 D_GP: 0.034 D_real: 1.168 D_fake: 0.612 +(epoch: 28, iters: 4384, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.567 G_ID: 0.344 G_Rec: 0.415 D_GP: 0.030 D_real: 0.979 D_fake: 0.812 +(epoch: 28, iters: 4784, time: 0.064) G_GAN: -0.116 G_GAN_Feat: 0.717 G_ID: 0.715 G_Rec: 0.452 D_GP: 0.048 D_real: 0.472 D_fake: 1.116 +(epoch: 28, iters: 5184, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.584 G_ID: 0.345 G_Rec: 0.482 D_GP: 0.031 D_real: 0.906 D_fake: 0.883 +(epoch: 28, iters: 5584, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.824 G_ID: 0.717 G_Rec: 0.357 D_GP: 0.047 D_real: 0.630 D_fake: 0.877 +(epoch: 28, iters: 5984, time: 0.064) G_GAN: 0.563 G_GAN_Feat: 0.769 G_ID: 0.383 G_Rec: 0.373 D_GP: 0.045 D_real: 1.135 D_fake: 0.477 +(epoch: 28, iters: 6384, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.699 G_ID: 0.711 G_Rec: 0.399 D_GP: 0.033 D_real: 1.115 D_fake: 0.780 +(epoch: 28, iters: 6784, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.680 G_ID: 0.352 G_Rec: 0.380 D_GP: 0.022 D_real: 1.307 D_fake: 0.478 +(epoch: 28, iters: 7184, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.570 G_ID: 0.675 G_Rec: 0.356 D_GP: 0.028 D_real: 1.028 D_fake: 0.762 +(epoch: 28, iters: 7584, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.589 G_ID: 0.358 G_Rec: 0.354 D_GP: 0.037 D_real: 1.243 D_fake: 0.536 +(epoch: 28, iters: 7984, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 1.147 G_ID: 0.688 G_Rec: 0.435 D_GP: 0.548 D_real: 0.331 D_fake: 0.921 +(epoch: 28, iters: 8384, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.887 G_ID: 0.352 G_Rec: 0.377 D_GP: 0.067 D_real: 0.457 D_fake: 0.977 +(epoch: 29, iters: 176, time: 0.064) G_GAN: 0.685 G_GAN_Feat: 0.642 G_ID: 0.733 G_Rec: 0.383 D_GP: 0.035 D_real: 1.471 D_fake: 0.327 +(epoch: 29, iters: 576, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.594 G_ID: 0.248 G_Rec: 0.327 D_GP: 0.029 D_real: 0.915 D_fake: 0.813 +(epoch: 29, iters: 976, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.781 G_ID: 0.787 G_Rec: 0.349 D_GP: 0.095 D_real: 0.862 D_fake: 0.696 +(epoch: 29, iters: 1376, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.708 G_ID: 0.335 G_Rec: 0.440 D_GP: 0.040 D_real: 1.152 D_fake: 0.496 +(epoch: 29, iters: 1776, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 1.163 G_ID: 0.743 G_Rec: 0.426 D_GP: 0.072 D_real: 0.345 D_fake: 0.714 +(epoch: 29, iters: 2176, time: 0.064) G_GAN: 0.853 G_GAN_Feat: 0.822 G_ID: 0.304 G_Rec: 0.378 D_GP: 0.047 D_real: 1.446 D_fake: 0.482 +(epoch: 29, iters: 2576, time: 0.064) G_GAN: 0.634 G_GAN_Feat: 0.781 G_ID: 0.711 G_Rec: 0.429 D_GP: 0.046 D_real: 1.331 D_fake: 0.496 +(epoch: 29, iters: 2976, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.692 G_ID: 0.333 G_Rec: 0.360 D_GP: 0.029 D_real: 0.825 D_fake: 0.912 +(epoch: 29, iters: 3376, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.715 G_ID: 0.722 G_Rec: 0.463 D_GP: 0.041 D_real: 0.977 D_fake: 0.655 +(epoch: 29, iters: 3776, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.794 G_ID: 0.360 G_Rec: 0.395 D_GP: 0.051 D_real: 0.942 D_fake: 0.568 +(epoch: 29, iters: 4176, time: 0.064) G_GAN: 0.396 G_GAN_Feat: 0.706 G_ID: 0.702 G_Rec: 0.391 D_GP: 0.042 D_real: 1.098 D_fake: 0.604 +(epoch: 29, iters: 4576, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.802 G_ID: 0.354 G_Rec: 0.360 D_GP: 0.049 D_real: 1.097 D_fake: 0.766 +(epoch: 29, iters: 4976, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.840 G_ID: 0.726 G_Rec: 0.382 D_GP: 0.041 D_real: 0.831 D_fake: 0.774 +(epoch: 29, iters: 5376, time: 0.064) G_GAN: 0.089 G_GAN_Feat: 0.757 G_ID: 0.326 G_Rec: 0.381 D_GP: 0.071 D_real: 0.664 D_fake: 0.911 +(epoch: 29, iters: 5776, time: 0.064) G_GAN: -0.004 G_GAN_Feat: 0.820 G_ID: 0.686 G_Rec: 0.381 D_GP: 0.129 D_real: 0.469 D_fake: 1.004 +(epoch: 29, iters: 6176, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.538 G_ID: 0.316 G_Rec: 0.348 D_GP: 0.019 D_real: 1.269 D_fake: 0.635 +(epoch: 29, iters: 6576, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.602 G_ID: 0.669 G_Rec: 0.358 D_GP: 0.019 D_real: 0.991 D_fake: 0.853 +(epoch: 29, iters: 6976, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.734 G_ID: 0.406 G_Rec: 0.472 D_GP: 0.040 D_real: 0.756 D_fake: 0.853 +(epoch: 29, iters: 7376, time: 0.064) G_GAN: 0.781 G_GAN_Feat: 1.135 G_ID: 0.689 G_Rec: 0.478 D_GP: 0.079 D_real: 1.044 D_fake: 0.379 +(epoch: 29, iters: 7776, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.805 G_ID: 0.359 G_Rec: 0.360 D_GP: 0.081 D_real: 0.721 D_fake: 0.818 +(epoch: 29, iters: 8176, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 1.066 G_ID: 0.779 G_Rec: 0.365 D_GP: 0.194 D_real: 0.518 D_fake: 0.885 +(epoch: 29, iters: 8576, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 1.011 G_ID: 0.362 G_Rec: 0.422 D_GP: 0.076 D_real: 0.634 D_fake: 0.529 +(epoch: 30, iters: 368, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 1.532 G_ID: 0.704 G_Rec: 0.360 D_GP: 0.287 D_real: 0.491 D_fake: 0.660 +(epoch: 30, iters: 768, time: 0.064) G_GAN: 0.806 G_GAN_Feat: 0.725 G_ID: 0.338 G_Rec: 0.384 D_GP: 0.034 D_real: 1.540 D_fake: 0.427 +(epoch: 30, iters: 1168, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 1.095 G_ID: 0.739 G_Rec: 0.541 D_GP: 0.156 D_real: 0.422 D_fake: 0.813 +(epoch: 30, iters: 1568, time: 0.064) G_GAN: 0.470 G_GAN_Feat: 0.681 G_ID: 0.327 G_Rec: 0.379 D_GP: 0.031 D_real: 1.217 D_fake: 0.535 +(epoch: 30, iters: 1968, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.820 G_ID: 0.696 G_Rec: 0.374 D_GP: 0.086 D_real: 1.064 D_fake: 0.495 +(epoch: 30, iters: 2368, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.683 G_ID: 0.351 G_Rec: 0.378 D_GP: 0.030 D_real: 0.996 D_fake: 0.820 +(epoch: 30, iters: 2768, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.737 G_ID: 0.755 G_Rec: 0.388 D_GP: 0.027 D_real: 1.113 D_fake: 0.623 +(epoch: 30, iters: 3168, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.686 G_ID: 0.335 G_Rec: 0.418 D_GP: 0.051 D_real: 0.879 D_fake: 0.779 +(epoch: 30, iters: 3568, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.595 G_ID: 0.704 G_Rec: 0.368 D_GP: 0.024 D_real: 1.221 D_fake: 0.600 +(epoch: 30, iters: 3968, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 1.174 G_ID: 0.318 G_Rec: 0.448 D_GP: 0.237 D_real: 0.539 D_fake: 0.577 +(epoch: 30, iters: 4368, time: 0.064) G_GAN: -0.119 G_GAN_Feat: 0.937 G_ID: 0.725 G_Rec: 0.371 D_GP: 0.158 D_real: 0.546 D_fake: 1.119 +(epoch: 30, iters: 4768, time: 0.064) G_GAN: -0.039 G_GAN_Feat: 0.692 G_ID: 0.354 G_Rec: 0.490 D_GP: 0.032 D_real: 0.683 D_fake: 1.039 +(epoch: 30, iters: 5168, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.686 G_ID: 0.679 G_Rec: 0.451 D_GP: 0.027 D_real: 1.020 D_fake: 0.816 +(epoch: 30, iters: 5568, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.666 G_ID: 0.338 G_Rec: 0.433 D_GP: 0.032 D_real: 1.109 D_fake: 0.647 +(epoch: 30, iters: 5968, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.716 G_ID: 0.661 G_Rec: 0.378 D_GP: 0.035 D_real: 1.195 D_fake: 0.532 +(epoch: 30, iters: 6368, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.682 G_ID: 0.310 G_Rec: 0.354 D_GP: 0.048 D_real: 0.934 D_fake: 0.718 +(epoch: 30, iters: 6768, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.973 G_ID: 0.666 G_Rec: 0.403 D_GP: 0.173 D_real: 0.867 D_fake: 0.437 +(epoch: 30, iters: 7168, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.856 G_ID: 0.272 G_Rec: 0.436 D_GP: 0.048 D_real: 0.773 D_fake: 0.765 +(epoch: 30, iters: 7568, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.558 G_ID: 0.697 G_Rec: 0.351 D_GP: 0.018 D_real: 1.201 D_fake: 0.698 +(epoch: 30, iters: 7968, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.598 G_ID: 0.332 G_Rec: 0.384 D_GP: 0.028 D_real: 1.389 D_fake: 0.477 +(epoch: 30, iters: 8368, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.774 G_ID: 0.694 G_Rec: 0.366 D_GP: 0.055 D_real: 1.054 D_fake: 0.588 +(epoch: 31, iters: 160, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.841 G_ID: 0.321 G_Rec: 0.387 D_GP: 0.054 D_real: 1.006 D_fake: 0.509 +(epoch: 31, iters: 560, time: 0.064) G_GAN: 0.558 G_GAN_Feat: 1.008 G_ID: 0.713 G_Rec: 0.392 D_GP: 0.115 D_real: 0.739 D_fake: 0.450 +(epoch: 31, iters: 960, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 0.823 G_ID: 0.360 G_Rec: 0.393 D_GP: 0.044 D_real: 1.211 D_fake: 0.442 +(epoch: 31, iters: 1360, time: 0.064) G_GAN: 0.647 G_GAN_Feat: 0.861 G_ID: 0.677 G_Rec: 0.362 D_GP: 0.045 D_real: 1.428 D_fake: 0.470 +(epoch: 31, iters: 1760, time: 0.064) G_GAN: 0.055 G_GAN_Feat: 0.854 G_ID: 0.371 G_Rec: 0.356 D_GP: 0.083 D_real: 0.605 D_fake: 0.945 +(epoch: 31, iters: 2160, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.522 G_ID: 0.709 G_Rec: 0.391 D_GP: 0.018 D_real: 1.444 D_fake: 0.487 +(epoch: 31, iters: 2560, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.542 G_ID: 0.340 G_Rec: 0.374 D_GP: 0.022 D_real: 1.078 D_fake: 0.740 +(epoch: 31, iters: 2960, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.591 G_ID: 0.651 G_Rec: 0.326 D_GP: 0.034 D_real: 1.009 D_fake: 0.792 +(epoch: 31, iters: 3360, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.731 G_ID: 0.350 G_Rec: 0.381 D_GP: 0.092 D_real: 0.909 D_fake: 0.661 +(epoch: 31, iters: 3760, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.742 G_ID: 0.713 G_Rec: 0.405 D_GP: 0.054 D_real: 0.903 D_fake: 0.855 +(epoch: 31, iters: 4160, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.675 G_ID: 0.301 G_Rec: 0.402 D_GP: 0.036 D_real: 1.278 D_fake: 0.475 +(epoch: 31, iters: 4560, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.740 G_ID: 0.648 G_Rec: 0.396 D_GP: 0.034 D_real: 1.047 D_fake: 0.664 +(epoch: 31, iters: 4960, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.807 G_ID: 0.353 G_Rec: 0.360 D_GP: 0.064 D_real: 0.996 D_fake: 0.553 +(epoch: 31, iters: 5360, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.905 G_ID: 0.668 G_Rec: 0.375 D_GP: 0.108 D_real: 1.019 D_fake: 0.467 +(epoch: 31, iters: 5760, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.745 G_ID: 0.322 G_Rec: 0.325 D_GP: 0.063 D_real: 0.905 D_fake: 0.812 +(epoch: 31, iters: 6160, time: 0.064) G_GAN: 0.688 G_GAN_Feat: 0.725 G_ID: 0.736 G_Rec: 0.382 D_GP: 0.031 D_real: 1.440 D_fake: 0.395 +(epoch: 31, iters: 6560, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.766 G_ID: 0.296 G_Rec: 0.371 D_GP: 0.073 D_real: 1.034 D_fake: 0.461 +(epoch: 31, iters: 6960, time: 0.064) G_GAN: 1.075 G_GAN_Feat: 0.714 G_ID: 0.665 G_Rec: 0.389 D_GP: 0.023 D_real: 1.808 D_fake: 0.085 +(epoch: 31, iters: 7360, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.658 G_ID: 0.356 G_Rec: 0.449 D_GP: 0.030 D_real: 1.357 D_fake: 0.465 +(epoch: 31, iters: 7760, time: 0.064) G_GAN: 0.702 G_GAN_Feat: 0.788 G_ID: 0.661 G_Rec: 0.396 D_GP: 0.056 D_real: 1.351 D_fake: 0.336 +(epoch: 31, iters: 8160, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.688 G_ID: 0.359 G_Rec: 0.359 D_GP: 0.037 D_real: 1.049 D_fake: 0.655 +(epoch: 31, iters: 8560, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.635 G_ID: 0.672 G_Rec: 0.390 D_GP: 0.029 D_real: 1.207 D_fake: 0.643 +(epoch: 32, iters: 352, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.698 G_ID: 0.342 G_Rec: 0.365 D_GP: 0.034 D_real: 1.164 D_fake: 0.576 +(epoch: 32, iters: 752, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 1.048 G_ID: 0.693 G_Rec: 0.486 D_GP: 0.117 D_real: 0.361 D_fake: 0.871 +(epoch: 32, iters: 1152, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.628 G_ID: 0.389 G_Rec: 0.408 D_GP: 0.019 D_real: 1.277 D_fake: 0.636 +(epoch: 32, iters: 1552, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.567 G_ID: 0.683 G_Rec: 0.360 D_GP: 0.017 D_real: 1.183 D_fake: 0.669 +(epoch: 32, iters: 1952, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.548 G_ID: 0.310 G_Rec: 0.394 D_GP: 0.022 D_real: 1.381 D_fake: 0.658 +(epoch: 32, iters: 2352, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.603 G_ID: 0.619 G_Rec: 0.369 D_GP: 0.029 D_real: 1.087 D_fake: 0.730 +(epoch: 32, iters: 2752, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.594 G_ID: 0.399 G_Rec: 0.366 D_GP: 0.029 D_real: 1.116 D_fake: 0.730 +(epoch: 32, iters: 3152, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 0.683 G_ID: 0.733 G_Rec: 0.355 D_GP: 0.058 D_real: 0.818 D_fake: 0.928 +(epoch: 32, iters: 3552, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.753 G_ID: 0.311 G_Rec: 0.358 D_GP: 0.067 D_real: 1.017 D_fake: 0.712 +(epoch: 32, iters: 3952, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.981 G_ID: 0.685 G_Rec: 0.395 D_GP: 0.065 D_real: 0.816 D_fake: 0.662 +(epoch: 32, iters: 4352, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.738 G_ID: 0.331 G_Rec: 0.341 D_GP: 0.037 D_real: 0.831 D_fake: 0.803 +(epoch: 32, iters: 4752, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 0.789 G_ID: 0.657 G_Rec: 0.479 D_GP: 0.061 D_real: 1.165 D_fake: 0.478 +(epoch: 32, iters: 5152, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.731 G_ID: 0.352 G_Rec: 0.409 D_GP: 0.034 D_real: 1.105 D_fake: 0.543 +(epoch: 32, iters: 5552, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.903 G_ID: 0.634 G_Rec: 0.365 D_GP: 0.083 D_real: 0.911 D_fake: 0.489 +(epoch: 32, iters: 5952, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.618 G_ID: 0.278 G_Rec: 0.437 D_GP: 0.022 D_real: 1.033 D_fake: 0.791 +(epoch: 32, iters: 6352, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.660 G_ID: 0.642 G_Rec: 0.411 D_GP: 0.033 D_real: 1.046 D_fake: 0.811 +(epoch: 32, iters: 6752, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.732 G_ID: 0.330 G_Rec: 0.455 D_GP: 0.049 D_real: 1.001 D_fake: 0.672 +(epoch: 32, iters: 7152, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.775 G_ID: 0.679 G_Rec: 0.335 D_GP: 0.043 D_real: 0.975 D_fake: 0.686 +(epoch: 32, iters: 7552, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.847 G_ID: 0.346 G_Rec: 0.432 D_GP: 0.042 D_real: 0.750 D_fake: 0.733 +(epoch: 32, iters: 7952, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.605 G_ID: 0.640 G_Rec: 0.325 D_GP: 0.028 D_real: 1.266 D_fake: 0.565 +(epoch: 32, iters: 8352, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.870 G_ID: 0.328 G_Rec: 0.363 D_GP: 0.099 D_real: 0.898 D_fake: 0.577 +(epoch: 33, iters: 144, time: 0.064) G_GAN: -0.015 G_GAN_Feat: 0.844 G_ID: 0.682 G_Rec: 0.354 D_GP: 0.160 D_real: 0.530 D_fake: 1.015 +(epoch: 33, iters: 544, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.924 G_ID: 0.323 G_Rec: 0.341 D_GP: 0.058 D_real: 0.568 D_fake: 0.824 +(epoch: 33, iters: 944, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 1.024 G_ID: 0.693 G_Rec: 0.470 D_GP: 0.136 D_real: 0.581 D_fake: 0.646 +(epoch: 33, iters: 1344, time: 0.064) G_GAN: 0.759 G_GAN_Feat: 1.042 G_ID: 0.324 G_Rec: 0.391 D_GP: 0.075 D_real: 0.951 D_fake: 0.285 +(epoch: 33, iters: 1744, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.489 G_ID: 0.650 G_Rec: 0.362 D_GP: 0.017 D_real: 1.209 D_fake: 0.693 +(epoch: 33, iters: 2144, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.494 G_ID: 0.303 G_Rec: 0.326 D_GP: 0.020 D_real: 1.077 D_fake: 0.744 +(epoch: 33, iters: 2544, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.546 G_ID: 0.703 G_Rec: 0.398 D_GP: 0.022 D_real: 0.943 D_fake: 0.848 +(epoch: 33, iters: 2944, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.560 G_ID: 0.311 G_Rec: 0.446 D_GP: 0.027 D_real: 1.281 D_fake: 0.594 +(epoch: 33, iters: 3344, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.570 G_ID: 0.644 G_Rec: 0.410 D_GP: 0.029 D_real: 0.859 D_fake: 0.879 +(epoch: 33, iters: 3744, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.648 G_ID: 0.361 G_Rec: 0.377 D_GP: 0.039 D_real: 0.953 D_fake: 0.745 +(epoch: 33, iters: 4144, time: 0.064) G_GAN: 0.024 G_GAN_Feat: 0.654 G_ID: 0.683 G_Rec: 0.393 D_GP: 0.055 D_real: 0.723 D_fake: 0.976 +(epoch: 33, iters: 4544, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.960 G_ID: 0.382 G_Rec: 0.382 D_GP: 0.120 D_real: 0.701 D_fake: 0.616 +(epoch: 33, iters: 4944, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.800 G_ID: 0.605 G_Rec: 0.370 D_GP: 0.051 D_real: 1.115 D_fake: 0.574 +(epoch: 33, iters: 5344, time: 0.064) G_GAN: 0.728 G_GAN_Feat: 0.789 G_ID: 0.301 G_Rec: 0.416 D_GP: 0.061 D_real: 1.268 D_fake: 0.444 +(epoch: 33, iters: 5744, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.572 G_ID: 0.648 G_Rec: 0.353 D_GP: 0.020 D_real: 1.289 D_fake: 0.615 +(epoch: 33, iters: 6144, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.643 G_ID: 0.319 G_Rec: 0.373 D_GP: 0.032 D_real: 1.178 D_fake: 0.650 +(epoch: 33, iters: 6544, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.816 G_ID: 0.639 G_Rec: 0.376 D_GP: 0.049 D_real: 1.008 D_fake: 0.668 +(epoch: 33, iters: 6944, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.872 G_ID: 0.331 G_Rec: 0.505 D_GP: 0.055 D_real: 0.700 D_fake: 0.780 +(epoch: 33, iters: 7344, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.657 G_ID: 0.664 G_Rec: 0.405 D_GP: 0.038 D_real: 0.973 D_fake: 0.721 +(epoch: 33, iters: 7744, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.729 G_ID: 0.270 G_Rec: 0.429 D_GP: 0.056 D_real: 1.037 D_fake: 0.579 +(epoch: 33, iters: 8144, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.684 G_ID: 0.704 G_Rec: 0.373 D_GP: 0.041 D_real: 1.096 D_fake: 0.709 +(epoch: 33, iters: 8544, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.675 G_ID: 0.367 G_Rec: 0.364 D_GP: 0.019 D_real: 0.964 D_fake: 0.850 +(epoch: 34, iters: 336, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.647 G_ID: 0.618 G_Rec: 0.367 D_GP: 0.034 D_real: 0.807 D_fake: 0.825 +(epoch: 34, iters: 736, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.866 G_ID: 0.294 G_Rec: 0.351 D_GP: 0.122 D_real: 0.740 D_fake: 0.541 +(epoch: 34, iters: 1136, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.738 G_ID: 0.651 G_Rec: 0.448 D_GP: 0.028 D_real: 0.836 D_fake: 0.882 +(epoch: 34, iters: 1536, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.753 G_ID: 0.346 G_Rec: 0.345 D_GP: 0.040 D_real: 1.145 D_fake: 0.555 +(epoch: 34, iters: 1936, time: 0.064) G_GAN: 0.655 G_GAN_Feat: 0.730 G_ID: 0.644 G_Rec: 0.369 D_GP: 0.036 D_real: 1.423 D_fake: 0.354 +(epoch: 34, iters: 2336, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.994 G_ID: 0.330 G_Rec: 0.375 D_GP: 0.107 D_real: 1.108 D_fake: 0.584 +(epoch: 34, iters: 2736, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.770 G_ID: 0.648 G_Rec: 0.359 D_GP: 0.058 D_real: 0.891 D_fake: 0.821 +(epoch: 34, iters: 3136, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.726 G_ID: 0.290 G_Rec: 0.404 D_GP: 0.031 D_real: 1.027 D_fake: 0.719 +(epoch: 34, iters: 3536, time: 0.064) G_GAN: 0.667 G_GAN_Feat: 0.794 G_ID: 0.642 G_Rec: 0.351 D_GP: 0.046 D_real: 1.411 D_fake: 0.348 +(epoch: 34, iters: 3936, time: 0.064) G_GAN: -0.065 G_GAN_Feat: 0.723 G_ID: 0.332 G_Rec: 0.356 D_GP: 0.031 D_real: 0.641 D_fake: 1.065 +(epoch: 34, iters: 4336, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.762 G_ID: 0.607 G_Rec: 0.348 D_GP: 0.047 D_real: 0.974 D_fake: 0.626 +(epoch: 34, iters: 4736, time: 0.064) G_GAN: 0.563 G_GAN_Feat: 1.106 G_ID: 0.295 G_Rec: 0.354 D_GP: 0.084 D_real: 0.690 D_fake: 0.449 +(epoch: 34, iters: 5136, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.575 G_ID: 0.590 G_Rec: 0.353 D_GP: 0.030 D_real: 0.971 D_fake: 0.900 +(epoch: 34, iters: 5536, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.716 G_ID: 0.358 G_Rec: 0.409 D_GP: 0.052 D_real: 1.086 D_fake: 0.659 +(epoch: 34, iters: 5936, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.712 G_ID: 0.634 G_Rec: 0.389 D_GP: 0.056 D_real: 1.066 D_fake: 0.660 +(epoch: 34, iters: 6336, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.783 G_ID: 0.320 G_Rec: 0.400 D_GP: 0.088 D_real: 0.850 D_fake: 0.636 +(epoch: 34, iters: 6736, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.995 G_ID: 0.643 G_Rec: 0.486 D_GP: 0.086 D_real: 0.387 D_fake: 0.947 +(epoch: 34, iters: 7136, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.784 G_ID: 0.325 G_Rec: 0.356 D_GP: 0.046 D_real: 0.974 D_fake: 0.672 +(epoch: 34, iters: 7536, time: 0.064) G_GAN: 0.777 G_GAN_Feat: 0.729 G_ID: 0.572 G_Rec: 0.393 D_GP: 0.036 D_real: 1.523 D_fake: 0.393 +(epoch: 34, iters: 7936, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.541 G_ID: 0.275 G_Rec: 0.314 D_GP: 0.028 D_real: 1.003 D_fake: 0.846 +(epoch: 34, iters: 8336, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.702 G_ID: 0.681 G_Rec: 0.415 D_GP: 0.032 D_real: 0.776 D_fake: 0.909 +(epoch: 35, iters: 128, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.668 G_ID: 0.358 G_Rec: 0.500 D_GP: 0.059 D_real: 0.983 D_fake: 0.695 +(epoch: 35, iters: 528, time: 0.064) G_GAN: 0.537 G_GAN_Feat: 0.852 G_ID: 0.592 G_Rec: 0.420 D_GP: 0.091 D_real: 1.163 D_fake: 0.515 +(epoch: 35, iters: 928, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.790 G_ID: 0.284 G_Rec: 0.419 D_GP: 0.066 D_real: 1.076 D_fake: 0.564 +(epoch: 35, iters: 1328, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.569 G_ID: 0.653 G_Rec: 0.345 D_GP: 0.021 D_real: 1.390 D_fake: 0.479 +(epoch: 35, iters: 1728, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.619 G_ID: 0.303 G_Rec: 0.452 D_GP: 0.035 D_real: 1.120 D_fake: 0.690 +(epoch: 35, iters: 2128, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.624 G_ID: 0.609 G_Rec: 0.381 D_GP: 0.039 D_real: 1.149 D_fake: 0.515 +(epoch: 35, iters: 2528, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.788 G_ID: 0.311 G_Rec: 0.474 D_GP: 0.087 D_real: 0.708 D_fake: 0.746 +(epoch: 35, iters: 2928, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.746 G_ID: 0.612 G_Rec: 0.347 D_GP: 0.086 D_real: 0.875 D_fake: 0.767 +(epoch: 35, iters: 3328, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.923 G_ID: 0.302 G_Rec: 0.352 D_GP: 0.072 D_real: 0.565 D_fake: 0.783 +(epoch: 35, iters: 3728, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.855 G_ID: 0.623 G_Rec: 0.352 D_GP: 0.153 D_real: 0.774 D_fake: 0.674 +(epoch: 35, iters: 4128, time: 0.064) G_GAN: 0.703 G_GAN_Feat: 0.697 G_ID: 0.331 G_Rec: 0.452 D_GP: 0.029 D_real: 1.490 D_fake: 0.313 +(epoch: 35, iters: 4528, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 1.183 G_ID: 0.644 G_Rec: 0.370 D_GP: 0.301 D_real: 0.466 D_fake: 0.613 +(epoch: 35, iters: 4928, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 1.097 G_ID: 0.362 G_Rec: 0.430 D_GP: 0.219 D_real: 0.451 D_fake: 0.571 +(epoch: 35, iters: 5328, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.564 G_ID: 0.686 G_Rec: 0.450 D_GP: 0.019 D_real: 1.137 D_fake: 0.810 +(epoch: 35, iters: 5728, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.569 G_ID: 0.325 G_Rec: 0.356 D_GP: 0.027 D_real: 1.020 D_fake: 0.826 +(epoch: 35, iters: 6128, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.516 G_ID: 0.636 G_Rec: 0.451 D_GP: 0.021 D_real: 1.043 D_fake: 0.819 +(epoch: 35, iters: 6528, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.553 G_ID: 0.290 G_Rec: 0.353 D_GP: 0.024 D_real: 1.162 D_fake: 0.761 +(epoch: 35, iters: 6928, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.571 G_ID: 0.641 G_Rec: 0.409 D_GP: 0.034 D_real: 0.961 D_fake: 0.850 +(epoch: 35, iters: 7328, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 0.629 G_ID: 0.276 G_Rec: 0.348 D_GP: 0.059 D_real: 0.870 D_fake: 0.935 +(epoch: 35, iters: 7728, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.649 G_ID: 0.606 G_Rec: 0.365 D_GP: 0.056 D_real: 0.973 D_fake: 0.782 +(epoch: 35, iters: 8128, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.654 G_ID: 0.291 G_Rec: 0.430 D_GP: 0.036 D_real: 1.039 D_fake: 0.755 +(epoch: 35, iters: 8528, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.732 G_ID: 0.649 G_Rec: 0.366 D_GP: 0.059 D_real: 1.098 D_fake: 0.561 +(epoch: 36, iters: 320, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.891 G_ID: 0.360 G_Rec: 0.368 D_GP: 0.100 D_real: 0.506 D_fake: 0.897 +(epoch: 36, iters: 720, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.705 G_ID: 0.603 G_Rec: 0.357 D_GP: 0.074 D_real: 1.050 D_fake: 0.546 +(epoch: 36, iters: 1120, time: 0.064) G_GAN: 0.582 G_GAN_Feat: 0.710 G_ID: 0.325 G_Rec: 0.333 D_GP: 0.060 D_real: 1.233 D_fake: 0.450 +(epoch: 36, iters: 1520, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.719 G_ID: 0.609 G_Rec: 0.406 D_GP: 0.034 D_real: 1.041 D_fake: 0.722 +(epoch: 36, iters: 1920, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.827 G_ID: 0.298 G_Rec: 0.385 D_GP: 0.093 D_real: 0.938 D_fake: 0.631 +(epoch: 36, iters: 2320, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.651 G_ID: 0.568 G_Rec: 0.370 D_GP: 0.030 D_real: 1.197 D_fake: 0.598 +(epoch: 36, iters: 2720, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.796 G_ID: 0.328 G_Rec: 0.374 D_GP: 0.103 D_real: 0.712 D_fake: 0.808 +(epoch: 36, iters: 3120, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.781 G_ID: 0.599 G_Rec: 0.390 D_GP: 0.071 D_real: 0.805 D_fake: 0.679 +(epoch: 36, iters: 3520, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.897 G_ID: 0.242 G_Rec: 0.400 D_GP: 0.110 D_real: 0.473 D_fake: 0.773 +(epoch: 36, iters: 3920, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.689 G_ID: 0.664 G_Rec: 0.361 D_GP: 0.042 D_real: 0.819 D_fake: 0.875 +(epoch: 36, iters: 4320, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.652 G_ID: 0.292 G_Rec: 0.409 D_GP: 0.032 D_real: 0.955 D_fake: 0.798 +(epoch: 36, iters: 4720, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.810 G_ID: 0.636 G_Rec: 0.328 D_GP: 0.056 D_real: 1.225 D_fake: 0.596 +(epoch: 36, iters: 5120, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.722 G_ID: 0.312 G_Rec: 0.338 D_GP: 0.042 D_real: 1.134 D_fake: 0.499 +(epoch: 36, iters: 5520, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.796 G_ID: 0.619 G_Rec: 0.342 D_GP: 0.049 D_real: 0.950 D_fake: 0.678 +(epoch: 36, iters: 5920, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.764 G_ID: 0.296 G_Rec: 0.480 D_GP: 0.029 D_real: 0.940 D_fake: 0.785 +(epoch: 36, iters: 6320, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.895 G_ID: 0.602 G_Rec: 0.372 D_GP: 0.083 D_real: 0.768 D_fake: 0.693 +(epoch: 36, iters: 6720, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.633 G_ID: 0.296 G_Rec: 0.332 D_GP: 0.025 D_real: 1.207 D_fake: 0.632 +(epoch: 36, iters: 7120, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.623 G_ID: 0.628 G_Rec: 0.326 D_GP: 0.026 D_real: 1.114 D_fake: 0.777 +(epoch: 36, iters: 7520, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.669 G_ID: 0.321 G_Rec: 0.374 D_GP: 0.020 D_real: 1.009 D_fake: 0.877 +(epoch: 36, iters: 7920, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.791 G_ID: 0.625 G_Rec: 0.415 D_GP: 0.032 D_real: 0.815 D_fake: 0.897 +(epoch: 36, iters: 8320, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.721 G_ID: 0.279 G_Rec: 0.416 D_GP: 0.049 D_real: 1.027 D_fake: 0.759 +(epoch: 37, iters: 112, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.802 G_ID: 0.592 G_Rec: 0.370 D_GP: 0.073 D_real: 1.043 D_fake: 0.720 +(epoch: 37, iters: 512, time: 0.064) G_GAN: 0.617 G_GAN_Feat: 0.774 G_ID: 0.327 G_Rec: 0.400 D_GP: 0.046 D_real: 1.309 D_fake: 0.401 +(epoch: 37, iters: 912, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.738 G_ID: 0.615 G_Rec: 0.336 D_GP: 0.057 D_real: 0.741 D_fake: 0.914 +(epoch: 37, iters: 1312, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.732 G_ID: 0.273 G_Rec: 0.346 D_GP: 0.035 D_real: 1.197 D_fake: 0.526 +(epoch: 37, iters: 1712, time: 0.064) G_GAN: -0.008 G_GAN_Feat: 1.181 G_ID: 0.656 G_Rec: 0.374 D_GP: 0.154 D_real: 0.289 D_fake: 1.008 +(epoch: 37, iters: 2112, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.738 G_ID: 0.304 G_Rec: 0.419 D_GP: 0.031 D_real: 0.976 D_fake: 0.762 +(epoch: 37, iters: 2512, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.605 G_ID: 0.607 G_Rec: 0.331 D_GP: 0.022 D_real: 1.075 D_fake: 0.816 +(epoch: 37, iters: 2912, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 1.518 G_ID: 0.284 G_Rec: 0.461 D_GP: 0.080 D_real: 0.462 D_fake: 0.516 +(epoch: 37, iters: 3312, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 0.602 G_ID: 0.635 G_Rec: 0.401 D_GP: 0.024 D_real: 1.335 D_fake: 0.499 +(epoch: 37, iters: 3712, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.611 G_ID: 0.252 G_Rec: 0.366 D_GP: 0.034 D_real: 0.904 D_fake: 0.913 +(epoch: 37, iters: 4112, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.636 G_ID: 0.626 G_Rec: 0.358 D_GP: 0.027 D_real: 1.218 D_fake: 0.568 +(epoch: 37, iters: 4512, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.895 G_ID: 0.333 G_Rec: 0.360 D_GP: 0.075 D_real: 0.752 D_fake: 0.726 +(epoch: 37, iters: 4912, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.842 G_ID: 0.557 G_Rec: 0.435 D_GP: 0.045 D_real: 0.964 D_fake: 0.633 +(epoch: 37, iters: 5312, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.735 G_ID: 0.290 G_Rec: 0.363 D_GP: 0.062 D_real: 1.030 D_fake: 0.576 +(epoch: 37, iters: 5712, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.787 G_ID: 0.620 G_Rec: 0.395 D_GP: 0.044 D_real: 1.289 D_fake: 0.409 +(epoch: 37, iters: 6112, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.786 G_ID: 0.292 G_Rec: 0.437 D_GP: 0.033 D_real: 1.254 D_fake: 0.536 +(epoch: 37, iters: 6512, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.752 G_ID: 0.615 G_Rec: 0.363 D_GP: 0.047 D_real: 1.223 D_fake: 0.571 +(epoch: 37, iters: 6912, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.850 G_ID: 0.279 G_Rec: 0.375 D_GP: 0.053 D_real: 0.939 D_fake: 0.629 +(epoch: 37, iters: 7312, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.781 G_ID: 0.583 G_Rec: 0.377 D_GP: 0.040 D_real: 0.730 D_fake: 0.846 +(epoch: 37, iters: 7712, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.767 G_ID: 0.318 G_Rec: 0.381 D_GP: 0.070 D_real: 0.899 D_fake: 0.794 +(epoch: 37, iters: 8112, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 1.050 G_ID: 0.577 G_Rec: 0.463 D_GP: 0.195 D_real: 0.670 D_fake: 0.532 +(epoch: 37, iters: 8512, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.697 G_ID: 0.278 G_Rec: 0.359 D_GP: 0.030 D_real: 1.238 D_fake: 0.492 +(epoch: 38, iters: 304, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 1.157 G_ID: 0.573 G_Rec: 0.359 D_GP: 0.165 D_real: 0.457 D_fake: 0.832 +(epoch: 38, iters: 704, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.777 G_ID: 0.328 G_Rec: 0.348 D_GP: 0.044 D_real: 0.764 D_fake: 0.824 +(epoch: 38, iters: 1104, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.707 G_ID: 0.620 G_Rec: 0.379 D_GP: 0.030 D_real: 1.345 D_fake: 0.459 +(epoch: 38, iters: 1504, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 0.661 G_ID: 0.265 G_Rec: 0.363 D_GP: 0.029 D_real: 1.320 D_fake: 0.532 +(epoch: 38, iters: 1904, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 1.124 G_ID: 0.573 G_Rec: 0.455 D_GP: 0.052 D_real: 0.677 D_fake: 0.565 +(epoch: 38, iters: 2304, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.634 G_ID: 0.324 G_Rec: 0.381 D_GP: 0.022 D_real: 1.338 D_fake: 0.516 +(epoch: 38, iters: 2704, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.825 G_ID: 0.650 G_Rec: 0.377 D_GP: 0.042 D_real: 0.733 D_fake: 0.815 +(epoch: 38, iters: 3104, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.848 G_ID: 0.289 G_Rec: 0.434 D_GP: 0.075 D_real: 0.711 D_fake: 0.826 +(epoch: 38, iters: 3504, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 0.842 G_ID: 0.617 G_Rec: 0.404 D_GP: 0.039 D_real: 0.585 D_fake: 0.988 +(epoch: 38, iters: 3904, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.845 G_ID: 0.331 G_Rec: 0.395 D_GP: 0.039 D_real: 0.857 D_fake: 0.729 +(epoch: 38, iters: 4304, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.894 G_ID: 0.587 G_Rec: 0.378 D_GP: 0.040 D_real: 1.047 D_fake: 0.515 +(epoch: 38, iters: 4704, time: 0.064) G_GAN: 0.582 G_GAN_Feat: 0.554 G_ID: 0.304 G_Rec: 0.367 D_GP: 0.020 D_real: 1.494 D_fake: 0.420 +(epoch: 38, iters: 5104, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.509 G_ID: 0.617 G_Rec: 0.352 D_GP: 0.021 D_real: 1.240 D_fake: 0.675 +(epoch: 38, iters: 5504, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.550 G_ID: 0.291 G_Rec: 0.596 D_GP: 0.022 D_real: 1.294 D_fake: 0.565 +(epoch: 38, iters: 5904, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.574 G_ID: 0.581 G_Rec: 0.405 D_GP: 0.022 D_real: 1.142 D_fake: 0.714 +(epoch: 38, iters: 6304, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.645 G_ID: 0.303 G_Rec: 0.369 D_GP: 0.032 D_real: 1.081 D_fake: 0.769 +(epoch: 38, iters: 6704, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.703 G_ID: 0.609 G_Rec: 0.431 D_GP: 0.040 D_real: 1.157 D_fake: 0.583 +(epoch: 38, iters: 7104, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.660 G_ID: 0.310 G_Rec: 0.349 D_GP: 0.050 D_real: 1.110 D_fake: 0.629 +(epoch: 38, iters: 7504, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 0.808 G_ID: 0.626 G_Rec: 0.406 D_GP: 0.040 D_real: 1.207 D_fake: 0.565 +(epoch: 38, iters: 7904, time: 0.064) G_GAN: 0.592 G_GAN_Feat: 0.710 G_ID: 0.270 G_Rec: 0.392 D_GP: 0.030 D_real: 1.269 D_fake: 0.411 +(epoch: 38, iters: 8304, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 0.711 G_ID: 0.575 G_Rec: 0.374 D_GP: 0.035 D_real: 1.357 D_fake: 0.462 +(epoch: 39, iters: 96, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.741 G_ID: 0.311 G_Rec: 0.505 D_GP: 0.076 D_real: 0.981 D_fake: 0.690 +(epoch: 39, iters: 496, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.708 G_ID: 0.643 G_Rec: 0.347 D_GP: 0.064 D_real: 1.029 D_fake: 0.722 +(epoch: 39, iters: 896, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.783 G_ID: 0.341 G_Rec: 0.358 D_GP: 0.077 D_real: 1.099 D_fake: 0.567 +(epoch: 39, iters: 1296, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.770 G_ID: 0.622 G_Rec: 0.373 D_GP: 0.069 D_real: 1.013 D_fake: 0.621 +(epoch: 39, iters: 1696, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.672 G_ID: 0.276 G_Rec: 0.382 D_GP: 0.035 D_real: 1.086 D_fake: 0.605 +(epoch: 39, iters: 2096, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.646 G_ID: 0.545 G_Rec: 0.354 D_GP: 0.051 D_real: 1.117 D_fake: 0.683 +(epoch: 39, iters: 2496, time: 0.064) G_GAN: 0.614 G_GAN_Feat: 0.832 G_ID: 0.261 G_Rec: 0.428 D_GP: 0.061 D_real: 1.080 D_fake: 0.452 +(epoch: 39, iters: 2896, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 1.056 G_ID: 0.602 G_Rec: 0.362 D_GP: 0.137 D_real: 0.461 D_fake: 0.729 +(epoch: 39, iters: 3296, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.888 G_ID: 0.267 G_Rec: 0.410 D_GP: 0.049 D_real: 1.179 D_fake: 0.481 +(epoch: 39, iters: 3696, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.980 G_ID: 0.566 G_Rec: 0.412 D_GP: 0.064 D_real: 0.568 D_fake: 0.774 +(epoch: 39, iters: 4096, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 1.037 G_ID: 0.289 G_Rec: 0.383 D_GP: 0.318 D_real: 0.700 D_fake: 0.525 +(epoch: 39, iters: 4496, time: 0.064) G_GAN: 0.705 G_GAN_Feat: 1.442 G_ID: 0.637 G_Rec: 0.478 D_GP: 0.615 D_real: 0.428 D_fake: 0.445 +(epoch: 39, iters: 4896, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.706 G_ID: 0.283 G_Rec: 0.379 D_GP: 0.054 D_real: 1.123 D_fake: 0.509 +(epoch: 39, iters: 5296, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.689 G_ID: 0.556 G_Rec: 0.351 D_GP: 0.034 D_real: 1.175 D_fake: 0.557 +(epoch: 39, iters: 5696, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 0.643 G_ID: 0.265 G_Rec: 0.363 D_GP: 0.020 D_real: 1.345 D_fake: 0.465 +(epoch: 39, iters: 6096, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.656 G_ID: 0.538 G_Rec: 0.407 D_GP: 0.031 D_real: 0.949 D_fake: 0.816 +(epoch: 39, iters: 6496, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 1.075 G_ID: 0.311 G_Rec: 0.395 D_GP: 0.058 D_real: 0.474 D_fake: 0.784 +(epoch: 39, iters: 6896, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.823 G_ID: 0.567 G_Rec: 0.395 D_GP: 0.034 D_real: 0.967 D_fake: 0.700 +(epoch: 39, iters: 7296, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.788 G_ID: 0.284 G_Rec: 0.495 D_GP: 0.067 D_real: 1.087 D_fake: 0.526 +(epoch: 39, iters: 7696, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.745 G_ID: 0.590 G_Rec: 0.412 D_GP: 0.034 D_real: 1.015 D_fake: 0.711 +(epoch: 39, iters: 8096, time: 0.064) G_GAN: 0.759 G_GAN_Feat: 0.898 G_ID: 0.273 G_Rec: 0.354 D_GP: 0.049 D_real: 1.493 D_fake: 0.267 +(epoch: 39, iters: 8496, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.670 G_ID: 0.586 G_Rec: 0.378 D_GP: 0.019 D_real: 1.218 D_fake: 0.583 +(epoch: 40, iters: 288, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.550 G_ID: 0.307 G_Rec: 0.362 D_GP: 0.025 D_real: 1.007 D_fake: 0.807 +(epoch: 40, iters: 688, time: 0.064) G_GAN: 0.009 G_GAN_Feat: 0.607 G_ID: 0.588 G_Rec: 0.307 D_GP: 0.031 D_real: 0.897 D_fake: 0.991 +(epoch: 40, iters: 1088, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.739 G_ID: 0.274 G_Rec: 0.434 D_GP: 0.040 D_real: 0.909 D_fake: 0.727 +(epoch: 40, iters: 1488, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.697 G_ID: 0.578 G_Rec: 0.381 D_GP: 0.034 D_real: 1.072 D_fake: 0.823 +(epoch: 40, iters: 1888, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 0.829 G_ID: 0.353 G_Rec: 0.380 D_GP: 0.277 D_real: 0.459 D_fake: 0.930 +(epoch: 40, iters: 2288, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 1.009 G_ID: 0.620 G_Rec: 0.361 D_GP: 0.127 D_real: 0.573 D_fake: 0.513 +(epoch: 40, iters: 2688, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.710 G_ID: 0.319 G_Rec: 0.400 D_GP: 0.041 D_real: 0.946 D_fake: 0.874 +(epoch: 40, iters: 3088, time: 0.064) G_GAN: 0.752 G_GAN_Feat: 1.033 G_ID: 0.526 G_Rec: 0.421 D_GP: 0.110 D_real: 1.194 D_fake: 0.294 +(epoch: 40, iters: 3488, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.652 G_ID: 0.335 G_Rec: 0.347 D_GP: 0.022 D_real: 0.964 D_fake: 0.886 +(epoch: 40, iters: 3888, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.883 G_ID: 0.605 G_Rec: 0.434 D_GP: 0.068 D_real: 1.105 D_fake: 0.666 +(epoch: 40, iters: 4288, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.744 G_ID: 0.260 G_Rec: 0.374 D_GP: 0.055 D_real: 0.743 D_fake: 0.977 +(epoch: 40, iters: 4688, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 1.020 G_ID: 0.575 G_Rec: 0.456 D_GP: 0.178 D_real: 0.675 D_fake: 0.480 +(epoch: 40, iters: 5088, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.726 G_ID: 0.255 G_Rec: 0.426 D_GP: 0.021 D_real: 1.249 D_fake: 0.560 +(epoch: 40, iters: 5488, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.644 G_ID: 0.572 G_Rec: 0.351 D_GP: 0.028 D_real: 1.092 D_fake: 0.692 +(epoch: 40, iters: 5888, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 0.607 G_ID: 0.277 G_Rec: 0.363 D_GP: 0.030 D_real: 1.312 D_fake: 0.511 +(epoch: 40, iters: 6288, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.769 G_ID: 0.568 G_Rec: 0.382 D_GP: 0.044 D_real: 1.220 D_fake: 0.496 +(epoch: 40, iters: 6688, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.802 G_ID: 0.297 G_Rec: 0.393 D_GP: 0.071 D_real: 0.971 D_fake: 0.662 +(epoch: 40, iters: 7088, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.669 G_ID: 0.555 G_Rec: 0.397 D_GP: 0.029 D_real: 1.244 D_fake: 0.651 +(epoch: 40, iters: 7488, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.754 G_ID: 0.293 G_Rec: 0.352 D_GP: 0.081 D_real: 0.932 D_fake: 0.747 +(epoch: 40, iters: 7888, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 1.500 G_ID: 0.559 G_Rec: 0.404 D_GP: 1.063 D_real: 0.541 D_fake: 0.992 +(epoch: 40, iters: 8288, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.859 G_ID: 0.273 G_Rec: 0.444 D_GP: 0.075 D_real: 0.813 D_fake: 0.668 +(epoch: 41, iters: 80, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.745 G_ID: 0.553 G_Rec: 0.360 D_GP: 0.036 D_real: 1.219 D_fake: 0.583 +(epoch: 41, iters: 480, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.708 G_ID: 0.356 G_Rec: 0.353 D_GP: 0.043 D_real: 0.949 D_fake: 0.722 +(epoch: 41, iters: 880, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.886 G_ID: 0.614 G_Rec: 0.371 D_GP: 0.050 D_real: 0.831 D_fake: 0.783 +(epoch: 41, iters: 1280, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.948 G_ID: 0.309 G_Rec: 0.342 D_GP: 0.095 D_real: 0.825 D_fake: 0.789 +(epoch: 41, iters: 1680, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.756 G_ID: 0.557 G_Rec: 0.433 D_GP: 0.028 D_real: 1.328 D_fake: 0.441 +(epoch: 41, iters: 2080, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.989 G_ID: 0.291 G_Rec: 0.402 D_GP: 0.042 D_real: 0.763 D_fake: 0.687 +(epoch: 41, iters: 2480, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 0.743 G_ID: 0.501 G_Rec: 0.341 D_GP: 0.053 D_real: 1.188 D_fake: 0.507 +(epoch: 41, iters: 2880, time: 0.064) G_GAN: 0.054 G_GAN_Feat: 0.706 G_ID: 0.305 G_Rec: 0.346 D_GP: 0.040 D_real: 0.788 D_fake: 0.946 +(epoch: 41, iters: 3280, time: 0.064) G_GAN: 0.564 G_GAN_Feat: 0.873 G_ID: 0.553 G_Rec: 0.367 D_GP: 0.184 D_real: 0.965 D_fake: 0.439 +(epoch: 41, iters: 3680, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.642 G_ID: 0.333 G_Rec: 0.385 D_GP: 0.033 D_real: 1.080 D_fake: 0.671 +(epoch: 41, iters: 4080, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.927 G_ID: 0.531 G_Rec: 0.420 D_GP: 0.099 D_real: 0.766 D_fake: 0.736 +(epoch: 41, iters: 4480, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.605 G_ID: 0.268 G_Rec: 0.339 D_GP: 0.023 D_real: 0.940 D_fake: 0.817 +(epoch: 41, iters: 4880, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.792 G_ID: 0.538 G_Rec: 0.384 D_GP: 0.037 D_real: 0.934 D_fake: 0.617 +(epoch: 41, iters: 5280, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.759 G_ID: 0.257 G_Rec: 0.384 D_GP: 0.069 D_real: 0.885 D_fake: 0.849 +(epoch: 41, iters: 5680, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.716 G_ID: 0.579 G_Rec: 0.379 D_GP: 0.047 D_real: 1.044 D_fake: 0.749 +(epoch: 41, iters: 6080, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.926 G_ID: 0.308 G_Rec: 0.376 D_GP: 0.092 D_real: 0.827 D_fake: 0.548 +(epoch: 41, iters: 6480, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.611 G_ID: 0.595 G_Rec: 0.347 D_GP: 0.025 D_real: 1.286 D_fake: 0.618 +(epoch: 41, iters: 6880, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.564 G_ID: 0.289 G_Rec: 0.394 D_GP: 0.026 D_real: 1.018 D_fake: 0.808 +(epoch: 41, iters: 7280, time: 0.064) G_GAN: -0.129 G_GAN_Feat: 0.623 G_ID: 0.539 G_Rec: 0.398 D_GP: 0.039 D_real: 0.642 D_fake: 1.129 +(epoch: 41, iters: 7680, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.701 G_ID: 0.272 G_Rec: 0.389 D_GP: 0.049 D_real: 0.887 D_fake: 0.770 +(epoch: 41, iters: 8080, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.729 G_ID: 0.547 G_Rec: 0.340 D_GP: 0.045 D_real: 1.183 D_fake: 0.556 +(epoch: 41, iters: 8480, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.777 G_ID: 0.270 G_Rec: 0.357 D_GP: 0.092 D_real: 0.812 D_fake: 0.651 +(epoch: 42, iters: 272, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.884 G_ID: 0.574 G_Rec: 0.351 D_GP: 0.043 D_real: 0.659 D_fake: 0.838 +(epoch: 42, iters: 672, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 0.725 G_ID: 0.334 G_Rec: 0.379 D_GP: 0.045 D_real: 0.777 D_fake: 0.990 +(epoch: 42, iters: 1072, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.828 G_ID: 0.619 G_Rec: 0.430 D_GP: 0.074 D_real: 0.809 D_fake: 0.812 +(epoch: 42, iters: 1472, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.867 G_ID: 0.230 G_Rec: 0.412 D_GP: 0.041 D_real: 0.912 D_fake: 0.623 +(epoch: 42, iters: 1872, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.787 G_ID: 0.592 G_Rec: 0.426 D_GP: 0.029 D_real: 1.134 D_fake: 0.626 +(epoch: 42, iters: 2272, time: 0.064) G_GAN: 0.139 G_GAN_Feat: 0.782 G_ID: 0.286 G_Rec: 0.348 D_GP: 0.079 D_real: 0.671 D_fake: 0.861 +(epoch: 42, iters: 2672, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.790 G_ID: 0.581 G_Rec: 0.352 D_GP: 0.055 D_real: 1.134 D_fake: 0.545 +(epoch: 42, iters: 3072, time: 0.064) G_GAN: 0.666 G_GAN_Feat: 0.796 G_ID: 0.293 G_Rec: 0.368 D_GP: 0.039 D_real: 1.467 D_fake: 0.340 +(epoch: 42, iters: 3472, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.918 G_ID: 0.569 G_Rec: 0.365 D_GP: 0.046 D_real: 1.139 D_fake: 0.542 +(epoch: 42, iters: 3872, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.918 G_ID: 0.270 G_Rec: 0.384 D_GP: 0.088 D_real: 0.642 D_fake: 0.711 +(epoch: 42, iters: 4272, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.811 G_ID: 0.530 G_Rec: 0.391 D_GP: 0.040 D_real: 1.056 D_fake: 0.621 +(epoch: 42, iters: 4672, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.725 G_ID: 0.242 G_Rec: 0.467 D_GP: 0.029 D_real: 1.151 D_fake: 0.543 +(epoch: 42, iters: 5072, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.593 G_ID: 0.477 G_Rec: 0.378 D_GP: 0.021 D_real: 1.218 D_fake: 0.653 +(epoch: 42, iters: 5472, time: 0.064) G_GAN: 0.756 G_GAN_Feat: 0.670 G_ID: 0.245 G_Rec: 0.485 D_GP: 0.028 D_real: 1.460 D_fake: 0.285 +(epoch: 42, iters: 5872, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.673 G_ID: 0.538 G_Rec: 0.379 D_GP: 0.036 D_real: 1.222 D_fake: 0.614 +(epoch: 42, iters: 6272, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.784 G_ID: 0.270 G_Rec: 0.374 D_GP: 0.073 D_real: 0.743 D_fake: 0.747 +(epoch: 42, iters: 6672, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.800 G_ID: 0.612 G_Rec: 0.356 D_GP: 0.040 D_real: 1.085 D_fake: 0.552 +(epoch: 42, iters: 7072, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.783 G_ID: 0.272 G_Rec: 0.375 D_GP: 0.044 D_real: 0.992 D_fake: 0.650 +(epoch: 42, iters: 7472, time: 0.064) G_GAN: 0.643 G_GAN_Feat: 0.717 G_ID: 0.571 G_Rec: 0.395 D_GP: 0.037 D_real: 1.357 D_fake: 0.359 +(epoch: 42, iters: 7872, time: 0.064) G_GAN: -0.068 G_GAN_Feat: 0.970 G_ID: 0.309 G_Rec: 0.383 D_GP: 0.114 D_real: 0.531 D_fake: 1.068 +(epoch: 42, iters: 8272, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.813 G_ID: 0.546 G_Rec: 0.379 D_GP: 0.038 D_real: 0.775 D_fake: 0.779 +(epoch: 43, iters: 64, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.949 G_ID: 0.274 G_Rec: 0.371 D_GP: 0.158 D_real: 0.368 D_fake: 0.724 +(epoch: 43, iters: 464, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.866 G_ID: 0.559 G_Rec: 0.412 D_GP: 0.029 D_real: 1.099 D_fake: 0.721 +(epoch: 43, iters: 864, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.892 G_ID: 0.326 G_Rec: 0.349 D_GP: 0.142 D_real: 0.408 D_fake: 0.978 +(epoch: 43, iters: 1264, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 1.030 G_ID: 0.534 G_Rec: 0.378 D_GP: 0.051 D_real: 0.789 D_fake: 0.570 +(epoch: 43, iters: 1664, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 1.053 G_ID: 0.245 G_Rec: 0.387 D_GP: 0.066 D_real: 0.578 D_fake: 0.612 +(epoch: 43, iters: 2064, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.696 G_ID: 0.551 G_Rec: 0.395 D_GP: 0.024 D_real: 1.132 D_fake: 0.688 +(epoch: 43, iters: 2464, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.592 G_ID: 0.249 G_Rec: 0.381 D_GP: 0.028 D_real: 1.130 D_fake: 0.768 +(epoch: 43, iters: 2864, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.670 G_ID: 0.523 G_Rec: 0.401 D_GP: 0.036 D_real: 0.943 D_fake: 0.843 +(epoch: 43, iters: 3264, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.747 G_ID: 0.278 G_Rec: 0.383 D_GP: 0.049 D_real: 1.077 D_fake: 0.553 +(epoch: 43, iters: 3664, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.843 G_ID: 0.551 G_Rec: 0.405 D_GP: 0.097 D_real: 0.691 D_fake: 0.844 +(epoch: 43, iters: 4064, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.832 G_ID: 0.284 G_Rec: 0.337 D_GP: 0.045 D_real: 0.635 D_fake: 0.884 +(epoch: 43, iters: 4464, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.720 G_ID: 0.612 G_Rec: 0.357 D_GP: 0.039 D_real: 0.978 D_fake: 0.747 +(epoch: 43, iters: 4864, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.806 G_ID: 0.320 G_Rec: 0.349 D_GP: 0.050 D_real: 0.758 D_fake: 0.701 +(epoch: 43, iters: 5264, time: 0.064) G_GAN: 0.576 G_GAN_Feat: 0.740 G_ID: 0.599 G_Rec: 0.365 D_GP: 0.028 D_real: 1.379 D_fake: 0.458 +(epoch: 43, iters: 5664, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.707 G_ID: 0.301 G_Rec: 0.376 D_GP: 0.032 D_real: 1.169 D_fake: 0.570 +(epoch: 43, iters: 6064, time: 0.064) G_GAN: 0.043 G_GAN_Feat: 0.771 G_ID: 0.579 G_Rec: 0.387 D_GP: 0.025 D_real: 0.894 D_fake: 0.957 +(epoch: 43, iters: 6464, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.847 G_ID: 0.276 G_Rec: 0.363 D_GP: 0.066 D_real: 0.928 D_fake: 0.601 +(epoch: 43, iters: 6864, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.790 G_ID: 0.541 G_Rec: 0.398 D_GP: 0.027 D_real: 1.122 D_fake: 0.579 +(epoch: 43, iters: 7264, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.879 G_ID: 0.263 G_Rec: 0.401 D_GP: 0.070 D_real: 0.990 D_fake: 0.517 +(epoch: 43, iters: 7664, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.800 G_ID: 0.567 G_Rec: 0.382 D_GP: 0.026 D_real: 1.146 D_fake: 0.624 +(epoch: 43, iters: 8064, time: 0.064) G_GAN: 0.783 G_GAN_Feat: 0.805 G_ID: 0.304 G_Rec: 0.427 D_GP: 0.039 D_real: 1.600 D_fake: 0.322 +(epoch: 43, iters: 8464, time: 0.064) G_GAN: 0.543 G_GAN_Feat: 1.147 G_ID: 0.551 G_Rec: 0.381 D_GP: 0.101 D_real: 0.519 D_fake: 0.459 +(epoch: 44, iters: 256, time: 0.064) G_GAN: -0.319 G_GAN_Feat: 0.838 G_ID: 0.274 G_Rec: 0.414 D_GP: 0.044 D_real: 0.506 D_fake: 1.319 +(epoch: 44, iters: 656, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.640 G_ID: 0.565 G_Rec: 0.494 D_GP: 0.029 D_real: 1.141 D_fake: 0.700 +(epoch: 44, iters: 1056, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.762 G_ID: 0.270 G_Rec: 0.357 D_GP: 0.089 D_real: 1.047 D_fake: 0.725 +(epoch: 44, iters: 1456, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.888 G_ID: 0.542 G_Rec: 0.366 D_GP: 0.047 D_real: 0.921 D_fake: 0.634 +(epoch: 44, iters: 1856, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.972 G_ID: 0.250 G_Rec: 0.341 D_GP: 0.158 D_real: 0.333 D_fake: 0.870 +(epoch: 44, iters: 2256, time: 0.064) G_GAN: 0.047 G_GAN_Feat: 0.906 G_ID: 0.545 G_Rec: 0.348 D_GP: 0.179 D_real: 0.440 D_fake: 0.954 +(epoch: 44, iters: 2656, time: 0.064) G_GAN: 0.743 G_GAN_Feat: 0.981 G_ID: 0.305 G_Rec: 0.373 D_GP: 0.064 D_real: 1.328 D_fake: 0.395 +(epoch: 44, iters: 3056, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.892 G_ID: 0.584 G_Rec: 0.356 D_GP: 0.056 D_real: 0.951 D_fake: 0.562 +(epoch: 44, iters: 3456, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.856 G_ID: 0.249 G_Rec: 0.381 D_GP: 0.045 D_real: 1.105 D_fake: 0.533 +(epoch: 44, iters: 3856, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.659 G_ID: 0.560 G_Rec: 0.380 D_GP: 0.022 D_real: 1.135 D_fake: 0.748 +(epoch: 44, iters: 4256, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.613 G_ID: 0.339 G_Rec: 0.330 D_GP: 0.030 D_real: 1.084 D_fake: 0.682 +(epoch: 44, iters: 4656, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.859 G_ID: 0.530 G_Rec: 0.441 D_GP: 0.115 D_real: 0.945 D_fake: 0.629 +(epoch: 44, iters: 5056, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.904 G_ID: 0.276 G_Rec: 0.366 D_GP: 0.044 D_real: 1.178 D_fake: 0.482 +(epoch: 44, iters: 5456, time: 0.064) G_GAN: 0.630 G_GAN_Feat: 1.709 G_ID: 0.619 G_Rec: 0.512 D_GP: 1.071 D_real: 0.515 D_fake: 0.386 +(epoch: 44, iters: 5856, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.576 G_ID: 0.268 G_Rec: 0.462 D_GP: 0.024 D_real: 1.046 D_fake: 0.782 +(epoch: 44, iters: 6256, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.716 G_ID: 0.534 G_Rec: 0.380 D_GP: 0.052 D_real: 1.075 D_fake: 0.696 +(epoch: 44, iters: 6656, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.831 G_ID: 0.265 G_Rec: 0.392 D_GP: 0.060 D_real: 0.983 D_fake: 0.505 +(epoch: 44, iters: 7056, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.688 G_ID: 0.564 G_Rec: 0.410 D_GP: 0.035 D_real: 1.057 D_fake: 0.709 +(epoch: 44, iters: 7456, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.759 G_ID: 0.249 G_Rec: 0.337 D_GP: 0.054 D_real: 0.747 D_fake: 0.881 +(epoch: 44, iters: 7856, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 1.039 G_ID: 0.544 G_Rec: 0.386 D_GP: 0.216 D_real: 0.652 D_fake: 0.631 +(epoch: 44, iters: 8256, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.800 G_ID: 0.284 G_Rec: 0.517 D_GP: 0.064 D_real: 0.956 D_fake: 0.634 +(epoch: 45, iters: 48, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.946 G_ID: 0.527 G_Rec: 0.361 D_GP: 0.063 D_real: 0.752 D_fake: 0.705 +(epoch: 45, iters: 448, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 1.196 G_ID: 0.282 G_Rec: 0.395 D_GP: 0.291 D_real: 0.517 D_fake: 0.723 +(epoch: 45, iters: 848, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.697 G_ID: 0.518 G_Rec: 0.358 D_GP: 0.027 D_real: 1.217 D_fake: 0.548 +(epoch: 45, iters: 1248, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.773 G_ID: 0.272 G_Rec: 0.372 D_GP: 0.054 D_real: 0.952 D_fake: 0.599 +(epoch: 45, iters: 1648, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.729 G_ID: 0.547 G_Rec: 0.378 D_GP: 0.034 D_real: 1.132 D_fake: 0.608 +(epoch: 45, iters: 2048, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.727 G_ID: 0.242 G_Rec: 0.339 D_GP: 0.057 D_real: 0.894 D_fake: 0.747 +(epoch: 45, iters: 2448, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.581 G_ID: 0.521 G_Rec: 0.373 D_GP: 0.020 D_real: 1.097 D_fake: 0.784 +(epoch: 45, iters: 2848, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.805 G_ID: 0.274 G_Rec: 0.383 D_GP: 0.048 D_real: 0.923 D_fake: 0.668 +(epoch: 45, iters: 3248, time: 0.064) G_GAN: 0.046 G_GAN_Feat: 1.211 G_ID: 0.516 G_Rec: 0.395 D_GP: 0.298 D_real: 0.423 D_fake: 0.955 +(epoch: 45, iters: 3648, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.915 G_ID: 0.293 G_Rec: 0.399 D_GP: 0.131 D_real: 0.935 D_fake: 0.494 +(epoch: 45, iters: 4048, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.843 G_ID: 0.525 G_Rec: 0.414 D_GP: 0.025 D_real: 1.214 D_fake: 0.608 +(epoch: 45, iters: 4448, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.670 G_ID: 0.238 G_Rec: 0.374 D_GP: 0.029 D_real: 1.127 D_fake: 0.653 +(epoch: 45, iters: 4848, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.800 G_ID: 0.537 G_Rec: 0.401 D_GP: 0.031 D_real: 1.296 D_fake: 0.540 +(epoch: 45, iters: 5248, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.753 G_ID: 0.235 G_Rec: 0.348 D_GP: 0.070 D_real: 1.248 D_fake: 0.426 +(epoch: 45, iters: 5648, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.842 G_ID: 0.543 G_Rec: 0.365 D_GP: 0.070 D_real: 0.910 D_fake: 0.615 +(epoch: 45, iters: 6048, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.840 G_ID: 0.285 G_Rec: 0.386 D_GP: 0.043 D_real: 1.093 D_fake: 0.595 +(epoch: 45, iters: 6448, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.708 G_ID: 0.583 G_Rec: 0.372 D_GP: 0.030 D_real: 0.958 D_fake: 0.799 +(epoch: 45, iters: 6848, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.624 G_ID: 0.240 G_Rec: 0.309 D_GP: 0.025 D_real: 1.266 D_fake: 0.580 +(epoch: 45, iters: 7248, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.876 G_ID: 0.562 G_Rec: 0.349 D_GP: 0.078 D_real: 0.929 D_fake: 0.610 +(epoch: 45, iters: 7648, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.751 G_ID: 0.227 G_Rec: 0.391 D_GP: 0.032 D_real: 1.075 D_fake: 0.760 +(epoch: 45, iters: 8048, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.790 G_ID: 0.537 G_Rec: 0.338 D_GP: 0.035 D_real: 1.024 D_fake: 0.720 +(epoch: 45, iters: 8448, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.977 G_ID: 0.288 G_Rec: 0.408 D_GP: 0.111 D_real: 0.693 D_fake: 0.589 +(epoch: 46, iters: 240, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.746 G_ID: 0.590 G_Rec: 0.378 D_GP: 0.038 D_real: 1.212 D_fake: 0.540 +(epoch: 46, iters: 640, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.710 G_ID: 0.262 G_Rec: 0.334 D_GP: 0.032 D_real: 1.159 D_fake: 0.600 +(epoch: 46, iters: 1040, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.614 G_ID: 0.548 G_Rec: 0.388 D_GP: 0.024 D_real: 1.389 D_fake: 0.476 +(epoch: 46, iters: 1440, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.785 G_ID: 0.293 G_Rec: 0.366 D_GP: 0.065 D_real: 0.979 D_fake: 0.594 +(epoch: 46, iters: 1840, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.885 G_ID: 0.500 G_Rec: 0.363 D_GP: 0.148 D_real: 0.852 D_fake: 0.779 +(epoch: 46, iters: 2240, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.725 G_ID: 0.291 G_Rec: 0.364 D_GP: 0.035 D_real: 0.771 D_fake: 0.915 +(epoch: 46, iters: 2640, time: 0.064) G_GAN: 0.560 G_GAN_Feat: 0.840 G_ID: 0.519 G_Rec: 0.390 D_GP: 0.054 D_real: 1.165 D_fake: 0.443 +(epoch: 46, iters: 3040, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.737 G_ID: 0.312 G_Rec: 0.387 D_GP: 0.036 D_real: 0.983 D_fake: 0.670 +(epoch: 46, iters: 3440, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.832 G_ID: 0.587 G_Rec: 0.373 D_GP: 0.049 D_real: 0.857 D_fake: 0.681 +(epoch: 46, iters: 3840, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 1.066 G_ID: 0.217 G_Rec: 0.396 D_GP: 0.310 D_real: 0.677 D_fake: 0.592 +(epoch: 46, iters: 4240, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.789 G_ID: 0.510 G_Rec: 0.369 D_GP: 0.041 D_real: 0.889 D_fake: 0.681 +(epoch: 46, iters: 4640, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.673 G_ID: 0.279 G_Rec: 0.400 D_GP: 0.033 D_real: 0.994 D_fake: 0.789 +(epoch: 46, iters: 5040, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.957 G_ID: 0.529 G_Rec: 0.402 D_GP: 0.057 D_real: 0.687 D_fake: 0.698 +(epoch: 46, iters: 5440, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.902 G_ID: 0.279 G_Rec: 0.371 D_GP: 0.075 D_real: 0.806 D_fake: 0.654 +(epoch: 46, iters: 5840, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.819 G_ID: 0.551 G_Rec: 0.368 D_GP: 0.042 D_real: 1.148 D_fake: 0.520 +(epoch: 46, iters: 6240, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.694 G_ID: 0.250 G_Rec: 0.338 D_GP: 0.039 D_real: 1.129 D_fake: 0.639 +(epoch: 46, iters: 6640, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.940 G_ID: 0.495 G_Rec: 0.397 D_GP: 0.102 D_real: 0.549 D_fake: 0.744 +(epoch: 46, iters: 7040, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 1.309 G_ID: 0.266 G_Rec: 0.388 D_GP: 0.107 D_real: 0.531 D_fake: 0.392 +(epoch: 46, iters: 7440, time: 0.064) G_GAN: 0.475 G_GAN_Feat: 0.832 G_ID: 0.544 G_Rec: 0.361 D_GP: 0.058 D_real: 1.193 D_fake: 0.530 +(epoch: 46, iters: 7840, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 0.784 G_ID: 0.246 G_Rec: 0.346 D_GP: 0.036 D_real: 1.198 D_fake: 0.476 +(epoch: 46, iters: 8240, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.892 G_ID: 0.519 G_Rec: 0.395 D_GP: 0.044 D_real: 0.696 D_fake: 0.818 +(epoch: 47, iters: 32, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 1.384 G_ID: 0.272 G_Rec: 0.360 D_GP: 0.099 D_real: 0.215 D_fake: 0.807 +(epoch: 47, iters: 432, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.855 G_ID: 0.567 G_Rec: 0.339 D_GP: 0.067 D_real: 0.971 D_fake: 0.660 +(epoch: 47, iters: 832, time: 0.064) G_GAN: 0.577 G_GAN_Feat: 0.820 G_ID: 0.280 G_Rec: 0.409 D_GP: 0.047 D_real: 1.259 D_fake: 0.447 +(epoch: 47, iters: 1232, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.738 G_ID: 0.516 G_Rec: 0.440 D_GP: 0.034 D_real: 0.795 D_fake: 0.938 +(epoch: 47, iters: 1632, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.632 G_ID: 0.284 G_Rec: 0.347 D_GP: 0.026 D_real: 1.184 D_fake: 0.621 +(epoch: 47, iters: 2032, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.737 G_ID: 0.554 G_Rec: 0.361 D_GP: 0.054 D_real: 1.182 D_fake: 0.578 +(epoch: 47, iters: 2432, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.811 G_ID: 0.230 G_Rec: 0.440 D_GP: 0.045 D_real: 0.663 D_fake: 0.873 +(epoch: 47, iters: 2832, time: 0.064) G_GAN: 0.841 G_GAN_Feat: 1.052 G_ID: 0.490 G_Rec: 0.380 D_GP: 0.080 D_real: 1.423 D_fake: 0.273 +(epoch: 47, iters: 3232, time: 0.064) G_GAN: 0.470 G_GAN_Feat: 0.610 G_ID: 0.284 G_Rec: 0.351 D_GP: 0.022 D_real: 1.406 D_fake: 0.532 +(epoch: 47, iters: 3632, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.635 G_ID: 0.523 G_Rec: 0.369 D_GP: 0.022 D_real: 1.206 D_fake: 0.651 +(epoch: 47, iters: 4032, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.572 G_ID: 0.263 G_Rec: 0.374 D_GP: 0.024 D_real: 1.142 D_fake: 0.719 +(epoch: 47, iters: 4432, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.650 G_ID: 0.561 G_Rec: 0.420 D_GP: 0.037 D_real: 0.874 D_fake: 0.877 +(epoch: 47, iters: 4832, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.655 G_ID: 0.280 G_Rec: 0.417 D_GP: 0.041 D_real: 0.771 D_fake: 0.964 +(epoch: 47, iters: 5232, time: 0.064) G_GAN: 0.444 G_GAN_Feat: 0.696 G_ID: 0.493 G_Rec: 0.358 D_GP: 0.042 D_real: 1.145 D_fake: 0.560 +(epoch: 47, iters: 5632, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.751 G_ID: 0.288 G_Rec: 0.423 D_GP: 0.067 D_real: 0.772 D_fake: 0.822 +(epoch: 47, iters: 6032, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.682 G_ID: 0.589 G_Rec: 0.370 D_GP: 0.036 D_real: 1.037 D_fake: 0.746 +(epoch: 47, iters: 6432, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.705 G_ID: 0.288 G_Rec: 0.345 D_GP: 0.046 D_real: 1.023 D_fake: 0.678 +(epoch: 47, iters: 6832, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 1.209 G_ID: 0.544 G_Rec: 0.363 D_GP: 0.480 D_real: 0.486 D_fake: 0.738 +(epoch: 47, iters: 7232, time: 0.064) G_GAN: -0.062 G_GAN_Feat: 0.693 G_ID: 0.270 G_Rec: 0.365 D_GP: 0.041 D_real: 0.670 D_fake: 1.062 +(epoch: 47, iters: 7632, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 1.098 G_ID: 0.542 G_Rec: 0.374 D_GP: 0.092 D_real: 0.430 D_fake: 0.767 +(epoch: 47, iters: 8032, time: 0.064) G_GAN: 0.631 G_GAN_Feat: 0.796 G_ID: 0.259 G_Rec: 0.442 D_GP: 0.053 D_real: 1.214 D_fake: 0.376 +(epoch: 47, iters: 8432, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.773 G_ID: 0.508 G_Rec: 0.379 D_GP: 0.039 D_real: 1.155 D_fake: 0.560 +(epoch: 48, iters: 224, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.707 G_ID: 0.266 G_Rec: 0.444 D_GP: 0.029 D_real: 0.850 D_fake: 0.840 +(epoch: 48, iters: 624, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.829 G_ID: 0.525 G_Rec: 0.380 D_GP: 0.041 D_real: 1.105 D_fake: 0.508 +(epoch: 48, iters: 1024, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.648 G_ID: 0.262 G_Rec: 0.400 D_GP: 0.024 D_real: 0.996 D_fake: 0.829 +(epoch: 48, iters: 1424, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.745 G_ID: 0.507 G_Rec: 0.369 D_GP: 0.029 D_real: 0.929 D_fake: 0.848 +(epoch: 48, iters: 1824, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.698 G_ID: 0.249 G_Rec: 0.370 D_GP: 0.030 D_real: 1.020 D_fake: 0.826 +(epoch: 48, iters: 2224, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.859 G_ID: 0.509 G_Rec: 0.384 D_GP: 0.052 D_real: 0.912 D_fake: 0.804 +(epoch: 48, iters: 2624, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.919 G_ID: 0.302 G_Rec: 0.354 D_GP: 0.037 D_real: 1.114 D_fake: 0.807 +(epoch: 48, iters: 3024, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 1.001 G_ID: 0.501 G_Rec: 0.375 D_GP: 0.047 D_real: 0.974 D_fake: 0.614 +(epoch: 48, iters: 3424, time: 0.064) G_GAN: 0.509 G_GAN_Feat: 0.772 G_ID: 0.255 G_Rec: 0.339 D_GP: 0.049 D_real: 1.243 D_fake: 0.544 +(epoch: 48, iters: 3824, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.798 G_ID: 0.565 G_Rec: 0.374 D_GP: 0.032 D_real: 1.082 D_fake: 0.793 +(epoch: 48, iters: 4224, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.794 G_ID: 0.289 G_Rec: 0.339 D_GP: 0.044 D_real: 0.949 D_fake: 0.712 +(epoch: 48, iters: 4624, time: 0.064) G_GAN: -0.050 G_GAN_Feat: 0.874 G_ID: 0.490 G_Rec: 0.388 D_GP: 0.037 D_real: 0.548 D_fake: 1.050 +(epoch: 48, iters: 5024, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.708 G_ID: 0.249 G_Rec: 0.346 D_GP: 0.023 D_real: 1.069 D_fake: 0.705 +(epoch: 48, iters: 5424, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.704 G_ID: 0.496 G_Rec: 0.333 D_GP: 0.037 D_real: 0.955 D_fake: 0.875 +(epoch: 48, iters: 5824, time: 0.064) G_GAN: -0.213 G_GAN_Feat: 0.858 G_ID: 0.253 G_Rec: 0.376 D_GP: 0.047 D_real: 0.513 D_fake: 1.213 +(epoch: 48, iters: 6224, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.918 G_ID: 0.493 G_Rec: 0.348 D_GP: 0.052 D_real: 1.234 D_fake: 0.582 +(epoch: 48, iters: 6624, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.590 G_ID: 0.259 G_Rec: 0.328 D_GP: 0.022 D_real: 1.079 D_fake: 0.789 +(epoch: 48, iters: 7024, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.682 G_ID: 0.513 G_Rec: 0.458 D_GP: 0.026 D_real: 1.002 D_fake: 0.741 +(epoch: 48, iters: 7424, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.721 G_ID: 0.291 G_Rec: 0.370 D_GP: 0.053 D_real: 0.915 D_fake: 0.812 +(epoch: 48, iters: 7824, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.796 G_ID: 0.565 G_Rec: 0.380 D_GP: 0.039 D_real: 0.992 D_fake: 0.678 +(epoch: 48, iters: 8224, time: 0.064) G_GAN: 0.666 G_GAN_Feat: 1.164 G_ID: 0.272 G_Rec: 0.438 D_GP: 0.114 D_real: 1.092 D_fake: 0.339 +(epoch: 49, iters: 16, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.975 G_ID: 0.546 G_Rec: 0.350 D_GP: 0.107 D_real: 0.411 D_fake: 0.894 +(epoch: 49, iters: 416, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.847 G_ID: 0.270 G_Rec: 0.400 D_GP: 0.046 D_real: 1.108 D_fake: 0.511 +(epoch: 49, iters: 816, time: 0.064) G_GAN: 0.659 G_GAN_Feat: 0.967 G_ID: 0.535 G_Rec: 0.347 D_GP: 0.160 D_real: 0.997 D_fake: 0.448 +(epoch: 49, iters: 1216, time: 0.064) G_GAN: 0.771 G_GAN_Feat: 0.986 G_ID: 0.304 G_Rec: 0.406 D_GP: 0.102 D_real: 1.035 D_fake: 0.256 +(epoch: 49, iters: 1616, time: 0.064) G_GAN: 0.767 G_GAN_Feat: 1.042 G_ID: 0.509 G_Rec: 0.479 D_GP: 0.073 D_real: 1.235 D_fake: 0.446 +(epoch: 49, iters: 2016, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.850 G_ID: 0.267 G_Rec: 0.348 D_GP: 0.060 D_real: 0.860 D_fake: 0.638 +(epoch: 49, iters: 2416, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.890 G_ID: 0.528 G_Rec: 0.399 D_GP: 0.071 D_real: 0.779 D_fake: 0.684 +(epoch: 49, iters: 2816, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.717 G_ID: 0.291 G_Rec: 0.369 D_GP: 0.029 D_real: 1.219 D_fake: 0.541 +(epoch: 49, iters: 3216, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.637 G_ID: 0.534 G_Rec: 0.400 D_GP: 0.025 D_real: 1.210 D_fake: 0.660 +(epoch: 49, iters: 3616, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.547 G_ID: 0.233 G_Rec: 0.489 D_GP: 0.020 D_real: 1.155 D_fake: 0.595 +(epoch: 49, iters: 4016, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.598 G_ID: 0.532 G_Rec: 0.349 D_GP: 0.022 D_real: 1.290 D_fake: 0.656 +(epoch: 49, iters: 4416, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.672 G_ID: 0.232 G_Rec: 0.346 D_GP: 0.030 D_real: 1.038 D_fake: 0.722 +(epoch: 49, iters: 4816, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.753 G_ID: 0.481 G_Rec: 0.338 D_GP: 0.076 D_real: 0.857 D_fake: 0.728 +(epoch: 49, iters: 5216, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.821 G_ID: 0.268 G_Rec: 0.384 D_GP: 0.066 D_real: 1.075 D_fake: 0.631 +(epoch: 49, iters: 5616, time: 0.064) G_GAN: 0.721 G_GAN_Feat: 0.890 G_ID: 0.568 G_Rec: 0.440 D_GP: 0.084 D_real: 1.115 D_fake: 0.443 +(epoch: 49, iters: 6016, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.725 G_ID: 0.233 G_Rec: 0.356 D_GP: 0.042 D_real: 1.021 D_fake: 0.754 +(epoch: 49, iters: 6416, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.695 G_ID: 0.531 G_Rec: 0.359 D_GP: 0.030 D_real: 0.847 D_fake: 0.912 +(epoch: 49, iters: 6816, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 0.672 G_ID: 0.262 G_Rec: 0.363 D_GP: 0.029 D_real: 1.305 D_fake: 0.512 +(epoch: 49, iters: 7216, time: 0.064) G_GAN: 0.635 G_GAN_Feat: 0.903 G_ID: 0.488 G_Rec: 0.408 D_GP: 0.064 D_real: 1.338 D_fake: 0.386 +(epoch: 49, iters: 7616, time: 0.064) G_GAN: 0.470 G_GAN_Feat: 0.694 G_ID: 0.277 G_Rec: 0.373 D_GP: 0.030 D_real: 1.190 D_fake: 0.531 +(epoch: 49, iters: 8016, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.715 G_ID: 0.544 G_Rec: 0.337 D_GP: 0.029 D_real: 1.112 D_fake: 0.683 +(epoch: 49, iters: 8416, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.987 G_ID: 0.242 G_Rec: 0.364 D_GP: 0.061 D_real: 0.585 D_fake: 0.675 +(epoch: 50, iters: 208, time: 0.064) G_GAN: 0.761 G_GAN_Feat: 1.155 G_ID: 0.451 G_Rec: 0.411 D_GP: 0.059 D_real: 1.154 D_fake: 0.385 +(epoch: 50, iters: 608, time: 0.064) G_GAN: 0.061 G_GAN_Feat: 0.697 G_ID: 0.263 G_Rec: 0.403 D_GP: 0.031 D_real: 0.892 D_fake: 0.940 +(epoch: 50, iters: 1008, time: 0.064) G_GAN: 0.694 G_GAN_Feat: 0.848 G_ID: 0.512 G_Rec: 0.374 D_GP: 0.045 D_real: 1.268 D_fake: 0.335 +(epoch: 50, iters: 1408, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.782 G_ID: 0.242 G_Rec: 0.369 D_GP: 0.037 D_real: 1.045 D_fake: 0.548 +(epoch: 50, iters: 1808, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.807 G_ID: 0.550 G_Rec: 0.335 D_GP: 0.049 D_real: 1.120 D_fake: 0.490 +(epoch: 50, iters: 2208, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.766 G_ID: 0.274 G_Rec: 0.453 D_GP: 0.047 D_real: 1.260 D_fake: 0.493 +(epoch: 50, iters: 2608, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 1.035 G_ID: 0.502 G_Rec: 0.459 D_GP: 0.032 D_real: 1.054 D_fake: 0.881 +(epoch: 50, iters: 3008, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.629 G_ID: 0.241 G_Rec: 0.379 D_GP: 0.020 D_real: 1.200 D_fake: 0.645 +(epoch: 50, iters: 3408, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.625 G_ID: 0.520 G_Rec: 0.350 D_GP: 0.021 D_real: 0.994 D_fake: 0.889 +(epoch: 50, iters: 3808, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.656 G_ID: 0.262 G_Rec: 0.343 D_GP: 0.029 D_real: 1.054 D_fake: 0.771 +(epoch: 50, iters: 4208, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.766 G_ID: 0.501 G_Rec: 0.360 D_GP: 0.031 D_real: 1.136 D_fake: 0.689 +(epoch: 50, iters: 4608, time: 0.064) G_GAN: 0.600 G_GAN_Feat: 0.793 G_ID: 0.275 G_Rec: 0.451 D_GP: 0.059 D_real: 1.283 D_fake: 0.408 +(epoch: 50, iters: 5008, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.783 G_ID: 0.516 G_Rec: 0.404 D_GP: 0.083 D_real: 0.795 D_fake: 0.937 +(epoch: 50, iters: 5408, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 1.103 G_ID: 0.254 G_Rec: 0.388 D_GP: 0.080 D_real: 0.584 D_fake: 0.691 +(epoch: 50, iters: 5808, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 1.068 G_ID: 0.525 G_Rec: 0.456 D_GP: 0.092 D_real: 0.743 D_fake: 0.614 +(epoch: 50, iters: 6208, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.743 G_ID: 0.227 G_Rec: 0.373 D_GP: 0.045 D_real: 0.978 D_fake: 0.738 +(epoch: 50, iters: 6608, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.809 G_ID: 0.481 G_Rec: 0.393 D_GP: 0.062 D_real: 0.665 D_fake: 0.881 +(epoch: 50, iters: 7008, time: 0.064) G_GAN: 0.774 G_GAN_Feat: 1.046 G_ID: 0.266 G_Rec: 0.502 D_GP: 0.282 D_real: 1.175 D_fake: 0.273 +(epoch: 50, iters: 7408, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 1.229 G_ID: 0.510 G_Rec: 0.425 D_GP: 0.146 D_real: 0.299 D_fake: 0.854 +(epoch: 50, iters: 7808, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.825 G_ID: 0.296 G_Rec: 0.369 D_GP: 0.052 D_real: 0.932 D_fake: 0.706 +(epoch: 50, iters: 8208, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.771 G_ID: 0.524 G_Rec: 0.348 D_GP: 0.027 D_real: 1.042 D_fake: 0.700 +(epoch: 50, iters: 8608, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.723 G_ID: 0.267 G_Rec: 0.377 D_GP: 0.039 D_real: 0.898 D_fake: 0.763 +(epoch: 51, iters: 400, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.875 G_ID: 0.466 G_Rec: 0.363 D_GP: 0.048 D_real: 0.627 D_fake: 0.882 +(epoch: 51, iters: 800, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.845 G_ID: 0.285 G_Rec: 0.368 D_GP: 0.060 D_real: 0.774 D_fake: 0.665 +(epoch: 51, iters: 1200, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.790 G_ID: 0.540 G_Rec: 0.419 D_GP: 0.038 D_real: 1.254 D_fake: 0.495 +(epoch: 51, iters: 1600, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 1.325 G_ID: 0.213 G_Rec: 0.374 D_GP: 0.357 D_real: 0.511 D_fake: 0.750 +(epoch: 51, iters: 2000, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.620 G_ID: 0.482 G_Rec: 0.383 D_GP: 0.024 D_real: 1.238 D_fake: 0.570 +(epoch: 51, iters: 2400, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 0.851 G_ID: 0.265 G_Rec: 0.330 D_GP: 0.033 D_real: 1.379 D_fake: 0.470 +(epoch: 51, iters: 2800, time: 0.064) G_GAN: -0.086 G_GAN_Feat: 0.759 G_ID: 0.498 G_Rec: 0.432 D_GP: 0.047 D_real: 0.652 D_fake: 1.086 +(epoch: 51, iters: 3200, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 0.832 G_ID: 0.222 G_Rec: 0.388 D_GP: 0.041 D_real: 1.068 D_fake: 0.561 +(epoch: 51, iters: 3600, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 0.801 G_ID: 0.501 G_Rec: 0.419 D_GP: 0.059 D_real: 1.270 D_fake: 0.377 +(epoch: 51, iters: 4000, time: 0.064) G_GAN: 0.709 G_GAN_Feat: 0.889 G_ID: 0.224 G_Rec: 0.439 D_GP: 0.080 D_real: 1.144 D_fake: 0.310 +(epoch: 51, iters: 4400, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.819 G_ID: 0.439 G_Rec: 0.366 D_GP: 0.064 D_real: 0.936 D_fake: 0.801 +(epoch: 51, iters: 4800, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.873 G_ID: 0.238 G_Rec: 0.401 D_GP: 0.037 D_real: 1.138 D_fake: 0.562 +(epoch: 51, iters: 5200, time: 0.064) G_GAN: 0.911 G_GAN_Feat: 0.749 G_ID: 0.470 G_Rec: 0.429 D_GP: 0.026 D_real: 1.645 D_fake: 0.219 +(epoch: 51, iters: 5600, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 0.685 G_ID: 0.221 G_Rec: 0.444 D_GP: 0.024 D_real: 1.258 D_fake: 0.527 +(epoch: 51, iters: 6000, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.823 G_ID: 0.550 G_Rec: 0.430 D_GP: 0.040 D_real: 1.012 D_fake: 0.657 +(epoch: 51, iters: 6400, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.886 G_ID: 0.248 G_Rec: 0.370 D_GP: 0.107 D_real: 0.782 D_fake: 0.625 +(epoch: 51, iters: 6800, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 1.157 G_ID: 0.510 G_Rec: 0.372 D_GP: 0.621 D_real: 0.273 D_fake: 0.706 +(epoch: 51, iters: 7200, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 1.245 G_ID: 0.248 G_Rec: 0.374 D_GP: 0.592 D_real: 0.356 D_fake: 0.751 +(epoch: 51, iters: 7600, time: 0.064) G_GAN: 0.815 G_GAN_Feat: 0.930 G_ID: 0.514 G_Rec: 0.379 D_GP: 0.039 D_real: 1.413 D_fake: 0.335 +(epoch: 51, iters: 8000, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.861 G_ID: 0.319 G_Rec: 0.376 D_GP: 0.032 D_real: 1.088 D_fake: 0.602 +(epoch: 51, iters: 8400, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 1.222 G_ID: 0.542 G_Rec: 0.401 D_GP: 0.142 D_real: 0.348 D_fake: 0.734 +(epoch: 52, iters: 192, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.906 G_ID: 0.269 G_Rec: 0.360 D_GP: 0.045 D_real: 1.082 D_fake: 0.525 +(epoch: 52, iters: 592, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.888 G_ID: 0.520 G_Rec: 0.377 D_GP: 0.067 D_real: 0.926 D_fake: 0.611 +(epoch: 52, iters: 992, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 1.152 G_ID: 0.283 G_Rec: 0.411 D_GP: 0.137 D_real: 0.351 D_fake: 0.859 +(epoch: 52, iters: 1392, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 1.085 G_ID: 0.502 G_Rec: 0.390 D_GP: 0.085 D_real: 0.651 D_fake: 0.613 +(epoch: 52, iters: 1792, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.931 G_ID: 0.246 G_Rec: 0.413 D_GP: 0.028 D_real: 1.224 D_fake: 0.548 +(epoch: 52, iters: 2192, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.798 G_ID: 0.542 G_Rec: 0.355 D_GP: 0.025 D_real: 1.119 D_fake: 0.621 +(epoch: 52, iters: 2592, time: 0.064) G_GAN: 0.063 G_GAN_Feat: 0.989 G_ID: 0.267 G_Rec: 0.371 D_GP: 0.116 D_real: 0.407 D_fake: 0.937 +(epoch: 52, iters: 2992, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.771 G_ID: 0.517 G_Rec: 0.388 D_GP: 0.026 D_real: 1.271 D_fake: 0.622 +(epoch: 52, iters: 3392, time: 0.064) G_GAN: -0.042 G_GAN_Feat: 0.522 G_ID: 0.267 G_Rec: 0.343 D_GP: 0.022 D_real: 0.939 D_fake: 1.042 +(epoch: 52, iters: 3792, time: 0.064) G_GAN: 0.623 G_GAN_Feat: 0.600 G_ID: 0.545 G_Rec: 0.397 D_GP: 0.018 D_real: 1.488 D_fake: 0.432 +(epoch: 52, iters: 4192, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.541 G_ID: 0.216 G_Rec: 0.387 D_GP: 0.020 D_real: 1.044 D_fake: 0.826 +(epoch: 52, iters: 4592, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.606 G_ID: 0.476 G_Rec: 0.510 D_GP: 0.026 D_real: 1.064 D_fake: 0.754 +(epoch: 52, iters: 4992, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.671 G_ID: 0.267 G_Rec: 0.382 D_GP: 0.032 D_real: 1.036 D_fake: 0.678 +(epoch: 52, iters: 5392, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.642 G_ID: 0.547 G_Rec: 0.364 D_GP: 0.044 D_real: 0.896 D_fake: 0.822 +(epoch: 52, iters: 5792, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.637 G_ID: 0.230 G_Rec: 0.315 D_GP: 0.045 D_real: 1.062 D_fake: 0.659 +(epoch: 52, iters: 6192, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.760 G_ID: 0.512 G_Rec: 0.393 D_GP: 0.051 D_real: 0.845 D_fake: 0.838 +(epoch: 52, iters: 6592, time: 0.064) G_GAN: -0.081 G_GAN_Feat: 0.839 G_ID: 0.228 G_Rec: 0.446 D_GP: 0.080 D_real: 0.308 D_fake: 1.081 +(epoch: 52, iters: 6992, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.919 G_ID: 0.532 G_Rec: 0.361 D_GP: 0.069 D_real: 0.669 D_fake: 0.772 +(epoch: 52, iters: 7392, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.648 G_ID: 0.265 G_Rec: 0.394 D_GP: 0.025 D_real: 1.012 D_fake: 0.751 +(epoch: 52, iters: 7792, time: 0.064) G_GAN: 0.595 G_GAN_Feat: 0.771 G_ID: 0.502 G_Rec: 0.382 D_GP: 0.055 D_real: 1.312 D_fake: 0.408 +(epoch: 52, iters: 8192, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.873 G_ID: 0.254 G_Rec: 0.503 D_GP: 0.039 D_real: 1.159 D_fake: 0.600 +(epoch: 52, iters: 8592, time: 0.064) G_GAN: 0.562 G_GAN_Feat: 0.733 G_ID: 0.488 G_Rec: 0.353 D_GP: 0.044 D_real: 1.305 D_fake: 0.461 +(epoch: 53, iters: 384, time: 0.064) G_GAN: 0.060 G_GAN_Feat: 1.258 G_ID: 0.229 G_Rec: 0.350 D_GP: 0.112 D_real: 0.348 D_fake: 0.940 +(epoch: 53, iters: 784, time: 0.064) G_GAN: 0.480 G_GAN_Feat: 0.709 G_ID: 0.477 G_Rec: 0.430 D_GP: 0.030 D_real: 1.341 D_fake: 0.521 +(epoch: 53, iters: 1184, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.882 G_ID: 0.256 G_Rec: 0.395 D_GP: 0.048 D_real: 1.080 D_fake: 0.594 +(epoch: 53, iters: 1584, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.823 G_ID: 0.520 G_Rec: 0.407 D_GP: 0.030 D_real: 1.040 D_fake: 0.664 +(epoch: 53, iters: 1984, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.684 G_ID: 0.248 G_Rec: 0.419 D_GP: 0.027 D_real: 1.025 D_fake: 0.729 +(epoch: 53, iters: 2384, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.832 G_ID: 0.508 G_Rec: 0.368 D_GP: 0.054 D_real: 0.931 D_fake: 0.711 +(epoch: 53, iters: 2784, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 1.146 G_ID: 0.295 G_Rec: 0.393 D_GP: 0.055 D_real: 0.469 D_fake: 0.583 +(epoch: 53, iters: 3184, time: 0.064) G_GAN: 0.725 G_GAN_Feat: 0.985 G_ID: 0.504 G_Rec: 0.372 D_GP: 0.090 D_real: 1.224 D_fake: 0.311 +(epoch: 53, iters: 3584, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 0.745 G_ID: 0.242 G_Rec: 0.395 D_GP: 0.035 D_real: 1.345 D_fake: 0.435 +(epoch: 53, iters: 3984, time: 0.064) G_GAN: 0.735 G_GAN_Feat: 1.027 G_ID: 0.510 G_Rec: 0.418 D_GP: 0.033 D_real: 1.309 D_fake: 0.503 +(epoch: 53, iters: 4384, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.953 G_ID: 0.252 G_Rec: 0.383 D_GP: 0.051 D_real: 0.905 D_fake: 0.617 +(epoch: 53, iters: 4784, time: 0.064) G_GAN: 1.063 G_GAN_Feat: 0.949 G_ID: 0.446 G_Rec: 0.391 D_GP: 0.096 D_real: 1.526 D_fake: 0.229 +(epoch: 53, iters: 5184, time: 0.064) G_GAN: 0.582 G_GAN_Feat: 0.802 G_ID: 0.201 G_Rec: 0.426 D_GP: 0.040 D_real: 1.252 D_fake: 0.429 +(epoch: 53, iters: 5584, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.726 G_ID: 0.509 G_Rec: 0.349 D_GP: 0.028 D_real: 1.211 D_fake: 0.572 +(epoch: 53, iters: 5984, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.874 G_ID: 0.246 G_Rec: 0.405 D_GP: 0.037 D_real: 0.928 D_fake: 0.618 +(epoch: 53, iters: 6384, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.832 G_ID: 0.495 G_Rec: 0.351 D_GP: 0.035 D_real: 1.194 D_fake: 0.594 +(epoch: 53, iters: 6784, time: 0.064) G_GAN: 0.811 G_GAN_Feat: 0.852 G_ID: 0.275 G_Rec: 0.361 D_GP: 0.066 D_real: 1.361 D_fake: 0.293 +(epoch: 53, iters: 7184, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.768 G_ID: 0.456 G_Rec: 0.363 D_GP: 0.056 D_real: 0.838 D_fake: 0.795 +(epoch: 53, iters: 7584, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.725 G_ID: 0.235 G_Rec: 0.460 D_GP: 0.022 D_real: 0.962 D_fake: 0.845 +(epoch: 53, iters: 7984, time: 0.064) G_GAN: 0.044 G_GAN_Feat: 0.971 G_ID: 0.470 G_Rec: 0.385 D_GP: 0.175 D_real: 0.508 D_fake: 0.956 +(epoch: 53, iters: 8384, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 1.015 G_ID: 0.219 G_Rec: 0.455 D_GP: 0.108 D_real: 0.558 D_fake: 0.630 +(epoch: 54, iters: 176, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.934 G_ID: 0.483 G_Rec: 0.427 D_GP: 0.032 D_real: 0.833 D_fake: 0.844 +(epoch: 54, iters: 576, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.846 G_ID: 0.226 G_Rec: 0.356 D_GP: 0.046 D_real: 1.087 D_fake: 0.501 +(epoch: 54, iters: 976, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.828 G_ID: 0.484 G_Rec: 0.412 D_GP: 0.026 D_real: 0.981 D_fake: 0.813 +(epoch: 54, iters: 1376, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.666 G_ID: 0.228 G_Rec: 0.345 D_GP: 0.027 D_real: 1.301 D_fake: 0.479 +(epoch: 54, iters: 1776, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.856 G_ID: 0.507 G_Rec: 0.419 D_GP: 0.039 D_real: 1.230 D_fake: 0.506 +(epoch: 54, iters: 2176, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.843 G_ID: 0.238 G_Rec: 0.408 D_GP: 0.068 D_real: 1.014 D_fake: 0.624 +(epoch: 54, iters: 2576, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.688 G_ID: 0.473 G_Rec: 0.426 D_GP: 0.020 D_real: 1.334 D_fake: 0.594 +(epoch: 54, iters: 2976, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.656 G_ID: 0.221 G_Rec: 0.390 D_GP: 0.025 D_real: 1.078 D_fake: 0.729 +(epoch: 54, iters: 3376, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.745 G_ID: 0.512 G_Rec: 0.398 D_GP: 0.043 D_real: 0.995 D_fake: 0.719 +(epoch: 54, iters: 3776, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.791 G_ID: 0.213 G_Rec: 0.384 D_GP: 0.057 D_real: 0.849 D_fake: 0.717 +(epoch: 54, iters: 4176, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 1.036 G_ID: 0.472 G_Rec: 0.432 D_GP: 0.039 D_real: 0.736 D_fake: 0.659 +(epoch: 54, iters: 4576, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.872 G_ID: 0.212 G_Rec: 0.389 D_GP: 0.033 D_real: 1.123 D_fake: 0.577 +(epoch: 54, iters: 4976, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.910 G_ID: 0.491 G_Rec: 0.376 D_GP: 0.094 D_real: 0.729 D_fake: 0.716 +(epoch: 54, iters: 5376, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.558 G_ID: 0.238 G_Rec: 0.344 D_GP: 0.020 D_real: 1.082 D_fake: 0.852 +(epoch: 54, iters: 5776, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.680 G_ID: 0.514 G_Rec: 0.369 D_GP: 0.029 D_real: 1.007 D_fake: 0.739 +(epoch: 54, iters: 6176, time: 0.064) G_GAN: -0.017 G_GAN_Feat: 0.749 G_ID: 0.268 G_Rec: 0.467 D_GP: 0.067 D_real: 0.560 D_fake: 1.017 +(epoch: 54, iters: 6576, time: 0.064) G_GAN: -0.172 G_GAN_Feat: 0.793 G_ID: 0.486 G_Rec: 0.363 D_GP: 0.238 D_real: 0.646 D_fake: 1.172 +(epoch: 54, iters: 6976, time: 0.064) G_GAN: -0.296 G_GAN_Feat: 0.828 G_ID: 0.218 G_Rec: 0.425 D_GP: 0.108 D_real: 0.515 D_fake: 1.296 +(epoch: 54, iters: 7376, time: 0.064) G_GAN: 0.755 G_GAN_Feat: 0.721 G_ID: 0.467 G_Rec: 0.364 D_GP: 0.034 D_real: 1.498 D_fake: 0.279 +(epoch: 54, iters: 7776, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.761 G_ID: 0.233 G_Rec: 0.391 D_GP: 0.040 D_real: 1.053 D_fake: 0.684 +(epoch: 54, iters: 8176, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 1.009 G_ID: 0.442 G_Rec: 0.413 D_GP: 0.057 D_real: 0.768 D_fake: 0.540 +(epoch: 54, iters: 8576, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.666 G_ID: 0.258 G_Rec: 0.378 D_GP: 0.030 D_real: 1.099 D_fake: 0.666 +(epoch: 55, iters: 368, time: 0.064) G_GAN: 0.631 G_GAN_Feat: 0.889 G_ID: 0.476 G_Rec: 0.411 D_GP: 0.060 D_real: 1.222 D_fake: 0.456 +(epoch: 55, iters: 768, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 1.137 G_ID: 0.233 G_Rec: 0.403 D_GP: 0.449 D_real: 0.636 D_fake: 0.669 +(epoch: 55, iters: 1168, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 1.004 G_ID: 0.506 G_Rec: 0.378 D_GP: 0.063 D_real: 0.683 D_fake: 0.676 +(epoch: 55, iters: 1568, time: 0.064) G_GAN: 0.550 G_GAN_Feat: 0.932 G_ID: 0.241 G_Rec: 0.357 D_GP: 0.063 D_real: 0.990 D_fake: 0.468 +(epoch: 55, iters: 1968, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.804 G_ID: 0.500 G_Rec: 0.406 D_GP: 0.037 D_real: 0.983 D_fake: 0.754 +(epoch: 55, iters: 2368, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.752 G_ID: 0.256 G_Rec: 0.362 D_GP: 0.031 D_real: 1.103 D_fake: 0.630 +(epoch: 55, iters: 2768, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.820 G_ID: 0.454 G_Rec: 0.493 D_GP: 0.056 D_real: 0.712 D_fake: 0.907 +(epoch: 55, iters: 3168, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.818 G_ID: 0.319 G_Rec: 0.368 D_GP: 0.042 D_real: 1.191 D_fake: 0.508 +(epoch: 55, iters: 3568, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.788 G_ID: 0.458 G_Rec: 0.345 D_GP: 0.030 D_real: 0.966 D_fake: 0.717 +(epoch: 55, iters: 3968, time: 0.064) G_GAN: 0.965 G_GAN_Feat: 0.896 G_ID: 0.214 G_Rec: 0.382 D_GP: 0.037 D_real: 1.536 D_fake: 0.297 +(epoch: 55, iters: 4368, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.824 G_ID: 0.454 G_Rec: 0.382 D_GP: 0.020 D_real: 1.041 D_fake: 0.755 +(epoch: 55, iters: 4768, time: 0.064) G_GAN: -0.098 G_GAN_Feat: 0.719 G_ID: 0.261 G_Rec: 0.372 D_GP: 0.026 D_real: 0.637 D_fake: 1.098 +(epoch: 55, iters: 5168, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 0.839 G_ID: 0.481 G_Rec: 0.378 D_GP: 0.077 D_real: 0.654 D_fake: 0.943 +(epoch: 55, iters: 5568, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.633 G_ID: 0.220 G_Rec: 0.336 D_GP: 0.027 D_real: 1.162 D_fake: 0.666 +(epoch: 55, iters: 5968, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.836 G_ID: 0.452 G_Rec: 0.390 D_GP: 0.073 D_real: 0.926 D_fake: 0.672 +(epoch: 55, iters: 6368, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.854 G_ID: 0.275 G_Rec: 0.428 D_GP: 0.051 D_real: 0.803 D_fake: 0.766 +(epoch: 55, iters: 6768, time: 0.064) G_GAN: 0.764 G_GAN_Feat: 0.690 G_ID: 0.449 G_Rec: 0.524 D_GP: 0.022 D_real: 1.564 D_fake: 0.344 +(epoch: 55, iters: 7168, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 1.077 G_ID: 0.261 G_Rec: 0.375 D_GP: 0.046 D_real: 0.773 D_fake: 0.735 +(epoch: 55, iters: 7568, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 0.777 G_ID: 0.496 G_Rec: 0.378 D_GP: 0.037 D_real: 1.248 D_fake: 0.382 +(epoch: 55, iters: 7968, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.775 G_ID: 0.271 G_Rec: 0.321 D_GP: 0.041 D_real: 1.207 D_fake: 0.581 +(epoch: 55, iters: 8368, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.849 G_ID: 0.466 G_Rec: 0.379 D_GP: 0.042 D_real: 1.117 D_fake: 0.479 +(epoch: 56, iters: 160, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.951 G_ID: 0.246 G_Rec: 0.399 D_GP: 0.052 D_real: 0.534 D_fake: 0.754 +(epoch: 56, iters: 560, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.705 G_ID: 0.496 G_Rec: 0.391 D_GP: 0.029 D_real: 1.185 D_fake: 0.716 +(epoch: 56, iters: 960, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.609 G_ID: 0.288 G_Rec: 0.297 D_GP: 0.025 D_real: 1.349 D_fake: 0.569 +(epoch: 56, iters: 1360, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 0.857 G_ID: 0.480 G_Rec: 0.418 D_GP: 0.034 D_real: 1.105 D_fake: 0.538 +(epoch: 56, iters: 1760, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.851 G_ID: 0.253 G_Rec: 0.350 D_GP: 0.105 D_real: 0.628 D_fake: 0.839 +(epoch: 56, iters: 2160, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.897 G_ID: 0.496 G_Rec: 0.377 D_GP: 0.033 D_real: 1.076 D_fake: 0.603 +(epoch: 56, iters: 2560, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.763 G_ID: 0.260 G_Rec: 0.390 D_GP: 0.037 D_real: 1.147 D_fake: 0.513 +(epoch: 56, iters: 2960, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.810 G_ID: 0.473 G_Rec: 0.362 D_GP: 0.076 D_real: 1.150 D_fake: 0.551 +(epoch: 56, iters: 3360, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.933 G_ID: 0.240 G_Rec: 0.348 D_GP: 0.050 D_real: 0.997 D_fake: 0.642 +(epoch: 56, iters: 3760, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 1.205 G_ID: 0.467 G_Rec: 0.387 D_GP: 0.090 D_real: 0.604 D_fake: 0.565 +(epoch: 56, iters: 4160, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 1.041 G_ID: 0.243 G_Rec: 0.379 D_GP: 0.320 D_real: 0.408 D_fake: 0.662 +(epoch: 56, iters: 4560, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.742 G_ID: 0.476 G_Rec: 0.358 D_GP: 0.034 D_real: 0.857 D_fake: 0.927 +(epoch: 56, iters: 4960, time: 0.064) G_GAN: 0.623 G_GAN_Feat: 0.891 G_ID: 0.221 G_Rec: 0.406 D_GP: 0.048 D_real: 1.204 D_fake: 0.392 +(epoch: 56, iters: 5360, time: 0.064) G_GAN: 0.706 G_GAN_Feat: 0.696 G_ID: 0.501 G_Rec: 0.409 D_GP: 0.021 D_real: 1.580 D_fake: 0.307 +(epoch: 56, iters: 5760, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.713 G_ID: 0.277 G_Rec: 0.340 D_GP: 0.037 D_real: 1.125 D_fake: 0.627 +(epoch: 56, iters: 6160, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.708 G_ID: 0.502 G_Rec: 0.376 D_GP: 0.028 D_real: 1.115 D_fake: 0.687 +(epoch: 56, iters: 6560, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.877 G_ID: 0.214 G_Rec: 0.382 D_GP: 0.061 D_real: 0.781 D_fake: 0.790 +(epoch: 56, iters: 6960, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.698 G_ID: 0.476 G_Rec: 0.381 D_GP: 0.024 D_real: 1.121 D_fake: 0.662 +(epoch: 56, iters: 7360, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.593 G_ID: 0.237 G_Rec: 0.337 D_GP: 0.023 D_real: 0.978 D_fake: 0.919 +(epoch: 56, iters: 7760, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.841 G_ID: 0.487 G_Rec: 0.366 D_GP: 0.051 D_real: 1.147 D_fake: 0.517 +(epoch: 56, iters: 8160, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.700 G_ID: 0.233 G_Rec: 0.370 D_GP: 0.031 D_real: 1.363 D_fake: 0.485 +(epoch: 56, iters: 8560, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.851 G_ID: 0.469 G_Rec: 0.355 D_GP: 0.066 D_real: 0.879 D_fake: 0.649 +(epoch: 57, iters: 352, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.775 G_ID: 0.252 G_Rec: 0.406 D_GP: 0.028 D_real: 1.206 D_fake: 0.623 +(epoch: 57, iters: 752, time: 0.064) G_GAN: 0.806 G_GAN_Feat: 0.765 G_ID: 0.488 G_Rec: 0.412 D_GP: 0.027 D_real: 1.577 D_fake: 0.437 +(epoch: 57, iters: 1152, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.597 G_ID: 0.238 G_Rec: 0.344 D_GP: 0.027 D_real: 1.162 D_fake: 0.673 +(epoch: 57, iters: 1552, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.877 G_ID: 0.454 G_Rec: 0.434 D_GP: 0.092 D_real: 0.692 D_fake: 0.844 +(epoch: 57, iters: 1952, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.784 G_ID: 0.266 G_Rec: 0.471 D_GP: 0.037 D_real: 1.076 D_fake: 0.686 +(epoch: 57, iters: 2352, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.888 G_ID: 0.455 G_Rec: 0.399 D_GP: 0.054 D_real: 1.139 D_fake: 0.493 +(epoch: 57, iters: 2752, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.981 G_ID: 0.279 G_Rec: 0.363 D_GP: 0.070 D_real: 0.671 D_fake: 0.554 +(epoch: 57, iters: 3152, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.705 G_ID: 0.461 G_Rec: 0.376 D_GP: 0.021 D_real: 1.272 D_fake: 0.544 +(epoch: 57, iters: 3552, time: 0.064) G_GAN: 0.690 G_GAN_Feat: 0.965 G_ID: 0.244 G_Rec: 0.384 D_GP: 0.045 D_real: 1.383 D_fake: 0.353 +(epoch: 57, iters: 3952, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 1.094 G_ID: 0.490 G_Rec: 0.408 D_GP: 0.142 D_real: 0.583 D_fake: 0.804 +(epoch: 57, iters: 4352, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.773 G_ID: 0.258 G_Rec: 0.381 D_GP: 0.028 D_real: 1.019 D_fake: 0.799 +(epoch: 57, iters: 4752, time: 0.064) G_GAN: 0.822 G_GAN_Feat: 0.847 G_ID: 0.456 G_Rec: 0.384 D_GP: 0.033 D_real: 1.484 D_fake: 0.424 +(epoch: 57, iters: 5152, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.654 G_ID: 0.266 G_Rec: 0.392 D_GP: 0.027 D_real: 1.097 D_fake: 0.668 +(epoch: 57, iters: 5552, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.665 G_ID: 0.523 G_Rec: 0.349 D_GP: 0.026 D_real: 0.929 D_fake: 0.860 +(epoch: 57, iters: 5952, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.692 G_ID: 0.214 G_Rec: 0.391 D_GP: 0.033 D_real: 1.165 D_fake: 0.676 +(epoch: 57, iters: 6352, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.837 G_ID: 0.498 G_Rec: 0.447 D_GP: 0.057 D_real: 0.957 D_fake: 0.642 +(epoch: 57, iters: 6752, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.735 G_ID: 0.268 G_Rec: 0.374 D_GP: 0.027 D_real: 1.131 D_fake: 0.648 +(epoch: 57, iters: 7152, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.702 G_ID: 0.467 G_Rec: 0.362 D_GP: 0.030 D_real: 1.305 D_fake: 0.569 +(epoch: 57, iters: 7552, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.723 G_ID: 0.228 G_Rec: 0.361 D_GP: 0.039 D_real: 1.097 D_fake: 0.721 +(epoch: 57, iters: 7952, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.798 G_ID: 0.509 G_Rec: 0.359 D_GP: 0.037 D_real: 0.925 D_fake: 0.771 +(epoch: 57, iters: 8352, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.742 G_ID: 0.237 G_Rec: 0.330 D_GP: 0.053 D_real: 1.033 D_fake: 0.719 +(epoch: 58, iters: 144, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.741 G_ID: 0.497 G_Rec: 0.401 D_GP: 0.032 D_real: 1.072 D_fake: 0.738 +(epoch: 58, iters: 544, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.781 G_ID: 0.270 G_Rec: 0.371 D_GP: 0.044 D_real: 1.251 D_fake: 0.538 +(epoch: 58, iters: 944, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.979 G_ID: 0.483 G_Rec: 0.404 D_GP: 0.277 D_real: 0.531 D_fake: 0.707 +(epoch: 58, iters: 1344, time: 0.064) G_GAN: 0.784 G_GAN_Feat: 0.806 G_ID: 0.226 G_Rec: 0.362 D_GP: 0.042 D_real: 1.398 D_fake: 0.234 +(epoch: 58, iters: 1744, time: 0.064) G_GAN: 0.589 G_GAN_Feat: 0.956 G_ID: 0.467 G_Rec: 0.384 D_GP: 0.096 D_real: 0.961 D_fake: 0.523 +(epoch: 58, iters: 2144, time: 0.064) G_GAN: 0.109 G_GAN_Feat: 0.790 G_ID: 0.231 G_Rec: 0.361 D_GP: 0.034 D_real: 0.724 D_fake: 0.891 +(epoch: 58, iters: 2544, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.044 G_ID: 0.491 G_Rec: 0.400 D_GP: 0.072 D_real: 0.454 D_fake: 0.651 +(epoch: 58, iters: 2944, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.726 G_ID: 0.256 G_Rec: 0.399 D_GP: 0.029 D_real: 1.203 D_fake: 0.613 +(epoch: 58, iters: 3344, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 1.014 G_ID: 0.441 G_Rec: 0.366 D_GP: 0.098 D_real: 0.556 D_fake: 0.732 +(epoch: 58, iters: 3744, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.925 G_ID: 0.244 G_Rec: 0.360 D_GP: 0.049 D_real: 0.547 D_fake: 0.862 +(epoch: 58, iters: 4144, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 1.150 G_ID: 0.494 G_Rec: 0.435 D_GP: 0.149 D_real: 0.474 D_fake: 0.579 +(epoch: 58, iters: 4544, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.614 G_ID: 0.222 G_Rec: 0.341 D_GP: 0.025 D_real: 1.124 D_fake: 0.685 +(epoch: 58, iters: 4944, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.689 G_ID: 0.467 G_Rec: 0.352 D_GP: 0.032 D_real: 1.091 D_fake: 0.716 +(epoch: 58, iters: 5344, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.777 G_ID: 0.258 G_Rec: 0.363 D_GP: 0.036 D_real: 1.025 D_fake: 0.659 +(epoch: 58, iters: 5744, time: 0.064) G_GAN: 0.532 G_GAN_Feat: 1.180 G_ID: 0.483 G_Rec: 0.396 D_GP: 0.150 D_real: 0.681 D_fake: 0.495 +(epoch: 58, iters: 6144, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.908 G_ID: 0.221 G_Rec: 0.395 D_GP: 0.100 D_real: 0.607 D_fake: 0.802 +(epoch: 58, iters: 6544, time: 0.064) G_GAN: 0.596 G_GAN_Feat: 0.670 G_ID: 0.469 G_Rec: 0.370 D_GP: 0.021 D_real: 1.468 D_fake: 0.438 +(epoch: 58, iters: 6944, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.782 G_ID: 0.210 G_Rec: 0.422 D_GP: 0.034 D_real: 1.301 D_fake: 0.543 +(epoch: 58, iters: 7344, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.815 G_ID: 0.478 G_Rec: 0.392 D_GP: 0.090 D_real: 0.893 D_fake: 0.680 +(epoch: 58, iters: 7744, time: 0.064) G_GAN: 0.630 G_GAN_Feat: 0.813 G_ID: 0.248 G_Rec: 0.415 D_GP: 0.047 D_real: 1.299 D_fake: 0.376 +(epoch: 58, iters: 8144, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.909 G_ID: 0.511 G_Rec: 0.402 D_GP: 0.166 D_real: 0.651 D_fake: 0.632 +(epoch: 58, iters: 8544, time: 0.064) G_GAN: 0.582 G_GAN_Feat: 1.024 G_ID: 0.230 G_Rec: 0.390 D_GP: 0.095 D_real: 0.733 D_fake: 0.424 +(epoch: 59, iters: 336, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 0.856 G_ID: 0.438 G_Rec: 0.370 D_GP: 0.073 D_real: 0.701 D_fake: 0.892 +(epoch: 59, iters: 736, time: 0.064) G_GAN: 0.830 G_GAN_Feat: 1.335 G_ID: 0.217 G_Rec: 0.391 D_GP: 0.405 D_real: 0.730 D_fake: 0.225 +(epoch: 59, iters: 1136, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 1.002 G_ID: 0.470 G_Rec: 0.388 D_GP: 0.040 D_real: 0.750 D_fake: 0.691 +(epoch: 59, iters: 1536, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.772 G_ID: 0.276 G_Rec: 0.356 D_GP: 0.040 D_real: 1.147 D_fake: 0.566 +(epoch: 59, iters: 1936, time: 0.064) G_GAN: 0.661 G_GAN_Feat: 0.642 G_ID: 0.496 G_Rec: 0.400 D_GP: 0.020 D_real: 1.477 D_fake: 0.397 +(epoch: 59, iters: 2336, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.614 G_ID: 0.252 G_Rec: 0.420 D_GP: 0.028 D_real: 1.127 D_fake: 0.744 +(epoch: 59, iters: 2736, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.860 G_ID: 0.483 G_Rec: 0.432 D_GP: 0.065 D_real: 0.914 D_fake: 0.553 +(epoch: 59, iters: 3136, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.835 G_ID: 0.195 G_Rec: 0.399 D_GP: 0.043 D_real: 0.960 D_fake: 0.638 +(epoch: 59, iters: 3536, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.813 G_ID: 0.517 G_Rec: 0.365 D_GP: 0.060 D_real: 0.814 D_fake: 0.923 +(epoch: 59, iters: 3936, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.841 G_ID: 0.269 G_Rec: 0.458 D_GP: 0.025 D_real: 0.792 D_fake: 0.856 +(epoch: 59, iters: 4336, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.871 G_ID: 0.469 G_Rec: 0.341 D_GP: 0.083 D_real: 0.782 D_fake: 0.730 +(epoch: 59, iters: 4736, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.923 G_ID: 0.278 G_Rec: 0.406 D_GP: 0.069 D_real: 0.614 D_fake: 0.729 +(epoch: 59, iters: 5136, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.956 G_ID: 0.455 G_Rec: 0.352 D_GP: 0.116 D_real: 0.773 D_fake: 0.587 +(epoch: 59, iters: 5536, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 0.544 G_ID: 0.237 G_Rec: 0.378 D_GP: 0.015 D_real: 1.431 D_fake: 0.514 +(epoch: 59, iters: 5936, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.585 G_ID: 0.486 G_Rec: 0.371 D_GP: 0.018 D_real: 1.286 D_fake: 0.568 +(epoch: 59, iters: 6336, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.584 G_ID: 0.183 G_Rec: 0.385 D_GP: 0.022 D_real: 1.267 D_fake: 0.710 +(epoch: 59, iters: 6736, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.687 G_ID: 0.457 G_Rec: 0.370 D_GP: 0.045 D_real: 0.925 D_fake: 0.836 +(epoch: 59, iters: 7136, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.697 G_ID: 0.235 G_Rec: 0.372 D_GP: 0.039 D_real: 1.043 D_fake: 0.718 +(epoch: 59, iters: 7536, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.737 G_ID: 0.467 G_Rec: 0.408 D_GP: 0.075 D_real: 0.964 D_fake: 0.639 +(epoch: 59, iters: 7936, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.751 G_ID: 0.257 G_Rec: 0.384 D_GP: 0.070 D_real: 0.729 D_fake: 0.816 +(epoch: 59, iters: 8336, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.934 G_ID: 0.494 G_Rec: 0.382 D_GP: 0.080 D_real: 0.893 D_fake: 0.493 +(epoch: 60, iters: 128, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 1.045 G_ID: 0.268 G_Rec: 0.377 D_GP: 0.060 D_real: 0.693 D_fake: 0.564 +(epoch: 60, iters: 528, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.748 G_ID: 0.481 G_Rec: 0.383 D_GP: 0.032 D_real: 1.023 D_fake: 0.710 +(epoch: 60, iters: 928, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.688 G_ID: 0.264 G_Rec: 0.348 D_GP: 0.030 D_real: 1.275 D_fake: 0.477 +(epoch: 60, iters: 1328, time: 0.064) G_GAN: 0.729 G_GAN_Feat: 0.968 G_ID: 0.465 G_Rec: 0.371 D_GP: 0.120 D_real: 1.170 D_fake: 0.491 +(epoch: 60, iters: 1728, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.698 G_ID: 0.278 G_Rec: 0.368 D_GP: 0.024 D_real: 1.142 D_fake: 0.742 +(epoch: 60, iters: 2128, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 0.700 G_ID: 0.448 G_Rec: 0.385 D_GP: 0.044 D_real: 0.855 D_fake: 0.992 +(epoch: 60, iters: 2528, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.766 G_ID: 0.225 G_Rec: 0.386 D_GP: 0.062 D_real: 0.794 D_fake: 0.844 +(epoch: 60, iters: 2928, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 1.343 G_ID: 0.499 G_Rec: 0.401 D_GP: 0.776 D_real: 0.430 D_fake: 0.829 +(epoch: 60, iters: 3328, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.649 G_ID: 0.250 G_Rec: 0.387 D_GP: 0.024 D_real: 1.060 D_fake: 0.691 +(epoch: 60, iters: 3728, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.725 G_ID: 0.495 G_Rec: 0.378 D_GP: 0.043 D_real: 0.890 D_fake: 0.833 +(epoch: 60, iters: 4128, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.725 G_ID: 0.260 G_Rec: 0.395 D_GP: 0.042 D_real: 0.992 D_fake: 0.735 +(epoch: 60, iters: 4528, time: 0.064) G_GAN: 0.078 G_GAN_Feat: 0.778 G_ID: 0.432 G_Rec: 0.372 D_GP: 0.056 D_real: 0.739 D_fake: 0.922 +(epoch: 60, iters: 4928, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.912 G_ID: 0.260 G_Rec: 0.385 D_GP: 0.058 D_real: 0.741 D_fake: 0.743 +(epoch: 60, iters: 5328, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.748 G_ID: 0.465 G_Rec: 0.349 D_GP: 0.047 D_real: 0.992 D_fake: 0.658 +(epoch: 60, iters: 5728, time: 0.064) G_GAN: 0.660 G_GAN_Feat: 0.874 G_ID: 0.260 G_Rec: 0.373 D_GP: 0.081 D_real: 1.166 D_fake: 0.396 +(epoch: 60, iters: 6128, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 1.213 G_ID: 0.454 G_Rec: 0.430 D_GP: 0.837 D_real: 0.389 D_fake: 0.671 +(epoch: 60, iters: 6528, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.688 G_ID: 0.189 G_Rec: 0.352 D_GP: 0.031 D_real: 1.111 D_fake: 0.807 +(epoch: 60, iters: 6928, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 1.018 G_ID: 0.407 G_Rec: 0.411 D_GP: 0.262 D_real: 0.478 D_fake: 0.637 +(epoch: 60, iters: 7328, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.968 G_ID: 0.238 G_Rec: 0.375 D_GP: 0.120 D_real: 0.415 D_fake: 0.917 +(epoch: 60, iters: 7728, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.730 G_ID: 0.469 G_Rec: 0.421 D_GP: 0.033 D_real: 0.981 D_fake: 0.760 +(epoch: 60, iters: 8128, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.890 G_ID: 0.241 G_Rec: 0.375 D_GP: 0.031 D_real: 1.034 D_fake: 0.617 +(epoch: 60, iters: 8528, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 1.101 G_ID: 0.490 G_Rec: 0.412 D_GP: 0.079 D_real: 0.454 D_fake: 0.885 +(epoch: 61, iters: 320, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.981 G_ID: 0.244 G_Rec: 0.398 D_GP: 0.156 D_real: 0.734 D_fake: 0.712 +(epoch: 61, iters: 720, time: 0.064) G_GAN: 0.652 G_GAN_Feat: 0.814 G_ID: 0.446 G_Rec: 0.360 D_GP: 0.051 D_real: 1.321 D_fake: 0.358 +(epoch: 61, iters: 1120, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.930 G_ID: 0.227 G_Rec: 0.380 D_GP: 0.043 D_real: 0.886 D_fake: 0.552 +(epoch: 61, iters: 1520, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 1.078 G_ID: 0.460 G_Rec: 0.404 D_GP: 0.064 D_real: 0.660 D_fake: 0.680 +(epoch: 61, iters: 1920, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.733 G_ID: 0.240 G_Rec: 0.372 D_GP: 0.031 D_real: 1.115 D_fake: 0.625 +(epoch: 61, iters: 2320, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.634 G_ID: 0.428 G_Rec: 0.400 D_GP: 0.022 D_real: 1.347 D_fake: 0.512 +(epoch: 61, iters: 2720, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.654 G_ID: 0.244 G_Rec: 0.406 D_GP: 0.028 D_real: 0.992 D_fake: 0.780 +(epoch: 61, iters: 3120, time: 0.064) G_GAN: 0.563 G_GAN_Feat: 0.851 G_ID: 0.464 G_Rec: 0.389 D_GP: 0.081 D_real: 1.056 D_fake: 0.445 +(epoch: 61, iters: 3520, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.730 G_ID: 0.198 G_Rec: 0.373 D_GP: 0.038 D_real: 1.122 D_fake: 0.566 +(epoch: 61, iters: 3920, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.932 G_ID: 0.492 G_Rec: 0.396 D_GP: 0.075 D_real: 0.498 D_fake: 0.868 +(epoch: 61, iters: 4320, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.657 G_ID: 0.257 G_Rec: 0.376 D_GP: 0.025 D_real: 1.068 D_fake: 0.768 +(epoch: 61, iters: 4720, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.705 G_ID: 0.456 G_Rec: 0.372 D_GP: 0.036 D_real: 0.981 D_fake: 0.737 +(epoch: 61, iters: 5120, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.672 G_ID: 0.229 G_Rec: 0.333 D_GP: 0.042 D_real: 0.965 D_fake: 0.792 +(epoch: 61, iters: 5520, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.858 G_ID: 0.496 G_Rec: 0.376 D_GP: 0.041 D_real: 0.915 D_fake: 0.678 +(epoch: 61, iters: 5920, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.797 G_ID: 0.246 G_Rec: 0.368 D_GP: 0.051 D_real: 1.193 D_fake: 0.659 +(epoch: 61, iters: 6320, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.821 G_ID: 0.429 G_Rec: 0.362 D_GP: 0.105 D_real: 0.963 D_fake: 0.591 +(epoch: 61, iters: 6720, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.624 G_ID: 0.236 G_Rec: 0.447 D_GP: 0.020 D_real: 1.115 D_fake: 0.791 +(epoch: 61, iters: 7120, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.862 G_ID: 0.431 G_Rec: 0.401 D_GP: 0.040 D_real: 1.003 D_fake: 0.693 +(epoch: 61, iters: 7520, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.816 G_ID: 0.265 G_Rec: 0.391 D_GP: 0.052 D_real: 0.829 D_fake: 0.801 +(epoch: 61, iters: 7920, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.830 G_ID: 0.435 G_Rec: 0.403 D_GP: 0.046 D_real: 1.099 D_fake: 0.607 +(epoch: 61, iters: 8320, time: 0.064) G_GAN: 0.470 G_GAN_Feat: 0.767 G_ID: 0.290 G_Rec: 0.366 D_GP: 0.048 D_real: 1.142 D_fake: 0.547 +(epoch: 62, iters: 112, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.840 G_ID: 0.480 G_Rec: 0.424 D_GP: 0.037 D_real: 1.037 D_fake: 0.639 +(epoch: 62, iters: 512, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.730 G_ID: 0.221 G_Rec: 0.325 D_GP: 0.042 D_real: 1.127 D_fake: 0.709 +(epoch: 62, iters: 912, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.862 G_ID: 0.446 G_Rec: 0.405 D_GP: 0.034 D_real: 0.864 D_fake: 0.781 +(epoch: 62, iters: 1312, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.883 G_ID: 0.253 G_Rec: 0.395 D_GP: 0.030 D_real: 1.030 D_fake: 0.663 +(epoch: 62, iters: 1712, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 0.890 G_ID: 0.439 G_Rec: 0.363 D_GP: 0.085 D_real: 0.642 D_fake: 0.808 +(epoch: 62, iters: 2112, time: 0.064) G_GAN: 0.658 G_GAN_Feat: 1.064 G_ID: 0.250 G_Rec: 0.397 D_GP: 0.060 D_real: 1.323 D_fake: 0.558 +(epoch: 62, iters: 2512, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 1.194 G_ID: 0.480 G_Rec: 0.424 D_GP: 0.146 D_real: 0.494 D_fake: 0.634 +(epoch: 62, iters: 2912, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 1.057 G_ID: 0.220 G_Rec: 0.443 D_GP: 0.105 D_real: 0.243 D_fake: 0.885 +(epoch: 62, iters: 3312, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.762 G_ID: 0.498 G_Rec: 0.390 D_GP: 0.024 D_real: 1.044 D_fake: 0.716 +(epoch: 62, iters: 3712, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.746 G_ID: 0.240 G_Rec: 0.358 D_GP: 0.027 D_real: 1.174 D_fake: 0.555 +(epoch: 62, iters: 4112, time: 0.064) G_GAN: 0.550 G_GAN_Feat: 0.896 G_ID: 0.445 G_Rec: 0.362 D_GP: 0.051 D_real: 1.118 D_fake: 0.451 +(epoch: 62, iters: 4512, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.685 G_ID: 0.290 G_Rec: 0.372 D_GP: 0.022 D_real: 1.123 D_fake: 0.655 +(epoch: 62, iters: 4912, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.815 G_ID: 0.432 G_Rec: 0.387 D_GP: 0.101 D_real: 0.593 D_fake: 0.912 +(epoch: 62, iters: 5312, time: 0.064) G_GAN: 0.572 G_GAN_Feat: 0.715 G_ID: 0.223 G_Rec: 0.362 D_GP: 0.035 D_real: 1.371 D_fake: 0.430 +(epoch: 62, iters: 5712, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 1.145 G_ID: 0.411 G_Rec: 0.434 D_GP: 0.106 D_real: 0.491 D_fake: 0.686 +(epoch: 62, iters: 6112, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.857 G_ID: 0.220 G_Rec: 0.481 D_GP: 0.056 D_real: 0.908 D_fake: 0.649 +(epoch: 62, iters: 6512, time: 0.064) G_GAN: 0.664 G_GAN_Feat: 1.151 G_ID: 0.440 G_Rec: 0.405 D_GP: 0.298 D_real: 0.664 D_fake: 0.386 +(epoch: 62, iters: 6912, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 1.093 G_ID: 0.232 G_Rec: 0.438 D_GP: 0.096 D_real: 0.467 D_fake: 0.586 +(epoch: 62, iters: 7312, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 1.073 G_ID: 0.440 G_Rec: 0.402 D_GP: 0.055 D_real: 0.539 D_fake: 0.762 +(epoch: 62, iters: 7712, time: 0.064) G_GAN: 0.685 G_GAN_Feat: 0.764 G_ID: 0.218 G_Rec: 0.331 D_GP: 0.047 D_real: 1.400 D_fake: 0.353 +(epoch: 62, iters: 8112, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.674 G_ID: 0.446 G_Rec: 0.405 D_GP: 0.024 D_real: 1.056 D_fake: 0.768 +(epoch: 62, iters: 8512, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.842 G_ID: 0.208 G_Rec: 0.365 D_GP: 0.057 D_real: 0.755 D_fake: 0.702 +(epoch: 63, iters: 304, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.654 G_ID: 0.442 G_Rec: 0.382 D_GP: 0.027 D_real: 1.212 D_fake: 0.584 +(epoch: 63, iters: 704, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.847 G_ID: 0.248 G_Rec: 0.380 D_GP: 0.080 D_real: 0.780 D_fake: 0.725 +(epoch: 63, iters: 1104, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.805 G_ID: 0.460 G_Rec: 0.380 D_GP: 0.035 D_real: 0.929 D_fake: 0.734 +(epoch: 63, iters: 1504, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.626 G_ID: 0.308 G_Rec: 0.348 D_GP: 0.024 D_real: 1.162 D_fake: 0.591 +(epoch: 63, iters: 1904, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.659 G_ID: 0.427 G_Rec: 0.358 D_GP: 0.031 D_real: 1.043 D_fake: 0.749 +(epoch: 63, iters: 2304, time: 0.064) G_GAN: 0.000 G_GAN_Feat: 0.738 G_ID: 0.224 G_Rec: 0.334 D_GP: 0.050 D_real: 0.850 D_fake: 1.000 +(epoch: 63, iters: 2704, time: 0.064) G_GAN: 0.038 G_GAN_Feat: 0.793 G_ID: 0.426 G_Rec: 0.370 D_GP: 0.060 D_real: 0.714 D_fake: 0.962 +(epoch: 63, iters: 3104, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.750 G_ID: 0.228 G_Rec: 0.369 D_GP: 0.039 D_real: 1.154 D_fake: 0.537 +(epoch: 63, iters: 3504, time: 0.064) G_GAN: 0.620 G_GAN_Feat: 0.893 G_ID: 0.414 G_Rec: 0.411 D_GP: 0.077 D_real: 1.113 D_fake: 0.414 +(epoch: 63, iters: 3904, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.994 G_ID: 0.195 G_Rec: 0.375 D_GP: 0.042 D_real: 0.785 D_fake: 0.590 +(epoch: 63, iters: 4304, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.833 G_ID: 0.513 G_Rec: 0.369 D_GP: 0.044 D_real: 1.149 D_fake: 0.577 +(epoch: 63, iters: 4704, time: 0.064) G_GAN: 0.444 G_GAN_Feat: 0.849 G_ID: 0.200 G_Rec: 0.361 D_GP: 0.089 D_real: 0.910 D_fake: 0.632 +(epoch: 63, iters: 5104, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 1.236 G_ID: 0.482 G_Rec: 0.361 D_GP: 0.070 D_real: 0.429 D_fake: 0.604 +(epoch: 63, iters: 5504, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.718 G_ID: 0.212 G_Rec: 0.382 D_GP: 0.022 D_real: 0.924 D_fake: 0.893 +(epoch: 63, iters: 5904, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.759 G_ID: 0.487 G_Rec: 0.363 D_GP: 0.036 D_real: 1.212 D_fake: 0.515 +(epoch: 63, iters: 6304, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.840 G_ID: 0.233 G_Rec: 0.417 D_GP: 0.030 D_real: 1.122 D_fake: 0.578 +(epoch: 63, iters: 6704, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.686 G_ID: 0.428 G_Rec: 0.425 D_GP: 0.022 D_real: 1.167 D_fake: 0.697 +(epoch: 63, iters: 7104, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.703 G_ID: 0.249 G_Rec: 0.352 D_GP: 0.027 D_real: 1.262 D_fake: 0.505 +(epoch: 63, iters: 7504, time: 0.064) G_GAN: 0.550 G_GAN_Feat: 0.698 G_ID: 0.432 G_Rec: 0.338 D_GP: 0.032 D_real: 1.329 D_fake: 0.453 +(epoch: 63, iters: 7904, time: 0.064) G_GAN: 0.733 G_GAN_Feat: 0.864 G_ID: 0.256 G_Rec: 0.394 D_GP: 0.085 D_real: 1.191 D_fake: 0.300 +(epoch: 63, iters: 8304, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.931 G_ID: 0.447 G_Rec: 0.406 D_GP: 0.041 D_real: 0.968 D_fake: 0.563 +(epoch: 64, iters: 96, time: 0.064) G_GAN: -0.106 G_GAN_Feat: 1.209 G_ID: 0.261 G_Rec: 0.390 D_GP: 0.300 D_real: 0.335 D_fake: 1.106 +(epoch: 64, iters: 496, time: 0.064) G_GAN: 0.727 G_GAN_Feat: 0.886 G_ID: 0.450 G_Rec: 0.402 D_GP: 0.033 D_real: 1.333 D_fake: 0.296 +(epoch: 64, iters: 896, time: 0.064) G_GAN: 0.852 G_GAN_Feat: 0.846 G_ID: 0.240 G_Rec: 0.372 D_GP: 0.074 D_real: 1.486 D_fake: 0.315 +(epoch: 64, iters: 1296, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 1.165 G_ID: 0.482 G_Rec: 0.389 D_GP: 0.086 D_real: 0.376 D_fake: 0.620 +(epoch: 64, iters: 1696, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.721 G_ID: 0.239 G_Rec: 0.399 D_GP: 0.021 D_real: 1.002 D_fake: 0.835 +(epoch: 64, iters: 2096, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.726 G_ID: 0.468 G_Rec: 0.402 D_GP: 0.027 D_real: 0.989 D_fake: 0.717 +(epoch: 64, iters: 2496, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 0.682 G_ID: 0.241 G_Rec: 0.332 D_GP: 0.033 D_real: 0.925 D_fake: 0.892 +(epoch: 64, iters: 2896, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.837 G_ID: 0.462 G_Rec: 0.386 D_GP: 0.071 D_real: 0.926 D_fake: 0.701 +(epoch: 64, iters: 3296, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.731 G_ID: 0.219 G_Rec: 0.409 D_GP: 0.037 D_real: 1.196 D_fake: 0.519 +(epoch: 64, iters: 3696, time: 0.064) G_GAN: 0.747 G_GAN_Feat: 0.978 G_ID: 0.462 G_Rec: 0.406 D_GP: 0.098 D_real: 0.972 D_fake: 0.351 +(epoch: 64, iters: 4096, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.877 G_ID: 0.241 G_Rec: 0.426 D_GP: 0.040 D_real: 0.923 D_fake: 0.774 +(epoch: 64, iters: 4496, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.881 G_ID: 0.475 G_Rec: 0.347 D_GP: 0.092 D_real: 0.973 D_fake: 0.483 +(epoch: 64, iters: 4896, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.573 G_ID: 0.221 G_Rec: 0.406 D_GP: 0.023 D_real: 1.194 D_fake: 0.714 +(epoch: 64, iters: 5296, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.655 G_ID: 0.435 G_Rec: 0.371 D_GP: 0.021 D_real: 1.159 D_fake: 0.630 +(epoch: 64, iters: 5696, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.611 G_ID: 0.225 G_Rec: 0.391 D_GP: 0.027 D_real: 1.039 D_fake: 0.822 +(epoch: 64, iters: 6096, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.607 G_ID: 0.436 G_Rec: 0.352 D_GP: 0.029 D_real: 1.069 D_fake: 0.743 +(epoch: 64, iters: 6496, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.674 G_ID: 0.215 G_Rec: 0.446 D_GP: 0.043 D_real: 1.021 D_fake: 0.714 +(epoch: 64, iters: 6896, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.759 G_ID: 0.426 G_Rec: 0.389 D_GP: 0.044 D_real: 0.893 D_fake: 0.750 +(epoch: 64, iters: 7296, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.920 G_ID: 0.255 G_Rec: 0.346 D_GP: 0.126 D_real: 0.575 D_fake: 0.715 +(epoch: 64, iters: 7696, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 0.734 G_ID: 0.446 G_Rec: 0.357 D_GP: 0.053 D_real: 1.260 D_fake: 0.438 +(epoch: 64, iters: 8096, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 0.864 G_ID: 0.236 G_Rec: 0.372 D_GP: 0.068 D_real: 1.199 D_fake: 0.445 +(epoch: 64, iters: 8496, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.779 G_ID: 0.483 G_Rec: 0.384 D_GP: 0.039 D_real: 1.068 D_fake: 0.669 +(epoch: 65, iters: 288, time: 0.064) G_GAN: 0.787 G_GAN_Feat: 0.860 G_ID: 0.233 G_Rec: 0.358 D_GP: 0.060 D_real: 1.353 D_fake: 0.409 +(epoch: 65, iters: 688, time: 0.064) G_GAN: 0.621 G_GAN_Feat: 0.974 G_ID: 0.463 G_Rec: 0.374 D_GP: 0.056 D_real: 0.990 D_fake: 0.416 +(epoch: 65, iters: 1088, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.739 G_ID: 0.221 G_Rec: 0.425 D_GP: 0.032 D_real: 1.264 D_fake: 0.671 +(epoch: 65, iters: 1488, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.718 G_ID: 0.415 G_Rec: 0.369 D_GP: 0.026 D_real: 1.063 D_fake: 0.728 +(epoch: 65, iters: 1888, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 1.117 G_ID: 0.205 G_Rec: 0.384 D_GP: 0.212 D_real: 0.556 D_fake: 0.569 +(epoch: 65, iters: 2288, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 0.964 G_ID: 0.473 G_Rec: 0.425 D_GP: 0.105 D_real: 0.289 D_fake: 0.934 +(epoch: 65, iters: 2688, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.835 G_ID: 0.255 G_Rec: 0.396 D_GP: 0.065 D_real: 0.688 D_fake: 0.794 +(epoch: 65, iters: 3088, time: 0.064) G_GAN: 0.726 G_GAN_Feat: 0.769 G_ID: 0.416 G_Rec: 0.414 D_GP: 0.034 D_real: 1.426 D_fake: 0.310 +(epoch: 65, iters: 3488, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.661 G_ID: 0.195 G_Rec: 0.358 D_GP: 0.026 D_real: 1.282 D_fake: 0.581 +(epoch: 65, iters: 3888, time: 0.064) G_GAN: 0.933 G_GAN_Feat: 1.078 G_ID: 0.378 G_Rec: 0.403 D_GP: 0.082 D_real: 1.074 D_fake: 0.200 +(epoch: 65, iters: 4288, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.991 G_ID: 0.229 G_Rec: 0.375 D_GP: 0.152 D_real: 0.367 D_fake: 0.791 +(epoch: 65, iters: 4688, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.759 G_ID: 0.438 G_Rec: 0.458 D_GP: 0.042 D_real: 0.967 D_fake: 0.686 +(epoch: 65, iters: 5088, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.768 G_ID: 0.247 G_Rec: 0.337 D_GP: 0.045 D_real: 1.139 D_fake: 0.568 +(epoch: 65, iters: 5488, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.929 G_ID: 0.399 G_Rec: 0.375 D_GP: 0.080 D_real: 0.917 D_fake: 0.848 +(epoch: 65, iters: 5888, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.956 G_ID: 0.229 G_Rec: 0.374 D_GP: 0.063 D_real: 0.647 D_fake: 0.808 +(epoch: 65, iters: 6288, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 1.133 G_ID: 0.437 G_Rec: 0.457 D_GP: 0.044 D_real: 0.532 D_fake: 0.666 +(epoch: 65, iters: 6688, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.718 G_ID: 0.238 G_Rec: 0.353 D_GP: 0.025 D_real: 1.065 D_fake: 0.855 +(epoch: 65, iters: 7088, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.808 G_ID: 0.450 G_Rec: 0.361 D_GP: 0.031 D_real: 0.966 D_fake: 0.719 +(epoch: 65, iters: 7488, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.670 G_ID: 0.283 G_Rec: 0.345 D_GP: 0.026 D_real: 0.923 D_fake: 0.844 +(epoch: 65, iters: 7888, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.760 G_ID: 0.417 G_Rec: 0.344 D_GP: 0.036 D_real: 1.190 D_fake: 0.525 +(epoch: 65, iters: 8288, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.711 G_ID: 0.236 G_Rec: 0.361 D_GP: 0.032 D_real: 1.045 D_fake: 0.768 +(epoch: 66, iters: 80, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.777 G_ID: 0.412 G_Rec: 0.358 D_GP: 0.066 D_real: 0.837 D_fake: 0.763 +(epoch: 66, iters: 480, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.996 G_ID: 0.207 G_Rec: 0.417 D_GP: 0.037 D_real: 0.570 D_fake: 0.732 +(epoch: 66, iters: 880, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.670 G_ID: 0.410 G_Rec: 0.380 D_GP: 0.020 D_real: 1.110 D_fake: 0.679 +(epoch: 66, iters: 1280, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.899 G_ID: 0.218 G_Rec: 0.351 D_GP: 0.069 D_real: 0.722 D_fake: 0.741 +(epoch: 66, iters: 1680, time: 0.064) G_GAN: 0.820 G_GAN_Feat: 1.213 G_ID: 0.444 G_Rec: 0.438 D_GP: 0.120 D_real: 0.699 D_fake: 0.235 +(epoch: 66, iters: 2080, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 1.223 G_ID: 0.216 G_Rec: 0.405 D_GP: 0.200 D_real: 0.499 D_fake: 0.816 +(epoch: 66, iters: 2480, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 1.461 G_ID: 0.441 G_Rec: 0.387 D_GP: 1.862 D_real: 0.440 D_fake: 0.579 +(epoch: 66, iters: 2880, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.597 G_ID: 0.218 G_Rec: 0.381 D_GP: 0.025 D_real: 1.174 D_fake: 0.676 +(epoch: 66, iters: 3280, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.614 G_ID: 0.462 G_Rec: 0.426 D_GP: 0.022 D_real: 1.067 D_fake: 0.848 +(epoch: 66, iters: 3680, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.574 G_ID: 0.222 G_Rec: 0.396 D_GP: 0.019 D_real: 1.238 D_fake: 0.642 +(epoch: 66, iters: 4080, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.725 G_ID: 0.423 G_Rec: 0.409 D_GP: 0.043 D_real: 1.227 D_fake: 0.554 +(epoch: 66, iters: 4480, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.776 G_ID: 0.219 G_Rec: 0.396 D_GP: 0.053 D_real: 1.110 D_fake: 0.586 +(epoch: 66, iters: 4880, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.762 G_ID: 0.428 G_Rec: 0.367 D_GP: 0.054 D_real: 1.240 D_fake: 0.527 +(epoch: 66, iters: 5280, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.644 G_ID: 0.251 G_Rec: 0.373 D_GP: 0.024 D_real: 0.934 D_fake: 0.879 +(epoch: 66, iters: 5680, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.968 G_ID: 0.465 G_Rec: 0.365 D_GP: 0.049 D_real: 0.869 D_fake: 0.596 +(epoch: 66, iters: 6080, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.725 G_ID: 0.245 G_Rec: 0.369 D_GP: 0.044 D_real: 0.908 D_fake: 0.859 +(epoch: 66, iters: 6480, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.828 G_ID: 0.497 G_Rec: 0.371 D_GP: 0.061 D_real: 0.956 D_fake: 0.630 +(epoch: 66, iters: 6880, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.667 G_ID: 0.223 G_Rec: 0.339 D_GP: 0.030 D_real: 1.074 D_fake: 0.774 +(epoch: 66, iters: 7280, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.863 G_ID: 0.404 G_Rec: 0.383 D_GP: 0.041 D_real: 1.000 D_fake: 0.577 +(epoch: 66, iters: 7680, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.796 G_ID: 0.226 G_Rec: 0.347 D_GP: 0.062 D_real: 0.961 D_fake: 0.599 +(epoch: 66, iters: 8080, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.883 G_ID: 0.429 G_Rec: 0.430 D_GP: 0.038 D_real: 0.964 D_fake: 0.574 +(epoch: 66, iters: 8480, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.748 G_ID: 0.205 G_Rec: 0.431 D_GP: 0.034 D_real: 1.134 D_fake: 0.622 +(epoch: 67, iters: 272, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.929 G_ID: 0.434 G_Rec: 0.370 D_GP: 0.065 D_real: 1.028 D_fake: 0.691 +(epoch: 67, iters: 672, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 1.081 G_ID: 0.198 G_Rec: 0.374 D_GP: 0.052 D_real: 0.722 D_fake: 0.620 +(epoch: 67, iters: 1072, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 1.121 G_ID: 0.462 G_Rec: 0.424 D_GP: 0.082 D_real: 0.217 D_fake: 0.824 +(epoch: 67, iters: 1472, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.829 G_ID: 0.194 G_Rec: 0.408 D_GP: 0.051 D_real: 0.991 D_fake: 0.546 +(epoch: 67, iters: 1872, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.784 G_ID: 0.428 G_Rec: 0.403 D_GP: 0.029 D_real: 1.151 D_fake: 0.602 +(epoch: 67, iters: 2272, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 1.408 G_ID: 0.249 G_Rec: 0.365 D_GP: 0.237 D_real: 0.239 D_fake: 0.918 +(epoch: 67, iters: 2672, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 1.056 G_ID: 0.393 G_Rec: 0.394 D_GP: 0.068 D_real: 0.634 D_fake: 0.576 +(epoch: 67, iters: 3072, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.662 G_ID: 0.213 G_Rec: 0.358 D_GP: 0.022 D_real: 1.254 D_fake: 0.548 +(epoch: 67, iters: 3472, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.746 G_ID: 0.392 G_Rec: 0.405 D_GP: 0.025 D_real: 1.144 D_fake: 0.553 +(epoch: 67, iters: 3872, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.826 G_ID: 0.255 G_Rec: 0.361 D_GP: 0.051 D_real: 0.954 D_fake: 0.618 +(epoch: 67, iters: 4272, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.997 G_ID: 0.425 G_Rec: 0.393 D_GP: 0.105 D_real: 0.762 D_fake: 0.834 +(epoch: 67, iters: 4672, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.684 G_ID: 0.250 G_Rec: 0.402 D_GP: 0.029 D_real: 1.191 D_fake: 0.739 +(epoch: 67, iters: 5072, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.717 G_ID: 0.381 G_Rec: 0.384 D_GP: 0.026 D_real: 1.348 D_fake: 0.477 +(epoch: 67, iters: 5472, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.784 G_ID: 0.237 G_Rec: 0.361 D_GP: 0.060 D_real: 1.067 D_fake: 0.714 +(epoch: 67, iters: 5872, time: 0.064) G_GAN: 0.520 G_GAN_Feat: 0.897 G_ID: 0.459 G_Rec: 0.436 D_GP: 0.038 D_real: 1.151 D_fake: 0.484 +(epoch: 67, iters: 6272, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.742 G_ID: 0.231 G_Rec: 0.376 D_GP: 0.027 D_real: 1.157 D_fake: 0.634 +(epoch: 67, iters: 6672, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.806 G_ID: 0.421 G_Rec: 0.403 D_GP: 0.047 D_real: 1.074 D_fake: 0.588 +(epoch: 67, iters: 7072, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.804 G_ID: 0.244 G_Rec: 0.437 D_GP: 0.093 D_real: 1.065 D_fake: 0.608 +(epoch: 67, iters: 7472, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 1.086 G_ID: 0.421 G_Rec: 0.431 D_GP: 0.097 D_real: 0.534 D_fake: 0.612 +(epoch: 67, iters: 7872, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.961 G_ID: 0.217 G_Rec: 0.370 D_GP: 0.069 D_real: 0.832 D_fake: 0.702 +(epoch: 67, iters: 8272, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.757 G_ID: 0.429 G_Rec: 0.369 D_GP: 0.040 D_real: 1.113 D_fake: 0.671 +(epoch: 68, iters: 64, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.975 G_ID: 0.240 G_Rec: 0.358 D_GP: 0.062 D_real: 0.651 D_fake: 0.726 +(epoch: 68, iters: 464, time: 0.064) G_GAN: 0.765 G_GAN_Feat: 0.818 G_ID: 0.415 G_Rec: 0.396 D_GP: 0.031 D_real: 1.509 D_fake: 0.262 +(epoch: 68, iters: 864, time: 0.064) G_GAN: 0.696 G_GAN_Feat: 0.869 G_ID: 0.208 G_Rec: 0.399 D_GP: 0.076 D_real: 1.111 D_fake: 0.362 +(epoch: 68, iters: 1264, time: 0.064) G_GAN: 0.618 G_GAN_Feat: 0.812 G_ID: 0.449 G_Rec: 0.419 D_GP: 0.031 D_real: 1.323 D_fake: 0.399 +(epoch: 68, iters: 1664, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.737 G_ID: 0.230 G_Rec: 0.369 D_GP: 0.030 D_real: 1.150 D_fake: 0.622 +(epoch: 68, iters: 2064, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 0.826 G_ID: 0.412 G_Rec: 0.385 D_GP: 0.038 D_real: 1.140 D_fake: 0.509 +(epoch: 68, iters: 2464, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.687 G_ID: 0.252 G_Rec: 0.366 D_GP: 0.029 D_real: 1.079 D_fake: 0.683 +(epoch: 68, iters: 2864, time: 0.064) G_GAN: 0.747 G_GAN_Feat: 1.004 G_ID: 0.436 G_Rec: 0.397 D_GP: 0.069 D_real: 1.359 D_fake: 0.305 +(epoch: 68, iters: 3264, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.826 G_ID: 0.236 G_Rec: 0.364 D_GP: 0.055 D_real: 1.091 D_fake: 0.614 +(epoch: 68, iters: 3664, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.971 G_ID: 0.465 G_Rec: 0.406 D_GP: 0.094 D_real: 1.029 D_fake: 0.456 +(epoch: 68, iters: 4064, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.678 G_ID: 0.259 G_Rec: 0.401 D_GP: 0.022 D_real: 1.074 D_fake: 0.839 +(epoch: 68, iters: 4464, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.994 G_ID: 0.413 G_Rec: 0.414 D_GP: 0.075 D_real: 0.623 D_fake: 0.652 +(epoch: 68, iters: 4864, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 0.817 G_ID: 0.235 G_Rec: 0.378 D_GP: 0.054 D_real: 1.148 D_fake: 0.506 +(epoch: 68, iters: 5264, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.825 G_ID: 0.442 G_Rec: 0.416 D_GP: 0.035 D_real: 1.084 D_fake: 0.541 +(epoch: 68, iters: 5664, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 0.926 G_ID: 0.203 G_Rec: 0.408 D_GP: 0.080 D_real: 0.970 D_fake: 0.519 +(epoch: 68, iters: 6064, time: 0.064) G_GAN: 0.694 G_GAN_Feat: 0.977 G_ID: 0.388 G_Rec: 0.378 D_GP: 0.034 D_real: 1.511 D_fake: 0.316 +(epoch: 68, iters: 6464, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 1.100 G_ID: 0.252 G_Rec: 0.392 D_GP: 0.086 D_real: 0.433 D_fake: 0.823 +(epoch: 68, iters: 6864, time: 0.064) G_GAN: 0.049 G_GAN_Feat: 0.866 G_ID: 0.443 G_Rec: 0.436 D_GP: 0.032 D_real: 0.758 D_fake: 0.951 +(epoch: 68, iters: 7264, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.909 G_ID: 0.226 G_Rec: 0.386 D_GP: 0.098 D_real: 0.702 D_fake: 0.847 +(epoch: 68, iters: 7664, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 0.767 G_ID: 0.392 G_Rec: 0.364 D_GP: 0.037 D_real: 0.773 D_fake: 0.904 +(epoch: 68, iters: 8064, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.833 G_ID: 0.261 G_Rec: 0.431 D_GP: 0.053 D_real: 0.958 D_fake: 0.598 +(epoch: 68, iters: 8464, time: 0.064) G_GAN: 0.864 G_GAN_Feat: 1.298 G_ID: 0.402 G_Rec: 0.394 D_GP: 0.115 D_real: 1.144 D_fake: 0.249 +(epoch: 69, iters: 256, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.666 G_ID: 0.206 G_Rec: 0.365 D_GP: 0.029 D_real: 1.035 D_fake: 0.788 +(epoch: 69, iters: 656, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.762 G_ID: 0.387 G_Rec: 0.510 D_GP: 0.035 D_real: 1.274 D_fake: 0.547 +(epoch: 69, iters: 1056, time: 0.064) G_GAN: -0.006 G_GAN_Feat: 1.153 G_ID: 0.293 G_Rec: 0.388 D_GP: 0.183 D_real: 0.258 D_fake: 1.006 +(epoch: 69, iters: 1456, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 0.954 G_ID: 0.443 G_Rec: 0.460 D_GP: 0.032 D_real: 1.243 D_fake: 0.476 +(epoch: 69, iters: 1856, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.688 G_ID: 0.254 G_Rec: 0.338 D_GP: 0.027 D_real: 1.227 D_fake: 0.596 +(epoch: 69, iters: 2256, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.965 G_ID: 0.410 G_Rec: 0.364 D_GP: 0.089 D_real: 0.951 D_fake: 0.497 +(epoch: 69, iters: 2656, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.941 G_ID: 0.224 G_Rec: 0.329 D_GP: 0.040 D_real: 0.758 D_fake: 0.750 +(epoch: 69, iters: 3056, time: 0.064) G_GAN: 0.674 G_GAN_Feat: 0.900 G_ID: 0.428 G_Rec: 0.396 D_GP: 0.062 D_real: 1.276 D_fake: 0.350 +(epoch: 69, iters: 3456, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.723 G_ID: 0.184 G_Rec: 0.357 D_GP: 0.030 D_real: 1.182 D_fake: 0.538 +(epoch: 69, iters: 3856, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.856 G_ID: 0.414 G_Rec: 0.371 D_GP: 0.039 D_real: 1.112 D_fake: 0.564 +(epoch: 69, iters: 4256, time: 0.064) G_GAN: 0.656 G_GAN_Feat: 0.977 G_ID: 0.194 G_Rec: 0.391 D_GP: 0.109 D_real: 0.874 D_fake: 0.408 +(epoch: 69, iters: 4656, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.699 G_ID: 0.414 G_Rec: 0.377 D_GP: 0.024 D_real: 1.355 D_fake: 0.501 +(epoch: 69, iters: 5056, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 1.043 G_ID: 0.211 G_Rec: 0.429 D_GP: 0.183 D_real: 0.415 D_fake: 0.710 +(epoch: 69, iters: 5456, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.922 G_ID: 0.417 G_Rec: 0.447 D_GP: 0.035 D_real: 1.123 D_fake: 0.666 +(epoch: 69, iters: 5856, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.727 G_ID: 0.237 G_Rec: 0.341 D_GP: 0.041 D_real: 1.188 D_fake: 0.546 +(epoch: 69, iters: 6256, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.845 G_ID: 0.397 G_Rec: 0.413 D_GP: 0.036 D_real: 1.168 D_fake: 0.533 +(epoch: 69, iters: 6656, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.928 G_ID: 0.201 G_Rec: 0.361 D_GP: 0.069 D_real: 0.507 D_fake: 0.913 +(epoch: 69, iters: 7056, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.775 G_ID: 0.420 G_Rec: 0.372 D_GP: 0.029 D_real: 1.042 D_fake: 0.694 +(epoch: 69, iters: 7456, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.761 G_ID: 0.262 G_Rec: 0.367 D_GP: 0.035 D_real: 0.827 D_fake: 0.869 +(epoch: 69, iters: 7856, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.783 G_ID: 0.419 G_Rec: 0.373 D_GP: 0.055 D_real: 0.809 D_fake: 0.799 +(epoch: 69, iters: 8256, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.783 G_ID: 0.221 G_Rec: 0.356 D_GP: 0.042 D_real: 1.201 D_fake: 0.518 +(epoch: 70, iters: 48, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.749 G_ID: 0.410 G_Rec: 0.385 D_GP: 0.026 D_real: 1.062 D_fake: 0.618 +(epoch: 70, iters: 448, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.785 G_ID: 0.223 G_Rec: 0.366 D_GP: 0.056 D_real: 1.189 D_fake: 0.521 +(epoch: 70, iters: 848, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 1.511 G_ID: 0.400 G_Rec: 0.431 D_GP: 0.226 D_real: 0.450 D_fake: 0.637 +(epoch: 70, iters: 1248, time: 0.064) G_GAN: 0.779 G_GAN_Feat: 0.901 G_ID: 0.190 G_Rec: 0.341 D_GP: 0.049 D_real: 1.324 D_fake: 0.238 +(epoch: 70, iters: 1648, time: 0.064) G_GAN: 1.037 G_GAN_Feat: 1.149 G_ID: 0.411 G_Rec: 0.371 D_GP: 0.054 D_real: 1.550 D_fake: 0.220 +(epoch: 70, iters: 2048, time: 0.064) G_GAN: 0.621 G_GAN_Feat: 0.724 G_ID: 0.213 G_Rec: 0.393 D_GP: 0.018 D_real: 1.536 D_fake: 0.391 +(epoch: 70, iters: 2448, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.642 G_ID: 0.441 G_Rec: 0.410 D_GP: 0.018 D_real: 1.140 D_fake: 0.659 +(epoch: 70, iters: 2848, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.615 G_ID: 0.205 G_Rec: 0.353 D_GP: 0.025 D_real: 1.155 D_fake: 0.700 +(epoch: 70, iters: 3248, time: 0.064) G_GAN: 0.029 G_GAN_Feat: 0.775 G_ID: 0.442 G_Rec: 0.395 D_GP: 0.042 D_real: 0.740 D_fake: 0.971 +(epoch: 70, iters: 3648, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.663 G_ID: 0.193 G_Rec: 0.364 D_GP: 0.032 D_real: 1.098 D_fake: 0.727 +(epoch: 70, iters: 4048, time: 0.064) G_GAN: 0.590 G_GAN_Feat: 0.818 G_ID: 0.374 G_Rec: 0.378 D_GP: 0.053 D_real: 1.209 D_fake: 0.425 +(epoch: 70, iters: 4448, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.763 G_ID: 0.189 G_Rec: 0.385 D_GP: 0.030 D_real: 1.174 D_fake: 0.642 +(epoch: 70, iters: 4848, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.822 G_ID: 0.377 G_Rec: 0.401 D_GP: 0.075 D_real: 0.827 D_fake: 0.741 +(epoch: 70, iters: 5248, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.771 G_ID: 0.217 G_Rec: 0.372 D_GP: 0.052 D_real: 1.094 D_fake: 0.545 +(epoch: 70, iters: 5648, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.883 G_ID: 0.375 G_Rec: 0.375 D_GP: 0.082 D_real: 0.878 D_fake: 0.635 +(epoch: 70, iters: 6048, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.823 G_ID: 0.254 G_Rec: 0.396 D_GP: 0.067 D_real: 0.911 D_fake: 0.736 +(epoch: 70, iters: 6448, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.919 G_ID: 0.379 G_Rec: 0.445 D_GP: 0.052 D_real: 0.972 D_fake: 0.507 +(epoch: 70, iters: 6848, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.821 G_ID: 0.210 G_Rec: 0.384 D_GP: 0.065 D_real: 0.895 D_fake: 0.653 +(epoch: 70, iters: 7248, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.904 G_ID: 0.385 G_Rec: 0.380 D_GP: 0.056 D_real: 0.832 D_fake: 0.726 +(epoch: 70, iters: 7648, time: 0.064) G_GAN: 0.509 G_GAN_Feat: 0.845 G_ID: 0.217 G_Rec: 0.351 D_GP: 0.051 D_real: 1.210 D_fake: 0.495 +(epoch: 70, iters: 8048, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.815 G_ID: 0.428 G_Rec: 0.353 D_GP: 0.029 D_real: 1.200 D_fake: 0.598 +(epoch: 70, iters: 8448, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.917 G_ID: 0.197 G_Rec: 0.371 D_GP: 0.088 D_real: 0.608 D_fake: 0.769 +(epoch: 71, iters: 240, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 1.406 G_ID: 0.420 G_Rec: 0.489 D_GP: 2.646 D_real: 0.458 D_fake: 0.953 +(epoch: 71, iters: 640, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.899 G_ID: 0.209 G_Rec: 0.423 D_GP: 0.027 D_real: 0.900 D_fake: 0.866 +(epoch: 71, iters: 1040, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.847 G_ID: 0.420 G_Rec: 0.363 D_GP: 0.036 D_real: 0.904 D_fake: 0.809 +(epoch: 71, iters: 1440, time: 0.064) G_GAN: -0.206 G_GAN_Feat: 0.897 G_ID: 0.238 G_Rec: 0.389 D_GP: 0.057 D_real: 0.656 D_fake: 1.206 +(epoch: 71, iters: 1840, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.988 G_ID: 0.387 G_Rec: 0.399 D_GP: 0.089 D_real: 0.713 D_fake: 0.837 +(epoch: 71, iters: 2240, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.834 G_ID: 0.232 G_Rec: 0.375 D_GP: 0.049 D_real: 1.041 D_fake: 0.650 +(epoch: 71, iters: 2640, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.802 G_ID: 0.350 G_Rec: 0.434 D_GP: 0.030 D_real: 1.247 D_fake: 0.563 +(epoch: 71, iters: 3040, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 1.129 G_ID: 0.221 G_Rec: 0.371 D_GP: 0.093 D_real: 0.774 D_fake: 0.711 +(epoch: 71, iters: 3440, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 0.848 G_ID: 0.382 G_Rec: 0.396 D_GP: 0.044 D_real: 1.176 D_fake: 0.547 +(epoch: 71, iters: 3840, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.987 G_ID: 0.255 G_Rec: 0.370 D_GP: 0.058 D_real: 0.705 D_fake: 0.886 +(epoch: 71, iters: 4240, time: 0.064) G_GAN: 0.664 G_GAN_Feat: 0.885 G_ID: 0.394 G_Rec: 0.365 D_GP: 0.041 D_real: 1.376 D_fake: 0.372 +(epoch: 71, iters: 4640, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.768 G_ID: 0.253 G_Rec: 0.369 D_GP: 0.032 D_real: 1.029 D_fake: 0.829 +(epoch: 71, iters: 5040, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.796 G_ID: 0.338 G_Rec: 0.391 D_GP: 0.038 D_real: 0.973 D_fake: 0.780 +(epoch: 71, iters: 5440, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.816 G_ID: 0.220 G_Rec: 0.368 D_GP: 0.057 D_real: 0.898 D_fake: 0.774 +(epoch: 71, iters: 5840, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.766 G_ID: 0.414 G_Rec: 0.370 D_GP: 0.034 D_real: 0.777 D_fake: 0.886 +(epoch: 71, iters: 6240, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.867 G_ID: 0.200 G_Rec: 0.414 D_GP: 0.058 D_real: 0.816 D_fake: 0.744 +(epoch: 71, iters: 6640, time: 0.064) G_GAN: -0.123 G_GAN_Feat: 0.943 G_ID: 0.424 G_Rec: 0.376 D_GP: 0.090 D_real: 0.694 D_fake: 1.123 +(epoch: 71, iters: 7040, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.922 G_ID: 0.195 G_Rec: 0.361 D_GP: 0.047 D_real: 0.738 D_fake: 0.764 +(epoch: 71, iters: 7440, time: 0.064) G_GAN: 0.624 G_GAN_Feat: 0.845 G_ID: 0.398 G_Rec: 0.392 D_GP: 0.035 D_real: 1.334 D_fake: 0.383 +(epoch: 71, iters: 7840, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 1.004 G_ID: 0.257 G_Rec: 0.371 D_GP: 0.037 D_real: 1.238 D_fake: 0.442 +(epoch: 71, iters: 8240, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.774 G_ID: 0.415 G_Rec: 0.466 D_GP: 0.028 D_real: 0.895 D_fake: 0.809 +(epoch: 72, iters: 32, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.939 G_ID: 0.209 G_Rec: 0.352 D_GP: 0.031 D_real: 1.029 D_fake: 0.555 +(epoch: 72, iters: 432, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.845 G_ID: 0.381 G_Rec: 0.433 D_GP: 0.032 D_real: 0.977 D_fake: 0.735 +(epoch: 72, iters: 832, time: 0.064) G_GAN: 0.804 G_GAN_Feat: 0.854 G_ID: 0.210 G_Rec: 0.388 D_GP: 0.052 D_real: 1.471 D_fake: 0.259 +(epoch: 72, iters: 1232, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 1.358 G_ID: 0.403 G_Rec: 0.410 D_GP: 0.230 D_real: 0.474 D_fake: 0.690 +(epoch: 72, iters: 1632, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 1.152 G_ID: 0.190 G_Rec: 0.368 D_GP: 0.065 D_real: 0.462 D_fake: 0.824 +(epoch: 72, iters: 2032, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.710 G_ID: 0.438 G_Rec: 0.371 D_GP: 0.029 D_real: 0.913 D_fake: 0.873 +(epoch: 72, iters: 2432, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.838 G_ID: 0.198 G_Rec: 0.395 D_GP: 0.058 D_real: 0.715 D_fake: 0.907 +(epoch: 72, iters: 2832, time: 0.064) G_GAN: -0.050 G_GAN_Feat: 0.804 G_ID: 0.337 G_Rec: 0.438 D_GP: 0.024 D_real: 0.869 D_fake: 1.050 +(epoch: 72, iters: 3232, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.701 G_ID: 0.176 G_Rec: 0.355 D_GP: 0.029 D_real: 0.938 D_fake: 0.831 +(epoch: 72, iters: 3632, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.800 G_ID: 0.391 G_Rec: 0.427 D_GP: 0.038 D_real: 0.872 D_fake: 0.801 +(epoch: 72, iters: 4032, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.805 G_ID: 0.217 G_Rec: 0.359 D_GP: 0.031 D_real: 1.127 D_fake: 0.591 +(epoch: 72, iters: 4432, time: 0.064) G_GAN: 0.700 G_GAN_Feat: 0.991 G_ID: 0.407 G_Rec: 0.468 D_GP: 0.079 D_real: 1.180 D_fake: 0.426 +(epoch: 72, iters: 4832, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.815 G_ID: 0.246 G_Rec: 0.384 D_GP: 0.038 D_real: 0.937 D_fake: 0.706 +(epoch: 72, iters: 5232, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 1.166 G_ID: 0.388 G_Rec: 0.406 D_GP: 0.162 D_real: 0.362 D_fake: 0.798 +(epoch: 72, iters: 5632, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.727 G_ID: 0.244 G_Rec: 0.345 D_GP: 0.033 D_real: 0.936 D_fake: 0.837 +(epoch: 72, iters: 6032, time: 0.064) G_GAN: 0.613 G_GAN_Feat: 0.716 G_ID: 0.384 G_Rec: 0.460 D_GP: 0.027 D_real: 1.412 D_fake: 0.395 +(epoch: 72, iters: 6432, time: 0.064) G_GAN: -0.041 G_GAN_Feat: 0.761 G_ID: 0.223 G_Rec: 0.400 D_GP: 0.034 D_real: 0.840 D_fake: 1.041 +(epoch: 72, iters: 6832, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.731 G_ID: 0.385 G_Rec: 0.382 D_GP: 0.029 D_real: 1.174 D_fake: 0.583 +(epoch: 72, iters: 7232, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.722 G_ID: 0.236 G_Rec: 0.353 D_GP: 0.028 D_real: 0.990 D_fake: 0.790 +(epoch: 72, iters: 7632, time: 0.064) G_GAN: -0.273 G_GAN_Feat: 0.991 G_ID: 0.382 G_Rec: 0.435 D_GP: 0.190 D_real: 0.310 D_fake: 1.273 +(epoch: 72, iters: 8032, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 1.227 G_ID: 0.208 G_Rec: 0.409 D_GP: 0.077 D_real: 0.358 D_fake: 0.923 +(epoch: 72, iters: 8432, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.984 G_ID: 0.421 G_Rec: 0.380 D_GP: 0.032 D_real: 0.886 D_fake: 0.688 +(epoch: 73, iters: 224, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.794 G_ID: 0.245 G_Rec: 0.348 D_GP: 0.049 D_real: 0.770 D_fake: 0.871 +(epoch: 73, iters: 624, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 1.083 G_ID: 0.387 G_Rec: 0.414 D_GP: 0.295 D_real: 0.543 D_fake: 0.511 +(epoch: 73, iters: 1024, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.874 G_ID: 0.213 G_Rec: 0.377 D_GP: 0.034 D_real: 0.881 D_fake: 0.663 +(epoch: 73, iters: 1424, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.848 G_ID: 0.396 G_Rec: 0.384 D_GP: 0.051 D_real: 0.928 D_fake: 0.800 +(epoch: 73, iters: 1824, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.676 G_ID: 0.226 G_Rec: 0.337 D_GP: 0.031 D_real: 1.214 D_fake: 0.606 +(epoch: 73, iters: 2224, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 1.215 G_ID: 0.393 G_Rec: 0.397 D_GP: 0.074 D_real: 0.512 D_fake: 0.539 +(epoch: 73, iters: 2624, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.934 G_ID: 0.203 G_Rec: 0.458 D_GP: 0.079 D_real: 0.698 D_fake: 0.691 +(epoch: 73, iters: 3024, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.914 G_ID: 0.440 G_Rec: 0.413 D_GP: 0.036 D_real: 0.928 D_fake: 0.684 +(epoch: 73, iters: 3424, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.880 G_ID: 0.256 G_Rec: 0.356 D_GP: 0.044 D_real: 1.068 D_fake: 0.470 +(epoch: 73, iters: 3824, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.857 G_ID: 0.422 G_Rec: 0.411 D_GP: 0.028 D_real: 1.028 D_fake: 0.623 +(epoch: 73, iters: 4224, time: 0.064) G_GAN: 0.583 G_GAN_Feat: 0.788 G_ID: 0.237 G_Rec: 0.397 D_GP: 0.038 D_real: 1.353 D_fake: 0.421 +(epoch: 73, iters: 4624, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.808 G_ID: 0.379 G_Rec: 0.406 D_GP: 0.026 D_real: 1.011 D_fake: 0.813 +(epoch: 73, iters: 5024, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.678 G_ID: 0.205 G_Rec: 0.397 D_GP: 0.022 D_real: 1.117 D_fake: 0.735 +(epoch: 73, iters: 5424, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.825 G_ID: 0.343 G_Rec: 0.410 D_GP: 0.030 D_real: 1.010 D_fake: 0.670 +(epoch: 73, iters: 5824, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.755 G_ID: 0.177 G_Rec: 0.359 D_GP: 0.048 D_real: 1.147 D_fake: 0.542 +(epoch: 73, iters: 6224, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.895 G_ID: 0.395 G_Rec: 0.373 D_GP: 0.029 D_real: 0.982 D_fake: 0.647 +(epoch: 73, iters: 6624, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.929 G_ID: 0.293 G_Rec: 0.376 D_GP: 0.067 D_real: 0.922 D_fake: 0.744 +(epoch: 73, iters: 7024, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 1.020 G_ID: 0.378 G_Rec: 0.371 D_GP: 0.043 D_real: 0.634 D_fake: 0.669 +(epoch: 73, iters: 7424, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.677 G_ID: 0.230 G_Rec: 0.349 D_GP: 0.026 D_real: 1.251 D_fake: 0.623 +(epoch: 73, iters: 7824, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.908 G_ID: 0.348 G_Rec: 0.410 D_GP: 0.030 D_real: 0.754 D_fake: 0.838 +(epoch: 73, iters: 8224, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.813 G_ID: 0.278 G_Rec: 0.359 D_GP: 0.032 D_real: 1.038 D_fake: 0.666 +(epoch: 74, iters: 16, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.755 G_ID: 0.385 G_Rec: 0.441 D_GP: 0.027 D_real: 1.197 D_fake: 0.574 +(epoch: 74, iters: 416, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.631 G_ID: 0.238 G_Rec: 0.348 D_GP: 0.024 D_real: 1.187 D_fake: 0.705 +(epoch: 74, iters: 816, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 1.044 G_ID: 0.391 G_Rec: 0.472 D_GP: 0.058 D_real: 1.045 D_fake: 0.548 +(epoch: 74, iters: 1216, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.723 G_ID: 0.206 G_Rec: 0.430 D_GP: 0.031 D_real: 1.097 D_fake: 0.671 +(epoch: 74, iters: 1616, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.865 G_ID: 0.378 G_Rec: 0.376 D_GP: 0.046 D_real: 1.098 D_fake: 0.516 +(epoch: 74, iters: 2016, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 0.879 G_ID: 0.221 G_Rec: 0.354 D_GP: 0.084 D_real: 1.005 D_fake: 0.530 +(epoch: 74, iters: 2416, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.896 G_ID: 0.431 G_Rec: 0.382 D_GP: 0.071 D_real: 0.906 D_fake: 0.613 +(epoch: 74, iters: 2816, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.675 G_ID: 0.185 G_Rec: 0.370 D_GP: 0.024 D_real: 1.202 D_fake: 0.669 +(epoch: 74, iters: 3216, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.784 G_ID: 0.356 G_Rec: 0.397 D_GP: 0.031 D_real: 0.980 D_fake: 0.708 +(epoch: 74, iters: 3616, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.979 G_ID: 0.292 G_Rec: 0.393 D_GP: 0.052 D_real: 1.045 D_fake: 0.735 +(epoch: 74, iters: 4016, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 1.154 G_ID: 0.353 G_Rec: 0.530 D_GP: 0.111 D_real: 0.533 D_fake: 0.634 +(epoch: 74, iters: 4416, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 1.098 G_ID: 0.220 G_Rec: 0.400 D_GP: 0.554 D_real: 0.497 D_fake: 0.788 +(epoch: 74, iters: 4816, time: 0.064) G_GAN: 0.016 G_GAN_Feat: 1.093 G_ID: 0.365 G_Rec: 0.413 D_GP: 0.102 D_real: 0.321 D_fake: 0.986 +(epoch: 74, iters: 5216, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 0.754 G_ID: 0.187 G_Rec: 0.375 D_GP: 0.022 D_real: 1.255 D_fake: 0.592 +(epoch: 74, iters: 5616, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 0.977 G_ID: 0.378 G_Rec: 0.412 D_GP: 0.079 D_real: 1.005 D_fake: 0.436 +(epoch: 74, iters: 6016, time: 0.064) G_GAN: 0.769 G_GAN_Feat: 1.065 G_ID: 0.218 G_Rec: 0.398 D_GP: 0.139 D_real: 0.731 D_fake: 0.330 +(epoch: 74, iters: 6416, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.969 G_ID: 0.374 G_Rec: 0.425 D_GP: 0.058 D_real: 0.500 D_fake: 0.905 +(epoch: 74, iters: 6816, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 1.009 G_ID: 0.202 G_Rec: 0.393 D_GP: 0.076 D_real: 0.504 D_fake: 0.705 +(epoch: 74, iters: 7216, time: 0.064) G_GAN: 0.709 G_GAN_Feat: 0.979 G_ID: 0.399 G_Rec: 0.396 D_GP: 0.052 D_real: 1.025 D_fake: 0.370 +(epoch: 74, iters: 7616, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.664 G_ID: 0.224 G_Rec: 0.365 D_GP: 0.020 D_real: 1.205 D_fake: 0.672 +(epoch: 74, iters: 8016, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.692 G_ID: 0.403 G_Rec: 0.379 D_GP: 0.023 D_real: 1.025 D_fake: 0.789 +(epoch: 74, iters: 8416, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.776 G_ID: 0.231 G_Rec: 0.399 D_GP: 0.059 D_real: 0.854 D_fake: 0.821 +(epoch: 75, iters: 208, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.762 G_ID: 0.359 G_Rec: 0.375 D_GP: 0.026 D_real: 1.185 D_fake: 0.618 +(epoch: 75, iters: 608, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 1.079 G_ID: 0.181 G_Rec: 0.403 D_GP: 0.467 D_real: 0.530 D_fake: 0.650 +(epoch: 75, iters: 1008, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 0.921 G_ID: 0.320 G_Rec: 0.437 D_GP: 0.091 D_real: 0.915 D_fake: 0.490 +(epoch: 75, iters: 1408, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.678 G_ID: 0.191 G_Rec: 0.366 D_GP: 0.028 D_real: 1.130 D_fake: 0.758 +(epoch: 75, iters: 1808, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.761 G_ID: 0.369 G_Rec: 0.337 D_GP: 0.034 D_real: 1.097 D_fake: 0.626 +(epoch: 75, iters: 2208, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 1.020 G_ID: 0.181 G_Rec: 0.385 D_GP: 0.091 D_real: 0.498 D_fake: 0.707 +(epoch: 75, iters: 2608, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.759 G_ID: 0.334 G_Rec: 0.379 D_GP: 0.025 D_real: 0.825 D_fake: 0.896 +(epoch: 75, iters: 3008, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 1.146 G_ID: 0.223 G_Rec: 0.421 D_GP: 0.149 D_real: 0.437 D_fake: 0.879 +(epoch: 75, iters: 3408, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.866 G_ID: 0.432 G_Rec: 0.415 D_GP: 0.039 D_real: 1.090 D_fake: 0.648 +(epoch: 75, iters: 3808, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.846 G_ID: 0.202 G_Rec: 0.373 D_GP: 0.036 D_real: 1.143 D_fake: 0.530 +(epoch: 75, iters: 4208, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.940 G_ID: 0.426 G_Rec: 0.386 D_GP: 0.093 D_real: 0.429 D_fake: 0.893 +(epoch: 75, iters: 4608, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.759 G_ID: 0.222 G_Rec: 0.357 D_GP: 0.033 D_real: 1.019 D_fake: 0.774 +(epoch: 75, iters: 5008, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.815 G_ID: 0.347 G_Rec: 0.408 D_GP: 0.034 D_real: 1.263 D_fake: 0.515 +(epoch: 75, iters: 5408, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.786 G_ID: 0.218 G_Rec: 0.334 D_GP: 0.044 D_real: 1.048 D_fake: 0.599 +(epoch: 75, iters: 5808, time: 0.064) G_GAN: 0.813 G_GAN_Feat: 0.948 G_ID: 0.318 G_Rec: 0.397 D_GP: 0.056 D_real: 1.335 D_fake: 0.232 +(epoch: 75, iters: 6208, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.648 G_ID: 0.205 G_Rec: 0.373 D_GP: 0.021 D_real: 1.229 D_fake: 0.702 +(epoch: 75, iters: 6608, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 1.146 G_ID: 0.400 G_Rec: 0.423 D_GP: 0.051 D_real: 0.837 D_fake: 0.672 +(epoch: 75, iters: 7008, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.960 G_ID: 0.200 G_Rec: 0.377 D_GP: 0.069 D_real: 1.132 D_fake: 0.663 +(epoch: 75, iters: 7408, time: 0.064) G_GAN: -0.194 G_GAN_Feat: 0.851 G_ID: 0.413 G_Rec: 0.397 D_GP: 0.060 D_real: 0.563 D_fake: 1.194 +(epoch: 75, iters: 7808, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.787 G_ID: 0.242 G_Rec: 0.370 D_GP: 0.070 D_real: 0.795 D_fake: 0.830 +(epoch: 75, iters: 8208, time: 0.064) G_GAN: 0.618 G_GAN_Feat: 0.741 G_ID: 0.357 G_Rec: 0.346 D_GP: 0.030 D_real: 1.364 D_fake: 0.391 +(epoch: 75, iters: 8608, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 1.012 G_ID: 0.193 G_Rec: 0.347 D_GP: 0.056 D_real: 0.624 D_fake: 0.712 +(epoch: 76, iters: 400, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.883 G_ID: 0.421 G_Rec: 0.428 D_GP: 0.029 D_real: 0.934 D_fake: 0.673 +(epoch: 76, iters: 800, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.688 G_ID: 0.197 G_Rec: 0.349 D_GP: 0.027 D_real: 0.939 D_fake: 0.869 +(epoch: 76, iters: 1200, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.946 G_ID: 0.350 G_Rec: 0.395 D_GP: 0.065 D_real: 0.823 D_fake: 0.735 +(epoch: 76, iters: 1600, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.936 G_ID: 0.206 G_Rec: 0.381 D_GP: 0.168 D_real: 0.722 D_fake: 0.865 +(epoch: 76, iters: 2000, time: 0.064) G_GAN: 0.651 G_GAN_Feat: 0.780 G_ID: 0.327 G_Rec: 0.351 D_GP: 0.057 D_real: 1.356 D_fake: 0.379 +(epoch: 76, iters: 2400, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 1.162 G_ID: 0.248 G_Rec: 0.368 D_GP: 0.173 D_real: 0.333 D_fake: 0.777 +(epoch: 76, iters: 2800, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.882 G_ID: 0.306 G_Rec: 0.398 D_GP: 0.030 D_real: 1.104 D_fake: 0.571 +(epoch: 76, iters: 3200, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.942 G_ID: 0.236 G_Rec: 0.377 D_GP: 0.087 D_real: 0.926 D_fake: 0.683 +(epoch: 76, iters: 3600, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.860 G_ID: 0.437 G_Rec: 0.421 D_GP: 0.038 D_real: 0.988 D_fake: 0.645 +(epoch: 76, iters: 4000, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 1.060 G_ID: 0.211 G_Rec: 0.392 D_GP: 0.404 D_real: 0.395 D_fake: 0.915 +(epoch: 76, iters: 4400, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.931 G_ID: 0.355 G_Rec: 0.388 D_GP: 0.045 D_real: 0.963 D_fake: 0.539 +(epoch: 76, iters: 4800, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.696 G_ID: 0.184 G_Rec: 0.346 D_GP: 0.025 D_real: 1.148 D_fake: 0.737 +(epoch: 76, iters: 5200, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.725 G_ID: 0.326 G_Rec: 0.397 D_GP: 0.022 D_real: 1.292 D_fake: 0.483 +(epoch: 76, iters: 5600, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.632 G_ID: 0.216 G_Rec: 0.350 D_GP: 0.025 D_real: 1.085 D_fake: 0.709 +(epoch: 76, iters: 6000, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 0.819 G_ID: 0.359 G_Rec: 0.450 D_GP: 0.041 D_real: 1.243 D_fake: 0.492 +(epoch: 76, iters: 6400, time: 0.064) G_GAN: 0.528 G_GAN_Feat: 0.746 G_ID: 0.218 G_Rec: 0.355 D_GP: 0.031 D_real: 1.321 D_fake: 0.479 +(epoch: 76, iters: 6800, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.906 G_ID: 0.354 G_Rec: 0.401 D_GP: 0.043 D_real: 1.089 D_fake: 0.612 +(epoch: 76, iters: 7200, time: 0.064) G_GAN: -0.272 G_GAN_Feat: 0.835 G_ID: 0.216 G_Rec: 0.396 D_GP: 0.066 D_real: 0.402 D_fake: 1.272 +(epoch: 76, iters: 7600, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.956 G_ID: 0.332 G_Rec: 0.384 D_GP: 0.032 D_real: 1.163 D_fake: 0.714 +(epoch: 76, iters: 8000, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.881 G_ID: 0.238 G_Rec: 0.437 D_GP: 0.034 D_real: 0.907 D_fake: 0.732 +(epoch: 76, iters: 8400, time: 0.064) G_GAN: 0.732 G_GAN_Feat: 0.868 G_ID: 0.333 G_Rec: 0.380 D_GP: 0.072 D_real: 1.407 D_fake: 0.309 +(epoch: 77, iters: 192, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.759 G_ID: 0.209 G_Rec: 0.362 D_GP: 0.034 D_real: 1.315 D_fake: 0.478 +(epoch: 77, iters: 592, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.940 G_ID: 0.314 G_Rec: 0.388 D_GP: 0.059 D_real: 0.652 D_fake: 0.762 +(epoch: 77, iters: 992, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.810 G_ID: 0.237 G_Rec: 0.367 D_GP: 0.064 D_real: 0.879 D_fake: 0.706 +(epoch: 77, iters: 1392, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.856 G_ID: 0.399 G_Rec: 0.384 D_GP: 0.045 D_real: 1.092 D_fake: 0.641 +(epoch: 77, iters: 1792, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.752 G_ID: 0.228 G_Rec: 0.343 D_GP: 0.033 D_real: 1.115 D_fake: 0.673 +(epoch: 77, iters: 2192, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.755 G_ID: 0.378 G_Rec: 0.383 D_GP: 0.026 D_real: 1.132 D_fake: 0.711 +(epoch: 77, iters: 2592, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.798 G_ID: 0.242 G_Rec: 0.406 D_GP: 0.044 D_real: 1.038 D_fake: 0.654 +(epoch: 77, iters: 2992, time: 0.064) G_GAN: 0.704 G_GAN_Feat: 0.891 G_ID: 0.345 G_Rec: 0.396 D_GP: 0.073 D_real: 1.208 D_fake: 0.314 +(epoch: 77, iters: 3392, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.771 G_ID: 0.228 G_Rec: 0.359 D_GP: 0.034 D_real: 0.974 D_fake: 0.758 +(epoch: 77, iters: 3792, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.996 G_ID: 0.330 G_Rec: 0.426 D_GP: 0.101 D_real: 0.266 D_fake: 0.921 +(epoch: 77, iters: 4192, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.766 G_ID: 0.187 G_Rec: 0.369 D_GP: 0.039 D_real: 1.047 D_fake: 0.715 +(epoch: 77, iters: 4592, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.770 G_ID: 0.369 G_Rec: 0.393 D_GP: 0.034 D_real: 1.112 D_fake: 0.618 +(epoch: 77, iters: 4992, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.887 G_ID: 0.202 G_Rec: 0.364 D_GP: 0.088 D_real: 0.668 D_fake: 0.744 +(epoch: 77, iters: 5392, time: 0.064) G_GAN: 0.635 G_GAN_Feat: 0.906 G_ID: 0.367 G_Rec: 0.380 D_GP: 0.054 D_real: 1.263 D_fake: 0.377 +(epoch: 77, iters: 5792, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 1.132 G_ID: 0.182 G_Rec: 0.420 D_GP: 0.354 D_real: 0.814 D_fake: 0.501 +(epoch: 77, iters: 6192, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 1.277 G_ID: 0.382 G_Rec: 0.421 D_GP: 0.127 D_real: 0.255 D_fake: 0.842 +(epoch: 77, iters: 6592, time: 0.064) G_GAN: -0.065 G_GAN_Feat: 0.975 G_ID: 0.226 G_Rec: 0.379 D_GP: 0.138 D_real: 0.461 D_fake: 1.065 +(epoch: 77, iters: 6992, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 1.517 G_ID: 0.382 G_Rec: 0.438 D_GP: 0.386 D_real: 0.553 D_fake: 0.595 +(epoch: 77, iters: 7392, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.857 G_ID: 0.215 G_Rec: 0.354 D_GP: 0.221 D_real: 0.865 D_fake: 0.684 +(epoch: 77, iters: 7792, time: 0.064) G_GAN: 0.731 G_GAN_Feat: 1.057 G_ID: 0.324 G_Rec: 0.432 D_GP: 0.128 D_real: 0.861 D_fake: 0.315 +(epoch: 77, iters: 8192, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.707 G_ID: 0.216 G_Rec: 0.359 D_GP: 0.031 D_real: 1.009 D_fake: 0.879 +(epoch: 77, iters: 8592, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.755 G_ID: 0.391 G_Rec: 0.375 D_GP: 0.026 D_real: 1.165 D_fake: 0.677 +(epoch: 78, iters: 384, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.875 G_ID: 0.198 G_Rec: 0.377 D_GP: 0.058 D_real: 0.864 D_fake: 0.729 +(epoch: 78, iters: 784, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.781 G_ID: 0.344 G_Rec: 0.427 D_GP: 0.027 D_real: 1.248 D_fake: 0.559 +(epoch: 78, iters: 1184, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.776 G_ID: 0.181 G_Rec: 0.351 D_GP: 0.034 D_real: 0.883 D_fake: 0.790 +(epoch: 78, iters: 1584, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.753 G_ID: 0.344 G_Rec: 0.386 D_GP: 0.024 D_real: 1.178 D_fake: 0.614 +(epoch: 78, iters: 1984, time: 0.064) G_GAN: 0.576 G_GAN_Feat: 0.873 G_ID: 0.267 G_Rec: 0.382 D_GP: 0.036 D_real: 1.166 D_fake: 0.474 +(epoch: 78, iters: 2384, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.898 G_ID: 0.358 G_Rec: 0.392 D_GP: 0.042 D_real: 1.012 D_fake: 0.513 +(epoch: 78, iters: 2784, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.749 G_ID: 0.258 G_Rec: 0.399 D_GP: 0.025 D_real: 1.260 D_fake: 0.578 +(epoch: 78, iters: 3184, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.841 G_ID: 0.368 G_Rec: 0.375 D_GP: 0.030 D_real: 1.053 D_fake: 0.721 +(epoch: 78, iters: 3584, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.928 G_ID: 0.193 G_Rec: 0.377 D_GP: 0.050 D_real: 0.797 D_fake: 0.649 +(epoch: 78, iters: 3984, time: 0.064) G_GAN: 0.811 G_GAN_Feat: 1.147 G_ID: 0.351 G_Rec: 0.398 D_GP: 0.181 D_real: 0.765 D_fake: 0.357 +(epoch: 78, iters: 4384, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 1.130 G_ID: 0.233 G_Rec: 0.396 D_GP: 0.074 D_real: 0.360 D_fake: 0.746 +(epoch: 78, iters: 4784, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.920 G_ID: 0.355 G_Rec: 0.386 D_GP: 0.076 D_real: 0.684 D_fake: 0.868 +(epoch: 78, iters: 5184, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.861 G_ID: 0.263 G_Rec: 0.393 D_GP: 0.025 D_real: 0.919 D_fake: 0.850 +(epoch: 78, iters: 5584, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 1.217 G_ID: 0.356 G_Rec: 0.470 D_GP: 0.043 D_real: 0.518 D_fake: 0.707 +(epoch: 78, iters: 5984, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.787 G_ID: 0.193 G_Rec: 0.388 D_GP: 0.023 D_real: 1.079 D_fake: 0.761 +(epoch: 78, iters: 6384, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.808 G_ID: 0.330 G_Rec: 0.364 D_GP: 0.031 D_real: 0.963 D_fake: 0.790 +(epoch: 78, iters: 6784, time: 0.064) G_GAN: 0.543 G_GAN_Feat: 0.734 G_ID: 0.203 G_Rec: 0.384 D_GP: 0.025 D_real: 1.372 D_fake: 0.458 +(epoch: 78, iters: 7184, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 0.904 G_ID: 0.331 G_Rec: 0.414 D_GP: 0.035 D_real: 1.223 D_fake: 0.406 +(epoch: 78, iters: 7584, time: 0.064) G_GAN: 0.529 G_GAN_Feat: 0.870 G_ID: 0.186 G_Rec: 0.379 D_GP: 0.080 D_real: 0.955 D_fake: 0.473 +(epoch: 78, iters: 7984, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.920 G_ID: 0.370 G_Rec: 0.383 D_GP: 0.045 D_real: 0.807 D_fake: 0.759 +(epoch: 78, iters: 8384, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.762 G_ID: 0.213 G_Rec: 0.369 D_GP: 0.047 D_real: 1.046 D_fake: 0.662 +(epoch: 79, iters: 176, time: 0.064) G_GAN: 0.165 G_GAN_Feat: 0.807 G_ID: 0.425 G_Rec: 0.398 D_GP: 0.026 D_real: 0.884 D_fake: 0.835 +(epoch: 79, iters: 576, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.917 G_ID: 0.203 G_Rec: 0.349 D_GP: 0.092 D_real: 0.559 D_fake: 0.799 +(epoch: 79, iters: 976, time: 0.064) G_GAN: 0.897 G_GAN_Feat: 1.038 G_ID: 0.329 G_Rec: 0.406 D_GP: 0.050 D_real: 1.438 D_fake: 0.187 +(epoch: 79, iters: 1376, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 1.265 G_ID: 0.223 G_Rec: 0.422 D_GP: 0.061 D_real: 0.404 D_fake: 0.626 +(epoch: 79, iters: 1776, time: 0.064) G_GAN: 0.791 G_GAN_Feat: 0.783 G_ID: 0.360 G_Rec: 0.394 D_GP: 0.026 D_real: 1.563 D_fake: 0.282 +(epoch: 79, iters: 2176, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 0.802 G_ID: 0.178 G_Rec: 0.365 D_GP: 0.043 D_real: 1.214 D_fake: 0.440 +(epoch: 79, iters: 2576, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.943 G_ID: 0.348 G_Rec: 0.427 D_GP: 0.055 D_real: 0.545 D_fake: 0.882 +(epoch: 79, iters: 2976, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.917 G_ID: 0.226 G_Rec: 0.387 D_GP: 0.042 D_real: 1.105 D_fake: 0.687 +(epoch: 79, iters: 3376, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.996 G_ID: 0.395 G_Rec: 0.400 D_GP: 0.079 D_real: 0.571 D_fake: 0.614 +(epoch: 79, iters: 3776, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.893 G_ID: 0.235 G_Rec: 0.395 D_GP: 0.149 D_real: 0.779 D_fake: 0.773 +(epoch: 79, iters: 4176, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.826 G_ID: 0.320 G_Rec: 0.483 D_GP: 0.032 D_real: 1.133 D_fake: 0.532 +(epoch: 79, iters: 4576, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.993 G_ID: 0.202 G_Rec: 0.346 D_GP: 0.040 D_real: 0.811 D_fake: 0.614 +(epoch: 79, iters: 4976, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.823 G_ID: 0.399 G_Rec: 0.385 D_GP: 0.039 D_real: 1.029 D_fake: 0.726 +(epoch: 79, iters: 5376, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.776 G_ID: 0.173 G_Rec: 0.388 D_GP: 0.041 D_real: 0.983 D_fake: 0.696 +(epoch: 79, iters: 5776, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 1.278 G_ID: 0.344 G_Rec: 0.431 D_GP: 0.292 D_real: 0.436 D_fake: 0.718 +(epoch: 79, iters: 6176, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.761 G_ID: 0.192 G_Rec: 0.390 D_GP: 0.021 D_real: 1.275 D_fake: 0.643 +(epoch: 79, iters: 6576, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.836 G_ID: 0.302 G_Rec: 0.391 D_GP: 0.050 D_real: 1.157 D_fake: 0.571 +(epoch: 79, iters: 6976, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.801 G_ID: 0.196 G_Rec: 0.356 D_GP: 0.045 D_real: 1.122 D_fake: 0.588 +(epoch: 79, iters: 7376, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.837 G_ID: 0.401 G_Rec: 0.445 D_GP: 0.028 D_real: 1.049 D_fake: 0.612 +(epoch: 79, iters: 7776, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.884 G_ID: 0.185 G_Rec: 0.432 D_GP: 0.044 D_real: 1.072 D_fake: 0.592 +(epoch: 79, iters: 8176, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.838 G_ID: 0.349 G_Rec: 0.366 D_GP: 0.027 D_real: 1.058 D_fake: 0.598 +(epoch: 79, iters: 8576, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 1.235 G_ID: 0.187 G_Rec: 0.384 D_GP: 0.275 D_real: 0.308 D_fake: 0.730 +(epoch: 80, iters: 368, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.801 G_ID: 0.323 G_Rec: 0.381 D_GP: 0.032 D_real: 1.183 D_fake: 0.593 +(epoch: 80, iters: 768, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.635 G_ID: 0.193 G_Rec: 0.363 D_GP: 0.020 D_real: 1.346 D_fake: 0.535 +(epoch: 80, iters: 1168, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.895 G_ID: 0.369 G_Rec: 0.502 D_GP: 0.051 D_real: 0.938 D_fake: 0.729 +(epoch: 80, iters: 1568, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.854 G_ID: 0.209 G_Rec: 0.432 D_GP: 0.090 D_real: 0.724 D_fake: 0.809 +(epoch: 80, iters: 1968, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.813 G_ID: 0.299 G_Rec: 0.390 D_GP: 0.034 D_real: 1.304 D_fake: 0.514 +(epoch: 80, iters: 2368, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.647 G_ID: 0.197 G_Rec: 0.393 D_GP: 0.022 D_real: 1.167 D_fake: 0.644 +(epoch: 80, iters: 2768, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.739 G_ID: 0.340 G_Rec: 0.385 D_GP: 0.028 D_real: 1.156 D_fake: 0.640 +(epoch: 80, iters: 3168, time: 0.064) G_GAN: -0.269 G_GAN_Feat: 0.643 G_ID: 0.207 G_Rec: 0.326 D_GP: 0.024 D_real: 0.858 D_fake: 1.269 +(epoch: 80, iters: 3568, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 1.044 G_ID: 0.329 G_Rec: 0.394 D_GP: 0.052 D_real: 0.696 D_fake: 0.598 +(epoch: 80, iters: 3968, time: 0.064) G_GAN: 0.139 G_GAN_Feat: 0.720 G_ID: 0.186 G_Rec: 0.418 D_GP: 0.040 D_real: 0.937 D_fake: 0.861 +(epoch: 80, iters: 4368, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.805 G_ID: 0.377 G_Rec: 0.425 D_GP: 0.048 D_real: 0.775 D_fake: 0.869 +(epoch: 80, iters: 4768, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.633 G_ID: 0.220 G_Rec: 0.337 D_GP: 0.031 D_real: 1.347 D_fake: 0.533 +(epoch: 80, iters: 5168, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.873 G_ID: 0.371 G_Rec: 0.390 D_GP: 0.036 D_real: 1.042 D_fake: 0.593 +(epoch: 80, iters: 5568, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.897 G_ID: 0.210 G_Rec: 0.413 D_GP: 0.057 D_real: 0.844 D_fake: 0.620 +(epoch: 80, iters: 5968, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.876 G_ID: 0.361 G_Rec: 0.425 D_GP: 0.062 D_real: 0.790 D_fake: 0.796 +(epoch: 80, iters: 6368, time: 0.064) G_GAN: 0.600 G_GAN_Feat: 1.043 G_ID: 0.184 G_Rec: 0.427 D_GP: 0.223 D_real: 0.755 D_fake: 0.523 +(epoch: 80, iters: 6768, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 0.896 G_ID: 0.340 G_Rec: 0.376 D_GP: 0.041 D_real: 1.125 D_fake: 0.510 +(epoch: 80, iters: 7168, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.821 G_ID: 0.187 G_Rec: 0.373 D_GP: 0.040 D_real: 1.035 D_fake: 0.664 +(epoch: 80, iters: 7568, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.828 G_ID: 0.336 G_Rec: 0.410 D_GP: 0.040 D_real: 1.091 D_fake: 0.670 +(epoch: 80, iters: 7968, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.987 G_ID: 0.234 G_Rec: 0.378 D_GP: 0.121 D_real: 0.379 D_fake: 0.824 +(epoch: 80, iters: 8368, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.887 G_ID: 0.372 G_Rec: 0.418 D_GP: 0.049 D_real: 1.072 D_fake: 0.552 +(epoch: 81, iters: 160, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.688 G_ID: 0.199 G_Rec: 0.329 D_GP: 0.034 D_real: 1.137 D_fake: 0.785 +(epoch: 81, iters: 560, time: 0.064) G_GAN: 0.554 G_GAN_Feat: 0.718 G_ID: 0.375 G_Rec: 0.404 D_GP: 0.024 D_real: 1.415 D_fake: 0.450 +(epoch: 81, iters: 960, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.705 G_ID: 0.227 G_Rec: 0.356 D_GP: 0.022 D_real: 1.109 D_fake: 0.797 +(epoch: 81, iters: 1360, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.723 G_ID: 0.305 G_Rec: 0.376 D_GP: 0.027 D_real: 1.293 D_fake: 0.477 +(epoch: 81, iters: 1760, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.695 G_ID: 0.240 G_Rec: 0.361 D_GP: 0.028 D_real: 1.142 D_fake: 0.686 +(epoch: 81, iters: 2160, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.726 G_ID: 0.351 G_Rec: 0.353 D_GP: 0.032 D_real: 1.195 D_fake: 0.591 +(epoch: 81, iters: 2560, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.757 G_ID: 0.219 G_Rec: 0.366 D_GP: 0.032 D_real: 1.234 D_fake: 0.559 +(epoch: 81, iters: 2960, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.992 G_ID: 0.365 G_Rec: 0.422 D_GP: 0.239 D_real: 0.549 D_fake: 0.865 +(epoch: 81, iters: 3360, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.794 G_ID: 0.250 G_Rec: 0.361 D_GP: 0.036 D_real: 1.077 D_fake: 0.698 +(epoch: 81, iters: 3760, time: 0.064) G_GAN: 0.688 G_GAN_Feat: 0.969 G_ID: 0.324 G_Rec: 0.413 D_GP: 0.129 D_real: 1.074 D_fake: 0.325 +(epoch: 81, iters: 4160, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.817 G_ID: 0.190 G_Rec: 0.460 D_GP: 0.046 D_real: 1.080 D_fake: 0.725 +(epoch: 81, iters: 4560, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 0.730 G_ID: 0.335 G_Rec: 0.378 D_GP: 0.023 D_real: 1.372 D_fake: 0.476 +(epoch: 81, iters: 4960, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.696 G_ID: 0.170 G_Rec: 0.325 D_GP: 0.034 D_real: 1.218 D_fake: 0.545 +(epoch: 81, iters: 5360, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.983 G_ID: 0.385 G_Rec: 0.400 D_GP: 0.057 D_real: 0.769 D_fake: 0.725 +(epoch: 81, iters: 5760, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 1.241 G_ID: 0.215 G_Rec: 0.408 D_GP: 0.472 D_real: 0.242 D_fake: 0.781 +(epoch: 81, iters: 6160, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 1.043 G_ID: 0.320 G_Rec: 0.426 D_GP: 0.116 D_real: 0.358 D_fake: 0.732 +(epoch: 81, iters: 6560, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.990 G_ID: 0.191 G_Rec: 0.432 D_GP: 0.116 D_real: 1.011 D_fake: 0.542 +(epoch: 81, iters: 6960, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.904 G_ID: 0.355 G_Rec: 0.390 D_GP: 0.036 D_real: 0.835 D_fake: 0.776 +(epoch: 81, iters: 7360, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.856 G_ID: 0.178 G_Rec: 0.405 D_GP: 0.071 D_real: 0.865 D_fake: 0.727 +(epoch: 81, iters: 7760, time: 0.064) G_GAN: 0.535 G_GAN_Feat: 0.900 G_ID: 0.357 G_Rec: 0.377 D_GP: 0.053 D_real: 1.033 D_fake: 0.473 +(epoch: 81, iters: 8160, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.743 G_ID: 0.185 G_Rec: 0.376 D_GP: 0.035 D_real: 1.251 D_fake: 0.571 +(epoch: 81, iters: 8560, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.861 G_ID: 0.339 G_Rec: 0.464 D_GP: 0.035 D_real: 1.311 D_fake: 0.543 +(epoch: 82, iters: 352, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.759 G_ID: 0.193 G_Rec: 0.394 D_GP: 0.025 D_real: 0.985 D_fake: 0.798 +(epoch: 82, iters: 752, time: 0.064) G_GAN: 0.606 G_GAN_Feat: 0.797 G_ID: 0.322 G_Rec: 0.413 D_GP: 0.024 D_real: 1.345 D_fake: 0.433 +(epoch: 82, iters: 1152, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.779 G_ID: 0.174 G_Rec: 0.346 D_GP: 0.037 D_real: 1.090 D_fake: 0.724 +(epoch: 82, iters: 1552, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 1.059 G_ID: 0.287 G_Rec: 0.421 D_GP: 0.047 D_real: 1.010 D_fake: 0.575 +(epoch: 82, iters: 1952, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.990 G_ID: 0.188 G_Rec: 0.387 D_GP: 0.089 D_real: 1.004 D_fake: 0.688 +(epoch: 82, iters: 2352, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.908 G_ID: 0.312 G_Rec: 0.406 D_GP: 0.066 D_real: 0.721 D_fake: 0.804 +(epoch: 82, iters: 2752, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.988 G_ID: 0.191 G_Rec: 0.385 D_GP: 0.197 D_real: 0.825 D_fake: 0.517 +(epoch: 82, iters: 3152, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.823 G_ID: 0.312 G_Rec: 0.373 D_GP: 0.024 D_real: 1.267 D_fake: 0.476 +(epoch: 82, iters: 3552, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.718 G_ID: 0.226 G_Rec: 0.383 D_GP: 0.033 D_real: 1.061 D_fake: 0.787 +(epoch: 82, iters: 3952, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.750 G_ID: 0.368 G_Rec: 0.369 D_GP: 0.030 D_real: 1.159 D_fake: 0.599 +(epoch: 82, iters: 4352, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.767 G_ID: 0.229 G_Rec: 0.340 D_GP: 0.034 D_real: 1.233 D_fake: 0.659 +(epoch: 82, iters: 4752, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.801 G_ID: 0.330 G_Rec: 0.340 D_GP: 0.029 D_real: 1.286 D_fake: 0.459 +(epoch: 82, iters: 5152, time: 0.064) G_GAN: 0.571 G_GAN_Feat: 0.768 G_ID: 0.184 G_Rec: 0.351 D_GP: 0.026 D_real: 1.474 D_fake: 0.484 +(epoch: 82, iters: 5552, time: 0.064) G_GAN: 0.576 G_GAN_Feat: 0.833 G_ID: 0.294 G_Rec: 0.393 D_GP: 0.027 D_real: 1.209 D_fake: 0.439 +(epoch: 82, iters: 5952, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.806 G_ID: 0.163 G_Rec: 0.397 D_GP: 0.062 D_real: 1.115 D_fake: 0.643 +(epoch: 82, iters: 6352, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.923 G_ID: 0.378 G_Rec: 0.401 D_GP: 0.045 D_real: 0.638 D_fake: 0.839 +(epoch: 82, iters: 6752, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.877 G_ID: 0.183 G_Rec: 0.356 D_GP: 0.054 D_real: 0.901 D_fake: 0.618 +(epoch: 82, iters: 7152, time: 0.064) G_GAN: 0.894 G_GAN_Feat: 0.715 G_ID: 0.314 G_Rec: 0.393 D_GP: 0.024 D_real: 1.620 D_fake: 0.162 +(epoch: 82, iters: 7552, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.643 G_ID: 0.178 G_Rec: 0.346 D_GP: 0.025 D_real: 1.233 D_fake: 0.669 +(epoch: 82, iters: 7952, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 1.045 G_ID: 0.366 G_Rec: 0.469 D_GP: 0.063 D_real: 0.591 D_fake: 0.714 +(epoch: 82, iters: 8352, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.813 G_ID: 0.214 G_Rec: 0.392 D_GP: 0.062 D_real: 0.795 D_fake: 0.927 +(epoch: 83, iters: 144, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 1.152 G_ID: 0.375 G_Rec: 0.430 D_GP: 0.269 D_real: 0.503 D_fake: 0.589 +(epoch: 83, iters: 544, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.770 G_ID: 0.173 G_Rec: 0.331 D_GP: 0.048 D_real: 1.188 D_fake: 0.510 +(epoch: 83, iters: 944, time: 0.064) G_GAN: -0.063 G_GAN_Feat: 0.809 G_ID: 0.299 G_Rec: 0.441 D_GP: 0.030 D_real: 0.769 D_fake: 1.063 +(epoch: 83, iters: 1344, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.812 G_ID: 0.203 G_Rec: 0.385 D_GP: 0.045 D_real: 0.997 D_fake: 0.670 +(epoch: 83, iters: 1744, time: 0.064) G_GAN: 0.027 G_GAN_Feat: 0.847 G_ID: 0.350 G_Rec: 0.387 D_GP: 0.081 D_real: 0.571 D_fake: 0.973 +(epoch: 83, iters: 2144, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.913 G_ID: 0.209 G_Rec: 0.337 D_GP: 0.047 D_real: 0.703 D_fake: 0.680 +(epoch: 83, iters: 2544, time: 0.064) G_GAN: 0.693 G_GAN_Feat: 0.719 G_ID: 0.352 G_Rec: 0.370 D_GP: 0.026 D_real: 1.497 D_fake: 0.319 +(epoch: 83, iters: 2944, time: 0.064) G_GAN: 0.712 G_GAN_Feat: 0.878 G_ID: 0.220 G_Rec: 0.367 D_GP: 0.055 D_real: 1.270 D_fake: 0.333 +(epoch: 83, iters: 3344, time: 0.064) G_GAN: -0.064 G_GAN_Feat: 0.887 G_ID: 0.434 G_Rec: 0.406 D_GP: 0.072 D_real: 0.570 D_fake: 1.064 +(epoch: 83, iters: 3744, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 1.000 G_ID: 0.228 G_Rec: 0.405 D_GP: 0.067 D_real: 0.636 D_fake: 0.611 +(epoch: 83, iters: 4144, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 1.060 G_ID: 0.324 G_Rec: 0.411 D_GP: 0.127 D_real: 0.464 D_fake: 0.627 +(epoch: 83, iters: 4544, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.780 G_ID: 0.208 G_Rec: 0.350 D_GP: 0.035 D_real: 1.025 D_fake: 0.724 +(epoch: 83, iters: 4944, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 1.089 G_ID: 0.334 G_Rec: 0.413 D_GP: 0.098 D_real: 0.593 D_fake: 0.474 +(epoch: 83, iters: 5344, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.754 G_ID: 0.211 G_Rec: 0.429 D_GP: 0.023 D_real: 0.980 D_fake: 0.906 +(epoch: 83, iters: 5744, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.739 G_ID: 0.307 G_Rec: 0.453 D_GP: 0.024 D_real: 1.177 D_fake: 0.535 +(epoch: 83, iters: 6144, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.697 G_ID: 0.172 G_Rec: 0.356 D_GP: 0.027 D_real: 1.133 D_fake: 0.689 +(epoch: 83, iters: 6544, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.845 G_ID: 0.354 G_Rec: 0.404 D_GP: 0.069 D_real: 0.805 D_fake: 0.836 +(epoch: 83, iters: 6944, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.826 G_ID: 0.157 G_Rec: 0.357 D_GP: 0.037 D_real: 0.988 D_fake: 0.675 +(epoch: 83, iters: 7344, time: 0.064) G_GAN: 0.985 G_GAN_Feat: 1.213 G_ID: 0.345 G_Rec: 0.466 D_GP: 0.405 D_real: 0.853 D_fake: 0.135 +(epoch: 83, iters: 7744, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.885 G_ID: 0.249 G_Rec: 0.424 D_GP: 0.062 D_real: 0.694 D_fake: 0.800 +(epoch: 83, iters: 8144, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.817 G_ID: 0.302 G_Rec: 0.378 D_GP: 0.034 D_real: 1.215 D_fake: 0.554 +(epoch: 83, iters: 8544, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.731 G_ID: 0.193 G_Rec: 0.375 D_GP: 0.033 D_real: 1.272 D_fake: 0.557 +(epoch: 84, iters: 336, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 1.004 G_ID: 0.354 G_Rec: 0.400 D_GP: 0.063 D_real: 0.651 D_fake: 0.720 +(epoch: 84, iters: 736, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.944 G_ID: 0.184 G_Rec: 0.366 D_GP: 0.035 D_real: 1.038 D_fake: 0.635 +(epoch: 84, iters: 1136, time: 0.064) G_GAN: -0.063 G_GAN_Feat: 0.832 G_ID: 0.363 G_Rec: 0.417 D_GP: 0.037 D_real: 0.663 D_fake: 1.065 +(epoch: 84, iters: 1536, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.790 G_ID: 0.222 G_Rec: 0.379 D_GP: 0.039 D_real: 1.100 D_fake: 0.757 +(epoch: 84, iters: 1936, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.795 G_ID: 0.313 G_Rec: 0.397 D_GP: 0.026 D_real: 1.131 D_fake: 0.639 +(epoch: 84, iters: 2336, time: 0.064) G_GAN: -0.136 G_GAN_Feat: 0.881 G_ID: 0.235 G_Rec: 0.407 D_GP: 0.064 D_real: 0.615 D_fake: 1.136 +(epoch: 84, iters: 2736, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.957 G_ID: 0.353 G_Rec: 0.402 D_GP: 0.068 D_real: 1.199 D_fake: 0.420 +(epoch: 84, iters: 3136, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.979 G_ID: 0.187 G_Rec: 0.425 D_GP: 0.026 D_real: 0.669 D_fake: 0.890 +(epoch: 84, iters: 3536, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.791 G_ID: 0.364 G_Rec: 0.374 D_GP: 0.031 D_real: 1.034 D_fake: 0.753 +(epoch: 84, iters: 3936, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.867 G_ID: 0.209 G_Rec: 0.383 D_GP: 0.036 D_real: 1.138 D_fake: 0.626 +(epoch: 84, iters: 4336, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 1.132 G_ID: 0.302 G_Rec: 0.428 D_GP: 0.095 D_real: 0.463 D_fake: 0.547 +(epoch: 84, iters: 4736, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.867 G_ID: 0.181 G_Rec: 0.410 D_GP: 0.035 D_real: 0.892 D_fake: 0.697 +(epoch: 84, iters: 5136, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.995 G_ID: 0.350 G_Rec: 0.436 D_GP: 0.148 D_real: 0.682 D_fake: 0.746 +(epoch: 84, iters: 5536, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.830 G_ID: 0.193 G_Rec: 0.362 D_GP: 0.066 D_real: 0.748 D_fake: 0.908 +(epoch: 84, iters: 5936, time: 0.064) G_GAN: 0.585 G_GAN_Feat: 0.961 G_ID: 0.321 G_Rec: 0.483 D_GP: 0.054 D_real: 0.973 D_fake: 0.430 +(epoch: 84, iters: 6336, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.867 G_ID: 0.178 G_Rec: 0.374 D_GP: 0.033 D_real: 0.668 D_fake: 0.910 +(epoch: 84, iters: 6736, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.915 G_ID: 0.348 G_Rec: 0.367 D_GP: 0.035 D_real: 1.052 D_fake: 0.605 +(epoch: 84, iters: 7136, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.751 G_ID: 0.193 G_Rec: 0.363 D_GP: 0.023 D_real: 1.251 D_fake: 0.620 +(epoch: 84, iters: 7536, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 1.016 G_ID: 0.353 G_Rec: 0.412 D_GP: 0.046 D_real: 1.069 D_fake: 0.528 +(epoch: 84, iters: 7936, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.819 G_ID: 0.184 G_Rec: 0.357 D_GP: 0.075 D_real: 0.856 D_fake: 0.800 +(epoch: 84, iters: 8336, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.998 G_ID: 0.351 G_Rec: 0.434 D_GP: 0.066 D_real: 0.736 D_fake: 0.584 +(epoch: 85, iters: 128, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 1.168 G_ID: 0.223 G_Rec: 0.360 D_GP: 0.102 D_real: 0.305 D_fake: 0.795 +(epoch: 85, iters: 528, time: 0.064) G_GAN: -0.478 G_GAN_Feat: 1.075 G_ID: 0.339 G_Rec: 0.469 D_GP: 0.258 D_real: 0.179 D_fake: 1.478 +(epoch: 85, iters: 928, time: 0.064) G_GAN: 0.537 G_GAN_Feat: 0.758 G_ID: 0.168 G_Rec: 0.348 D_GP: 0.030 D_real: 1.364 D_fake: 0.465 +(epoch: 85, iters: 1328, time: 0.064) G_GAN: 0.555 G_GAN_Feat: 0.812 G_ID: 0.319 G_Rec: 0.400 D_GP: 0.029 D_real: 1.233 D_fake: 0.448 +(epoch: 85, iters: 1728, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.707 G_ID: 0.208 G_Rec: 0.391 D_GP: 0.024 D_real: 1.416 D_fake: 0.485 +(epoch: 85, iters: 2128, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.978 G_ID: 0.347 G_Rec: 0.404 D_GP: 0.080 D_real: 0.624 D_fake: 0.726 +(epoch: 85, iters: 2528, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.894 G_ID: 0.208 G_Rec: 0.387 D_GP: 0.072 D_real: 1.047 D_fake: 0.509 +(epoch: 85, iters: 2928, time: 0.064) G_GAN: 0.778 G_GAN_Feat: 0.980 G_ID: 0.277 G_Rec: 0.442 D_GP: 0.044 D_real: 1.342 D_fake: 0.300 +(epoch: 85, iters: 3328, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.611 G_ID: 0.208 G_Rec: 0.390 D_GP: 0.022 D_real: 0.976 D_fake: 0.823 +(epoch: 85, iters: 3728, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.719 G_ID: 0.299 G_Rec: 0.398 D_GP: 0.020 D_real: 1.121 D_fake: 0.610 +(epoch: 85, iters: 4128, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.636 G_ID: 0.170 G_Rec: 0.345 D_GP: 0.023 D_real: 1.262 D_fake: 0.579 +(epoch: 85, iters: 4528, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.966 G_ID: 0.278 G_Rec: 0.526 D_GP: 0.216 D_real: 0.903 D_fake: 0.584 +(epoch: 85, iters: 4928, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.753 G_ID: 0.191 G_Rec: 0.339 D_GP: 0.036 D_real: 1.026 D_fake: 0.749 +(epoch: 85, iters: 5328, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.795 G_ID: 0.357 G_Rec: 0.366 D_GP: 0.029 D_real: 1.131 D_fake: 0.619 +(epoch: 85, iters: 5728, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.696 G_ID: 0.163 G_Rec: 0.362 D_GP: 0.024 D_real: 1.287 D_fake: 0.515 +(epoch: 85, iters: 6128, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 1.178 G_ID: 0.321 G_Rec: 0.432 D_GP: 0.477 D_real: 0.587 D_fake: 0.513 +(epoch: 85, iters: 6528, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.757 G_ID: 0.188 G_Rec: 0.351 D_GP: 0.035 D_real: 1.221 D_fake: 0.568 +(epoch: 85, iters: 6928, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 1.059 G_ID: 0.331 G_Rec: 0.426 D_GP: 0.052 D_real: 0.546 D_fake: 0.737 +(epoch: 85, iters: 7328, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.810 G_ID: 0.211 G_Rec: 0.396 D_GP: 0.024 D_real: 1.079 D_fake: 0.691 +(epoch: 85, iters: 7728, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 0.816 G_ID: 0.323 G_Rec: 0.368 D_GP: 0.036 D_real: 1.283 D_fake: 0.391 +(epoch: 85, iters: 8128, time: 0.064) G_GAN: 0.017 G_GAN_Feat: 1.081 G_ID: 0.228 G_Rec: 0.348 D_GP: 0.048 D_real: 0.363 D_fake: 0.983 +(epoch: 85, iters: 8528, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.823 G_ID: 0.336 G_Rec: 0.421 D_GP: 0.023 D_real: 1.232 D_fake: 0.605 +(epoch: 86, iters: 320, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 0.733 G_ID: 0.202 G_Rec: 0.343 D_GP: 0.034 D_real: 0.890 D_fake: 0.992 +(epoch: 86, iters: 720, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.879 G_ID: 0.371 G_Rec: 0.405 D_GP: 0.026 D_real: 1.201 D_fake: 0.487 +(epoch: 86, iters: 1120, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.897 G_ID: 0.212 G_Rec: 0.359 D_GP: 0.065 D_real: 0.800 D_fake: 0.662 +(epoch: 86, iters: 1520, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.785 G_ID: 0.304 G_Rec: 0.363 D_GP: 0.033 D_real: 1.313 D_fake: 0.396 +(epoch: 86, iters: 1920, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.897 G_ID: 0.209 G_Rec: 0.368 D_GP: 0.053 D_real: 0.786 D_fake: 0.753 +(epoch: 86, iters: 2320, time: 0.064) G_GAN: 0.548 G_GAN_Feat: 0.868 G_ID: 0.341 G_Rec: 0.394 D_GP: 0.038 D_real: 1.209 D_fake: 0.457 +(epoch: 86, iters: 2720, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.684 G_ID: 0.205 G_Rec: 0.392 D_GP: 0.023 D_real: 1.226 D_fake: 0.673 +(epoch: 86, iters: 3120, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 1.041 G_ID: 0.313 G_Rec: 0.443 D_GP: 0.074 D_real: 0.910 D_fake: 0.427 +(epoch: 86, iters: 3520, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.822 G_ID: 0.187 G_Rec: 0.379 D_GP: 0.035 D_real: 0.922 D_fake: 0.826 +(epoch: 86, iters: 3920, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 1.131 G_ID: 0.356 G_Rec: 0.387 D_GP: 0.064 D_real: 0.395 D_fake: 0.751 +(epoch: 86, iters: 4320, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.712 G_ID: 0.193 G_Rec: 0.400 D_GP: 0.024 D_real: 1.262 D_fake: 0.626 +(epoch: 86, iters: 4720, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.848 G_ID: 0.285 G_Rec: 0.428 D_GP: 0.034 D_real: 1.042 D_fake: 0.593 +(epoch: 86, iters: 5120, time: 0.064) G_GAN: -0.022 G_GAN_Feat: 1.048 G_ID: 0.223 G_Rec: 0.423 D_GP: 0.131 D_real: 0.169 D_fake: 1.022 +(epoch: 86, iters: 5520, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.900 G_ID: 0.370 G_Rec: 0.399 D_GP: 0.035 D_real: 0.896 D_fake: 0.708 +(epoch: 86, iters: 5920, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.774 G_ID: 0.195 G_Rec: 0.340 D_GP: 0.039 D_real: 1.017 D_fake: 0.634 +(epoch: 86, iters: 6320, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 0.842 G_ID: 0.325 G_Rec: 0.438 D_GP: 0.032 D_real: 1.288 D_fake: 0.386 +(epoch: 86, iters: 6720, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.739 G_ID: 0.231 G_Rec: 0.353 D_GP: 0.037 D_real: 1.285 D_fake: 0.533 +(epoch: 86, iters: 7120, time: 0.064) G_GAN: 0.690 G_GAN_Feat: 0.859 G_ID: 0.332 G_Rec: 0.421 D_GP: 0.036 D_real: 1.312 D_fake: 0.322 +(epoch: 86, iters: 7520, time: 0.064) G_GAN: 0.563 G_GAN_Feat: 0.831 G_ID: 0.200 G_Rec: 0.391 D_GP: 0.027 D_real: 1.204 D_fake: 0.448 +(epoch: 86, iters: 7920, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 1.238 G_ID: 0.312 G_Rec: 0.415 D_GP: 0.060 D_real: 0.450 D_fake: 0.698 +(epoch: 86, iters: 8320, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 1.133 G_ID: 0.172 G_Rec: 0.361 D_GP: 0.097 D_real: 0.426 D_fake: 0.580 +(epoch: 87, iters: 112, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.761 G_ID: 0.341 G_Rec: 0.399 D_GP: 0.025 D_real: 1.151 D_fake: 0.704 +(epoch: 87, iters: 512, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.679 G_ID: 0.175 G_Rec: 0.357 D_GP: 0.024 D_real: 1.027 D_fake: 0.839 +(epoch: 87, iters: 912, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 1.003 G_ID: 0.309 G_Rec: 0.466 D_GP: 0.065 D_real: 0.597 D_fake: 0.714 +(epoch: 87, iters: 1312, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 0.754 G_ID: 0.182 G_Rec: 0.370 D_GP: 0.025 D_real: 1.379 D_fake: 0.533 +(epoch: 87, iters: 1712, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.769 G_ID: 0.305 G_Rec: 0.362 D_GP: 0.034 D_real: 1.095 D_fake: 0.693 +(epoch: 87, iters: 2112, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.659 G_ID: 0.200 G_Rec: 0.345 D_GP: 0.027 D_real: 1.029 D_fake: 0.811 +(epoch: 87, iters: 2512, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.842 G_ID: 0.304 G_Rec: 0.359 D_GP: 0.067 D_real: 0.935 D_fake: 0.632 +(epoch: 87, iters: 2912, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.840 G_ID: 0.180 G_Rec: 0.360 D_GP: 0.038 D_real: 1.094 D_fake: 0.709 +(epoch: 87, iters: 3312, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.900 G_ID: 0.311 G_Rec: 0.383 D_GP: 0.084 D_real: 0.983 D_fake: 0.524 +(epoch: 87, iters: 3712, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.627 G_ID: 0.260 G_Rec: 0.342 D_GP: 0.023 D_real: 1.091 D_fake: 0.790 +(epoch: 87, iters: 4112, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.674 G_ID: 0.344 G_Rec: 0.362 D_GP: 0.025 D_real: 1.299 D_fake: 0.582 +(epoch: 87, iters: 4512, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.739 G_ID: 0.182 G_Rec: 0.353 D_GP: 0.065 D_real: 0.909 D_fake: 0.849 +(epoch: 87, iters: 4912, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.705 G_ID: 0.331 G_Rec: 0.436 D_GP: 0.023 D_real: 1.163 D_fake: 0.621 +(epoch: 87, iters: 5312, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.702 G_ID: 0.186 G_Rec: 0.365 D_GP: 0.035 D_real: 1.254 D_fake: 0.580 +(epoch: 87, iters: 5712, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.875 G_ID: 0.298 G_Rec: 0.397 D_GP: 0.043 D_real: 1.123 D_fake: 0.575 +(epoch: 87, iters: 6112, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 0.681 G_ID: 0.226 G_Rec: 0.376 D_GP: 0.026 D_real: 0.954 D_fake: 0.928 +(epoch: 87, iters: 6512, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 1.263 G_ID: 0.306 G_Rec: 0.456 D_GP: 0.148 D_real: 0.517 D_fake: 0.405 +(epoch: 87, iters: 6912, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 1.052 G_ID: 0.212 G_Rec: 0.381 D_GP: 0.183 D_real: 0.255 D_fake: 0.907 +(epoch: 87, iters: 7312, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.941 G_ID: 0.294 G_Rec: 0.415 D_GP: 0.071 D_real: 0.667 D_fake: 0.674 +(epoch: 87, iters: 7712, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.624 G_ID: 0.181 G_Rec: 0.342 D_GP: 0.022 D_real: 1.476 D_fake: 0.484 +(epoch: 87, iters: 8112, time: 0.064) G_GAN: 0.613 G_GAN_Feat: 0.960 G_ID: 0.290 G_Rec: 0.367 D_GP: 0.060 D_real: 0.984 D_fake: 0.391 +(epoch: 87, iters: 8512, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.729 G_ID: 0.202 G_Rec: 0.395 D_GP: 0.026 D_real: 1.178 D_fake: 0.660 +(epoch: 88, iters: 304, time: 0.064) G_GAN: -0.161 G_GAN_Feat: 0.940 G_ID: 0.320 G_Rec: 0.423 D_GP: 0.077 D_real: 0.316 D_fake: 1.161 +(epoch: 88, iters: 704, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.772 G_ID: 0.197 G_Rec: 0.360 D_GP: 0.024 D_real: 1.047 D_fake: 0.760 +(epoch: 88, iters: 1104, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.804 G_ID: 0.327 G_Rec: 0.464 D_GP: 0.042 D_real: 1.063 D_fake: 0.600 +(epoch: 88, iters: 1504, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.828 G_ID: 0.193 G_Rec: 0.367 D_GP: 0.056 D_real: 1.043 D_fake: 0.586 +(epoch: 88, iters: 1904, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.729 G_ID: 0.294 G_Rec: 0.396 D_GP: 0.026 D_real: 1.077 D_fake: 0.717 +(epoch: 88, iters: 2304, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.780 G_ID: 0.192 G_Rec: 0.443 D_GP: 0.061 D_real: 0.949 D_fake: 0.715 +(epoch: 88, iters: 2704, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.918 G_ID: 0.319 G_Rec: 0.404 D_GP: 0.027 D_real: 0.773 D_fake: 0.758 +(epoch: 88, iters: 3104, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.725 G_ID: 0.188 G_Rec: 0.380 D_GP: 0.043 D_real: 1.006 D_fake: 0.827 +(epoch: 88, iters: 3504, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 1.234 G_ID: 0.309 G_Rec: 0.389 D_GP: 0.079 D_real: 0.308 D_fake: 0.731 +(epoch: 88, iters: 3904, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.992 G_ID: 0.199 G_Rec: 0.400 D_GP: 0.114 D_real: 0.378 D_fake: 0.870 +(epoch: 88, iters: 4304, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.868 G_ID: 0.350 G_Rec: 0.416 D_GP: 0.031 D_real: 1.062 D_fake: 0.594 +(epoch: 88, iters: 4704, time: 0.064) G_GAN: 0.687 G_GAN_Feat: 1.040 G_ID: 0.160 G_Rec: 0.412 D_GP: 0.115 D_real: 1.227 D_fake: 0.473 +(epoch: 88, iters: 5104, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.894 G_ID: 0.303 G_Rec: 0.440 D_GP: 0.045 D_real: 0.848 D_fake: 0.662 +(epoch: 88, iters: 5504, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.691 G_ID: 0.204 G_Rec: 0.347 D_GP: 0.032 D_real: 0.848 D_fake: 0.964 +(epoch: 88, iters: 5904, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.818 G_ID: 0.280 G_Rec: 0.395 D_GP: 0.028 D_real: 1.246 D_fake: 0.552 +(epoch: 88, iters: 6304, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.877 G_ID: 0.228 G_Rec: 0.407 D_GP: 0.062 D_real: 1.074 D_fake: 0.533 +(epoch: 88, iters: 6704, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.873 G_ID: 0.352 G_Rec: 0.371 D_GP: 0.070 D_real: 0.735 D_fake: 0.721 +(epoch: 88, iters: 7104, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.928 G_ID: 0.189 G_Rec: 0.359 D_GP: 0.060 D_real: 0.573 D_fake: 0.699 +(epoch: 88, iters: 7504, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.953 G_ID: 0.310 G_Rec: 0.388 D_GP: 0.057 D_real: 0.881 D_fake: 0.486 +(epoch: 88, iters: 7904, time: 0.064) G_GAN: 0.650 G_GAN_Feat: 0.891 G_ID: 0.203 G_Rec: 0.359 D_GP: 0.087 D_real: 1.188 D_fake: 0.390 +(epoch: 88, iters: 8304, time: 0.064) G_GAN: 0.632 G_GAN_Feat: 1.012 G_ID: 0.328 G_Rec: 0.411 D_GP: 0.111 D_real: 0.964 D_fake: 0.433 +(epoch: 89, iters: 96, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.810 G_ID: 0.187 G_Rec: 0.371 D_GP: 0.045 D_real: 1.204 D_fake: 0.622 +(epoch: 89, iters: 496, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.796 G_ID: 0.316 G_Rec: 0.367 D_GP: 0.032 D_real: 1.121 D_fake: 0.773 +(epoch: 89, iters: 896, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.744 G_ID: 0.168 G_Rec: 0.353 D_GP: 0.026 D_real: 0.894 D_fake: 0.926 +(epoch: 89, iters: 1296, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 1.238 G_ID: 0.317 G_Rec: 0.483 D_GP: 0.948 D_real: 0.587 D_fake: 0.836 +(epoch: 89, iters: 1696, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.724 G_ID: 0.201 G_Rec: 0.407 D_GP: 0.026 D_real: 0.959 D_fake: 0.863 +(epoch: 89, iters: 2096, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.950 G_ID: 0.314 G_Rec: 0.405 D_GP: 0.074 D_real: 0.876 D_fake: 0.576 +(epoch: 89, iters: 2496, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.696 G_ID: 0.177 G_Rec: 0.328 D_GP: 0.029 D_real: 1.195 D_fake: 0.708 +(epoch: 89, iters: 2896, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.887 G_ID: 0.346 G_Rec: 0.472 D_GP: 0.039 D_real: 1.030 D_fake: 0.686 +(epoch: 89, iters: 3296, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.977 G_ID: 0.194 G_Rec: 0.366 D_GP: 0.041 D_real: 0.510 D_fake: 0.920 +(epoch: 89, iters: 3696, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.816 G_ID: 0.325 G_Rec: 0.375 D_GP: 0.030 D_real: 1.151 D_fake: 0.594 +(epoch: 89, iters: 4096, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.768 G_ID: 0.163 G_Rec: 0.394 D_GP: 0.023 D_real: 1.132 D_fake: 0.684 +(epoch: 89, iters: 4496, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.802 G_ID: 0.287 G_Rec: 0.403 D_GP: 0.028 D_real: 1.096 D_fake: 0.662 +(epoch: 89, iters: 4896, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.723 G_ID: 0.189 G_Rec: 0.381 D_GP: 0.028 D_real: 1.138 D_fake: 0.651 +(epoch: 89, iters: 5296, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 0.879 G_ID: 0.337 G_Rec: 0.371 D_GP: 0.030 D_real: 1.226 D_fake: 0.437 +(epoch: 89, iters: 5696, time: 0.064) G_GAN: -0.010 G_GAN_Feat: 0.669 G_ID: 0.241 G_Rec: 0.376 D_GP: 0.025 D_real: 0.948 D_fake: 1.011 +(epoch: 89, iters: 6096, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.741 G_ID: 0.300 G_Rec: 0.402 D_GP: 0.025 D_real: 1.225 D_fake: 0.584 +(epoch: 89, iters: 6496, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.836 G_ID: 0.188 G_Rec: 0.357 D_GP: 0.069 D_real: 0.883 D_fake: 0.713 +(epoch: 89, iters: 6896, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.954 G_ID: 0.307 G_Rec: 0.420 D_GP: 0.069 D_real: 0.887 D_fake: 0.506 +(epoch: 89, iters: 7296, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.787 G_ID: 0.158 G_Rec: 0.341 D_GP: 0.034 D_real: 0.843 D_fake: 0.853 +(epoch: 89, iters: 7696, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.952 G_ID: 0.284 G_Rec: 0.429 D_GP: 0.098 D_real: 1.136 D_fake: 0.488 +(epoch: 89, iters: 8096, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 1.019 G_ID: 0.204 G_Rec: 0.386 D_GP: 0.100 D_real: 1.039 D_fake: 0.830 +(epoch: 89, iters: 8496, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.919 G_ID: 0.299 G_Rec: 0.436 D_GP: 0.050 D_real: 0.799 D_fake: 0.833 +(epoch: 90, iters: 288, time: 0.064) G_GAN: 0.827 G_GAN_Feat: 1.069 G_ID: 0.218 G_Rec: 0.399 D_GP: 0.236 D_real: 1.116 D_fake: 0.605 +(epoch: 90, iters: 688, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.726 G_ID: 0.310 G_Rec: 0.347 D_GP: 0.028 D_real: 1.209 D_fake: 0.504 +(epoch: 90, iters: 1088, time: 0.064) G_GAN: 0.543 G_GAN_Feat: 0.681 G_ID: 0.159 G_Rec: 0.380 D_GP: 0.026 D_real: 1.376 D_fake: 0.459 +(epoch: 90, iters: 1488, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.991 G_ID: 0.336 G_Rec: 0.389 D_GP: 0.111 D_real: 0.585 D_fake: 0.660 +(epoch: 90, iters: 1888, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 1.040 G_ID: 0.193 G_Rec: 0.382 D_GP: 0.114 D_real: 0.553 D_fake: 0.627 +(epoch: 90, iters: 2288, time: 0.064) G_GAN: 0.475 G_GAN_Feat: 1.033 G_ID: 0.309 G_Rec: 0.420 D_GP: 0.070 D_real: 0.833 D_fake: 0.533 +(epoch: 90, iters: 2688, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 1.022 G_ID: 0.179 G_Rec: 0.366 D_GP: 0.103 D_real: 0.557 D_fake: 0.718 +(epoch: 90, iters: 3088, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.829 G_ID: 0.338 G_Rec: 0.383 D_GP: 0.033 D_real: 1.057 D_fake: 0.592 +(epoch: 90, iters: 3488, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.769 G_ID: 0.177 G_Rec: 0.386 D_GP: 0.027 D_real: 1.186 D_fake: 0.582 +(epoch: 90, iters: 3888, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.864 G_ID: 0.296 G_Rec: 0.419 D_GP: 0.043 D_real: 1.161 D_fake: 0.521 +(epoch: 90, iters: 4288, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.731 G_ID: 0.214 G_Rec: 0.356 D_GP: 0.029 D_real: 1.043 D_fake: 0.767 +(epoch: 90, iters: 4688, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 1.026 G_ID: 0.274 G_Rec: 0.442 D_GP: 0.138 D_real: 0.567 D_fake: 0.611 +(epoch: 90, iters: 5088, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.780 G_ID: 0.198 G_Rec: 0.359 D_GP: 0.042 D_real: 1.122 D_fake: 0.658 +(epoch: 90, iters: 5488, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.819 G_ID: 0.304 G_Rec: 0.378 D_GP: 0.030 D_real: 0.989 D_fake: 0.749 +(epoch: 90, iters: 5888, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 1.083 G_ID: 0.218 G_Rec: 0.430 D_GP: 0.183 D_real: 0.231 D_fake: 0.898 +(epoch: 90, iters: 6288, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 0.905 G_ID: 0.311 G_Rec: 0.437 D_GP: 0.044 D_real: 1.145 D_fake: 0.638 +(epoch: 90, iters: 6688, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 1.139 G_ID: 0.176 G_Rec: 0.376 D_GP: 0.716 D_real: 0.492 D_fake: 0.936 +(epoch: 90, iters: 7088, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.922 G_ID: 0.301 G_Rec: 0.416 D_GP: 0.026 D_real: 1.109 D_fake: 0.591 +(epoch: 90, iters: 7488, time: 0.064) G_GAN: 0.555 G_GAN_Feat: 0.943 G_ID: 0.191 G_Rec: 0.350 D_GP: 0.102 D_real: 1.166 D_fake: 0.462 +(epoch: 90, iters: 7888, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.806 G_ID: 0.303 G_Rec: 0.392 D_GP: 0.028 D_real: 1.219 D_fake: 0.496 +(epoch: 90, iters: 8288, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.995 G_ID: 0.199 G_Rec: 0.383 D_GP: 0.044 D_real: 0.506 D_fake: 0.866 +(epoch: 91, iters: 80, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.869 G_ID: 0.295 G_Rec: 0.421 D_GP: 0.024 D_real: 0.955 D_fake: 0.731 +(epoch: 91, iters: 480, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.800 G_ID: 0.175 G_Rec: 0.376 D_GP: 0.038 D_real: 0.877 D_fake: 0.850 +(epoch: 91, iters: 880, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.891 G_ID: 0.305 G_Rec: 0.361 D_GP: 0.040 D_real: 1.033 D_fake: 0.554 +(epoch: 91, iters: 1280, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.934 G_ID: 0.212 G_Rec: 0.376 D_GP: 0.118 D_real: 0.639 D_fake: 0.852 +(epoch: 91, iters: 1680, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 1.260 G_ID: 0.281 G_Rec: 0.414 D_GP: 0.204 D_real: 0.514 D_fake: 0.472 +(epoch: 91, iters: 2080, time: 0.064) G_GAN: 0.133 G_GAN_Feat: 0.717 G_ID: 0.192 G_Rec: 0.342 D_GP: 0.027 D_real: 1.013 D_fake: 0.867 +(epoch: 91, iters: 2480, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.849 G_ID: 0.300 G_Rec: 0.392 D_GP: 0.029 D_real: 1.187 D_fake: 0.476 +(epoch: 91, iters: 2880, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.831 G_ID: 0.177 G_Rec: 0.361 D_GP: 0.039 D_real: 1.188 D_fake: 0.631 +(epoch: 91, iters: 3280, time: 0.064) G_GAN: 0.644 G_GAN_Feat: 0.865 G_ID: 0.317 G_Rec: 0.464 D_GP: 0.023 D_real: 1.450 D_fake: 0.363 +(epoch: 91, iters: 3680, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.830 G_ID: 0.175 G_Rec: 0.427 D_GP: 0.050 D_real: 1.129 D_fake: 0.579 +(epoch: 91, iters: 4080, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.800 G_ID: 0.299 G_Rec: 0.381 D_GP: 0.025 D_real: 1.069 D_fake: 0.653 +(epoch: 91, iters: 4480, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.800 G_ID: 0.187 G_Rec: 0.375 D_GP: 0.035 D_real: 1.097 D_fake: 0.593 +(epoch: 91, iters: 4880, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 1.155 G_ID: 0.344 G_Rec: 0.381 D_GP: 0.069 D_real: 0.504 D_fake: 0.485 +(epoch: 91, iters: 5280, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.706 G_ID: 0.204 G_Rec: 0.446 D_GP: 0.023 D_real: 1.194 D_fake: 0.678 +(epoch: 91, iters: 5680, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.959 G_ID: 0.283 G_Rec: 0.384 D_GP: 0.040 D_real: 0.904 D_fake: 0.550 +(epoch: 91, iters: 6080, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 1.003 G_ID: 0.197 G_Rec: 0.421 D_GP: 0.040 D_real: 1.167 D_fake: 0.656 +(epoch: 91, iters: 6480, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 1.039 G_ID: 0.325 G_Rec: 0.398 D_GP: 0.079 D_real: 0.840 D_fake: 0.478 +(epoch: 91, iters: 6880, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.770 G_ID: 0.158 G_Rec: 0.363 D_GP: 0.024 D_real: 1.252 D_fake: 0.530 +(epoch: 91, iters: 7280, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 1.223 G_ID: 0.311 G_Rec: 0.419 D_GP: 0.066 D_real: 0.554 D_fake: 0.548 +(epoch: 91, iters: 7680, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 1.147 G_ID: 0.177 G_Rec: 0.434 D_GP: 0.385 D_real: 0.432 D_fake: 0.805 +(epoch: 91, iters: 8080, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.811 G_ID: 0.334 G_Rec: 0.386 D_GP: 0.037 D_real: 0.806 D_fake: 0.926 +(epoch: 91, iters: 8480, time: 0.064) G_GAN: 0.035 G_GAN_Feat: 0.751 G_ID: 0.192 G_Rec: 0.363 D_GP: 0.027 D_real: 0.872 D_fake: 0.965 +(epoch: 92, iters: 272, time: 0.064) G_GAN: 0.664 G_GAN_Feat: 0.931 G_ID: 0.308 G_Rec: 0.387 D_GP: 0.042 D_real: 1.213 D_fake: 0.348 +(epoch: 92, iters: 672, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.654 G_ID: 0.199 G_Rec: 0.360 D_GP: 0.023 D_real: 1.361 D_fake: 0.617 +(epoch: 92, iters: 1072, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.900 G_ID: 0.333 G_Rec: 0.375 D_GP: 0.037 D_real: 1.035 D_fake: 0.720 +(epoch: 92, iters: 1472, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.668 G_ID: 0.245 G_Rec: 0.345 D_GP: 0.024 D_real: 1.284 D_fake: 0.602 +(epoch: 92, iters: 1872, time: 0.064) G_GAN: 0.487 G_GAN_Feat: 0.740 G_ID: 0.311 G_Rec: 0.387 D_GP: 0.025 D_real: 1.233 D_fake: 0.514 +(epoch: 92, iters: 2272, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.870 G_ID: 0.194 G_Rec: 0.361 D_GP: 0.060 D_real: 0.840 D_fake: 0.690 +(epoch: 92, iters: 2672, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 1.151 G_ID: 0.278 G_Rec: 0.399 D_GP: 0.281 D_real: 0.467 D_fake: 0.448 +(epoch: 92, iters: 3072, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 0.850 G_ID: 0.230 G_Rec: 0.415 D_GP: 0.046 D_real: 1.235 D_fake: 0.496 +(epoch: 92, iters: 3472, time: 0.064) G_GAN: 0.761 G_GAN_Feat: 1.052 G_ID: 0.303 G_Rec: 0.437 D_GP: 0.169 D_real: 1.014 D_fake: 0.338 +(epoch: 92, iters: 3872, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.585 G_ID: 0.165 G_Rec: 0.414 D_GP: 0.016 D_real: 1.260 D_fake: 0.654 +(epoch: 92, iters: 4272, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.683 G_ID: 0.288 G_Rec: 0.416 D_GP: 0.018 D_real: 1.316 D_fake: 0.592 +(epoch: 92, iters: 4672, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.737 G_ID: 0.173 G_Rec: 0.406 D_GP: 0.023 D_real: 1.151 D_fake: 0.709 +(epoch: 92, iters: 5072, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.766 G_ID: 0.292 G_Rec: 0.419 D_GP: 0.027 D_real: 0.906 D_fake: 0.823 +(epoch: 92, iters: 5472, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.716 G_ID: 0.193 G_Rec: 0.344 D_GP: 0.040 D_real: 1.026 D_fake: 0.719 +(epoch: 92, iters: 5872, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.826 G_ID: 0.302 G_Rec: 0.377 D_GP: 0.054 D_real: 0.852 D_fake: 0.763 +(epoch: 92, iters: 6272, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.914 G_ID: 0.170 G_Rec: 0.404 D_GP: 0.075 D_real: 0.739 D_fake: 0.704 +(epoch: 92, iters: 6672, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.873 G_ID: 0.313 G_Rec: 0.387 D_GP: 0.043 D_real: 1.039 D_fake: 0.693 +(epoch: 92, iters: 7072, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.722 G_ID: 0.202 G_Rec: 0.367 D_GP: 0.029 D_real: 1.293 D_fake: 0.541 +(epoch: 92, iters: 7472, time: 0.064) G_GAN: 0.614 G_GAN_Feat: 0.937 G_ID: 0.309 G_Rec: 0.415 D_GP: 0.086 D_real: 0.985 D_fake: 0.395 +(epoch: 92, iters: 7872, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.710 G_ID: 0.172 G_Rec: 0.391 D_GP: 0.025 D_real: 1.147 D_fake: 0.696 +(epoch: 92, iters: 8272, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.787 G_ID: 0.303 G_Rec: 0.400 D_GP: 0.033 D_real: 0.955 D_fake: 0.774 +(epoch: 93, iters: 64, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.836 G_ID: 0.182 G_Rec: 0.396 D_GP: 0.108 D_real: 0.759 D_fake: 0.837 +(epoch: 93, iters: 464, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.854 G_ID: 0.333 G_Rec: 0.405 D_GP: 0.023 D_real: 1.024 D_fake: 0.691 +(epoch: 93, iters: 864, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.863 G_ID: 0.220 G_Rec: 0.397 D_GP: 0.049 D_real: 0.974 D_fake: 0.655 +(epoch: 93, iters: 1264, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.971 G_ID: 0.319 G_Rec: 0.387 D_GP: 0.207 D_real: 0.612 D_fake: 0.821 +(epoch: 93, iters: 1664, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.706 G_ID: 0.205 G_Rec: 0.333 D_GP: 0.033 D_real: 1.022 D_fake: 0.795 +(epoch: 93, iters: 2064, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.753 G_ID: 0.338 G_Rec: 0.428 D_GP: 0.025 D_real: 0.879 D_fake: 0.901 +(epoch: 93, iters: 2464, time: 0.064) G_GAN: 0.031 G_GAN_Feat: 0.666 G_ID: 0.154 G_Rec: 0.360 D_GP: 0.025 D_real: 0.872 D_fake: 0.969 +(epoch: 93, iters: 2864, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.853 G_ID: 0.309 G_Rec: 0.378 D_GP: 0.047 D_real: 1.020 D_fake: 0.631 +(epoch: 93, iters: 3264, time: 0.064) G_GAN: 0.000 G_GAN_Feat: 0.748 G_ID: 0.155 G_Rec: 0.370 D_GP: 0.053 D_real: 0.699 D_fake: 1.000 +(epoch: 93, iters: 3664, time: 0.064) G_GAN: 0.802 G_GAN_Feat: 0.808 G_ID: 0.305 G_Rec: 0.357 D_GP: 0.032 D_real: 1.417 D_fake: 0.232 +(epoch: 93, iters: 4064, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.798 G_ID: 0.188 G_Rec: 0.339 D_GP: 0.032 D_real: 1.269 D_fake: 0.622 +(epoch: 93, iters: 4464, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.936 G_ID: 0.248 G_Rec: 0.376 D_GP: 0.032 D_real: 0.734 D_fake: 0.906 +(epoch: 93, iters: 4864, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.846 G_ID: 0.199 G_Rec: 0.398 D_GP: 0.058 D_real: 1.208 D_fake: 0.579 +(epoch: 93, iters: 5264, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 1.089 G_ID: 0.268 G_Rec: 0.414 D_GP: 0.083 D_real: 0.365 D_fake: 0.670 +(epoch: 93, iters: 5664, time: 0.064) G_GAN: -0.014 G_GAN_Feat: 0.766 G_ID: 0.194 G_Rec: 0.446 D_GP: 0.025 D_real: 0.779 D_fake: 1.014 +(epoch: 93, iters: 6064, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.839 G_ID: 0.294 G_Rec: 0.368 D_GP: 0.036 D_real: 0.707 D_fake: 0.959 +(epoch: 93, iters: 6464, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 1.039 G_ID: 0.174 G_Rec: 0.393 D_GP: 0.205 D_real: 0.538 D_fake: 0.625 +(epoch: 93, iters: 6864, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.975 G_ID: 0.297 G_Rec: 0.445 D_GP: 0.049 D_real: 0.690 D_fake: 0.784 +(epoch: 93, iters: 7264, time: 0.064) G_GAN: 0.664 G_GAN_Feat: 0.872 G_ID: 0.183 G_Rec: 0.342 D_GP: 0.063 D_real: 1.233 D_fake: 0.496 +(epoch: 93, iters: 7664, time: 0.064) G_GAN: 0.685 G_GAN_Feat: 1.040 G_ID: 0.312 G_Rec: 0.446 D_GP: 0.112 D_real: 1.056 D_fake: 0.388 +(epoch: 93, iters: 8064, time: 0.064) G_GAN: 0.856 G_GAN_Feat: 0.879 G_ID: 0.210 G_Rec: 0.381 D_GP: 0.064 D_real: 1.354 D_fake: 0.403 +(epoch: 93, iters: 8464, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 1.010 G_ID: 0.278 G_Rec: 0.413 D_GP: 0.035 D_real: 0.783 D_fake: 0.704 +(epoch: 94, iters: 256, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.619 G_ID: 0.221 G_Rec: 0.408 D_GP: 0.020 D_real: 1.334 D_fake: 0.551 +(epoch: 94, iters: 656, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 0.762 G_ID: 0.302 G_Rec: 0.398 D_GP: 0.027 D_real: 1.233 D_fake: 0.514 +(epoch: 94, iters: 1056, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.969 G_ID: 0.200 G_Rec: 0.378 D_GP: 0.070 D_real: 0.793 D_fake: 0.670 +(epoch: 94, iters: 1456, time: 0.064) G_GAN: 0.674 G_GAN_Feat: 0.747 G_ID: 0.270 G_Rec: 0.411 D_GP: 0.026 D_real: 1.398 D_fake: 0.345 +(epoch: 94, iters: 1856, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.712 G_ID: 0.165 G_Rec: 0.364 D_GP: 0.033 D_real: 1.375 D_fake: 0.475 +(epoch: 94, iters: 2256, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.728 G_ID: 0.305 G_Rec: 0.347 D_GP: 0.029 D_real: 1.250 D_fake: 0.569 +(epoch: 94, iters: 2656, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.665 G_ID: 0.197 G_Rec: 0.361 D_GP: 0.025 D_real: 1.078 D_fake: 0.704 +(epoch: 94, iters: 3056, time: 0.064) G_GAN: 0.588 G_GAN_Feat: 0.961 G_ID: 0.310 G_Rec: 0.389 D_GP: 0.054 D_real: 1.054 D_fake: 0.423 +(epoch: 94, iters: 3456, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.745 G_ID: 0.185 G_Rec: 0.354 D_GP: 0.036 D_real: 0.958 D_fake: 0.781 +(epoch: 94, iters: 3856, time: 0.064) G_GAN: 0.568 G_GAN_Feat: 0.804 G_ID: 0.263 G_Rec: 0.368 D_GP: 0.035 D_real: 1.342 D_fake: 0.443 +(epoch: 94, iters: 4256, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.630 G_ID: 0.186 G_Rec: 0.366 D_GP: 0.023 D_real: 1.114 D_fake: 0.728 +(epoch: 94, iters: 4656, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.819 G_ID: 0.320 G_Rec: 0.387 D_GP: 0.043 D_real: 0.773 D_fake: 0.847 +(epoch: 94, iters: 5056, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.642 G_ID: 0.172 G_Rec: 0.378 D_GP: 0.030 D_real: 1.077 D_fake: 0.813 +(epoch: 94, iters: 5456, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.892 G_ID: 0.316 G_Rec: 0.393 D_GP: 0.062 D_real: 1.066 D_fake: 0.499 +(epoch: 94, iters: 5856, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.680 G_ID: 0.183 G_Rec: 0.374 D_GP: 0.027 D_real: 1.086 D_fake: 0.704 +(epoch: 94, iters: 6256, time: 0.064) G_GAN: 0.595 G_GAN_Feat: 0.836 G_ID: 0.313 G_Rec: 0.375 D_GP: 0.044 D_real: 1.223 D_fake: 0.409 +(epoch: 94, iters: 6656, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 1.022 G_ID: 0.193 G_Rec: 0.379 D_GP: 0.201 D_real: 0.454 D_fake: 0.694 +(epoch: 94, iters: 7056, time: 0.064) G_GAN: 0.556 G_GAN_Feat: 1.168 G_ID: 0.271 G_Rec: 0.419 D_GP: 0.263 D_real: 0.653 D_fake: 0.457 +(epoch: 94, iters: 7456, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.640 G_ID: 0.161 G_Rec: 0.346 D_GP: 0.024 D_real: 1.238 D_fake: 0.662 +(epoch: 94, iters: 7856, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 0.787 G_ID: 0.275 G_Rec: 0.381 D_GP: 0.034 D_real: 1.293 D_fake: 0.488 +(epoch: 94, iters: 8256, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.796 G_ID: 0.241 G_Rec: 0.357 D_GP: 0.054 D_real: 1.208 D_fake: 0.526 +(epoch: 95, iters: 48, time: 0.064) G_GAN: 0.480 G_GAN_Feat: 1.107 G_ID: 0.267 G_Rec: 0.435 D_GP: 0.122 D_real: 0.533 D_fake: 0.521 +(epoch: 95, iters: 448, time: 0.064) G_GAN: -0.071 G_GAN_Feat: 0.872 G_ID: 0.200 G_Rec: 0.391 D_GP: 0.070 D_real: 0.561 D_fake: 1.071 +(epoch: 95, iters: 848, time: 0.064) G_GAN: 0.634 G_GAN_Feat: 1.181 G_ID: 0.275 G_Rec: 0.387 D_GP: 0.449 D_real: 0.591 D_fake: 0.382 +(epoch: 95, iters: 1248, time: 0.064) G_GAN: 0.024 G_GAN_Feat: 0.871 G_ID: 0.226 G_Rec: 0.383 D_GP: 0.093 D_real: 0.593 D_fake: 0.976 +(epoch: 95, iters: 1648, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.670 G_ID: 0.275 G_Rec: 0.416 D_GP: 0.018 D_real: 1.343 D_fake: 0.567 +(epoch: 95, iters: 2048, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.624 G_ID: 0.223 G_Rec: 0.356 D_GP: 0.019 D_real: 1.301 D_fake: 0.574 +(epoch: 95, iters: 2448, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.729 G_ID: 0.261 G_Rec: 0.403 D_GP: 0.023 D_real: 0.976 D_fake: 0.877 +(epoch: 95, iters: 2848, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.634 G_ID: 0.166 G_Rec: 0.359 D_GP: 0.025 D_real: 1.165 D_fake: 0.709 +(epoch: 95, iters: 3248, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.744 G_ID: 0.309 G_Rec: 0.383 D_GP: 0.031 D_real: 0.952 D_fake: 0.830 +(epoch: 95, iters: 3648, time: 0.064) G_GAN: -0.005 G_GAN_Feat: 0.720 G_ID: 0.225 G_Rec: 0.390 D_GP: 0.047 D_real: 0.750 D_fake: 1.005 +(epoch: 95, iters: 4048, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.752 G_ID: 0.306 G_Rec: 0.403 D_GP: 0.022 D_real: 0.986 D_fake: 0.685 +(epoch: 95, iters: 4448, time: 0.064) G_GAN: -0.037 G_GAN_Feat: 0.797 G_ID: 0.160 G_Rec: 0.362 D_GP: 0.081 D_real: 0.670 D_fake: 1.037 +(epoch: 95, iters: 4848, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.805 G_ID: 0.330 G_Rec: 0.418 D_GP: 0.033 D_real: 1.093 D_fake: 0.682 +(epoch: 95, iters: 5248, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.822 G_ID: 0.215 G_Rec: 0.353 D_GP: 0.040 D_real: 0.965 D_fake: 0.727 +(epoch: 95, iters: 5648, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.815 G_ID: 0.268 G_Rec: 0.384 D_GP: 0.039 D_real: 1.038 D_fake: 0.677 +(epoch: 95, iters: 6048, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.784 G_ID: 0.200 G_Rec: 0.335 D_GP: 0.041 D_real: 0.935 D_fake: 0.675 +(epoch: 95, iters: 6448, time: 0.064) G_GAN: -0.013 G_GAN_Feat: 1.069 G_ID: 0.309 G_Rec: 0.388 D_GP: 0.047 D_real: 0.220 D_fake: 1.013 +(epoch: 95, iters: 6848, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.676 G_ID: 0.200 G_Rec: 0.324 D_GP: 0.020 D_real: 1.170 D_fake: 0.682 +(epoch: 95, iters: 7248, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 0.870 G_ID: 0.270 G_Rec: 0.405 D_GP: 0.029 D_real: 1.116 D_fake: 0.457 +(epoch: 95, iters: 7648, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.790 G_ID: 0.170 G_Rec: 0.350 D_GP: 0.075 D_real: 1.140 D_fake: 0.549 +(epoch: 95, iters: 8048, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 1.308 G_ID: 0.320 G_Rec: 0.402 D_GP: 0.421 D_real: 0.552 D_fake: 0.678 +(epoch: 95, iters: 8448, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 1.082 G_ID: 0.167 G_Rec: 0.391 D_GP: 0.193 D_real: 0.457 D_fake: 0.653 +(epoch: 96, iters: 240, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 1.021 G_ID: 0.273 G_Rec: 0.432 D_GP: 0.111 D_real: 1.027 D_fake: 0.559 +(epoch: 96, iters: 640, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.747 G_ID: 0.173 G_Rec: 0.337 D_GP: 0.031 D_real: 0.857 D_fake: 0.963 +(epoch: 96, iters: 1040, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.729 G_ID: 0.336 G_Rec: 0.393 D_GP: 0.022 D_real: 1.153 D_fake: 0.677 +(epoch: 96, iters: 1440, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.624 G_ID: 0.165 G_Rec: 0.367 D_GP: 0.020 D_real: 1.308 D_fake: 0.620 +(epoch: 96, iters: 1840, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.759 G_ID: 0.307 G_Rec: 0.394 D_GP: 0.027 D_real: 1.066 D_fake: 0.636 +(epoch: 96, iters: 2240, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.727 G_ID: 0.190 G_Rec: 0.369 D_GP: 0.048 D_real: 1.012 D_fake: 0.815 +(epoch: 96, iters: 2640, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.898 G_ID: 0.277 G_Rec: 0.390 D_GP: 0.069 D_real: 1.019 D_fake: 0.531 +(epoch: 96, iters: 3040, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.751 G_ID: 0.196 G_Rec: 0.351 D_GP: 0.050 D_real: 1.047 D_fake: 0.675 +(epoch: 96, iters: 3440, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 0.903 G_ID: 0.279 G_Rec: 0.395 D_GP: 0.043 D_real: 0.955 D_fake: 0.593 +(epoch: 96, iters: 3840, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.867 G_ID: 0.190 G_Rec: 0.374 D_GP: 0.048 D_real: 0.782 D_fake: 0.727 +(epoch: 96, iters: 4240, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.755 G_ID: 0.293 G_Rec: 0.380 D_GP: 0.028 D_real: 0.813 D_fake: 0.913 +(epoch: 96, iters: 4640, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.962 G_ID: 0.168 G_Rec: 0.394 D_GP: 0.141 D_real: 0.509 D_fake: 0.776 +(epoch: 96, iters: 5040, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.806 G_ID: 0.297 G_Rec: 0.403 D_GP: 0.035 D_real: 1.100 D_fake: 0.686 +(epoch: 96, iters: 5440, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.690 G_ID: 0.183 G_Rec: 0.334 D_GP: 0.024 D_real: 1.002 D_fake: 0.853 +(epoch: 96, iters: 5840, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 1.015 G_ID: 0.274 G_Rec: 0.397 D_GP: 0.139 D_real: 0.762 D_fake: 0.553 +(epoch: 96, iters: 6240, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.667 G_ID: 0.185 G_Rec: 0.350 D_GP: 0.027 D_real: 1.164 D_fake: 0.719 +(epoch: 96, iters: 6640, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.867 G_ID: 0.264 G_Rec: 0.406 D_GP: 0.044 D_real: 0.978 D_fake: 0.661 +(epoch: 96, iters: 7040, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 0.764 G_ID: 0.189 G_Rec: 0.358 D_GP: 0.031 D_real: 1.257 D_fake: 0.513 +(epoch: 96, iters: 7440, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.821 G_ID: 0.243 G_Rec: 0.365 D_GP: 0.034 D_real: 1.058 D_fake: 0.698 +(epoch: 96, iters: 7840, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.901 G_ID: 0.174 G_Rec: 0.361 D_GP: 0.043 D_real: 1.159 D_fake: 0.581 +(epoch: 96, iters: 8240, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.999 G_ID: 0.304 G_Rec: 0.393 D_GP: 0.088 D_real: 0.528 D_fake: 0.688 +(epoch: 97, iters: 32, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.786 G_ID: 0.190 G_Rec: 0.364 D_GP: 0.048 D_real: 1.145 D_fake: 0.663 +(epoch: 97, iters: 432, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.799 G_ID: 0.333 G_Rec: 0.397 D_GP: 0.041 D_real: 0.840 D_fake: 0.861 +(epoch: 97, iters: 832, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 1.120 G_ID: 0.218 G_Rec: 0.404 D_GP: 0.161 D_real: 0.382 D_fake: 0.836 +(epoch: 97, iters: 1232, time: 0.064) G_GAN: 0.655 G_GAN_Feat: 0.976 G_ID: 0.276 G_Rec: 0.412 D_GP: 0.050 D_real: 1.069 D_fake: 0.361 +(epoch: 97, iters: 1632, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.914 G_ID: 0.200 G_Rec: 0.335 D_GP: 0.046 D_real: 0.964 D_fake: 0.671 +(epoch: 97, iters: 2032, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.716 G_ID: 0.228 G_Rec: 0.357 D_GP: 0.024 D_real: 1.333 D_fake: 0.498 +(epoch: 97, iters: 2432, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.658 G_ID: 0.186 G_Rec: 0.332 D_GP: 0.025 D_real: 1.149 D_fake: 0.703 +(epoch: 97, iters: 2832, time: 0.064) G_GAN: 0.612 G_GAN_Feat: 0.833 G_ID: 0.295 G_Rec: 0.374 D_GP: 0.035 D_real: 1.345 D_fake: 0.393 +(epoch: 97, iters: 3232, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.959 G_ID: 0.160 G_Rec: 0.376 D_GP: 0.054 D_real: 0.683 D_fake: 0.642 +(epoch: 97, iters: 3632, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.839 G_ID: 0.302 G_Rec: 0.397 D_GP: 0.032 D_real: 1.124 D_fake: 0.588 +(epoch: 97, iters: 4032, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.888 G_ID: 0.150 G_Rec: 0.341 D_GP: 0.102 D_real: 0.751 D_fake: 0.724 +(epoch: 97, iters: 4432, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.965 G_ID: 0.296 G_Rec: 0.396 D_GP: 0.045 D_real: 1.031 D_fake: 0.548 +(epoch: 97, iters: 4832, time: 0.064) G_GAN: 0.577 G_GAN_Feat: 0.789 G_ID: 0.209 G_Rec: 0.388 D_GP: 0.062 D_real: 1.308 D_fake: 0.443 +(epoch: 97, iters: 5232, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.832 G_ID: 0.285 G_Rec: 0.393 D_GP: 0.032 D_real: 0.723 D_fake: 0.914 +(epoch: 97, iters: 5632, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.754 G_ID: 0.187 G_Rec: 0.388 D_GP: 0.041 D_real: 1.057 D_fake: 0.683 +(epoch: 97, iters: 6032, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.989 G_ID: 0.318 G_Rec: 0.381 D_GP: 0.086 D_real: 0.670 D_fake: 0.555 +(epoch: 97, iters: 6432, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.682 G_ID: 0.179 G_Rec: 0.369 D_GP: 0.023 D_real: 1.106 D_fake: 0.799 +(epoch: 97, iters: 6832, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.758 G_ID: 0.270 G_Rec: 0.387 D_GP: 0.025 D_real: 1.109 D_fake: 0.604 +(epoch: 97, iters: 7232, time: 0.064) G_GAN: 0.015 G_GAN_Feat: 0.906 G_ID: 0.178 G_Rec: 0.390 D_GP: 0.190 D_real: 0.594 D_fake: 0.985 +(epoch: 97, iters: 7632, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.876 G_ID: 0.317 G_Rec: 0.408 D_GP: 0.050 D_real: 1.175 D_fake: 0.502 +(epoch: 97, iters: 8032, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.786 G_ID: 0.173 G_Rec: 0.348 D_GP: 0.033 D_real: 1.290 D_fake: 0.488 +(epoch: 97, iters: 8432, time: 0.064) G_GAN: 0.585 G_GAN_Feat: 1.036 G_ID: 0.307 G_Rec: 0.432 D_GP: 0.125 D_real: 0.794 D_fake: 0.428 +(epoch: 98, iters: 224, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.692 G_ID: 0.173 G_Rec: 0.345 D_GP: 0.030 D_real: 1.170 D_fake: 0.626 +(epoch: 98, iters: 624, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.762 G_ID: 0.321 G_Rec: 0.407 D_GP: 0.027 D_real: 1.108 D_fake: 0.649 +(epoch: 98, iters: 1024, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.962 G_ID: 0.182 G_Rec: 0.399 D_GP: 0.076 D_real: 0.500 D_fake: 0.823 +(epoch: 98, iters: 1424, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.978 G_ID: 0.261 G_Rec: 0.443 D_GP: 0.045 D_real: 0.920 D_fake: 0.577 +(epoch: 98, iters: 1824, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.927 G_ID: 0.174 G_Rec: 0.391 D_GP: 0.028 D_real: 1.051 D_fake: 0.710 +(epoch: 98, iters: 2224, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.780 G_ID: 0.288 G_Rec: 0.402 D_GP: 0.022 D_real: 0.942 D_fake: 0.833 +(epoch: 98, iters: 2624, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.835 G_ID: 0.176 G_Rec: 0.361 D_GP: 0.055 D_real: 0.792 D_fake: 0.857 +(epoch: 98, iters: 3024, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 1.069 G_ID: 0.318 G_Rec: 0.459 D_GP: 0.140 D_real: 0.429 D_fake: 0.762 +(epoch: 98, iters: 3424, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.826 G_ID: 0.164 G_Rec: 0.374 D_GP: 0.037 D_real: 1.342 D_fake: 0.423 +(epoch: 98, iters: 3824, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 0.926 G_ID: 0.330 G_Rec: 0.387 D_GP: 0.049 D_real: 0.994 D_fake: 0.528 +(epoch: 98, iters: 4224, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.760 G_ID: 0.191 G_Rec: 0.357 D_GP: 0.031 D_real: 1.024 D_fake: 0.715 +(epoch: 98, iters: 4624, time: 0.064) G_GAN: 0.594 G_GAN_Feat: 1.090 G_ID: 0.263 G_Rec: 0.401 D_GP: 0.103 D_real: 0.863 D_fake: 0.442 +(epoch: 98, iters: 5024, time: 0.064) G_GAN: 0.049 G_GAN_Feat: 0.795 G_ID: 0.163 G_Rec: 0.347 D_GP: 0.045 D_real: 0.762 D_fake: 0.952 +(epoch: 98, iters: 5424, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.824 G_ID: 0.287 G_Rec: 0.372 D_GP: 0.030 D_real: 1.030 D_fake: 0.659 +(epoch: 98, iters: 5824, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.910 G_ID: 0.168 G_Rec: 0.425 D_GP: 0.061 D_real: 0.591 D_fake: 0.916 +(epoch: 98, iters: 6224, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 1.198 G_ID: 0.255 G_Rec: 0.386 D_GP: 0.368 D_real: 0.549 D_fake: 0.735 +(epoch: 98, iters: 6624, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.751 G_ID: 0.188 G_Rec: 0.372 D_GP: 0.030 D_real: 1.237 D_fake: 0.604 +(epoch: 98, iters: 7024, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.812 G_ID: 0.260 G_Rec: 0.401 D_GP: 0.032 D_real: 0.875 D_fake: 0.846 +(epoch: 98, iters: 7424, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.841 G_ID: 0.172 G_Rec: 0.356 D_GP: 0.062 D_real: 0.803 D_fake: 0.754 +(epoch: 98, iters: 7824, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.709 G_ID: 0.310 G_Rec: 0.393 D_GP: 0.020 D_real: 1.324 D_fake: 0.504 +(epoch: 98, iters: 8224, time: 0.064) G_GAN: 0.024 G_GAN_Feat: 0.717 G_ID: 0.170 G_Rec: 0.374 D_GP: 0.023 D_real: 0.944 D_fake: 0.976 +(epoch: 99, iters: 16, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.863 G_ID: 0.308 G_Rec: 0.400 D_GP: 0.034 D_real: 0.918 D_fake: 0.713 +(epoch: 99, iters: 416, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.781 G_ID: 0.160 G_Rec: 0.361 D_GP: 0.037 D_real: 1.394 D_fake: 0.434 +(epoch: 99, iters: 816, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.903 G_ID: 0.290 G_Rec: 0.366 D_GP: 0.055 D_real: 0.860 D_fake: 0.703 +(epoch: 99, iters: 1216, time: 0.064) G_GAN: 0.735 G_GAN_Feat: 0.858 G_ID: 0.168 G_Rec: 0.344 D_GP: 0.078 D_real: 1.299 D_fake: 0.336 +(epoch: 99, iters: 1616, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 1.304 G_ID: 0.291 G_Rec: 0.384 D_GP: 0.108 D_real: 0.440 D_fake: 0.631 +(epoch: 99, iters: 2016, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.617 G_ID: 0.162 G_Rec: 0.391 D_GP: 0.024 D_real: 1.259 D_fake: 0.706 +(epoch: 99, iters: 2416, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.957 G_ID: 0.313 G_Rec: 0.451 D_GP: 0.059 D_real: 0.790 D_fake: 0.773 +(epoch: 99, iters: 2816, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.720 G_ID: 0.185 G_Rec: 0.355 D_GP: 0.026 D_real: 1.118 D_fake: 0.692 +(epoch: 99, iters: 3216, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.816 G_ID: 0.270 G_Rec: 0.356 D_GP: 0.053 D_real: 1.133 D_fake: 0.522 +(epoch: 99, iters: 3616, time: 0.064) G_GAN: 0.042 G_GAN_Feat: 0.754 G_ID: 0.216 G_Rec: 0.380 D_GP: 0.031 D_real: 0.903 D_fake: 0.958 +(epoch: 99, iters: 4016, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.868 G_ID: 0.240 G_Rec: 0.386 D_GP: 0.033 D_real: 1.036 D_fake: 0.608 +(epoch: 99, iters: 4416, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.806 G_ID: 0.169 G_Rec: 0.362 D_GP: 0.038 D_real: 1.211 D_fake: 0.681 +(epoch: 99, iters: 4816, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 1.025 G_ID: 0.298 G_Rec: 0.392 D_GP: 0.075 D_real: 0.773 D_fake: 0.588 +(epoch: 99, iters: 5216, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.957 G_ID: 0.183 G_Rec: 0.356 D_GP: 0.087 D_real: 0.868 D_fake: 0.652 +(epoch: 99, iters: 5616, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.986 G_ID: 0.304 G_Rec: 0.422 D_GP: 0.112 D_real: 0.756 D_fake: 0.756 +(epoch: 99, iters: 6016, time: 0.064) G_GAN: 0.633 G_GAN_Feat: 1.019 G_ID: 0.175 G_Rec: 0.400 D_GP: 0.139 D_real: 1.024 D_fake: 0.499 +(epoch: 99, iters: 6416, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 1.122 G_ID: 0.290 G_Rec: 0.450 D_GP: 0.189 D_real: 0.560 D_fake: 0.745 +(epoch: 99, iters: 6816, time: 0.064) G_GAN: 0.071 G_GAN_Feat: 0.854 G_ID: 0.179 G_Rec: 0.342 D_GP: 0.043 D_real: 0.693 D_fake: 0.929 +(epoch: 99, iters: 7216, time: 0.064) G_GAN: 0.434 G_GAN_Feat: 1.059 G_ID: 0.274 G_Rec: 0.374 D_GP: 0.062 D_real: 0.752 D_fake: 0.578 +(epoch: 99, iters: 7616, time: 0.064) G_GAN: 0.691 G_GAN_Feat: 1.015 G_ID: 0.178 G_Rec: 0.370 D_GP: 0.115 D_real: 0.837 D_fake: 0.331 +(epoch: 99, iters: 8016, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.936 G_ID: 0.328 G_Rec: 0.374 D_GP: 0.093 D_real: 0.807 D_fake: 0.579 +(epoch: 99, iters: 8416, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.873 G_ID: 0.160 G_Rec: 0.381 D_GP: 0.031 D_real: 1.032 D_fake: 0.676 +(epoch: 100, iters: 208, time: 0.064) G_GAN: 0.745 G_GAN_Feat: 0.973 G_ID: 0.293 G_Rec: 0.393 D_GP: 0.050 D_real: 1.343 D_fake: 0.344 +(epoch: 100, iters: 608, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.784 G_ID: 0.187 G_Rec: 0.360 D_GP: 0.045 D_real: 1.105 D_fake: 0.634 +(epoch: 100, iters: 1008, time: 0.064) G_GAN: 0.634 G_GAN_Feat: 1.135 G_ID: 0.303 G_Rec: 0.407 D_GP: 0.197 D_real: 0.779 D_fake: 0.448 +(epoch: 100, iters: 1408, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.724 G_ID: 0.160 G_Rec: 0.402 D_GP: 0.029 D_real: 1.147 D_fake: 0.689 +(epoch: 100, iters: 1808, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.993 G_ID: 0.274 G_Rec: 0.387 D_GP: 0.046 D_real: 0.629 D_fake: 0.681 +(epoch: 100, iters: 2208, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 1.004 G_ID: 0.186 G_Rec: 0.400 D_GP: 0.101 D_real: 0.568 D_fake: 0.763 +(epoch: 100, iters: 2608, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.894 G_ID: 0.344 G_Rec: 0.399 D_GP: 0.024 D_real: 0.935 D_fake: 0.822 +(epoch: 100, iters: 3008, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.778 G_ID: 0.180 G_Rec: 0.366 D_GP: 0.032 D_real: 1.063 D_fake: 0.847 +(epoch: 100, iters: 3408, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.838 G_ID: 0.273 G_Rec: 0.444 D_GP: 0.020 D_real: 1.137 D_fake: 0.713 +(epoch: 100, iters: 3808, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.832 G_ID: 0.194 G_Rec: 0.389 D_GP: 0.031 D_real: 1.034 D_fake: 0.714 +(epoch: 100, iters: 4208, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.820 G_ID: 0.300 G_Rec: 0.448 D_GP: 0.022 D_real: 1.021 D_fake: 0.710 +(epoch: 100, iters: 4608, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.716 G_ID: 0.170 G_Rec: 0.317 D_GP: 0.036 D_real: 1.090 D_fake: 0.698 +(epoch: 100, iters: 5008, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 1.058 G_ID: 0.261 G_Rec: 0.405 D_GP: 0.050 D_real: 0.481 D_fake: 0.662 +(epoch: 100, iters: 5408, time: 0.064) G_GAN: 0.528 G_GAN_Feat: 1.171 G_ID: 0.172 G_Rec: 0.401 D_GP: 0.259 D_real: 1.067 D_fake: 0.523 +(epoch: 100, iters: 5808, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.989 G_ID: 0.294 G_Rec: 0.399 D_GP: 0.029 D_real: 1.040 D_fake: 0.411 +(epoch: 100, iters: 6208, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.846 G_ID: 0.172 G_Rec: 0.403 D_GP: 0.049 D_real: 1.210 D_fake: 0.552 +(epoch: 100, iters: 6608, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.954 G_ID: 0.265 G_Rec: 0.454 D_GP: 0.033 D_real: 0.674 D_fake: 0.762 +(epoch: 100, iters: 7008, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.745 G_ID: 0.151 G_Rec: 0.356 D_GP: 0.022 D_real: 1.046 D_fake: 0.786 +(epoch: 100, iters: 7408, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.738 G_ID: 0.286 G_Rec: 0.356 D_GP: 0.027 D_real: 0.928 D_fake: 0.894 +(epoch: 100, iters: 7808, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 1.120 G_ID: 0.187 G_Rec: 0.370 D_GP: 0.102 D_real: 0.413 D_fake: 0.866 +(epoch: 100, iters: 8208, time: 0.064) G_GAN: 0.051 G_GAN_Feat: 1.090 G_ID: 0.322 G_Rec: 0.414 D_GP: 0.390 D_real: 0.526 D_fake: 0.950 +(epoch: 100, iters: 8608, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.715 G_ID: 0.187 G_Rec: 0.339 D_GP: 0.033 D_real: 1.008 D_fake: 0.810 +(epoch: 101, iters: 400, time: 0.064) G_GAN: 0.617 G_GAN_Feat: 0.800 G_ID: 0.295 G_Rec: 0.403 D_GP: 0.030 D_real: 1.377 D_fake: 0.389 +(epoch: 101, iters: 800, time: 0.064) G_GAN: 0.827 G_GAN_Feat: 0.825 G_ID: 0.156 G_Rec: 0.389 D_GP: 0.021 D_real: 1.616 D_fake: 0.234 +(epoch: 101, iters: 1200, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.776 G_ID: 0.267 G_Rec: 0.404 D_GP: 0.029 D_real: 1.045 D_fake: 0.670 +(epoch: 101, iters: 1600, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.865 G_ID: 0.164 G_Rec: 0.411 D_GP: 0.067 D_real: 0.733 D_fake: 0.752 +(epoch: 101, iters: 2000, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.930 G_ID: 0.279 G_Rec: 0.407 D_GP: 0.084 D_real: 0.772 D_fake: 0.629 +(epoch: 101, iters: 2400, time: 0.064) G_GAN: 0.677 G_GAN_Feat: 0.845 G_ID: 0.218 G_Rec: 0.335 D_GP: 0.041 D_real: 1.510 D_fake: 0.357 +(epoch: 101, iters: 2800, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 1.006 G_ID: 0.289 G_Rec: 0.417 D_GP: 0.036 D_real: 0.857 D_fake: 0.707 +(epoch: 101, iters: 3200, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.875 G_ID: 0.176 G_Rec: 0.368 D_GP: 0.027 D_real: 1.063 D_fake: 0.836 +(epoch: 101, iters: 3600, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 1.347 G_ID: 0.275 G_Rec: 0.463 D_GP: 0.059 D_real: 0.580 D_fake: 0.560 +(epoch: 101, iters: 4000, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.705 G_ID: 0.172 G_Rec: 0.354 D_GP: 0.028 D_real: 1.209 D_fake: 0.648 +(epoch: 101, iters: 4400, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 1.155 G_ID: 0.265 G_Rec: 0.396 D_GP: 0.069 D_real: 0.738 D_fake: 0.443 +(epoch: 101, iters: 4800, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.706 G_ID: 0.188 G_Rec: 0.347 D_GP: 0.024 D_real: 0.971 D_fake: 0.862 +(epoch: 101, iters: 5200, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 1.010 G_ID: 0.255 G_Rec: 0.434 D_GP: 0.104 D_real: 0.591 D_fake: 0.755 +(epoch: 101, iters: 5600, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.700 G_ID: 0.185 G_Rec: 0.308 D_GP: 0.025 D_real: 1.125 D_fake: 0.824 +(epoch: 101, iters: 6000, time: 0.064) G_GAN: 0.726 G_GAN_Feat: 1.096 G_ID: 0.244 G_Rec: 0.431 D_GP: 0.114 D_real: 0.923 D_fake: 0.311 +(epoch: 101, iters: 6400, time: 0.064) G_GAN: 0.841 G_GAN_Feat: 0.856 G_ID: 0.164 G_Rec: 0.372 D_GP: 0.031 D_real: 1.522 D_fake: 0.360 +(epoch: 101, iters: 6800, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.881 G_ID: 0.274 G_Rec: 0.416 D_GP: 0.042 D_real: 1.061 D_fake: 0.633 +(epoch: 101, iters: 7200, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.779 G_ID: 0.164 G_Rec: 0.350 D_GP: 0.035 D_real: 1.232 D_fake: 0.557 +(epoch: 101, iters: 7600, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.907 G_ID: 0.254 G_Rec: 0.397 D_GP: 0.039 D_real: 0.846 D_fake: 0.804 +(epoch: 101, iters: 8000, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.740 G_ID: 0.193 G_Rec: 0.423 D_GP: 0.020 D_real: 1.117 D_fake: 0.783 +(epoch: 101, iters: 8400, time: 0.064) G_GAN: 0.600 G_GAN_Feat: 0.707 G_ID: 0.268 G_Rec: 0.473 D_GP: 0.020 D_real: 1.385 D_fake: 0.405 +(epoch: 102, iters: 192, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.614 G_ID: 0.199 G_Rec: 0.328 D_GP: 0.021 D_real: 1.336 D_fake: 0.613 +(epoch: 102, iters: 592, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.735 G_ID: 0.252 G_Rec: 0.363 D_GP: 0.026 D_real: 1.138 D_fake: 0.725 +(epoch: 102, iters: 992, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.637 G_ID: 0.204 G_Rec: 0.351 D_GP: 0.027 D_real: 1.137 D_fake: 0.722 +(epoch: 102, iters: 1392, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.850 G_ID: 0.243 G_Rec: 0.413 D_GP: 0.042 D_real: 1.137 D_fake: 0.465 +(epoch: 102, iters: 1792, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.881 G_ID: 0.199 G_Rec: 0.389 D_GP: 0.272 D_real: 0.540 D_fake: 1.030 +(epoch: 102, iters: 2192, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 0.794 G_ID: 0.283 G_Rec: 0.402 D_GP: 0.025 D_real: 1.340 D_fake: 0.479 +(epoch: 102, iters: 2592, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.798 G_ID: 0.193 G_Rec: 0.369 D_GP: 0.042 D_real: 0.971 D_fake: 0.739 +(epoch: 102, iters: 2992, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.913 G_ID: 0.269 G_Rec: 0.403 D_GP: 0.078 D_real: 0.838 D_fake: 0.544 +(epoch: 102, iters: 3392, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.891 G_ID: 0.159 G_Rec: 0.378 D_GP: 0.122 D_real: 0.953 D_fake: 0.658 +(epoch: 102, iters: 3792, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.992 G_ID: 0.293 G_Rec: 0.440 D_GP: 0.084 D_real: 0.596 D_fake: 0.787 +(epoch: 102, iters: 4192, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.735 G_ID: 0.196 G_Rec: 0.410 D_GP: 0.040 D_real: 1.128 D_fake: 0.777 +(epoch: 102, iters: 4592, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.789 G_ID: 0.249 G_Rec: 0.370 D_GP: 0.034 D_real: 1.126 D_fake: 0.553 +(epoch: 102, iters: 4992, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.874 G_ID: 0.178 G_Rec: 0.344 D_GP: 0.063 D_real: 1.047 D_fake: 0.516 +(epoch: 102, iters: 5392, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.783 G_ID: 0.300 G_Rec: 0.440 D_GP: 0.026 D_real: 1.064 D_fake: 0.649 +(epoch: 102, iters: 5792, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.726 G_ID: 0.172 G_Rec: 0.366 D_GP: 0.034 D_real: 1.069 D_fake: 0.772 +(epoch: 102, iters: 6192, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.853 G_ID: 0.304 G_Rec: 0.425 D_GP: 0.033 D_real: 1.101 D_fake: 0.594 +(epoch: 102, iters: 6592, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.798 G_ID: 0.186 G_Rec: 0.356 D_GP: 0.052 D_real: 0.867 D_fake: 0.829 +(epoch: 102, iters: 6992, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 1.008 G_ID: 0.275 G_Rec: 0.400 D_GP: 0.086 D_real: 0.571 D_fake: 0.740 +(epoch: 102, iters: 7392, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.804 G_ID: 0.169 G_Rec: 0.334 D_GP: 0.064 D_real: 1.192 D_fake: 0.440 +(epoch: 102, iters: 7792, time: 0.064) G_GAN: 0.551 G_GAN_Feat: 0.985 G_ID: 0.289 G_Rec: 0.429 D_GP: 0.039 D_real: 0.975 D_fake: 0.466 +(epoch: 102, iters: 8192, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.913 G_ID: 0.183 G_Rec: 0.347 D_GP: 0.038 D_real: 0.659 D_fake: 0.868 +(epoch: 102, iters: 8592, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 1.145 G_ID: 0.249 G_Rec: 0.424 D_GP: 0.023 D_real: 1.071 D_fake: 0.454 +(epoch: 103, iters: 384, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.950 G_ID: 0.172 G_Rec: 0.396 D_GP: 0.473 D_real: 0.577 D_fake: 0.756 +(epoch: 103, iters: 784, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.992 G_ID: 0.283 G_Rec: 0.395 D_GP: 0.121 D_real: 0.435 D_fake: 0.921 +(epoch: 103, iters: 1184, time: 0.064) G_GAN: 0.067 G_GAN_Feat: 0.902 G_ID: 0.231 G_Rec: 0.362 D_GP: 0.087 D_real: 0.705 D_fake: 0.934 +(epoch: 103, iters: 1584, time: 0.064) G_GAN: 0.810 G_GAN_Feat: 0.876 G_ID: 0.301 G_Rec: 0.390 D_GP: 0.033 D_real: 1.448 D_fake: 0.235 +(epoch: 103, iters: 1984, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.710 G_ID: 0.223 G_Rec: 0.331 D_GP: 0.023 D_real: 1.098 D_fake: 0.693 +(epoch: 103, iters: 2384, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.711 G_ID: 0.249 G_Rec: 0.351 D_GP: 0.023 D_real: 1.355 D_fake: 0.412 +(epoch: 103, iters: 2784, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.748 G_ID: 0.163 G_Rec: 0.356 D_GP: 0.034 D_real: 1.219 D_fake: 0.583 +(epoch: 103, iters: 3184, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 1.020 G_ID: 0.293 G_Rec: 0.392 D_GP: 0.062 D_real: 0.639 D_fake: 0.648 +(epoch: 103, iters: 3584, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.620 G_ID: 0.187 G_Rec: 0.345 D_GP: 0.020 D_real: 1.312 D_fake: 0.650 +(epoch: 103, iters: 3984, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.813 G_ID: 0.269 G_Rec: 0.454 D_GP: 0.028 D_real: 0.983 D_fake: 0.733 +(epoch: 103, iters: 4384, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.795 G_ID: 0.173 G_Rec: 0.472 D_GP: 0.052 D_real: 1.046 D_fake: 0.754 +(epoch: 103, iters: 4784, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 0.925 G_ID: 0.264 G_Rec: 0.421 D_GP: 0.045 D_real: 1.124 D_fake: 0.480 +(epoch: 103, iters: 5184, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.756 G_ID: 0.178 G_Rec: 0.365 D_GP: 0.024 D_real: 0.986 D_fake: 0.851 +(epoch: 103, iters: 5584, time: 0.064) G_GAN: 0.764 G_GAN_Feat: 0.828 G_ID: 0.283 G_Rec: 0.375 D_GP: 0.032 D_real: 1.475 D_fake: 0.270 +(epoch: 103, iters: 5984, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 1.027 G_ID: 0.161 G_Rec: 0.371 D_GP: 0.055 D_real: 0.583 D_fake: 0.756 +(epoch: 103, iters: 6384, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.818 G_ID: 0.280 G_Rec: 0.381 D_GP: 0.039 D_real: 0.959 D_fake: 0.731 +(epoch: 103, iters: 6784, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.754 G_ID: 0.199 G_Rec: 0.320 D_GP: 0.042 D_real: 1.219 D_fake: 0.531 +(epoch: 103, iters: 7184, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.889 G_ID: 0.266 G_Rec: 0.389 D_GP: 0.029 D_real: 0.958 D_fake: 0.682 +(epoch: 103, iters: 7584, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.814 G_ID: 0.190 G_Rec: 0.326 D_GP: 0.052 D_real: 0.972 D_fake: 0.652 +(epoch: 103, iters: 7984, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.838 G_ID: 0.258 G_Rec: 0.439 D_GP: 0.027 D_real: 1.166 D_fake: 0.551 +(epoch: 103, iters: 8384, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.758 G_ID: 0.169 G_Rec: 0.355 D_GP: 0.044 D_real: 0.950 D_fake: 0.916 +(epoch: 104, iters: 176, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.813 G_ID: 0.330 G_Rec: 0.369 D_GP: 0.030 D_real: 1.014 D_fake: 0.720 +(epoch: 104, iters: 576, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.658 G_ID: 0.181 G_Rec: 0.326 D_GP: 0.028 D_real: 1.349 D_fake: 0.520 +(epoch: 104, iters: 976, time: 0.064) G_GAN: 0.529 G_GAN_Feat: 0.759 G_ID: 0.259 G_Rec: 0.375 D_GP: 0.035 D_real: 1.252 D_fake: 0.480 +(epoch: 104, iters: 1376, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.884 G_ID: 0.152 G_Rec: 0.326 D_GP: 0.236 D_real: 0.509 D_fake: 0.785 +(epoch: 104, iters: 1776, time: 0.064) G_GAN: 0.594 G_GAN_Feat: 0.768 G_ID: 0.271 G_Rec: 0.433 D_GP: 0.023 D_real: 1.310 D_fake: 0.413 +(epoch: 104, iters: 2176, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.871 G_ID: 0.170 G_Rec: 0.353 D_GP: 0.033 D_real: 1.054 D_fake: 0.654 +(epoch: 104, iters: 2576, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.875 G_ID: 0.317 G_Rec: 0.383 D_GP: 0.026 D_real: 1.253 D_fake: 0.515 +(epoch: 104, iters: 2976, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 1.163 G_ID: 0.174 G_Rec: 0.383 D_GP: 3.192 D_real: 0.560 D_fake: 0.883 +(epoch: 104, iters: 3376, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.641 G_ID: 0.301 G_Rec: 0.345 D_GP: 0.020 D_real: 1.168 D_fake: 0.687 +(epoch: 104, iters: 3776, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.651 G_ID: 0.160 G_Rec: 0.372 D_GP: 0.022 D_real: 1.216 D_fake: 0.641 +(epoch: 104, iters: 4176, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.669 G_ID: 0.273 G_Rec: 0.365 D_GP: 0.023 D_real: 1.266 D_fake: 0.563 +(epoch: 104, iters: 4576, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.618 G_ID: 0.188 G_Rec: 0.331 D_GP: 0.023 D_real: 1.231 D_fake: 0.681 +(epoch: 104, iters: 4976, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.845 G_ID: 0.243 G_Rec: 0.396 D_GP: 0.038 D_real: 1.059 D_fake: 0.660 +(epoch: 104, iters: 5376, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.752 G_ID: 0.172 G_Rec: 0.366 D_GP: 0.055 D_real: 1.005 D_fake: 0.649 +(epoch: 104, iters: 5776, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.883 G_ID: 0.242 G_Rec: 0.412 D_GP: 0.057 D_real: 0.856 D_fake: 0.824 +(epoch: 104, iters: 6176, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.955 G_ID: 0.187 G_Rec: 0.385 D_GP: 0.080 D_real: 0.628 D_fake: 0.699 +(epoch: 104, iters: 6576, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 1.101 G_ID: 0.229 G_Rec: 0.444 D_GP: 0.232 D_real: 0.509 D_fake: 0.699 +(epoch: 104, iters: 6976, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.830 G_ID: 0.160 G_Rec: 0.349 D_GP: 0.056 D_real: 1.042 D_fake: 0.771 +(epoch: 104, iters: 7376, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.917 G_ID: 0.287 G_Rec: 0.409 D_GP: 0.076 D_real: 0.739 D_fake: 0.813 +(epoch: 104, iters: 7776, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.765 G_ID: 0.143 G_Rec: 0.377 D_GP: 0.034 D_real: 1.172 D_fake: 0.529 +(epoch: 104, iters: 8176, time: 0.064) G_GAN: 0.774 G_GAN_Feat: 0.906 G_ID: 0.255 G_Rec: 0.397 D_GP: 0.041 D_real: 1.385 D_fake: 0.283 +(epoch: 104, iters: 8576, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.977 G_ID: 0.200 G_Rec: 0.367 D_GP: 0.112 D_real: 0.408 D_fake: 0.841 +(epoch: 105, iters: 368, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.988 G_ID: 0.312 G_Rec: 0.369 D_GP: 0.047 D_real: 0.992 D_fake: 0.534 +(epoch: 105, iters: 768, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.855 G_ID: 0.160 G_Rec: 0.341 D_GP: 0.058 D_real: 1.197 D_fake: 0.694 +(epoch: 105, iters: 1168, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.937 G_ID: 0.295 G_Rec: 0.389 D_GP: 0.036 D_real: 1.003 D_fake: 0.603 +(epoch: 105, iters: 1568, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.936 G_ID: 0.177 G_Rec: 0.362 D_GP: 0.100 D_real: 0.530 D_fake: 0.906 +(epoch: 105, iters: 1968, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 1.135 G_ID: 0.294 G_Rec: 0.422 D_GP: 0.068 D_real: 0.757 D_fake: 0.372 +(epoch: 105, iters: 2368, time: 0.064) G_GAN: -0.263 G_GAN_Feat: 0.875 G_ID: 0.209 G_Rec: 0.430 D_GP: 0.119 D_real: 0.577 D_fake: 1.268 +(epoch: 105, iters: 2768, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 0.841 G_ID: 0.251 G_Rec: 0.379 D_GP: 0.031 D_real: 1.313 D_fake: 0.449 +(epoch: 105, iters: 3168, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.823 G_ID: 0.170 G_Rec: 0.338 D_GP: 0.042 D_real: 0.904 D_fake: 0.788 +(epoch: 105, iters: 3568, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.936 G_ID: 0.287 G_Rec: 0.418 D_GP: 0.037 D_real: 0.769 D_fake: 0.791 +(epoch: 105, iters: 3968, time: 0.064) G_GAN: -0.465 G_GAN_Feat: 0.915 G_ID: 0.215 G_Rec: 0.360 D_GP: 0.115 D_real: 0.177 D_fake: 1.465 +(epoch: 105, iters: 4368, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.964 G_ID: 0.273 G_Rec: 0.424 D_GP: 0.080 D_real: 0.593 D_fake: 0.882 +(epoch: 105, iters: 4768, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.767 G_ID: 0.154 G_Rec: 0.326 D_GP: 0.027 D_real: 1.232 D_fake: 0.617 +(epoch: 105, iters: 5168, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.841 G_ID: 0.326 G_Rec: 0.405 D_GP: 0.023 D_real: 1.116 D_fake: 0.673 +(epoch: 105, iters: 5568, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.789 G_ID: 0.177 G_Rec: 0.366 D_GP: 0.031 D_real: 0.988 D_fake: 0.791 +(epoch: 105, iters: 5968, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.892 G_ID: 0.223 G_Rec: 0.417 D_GP: 0.028 D_real: 0.935 D_fake: 0.669 +(epoch: 105, iters: 6368, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.750 G_ID: 0.191 G_Rec: 0.349 D_GP: 0.050 D_real: 0.942 D_fake: 0.782 +(epoch: 105, iters: 6768, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.811 G_ID: 0.257 G_Rec: 0.398 D_GP: 0.032 D_real: 0.985 D_fake: 0.631 +(epoch: 105, iters: 7168, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 1.161 G_ID: 0.184 G_Rec: 0.379 D_GP: 0.848 D_real: 0.369 D_fake: 0.756 +(epoch: 105, iters: 7568, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 1.034 G_ID: 0.285 G_Rec: 0.404 D_GP: 0.031 D_real: 1.040 D_fake: 0.507 +(epoch: 105, iters: 7968, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.724 G_ID: 0.142 G_Rec: 0.324 D_GP: 0.027 D_real: 1.330 D_fake: 0.586 +(epoch: 105, iters: 8368, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 1.019 G_ID: 0.251 G_Rec: 0.373 D_GP: 0.107 D_real: 0.283 D_fake: 0.762 +(epoch: 106, iters: 160, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.857 G_ID: 0.177 G_Rec: 0.412 D_GP: 0.038 D_real: 1.185 D_fake: 0.666 +(epoch: 106, iters: 560, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 0.852 G_ID: 0.266 G_Rec: 0.383 D_GP: 0.032 D_real: 1.239 D_fake: 0.488 +(epoch: 106, iters: 960, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 1.048 G_ID: 0.180 G_Rec: 0.395 D_GP: 0.095 D_real: 0.549 D_fake: 0.780 +(epoch: 106, iters: 1360, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 0.871 G_ID: 0.291 G_Rec: 0.406 D_GP: 0.022 D_real: 1.202 D_fake: 0.471 +(epoch: 106, iters: 1760, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.747 G_ID: 0.214 G_Rec: 0.326 D_GP: 0.036 D_real: 1.089 D_fake: 0.714 +(epoch: 106, iters: 2160, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.871 G_ID: 0.285 G_Rec: 0.416 D_GP: 0.029 D_real: 1.122 D_fake: 0.697 +(epoch: 106, iters: 2560, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.910 G_ID: 0.170 G_Rec: 0.365 D_GP: 0.059 D_real: 1.072 D_fake: 0.521 +(epoch: 106, iters: 2960, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.879 G_ID: 0.282 G_Rec: 0.397 D_GP: 0.051 D_real: 0.952 D_fake: 0.724 +(epoch: 106, iters: 3360, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.712 G_ID: 0.169 G_Rec: 0.321 D_GP: 0.029 D_real: 0.979 D_fake: 0.866 +(epoch: 106, iters: 3760, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.968 G_ID: 0.324 G_Rec: 0.408 D_GP: 0.061 D_real: 0.952 D_fake: 0.569 +(epoch: 106, iters: 4160, time: 0.064) G_GAN: -0.348 G_GAN_Feat: 0.800 G_ID: 0.159 G_Rec: 0.384 D_GP: 0.033 D_real: 0.446 D_fake: 1.348 +(epoch: 106, iters: 4560, time: 0.064) G_GAN: 0.044 G_GAN_Feat: 0.852 G_ID: 0.259 G_Rec: 0.435 D_GP: 0.031 D_real: 0.684 D_fake: 0.956 +(epoch: 106, iters: 4960, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.654 G_ID: 0.183 G_Rec: 0.347 D_GP: 0.021 D_real: 1.380 D_fake: 0.500 +(epoch: 106, iters: 5360, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 0.828 G_ID: 0.222 G_Rec: 0.407 D_GP: 0.027 D_real: 1.258 D_fake: 0.406 +(epoch: 106, iters: 5760, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.636 G_ID: 0.189 G_Rec: 0.346 D_GP: 0.024 D_real: 1.170 D_fake: 0.740 +(epoch: 106, iters: 6160, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 1.017 G_ID: 0.258 G_Rec: 0.396 D_GP: 0.080 D_real: 0.736 D_fake: 0.590 +(epoch: 106, iters: 6560, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.911 G_ID: 0.159 G_Rec: 0.346 D_GP: 0.039 D_real: 1.035 D_fake: 0.493 +(epoch: 106, iters: 6960, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 1.052 G_ID: 0.234 G_Rec: 0.415 D_GP: 0.119 D_real: 0.731 D_fake: 0.438 +(epoch: 106, iters: 7360, time: 0.064) G_GAN: -0.261 G_GAN_Feat: 1.081 G_ID: 0.160 G_Rec: 0.384 D_GP: 0.074 D_real: 0.097 D_fake: 1.261 +(epoch: 106, iters: 7760, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 1.044 G_ID: 0.260 G_Rec: 0.399 D_GP: 0.057 D_real: 0.886 D_fake: 0.495 +(epoch: 106, iters: 8160, time: 0.064) G_GAN: -0.028 G_GAN_Feat: 0.749 G_ID: 0.176 G_Rec: 0.369 D_GP: 0.022 D_real: 0.792 D_fake: 1.028 +(epoch: 106, iters: 8560, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.913 G_ID: 0.277 G_Rec: 0.442 D_GP: 0.038 D_real: 0.839 D_fake: 0.688 +(epoch: 107, iters: 352, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 1.035 G_ID: 0.147 G_Rec: 0.361 D_GP: 0.133 D_real: 0.387 D_fake: 0.739 +(epoch: 107, iters: 752, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.924 G_ID: 0.286 G_Rec: 0.416 D_GP: 0.035 D_real: 0.921 D_fake: 0.814 +(epoch: 107, iters: 1152, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.802 G_ID: 0.155 G_Rec: 0.352 D_GP: 0.087 D_real: 1.089 D_fake: 0.647 +(epoch: 107, iters: 1552, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.861 G_ID: 0.287 G_Rec: 0.416 D_GP: 0.061 D_real: 0.953 D_fake: 0.623 +(epoch: 107, iters: 1952, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.795 G_ID: 0.156 G_Rec: 0.351 D_GP: 0.046 D_real: 0.887 D_fake: 0.706 +(epoch: 107, iters: 2352, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.970 G_ID: 0.240 G_Rec: 0.444 D_GP: 0.046 D_real: 0.991 D_fake: 0.449 +(epoch: 107, iters: 2752, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.666 G_ID: 0.174 G_Rec: 0.331 D_GP: 0.026 D_real: 1.203 D_fake: 0.689 +(epoch: 107, iters: 3152, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.938 G_ID: 0.251 G_Rec: 0.373 D_GP: 0.065 D_real: 1.034 D_fake: 0.509 +(epoch: 107, iters: 3552, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 1.182 G_ID: 0.175 G_Rec: 0.364 D_GP: 0.188 D_real: 1.159 D_fake: 0.750 +(epoch: 107, iters: 3952, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.694 G_ID: 0.285 G_Rec: 0.406 D_GP: 0.021 D_real: 1.284 D_fake: 0.609 +(epoch: 107, iters: 4352, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.667 G_ID: 0.141 G_Rec: 0.343 D_GP: 0.026 D_real: 1.108 D_fake: 0.742 +(epoch: 107, iters: 4752, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.882 G_ID: 0.277 G_Rec: 0.474 D_GP: 0.033 D_real: 0.999 D_fake: 0.502 +(epoch: 107, iters: 5152, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.658 G_ID: 0.170 G_Rec: 0.349 D_GP: 0.024 D_real: 1.021 D_fake: 0.839 +(epoch: 107, iters: 5552, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.959 G_ID: 0.273 G_Rec: 0.393 D_GP: 0.068 D_real: 0.794 D_fake: 0.552 +(epoch: 107, iters: 5952, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.814 G_ID: 0.156 G_Rec: 0.346 D_GP: 0.048 D_real: 1.112 D_fake: 0.550 +(epoch: 107, iters: 6352, time: 0.064) G_GAN: 0.652 G_GAN_Feat: 0.750 G_ID: 0.252 G_Rec: 0.361 D_GP: 0.024 D_real: 1.383 D_fake: 0.354 +(epoch: 107, iters: 6752, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.964 G_ID: 0.162 G_Rec: 0.330 D_GP: 0.046 D_real: 0.620 D_fake: 0.841 +(epoch: 107, iters: 7152, time: 0.064) G_GAN: 0.805 G_GAN_Feat: 0.828 G_ID: 0.249 G_Rec: 0.409 D_GP: 0.027 D_real: 1.548 D_fake: 0.238 +(epoch: 107, iters: 7552, time: 0.064) G_GAN: 0.124 G_GAN_Feat: 1.030 G_ID: 0.151 G_Rec: 0.384 D_GP: 0.172 D_real: 0.409 D_fake: 0.876 +(epoch: 107, iters: 7952, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.907 G_ID: 0.263 G_Rec: 0.398 D_GP: 0.032 D_real: 0.850 D_fake: 0.755 +(epoch: 107, iters: 8352, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.686 G_ID: 0.173 G_Rec: 0.335 D_GP: 0.027 D_real: 1.079 D_fake: 0.801 +(epoch: 108, iters: 144, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 1.008 G_ID: 0.299 G_Rec: 0.391 D_GP: 0.037 D_real: 0.693 D_fake: 0.734 +(epoch: 108, iters: 544, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.750 G_ID: 0.186 G_Rec: 0.415 D_GP: 0.033 D_real: 1.378 D_fake: 0.475 +(epoch: 108, iters: 944, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.925 G_ID: 0.249 G_Rec: 0.420 D_GP: 0.023 D_real: 0.796 D_fake: 0.786 +(epoch: 108, iters: 1344, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.915 G_ID: 0.183 G_Rec: 0.350 D_GP: 0.032 D_real: 0.865 D_fake: 0.638 +(epoch: 108, iters: 1744, time: 0.064) G_GAN: 0.548 G_GAN_Feat: 0.902 G_ID: 0.255 G_Rec: 0.384 D_GP: 0.029 D_real: 1.200 D_fake: 0.454 +(epoch: 108, iters: 2144, time: 0.064) G_GAN: 0.592 G_GAN_Feat: 0.814 G_ID: 0.163 G_Rec: 0.457 D_GP: 0.026 D_real: 1.329 D_fake: 0.426 +(epoch: 108, iters: 2544, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 1.065 G_ID: 0.263 G_Rec: 0.382 D_GP: 0.122 D_real: 0.791 D_fake: 0.424 +(epoch: 108, iters: 2944, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.710 G_ID: 0.147 G_Rec: 0.377 D_GP: 0.025 D_real: 1.125 D_fake: 0.731 +(epoch: 108, iters: 3344, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.963 G_ID: 0.249 G_Rec: 0.397 D_GP: 0.049 D_real: 1.029 D_fake: 0.624 +(epoch: 108, iters: 3744, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.776 G_ID: 0.147 G_Rec: 0.348 D_GP: 0.024 D_real: 1.078 D_fake: 0.770 +(epoch: 108, iters: 4144, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.884 G_ID: 0.265 G_Rec: 0.423 D_GP: 0.029 D_real: 1.110 D_fake: 0.542 +(epoch: 108, iters: 4544, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.797 G_ID: 0.159 G_Rec: 0.387 D_GP: 0.047 D_real: 0.943 D_fake: 0.852 +(epoch: 108, iters: 4944, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.872 G_ID: 0.272 G_Rec: 0.422 D_GP: 0.028 D_real: 0.906 D_fake: 0.831 +(epoch: 108, iters: 5344, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.869 G_ID: 0.207 G_Rec: 0.368 D_GP: 0.094 D_real: 0.745 D_fake: 0.788 +(epoch: 108, iters: 5744, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.857 G_ID: 0.247 G_Rec: 0.370 D_GP: 0.035 D_real: 0.951 D_fake: 0.615 +(epoch: 108, iters: 6144, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.863 G_ID: 0.173 G_Rec: 0.398 D_GP: 0.057 D_real: 1.006 D_fake: 0.632 +(epoch: 108, iters: 6544, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.956 G_ID: 0.232 G_Rec: 0.476 D_GP: 0.080 D_real: 0.788 D_fake: 0.666 +(epoch: 108, iters: 6944, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 1.266 G_ID: 0.153 G_Rec: 0.640 D_GP: 0.204 D_real: 0.391 D_fake: 0.680 +(epoch: 108, iters: 7344, time: 0.064) G_GAN: 0.495 G_GAN_Feat: 1.201 G_ID: 0.289 G_Rec: 0.440 D_GP: 0.043 D_real: 0.925 D_fake: 0.507 +(epoch: 108, iters: 7744, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 0.946 G_ID: 0.192 G_Rec: 0.365 D_GP: 0.228 D_real: 0.510 D_fake: 0.808 +(epoch: 108, iters: 8144, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 0.806 G_ID: 0.250 G_Rec: 0.420 D_GP: 0.025 D_real: 1.372 D_fake: 0.350 +(epoch: 108, iters: 8544, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.820 G_ID: 0.154 G_Rec: 0.377 D_GP: 0.046 D_real: 0.989 D_fake: 0.699 +(epoch: 109, iters: 336, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.825 G_ID: 0.256 G_Rec: 0.415 D_GP: 0.023 D_real: 1.121 D_fake: 0.609 +(epoch: 109, iters: 736, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.839 G_ID: 0.150 G_Rec: 0.338 D_GP: 0.055 D_real: 0.784 D_fake: 0.813 +(epoch: 109, iters: 1136, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 1.022 G_ID: 0.238 G_Rec: 0.400 D_GP: 0.065 D_real: 0.811 D_fake: 0.499 +(epoch: 109, iters: 1536, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.793 G_ID: 0.147 G_Rec: 0.325 D_GP: 0.033 D_real: 1.018 D_fake: 0.749 +(epoch: 109, iters: 1936, time: 0.064) G_GAN: 0.629 G_GAN_Feat: 0.924 G_ID: 0.239 G_Rec: 0.415 D_GP: 0.035 D_real: 1.086 D_fake: 0.379 +(epoch: 109, iters: 2336, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.724 G_ID: 0.175 G_Rec: 0.385 D_GP: 0.028 D_real: 1.127 D_fake: 0.769 +(epoch: 109, iters: 2736, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.834 G_ID: 0.306 G_Rec: 0.446 D_GP: 0.025 D_real: 1.130 D_fake: 0.656 +(epoch: 109, iters: 3136, time: 0.064) G_GAN: -0.056 G_GAN_Feat: 0.843 G_ID: 0.141 G_Rec: 0.360 D_GP: 0.145 D_real: 0.639 D_fake: 1.056 +(epoch: 109, iters: 3536, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 1.003 G_ID: 0.302 G_Rec: 0.421 D_GP: 0.048 D_real: 1.201 D_fake: 0.531 +(epoch: 109, iters: 3936, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.675 G_ID: 0.195 G_Rec: 0.330 D_GP: 0.024 D_real: 1.019 D_fake: 0.814 +(epoch: 109, iters: 4336, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 1.299 G_ID: 0.241 G_Rec: 0.430 D_GP: 0.692 D_real: 0.609 D_fake: 0.495 +(epoch: 109, iters: 4736, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.744 G_ID: 0.154 G_Rec: 0.357 D_GP: 0.027 D_real: 1.154 D_fake: 0.659 +(epoch: 109, iters: 5136, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.915 G_ID: 0.280 G_Rec: 0.419 D_GP: 0.028 D_real: 0.936 D_fake: 0.719 +(epoch: 109, iters: 5536, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 0.882 G_ID: 0.165 G_Rec: 0.362 D_GP: 0.078 D_real: 0.968 D_fake: 0.483 +(epoch: 109, iters: 5936, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 0.874 G_ID: 0.245 G_Rec: 0.431 D_GP: 0.023 D_real: 1.193 D_fake: 0.466 +(epoch: 109, iters: 6336, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.752 G_ID: 0.166 G_Rec: 0.346 D_GP: 0.030 D_real: 1.219 D_fake: 0.593 +(epoch: 109, iters: 6736, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.873 G_ID: 0.254 G_Rec: 0.393 D_GP: 0.036 D_real: 1.069 D_fake: 0.578 +(epoch: 109, iters: 7136, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.790 G_ID: 0.151 G_Rec: 0.350 D_GP: 0.057 D_real: 0.993 D_fake: 0.693 +(epoch: 109, iters: 7536, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.699 G_ID: 0.281 G_Rec: 0.369 D_GP: 0.025 D_real: 1.148 D_fake: 0.648 +(epoch: 109, iters: 7936, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.772 G_ID: 0.175 G_Rec: 0.359 D_GP: 0.054 D_real: 0.972 D_fake: 0.808 +(epoch: 109, iters: 8336, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.849 G_ID: 0.262 G_Rec: 0.375 D_GP: 0.029 D_real: 1.177 D_fake: 0.569 +(epoch: 110, iters: 128, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.761 G_ID: 0.177 G_Rec: 0.332 D_GP: 0.039 D_real: 1.112 D_fake: 0.630 +(epoch: 110, iters: 528, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.847 G_ID: 0.259 G_Rec: 0.420 D_GP: 0.038 D_real: 0.945 D_fake: 0.781 +(epoch: 110, iters: 928, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.899 G_ID: 0.145 G_Rec: 0.360 D_GP: 0.128 D_real: 0.826 D_fake: 0.600 +(epoch: 110, iters: 1328, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.842 G_ID: 0.272 G_Rec: 0.407 D_GP: 0.028 D_real: 1.231 D_fake: 0.545 +(epoch: 110, iters: 1728, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.698 G_ID: 0.189 G_Rec: 0.319 D_GP: 0.033 D_real: 1.023 D_fake: 0.802 +(epoch: 110, iters: 2128, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.927 G_ID: 0.210 G_Rec: 0.408 D_GP: 0.037 D_real: 0.940 D_fake: 0.576 +(epoch: 110, iters: 2528, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.696 G_ID: 0.165 G_Rec: 0.341 D_GP: 0.024 D_real: 1.342 D_fake: 0.510 +(epoch: 110, iters: 2928, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.889 G_ID: 0.254 G_Rec: 0.388 D_GP: 0.036 D_real: 1.164 D_fake: 0.494 +(epoch: 110, iters: 3328, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.863 G_ID: 0.175 G_Rec: 0.366 D_GP: 0.053 D_real: 0.717 D_fake: 0.842 +(epoch: 110, iters: 3728, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.912 G_ID: 0.302 G_Rec: 0.401 D_GP: 0.056 D_real: 1.032 D_fake: 0.634 +(epoch: 110, iters: 4128, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.888 G_ID: 0.172 G_Rec: 0.345 D_GP: 0.051 D_real: 1.055 D_fake: 0.555 +(epoch: 110, iters: 4528, time: 0.064) G_GAN: 0.530 G_GAN_Feat: 0.929 G_ID: 0.247 G_Rec: 0.377 D_GP: 0.029 D_real: 1.207 D_fake: 0.472 +(epoch: 110, iters: 4928, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.655 G_ID: 0.196 G_Rec: 0.309 D_GP: 0.026 D_real: 1.348 D_fake: 0.541 +(epoch: 110, iters: 5328, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.948 G_ID: 0.250 G_Rec: 0.390 D_GP: 0.046 D_real: 1.030 D_fake: 0.686 +(epoch: 110, iters: 5728, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.765 G_ID: 0.152 G_Rec: 0.367 D_GP: 0.024 D_real: 1.237 D_fake: 0.598 +(epoch: 110, iters: 6128, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.832 G_ID: 0.229 G_Rec: 0.385 D_GP: 0.029 D_real: 1.241 D_fake: 0.495 +(epoch: 110, iters: 6528, time: 0.064) G_GAN: -0.085 G_GAN_Feat: 0.932 G_ID: 0.177 G_Rec: 0.355 D_GP: 0.073 D_real: 0.992 D_fake: 1.085 +(epoch: 110, iters: 6928, time: 0.064) G_GAN: 0.864 G_GAN_Feat: 0.975 G_ID: 0.265 G_Rec: 0.410 D_GP: 0.046 D_real: 1.439 D_fake: 0.183 +(epoch: 110, iters: 7328, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 0.816 G_ID: 0.177 G_Rec: 0.362 D_GP: 0.042 D_real: 1.295 D_fake: 0.494 +(epoch: 110, iters: 7728, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.765 G_ID: 0.264 G_Rec: 0.377 D_GP: 0.023 D_real: 1.377 D_fake: 0.434 +(epoch: 110, iters: 8128, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.895 G_ID: 0.147 G_Rec: 0.342 D_GP: 0.056 D_real: 0.750 D_fake: 0.746 +(epoch: 110, iters: 8528, time: 0.064) G_GAN: 0.787 G_GAN_Feat: 1.238 G_ID: 0.258 G_Rec: 0.464 D_GP: 0.154 D_real: 0.614 D_fake: 0.319 +(epoch: 111, iters: 320, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.786 G_ID: 0.165 G_Rec: 0.402 D_GP: 0.030 D_real: 1.214 D_fake: 0.636 +(epoch: 111, iters: 720, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 0.870 G_ID: 0.263 G_Rec: 0.371 D_GP: 0.039 D_real: 1.075 D_fake: 0.592 +(epoch: 111, iters: 1120, time: 0.064) G_GAN: -0.811 G_GAN_Feat: 1.135 G_ID: 0.174 G_Rec: 0.456 D_GP: 0.940 D_real: 0.077 D_fake: 1.811 +(epoch: 111, iters: 1520, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.738 G_ID: 0.247 G_Rec: 0.368 D_GP: 0.022 D_real: 1.113 D_fake: 0.640 +(epoch: 111, iters: 1920, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.620 G_ID: 0.173 G_Rec: 0.310 D_GP: 0.027 D_real: 1.412 D_fake: 0.521 +(epoch: 111, iters: 2320, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 0.814 G_ID: 0.269 G_Rec: 0.396 D_GP: 0.054 D_real: 1.034 D_fake: 0.549 +(epoch: 111, iters: 2720, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.889 G_ID: 0.184 G_Rec: 0.424 D_GP: 0.034 D_real: 0.662 D_fake: 0.882 +(epoch: 111, iters: 3120, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.789 G_ID: 0.227 G_Rec: 0.395 D_GP: 0.026 D_real: 1.232 D_fake: 0.518 +(epoch: 111, iters: 3520, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.956 G_ID: 0.170 G_Rec: 0.391 D_GP: 0.072 D_real: 0.717 D_fake: 0.768 +(epoch: 111, iters: 3920, time: 0.064) G_GAN: 0.633 G_GAN_Feat: 1.234 G_ID: 0.255 G_Rec: 0.466 D_GP: 0.141 D_real: 0.863 D_fake: 0.452 +(epoch: 111, iters: 4320, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.871 G_ID: 0.154 G_Rec: 0.337 D_GP: 0.033 D_real: 1.393 D_fake: 0.540 +(epoch: 111, iters: 4720, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.745 G_ID: 0.269 G_Rec: 0.385 D_GP: 0.023 D_real: 0.853 D_fake: 0.987 +(epoch: 111, iters: 5120, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.697 G_ID: 0.154 G_Rec: 0.357 D_GP: 0.027 D_real: 0.978 D_fake: 0.846 +(epoch: 111, iters: 5520, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.829 G_ID: 0.257 G_Rec: 0.397 D_GP: 0.031 D_real: 0.958 D_fake: 0.647 +(epoch: 111, iters: 5920, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.714 G_ID: 0.151 G_Rec: 0.309 D_GP: 0.026 D_real: 1.184 D_fake: 0.742 +(epoch: 111, iters: 6320, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.881 G_ID: 0.250 G_Rec: 0.430 D_GP: 0.061 D_real: 1.010 D_fake: 0.628 +(epoch: 111, iters: 6720, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.844 G_ID: 0.152 G_Rec: 0.372 D_GP: 0.074 D_real: 1.107 D_fake: 0.521 +(epoch: 111, iters: 7120, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.852 G_ID: 0.252 G_Rec: 0.386 D_GP: 0.027 D_real: 1.010 D_fake: 0.739 +(epoch: 111, iters: 7520, time: 0.064) G_GAN: 0.735 G_GAN_Feat: 0.796 G_ID: 0.170 G_Rec: 0.341 D_GP: 0.037 D_real: 1.465 D_fake: 0.303 +(epoch: 111, iters: 7920, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.834 G_ID: 0.269 G_Rec: 0.418 D_GP: 0.028 D_real: 1.163 D_fake: 0.594 +(epoch: 111, iters: 8320, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.849 G_ID: 0.178 G_Rec: 0.396 D_GP: 0.059 D_real: 0.796 D_fake: 0.794 +(epoch: 112, iters: 112, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.906 G_ID: 0.222 G_Rec: 0.427 D_GP: 0.043 D_real: 1.210 D_fake: 0.405 +(epoch: 112, iters: 512, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.813 G_ID: 0.154 G_Rec: 0.339 D_GP: 0.033 D_real: 0.982 D_fake: 0.681 +(epoch: 112, iters: 912, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.713 G_ID: 0.277 G_Rec: 0.390 D_GP: 0.023 D_real: 1.108 D_fake: 0.768 +(epoch: 112, iters: 1312, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.815 G_ID: 0.179 G_Rec: 0.381 D_GP: 0.062 D_real: 0.844 D_fake: 0.895 +(epoch: 112, iters: 1712, time: 0.064) G_GAN: 0.675 G_GAN_Feat: 0.824 G_ID: 0.280 G_Rec: 0.387 D_GP: 0.031 D_real: 1.358 D_fake: 0.349 +(epoch: 112, iters: 2112, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.795 G_ID: 0.178 G_Rec: 0.349 D_GP: 0.039 D_real: 1.030 D_fake: 0.701 +(epoch: 112, iters: 2512, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.949 G_ID: 0.273 G_Rec: 0.404 D_GP: 0.033 D_real: 0.839 D_fake: 0.672 +(epoch: 112, iters: 2912, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 1.091 G_ID: 0.220 G_Rec: 0.356 D_GP: 0.481 D_real: 0.246 D_fake: 0.902 +(epoch: 112, iters: 3312, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 1.155 G_ID: 0.232 G_Rec: 0.394 D_GP: 0.086 D_real: 0.450 D_fake: 0.620 +(epoch: 112, iters: 3712, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.762 G_ID: 0.153 G_Rec: 0.342 D_GP: 0.027 D_real: 1.257 D_fake: 0.548 +(epoch: 112, iters: 4112, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.740 G_ID: 0.241 G_Rec: 0.406 D_GP: 0.026 D_real: 1.170 D_fake: 0.660 +(epoch: 112, iters: 4512, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.751 G_ID: 0.156 G_Rec: 0.348 D_GP: 0.031 D_real: 1.138 D_fake: 0.594 +(epoch: 112, iters: 4912, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.885 G_ID: 0.270 G_Rec: 0.388 D_GP: 0.027 D_real: 1.025 D_fake: 0.653 +(epoch: 112, iters: 5312, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 0.674 G_ID: 0.183 G_Rec: 0.381 D_GP: 0.026 D_real: 1.066 D_fake: 0.807 +(epoch: 112, iters: 5712, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.844 G_ID: 0.262 G_Rec: 0.386 D_GP: 0.043 D_real: 0.924 D_fake: 0.735 +(epoch: 112, iters: 6112, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.777 G_ID: 0.157 G_Rec: 0.346 D_GP: 0.033 D_real: 1.286 D_fake: 0.545 +(epoch: 112, iters: 6512, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.976 G_ID: 0.258 G_Rec: 0.449 D_GP: 0.031 D_real: 0.991 D_fake: 0.543 +(epoch: 112, iters: 6912, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.886 G_ID: 0.157 G_Rec: 0.367 D_GP: 0.083 D_real: 0.728 D_fake: 0.779 +(epoch: 112, iters: 7312, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.834 G_ID: 0.240 G_Rec: 0.377 D_GP: 0.031 D_real: 1.090 D_fake: 0.678 +(epoch: 112, iters: 7712, time: 0.064) G_GAN: 0.204 G_GAN_Feat: 0.710 G_ID: 0.198 G_Rec: 0.329 D_GP: 0.030 D_real: 1.054 D_fake: 0.796 +(epoch: 112, iters: 8112, time: 0.064) G_GAN: 0.959 G_GAN_Feat: 0.929 G_ID: 0.253 G_Rec: 0.389 D_GP: 0.050 D_real: 1.422 D_fake: 0.139 +(epoch: 112, iters: 8512, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.719 G_ID: 0.148 G_Rec: 0.341 D_GP: 0.027 D_real: 1.309 D_fake: 0.586 +(epoch: 113, iters: 304, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.948 G_ID: 0.243 G_Rec: 0.409 D_GP: 0.046 D_real: 0.874 D_fake: 0.602 +(epoch: 113, iters: 704, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.766 G_ID: 0.194 G_Rec: 0.359 D_GP: 0.035 D_real: 1.128 D_fake: 0.734 +(epoch: 113, iters: 1104, time: 0.064) G_GAN: 0.560 G_GAN_Feat: 0.942 G_ID: 0.255 G_Rec: 0.369 D_GP: 0.059 D_real: 1.017 D_fake: 0.498 +(epoch: 113, iters: 1504, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.757 G_ID: 0.163 G_Rec: 0.330 D_GP: 0.036 D_real: 1.142 D_fake: 0.715 +(epoch: 113, iters: 1904, time: 0.064) G_GAN: 0.536 G_GAN_Feat: 0.842 G_ID: 0.273 G_Rec: 0.437 D_GP: 0.023 D_real: 1.310 D_fake: 0.467 +(epoch: 113, iters: 2304, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.902 G_ID: 0.171 G_Rec: 0.369 D_GP: 0.092 D_real: 0.614 D_fake: 0.725 +(epoch: 113, iters: 2704, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 1.052 G_ID: 0.271 G_Rec: 0.455 D_GP: 0.078 D_real: 0.960 D_fake: 0.520 +(epoch: 113, iters: 3104, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.982 G_ID: 0.164 G_Rec: 0.351 D_GP: 0.040 D_real: 0.803 D_fake: 0.679 +(epoch: 113, iters: 3504, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.869 G_ID: 0.221 G_Rec: 0.364 D_GP: 0.028 D_real: 0.977 D_fake: 0.674 +(epoch: 113, iters: 3904, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.760 G_ID: 0.167 G_Rec: 0.354 D_GP: 0.027 D_real: 1.275 D_fake: 0.603 +(epoch: 113, iters: 4304, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.898 G_ID: 0.242 G_Rec: 0.401 D_GP: 0.031 D_real: 0.938 D_fake: 0.678 +(epoch: 113, iters: 4704, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.720 G_ID: 0.140 G_Rec: 0.369 D_GP: 0.022 D_real: 1.104 D_fake: 0.720 +(epoch: 113, iters: 5104, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.851 G_ID: 0.271 G_Rec: 0.406 D_GP: 0.032 D_real: 1.184 D_fake: 0.529 +(epoch: 113, iters: 5504, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.928 G_ID: 0.167 G_Rec: 0.399 D_GP: 0.067 D_real: 0.853 D_fake: 0.691 +(epoch: 113, iters: 5904, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.823 G_ID: 0.277 G_Rec: 0.407 D_GP: 0.021 D_real: 1.084 D_fake: 0.576 +(epoch: 113, iters: 6304, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.797 G_ID: 0.160 G_Rec: 0.362 D_GP: 0.022 D_real: 1.103 D_fake: 0.714 +(epoch: 113, iters: 6704, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.885 G_ID: 0.231 G_Rec: 0.386 D_GP: 0.030 D_real: 1.138 D_fake: 0.598 +(epoch: 113, iters: 7104, time: 0.064) G_GAN: -0.012 G_GAN_Feat: 0.814 G_ID: 0.164 G_Rec: 0.352 D_GP: 0.053 D_real: 0.757 D_fake: 1.012 +(epoch: 113, iters: 7504, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.783 G_ID: 0.284 G_Rec: 0.392 D_GP: 0.026 D_real: 1.135 D_fake: 0.664 +(epoch: 113, iters: 7904, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.744 G_ID: 0.218 G_Rec: 0.379 D_GP: 0.032 D_real: 1.068 D_fake: 0.743 +(epoch: 113, iters: 8304, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 1.091 G_ID: 0.239 G_Rec: 0.500 D_GP: 0.090 D_real: 0.917 D_fake: 0.618 +(epoch: 114, iters: 96, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.811 G_ID: 0.159 G_Rec: 0.401 D_GP: 0.038 D_real: 1.014 D_fake: 0.720 +(epoch: 114, iters: 496, time: 0.064) G_GAN: 0.621 G_GAN_Feat: 1.083 G_ID: 0.267 G_Rec: 0.424 D_GP: 0.134 D_real: 0.898 D_fake: 0.420 +(epoch: 114, iters: 896, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 1.020 G_ID: 0.177 G_Rec: 0.423 D_GP: 0.290 D_real: 0.845 D_fake: 0.529 +(epoch: 114, iters: 1296, time: 0.064) G_GAN: 0.690 G_GAN_Feat: 0.862 G_ID: 0.240 G_Rec: 0.383 D_GP: 0.032 D_real: 1.412 D_fake: 0.333 +(epoch: 114, iters: 1696, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 1.013 G_ID: 0.164 G_Rec: 0.365 D_GP: 0.122 D_real: 0.470 D_fake: 0.862 +(epoch: 114, iters: 2096, time: 0.064) G_GAN: 0.677 G_GAN_Feat: 0.745 G_ID: 0.272 G_Rec: 0.371 D_GP: 0.022 D_real: 1.484 D_fake: 0.346 +(epoch: 114, iters: 2496, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.730 G_ID: 0.166 G_Rec: 0.389 D_GP: 0.031 D_real: 1.136 D_fake: 0.633 +(epoch: 114, iters: 2896, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.905 G_ID: 0.241 G_Rec: 0.380 D_GP: 0.060 D_real: 1.170 D_fake: 0.527 +(epoch: 114, iters: 3296, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.734 G_ID: 0.172 G_Rec: 0.357 D_GP: 0.030 D_real: 1.215 D_fake: 0.610 +(epoch: 114, iters: 3696, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.867 G_ID: 0.223 G_Rec: 0.403 D_GP: 0.050 D_real: 1.165 D_fake: 0.493 +(epoch: 114, iters: 4096, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.754 G_ID: 0.132 G_Rec: 0.374 D_GP: 0.028 D_real: 1.192 D_fake: 0.650 +(epoch: 114, iters: 4496, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.880 G_ID: 0.278 G_Rec: 0.389 D_GP: 0.043 D_real: 0.980 D_fake: 0.691 +(epoch: 114, iters: 4896, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.921 G_ID: 0.177 G_Rec: 0.381 D_GP: 0.046 D_real: 0.772 D_fake: 0.781 +(epoch: 114, iters: 5296, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.931 G_ID: 0.221 G_Rec: 0.390 D_GP: 0.060 D_real: 0.643 D_fake: 0.776 +(epoch: 114, iters: 5696, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 1.089 G_ID: 0.184 G_Rec: 0.396 D_GP: 0.518 D_real: 0.335 D_fake: 0.876 +(epoch: 114, iters: 6096, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 1.178 G_ID: 0.209 G_Rec: 0.423 D_GP: 0.137 D_real: 0.415 D_fake: 0.928 +(epoch: 114, iters: 6496, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.844 G_ID: 0.134 G_Rec: 0.356 D_GP: 0.068 D_real: 0.833 D_fake: 0.715 +(epoch: 114, iters: 6896, time: 0.064) G_GAN: 0.606 G_GAN_Feat: 0.947 G_ID: 0.299 G_Rec: 0.434 D_GP: 0.030 D_real: 1.152 D_fake: 0.405 +(epoch: 114, iters: 7296, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.900 G_ID: 0.155 G_Rec: 0.424 D_GP: 0.048 D_real: 0.735 D_fake: 0.705 +(epoch: 114, iters: 7696, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.835 G_ID: 0.245 G_Rec: 0.366 D_GP: 0.041 D_real: 1.078 D_fake: 0.729 +(epoch: 114, iters: 8096, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.759 G_ID: 0.188 G_Rec: 0.372 D_GP: 0.026 D_real: 1.168 D_fake: 0.677 +(epoch: 114, iters: 8496, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 1.087 G_ID: 0.237 G_Rec: 0.396 D_GP: 0.072 D_real: 0.570 D_fake: 0.564 +(epoch: 115, iters: 288, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.814 G_ID: 0.159 G_Rec: 0.342 D_GP: 0.037 D_real: 0.921 D_fake: 0.780 +(epoch: 115, iters: 688, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.873 G_ID: 0.272 G_Rec: 0.372 D_GP: 0.037 D_real: 1.181 D_fake: 0.531 +(epoch: 115, iters: 1088, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.800 G_ID: 0.153 G_Rec: 0.418 D_GP: 0.028 D_real: 0.870 D_fake: 0.941 +(epoch: 115, iters: 1488, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.974 G_ID: 0.264 G_Rec: 0.413 D_GP: 0.077 D_real: 0.861 D_fake: 0.595 +(epoch: 115, iters: 1888, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 1.015 G_ID: 0.148 G_Rec: 0.406 D_GP: 0.029 D_real: 0.855 D_fake: 0.619 +(epoch: 115, iters: 2288, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.759 G_ID: 0.252 G_Rec: 0.374 D_GP: 0.025 D_real: 1.268 D_fake: 0.495 +(epoch: 115, iters: 2688, time: 0.064) G_GAN: 0.663 G_GAN_Feat: 0.903 G_ID: 0.162 G_Rec: 0.374 D_GP: 0.121 D_real: 1.110 D_fake: 0.424 +(epoch: 115, iters: 3088, time: 0.064) G_GAN: 0.734 G_GAN_Feat: 0.917 G_ID: 0.252 G_Rec: 0.397 D_GP: 0.028 D_real: 1.366 D_fake: 0.339 +(epoch: 115, iters: 3488, time: 0.064) G_GAN: 0.075 G_GAN_Feat: 0.678 G_ID: 0.144 G_Rec: 0.428 D_GP: 0.026 D_real: 0.940 D_fake: 0.925 +(epoch: 115, iters: 3888, time: 0.064) G_GAN: 0.614 G_GAN_Feat: 0.917 G_ID: 0.241 G_Rec: 0.392 D_GP: 0.029 D_real: 1.264 D_fake: 0.392 +(epoch: 115, iters: 4288, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.715 G_ID: 0.188 G_Rec: 0.322 D_GP: 0.027 D_real: 1.046 D_fake: 0.737 +(epoch: 115, iters: 4688, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.891 G_ID: 0.245 G_Rec: 0.415 D_GP: 0.030 D_real: 0.920 D_fake: 0.737 +(epoch: 115, iters: 5088, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.806 G_ID: 0.171 G_Rec: 0.361 D_GP: 0.042 D_real: 1.054 D_fake: 0.705 +(epoch: 115, iters: 5488, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 1.214 G_ID: 0.241 G_Rec: 0.453 D_GP: 0.114 D_real: 0.346 D_fake: 0.833 +(epoch: 115, iters: 5888, time: 0.064) G_GAN: -0.023 G_GAN_Feat: 0.785 G_ID: 0.189 G_Rec: 0.525 D_GP: 0.033 D_real: 0.822 D_fake: 1.023 +(epoch: 115, iters: 6288, time: 0.064) G_GAN: 0.646 G_GAN_Feat: 1.036 G_ID: 0.249 G_Rec: 0.448 D_GP: 0.061 D_real: 0.941 D_fake: 0.364 +(epoch: 115, iters: 6688, time: 0.064) G_GAN: 0.029 G_GAN_Feat: 1.008 G_ID: 0.169 G_Rec: 0.343 D_GP: 0.049 D_real: 0.378 D_fake: 0.971 +(epoch: 115, iters: 7088, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.936 G_ID: 0.234 G_Rec: 0.429 D_GP: 0.048 D_real: 1.056 D_fake: 0.615 +(epoch: 115, iters: 7488, time: 0.064) G_GAN: 0.645 G_GAN_Feat: 0.900 G_ID: 0.170 G_Rec: 0.361 D_GP: 0.056 D_real: 1.142 D_fake: 0.377 +(epoch: 115, iters: 7888, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 0.970 G_ID: 0.244 G_Rec: 0.381 D_GP: 0.052 D_real: 0.996 D_fake: 0.440 +(epoch: 115, iters: 8288, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.581 G_ID: 0.151 G_Rec: 0.333 D_GP: 0.017 D_real: 1.387 D_fake: 0.601 +(epoch: 116, iters: 80, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.681 G_ID: 0.235 G_Rec: 0.402 D_GP: 0.018 D_real: 1.158 D_fake: 0.724 +(epoch: 116, iters: 480, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.619 G_ID: 0.141 G_Rec: 0.369 D_GP: 0.019 D_real: 1.328 D_fake: 0.626 +(epoch: 116, iters: 880, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.723 G_ID: 0.271 G_Rec: 0.386 D_GP: 0.025 D_real: 1.036 D_fake: 0.741 +(epoch: 116, iters: 1280, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.608 G_ID: 0.215 G_Rec: 0.350 D_GP: 0.024 D_real: 0.912 D_fake: 0.931 +(epoch: 116, iters: 1680, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.809 G_ID: 0.241 G_Rec: 0.406 D_GP: 0.044 D_real: 0.893 D_fake: 0.690 +(epoch: 116, iters: 2080, time: 0.064) G_GAN: 0.060 G_GAN_Feat: 0.611 G_ID: 0.165 G_Rec: 0.336 D_GP: 0.033 D_real: 0.983 D_fake: 0.940 +(epoch: 116, iters: 2480, time: 0.064) G_GAN: 0.068 G_GAN_Feat: 0.697 G_ID: 0.264 G_Rec: 0.373 D_GP: 0.034 D_real: 0.810 D_fake: 0.932 +(epoch: 116, iters: 2880, time: 0.064) G_GAN: -0.023 G_GAN_Feat: 0.690 G_ID: 0.175 G_Rec: 0.351 D_GP: 0.045 D_real: 0.776 D_fake: 1.023 +(epoch: 116, iters: 3280, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 0.792 G_ID: 0.239 G_Rec: 0.412 D_GP: 0.039 D_real: 0.787 D_fake: 0.990 +(epoch: 116, iters: 3680, time: 0.064) G_GAN: 0.018 G_GAN_Feat: 0.804 G_ID: 0.207 G_Rec: 0.359 D_GP: 0.102 D_real: 0.575 D_fake: 0.985 +(epoch: 116, iters: 4080, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.836 G_ID: 0.281 G_Rec: 0.394 D_GP: 0.032 D_real: 1.249 D_fake: 0.467 +(epoch: 116, iters: 4480, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.811 G_ID: 0.179 G_Rec: 0.363 D_GP: 0.057 D_real: 1.028 D_fake: 0.767 +(epoch: 116, iters: 4880, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 1.089 G_ID: 0.231 G_Rec: 0.429 D_GP: 0.092 D_real: 0.643 D_fake: 0.634 +(epoch: 116, iters: 5280, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.863 G_ID: 0.152 G_Rec: 0.413 D_GP: 0.098 D_real: 0.988 D_fake: 0.736 +(epoch: 116, iters: 5680, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 1.049 G_ID: 0.266 G_Rec: 0.409 D_GP: 0.074 D_real: 0.634 D_fake: 0.741 +(epoch: 116, iters: 6080, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 0.838 G_ID: 0.180 G_Rec: 0.365 D_GP: 0.094 D_real: 1.119 D_fake: 0.466 +(epoch: 116, iters: 6480, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.748 G_ID: 0.241 G_Rec: 0.383 D_GP: 0.023 D_real: 0.959 D_fake: 0.754 +(epoch: 116, iters: 6880, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.695 G_ID: 0.153 G_Rec: 0.331 D_GP: 0.035 D_real: 0.897 D_fake: 0.923 +(epoch: 116, iters: 7280, time: 0.064) G_GAN: 0.676 G_GAN_Feat: 0.862 G_ID: 0.252 G_Rec: 0.411 D_GP: 0.029 D_real: 1.423 D_fake: 0.345 +(epoch: 116, iters: 7680, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.713 G_ID: 0.168 G_Rec: 0.467 D_GP: 0.028 D_real: 1.149 D_fake: 0.751 +(epoch: 116, iters: 8080, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 1.079 G_ID: 0.261 G_Rec: 0.420 D_GP: 0.152 D_real: 0.575 D_fake: 0.550 +(epoch: 116, iters: 8480, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.895 G_ID: 0.132 G_Rec: 0.339 D_GP: 0.100 D_real: 0.743 D_fake: 0.662 +(epoch: 117, iters: 272, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.735 G_ID: 0.259 G_Rec: 0.421 D_GP: 0.022 D_real: 1.059 D_fake: 0.725 +(epoch: 117, iters: 672, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.644 G_ID: 0.184 G_Rec: 0.330 D_GP: 0.026 D_real: 1.047 D_fake: 0.872 +(epoch: 117, iters: 1072, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.805 G_ID: 0.280 G_Rec: 0.400 D_GP: 0.045 D_real: 1.187 D_fake: 0.556 +(epoch: 117, iters: 1472, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.721 G_ID: 0.160 G_Rec: 0.366 D_GP: 0.046 D_real: 1.229 D_fake: 0.560 +(epoch: 117, iters: 1872, time: 0.064) G_GAN: 0.778 G_GAN_Feat: 0.825 G_ID: 0.229 G_Rec: 0.398 D_GP: 0.042 D_real: 1.411 D_fake: 0.273 +(epoch: 117, iters: 2272, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.708 G_ID: 0.161 G_Rec: 0.345 D_GP: 0.039 D_real: 1.106 D_fake: 0.729 +(epoch: 117, iters: 2672, time: 0.064) G_GAN: 0.560 G_GAN_Feat: 0.835 G_ID: 0.220 G_Rec: 0.376 D_GP: 0.040 D_real: 1.151 D_fake: 0.455 +(epoch: 117, iters: 3072, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.641 G_ID: 0.149 G_Rec: 0.316 D_GP: 0.023 D_real: 1.128 D_fake: 0.774 +(epoch: 117, iters: 3472, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.985 G_ID: 0.255 G_Rec: 0.381 D_GP: 0.289 D_real: 0.539 D_fake: 0.689 +(epoch: 117, iters: 3872, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.874 G_ID: 0.143 G_Rec: 0.360 D_GP: 0.233 D_real: 0.502 D_fake: 0.860 +(epoch: 117, iters: 4272, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.952 G_ID: 0.249 G_Rec: 0.442 D_GP: 0.088 D_real: 0.792 D_fake: 0.583 +(epoch: 117, iters: 4672, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.672 G_ID: 0.187 G_Rec: 0.336 D_GP: 0.025 D_real: 1.113 D_fake: 0.739 +(epoch: 117, iters: 5072, time: 0.064) G_GAN: 0.812 G_GAN_Feat: 0.880 G_ID: 0.214 G_Rec: 0.409 D_GP: 0.040 D_real: 1.355 D_fake: 0.287 +(epoch: 117, iters: 5472, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 1.045 G_ID: 0.174 G_Rec: 0.388 D_GP: 0.333 D_real: 0.484 D_fake: 0.715 +(epoch: 117, iters: 5872, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.908 G_ID: 0.314 G_Rec: 0.369 D_GP: 0.049 D_real: 0.722 D_fake: 0.785 +(epoch: 117, iters: 6272, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.843 G_ID: 0.178 G_Rec: 0.369 D_GP: 0.152 D_real: 0.643 D_fake: 0.849 +(epoch: 117, iters: 6672, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 1.211 G_ID: 0.276 G_Rec: 0.471 D_GP: 0.147 D_real: 1.091 D_fake: 0.516 +(epoch: 117, iters: 7072, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.740 G_ID: 0.192 G_Rec: 0.402 D_GP: 0.035 D_real: 1.114 D_fake: 0.647 +(epoch: 117, iters: 7472, time: 0.064) G_GAN: 0.643 G_GAN_Feat: 0.933 G_ID: 0.301 G_Rec: 0.390 D_GP: 0.071 D_real: 1.061 D_fake: 0.378 +(epoch: 117, iters: 7872, time: 0.064) G_GAN: 0.434 G_GAN_Feat: 0.817 G_ID: 0.162 G_Rec: 0.356 D_GP: 0.047 D_real: 1.080 D_fake: 0.569 +(epoch: 117, iters: 8272, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 0.773 G_ID: 0.243 G_Rec: 0.393 D_GP: 0.027 D_real: 1.321 D_fake: 0.464 +(epoch: 118, iters: 64, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.729 G_ID: 0.189 G_Rec: 0.327 D_GP: 0.042 D_real: 1.058 D_fake: 0.784 +(epoch: 118, iters: 464, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 1.131 G_ID: 0.255 G_Rec: 0.396 D_GP: 0.190 D_real: 0.175 D_fake: 0.846 +(epoch: 118, iters: 864, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.923 G_ID: 0.161 G_Rec: 0.370 D_GP: 0.136 D_real: 0.778 D_fake: 0.633 +(epoch: 118, iters: 1264, time: 0.064) G_GAN: 0.551 G_GAN_Feat: 0.919 G_ID: 0.214 G_Rec: 0.405 D_GP: 0.026 D_real: 1.308 D_fake: 0.452 +(epoch: 118, iters: 1664, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.796 G_ID: 0.138 G_Rec: 0.328 D_GP: 0.061 D_real: 1.153 D_fake: 0.484 +(epoch: 118, iters: 2064, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 0.857 G_ID: 0.215 G_Rec: 0.394 D_GP: 0.032 D_real: 1.126 D_fake: 0.458 +(epoch: 118, iters: 2464, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.761 G_ID: 0.169 G_Rec: 0.396 D_GP: 0.028 D_real: 1.169 D_fake: 0.608 +(epoch: 118, iters: 2864, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 0.762 G_ID: 0.224 G_Rec: 0.396 D_GP: 0.023 D_real: 1.411 D_fake: 0.352 +(epoch: 118, iters: 3264, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.584 G_ID: 0.164 G_Rec: 0.350 D_GP: 0.022 D_real: 1.154 D_fake: 0.780 +(epoch: 118, iters: 3664, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.753 G_ID: 0.263 G_Rec: 0.370 D_GP: 0.029 D_real: 1.070 D_fake: 0.587 +(epoch: 118, iters: 4064, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.746 G_ID: 0.174 G_Rec: 0.337 D_GP: 0.045 D_real: 0.990 D_fake: 0.799 +(epoch: 118, iters: 4464, time: 0.064) G_GAN: 0.598 G_GAN_Feat: 0.941 G_ID: 0.224 G_Rec: 0.386 D_GP: 0.250 D_real: 0.879 D_fake: 0.422 +(epoch: 118, iters: 4864, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.779 G_ID: 0.149 G_Rec: 0.373 D_GP: 0.051 D_real: 0.986 D_fake: 0.718 +(epoch: 118, iters: 5264, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.894 G_ID: 0.255 G_Rec: 0.371 D_GP: 0.080 D_real: 0.820 D_fake: 0.662 +(epoch: 118, iters: 5664, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.820 G_ID: 0.168 G_Rec: 0.392 D_GP: 0.047 D_real: 0.724 D_fake: 0.878 +(epoch: 118, iters: 6064, time: 0.064) G_GAN: 0.924 G_GAN_Feat: 1.193 G_ID: 0.223 G_Rec: 0.467 D_GP: 0.052 D_real: 1.473 D_fake: 0.244 +(epoch: 118, iters: 6464, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.945 G_ID: 0.131 G_Rec: 0.339 D_GP: 0.116 D_real: 0.448 D_fake: 0.887 +(epoch: 118, iters: 6864, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.689 G_ID: 0.243 G_Rec: 0.415 D_GP: 0.018 D_real: 1.109 D_fake: 0.668 +(epoch: 118, iters: 7264, time: 0.064) G_GAN: 0.047 G_GAN_Feat: 0.692 G_ID: 0.182 G_Rec: 0.363 D_GP: 0.023 D_real: 0.972 D_fake: 0.953 +(epoch: 118, iters: 7664, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.777 G_ID: 0.204 G_Rec: 0.408 D_GP: 0.023 D_real: 1.165 D_fake: 0.620 +(epoch: 118, iters: 8064, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.653 G_ID: 0.168 G_Rec: 0.365 D_GP: 0.022 D_real: 0.970 D_fake: 0.895 +(epoch: 118, iters: 8464, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.796 G_ID: 0.235 G_Rec: 0.415 D_GP: 0.034 D_real: 0.912 D_fake: 0.826 +(epoch: 119, iters: 256, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.740 G_ID: 0.154 G_Rec: 0.375 D_GP: 0.078 D_real: 0.915 D_fake: 0.788 +(epoch: 119, iters: 656, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 1.097 G_ID: 0.216 G_Rec: 0.418 D_GP: 0.075 D_real: 0.446 D_fake: 0.670 +(epoch: 119, iters: 1056, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.703 G_ID: 0.197 G_Rec: 0.333 D_GP: 0.033 D_real: 1.051 D_fake: 0.842 +(epoch: 119, iters: 1456, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 1.001 G_ID: 0.207 G_Rec: 0.396 D_GP: 0.049 D_real: 0.765 D_fake: 0.547 +(epoch: 119, iters: 1856, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.712 G_ID: 0.171 G_Rec: 0.326 D_GP: 0.027 D_real: 1.252 D_fake: 0.621 +(epoch: 119, iters: 2256, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.713 G_ID: 0.249 G_Rec: 0.367 D_GP: 0.027 D_real: 1.059 D_fake: 0.768 +(epoch: 119, iters: 2656, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.695 G_ID: 0.138 G_Rec: 0.353 D_GP: 0.037 D_real: 0.932 D_fake: 0.896 +(epoch: 119, iters: 3056, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 0.882 G_ID: 0.276 G_Rec: 0.392 D_GP: 0.060 D_real: 1.073 D_fake: 0.459 +(epoch: 119, iters: 3456, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.707 G_ID: 0.166 G_Rec: 0.333 D_GP: 0.037 D_real: 0.947 D_fake: 0.823 +(epoch: 119, iters: 3856, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.859 G_ID: 0.260 G_Rec: 0.439 D_GP: 0.038 D_real: 0.852 D_fake: 0.811 +(epoch: 119, iters: 4256, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.701 G_ID: 0.138 G_Rec: 0.308 D_GP: 0.034 D_real: 1.027 D_fake: 0.777 +(epoch: 119, iters: 4656, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.922 G_ID: 0.260 G_Rec: 0.384 D_GP: 0.057 D_real: 0.966 D_fake: 0.726 +(epoch: 119, iters: 5056, time: 0.064) G_GAN: -0.072 G_GAN_Feat: 0.697 G_ID: 0.176 G_Rec: 0.346 D_GP: 0.028 D_real: 0.742 D_fake: 1.072 +(epoch: 119, iters: 5456, time: 0.064) G_GAN: 0.740 G_GAN_Feat: 0.805 G_ID: 0.221 G_Rec: 0.376 D_GP: 0.028 D_real: 1.429 D_fake: 0.280 +(epoch: 119, iters: 5856, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.770 G_ID: 0.152 G_Rec: 0.347 D_GP: 0.049 D_real: 1.043 D_fake: 0.777 +(epoch: 119, iters: 6256, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.838 G_ID: 0.282 G_Rec: 0.384 D_GP: 0.026 D_real: 1.144 D_fake: 0.591 +(epoch: 119, iters: 6656, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.912 G_ID: 0.157 G_Rec: 0.386 D_GP: 0.038 D_real: 0.595 D_fake: 0.789 +(epoch: 119, iters: 7056, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 1.007 G_ID: 0.213 G_Rec: 0.367 D_GP: 0.063 D_real: 0.496 D_fake: 0.774 +(epoch: 119, iters: 7456, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.867 G_ID: 0.174 G_Rec: 0.348 D_GP: 0.049 D_real: 0.771 D_fake: 0.694 +(epoch: 119, iters: 7856, time: 0.064) G_GAN: 0.629 G_GAN_Feat: 0.988 G_ID: 0.244 G_Rec: 0.418 D_GP: 0.036 D_real: 1.101 D_fake: 0.379 +(epoch: 119, iters: 8256, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.845 G_ID: 0.155 G_Rec: 0.349 D_GP: 0.051 D_real: 0.841 D_fake: 0.693 +(epoch: 120, iters: 48, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 0.841 G_ID: 0.196 G_Rec: 0.402 D_GP: 0.024 D_real: 1.160 D_fake: 0.500 +(epoch: 120, iters: 448, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 1.148 G_ID: 0.161 G_Rec: 0.401 D_GP: 1.558 D_real: 0.469 D_fake: 0.724 +(epoch: 120, iters: 848, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.824 G_ID: 0.248 G_Rec: 0.418 D_GP: 0.025 D_real: 1.095 D_fake: 0.656 +(epoch: 120, iters: 1248, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.770 G_ID: 0.159 G_Rec: 0.386 D_GP: 0.044 D_real: 1.215 D_fake: 0.537 +(epoch: 120, iters: 1648, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.898 G_ID: 0.225 G_Rec: 0.415 D_GP: 0.039 D_real: 0.968 D_fake: 0.596 +(epoch: 120, iters: 2048, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.745 G_ID: 0.153 G_Rec: 0.366 D_GP: 0.041 D_real: 1.021 D_fake: 0.722 +(epoch: 120, iters: 2448, time: 0.064) G_GAN: 0.589 G_GAN_Feat: 0.986 G_ID: 0.257 G_Rec: 0.397 D_GP: 0.067 D_real: 0.998 D_fake: 0.443 +(epoch: 120, iters: 2848, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.699 G_ID: 0.165 G_Rec: 0.327 D_GP: 0.034 D_real: 1.106 D_fake: 0.800 +(epoch: 120, iters: 3248, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.830 G_ID: 0.217 G_Rec: 0.388 D_GP: 0.027 D_real: 1.312 D_fake: 0.485 +(epoch: 120, iters: 3648, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.768 G_ID: 0.148 G_Rec: 0.347 D_GP: 0.038 D_real: 1.274 D_fake: 0.520 +(epoch: 120, iters: 4048, time: 0.064) G_GAN: 0.618 G_GAN_Feat: 0.885 G_ID: 0.206 G_Rec: 0.389 D_GP: 0.054 D_real: 1.253 D_fake: 0.427 +(epoch: 120, iters: 4448, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.879 G_ID: 0.164 G_Rec: 0.366 D_GP: 0.064 D_real: 0.966 D_fake: 0.529 +(epoch: 120, iters: 4848, time: 0.064) G_GAN: 0.647 G_GAN_Feat: 0.769 G_ID: 0.244 G_Rec: 0.374 D_GP: 0.026 D_real: 1.443 D_fake: 0.356 +(epoch: 120, iters: 5248, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.639 G_ID: 0.175 G_Rec: 0.327 D_GP: 0.023 D_real: 1.203 D_fake: 0.741 +(epoch: 120, iters: 5648, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.933 G_ID: 0.275 G_Rec: 0.392 D_GP: 0.052 D_real: 0.865 D_fake: 0.625 +(epoch: 120, iters: 6048, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.968 G_ID: 0.208 G_Rec: 0.355 D_GP: 0.085 D_real: 0.509 D_fake: 0.700 +(epoch: 120, iters: 6448, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.804 G_ID: 0.230 G_Rec: 0.385 D_GP: 0.033 D_real: 1.028 D_fake: 0.799 +(epoch: 120, iters: 6848, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.930 G_ID: 0.153 G_Rec: 0.366 D_GP: 0.245 D_real: 0.590 D_fake: 0.733 +(epoch: 120, iters: 7248, time: 0.064) G_GAN: 0.698 G_GAN_Feat: 0.902 G_ID: 0.240 G_Rec: 0.401 D_GP: 0.036 D_real: 1.237 D_fake: 0.315 +(epoch: 120, iters: 7648, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.748 G_ID: 0.196 G_Rec: 0.400 D_GP: 0.030 D_real: 0.959 D_fake: 0.845 +(epoch: 120, iters: 8048, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.869 G_ID: 0.262 G_Rec: 0.364 D_GP: 0.048 D_real: 0.917 D_fake: 0.660 +(epoch: 120, iters: 8448, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.700 G_ID: 0.151 G_Rec: 0.312 D_GP: 0.024 D_real: 1.153 D_fake: 0.669 +(epoch: 121, iters: 240, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.771 G_ID: 0.226 G_Rec: 0.383 D_GP: 0.022 D_real: 0.931 D_fake: 0.854 +(epoch: 121, iters: 640, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 0.733 G_ID: 0.182 G_Rec: 0.317 D_GP: 0.040 D_real: 0.951 D_fake: 0.944 +(epoch: 121, iters: 1040, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.871 G_ID: 0.251 G_Rec: 0.373 D_GP: 0.023 D_real: 1.073 D_fake: 0.657 +(epoch: 121, iters: 1440, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.853 G_ID: 0.156 G_Rec: 0.335 D_GP: 0.059 D_real: 1.016 D_fake: 0.488 +(epoch: 121, iters: 1840, time: 0.064) G_GAN: 0.528 G_GAN_Feat: 0.852 G_ID: 0.214 G_Rec: 0.373 D_GP: 0.028 D_real: 1.241 D_fake: 0.516 +(epoch: 121, iters: 2240, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.806 G_ID: 0.176 G_Rec: 0.361 D_GP: 0.028 D_real: 0.956 D_fake: 0.753 +(epoch: 121, iters: 2640, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.819 G_ID: 0.248 G_Rec: 0.391 D_GP: 0.022 D_real: 1.080 D_fake: 0.650 +(epoch: 121, iters: 3040, time: 0.064) G_GAN: 0.042 G_GAN_Feat: 0.892 G_ID: 0.158 G_Rec: 0.338 D_GP: 0.052 D_real: 0.393 D_fake: 0.958 +(epoch: 121, iters: 3440, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.853 G_ID: 0.231 G_Rec: 0.422 D_GP: 0.026 D_real: 1.124 D_fake: 0.614 +(epoch: 121, iters: 3840, time: 0.064) G_GAN: 0.704 G_GAN_Feat: 0.712 G_ID: 0.159 G_Rec: 0.308 D_GP: 0.025 D_real: 1.548 D_fake: 0.324 +(epoch: 121, iters: 4240, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.828 G_ID: 0.241 G_Rec: 0.379 D_GP: 0.036 D_real: 1.112 D_fake: 0.597 +(epoch: 121, iters: 4640, time: 0.064) G_GAN: 0.650 G_GAN_Feat: 0.837 G_ID: 0.140 G_Rec: 0.361 D_GP: 0.045 D_real: 1.312 D_fake: 0.425 +(epoch: 121, iters: 5040, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.802 G_ID: 0.218 G_Rec: 0.390 D_GP: 0.028 D_real: 1.203 D_fake: 0.526 +(epoch: 121, iters: 5440, time: 0.064) G_GAN: -0.101 G_GAN_Feat: 0.659 G_ID: 0.219 G_Rec: 0.354 D_GP: 0.024 D_real: 0.854 D_fake: 1.101 +(epoch: 121, iters: 5840, time: 0.064) G_GAN: 0.583 G_GAN_Feat: 0.876 G_ID: 0.235 G_Rec: 0.398 D_GP: 0.039 D_real: 1.189 D_fake: 0.422 +(epoch: 121, iters: 6240, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.819 G_ID: 0.162 G_Rec: 0.335 D_GP: 0.067 D_real: 0.791 D_fake: 0.854 +(epoch: 121, iters: 6640, time: 0.064) G_GAN: 0.738 G_GAN_Feat: 0.931 G_ID: 0.228 G_Rec: 0.384 D_GP: 0.030 D_real: 1.234 D_fake: 0.300 +(epoch: 121, iters: 7040, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 0.720 G_ID: 0.156 G_Rec: 0.388 D_GP: 0.025 D_real: 1.408 D_fake: 0.439 +(epoch: 121, iters: 7440, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.972 G_ID: 0.285 G_Rec: 0.416 D_GP: 0.051 D_real: 0.950 D_fake: 0.526 +(epoch: 121, iters: 7840, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.766 G_ID: 0.151 G_Rec: 0.330 D_GP: 0.034 D_real: 1.207 D_fake: 0.593 +(epoch: 121, iters: 8240, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.857 G_ID: 0.221 G_Rec: 0.500 D_GP: 0.024 D_real: 1.224 D_fake: 0.577 +(epoch: 122, iters: 32, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.668 G_ID: 0.166 G_Rec: 0.356 D_GP: 0.025 D_real: 1.008 D_fake: 0.810 +(epoch: 122, iters: 432, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.999 G_ID: 0.272 G_Rec: 0.401 D_GP: 0.068 D_real: 0.868 D_fake: 0.612 +(epoch: 122, iters: 832, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.745 G_ID: 0.134 G_Rec: 0.327 D_GP: 0.041 D_real: 0.995 D_fake: 0.791 +(epoch: 122, iters: 1232, time: 0.064) G_GAN: 0.693 G_GAN_Feat: 0.963 G_ID: 0.248 G_Rec: 0.376 D_GP: 0.031 D_real: 1.188 D_fake: 0.345 +(epoch: 122, iters: 1632, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.650 G_ID: 0.152 G_Rec: 0.339 D_GP: 0.022 D_real: 1.449 D_fake: 0.496 +(epoch: 122, iters: 2032, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.867 G_ID: 0.240 G_Rec: 0.378 D_GP: 0.055 D_real: 1.062 D_fake: 0.520 +(epoch: 122, iters: 2432, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.756 G_ID: 0.169 G_Rec: 0.361 D_GP: 0.030 D_real: 1.087 D_fake: 0.749 +(epoch: 122, iters: 2832, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 1.122 G_ID: 0.237 G_Rec: 0.438 D_GP: 0.254 D_real: 0.546 D_fake: 0.483 +(epoch: 122, iters: 3232, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.723 G_ID: 0.145 G_Rec: 0.327 D_GP: 0.030 D_real: 1.059 D_fake: 0.770 +(epoch: 122, iters: 3632, time: 0.064) G_GAN: 0.626 G_GAN_Feat: 0.824 G_ID: 0.268 G_Rec: 0.415 D_GP: 0.026 D_real: 1.386 D_fake: 0.390 +(epoch: 122, iters: 4032, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.703 G_ID: 0.158 G_Rec: 0.315 D_GP: 0.028 D_real: 1.148 D_fake: 0.733 +(epoch: 122, iters: 4432, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.995 G_ID: 0.266 G_Rec: 0.417 D_GP: 0.087 D_real: 0.426 D_fake: 0.846 +(epoch: 122, iters: 4832, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.730 G_ID: 0.155 G_Rec: 0.340 D_GP: 0.025 D_real: 1.278 D_fake: 0.586 +(epoch: 122, iters: 5232, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.821 G_ID: 0.223 G_Rec: 0.415 D_GP: 0.020 D_real: 1.282 D_fake: 0.538 +(epoch: 122, iters: 5632, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.968 G_ID: 0.157 G_Rec: 0.360 D_GP: 0.326 D_real: 0.536 D_fake: 0.729 +(epoch: 122, iters: 6032, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.875 G_ID: 0.218 G_Rec: 0.396 D_GP: 0.041 D_real: 0.949 D_fake: 0.771 +(epoch: 122, iters: 6432, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.818 G_ID: 0.156 G_Rec: 0.363 D_GP: 0.037 D_real: 1.086 D_fake: 0.577 +(epoch: 122, iters: 6832, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.976 G_ID: 0.223 G_Rec: 0.414 D_GP: 0.086 D_real: 0.771 D_fake: 0.552 +(epoch: 122, iters: 7232, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.679 G_ID: 0.157 G_Rec: 0.343 D_GP: 0.034 D_real: 0.932 D_fake: 0.913 +(epoch: 122, iters: 7632, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.902 G_ID: 0.243 G_Rec: 0.404 D_GP: 0.084 D_real: 0.483 D_fake: 0.963 +(epoch: 122, iters: 8032, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.738 G_ID: 0.164 G_Rec: 0.343 D_GP: 0.035 D_real: 1.134 D_fake: 0.738 +(epoch: 122, iters: 8432, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.948 G_ID: 0.236 G_Rec: 0.410 D_GP: 0.061 D_real: 0.767 D_fake: 0.620 +(epoch: 123, iters: 224, time: 0.064) G_GAN: 0.609 G_GAN_Feat: 1.026 G_ID: 0.168 G_Rec: 0.396 D_GP: 0.749 D_real: 0.695 D_fake: 0.466 +(epoch: 123, iters: 624, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 1.014 G_ID: 0.270 G_Rec: 0.414 D_GP: 0.184 D_real: 0.294 D_fake: 0.938 +(epoch: 123, iters: 1024, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.857 G_ID: 0.153 G_Rec: 0.341 D_GP: 0.054 D_real: 1.074 D_fake: 0.635 +(epoch: 123, iters: 1424, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.783 G_ID: 0.212 G_Rec: 0.411 D_GP: 0.021 D_real: 1.257 D_fake: 0.522 +(epoch: 123, iters: 1824, time: 0.064) G_GAN: -0.062 G_GAN_Feat: 0.740 G_ID: 0.167 G_Rec: 0.323 D_GP: 0.032 D_real: 0.855 D_fake: 1.062 +(epoch: 123, iters: 2224, time: 0.064) G_GAN: 0.885 G_GAN_Feat: 1.047 G_ID: 0.258 G_Rec: 0.424 D_GP: 0.183 D_real: 0.945 D_fake: 0.274 +(epoch: 123, iters: 2624, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.823 G_ID: 0.153 G_Rec: 0.353 D_GP: 0.033 D_real: 1.139 D_fake: 0.541 +(epoch: 123, iters: 3024, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.904 G_ID: 0.223 G_Rec: 0.403 D_GP: 0.032 D_real: 1.142 D_fake: 0.494 +(epoch: 123, iters: 3424, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.931 G_ID: 0.147 G_Rec: 0.354 D_GP: 0.195 D_real: 0.833 D_fake: 0.502 +(epoch: 123, iters: 3824, time: 0.064) G_GAN: 0.683 G_GAN_Feat: 0.973 G_ID: 0.260 G_Rec: 0.461 D_GP: 0.039 D_real: 1.352 D_fake: 0.350 +(epoch: 123, iters: 4224, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.679 G_ID: 0.163 G_Rec: 0.333 D_GP: 0.028 D_real: 1.145 D_fake: 0.686 +(epoch: 123, iters: 4624, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.918 G_ID: 0.240 G_Rec: 0.383 D_GP: 0.058 D_real: 0.972 D_fake: 0.511 +(epoch: 123, iters: 5024, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.627 G_ID: 0.150 G_Rec: 0.363 D_GP: 0.019 D_real: 1.272 D_fake: 0.575 +(epoch: 123, iters: 5424, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.821 G_ID: 0.233 G_Rec: 0.385 D_GP: 0.035 D_real: 1.116 D_fake: 0.485 +(epoch: 123, iters: 5824, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.703 G_ID: 0.142 G_Rec: 0.324 D_GP: 0.037 D_real: 0.939 D_fake: 0.881 +(epoch: 123, iters: 6224, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.826 G_ID: 0.247 G_Rec: 0.404 D_GP: 0.023 D_real: 1.339 D_fake: 0.435 +(epoch: 123, iters: 6624, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.805 G_ID: 0.169 G_Rec: 0.358 D_GP: 0.055 D_real: 0.939 D_fake: 0.704 +(epoch: 123, iters: 7024, time: 0.064) G_GAN: 0.756 G_GAN_Feat: 1.036 G_ID: 0.235 G_Rec: 0.421 D_GP: 0.121 D_real: 0.878 D_fake: 0.322 +(epoch: 123, iters: 7424, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.700 G_ID: 0.169 G_Rec: 0.352 D_GP: 0.031 D_real: 1.088 D_fake: 0.756 +(epoch: 123, iters: 7824, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 1.001 G_ID: 0.231 G_Rec: 0.398 D_GP: 0.068 D_real: 0.808 D_fake: 0.516 +(epoch: 123, iters: 8224, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.790 G_ID: 0.168 G_Rec: 0.337 D_GP: 0.036 D_real: 1.253 D_fake: 0.511 +(epoch: 124, iters: 16, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 0.947 G_ID: 0.224 G_Rec: 0.411 D_GP: 0.046 D_real: 1.129 D_fake: 0.462 +(epoch: 124, iters: 416, time: 0.064) G_GAN: 0.003 G_GAN_Feat: 0.846 G_ID: 0.239 G_Rec: 0.345 D_GP: 0.055 D_real: 0.625 D_fake: 0.999 +(epoch: 124, iters: 816, time: 0.064) G_GAN: 0.741 G_GAN_Feat: 1.034 G_ID: 0.251 G_Rec: 0.387 D_GP: 0.121 D_real: 1.060 D_fake: 0.287 +(epoch: 124, iters: 1216, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.682 G_ID: 0.148 G_Rec: 0.379 D_GP: 0.021 D_real: 1.194 D_fake: 0.762 +(epoch: 124, iters: 1616, time: 0.064) G_GAN: 0.532 G_GAN_Feat: 1.000 G_ID: 0.237 G_Rec: 0.438 D_GP: 0.139 D_real: 0.971 D_fake: 0.473 +(epoch: 124, iters: 2016, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.727 G_ID: 0.158 G_Rec: 0.318 D_GP: 0.047 D_real: 0.946 D_fake: 0.830 +(epoch: 124, iters: 2416, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.979 G_ID: 0.239 G_Rec: 0.419 D_GP: 0.067 D_real: 0.881 D_fake: 0.571 +(epoch: 124, iters: 2816, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.820 G_ID: 0.153 G_Rec: 0.328 D_GP: 0.040 D_real: 1.148 D_fake: 0.644 +(epoch: 124, iters: 3216, time: 0.064) G_GAN: 0.608 G_GAN_Feat: 0.981 G_ID: 0.216 G_Rec: 0.381 D_GP: 0.066 D_real: 0.851 D_fake: 0.417 +(epoch: 124, iters: 3616, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.709 G_ID: 0.182 G_Rec: 0.360 D_GP: 0.028 D_real: 1.210 D_fake: 0.671 +(epoch: 124, iters: 4016, time: 0.064) G_GAN: 0.633 G_GAN_Feat: 0.839 G_ID: 0.237 G_Rec: 0.399 D_GP: 0.026 D_real: 1.321 D_fake: 0.398 +(epoch: 124, iters: 4416, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.790 G_ID: 0.173 G_Rec: 0.359 D_GP: 0.043 D_real: 1.077 D_fake: 0.685 +(epoch: 124, iters: 4816, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.934 G_ID: 0.265 G_Rec: 0.398 D_GP: 0.060 D_real: 0.726 D_fake: 0.704 +(epoch: 124, iters: 5216, time: 0.064) G_GAN: -0.025 G_GAN_Feat: 0.862 G_ID: 0.168 G_Rec: 0.373 D_GP: 0.066 D_real: 0.758 D_fake: 1.025 +(epoch: 124, iters: 5616, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.802 G_ID: 0.237 G_Rec: 0.348 D_GP: 0.029 D_real: 1.017 D_fake: 0.721 +(epoch: 124, iters: 6016, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.806 G_ID: 0.149 G_Rec: 0.359 D_GP: 0.063 D_real: 1.078 D_fake: 0.624 +(epoch: 124, iters: 6416, time: 0.064) G_GAN: 0.588 G_GAN_Feat: 1.019 G_ID: 0.237 G_Rec: 0.441 D_GP: 0.106 D_real: 1.096 D_fake: 0.473 +(epoch: 124, iters: 6816, time: 0.064) G_GAN: -0.010 G_GAN_Feat: 0.873 G_ID: 0.157 G_Rec: 0.369 D_GP: 0.030 D_real: 0.750 D_fake: 1.012 +(epoch: 124, iters: 7216, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 1.126 G_ID: 0.269 G_Rec: 0.380 D_GP: 0.097 D_real: 0.622 D_fake: 0.444 +(epoch: 124, iters: 7616, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.726 G_ID: 0.125 G_Rec: 0.330 D_GP: 0.024 D_real: 1.026 D_fake: 0.856 +(epoch: 124, iters: 8016, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 0.912 G_ID: 0.217 G_Rec: 0.393 D_GP: 0.034 D_real: 1.225 D_fake: 0.403 +(epoch: 124, iters: 8416, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.945 G_ID: 0.161 G_Rec: 0.373 D_GP: 0.366 D_real: 0.520 D_fake: 0.848 +(epoch: 125, iters: 208, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.916 G_ID: 0.239 G_Rec: 0.384 D_GP: 0.035 D_real: 1.059 D_fake: 0.530 +(epoch: 125, iters: 608, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.702 G_ID: 0.178 G_Rec: 0.336 D_GP: 0.027 D_real: 0.980 D_fake: 0.919 +(epoch: 125, iters: 1008, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 1.068 G_ID: 0.216 G_Rec: 0.429 D_GP: 0.066 D_real: 0.766 D_fake: 0.771 +(epoch: 125, iters: 1408, time: 0.064) G_GAN: -0.032 G_GAN_Feat: 0.886 G_ID: 0.153 G_Rec: 0.337 D_GP: 0.053 D_real: 0.454 D_fake: 1.032 +(epoch: 125, iters: 1808, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 1.065 G_ID: 0.253 G_Rec: 0.390 D_GP: 0.069 D_real: 0.426 D_fake: 0.820 +(epoch: 125, iters: 2208, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.987 G_ID: 0.165 G_Rec: 0.334 D_GP: 0.194 D_real: 0.516 D_fake: 0.872 +(epoch: 125, iters: 2608, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.908 G_ID: 0.242 G_Rec: 0.424 D_GP: 0.025 D_real: 1.086 D_fake: 0.632 +(epoch: 125, iters: 3008, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.632 G_ID: 0.183 G_Rec: 0.342 D_GP: 0.020 D_real: 1.102 D_fake: 0.821 +(epoch: 125, iters: 3408, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.764 G_ID: 0.268 G_Rec: 0.372 D_GP: 0.024 D_real: 1.107 D_fake: 0.652 +(epoch: 125, iters: 3808, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.732 G_ID: 0.135 G_Rec: 0.336 D_GP: 0.062 D_real: 0.869 D_fake: 0.833 +(epoch: 125, iters: 4208, time: 0.064) G_GAN: 0.585 G_GAN_Feat: 0.800 G_ID: 0.249 G_Rec: 0.382 D_GP: 0.027 D_real: 1.313 D_fake: 0.424 +(epoch: 125, iters: 4608, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 1.047 G_ID: 0.183 G_Rec: 0.351 D_GP: 0.093 D_real: 0.528 D_fake: 0.821 +(epoch: 125, iters: 5008, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 0.866 G_ID: 0.245 G_Rec: 0.379 D_GP: 0.049 D_real: 1.131 D_fake: 0.476 +(epoch: 125, iters: 5408, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.869 G_ID: 0.147 G_Rec: 0.358 D_GP: 0.043 D_real: 1.127 D_fake: 0.549 +(epoch: 125, iters: 5808, time: 0.064) G_GAN: 0.549 G_GAN_Feat: 1.077 G_ID: 0.257 G_Rec: 0.471 D_GP: 0.210 D_real: 0.810 D_fake: 0.460 +(epoch: 125, iters: 6208, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.855 G_ID: 0.170 G_Rec: 0.348 D_GP: 0.068 D_real: 0.863 D_fake: 0.717 +(epoch: 125, iters: 6608, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.831 G_ID: 0.213 G_Rec: 0.361 D_GP: 0.026 D_real: 0.885 D_fake: 0.919 +(epoch: 125, iters: 7008, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.813 G_ID: 0.141 G_Rec: 0.376 D_GP: 0.040 D_real: 1.153 D_fake: 0.605 +(epoch: 125, iters: 7408, time: 0.064) G_GAN: 0.831 G_GAN_Feat: 1.029 G_ID: 0.268 G_Rec: 0.425 D_GP: 0.088 D_real: 1.023 D_fake: 0.254 +(epoch: 125, iters: 7808, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.799 G_ID: 0.149 G_Rec: 0.358 D_GP: 0.033 D_real: 0.979 D_fake: 0.718 +(epoch: 125, iters: 8208, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 1.034 G_ID: 0.257 G_Rec: 0.410 D_GP: 0.046 D_real: 0.767 D_fake: 0.551 +(epoch: 125, iters: 8608, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.723 G_ID: 0.174 G_Rec: 0.347 D_GP: 0.025 D_real: 1.328 D_fake: 0.608 +(epoch: 126, iters: 400, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.938 G_ID: 0.241 G_Rec: 0.383 D_GP: 0.046 D_real: 0.953 D_fake: 0.755 +(epoch: 126, iters: 800, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.855 G_ID: 0.150 G_Rec: 0.364 D_GP: 0.046 D_real: 1.070 D_fake: 0.627 +(epoch: 126, iters: 1200, time: 0.064) G_GAN: 0.648 G_GAN_Feat: 1.118 G_ID: 0.217 G_Rec: 0.415 D_GP: 0.097 D_real: 0.801 D_fake: 0.414 +(epoch: 126, iters: 1600, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.835 G_ID: 0.147 G_Rec: 0.363 D_GP: 0.029 D_real: 1.040 D_fake: 0.720 +(epoch: 126, iters: 2000, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 0.918 G_ID: 0.245 G_Rec: 0.404 D_GP: 0.027 D_real: 1.183 D_fake: 0.414 +(epoch: 126, iters: 2400, time: 0.064) G_GAN: 0.627 G_GAN_Feat: 0.839 G_ID: 0.136 G_Rec: 0.351 D_GP: 0.058 D_real: 1.328 D_fake: 0.386 +(epoch: 126, iters: 2800, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.966 G_ID: 0.227 G_Rec: 0.431 D_GP: 0.039 D_real: 0.905 D_fake: 0.624 +(epoch: 126, iters: 3200, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.651 G_ID: 0.165 G_Rec: 0.337 D_GP: 0.021 D_real: 1.219 D_fake: 0.664 +(epoch: 126, iters: 3600, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.981 G_ID: 0.233 G_Rec: 0.442 D_GP: 0.056 D_real: 0.843 D_fake: 0.586 +(epoch: 126, iters: 4000, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.709 G_ID: 0.189 G_Rec: 0.325 D_GP: 0.027 D_real: 1.094 D_fake: 0.768 +(epoch: 126, iters: 4400, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.831 G_ID: 0.257 G_Rec: 0.397 D_GP: 0.035 D_real: 1.044 D_fake: 0.685 +(epoch: 126, iters: 4800, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.818 G_ID: 0.192 G_Rec: 0.343 D_GP: 0.038 D_real: 1.091 D_fake: 0.654 +(epoch: 126, iters: 5200, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.921 G_ID: 0.222 G_Rec: 0.376 D_GP: 0.040 D_real: 0.954 D_fake: 0.608 +(epoch: 126, iters: 5600, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.715 G_ID: 0.190 G_Rec: 0.350 D_GP: 0.026 D_real: 1.051 D_fake: 0.887 +(epoch: 126, iters: 6000, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.831 G_ID: 0.193 G_Rec: 0.394 D_GP: 0.036 D_real: 1.003 D_fake: 0.652 +(epoch: 126, iters: 6400, time: 0.064) G_GAN: 0.396 G_GAN_Feat: 0.861 G_ID: 0.144 G_Rec: 0.371 D_GP: 0.052 D_real: 0.966 D_fake: 0.633 +(epoch: 126, iters: 6800, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 1.216 G_ID: 0.290 G_Rec: 0.424 D_GP: 0.241 D_real: 0.306 D_fake: 0.828 +(epoch: 126, iters: 7200, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.779 G_ID: 0.144 G_Rec: 0.326 D_GP: 0.030 D_real: 1.037 D_fake: 0.728 +(epoch: 126, iters: 7600, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.866 G_ID: 0.236 G_Rec: 0.398 D_GP: 0.030 D_real: 1.095 D_fake: 0.594 +(epoch: 126, iters: 8000, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.929 G_ID: 0.155 G_Rec: 0.352 D_GP: 0.051 D_real: 0.645 D_fake: 0.709 +(epoch: 126, iters: 8400, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.869 G_ID: 0.207 G_Rec: 0.355 D_GP: 0.033 D_real: 1.058 D_fake: 0.673 +(epoch: 127, iters: 192, time: 0.064) G_GAN: 0.595 G_GAN_Feat: 0.758 G_ID: 0.141 G_Rec: 0.381 D_GP: 0.037 D_real: 1.391 D_fake: 0.413 +(epoch: 127, iters: 592, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.877 G_ID: 0.232 G_Rec: 0.430 D_GP: 0.026 D_real: 0.967 D_fake: 0.733 +(epoch: 127, iters: 992, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.610 G_ID: 0.140 G_Rec: 0.310 D_GP: 0.025 D_real: 1.056 D_fake: 0.830 +(epoch: 127, iters: 1392, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.724 G_ID: 0.195 G_Rec: 0.354 D_GP: 0.026 D_real: 1.007 D_fake: 0.714 +(epoch: 127, iters: 1792, time: 0.064) G_GAN: -0.018 G_GAN_Feat: 0.745 G_ID: 0.160 G_Rec: 0.337 D_GP: 0.059 D_real: 0.753 D_fake: 1.018 +(epoch: 127, iters: 2192, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.912 G_ID: 0.271 G_Rec: 0.415 D_GP: 0.058 D_real: 0.855 D_fake: 0.620 +(epoch: 127, iters: 2592, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 0.773 G_ID: 0.131 G_Rec: 0.364 D_GP: 0.048 D_real: 1.401 D_fake: 0.412 +(epoch: 127, iters: 2992, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.864 G_ID: 0.248 G_Rec: 0.396 D_GP: 0.034 D_real: 1.134 D_fake: 0.584 +(epoch: 127, iters: 3392, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 0.943 G_ID: 0.147 G_Rec: 0.332 D_GP: 0.116 D_real: 0.360 D_fake: 0.944 +(epoch: 127, iters: 3792, time: 0.064) G_GAN: 0.632 G_GAN_Feat: 0.949 G_ID: 0.204 G_Rec: 0.392 D_GP: 0.064 D_real: 1.086 D_fake: 0.394 +(epoch: 127, iters: 4192, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 1.055 G_ID: 0.168 G_Rec: 0.354 D_GP: 0.246 D_real: 0.424 D_fake: 0.907 +(epoch: 127, iters: 4592, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.864 G_ID: 0.233 G_Rec: 0.401 D_GP: 0.027 D_real: 1.162 D_fake: 0.609 +(epoch: 127, iters: 4992, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.948 G_ID: 0.174 G_Rec: 0.381 D_GP: 0.210 D_real: 0.481 D_fake: 0.928 +(epoch: 127, iters: 5392, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 1.121 G_ID: 0.247 G_Rec: 0.438 D_GP: 0.332 D_real: 0.563 D_fake: 0.712 +(epoch: 127, iters: 5792, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.798 G_ID: 0.178 G_Rec: 0.334 D_GP: 0.040 D_real: 1.050 D_fake: 0.683 +(epoch: 127, iters: 6192, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 0.914 G_ID: 0.201 G_Rec: 0.393 D_GP: 0.040 D_real: 1.037 D_fake: 0.439 +(epoch: 127, iters: 6592, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.732 G_ID: 0.173 G_Rec: 0.310 D_GP: 0.032 D_real: 1.135 D_fake: 0.727 +(epoch: 127, iters: 6992, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 1.269 G_ID: 0.209 G_Rec: 0.420 D_GP: 0.058 D_real: 0.808 D_fake: 0.455 +(epoch: 127, iters: 7392, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.971 G_ID: 0.133 G_Rec: 0.366 D_GP: 0.055 D_real: 0.626 D_fake: 0.777 +(epoch: 127, iters: 7792, time: 0.064) G_GAN: 0.873 G_GAN_Feat: 1.044 G_ID: 0.224 G_Rec: 0.441 D_GP: 0.157 D_real: 1.217 D_fake: 0.369 +(epoch: 127, iters: 8192, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.974 G_ID: 0.149 G_Rec: 0.410 D_GP: 0.067 D_real: 0.627 D_fake: 0.609 +(epoch: 127, iters: 8592, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 1.075 G_ID: 0.288 G_Rec: 0.412 D_GP: 0.088 D_real: 0.534 D_fake: 0.605 +(epoch: 128, iters: 384, time: 0.064) G_GAN: -0.048 G_GAN_Feat: 0.785 G_ID: 0.205 G_Rec: 0.389 D_GP: 0.063 D_real: 0.935 D_fake: 1.051 +(epoch: 128, iters: 784, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.803 G_ID: 0.232 G_Rec: 0.388 D_GP: 0.033 D_real: 0.994 D_fake: 0.698 +(epoch: 128, iters: 1184, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.819 G_ID: 0.167 G_Rec: 0.368 D_GP: 0.039 D_real: 1.056 D_fake: 0.681 +(epoch: 128, iters: 1584, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 1.005 G_ID: 0.205 G_Rec: 0.403 D_GP: 0.032 D_real: 0.840 D_fake: 0.906 +(epoch: 128, iters: 1984, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.969 G_ID: 0.150 G_Rec: 0.374 D_GP: 0.041 D_real: 1.124 D_fake: 0.585 +(epoch: 128, iters: 2384, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.877 G_ID: 0.230 G_Rec: 0.367 D_GP: 0.044 D_real: 0.924 D_fake: 0.666 +(epoch: 128, iters: 2784, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 1.163 G_ID: 0.154 G_Rec: 0.368 D_GP: 0.139 D_real: 1.110 D_fake: 0.947 +(epoch: 128, iters: 3184, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.990 G_ID: 0.234 G_Rec: 0.421 D_GP: 0.047 D_real: 1.118 D_fake: 0.625 +(epoch: 128, iters: 3584, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.734 G_ID: 0.158 G_Rec: 0.357 D_GP: 0.026 D_real: 1.105 D_fake: 0.753 +(epoch: 128, iters: 3984, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.848 G_ID: 0.255 G_Rec: 0.404 D_GP: 0.032 D_real: 0.916 D_fake: 0.784 +(epoch: 128, iters: 4384, time: 0.064) G_GAN: -0.008 G_GAN_Feat: 0.767 G_ID: 0.170 G_Rec: 0.371 D_GP: 0.032 D_real: 0.742 D_fake: 1.009 +(epoch: 128, iters: 4784, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 1.194 G_ID: 0.232 G_Rec: 0.369 D_GP: 0.072 D_real: 0.439 D_fake: 0.437 +(epoch: 128, iters: 5184, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.677 G_ID: 0.156 G_Rec: 0.374 D_GP: 0.021 D_real: 1.207 D_fake: 0.700 +(epoch: 128, iters: 5584, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 0.792 G_ID: 0.215 G_Rec: 0.390 D_GP: 0.027 D_real: 1.174 D_fake: 0.522 +(epoch: 128, iters: 5984, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.696 G_ID: 0.133 G_Rec: 0.336 D_GP: 0.049 D_real: 1.119 D_fake: 0.724 +(epoch: 128, iters: 6384, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.843 G_ID: 0.235 G_Rec: 0.411 D_GP: 0.025 D_real: 1.090 D_fake: 0.665 +(epoch: 128, iters: 6784, time: 0.064) G_GAN: -0.064 G_GAN_Feat: 0.831 G_ID: 0.181 G_Rec: 0.348 D_GP: 0.133 D_real: 0.521 D_fake: 1.065 +(epoch: 128, iters: 7184, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.751 G_ID: 0.248 G_Rec: 0.378 D_GP: 0.020 D_real: 1.151 D_fake: 0.669 +(epoch: 128, iters: 7584, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.675 G_ID: 0.165 G_Rec: 0.307 D_GP: 0.041 D_real: 1.054 D_fake: 0.748 +(epoch: 128, iters: 7984, time: 0.064) G_GAN: -0.039 G_GAN_Feat: 0.986 G_ID: 0.226 G_Rec: 0.382 D_GP: 0.229 D_real: 0.313 D_fake: 1.040 +(epoch: 128, iters: 8384, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 1.019 G_ID: 0.146 G_Rec: 0.364 D_GP: 0.469 D_real: 0.464 D_fake: 0.974 +(epoch: 129, iters: 176, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 0.870 G_ID: 0.249 G_Rec: 0.363 D_GP: 0.061 D_real: 0.943 D_fake: 0.444 +(epoch: 129, iters: 576, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.801 G_ID: 0.161 G_Rec: 0.357 D_GP: 0.030 D_real: 1.257 D_fake: 0.553 +(epoch: 129, iters: 976, time: 0.064) G_GAN: 0.537 G_GAN_Feat: 0.980 G_ID: 0.214 G_Rec: 0.424 D_GP: 0.034 D_real: 0.985 D_fake: 0.470 +(epoch: 129, iters: 1376, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.840 G_ID: 0.154 G_Rec: 0.360 D_GP: 0.031 D_real: 1.073 D_fake: 0.657 +(epoch: 129, iters: 1776, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 1.039 G_ID: 0.215 G_Rec: 0.442 D_GP: 0.091 D_real: 0.830 D_fake: 0.562 +(epoch: 129, iters: 2176, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.863 G_ID: 0.146 G_Rec: 0.330 D_GP: 0.033 D_real: 0.972 D_fake: 0.695 +(epoch: 129, iters: 2576, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.838 G_ID: 0.245 G_Rec: 0.393 D_GP: 0.033 D_real: 0.881 D_fake: 0.790 +(epoch: 129, iters: 2976, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.788 G_ID: 0.175 G_Rec: 0.313 D_GP: 0.040 D_real: 1.011 D_fake: 0.732 +(epoch: 129, iters: 3376, time: 0.064) G_GAN: 0.511 G_GAN_Feat: 0.835 G_ID: 0.248 G_Rec: 0.373 D_GP: 0.030 D_real: 1.278 D_fake: 0.492 +(epoch: 129, iters: 3776, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.751 G_ID: 0.148 G_Rec: 0.359 D_GP: 0.028 D_real: 0.973 D_fake: 0.854 +(epoch: 129, iters: 4176, time: 0.064) G_GAN: 0.714 G_GAN_Feat: 1.004 G_ID: 0.177 G_Rec: 0.404 D_GP: 0.042 D_real: 1.085 D_fake: 0.316 +(epoch: 129, iters: 4576, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.836 G_ID: 0.147 G_Rec: 0.348 D_GP: 0.041 D_real: 0.867 D_fake: 0.895 +(epoch: 129, iters: 4976, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 1.134 G_ID: 0.210 G_Rec: 0.418 D_GP: 0.332 D_real: 0.543 D_fake: 0.615 +(epoch: 129, iters: 5376, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.689 G_ID: 0.165 G_Rec: 0.366 D_GP: 0.025 D_real: 1.103 D_fake: 0.783 +(epoch: 129, iters: 5776, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.938 G_ID: 0.216 G_Rec: 0.399 D_GP: 0.069 D_real: 0.929 D_fake: 0.633 +(epoch: 129, iters: 6176, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.860 G_ID: 0.165 G_Rec: 0.314 D_GP: 0.107 D_real: 0.736 D_fake: 0.705 +(epoch: 129, iters: 6576, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.952 G_ID: 0.239 G_Rec: 0.431 D_GP: 0.037 D_real: 0.934 D_fake: 0.512 +(epoch: 129, iters: 6976, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.737 G_ID: 0.171 G_Rec: 0.347 D_GP: 0.031 D_real: 1.012 D_fake: 0.790 +(epoch: 129, iters: 7376, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 0.866 G_ID: 0.206 G_Rec: 0.381 D_GP: 0.032 D_real: 1.196 D_fake: 0.399 +(epoch: 129, iters: 7776, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.641 G_ID: 0.167 G_Rec: 0.336 D_GP: 0.018 D_real: 1.226 D_fake: 0.670 +(epoch: 129, iters: 8176, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.858 G_ID: 0.277 G_Rec: 0.418 D_GP: 0.023 D_real: 1.206 D_fake: 0.509 +(epoch: 129, iters: 8576, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.721 G_ID: 0.157 G_Rec: 0.328 D_GP: 0.039 D_real: 1.083 D_fake: 0.683 +(epoch: 130, iters: 368, time: 0.064) G_GAN: 0.487 G_GAN_Feat: 0.923 G_ID: 0.225 G_Rec: 0.394 D_GP: 0.042 D_real: 0.917 D_fake: 0.515 +(epoch: 130, iters: 768, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.706 G_ID: 0.197 G_Rec: 0.328 D_GP: 0.035 D_real: 0.989 D_fake: 0.866 +(epoch: 130, iters: 1168, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.876 G_ID: 0.218 G_Rec: 0.386 D_GP: 0.044 D_real: 0.870 D_fake: 0.793 +(epoch: 130, iters: 1568, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.886 G_ID: 0.159 G_Rec: 0.336 D_GP: 0.168 D_real: 0.707 D_fake: 0.826 +(epoch: 130, iters: 1968, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 1.193 G_ID: 0.237 G_Rec: 0.394 D_GP: 0.165 D_real: 0.257 D_fake: 0.748 +(epoch: 130, iters: 2368, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 1.080 G_ID: 0.134 G_Rec: 0.354 D_GP: 0.071 D_real: 0.403 D_fake: 0.730 +(epoch: 130, iters: 2768, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.843 G_ID: 0.224 G_Rec: 0.398 D_GP: 0.027 D_real: 1.162 D_fake: 0.547 +(epoch: 130, iters: 3168, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.777 G_ID: 0.172 G_Rec: 0.351 D_GP: 0.038 D_real: 1.197 D_fake: 0.626 +(epoch: 130, iters: 3568, time: 0.064) G_GAN: 0.748 G_GAN_Feat: 1.039 G_ID: 0.195 G_Rec: 0.394 D_GP: 0.279 D_real: 0.878 D_fake: 0.387 +(epoch: 130, iters: 3968, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.795 G_ID: 0.159 G_Rec: 0.328 D_GP: 0.038 D_real: 1.155 D_fake: 0.650 +(epoch: 130, iters: 4368, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.882 G_ID: 0.211 G_Rec: 0.442 D_GP: 0.033 D_real: 1.191 D_fake: 0.594 +(epoch: 130, iters: 4768, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.685 G_ID: 0.153 G_Rec: 0.328 D_GP: 0.022 D_real: 1.279 D_fake: 0.656 +(epoch: 130, iters: 5168, time: 0.064) G_GAN: 0.729 G_GAN_Feat: 0.883 G_ID: 0.207 G_Rec: 0.395 D_GP: 0.027 D_real: 1.403 D_fake: 0.288 +(epoch: 130, iters: 5568, time: 0.064) G_GAN: -0.156 G_GAN_Feat: 0.928 G_ID: 0.159 G_Rec: 0.360 D_GP: 0.062 D_real: 0.958 D_fake: 1.156 +(epoch: 130, iters: 5968, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 1.093 G_ID: 0.190 G_Rec: 0.394 D_GP: 0.042 D_real: 0.654 D_fake: 0.468 +(epoch: 130, iters: 6368, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.680 G_ID: 0.154 G_Rec: 0.355 D_GP: 0.022 D_real: 1.162 D_fake: 0.794 +(epoch: 130, iters: 6768, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.826 G_ID: 0.227 G_Rec: 0.388 D_GP: 0.027 D_real: 1.005 D_fake: 0.724 +(epoch: 130, iters: 7168, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.670 G_ID: 0.147 G_Rec: 0.335 D_GP: 0.024 D_real: 1.221 D_fake: 0.699 +(epoch: 130, iters: 7568, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.878 G_ID: 0.246 G_Rec: 0.400 D_GP: 0.028 D_real: 1.217 D_fake: 0.514 +(epoch: 130, iters: 7968, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.751 G_ID: 0.150 G_Rec: 0.341 D_GP: 0.032 D_real: 1.093 D_fake: 0.777 +(epoch: 130, iters: 8368, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.892 G_ID: 0.242 G_Rec: 0.395 D_GP: 0.047 D_real: 0.900 D_fake: 0.631 +(epoch: 131, iters: 160, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.778 G_ID: 0.124 G_Rec: 0.339 D_GP: 0.040 D_real: 1.137 D_fake: 0.673 +(epoch: 131, iters: 560, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.828 G_ID: 0.228 G_Rec: 0.388 D_GP: 0.034 D_real: 1.080 D_fake: 0.662 +(epoch: 131, iters: 960, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.766 G_ID: 0.144 G_Rec: 0.323 D_GP: 0.038 D_real: 1.056 D_fake: 0.672 +(epoch: 131, iters: 1360, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.894 G_ID: 0.218 G_Rec: 0.408 D_GP: 0.040 D_real: 1.118 D_fake: 0.491 +(epoch: 131, iters: 1760, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.928 G_ID: 0.152 G_Rec: 0.353 D_GP: 0.123 D_real: 0.694 D_fake: 0.657 +(epoch: 131, iters: 2160, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.981 G_ID: 0.201 G_Rec: 0.432 D_GP: 0.051 D_real: 0.885 D_fake: 0.551 +(epoch: 131, iters: 2560, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.752 G_ID: 0.155 G_Rec: 0.312 D_GP: 0.030 D_real: 1.136 D_fake: 0.641 +(epoch: 131, iters: 2960, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.993 G_ID: 0.232 G_Rec: 0.379 D_GP: 0.042 D_real: 0.918 D_fake: 0.532 +(epoch: 131, iters: 3360, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 0.619 G_ID: 0.167 G_Rec: 0.317 D_GP: 0.023 D_real: 0.973 D_fake: 0.974 +(epoch: 131, iters: 3760, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.817 G_ID: 0.202 G_Rec: 0.387 D_GP: 0.022 D_real: 1.136 D_fake: 0.670 +(epoch: 131, iters: 4160, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.715 G_ID: 0.152 G_Rec: 0.363 D_GP: 0.043 D_real: 0.923 D_fake: 0.902 +(epoch: 131, iters: 4560, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.888 G_ID: 0.242 G_Rec: 0.398 D_GP: 0.094 D_real: 0.911 D_fake: 0.620 +(epoch: 131, iters: 4960, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.813 G_ID: 0.133 G_Rec: 0.329 D_GP: 0.092 D_real: 0.833 D_fake: 0.831 +(epoch: 131, iters: 5360, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.893 G_ID: 0.220 G_Rec: 0.391 D_GP: 0.029 D_real: 0.944 D_fake: 0.676 +(epoch: 131, iters: 5760, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 1.061 G_ID: 0.160 G_Rec: 0.432 D_GP: 1.313 D_real: 0.363 D_fake: 0.989 +(epoch: 131, iters: 6160, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.970 G_ID: 0.250 G_Rec: 0.439 D_GP: 0.037 D_real: 0.775 D_fake: 0.692 +(epoch: 131, iters: 6560, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.724 G_ID: 0.170 G_Rec: 0.315 D_GP: 0.035 D_real: 1.073 D_fake: 0.681 +(epoch: 131, iters: 6960, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.990 G_ID: 0.268 G_Rec: 0.366 D_GP: 0.036 D_real: 0.901 D_fake: 0.631 +(epoch: 131, iters: 7360, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.977 G_ID: 0.146 G_Rec: 0.355 D_GP: 0.198 D_real: 0.677 D_fake: 0.548 +(epoch: 131, iters: 7760, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 1.001 G_ID: 0.209 G_Rec: 0.421 D_GP: 0.050 D_real: 0.809 D_fake: 0.552 +(epoch: 131, iters: 8160, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.923 G_ID: 0.151 G_Rec: 0.355 D_GP: 0.030 D_real: 0.636 D_fake: 0.810 +(epoch: 131, iters: 8560, time: 0.064) G_GAN: 1.133 G_GAN_Feat: 0.864 G_ID: 0.241 G_Rec: 0.375 D_GP: 0.031 D_real: 1.804 D_fake: 0.135 +(epoch: 132, iters: 352, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.928 G_ID: 0.141 G_Rec: 0.357 D_GP: 0.056 D_real: 0.756 D_fake: 0.592 +(epoch: 132, iters: 752, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.833 G_ID: 0.259 G_Rec: 0.377 D_GP: 0.026 D_real: 0.975 D_fake: 0.829 +(epoch: 132, iters: 1152, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.732 G_ID: 0.166 G_Rec: 0.325 D_GP: 0.024 D_real: 1.311 D_fake: 0.603 +(epoch: 132, iters: 1552, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.877 G_ID: 0.189 G_Rec: 0.402 D_GP: 0.028 D_real: 1.208 D_fake: 0.463 +(epoch: 132, iters: 1952, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.788 G_ID: 0.180 G_Rec: 0.329 D_GP: 0.043 D_real: 0.911 D_fake: 0.848 +(epoch: 132, iters: 2352, time: 0.064) G_GAN: 0.736 G_GAN_Feat: 1.027 G_ID: 0.257 G_Rec: 0.393 D_GP: 0.052 D_real: 0.881 D_fake: 0.357 +(epoch: 132, iters: 2752, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.818 G_ID: 0.189 G_Rec: 0.365 D_GP: 0.025 D_real: 1.148 D_fake: 0.717 +(epoch: 132, iters: 3152, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.850 G_ID: 0.210 G_Rec: 0.343 D_GP: 0.029 D_real: 1.214 D_fake: 0.510 +(epoch: 132, iters: 3552, time: 0.064) G_GAN: 0.653 G_GAN_Feat: 1.002 G_ID: 0.137 G_Rec: 0.334 D_GP: 0.104 D_real: 0.783 D_fake: 0.409 +(epoch: 132, iters: 3952, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.706 G_ID: 0.207 G_Rec: 0.354 D_GP: 0.021 D_real: 1.307 D_fake: 0.494 +(epoch: 132, iters: 4352, time: 0.064) G_GAN: 0.045 G_GAN_Feat: 0.805 G_ID: 0.152 G_Rec: 0.376 D_GP: 0.069 D_real: 0.786 D_fake: 0.955 +(epoch: 132, iters: 4752, time: 0.064) G_GAN: -0.031 G_GAN_Feat: 1.121 G_ID: 0.195 G_Rec: 0.387 D_GP: 0.049 D_real: 1.077 D_fake: 1.032 +(epoch: 132, iters: 5152, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.813 G_ID: 0.130 G_Rec: 0.347 D_GP: 0.029 D_real: 1.005 D_fake: 0.770 +(epoch: 132, iters: 5552, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 1.189 G_ID: 0.228 G_Rec: 0.430 D_GP: 0.205 D_real: 0.479 D_fake: 0.613 +(epoch: 132, iters: 5952, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.934 G_ID: 0.157 G_Rec: 0.341 D_GP: 0.059 D_real: 1.041 D_fake: 0.605 +(epoch: 132, iters: 6352, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.883 G_ID: 0.214 G_Rec: 0.382 D_GP: 0.026 D_real: 1.092 D_fake: 0.670 +(epoch: 132, iters: 6752, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.825 G_ID: 0.141 G_Rec: 0.344 D_GP: 0.033 D_real: 0.915 D_fake: 0.673 +(epoch: 132, iters: 7152, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 0.852 G_ID: 0.212 G_Rec: 0.537 D_GP: 0.024 D_real: 1.328 D_fake: 0.404 +(epoch: 132, iters: 7552, time: 0.064) G_GAN: -0.014 G_GAN_Feat: 0.749 G_ID: 0.184 G_Rec: 0.317 D_GP: 0.036 D_real: 0.905 D_fake: 1.016 +(epoch: 132, iters: 7952, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.853 G_ID: 0.214 G_Rec: 0.369 D_GP: 0.027 D_real: 1.024 D_fake: 0.698 +(epoch: 132, iters: 8352, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.753 G_ID: 0.167 G_Rec: 0.357 D_GP: 0.025 D_real: 1.061 D_fake: 0.794 +(epoch: 133, iters: 144, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.854 G_ID: 0.217 G_Rec: 0.370 D_GP: 0.027 D_real: 1.130 D_fake: 0.607 +(epoch: 133, iters: 544, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 1.079 G_ID: 0.177 G_Rec: 0.374 D_GP: 1.310 D_real: 0.565 D_fake: 0.570 +(epoch: 133, iters: 944, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.860 G_ID: 0.224 G_Rec: 0.436 D_GP: 0.031 D_real: 1.062 D_fake: 0.574 +(epoch: 133, iters: 1344, time: 0.064) G_GAN: 0.742 G_GAN_Feat: 0.850 G_ID: 0.159 G_Rec: 0.363 D_GP: 0.059 D_real: 1.310 D_fake: 0.307 +(epoch: 133, iters: 1744, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.806 G_ID: 0.242 G_Rec: 0.386 D_GP: 0.021 D_real: 1.046 D_fake: 0.741 +(epoch: 133, iters: 2144, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.702 G_ID: 0.154 G_Rec: 0.323 D_GP: 0.026 D_real: 0.943 D_fake: 0.916 +(epoch: 133, iters: 2544, time: 0.064) G_GAN: 0.667 G_GAN_Feat: 1.187 G_ID: 0.259 G_Rec: 0.431 D_GP: 0.405 D_real: 0.625 D_fake: 0.357 +(epoch: 133, iters: 2944, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.816 G_ID: 0.145 G_Rec: 0.343 D_GP: 0.035 D_real: 1.183 D_fake: 0.499 +(epoch: 133, iters: 3344, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.877 G_ID: 0.218 G_Rec: 0.393 D_GP: 0.078 D_real: 0.786 D_fake: 0.868 +(epoch: 133, iters: 3744, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.718 G_ID: 0.186 G_Rec: 0.310 D_GP: 0.029 D_real: 1.051 D_fake: 0.769 +(epoch: 133, iters: 4144, time: 0.064) G_GAN: 0.606 G_GAN_Feat: 0.985 G_ID: 0.215 G_Rec: 0.409 D_GP: 0.046 D_real: 1.004 D_fake: 0.403 +(epoch: 133, iters: 4544, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.941 G_ID: 0.171 G_Rec: 0.351 D_GP: 0.193 D_real: 0.682 D_fake: 0.729 +(epoch: 133, iters: 4944, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.914 G_ID: 0.190 G_Rec: 0.387 D_GP: 0.034 D_real: 1.115 D_fake: 0.535 +(epoch: 133, iters: 5344, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.873 G_ID: 0.153 G_Rec: 0.348 D_GP: 0.053 D_real: 0.723 D_fake: 0.849 +(epoch: 133, iters: 5744, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 1.133 G_ID: 0.216 G_Rec: 0.449 D_GP: 0.402 D_real: 0.515 D_fake: 0.527 +(epoch: 133, iters: 6144, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.857 G_ID: 0.157 G_Rec: 0.325 D_GP: 0.028 D_real: 0.906 D_fake: 0.792 +(epoch: 133, iters: 6544, time: 0.064) G_GAN: 0.543 G_GAN_Feat: 1.165 G_ID: 0.231 G_Rec: 0.409 D_GP: 0.070 D_real: 0.826 D_fake: 0.464 +(epoch: 133, iters: 6944, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.735 G_ID: 0.158 G_Rec: 0.326 D_GP: 0.030 D_real: 1.225 D_fake: 0.731 +(epoch: 133, iters: 7344, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.918 G_ID: 0.203 G_Rec: 0.379 D_GP: 0.034 D_real: 1.107 D_fake: 0.578 +(epoch: 133, iters: 7744, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.813 G_ID: 0.159 G_Rec: 0.319 D_GP: 0.044 D_real: 1.082 D_fake: 0.593 +(epoch: 133, iters: 8144, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.981 G_ID: 0.221 G_Rec: 0.409 D_GP: 0.048 D_real: 0.962 D_fake: 0.486 +(epoch: 133, iters: 8544, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.852 G_ID: 0.158 G_Rec: 0.336 D_GP: 0.028 D_real: 1.046 D_fake: 0.667 +(epoch: 134, iters: 336, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 1.112 G_ID: 0.223 G_Rec: 0.424 D_GP: 0.046 D_real: 0.419 D_fake: 0.761 +(epoch: 134, iters: 736, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.931 G_ID: 0.149 G_Rec: 0.354 D_GP: 0.066 D_real: 0.606 D_fake: 0.752 +(epoch: 134, iters: 1136, time: 0.064) G_GAN: -0.017 G_GAN_Feat: 0.859 G_ID: 0.209 G_Rec: 0.396 D_GP: 0.030 D_real: 0.818 D_fake: 1.017 +(epoch: 134, iters: 1536, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.707 G_ID: 0.131 G_Rec: 0.335 D_GP: 0.028 D_real: 1.054 D_fake: 0.809 +(epoch: 134, iters: 1936, time: 0.064) G_GAN: 0.728 G_GAN_Feat: 1.032 G_ID: 0.224 G_Rec: 0.433 D_GP: 0.051 D_real: 1.223 D_fake: 0.313 +(epoch: 134, iters: 2336, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.901 G_ID: 0.159 G_Rec: 0.407 D_GP: 0.069 D_real: 0.787 D_fake: 0.698 +(epoch: 134, iters: 2736, time: 0.064) G_GAN: 0.644 G_GAN_Feat: 1.087 G_ID: 0.211 G_Rec: 0.416 D_GP: 0.068 D_real: 0.816 D_fake: 0.366 +(epoch: 134, iters: 3136, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.763 G_ID: 0.142 G_Rec: 0.382 D_GP: 0.027 D_real: 0.959 D_fake: 0.870 +(epoch: 134, iters: 3536, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.826 G_ID: 0.233 G_Rec: 0.351 D_GP: 0.027 D_real: 1.116 D_fake: 0.645 +(epoch: 134, iters: 3936, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 1.020 G_ID: 0.154 G_Rec: 0.380 D_GP: 0.119 D_real: 0.494 D_fake: 0.787 +(epoch: 134, iters: 4336, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.916 G_ID: 0.259 G_Rec: 0.378 D_GP: 0.027 D_real: 0.931 D_fake: 0.627 +(epoch: 134, iters: 4736, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 1.045 G_ID: 0.175 G_Rec: 0.321 D_GP: 0.150 D_real: 0.363 D_fake: 0.830 +(epoch: 134, iters: 5136, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.928 G_ID: 0.211 G_Rec: 0.426 D_GP: 0.050 D_real: 0.679 D_fake: 0.749 +(epoch: 134, iters: 5536, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.818 G_ID: 0.140 G_Rec: 0.359 D_GP: 0.062 D_real: 0.731 D_fake: 0.848 +(epoch: 134, iters: 5936, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.978 G_ID: 0.199 G_Rec: 0.430 D_GP: 0.037 D_real: 0.844 D_fake: 0.692 +(epoch: 134, iters: 6336, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.779 G_ID: 0.130 G_Rec: 0.328 D_GP: 0.029 D_real: 1.305 D_fake: 0.501 +(epoch: 134, iters: 6736, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.757 G_ID: 0.212 G_Rec: 0.390 D_GP: 0.021 D_real: 1.258 D_fake: 0.564 +(epoch: 134, iters: 7136, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.932 G_ID: 0.189 G_Rec: 0.348 D_GP: 0.052 D_real: 0.613 D_fake: 0.658 +(epoch: 134, iters: 7536, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 1.105 G_ID: 0.288 G_Rec: 0.411 D_GP: 0.112 D_real: 0.832 D_fake: 0.596 +(epoch: 134, iters: 7936, time: 0.064) G_GAN: 0.434 G_GAN_Feat: 0.812 G_ID: 0.162 G_Rec: 0.320 D_GP: 0.029 D_real: 1.189 D_fake: 0.566 +(epoch: 134, iters: 8336, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.981 G_ID: 0.244 G_Rec: 0.431 D_GP: 0.059 D_real: 0.947 D_fake: 0.483 +(epoch: 135, iters: 128, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.715 G_ID: 0.154 G_Rec: 0.322 D_GP: 0.026 D_real: 1.109 D_fake: 0.762 +(epoch: 135, iters: 528, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.971 G_ID: 0.224 G_Rec: 0.381 D_GP: 0.031 D_real: 1.267 D_fake: 0.623 +(epoch: 135, iters: 928, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.730 G_ID: 0.164 G_Rec: 0.307 D_GP: 0.032 D_real: 1.111 D_fake: 0.745 +(epoch: 135, iters: 1328, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.830 G_ID: 0.246 G_Rec: 0.381 D_GP: 0.026 D_real: 0.839 D_fake: 0.826 +(epoch: 135, iters: 1728, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.883 G_ID: 0.157 G_Rec: 0.347 D_GP: 0.085 D_real: 0.821 D_fake: 0.494 +(epoch: 135, iters: 2128, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.853 G_ID: 0.212 G_Rec: 0.392 D_GP: 0.022 D_real: 1.112 D_fake: 0.636 +(epoch: 135, iters: 2528, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.850 G_ID: 0.172 G_Rec: 0.320 D_GP: 0.037 D_real: 0.756 D_fake: 0.766 +(epoch: 135, iters: 2928, time: 0.064) G_GAN: 0.560 G_GAN_Feat: 0.930 G_ID: 0.224 G_Rec: 0.381 D_GP: 0.039 D_real: 1.047 D_fake: 0.447 +(epoch: 135, iters: 3328, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.708 G_ID: 0.172 G_Rec: 0.328 D_GP: 0.028 D_real: 1.020 D_fake: 0.873 +(epoch: 135, iters: 3728, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 1.139 G_ID: 0.203 G_Rec: 0.434 D_GP: 0.183 D_real: 0.453 D_fake: 0.598 +(epoch: 135, iters: 4128, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.758 G_ID: 0.161 G_Rec: 0.303 D_GP: 0.036 D_real: 1.082 D_fake: 0.874 +(epoch: 135, iters: 4528, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 1.092 G_ID: 0.202 G_Rec: 0.416 D_GP: 0.143 D_real: 0.882 D_fake: 0.642 +(epoch: 135, iters: 4928, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.708 G_ID: 0.152 G_Rec: 0.342 D_GP: 0.021 D_real: 1.143 D_fake: 0.798 +(epoch: 135, iters: 5328, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.792 G_ID: 0.231 G_Rec: 0.351 D_GP: 0.025 D_real: 1.222 D_fake: 0.553 +(epoch: 135, iters: 5728, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.686 G_ID: 0.156 G_Rec: 0.321 D_GP: 0.025 D_real: 1.194 D_fake: 0.711 +(epoch: 135, iters: 6128, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 1.040 G_ID: 0.245 G_Rec: 0.430 D_GP: 0.041 D_real: 1.050 D_fake: 0.452 +(epoch: 135, iters: 6528, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.934 G_ID: 0.135 G_Rec: 0.301 D_GP: 0.045 D_real: 0.626 D_fake: 0.786 +(epoch: 135, iters: 6928, time: 0.064) G_GAN: 0.616 G_GAN_Feat: 0.891 G_ID: 0.200 G_Rec: 0.385 D_GP: 0.035 D_real: 1.247 D_fake: 0.409 +(epoch: 135, iters: 7328, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.910 G_ID: 0.139 G_Rec: 0.352 D_GP: 0.069 D_real: 0.896 D_fake: 0.688 +(epoch: 135, iters: 7728, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.891 G_ID: 0.251 G_Rec: 0.383 D_GP: 0.032 D_real: 0.779 D_fake: 0.787 +(epoch: 135, iters: 8128, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.853 G_ID: 0.144 G_Rec: 0.326 D_GP: 0.034 D_real: 0.938 D_fake: 0.776 +(epoch: 135, iters: 8528, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 1.028 G_ID: 0.221 G_Rec: 0.442 D_GP: 0.056 D_real: 0.899 D_fake: 0.647 +(epoch: 136, iters: 320, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.836 G_ID: 0.141 G_Rec: 0.326 D_GP: 0.029 D_real: 0.817 D_fake: 0.821 +(epoch: 136, iters: 720, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.838 G_ID: 0.205 G_Rec: 0.408 D_GP: 0.024 D_real: 1.227 D_fake: 0.490 +(epoch: 136, iters: 1120, time: 0.064) G_GAN: 0.071 G_GAN_Feat: 0.929 G_ID: 0.138 G_Rec: 0.429 D_GP: 0.136 D_real: 0.402 D_fake: 0.929 +(epoch: 136, iters: 1520, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 1.096 G_ID: 0.204 G_Rec: 0.405 D_GP: 0.132 D_real: 0.459 D_fake: 0.541 +(epoch: 136, iters: 1920, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 0.665 G_ID: 0.154 G_Rec: 0.299 D_GP: 0.027 D_real: 0.928 D_fake: 0.990 +(epoch: 136, iters: 2320, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.963 G_ID: 0.221 G_Rec: 0.416 D_GP: 0.043 D_real: 1.027 D_fake: 0.447 +(epoch: 136, iters: 2720, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.726 G_ID: 0.140 G_Rec: 0.325 D_GP: 0.026 D_real: 1.305 D_fake: 0.554 +(epoch: 136, iters: 3120, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.886 G_ID: 0.218 G_Rec: 0.377 D_GP: 0.040 D_real: 1.111 D_fake: 0.544 +(epoch: 136, iters: 3520, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.675 G_ID: 0.154 G_Rec: 0.333 D_GP: 0.022 D_real: 1.258 D_fake: 0.731 +(epoch: 136, iters: 3920, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.793 G_ID: 0.224 G_Rec: 0.394 D_GP: 0.023 D_real: 0.916 D_fake: 0.801 +(epoch: 136, iters: 4320, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.712 G_ID: 0.161 G_Rec: 0.414 D_GP: 0.049 D_real: 0.918 D_fake: 0.881 +(epoch: 136, iters: 4720, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.867 G_ID: 0.251 G_Rec: 0.420 D_GP: 0.036 D_real: 0.743 D_fake: 0.894 +(epoch: 136, iters: 5120, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.699 G_ID: 0.161 G_Rec: 0.351 D_GP: 0.028 D_real: 1.021 D_fake: 0.787 +(epoch: 136, iters: 5520, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 1.095 G_ID: 0.251 G_Rec: 0.461 D_GP: 0.131 D_real: 1.025 D_fake: 0.478 +(epoch: 136, iters: 5920, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.953 G_ID: 0.168 G_Rec: 0.362 D_GP: 0.225 D_real: 0.603 D_fake: 0.906 +(epoch: 136, iters: 6320, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.828 G_ID: 0.246 G_Rec: 0.340 D_GP: 0.049 D_real: 0.899 D_fake: 0.765 +(epoch: 136, iters: 6720, time: 0.064) G_GAN: 0.575 G_GAN_Feat: 0.834 G_ID: 0.157 G_Rec: 0.369 D_GP: 0.051 D_real: 1.159 D_fake: 0.452 +(epoch: 136, iters: 7120, time: 0.064) G_GAN: 0.803 G_GAN_Feat: 0.902 G_ID: 0.220 G_Rec: 0.399 D_GP: 0.033 D_real: 1.396 D_fake: 0.316 +(epoch: 136, iters: 7520, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.726 G_ID: 0.174 G_Rec: 0.302 D_GP: 0.035 D_real: 0.891 D_fake: 0.947 +(epoch: 136, iters: 7920, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.912 G_ID: 0.223 G_Rec: 0.393 D_GP: 0.049 D_real: 0.948 D_fake: 0.643 +(epoch: 136, iters: 8320, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.707 G_ID: 0.179 G_Rec: 0.340 D_GP: 0.023 D_real: 1.374 D_fake: 0.555 +(epoch: 137, iters: 112, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.946 G_ID: 0.222 G_Rec: 0.402 D_GP: 0.030 D_real: 1.026 D_fake: 0.607 +(epoch: 137, iters: 512, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 1.035 G_ID: 0.178 G_Rec: 0.354 D_GP: 0.191 D_real: 0.446 D_fake: 0.675 +(epoch: 137, iters: 912, time: 0.064) G_GAN: 0.755 G_GAN_Feat: 1.053 G_ID: 0.244 G_Rec: 0.427 D_GP: 0.040 D_real: 1.309 D_fake: 0.348 +(epoch: 137, iters: 1312, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.859 G_ID: 0.170 G_Rec: 0.319 D_GP: 0.063 D_real: 0.724 D_fake: 0.768 +(epoch: 137, iters: 1712, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 1.009 G_ID: 0.206 G_Rec: 0.395 D_GP: 0.064 D_real: 0.669 D_fake: 0.624 +(epoch: 137, iters: 2112, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.841 G_ID: 0.191 G_Rec: 0.365 D_GP: 0.037 D_real: 0.719 D_fake: 0.910 +(epoch: 137, iters: 2512, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 1.115 G_ID: 0.270 G_Rec: 0.424 D_GP: 0.272 D_real: 0.387 D_fake: 0.771 +(epoch: 137, iters: 2912, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 0.818 G_ID: 0.150 G_Rec: 0.298 D_GP: 0.082 D_real: 1.065 D_fake: 0.562 +(epoch: 137, iters: 3312, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.804 G_ID: 0.247 G_Rec: 0.392 D_GP: 0.024 D_real: 1.250 D_fake: 0.527 +(epoch: 137, iters: 3712, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.799 G_ID: 0.140 G_Rec: 0.339 D_GP: 0.046 D_real: 0.788 D_fake: 0.917 +(epoch: 137, iters: 4112, time: 0.064) G_GAN: 0.786 G_GAN_Feat: 0.931 G_ID: 0.192 G_Rec: 0.401 D_GP: 0.028 D_real: 1.435 D_fake: 0.244 +(epoch: 137, iters: 4512, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.827 G_ID: 0.181 G_Rec: 0.345 D_GP: 0.045 D_real: 1.082 D_fake: 0.580 +(epoch: 137, iters: 4912, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 1.103 G_ID: 0.237 G_Rec: 0.421 D_GP: 0.332 D_real: 0.467 D_fake: 0.543 +(epoch: 137, iters: 5312, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.676 G_ID: 0.141 G_Rec: 0.328 D_GP: 0.024 D_real: 1.245 D_fake: 0.661 +(epoch: 137, iters: 5712, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.899 G_ID: 0.199 G_Rec: 0.374 D_GP: 0.065 D_real: 0.849 D_fake: 0.752 +(epoch: 137, iters: 6112, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.816 G_ID: 0.123 G_Rec: 0.340 D_GP: 0.054 D_real: 0.779 D_fake: 0.840 +(epoch: 137, iters: 6512, time: 0.064) G_GAN: 0.564 G_GAN_Feat: 0.937 G_ID: 0.196 G_Rec: 0.400 D_GP: 0.038 D_real: 1.159 D_fake: 0.448 +(epoch: 137, iters: 6912, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.772 G_ID: 0.194 G_Rec: 0.340 D_GP: 0.035 D_real: 1.127 D_fake: 0.766 +(epoch: 137, iters: 7312, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.883 G_ID: 0.197 G_Rec: 0.408 D_GP: 0.023 D_real: 1.303 D_fake: 0.471 +(epoch: 137, iters: 7712, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 1.032 G_ID: 0.205 G_Rec: 0.384 D_GP: 0.381 D_real: 0.346 D_fake: 0.641 +(epoch: 137, iters: 8112, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.785 G_ID: 0.214 G_Rec: 0.379 D_GP: 0.023 D_real: 0.929 D_fake: 0.864 +(epoch: 137, iters: 8512, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.678 G_ID: 0.196 G_Rec: 0.330 D_GP: 0.023 D_real: 1.171 D_fake: 0.657 +(epoch: 138, iters: 304, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.909 G_ID: 0.254 G_Rec: 0.418 D_GP: 0.029 D_real: 0.996 D_fake: 0.700 +(epoch: 138, iters: 704, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.671 G_ID: 0.160 G_Rec: 0.344 D_GP: 0.023 D_real: 1.128 D_fake: 0.789 +(epoch: 138, iters: 1104, time: 0.064) G_GAN: 0.587 G_GAN_Feat: 1.110 G_ID: 0.201 G_Rec: 0.417 D_GP: 0.155 D_real: 0.622 D_fake: 0.423 +(epoch: 138, iters: 1504, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.911 G_ID: 0.171 G_Rec: 0.353 D_GP: 0.090 D_real: 1.020 D_fake: 0.709 +(epoch: 138, iters: 1904, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.990 G_ID: 0.205 G_Rec: 0.423 D_GP: 0.034 D_real: 1.069 D_fake: 0.540 +(epoch: 138, iters: 2304, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.699 G_ID: 0.142 G_Rec: 0.322 D_GP: 0.027 D_real: 1.318 D_fake: 0.655 +(epoch: 138, iters: 2704, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 1.059 G_ID: 0.214 G_Rec: 0.447 D_GP: 0.037 D_real: 0.804 D_fake: 0.545 +(epoch: 138, iters: 3104, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.795 G_ID: 0.141 G_Rec: 0.310 D_GP: 0.046 D_real: 1.201 D_fake: 0.485 +(epoch: 138, iters: 3504, time: 0.064) G_GAN: 0.071 G_GAN_Feat: 0.850 G_ID: 0.248 G_Rec: 0.402 D_GP: 0.025 D_real: 0.851 D_fake: 0.929 +(epoch: 138, iters: 3904, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.908 G_ID: 0.151 G_Rec: 0.345 D_GP: 0.074 D_real: 0.864 D_fake: 0.647 +(epoch: 138, iters: 4304, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 0.810 G_ID: 0.254 G_Rec: 0.390 D_GP: 0.027 D_real: 1.202 D_fake: 0.562 +(epoch: 138, iters: 4704, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.678 G_ID: 0.143 G_Rec: 0.351 D_GP: 0.029 D_real: 0.925 D_fake: 0.923 +(epoch: 138, iters: 5104, time: 0.064) G_GAN: 0.641 G_GAN_Feat: 0.942 G_ID: 0.204 G_Rec: 0.425 D_GP: 0.031 D_real: 1.185 D_fake: 0.384 +(epoch: 138, iters: 5504, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 1.229 G_ID: 0.176 G_Rec: 0.388 D_GP: 0.662 D_real: 0.309 D_fake: 0.953 +(epoch: 138, iters: 5904, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.963 G_ID: 0.194 G_Rec: 0.406 D_GP: 0.036 D_real: 0.918 D_fake: 0.611 +(epoch: 138, iters: 6304, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.861 G_ID: 0.184 G_Rec: 0.327 D_GP: 0.078 D_real: 0.826 D_fake: 0.896 +(epoch: 138, iters: 6704, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.871 G_ID: 0.234 G_Rec: 0.412 D_GP: 0.029 D_real: 1.032 D_fake: 0.646 +(epoch: 138, iters: 7104, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 1.124 G_ID: 0.164 G_Rec: 0.367 D_GP: 0.288 D_real: 0.476 D_fake: 0.639 +(epoch: 138, iters: 7504, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.923 G_ID: 0.216 G_Rec: 0.400 D_GP: 0.038 D_real: 0.923 D_fake: 0.590 +(epoch: 138, iters: 7904, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.039 G_ID: 0.154 G_Rec: 0.383 D_GP: 0.876 D_real: 0.382 D_fake: 0.665 +(epoch: 138, iters: 8304, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.890 G_ID: 0.223 G_Rec: 0.390 D_GP: 0.034 D_real: 1.006 D_fake: 0.570 +(epoch: 139, iters: 96, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.875 G_ID: 0.158 G_Rec: 0.321 D_GP: 0.115 D_real: 0.683 D_fake: 0.817 +(epoch: 139, iters: 496, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.801 G_ID: 0.202 G_Rec: 0.378 D_GP: 0.025 D_real: 1.137 D_fake: 0.627 +(epoch: 139, iters: 896, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.812 G_ID: 0.137 G_Rec: 0.317 D_GP: 0.050 D_real: 0.827 D_fake: 0.826 +(epoch: 139, iters: 1296, time: 0.064) G_GAN: 0.511 G_GAN_Feat: 0.846 G_ID: 0.244 G_Rec: 0.397 D_GP: 0.026 D_real: 1.175 D_fake: 0.504 +(epoch: 139, iters: 1696, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.776 G_ID: 0.160 G_Rec: 0.374 D_GP: 0.023 D_real: 1.122 D_fake: 0.774 +(epoch: 139, iters: 2096, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.927 G_ID: 0.276 G_Rec: 0.411 D_GP: 0.029 D_real: 0.874 D_fake: 0.724 +(epoch: 139, iters: 2496, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.654 G_ID: 0.148 G_Rec: 0.296 D_GP: 0.027 D_real: 1.147 D_fake: 0.707 +(epoch: 139, iters: 2896, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.833 G_ID: 0.224 G_Rec: 0.380 D_GP: 0.036 D_real: 0.917 D_fake: 0.821 +(epoch: 139, iters: 3296, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.695 G_ID: 0.182 G_Rec: 0.371 D_GP: 0.026 D_real: 1.048 D_fake: 0.816 +(epoch: 139, iters: 3696, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.954 G_ID: 0.203 G_Rec: 0.401 D_GP: 0.048 D_real: 0.986 D_fake: 0.482 +(epoch: 139, iters: 4096, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.707 G_ID: 0.151 G_Rec: 0.442 D_GP: 0.027 D_real: 1.083 D_fake: 0.782 +(epoch: 139, iters: 4496, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 1.039 G_ID: 0.210 G_Rec: 0.419 D_GP: 0.031 D_real: 0.968 D_fake: 0.612 +(epoch: 139, iters: 4896, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.763 G_ID: 0.153 G_Rec: 0.362 D_GP: 0.032 D_real: 1.219 D_fake: 0.612 +(epoch: 139, iters: 5296, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.803 G_ID: 0.209 G_Rec: 0.397 D_GP: 0.022 D_real: 1.285 D_fake: 0.547 +(epoch: 139, iters: 5696, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.882 G_ID: 0.135 G_Rec: 0.345 D_GP: 0.048 D_real: 0.964 D_fake: 0.657 +(epoch: 139, iters: 6096, time: 0.064) G_GAN: 0.768 G_GAN_Feat: 0.943 G_ID: 0.188 G_Rec: 0.406 D_GP: 0.034 D_real: 1.235 D_fake: 0.272 +(epoch: 139, iters: 6496, time: 0.064) G_GAN: -0.027 G_GAN_Feat: 0.975 G_ID: 0.159 G_Rec: 0.327 D_GP: 0.079 D_real: 1.021 D_fake: 1.028 +(epoch: 139, iters: 6896, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 1.193 G_ID: 0.221 G_Rec: 0.488 D_GP: 0.063 D_real: 0.684 D_fake: 0.552 +(epoch: 139, iters: 7296, time: 0.064) G_GAN: -0.019 G_GAN_Feat: 0.874 G_ID: 0.163 G_Rec: 0.317 D_GP: 0.079 D_real: 0.423 D_fake: 1.019 +(epoch: 139, iters: 7696, time: 0.064) G_GAN: 0.661 G_GAN_Feat: 0.952 G_ID: 0.217 G_Rec: 0.396 D_GP: 0.034 D_real: 1.306 D_fake: 0.345 +(epoch: 139, iters: 8096, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.771 G_ID: 0.175 G_Rec: 0.343 D_GP: 0.044 D_real: 0.952 D_fake: 0.813 +(epoch: 139, iters: 8496, time: 0.064) G_GAN: 0.658 G_GAN_Feat: 0.881 G_ID: 0.275 G_Rec: 0.408 D_GP: 0.024 D_real: 1.409 D_fake: 0.383 +(epoch: 140, iters: 288, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.905 G_ID: 0.163 G_Rec: 0.355 D_GP: 0.060 D_real: 0.546 D_fake: 0.910 +(epoch: 140, iters: 688, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.789 G_ID: 0.226 G_Rec: 0.404 D_GP: 0.023 D_real: 1.138 D_fake: 0.604 +(epoch: 140, iters: 1088, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.565 G_ID: 0.142 G_Rec: 0.289 D_GP: 0.021 D_real: 1.171 D_fake: 0.791 +(epoch: 140, iters: 1488, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.792 G_ID: 0.239 G_Rec: 0.417 D_GP: 0.028 D_real: 1.104 D_fake: 0.636 +(epoch: 140, iters: 1888, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.640 G_ID: 0.147 G_Rec: 0.346 D_GP: 0.025 D_real: 1.090 D_fake: 0.761 +(epoch: 140, iters: 2288, time: 0.064) G_GAN: 0.141 G_GAN_Feat: 0.895 G_ID: 0.222 G_Rec: 0.457 D_GP: 0.047 D_real: 0.779 D_fake: 0.859 +(epoch: 140, iters: 2688, time: 0.064) G_GAN: -0.026 G_GAN_Feat: 0.741 G_ID: 0.152 G_Rec: 0.328 D_GP: 0.034 D_real: 0.932 D_fake: 1.026 +(epoch: 140, iters: 3088, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.940 G_ID: 0.227 G_Rec: 0.424 D_GP: 0.035 D_real: 1.047 D_fake: 0.614 +(epoch: 140, iters: 3488, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.879 G_ID: 0.161 G_Rec: 0.345 D_GP: 0.036 D_real: 0.974 D_fake: 0.543 +(epoch: 140, iters: 3888, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.993 G_ID: 0.230 G_Rec: 0.404 D_GP: 0.045 D_real: 0.722 D_fake: 0.647 +(epoch: 140, iters: 4288, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.803 G_ID: 0.164 G_Rec: 0.326 D_GP: 0.031 D_real: 0.948 D_fake: 0.800 +(epoch: 140, iters: 4688, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 1.266 G_ID: 0.172 G_Rec: 0.461 D_GP: 0.165 D_real: 0.564 D_fake: 0.492 +(epoch: 140, iters: 5088, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.786 G_ID: 0.134 G_Rec: 0.310 D_GP: 0.038 D_real: 1.075 D_fake: 0.654 +(epoch: 140, iters: 5488, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.762 G_ID: 0.216 G_Rec: 0.391 D_GP: 0.024 D_real: 0.976 D_fake: 0.843 +(epoch: 140, iters: 5888, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.735 G_ID: 0.159 G_Rec: 0.351 D_GP: 0.030 D_real: 1.056 D_fake: 0.803 +(epoch: 140, iters: 6288, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.911 G_ID: 0.234 G_Rec: 0.397 D_GP: 0.061 D_real: 0.695 D_fake: 0.913 +(epoch: 140, iters: 6688, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.786 G_ID: 0.192 G_Rec: 0.318 D_GP: 0.034 D_real: 1.113 D_fake: 0.841 +(epoch: 140, iters: 7088, time: 0.064) G_GAN: 0.529 G_GAN_Feat: 1.075 G_ID: 0.223 G_Rec: 0.431 D_GP: 0.141 D_real: 0.578 D_fake: 0.475 +(epoch: 140, iters: 7488, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.701 G_ID: 0.135 G_Rec: 0.303 D_GP: 0.027 D_real: 1.073 D_fake: 0.808 +(epoch: 140, iters: 7888, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.999 G_ID: 0.225 G_Rec: 0.381 D_GP: 0.089 D_real: 0.947 D_fake: 0.518 +(epoch: 140, iters: 8288, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.698 G_ID: 0.159 G_Rec: 0.324 D_GP: 0.025 D_real: 1.181 D_fake: 0.751 +(epoch: 141, iters: 80, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 1.028 G_ID: 0.212 G_Rec: 0.439 D_GP: 0.031 D_real: 0.857 D_fake: 0.621 +(epoch: 141, iters: 480, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.778 G_ID: 0.178 G_Rec: 0.334 D_GP: 0.031 D_real: 0.982 D_fake: 0.811 +(epoch: 141, iters: 880, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.822 G_ID: 0.213 G_Rec: 0.376 D_GP: 0.025 D_real: 1.137 D_fake: 0.521 +(epoch: 141, iters: 1280, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.684 G_ID: 0.158 G_Rec: 0.301 D_GP: 0.024 D_real: 1.064 D_fake: 0.812 +(epoch: 141, iters: 1680, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.832 G_ID: 0.211 G_Rec: 0.356 D_GP: 0.030 D_real: 1.199 D_fake: 0.620 +(epoch: 141, iters: 2080, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.844 G_ID: 0.170 G_Rec: 0.379 D_GP: 0.055 D_real: 0.768 D_fake: 0.776 +(epoch: 141, iters: 2480, time: 0.064) G_GAN: 0.645 G_GAN_Feat: 1.002 G_ID: 0.224 G_Rec: 0.408 D_GP: 0.045 D_real: 1.097 D_fake: 0.392 +(epoch: 141, iters: 2880, time: 0.064) G_GAN: 0.075 G_GAN_Feat: 0.689 G_ID: 0.154 G_Rec: 0.352 D_GP: 0.030 D_real: 0.915 D_fake: 0.926 +(epoch: 141, iters: 3280, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.895 G_ID: 0.218 G_Rec: 0.434 D_GP: 0.033 D_real: 1.130 D_fake: 0.555 +(epoch: 141, iters: 3680, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.620 G_ID: 0.152 G_Rec: 0.336 D_GP: 0.030 D_real: 1.119 D_fake: 0.804 +(epoch: 141, iters: 4080, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.853 G_ID: 0.223 G_Rec: 0.402 D_GP: 0.053 D_real: 0.782 D_fake: 0.815 +(epoch: 141, iters: 4480, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.866 G_ID: 0.153 G_Rec: 0.416 D_GP: 0.105 D_real: 0.519 D_fake: 0.855 +(epoch: 141, iters: 4880, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.836 G_ID: 0.232 G_Rec: 0.371 D_GP: 0.027 D_real: 1.201 D_fake: 0.442 +(epoch: 141, iters: 5280, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.759 G_ID: 0.136 G_Rec: 0.314 D_GP: 0.048 D_real: 0.979 D_fake: 0.779 +(epoch: 141, iters: 5680, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 1.018 G_ID: 0.216 G_Rec: 0.442 D_GP: 0.100 D_real: 0.556 D_fake: 0.584 +(epoch: 141, iters: 6080, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.855 G_ID: 0.147 G_Rec: 0.406 D_GP: 0.291 D_real: 0.801 D_fake: 0.833 +(epoch: 141, iters: 6480, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.987 G_ID: 0.217 G_Rec: 0.431 D_GP: 0.042 D_real: 0.881 D_fake: 0.608 +(epoch: 141, iters: 6880, time: 0.064) G_GAN: 0.003 G_GAN_Feat: 0.928 G_ID: 0.159 G_Rec: 0.357 D_GP: 0.131 D_real: 0.391 D_fake: 0.997 +(epoch: 141, iters: 7280, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.985 G_ID: 0.247 G_Rec: 0.404 D_GP: 0.032 D_real: 0.808 D_fake: 0.690 +(epoch: 141, iters: 7680, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.936 G_ID: 0.187 G_Rec: 0.345 D_GP: 0.065 D_real: 0.739 D_fake: 0.584 +(epoch: 141, iters: 8080, time: 0.064) G_GAN: 0.555 G_GAN_Feat: 0.943 G_ID: 0.214 G_Rec: 0.386 D_GP: 0.028 D_real: 1.162 D_fake: 0.454 +(epoch: 141, iters: 8480, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.703 G_ID: 0.147 G_Rec: 0.361 D_GP: 0.020 D_real: 1.413 D_fake: 0.509 +(epoch: 142, iters: 272, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 1.042 G_ID: 0.189 G_Rec: 0.431 D_GP: 0.100 D_real: 0.706 D_fake: 0.705 +(epoch: 142, iters: 672, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.809 G_ID: 0.160 G_Rec: 0.356 D_GP: 0.028 D_real: 0.901 D_fake: 0.717 +(epoch: 142, iters: 1072, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.894 G_ID: 0.203 G_Rec: 0.374 D_GP: 0.038 D_real: 1.088 D_fake: 0.494 +(epoch: 142, iters: 1472, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.731 G_ID: 0.152 G_Rec: 0.365 D_GP: 0.019 D_real: 1.167 D_fake: 0.718 +(epoch: 142, iters: 1872, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.775 G_ID: 0.224 G_Rec: 0.358 D_GP: 0.027 D_real: 1.086 D_fake: 0.687 +(epoch: 142, iters: 2272, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.734 G_ID: 0.177 G_Rec: 0.387 D_GP: 0.037 D_real: 0.929 D_fake: 0.860 +(epoch: 142, iters: 2672, time: 0.064) G_GAN: 0.823 G_GAN_Feat: 0.892 G_ID: 0.206 G_Rec: 0.389 D_GP: 0.042 D_real: 1.342 D_fake: 0.334 +(epoch: 142, iters: 3072, time: 0.064) G_GAN: 0.049 G_GAN_Feat: 0.823 G_ID: 0.157 G_Rec: 0.382 D_GP: 0.066 D_real: 1.018 D_fake: 0.953 +(epoch: 142, iters: 3472, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.857 G_ID: 0.208 G_Rec: 0.429 D_GP: 0.029 D_real: 1.114 D_fake: 0.617 +(epoch: 142, iters: 3872, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.799 G_ID: 0.153 G_Rec: 0.324 D_GP: 0.031 D_real: 1.048 D_fake: 0.797 +(epoch: 142, iters: 4272, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.931 G_ID: 0.199 G_Rec: 0.439 D_GP: 0.042 D_real: 1.013 D_fake: 0.493 +(epoch: 142, iters: 4672, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.686 G_ID: 0.164 G_Rec: 0.304 D_GP: 0.025 D_real: 1.100 D_fake: 0.768 +(epoch: 142, iters: 5072, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.831 G_ID: 0.244 G_Rec: 0.387 D_GP: 0.027 D_real: 1.255 D_fake: 0.488 +(epoch: 142, iters: 5472, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.735 G_ID: 0.122 G_Rec: 0.301 D_GP: 0.044 D_real: 0.938 D_fake: 0.858 +(epoch: 142, iters: 5872, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.882 G_ID: 0.248 G_Rec: 0.414 D_GP: 0.026 D_real: 1.230 D_fake: 0.481 +(epoch: 142, iters: 6272, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.878 G_ID: 0.155 G_Rec: 0.311 D_GP: 0.092 D_real: 0.454 D_fake: 0.937 +(epoch: 142, iters: 6672, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.997 G_ID: 0.211 G_Rec: 0.417 D_GP: 0.077 D_real: 0.787 D_fake: 0.856 +(epoch: 142, iters: 7072, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.631 G_ID: 0.191 G_Rec: 0.305 D_GP: 0.024 D_real: 1.139 D_fake: 0.742 +(epoch: 142, iters: 7472, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 1.085 G_ID: 0.241 G_Rec: 0.392 D_GP: 0.083 D_real: 0.417 D_fake: 0.660 +(epoch: 142, iters: 7872, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.860 G_ID: 0.176 G_Rec: 0.342 D_GP: 0.074 D_real: 0.816 D_fake: 0.806 +(epoch: 142, iters: 8272, time: 0.064) G_GAN: 0.735 G_GAN_Feat: 0.860 G_ID: 0.247 G_Rec: 0.414 D_GP: 0.024 D_real: 1.431 D_fake: 0.355 +(epoch: 143, iters: 64, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.747 G_ID: 0.175 G_Rec: 0.349 D_GP: 0.034 D_real: 0.935 D_fake: 0.841 +(epoch: 143, iters: 464, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 1.016 G_ID: 0.222 G_Rec: 0.438 D_GP: 0.078 D_real: 0.885 D_fake: 0.850 +(epoch: 143, iters: 864, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.665 G_ID: 0.147 G_Rec: 0.315 D_GP: 0.021 D_real: 1.265 D_fake: 0.675 +(epoch: 143, iters: 1264, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 1.109 G_ID: 0.203 G_Rec: 0.377 D_GP: 0.048 D_real: 0.565 D_fake: 0.630 +(epoch: 143, iters: 1664, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 1.075 G_ID: 0.159 G_Rec: 0.410 D_GP: 0.068 D_real: 1.299 D_fake: 0.546 +(epoch: 143, iters: 2064, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.817 G_ID: 0.208 G_Rec: 0.399 D_GP: 0.021 D_real: 1.257 D_fake: 0.574 +(epoch: 143, iters: 2464, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.908 G_ID: 0.151 G_Rec: 0.349 D_GP: 0.070 D_real: 0.741 D_fake: 0.780 +(epoch: 143, iters: 2864, time: 0.064) G_GAN: 0.682 G_GAN_Feat: 1.008 G_ID: 0.214 G_Rec: 0.432 D_GP: 0.056 D_real: 1.047 D_fake: 0.365 +(epoch: 143, iters: 3264, time: 0.064) G_GAN: -0.041 G_GAN_Feat: 0.802 G_ID: 0.131 G_Rec: 0.409 D_GP: 0.033 D_real: 0.696 D_fake: 1.041 +(epoch: 143, iters: 3664, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.818 G_ID: 0.221 G_Rec: 0.392 D_GP: 0.023 D_real: 1.182 D_fake: 0.545 +(epoch: 143, iters: 4064, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.807 G_ID: 0.151 G_Rec: 0.319 D_GP: 0.050 D_real: 0.813 D_fake: 0.764 +(epoch: 143, iters: 4464, time: 0.064) G_GAN: 0.556 G_GAN_Feat: 0.902 G_ID: 0.212 G_Rec: 0.437 D_GP: 0.023 D_real: 1.200 D_fake: 0.448 +(epoch: 143, iters: 4864, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.818 G_ID: 0.152 G_Rec: 0.328 D_GP: 0.052 D_real: 0.963 D_fake: 0.759 +(epoch: 143, iters: 5264, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.939 G_ID: 0.239 G_Rec: 0.423 D_GP: 0.031 D_real: 0.860 D_fake: 0.803 +(epoch: 143, iters: 5664, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.855 G_ID: 0.117 G_Rec: 0.334 D_GP: 0.090 D_real: 0.825 D_fake: 0.923 +(epoch: 143, iters: 6064, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.917 G_ID: 0.216 G_Rec: 0.461 D_GP: 0.026 D_real: 1.159 D_fake: 0.703 +(epoch: 143, iters: 6464, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.697 G_ID: 0.145 G_Rec: 0.326 D_GP: 0.024 D_real: 1.112 D_fake: 0.810 +(epoch: 143, iters: 6864, time: 0.064) G_GAN: 0.641 G_GAN_Feat: 0.902 G_ID: 0.230 G_Rec: 0.404 D_GP: 0.040 D_real: 1.127 D_fake: 0.394 +(epoch: 143, iters: 7264, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 1.085 G_ID: 0.134 G_Rec: 0.343 D_GP: 0.109 D_real: 0.312 D_fake: 0.850 +(epoch: 143, iters: 7664, time: 0.064) G_GAN: 0.661 G_GAN_Feat: 0.939 G_ID: 0.198 G_Rec: 0.439 D_GP: 0.029 D_real: 1.348 D_fake: 0.345 +(epoch: 143, iters: 8064, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.831 G_ID: 0.149 G_Rec: 0.357 D_GP: 0.058 D_real: 0.989 D_fake: 0.696 +(epoch: 143, iters: 8464, time: 0.064) G_GAN: 0.820 G_GAN_Feat: 0.945 G_ID: 0.222 G_Rec: 0.415 D_GP: 0.043 D_real: 1.384 D_fake: 0.221 +(epoch: 144, iters: 256, time: 0.064) G_GAN: 0.070 G_GAN_Feat: 1.022 G_ID: 0.178 G_Rec: 0.328 D_GP: 0.064 D_real: 0.293 D_fake: 0.930 +(epoch: 144, iters: 656, time: 0.064) G_GAN: 0.648 G_GAN_Feat: 0.924 G_ID: 0.204 G_Rec: 0.422 D_GP: 0.025 D_real: 1.330 D_fake: 0.361 +(epoch: 144, iters: 1056, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.639 G_ID: 0.136 G_Rec: 0.298 D_GP: 0.026 D_real: 1.335 D_fake: 0.594 +(epoch: 144, iters: 1456, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.845 G_ID: 0.213 G_Rec: 0.402 D_GP: 0.037 D_real: 0.853 D_fake: 0.855 +(epoch: 144, iters: 1856, time: 0.064) G_GAN: -0.023 G_GAN_Feat: 0.724 G_ID: 0.172 G_Rec: 0.310 D_GP: 0.045 D_real: 0.870 D_fake: 1.023 +(epoch: 144, iters: 2256, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.840 G_ID: 0.192 G_Rec: 0.392 D_GP: 0.030 D_real: 1.155 D_fake: 0.576 +(epoch: 144, iters: 2656, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.814 G_ID: 0.173 G_Rec: 0.340 D_GP: 0.038 D_real: 1.035 D_fake: 0.704 +(epoch: 144, iters: 3056, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.949 G_ID: 0.232 G_Rec: 0.401 D_GP: 0.074 D_real: 0.746 D_fake: 0.771 +(epoch: 144, iters: 3456, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.746 G_ID: 0.164 G_Rec: 0.327 D_GP: 0.037 D_real: 1.046 D_fake: 0.714 +(epoch: 144, iters: 3856, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 1.013 G_ID: 0.229 G_Rec: 0.420 D_GP: 0.135 D_real: 0.831 D_fake: 0.615 +(epoch: 144, iters: 4256, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.654 G_ID: 0.153 G_Rec: 0.320 D_GP: 0.026 D_real: 1.165 D_fake: 0.740 +(epoch: 144, iters: 4656, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 1.095 G_ID: 0.213 G_Rec: 0.452 D_GP: 0.203 D_real: 0.293 D_fake: 0.919 +(epoch: 144, iters: 5056, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.949 G_ID: 0.154 G_Rec: 0.359 D_GP: 0.097 D_real: 0.541 D_fake: 0.825 +(epoch: 144, iters: 5456, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 1.064 G_ID: 0.219 G_Rec: 0.460 D_GP: 0.212 D_real: 0.523 D_fake: 0.750 +(epoch: 144, iters: 5856, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.651 G_ID: 0.151 G_Rec: 0.289 D_GP: 0.026 D_real: 1.266 D_fake: 0.595 +(epoch: 144, iters: 6256, time: 0.064) G_GAN: 0.804 G_GAN_Feat: 1.326 G_ID: 0.239 G_Rec: 0.466 D_GP: 0.497 D_real: 0.731 D_fake: 0.246 +(epoch: 144, iters: 6656, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.883 G_ID: 0.156 G_Rec: 0.360 D_GP: 0.042 D_real: 0.739 D_fake: 0.673 +(epoch: 144, iters: 7056, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 1.109 G_ID: 0.219 G_Rec: 0.410 D_GP: 0.501 D_real: 0.427 D_fake: 0.648 +(epoch: 144, iters: 7456, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.702 G_ID: 0.151 G_Rec: 0.329 D_GP: 0.021 D_real: 1.173 D_fake: 0.692 +(epoch: 144, iters: 7856, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 1.036 G_ID: 0.189 G_Rec: 0.417 D_GP: 0.144 D_real: 0.781 D_fake: 0.533 +(epoch: 144, iters: 8256, time: 0.064) G_GAN: 0.101 G_GAN_Feat: 0.631 G_ID: 0.177 G_Rec: 0.288 D_GP: 0.024 D_real: 1.019 D_fake: 0.899 +(epoch: 145, iters: 48, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.957 G_ID: 0.193 G_Rec: 0.435 D_GP: 0.047 D_real: 1.067 D_fake: 0.669 +(epoch: 145, iters: 448, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.614 G_ID: 0.161 G_Rec: 0.418 D_GP: 0.021 D_real: 1.177 D_fake: 0.735 +(epoch: 145, iters: 848, time: 0.064) G_GAN: 0.601 G_GAN_Feat: 0.787 G_ID: 0.231 G_Rec: 0.390 D_GP: 0.022 D_real: 1.272 D_fake: 0.422 +(epoch: 145, iters: 1248, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.737 G_ID: 0.170 G_Rec: 0.325 D_GP: 0.080 D_real: 0.975 D_fake: 0.730 +(epoch: 145, iters: 1648, time: 0.064) G_GAN: 0.676 G_GAN_Feat: 1.044 G_ID: 0.213 G_Rec: 0.426 D_GP: 0.070 D_real: 0.969 D_fake: 0.366 +(epoch: 145, iters: 2048, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 0.906 G_ID: 0.137 G_Rec: 0.337 D_GP: 0.059 D_real: 1.327 D_fake: 0.448 +(epoch: 145, iters: 2448, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.877 G_ID: 0.192 G_Rec: 0.400 D_GP: 0.031 D_real: 1.080 D_fake: 0.553 +(epoch: 145, iters: 2848, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.967 G_ID: 0.176 G_Rec: 0.357 D_GP: 0.105 D_real: 0.629 D_fake: 0.693 +(epoch: 145, iters: 3248, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.895 G_ID: 0.197 G_Rec: 0.416 D_GP: 0.032 D_real: 0.929 D_fake: 0.623 +(epoch: 145, iters: 3648, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.812 G_ID: 0.139 G_Rec: 0.379 D_GP: 0.033 D_real: 1.088 D_fake: 0.680 +(epoch: 145, iters: 4048, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.806 G_ID: 0.202 G_Rec: 0.377 D_GP: 0.025 D_real: 0.963 D_fake: 0.808 +(epoch: 145, iters: 4448, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.749 G_ID: 0.146 G_Rec: 0.339 D_GP: 0.044 D_real: 0.882 D_fake: 0.901 +(epoch: 145, iters: 4848, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.919 G_ID: 0.218 G_Rec: 0.415 D_GP: 0.030 D_real: 1.050 D_fake: 0.627 +(epoch: 145, iters: 5248, time: 0.064) G_GAN: 0.063 G_GAN_Feat: 0.750 G_ID: 0.144 G_Rec: 0.323 D_GP: 0.032 D_real: 0.874 D_fake: 0.937 +(epoch: 145, iters: 5648, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 0.946 G_ID: 0.206 G_Rec: 0.392 D_GP: 0.055 D_real: 1.223 D_fake: 0.442 +(epoch: 145, iters: 6048, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.880 G_ID: 0.143 G_Rec: 0.324 D_GP: 0.039 D_real: 0.770 D_fake: 0.807 +(epoch: 145, iters: 6448, time: 0.064) G_GAN: 0.665 G_GAN_Feat: 1.094 G_ID: 0.215 G_Rec: 0.425 D_GP: 0.113 D_real: 0.958 D_fake: 0.414 +(epoch: 145, iters: 6848, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.796 G_ID: 0.145 G_Rec: 0.330 D_GP: 0.027 D_real: 1.155 D_fake: 0.685 +(epoch: 145, iters: 7248, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.826 G_ID: 0.203 G_Rec: 0.410 D_GP: 0.022 D_real: 0.997 D_fake: 0.747 +(epoch: 145, iters: 7648, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.622 G_ID: 0.152 G_Rec: 0.321 D_GP: 0.027 D_real: 1.032 D_fake: 0.862 +(epoch: 145, iters: 8048, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.845 G_ID: 0.191 G_Rec: 0.394 D_GP: 0.036 D_real: 0.898 D_fake: 0.757 +(epoch: 145, iters: 8448, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.854 G_ID: 0.181 G_Rec: 0.344 D_GP: 0.037 D_real: 0.869 D_fake: 0.747 +(epoch: 146, iters: 240, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 0.853 G_ID: 0.185 G_Rec: 0.423 D_GP: 0.022 D_real: 1.189 D_fake: 0.454 +(epoch: 146, iters: 640, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.758 G_ID: 0.158 G_Rec: 0.333 D_GP: 0.051 D_real: 1.076 D_fake: 0.670 +(epoch: 146, iters: 1040, time: 0.064) G_GAN: 0.666 G_GAN_Feat: 1.046 G_ID: 0.217 G_Rec: 0.430 D_GP: 0.108 D_real: 0.980 D_fake: 0.345 +(epoch: 146, iters: 1440, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.873 G_ID: 0.129 G_Rec: 0.323 D_GP: 0.055 D_real: 0.760 D_fake: 0.618 +(epoch: 146, iters: 1840, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.832 G_ID: 0.214 G_Rec: 0.390 D_GP: 0.027 D_real: 1.285 D_fake: 0.483 +(epoch: 146, iters: 2240, time: 0.064) G_GAN: 0.038 G_GAN_Feat: 0.911 G_ID: 0.163 G_Rec: 0.337 D_GP: 0.096 D_real: 0.499 D_fake: 0.963 +(epoch: 146, iters: 2640, time: 0.064) G_GAN: 0.669 G_GAN_Feat: 1.052 G_ID: 0.197 G_Rec: 0.401 D_GP: 0.031 D_real: 1.038 D_fake: 0.362 +(epoch: 146, iters: 3040, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 1.187 G_ID: 0.145 G_Rec: 0.422 D_GP: 1.129 D_real: 0.859 D_fake: 0.876 +(epoch: 146, iters: 3440, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.862 G_ID: 0.236 G_Rec: 0.419 D_GP: 0.026 D_real: 1.088 D_fake: 0.713 +(epoch: 146, iters: 3840, time: 0.064) G_GAN: 0.014 G_GAN_Feat: 0.752 G_ID: 0.169 G_Rec: 0.354 D_GP: 0.038 D_real: 0.792 D_fake: 0.986 +(epoch: 146, iters: 4240, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.966 G_ID: 0.208 G_Rec: 0.410 D_GP: 0.053 D_real: 0.955 D_fake: 0.605 +(epoch: 146, iters: 4640, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.715 G_ID: 0.140 G_Rec: 0.294 D_GP: 0.033 D_real: 1.131 D_fake: 0.737 +(epoch: 146, iters: 5040, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.930 G_ID: 0.265 G_Rec: 0.415 D_GP: 0.033 D_real: 0.869 D_fake: 0.708 +(epoch: 146, iters: 5440, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.828 G_ID: 0.153 G_Rec: 0.315 D_GP: 0.027 D_real: 1.078 D_fake: 0.697 +(epoch: 146, iters: 5840, time: 0.064) G_GAN: 0.711 G_GAN_Feat: 0.846 G_ID: 0.206 G_Rec: 0.396 D_GP: 0.025 D_real: 1.345 D_fake: 0.302 +(epoch: 146, iters: 6240, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.671 G_ID: 0.152 G_Rec: 0.322 D_GP: 0.023 D_real: 1.231 D_fake: 0.685 +(epoch: 146, iters: 6640, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.993 G_ID: 0.207 G_Rec: 0.418 D_GP: 0.124 D_real: 0.830 D_fake: 0.491 +(epoch: 146, iters: 7040, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.786 G_ID: 0.128 G_Rec: 0.349 D_GP: 0.036 D_real: 1.019 D_fake: 0.841 +(epoch: 146, iters: 7440, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 1.240 G_ID: 0.223 G_Rec: 0.411 D_GP: 0.057 D_real: 1.244 D_fake: 0.991 +(epoch: 146, iters: 7840, time: 0.064) G_GAN: -0.070 G_GAN_Feat: 0.650 G_ID: 0.173 G_Rec: 0.338 D_GP: 0.020 D_real: 0.894 D_fake: 1.070 +(epoch: 146, iters: 8240, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.957 G_ID: 0.224 G_Rec: 0.425 D_GP: 0.069 D_real: 0.712 D_fake: 0.772 +(epoch: 147, iters: 32, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.680 G_ID: 0.128 G_Rec: 0.337 D_GP: 0.024 D_real: 1.338 D_fake: 0.600 +(epoch: 147, iters: 432, time: 0.064) G_GAN: 0.792 G_GAN_Feat: 1.117 G_ID: 0.217 G_Rec: 0.436 D_GP: 0.095 D_real: 0.850 D_fake: 0.263 +(epoch: 147, iters: 832, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.743 G_ID: 0.145 G_Rec: 0.352 D_GP: 0.028 D_real: 1.208 D_fake: 0.721 +(epoch: 147, iters: 1232, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.824 G_ID: 0.218 G_Rec: 0.376 D_GP: 0.027 D_real: 1.030 D_fake: 0.750 +(epoch: 147, iters: 1632, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.869 G_ID: 0.171 G_Rec: 0.320 D_GP: 0.045 D_real: 0.688 D_fake: 0.846 +(epoch: 147, iters: 2032, time: 0.064) G_GAN: 0.204 G_GAN_Feat: 1.225 G_ID: 0.226 G_Rec: 0.467 D_GP: 0.851 D_real: 0.573 D_fake: 0.825 +(epoch: 147, iters: 2432, time: 0.064) G_GAN: 0.089 G_GAN_Feat: 0.727 G_ID: 0.146 G_Rec: 0.302 D_GP: 0.037 D_real: 0.941 D_fake: 0.911 +(epoch: 147, iters: 2832, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.760 G_ID: 0.203 G_Rec: 0.399 D_GP: 0.021 D_real: 1.260 D_fake: 0.526 +(epoch: 147, iters: 3232, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.642 G_ID: 0.146 G_Rec: 0.301 D_GP: 0.023 D_real: 1.053 D_fake: 0.887 +(epoch: 147, iters: 3632, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.795 G_ID: 0.216 G_Rec: 0.407 D_GP: 0.032 D_real: 0.919 D_fake: 0.734 +(epoch: 147, iters: 4032, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.742 G_ID: 0.154 G_Rec: 0.328 D_GP: 0.081 D_real: 0.894 D_fake: 0.918 +(epoch: 147, iters: 4432, time: 0.064) G_GAN: 0.643 G_GAN_Feat: 0.847 G_ID: 0.223 G_Rec: 0.386 D_GP: 0.026 D_real: 1.351 D_fake: 0.362 +(epoch: 147, iters: 4832, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.824 G_ID: 0.182 G_Rec: 0.343 D_GP: 0.047 D_real: 0.936 D_fake: 0.700 +(epoch: 147, iters: 5232, time: 0.064) G_GAN: 0.595 G_GAN_Feat: 0.861 G_ID: 0.210 G_Rec: 0.391 D_GP: 0.027 D_real: 1.307 D_fake: 0.425 +(epoch: 147, iters: 5632, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.793 G_ID: 0.167 G_Rec: 0.331 D_GP: 0.044 D_real: 0.885 D_fake: 0.831 +(epoch: 147, iters: 6032, time: 0.064) G_GAN: 0.617 G_GAN_Feat: 0.851 G_ID: 0.197 G_Rec: 0.415 D_GP: 0.026 D_real: 1.357 D_fake: 0.413 +(epoch: 147, iters: 6432, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 0.830 G_ID: 0.138 G_Rec: 0.310 D_GP: 0.131 D_real: 0.447 D_fake: 0.936 +(epoch: 147, iters: 6832, time: 0.064) G_GAN: 0.557 G_GAN_Feat: 0.882 G_ID: 0.186 G_Rec: 0.456 D_GP: 0.022 D_real: 1.192 D_fake: 0.454 +(epoch: 147, iters: 7232, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.602 G_ID: 0.154 G_Rec: 0.299 D_GP: 0.022 D_real: 1.221 D_fake: 0.701 +(epoch: 147, iters: 7632, time: 0.064) G_GAN: 0.707 G_GAN_Feat: 0.810 G_ID: 0.216 G_Rec: 0.358 D_GP: 0.028 D_real: 1.385 D_fake: 0.336 +(epoch: 147, iters: 8032, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.807 G_ID: 0.163 G_Rec: 0.332 D_GP: 0.059 D_real: 0.822 D_fake: 0.748 +(epoch: 147, iters: 8432, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.801 G_ID: 0.244 G_Rec: 0.426 D_GP: 0.031 D_real: 0.880 D_fake: 0.797 +(epoch: 148, iters: 224, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.654 G_ID: 0.132 G_Rec: 0.296 D_GP: 0.027 D_real: 1.160 D_fake: 0.756 +(epoch: 148, iters: 624, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.900 G_ID: 0.237 G_Rec: 0.395 D_GP: 0.028 D_real: 1.196 D_fake: 0.532 +(epoch: 148, iters: 1024, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.707 G_ID: 0.146 G_Rec: 0.332 D_GP: 0.025 D_real: 1.089 D_fake: 0.808 +(epoch: 148, iters: 1424, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.974 G_ID: 0.206 G_Rec: 0.407 D_GP: 0.044 D_real: 0.804 D_fake: 0.645 +(epoch: 148, iters: 1824, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.836 G_ID: 0.163 G_Rec: 0.353 D_GP: 0.032 D_real: 1.149 D_fake: 0.696 +(epoch: 148, iters: 2224, time: 0.064) G_GAN: 0.684 G_GAN_Feat: 1.097 G_ID: 0.212 G_Rec: 0.436 D_GP: 0.091 D_real: 0.733 D_fake: 0.359 +(epoch: 148, iters: 2624, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.750 G_ID: 0.157 G_Rec: 0.356 D_GP: 0.031 D_real: 0.998 D_fake: 0.829 +(epoch: 148, iters: 3024, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.897 G_ID: 0.206 G_Rec: 0.410 D_GP: 0.060 D_real: 0.668 D_fake: 0.828 +(epoch: 148, iters: 3424, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.751 G_ID: 0.133 G_Rec: 0.313 D_GP: 0.096 D_real: 0.862 D_fake: 0.796 +(epoch: 148, iters: 3824, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.985 G_ID: 0.225 G_Rec: 0.382 D_GP: 0.058 D_real: 1.019 D_fake: 0.408 +(epoch: 148, iters: 4224, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.763 G_ID: 0.159 G_Rec: 0.321 D_GP: 0.026 D_real: 1.154 D_fake: 0.718 +(epoch: 148, iters: 4624, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.861 G_ID: 0.249 G_Rec: 0.394 D_GP: 0.027 D_real: 1.001 D_fake: 0.676 +(epoch: 148, iters: 5024, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.878 G_ID: 0.138 G_Rec: 0.329 D_GP: 0.060 D_real: 0.704 D_fake: 0.829 +(epoch: 148, iters: 5424, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 1.043 G_ID: 0.219 G_Rec: 0.416 D_GP: 0.054 D_real: 0.676 D_fake: 0.659 +(epoch: 148, iters: 5824, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 0.835 G_ID: 0.145 G_Rec: 0.315 D_GP: 0.073 D_real: 1.027 D_fake: 0.493 +(epoch: 148, iters: 6224, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 0.966 G_ID: 0.207 G_Rec: 0.414 D_GP: 0.055 D_real: 0.887 D_fake: 0.507 +(epoch: 148, iters: 6624, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.752 G_ID: 0.126 G_Rec: 0.342 D_GP: 0.028 D_real: 0.938 D_fake: 0.918 +(epoch: 148, iters: 7024, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.881 G_ID: 0.228 G_Rec: 0.423 D_GP: 0.024 D_real: 1.043 D_fake: 0.618 +(epoch: 148, iters: 7424, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.715 G_ID: 0.140 G_Rec: 0.342 D_GP: 0.035 D_real: 1.059 D_fake: 0.709 +(epoch: 148, iters: 7824, time: 0.064) G_GAN: 0.588 G_GAN_Feat: 0.887 G_ID: 0.181 G_Rec: 0.412 D_GP: 0.027 D_real: 1.220 D_fake: 0.427 +(epoch: 148, iters: 8224, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.739 G_ID: 0.154 G_Rec: 0.340 D_GP: 0.025 D_real: 1.060 D_fake: 0.782 +(epoch: 149, iters: 16, time: 0.064) G_GAN: 0.703 G_GAN_Feat: 0.783 G_ID: 0.235 G_Rec: 0.402 D_GP: 0.026 D_real: 1.415 D_fake: 0.364 +(epoch: 149, iters: 416, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.688 G_ID: 0.154 G_Rec: 0.328 D_GP: 0.020 D_real: 1.184 D_fake: 0.728 +(epoch: 149, iters: 816, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.941 G_ID: 0.192 G_Rec: 0.444 D_GP: 0.025 D_real: 0.868 D_fake: 0.668 +(epoch: 149, iters: 1216, time: 0.064) G_GAN: 0.005 G_GAN_Feat: 0.843 G_ID: 0.133 G_Rec: 0.361 D_GP: 0.098 D_real: 0.481 D_fake: 0.995 +(epoch: 149, iters: 1616, time: 0.064) G_GAN: 0.530 G_GAN_Feat: 0.862 G_ID: 0.211 G_Rec: 0.392 D_GP: 0.026 D_real: 1.154 D_fake: 0.479 +(epoch: 149, iters: 2016, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.935 G_ID: 0.136 G_Rec: 0.366 D_GP: 0.198 D_real: 0.477 D_fake: 0.794 +(epoch: 149, iters: 2416, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.900 G_ID: 0.216 G_Rec: 0.384 D_GP: 0.058 D_real: 0.920 D_fake: 0.547 +(epoch: 149, iters: 2816, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.732 G_ID: 0.148 G_Rec: 0.298 D_GP: 0.027 D_real: 1.152 D_fake: 0.689 +(epoch: 149, iters: 3216, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.798 G_ID: 0.210 G_Rec: 0.346 D_GP: 0.028 D_real: 1.077 D_fake: 0.624 +(epoch: 149, iters: 3616, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.967 G_ID: 0.149 G_Rec: 0.415 D_GP: 0.063 D_real: 0.768 D_fake: 0.764 +(epoch: 149, iters: 4016, time: 0.064) G_GAN: 1.019 G_GAN_Feat: 1.046 G_ID: 0.213 G_Rec: 0.421 D_GP: 0.062 D_real: 1.202 D_fake: 0.127 +(epoch: 149, iters: 4416, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 0.974 G_ID: 0.162 G_Rec: 0.375 D_GP: 0.085 D_real: 0.555 D_fake: 0.545 +(epoch: 149, iters: 4816, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 0.863 G_ID: 0.188 G_Rec: 0.420 D_GP: 0.025 D_real: 1.388 D_fake: 0.402 +(epoch: 149, iters: 5216, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.879 G_ID: 0.158 G_Rec: 0.353 D_GP: 0.042 D_real: 1.013 D_fake: 0.740 +(epoch: 149, iters: 5616, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 1.059 G_ID: 0.223 G_Rec: 0.423 D_GP: 0.063 D_real: 0.362 D_fake: 0.905 +(epoch: 149, iters: 6016, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.825 G_ID: 0.150 G_Rec: 0.377 D_GP: 0.055 D_real: 0.994 D_fake: 0.732 +(epoch: 149, iters: 6416, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.879 G_ID: 0.235 G_Rec: 0.450 D_GP: 0.025 D_real: 1.147 D_fake: 0.562 +(epoch: 149, iters: 6816, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.849 G_ID: 0.151 G_Rec: 0.404 D_GP: 0.031 D_real: 1.053 D_fake: 0.658 +(epoch: 149, iters: 7216, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 0.902 G_ID: 0.187 G_Rec: 0.383 D_GP: 0.028 D_real: 1.156 D_fake: 0.420 +(epoch: 149, iters: 7616, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.838 G_ID: 0.143 G_Rec: 0.366 D_GP: 0.061 D_real: 0.760 D_fake: 0.873 +(epoch: 149, iters: 8016, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 0.864 G_ID: 0.212 G_Rec: 0.425 D_GP: 0.025 D_real: 1.375 D_fake: 0.414 +(epoch: 149, iters: 8416, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.678 G_ID: 0.144 G_Rec: 0.332 D_GP: 0.021 D_real: 1.037 D_fake: 0.841 +(epoch: 150, iters: 208, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.764 G_ID: 0.203 G_Rec: 0.378 D_GP: 0.029 D_real: 1.069 D_fake: 0.713 +(epoch: 150, iters: 608, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.645 G_ID: 0.169 G_Rec: 0.362 D_GP: 0.021 D_real: 1.127 D_fake: 0.836 +(epoch: 150, iters: 1008, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.909 G_ID: 0.216 G_Rec: 0.415 D_GP: 0.065 D_real: 0.896 D_fake: 0.692 +(epoch: 150, iters: 1408, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.739 G_ID: 0.130 G_Rec: 0.331 D_GP: 0.033 D_real: 1.045 D_fake: 0.838 +(epoch: 150, iters: 1808, time: 0.064) G_GAN: 0.680 G_GAN_Feat: 1.296 G_ID: 0.187 G_Rec: 0.429 D_GP: 0.589 D_real: 0.471 D_fake: 0.381 +(epoch: 150, iters: 2208, time: 0.064) G_GAN: -0.039 G_GAN_Feat: 0.916 G_ID: 0.144 G_Rec: 0.351 D_GP: 0.029 D_real: 0.641 D_fake: 1.039 +(epoch: 150, iters: 2608, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 0.924 G_ID: 0.214 G_Rec: 0.385 D_GP: 0.032 D_real: 1.149 D_fake: 0.474 +(epoch: 150, iters: 3008, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.685 G_ID: 0.143 G_Rec: 0.343 D_GP: 0.023 D_real: 1.261 D_fake: 0.646 +(epoch: 150, iters: 3408, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.840 G_ID: 0.227 G_Rec: 0.417 D_GP: 0.031 D_real: 1.456 D_fake: 0.410 +(epoch: 150, iters: 3808, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.674 G_ID: 0.136 G_Rec: 0.344 D_GP: 0.028 D_real: 1.061 D_fake: 0.779 +(epoch: 150, iters: 4208, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.897 G_ID: 0.198 G_Rec: 0.406 D_GP: 0.044 D_real: 1.115 D_fake: 0.478 +(epoch: 150, iters: 4608, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.876 G_ID: 0.138 G_Rec: 0.331 D_GP: 0.221 D_real: 0.881 D_fake: 0.627 +(epoch: 150, iters: 5008, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.975 G_ID: 0.200 G_Rec: 0.395 D_GP: 0.056 D_real: 0.731 D_fake: 0.791 +(epoch: 150, iters: 5408, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.692 G_ID: 0.154 G_Rec: 0.320 D_GP: 0.035 D_real: 1.189 D_fake: 0.713 +(epoch: 150, iters: 5808, time: 0.064) G_GAN: 0.724 G_GAN_Feat: 1.092 G_ID: 0.192 G_Rec: 0.412 D_GP: 0.048 D_real: 0.846 D_fake: 0.318 +(epoch: 150, iters: 6208, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.911 G_ID: 0.137 G_Rec: 0.332 D_GP: 0.033 D_real: 0.928 D_fake: 0.675 +(epoch: 150, iters: 6608, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 1.071 G_ID: 0.209 G_Rec: 0.409 D_GP: 0.049 D_real: 0.630 D_fake: 0.573 +(epoch: 150, iters: 7008, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.673 G_ID: 0.132 G_Rec: 0.327 D_GP: 0.024 D_real: 1.087 D_fake: 0.878 +(epoch: 150, iters: 7408, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 1.043 G_ID: 0.199 G_Rec: 0.420 D_GP: 0.185 D_real: 0.853 D_fake: 0.487 +(epoch: 150, iters: 7808, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 1.065 G_ID: 0.161 G_Rec: 0.391 D_GP: 0.201 D_real: 0.364 D_fake: 0.889 +(epoch: 150, iters: 8208, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 1.018 G_ID: 0.172 G_Rec: 0.436 D_GP: 0.024 D_real: 0.848 D_fake: 0.778 +(epoch: 150, iters: 8608, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.854 G_ID: 0.148 G_Rec: 0.325 D_GP: 0.038 D_real: 1.112 D_fake: 0.831 +(epoch: 151, iters: 400, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.817 G_ID: 0.169 G_Rec: 0.373 D_GP: 0.024 D_real: 1.196 D_fake: 0.539 +(epoch: 151, iters: 800, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.762 G_ID: 0.173 G_Rec: 0.372 D_GP: 0.026 D_real: 0.957 D_fake: 0.885 +(epoch: 151, iters: 1200, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 1.035 G_ID: 0.227 G_Rec: 0.401 D_GP: 0.087 D_real: 0.465 D_fake: 0.621 +(epoch: 151, iters: 1600, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.812 G_ID: 0.128 G_Rec: 0.343 D_GP: 0.039 D_real: 1.103 D_fake: 0.688 +(epoch: 151, iters: 2000, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.806 G_ID: 0.186 G_Rec: 0.389 D_GP: 0.025 D_real: 1.259 D_fake: 0.602 +(epoch: 151, iters: 2400, time: 0.064) G_GAN: -0.120 G_GAN_Feat: 0.876 G_ID: 0.147 G_Rec: 0.313 D_GP: 0.049 D_real: 0.380 D_fake: 1.120 +(epoch: 151, iters: 2800, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.898 G_ID: 0.208 G_Rec: 0.406 D_GP: 0.037 D_real: 1.251 D_fake: 0.461 +(epoch: 151, iters: 3200, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.759 G_ID: 0.150 G_Rec: 0.332 D_GP: 0.035 D_real: 1.087 D_fake: 0.731 +(epoch: 151, iters: 3600, time: 0.064) G_GAN: 0.663 G_GAN_Feat: 0.910 G_ID: 0.188 G_Rec: 0.397 D_GP: 0.038 D_real: 1.130 D_fake: 0.354 +(epoch: 151, iters: 4000, time: 0.064) G_GAN: 0.015 G_GAN_Feat: 1.077 G_ID: 0.159 G_Rec: 0.353 D_GP: 0.299 D_real: 0.226 D_fake: 0.985 +(epoch: 151, iters: 4400, time: 0.064) G_GAN: 0.637 G_GAN_Feat: 1.154 G_ID: 0.200 G_Rec: 0.507 D_GP: 0.132 D_real: 1.082 D_fake: 0.397 +(epoch: 151, iters: 4800, time: 0.064) G_GAN: -0.154 G_GAN_Feat: 0.822 G_ID: 0.162 G_Rec: 0.366 D_GP: 0.076 D_real: 0.446 D_fake: 1.154 +(epoch: 151, iters: 5200, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 1.012 G_ID: 0.252 G_Rec: 0.442 D_GP: 0.043 D_real: 0.799 D_fake: 0.803 +(epoch: 151, iters: 5600, time: 0.064) G_GAN: -0.118 G_GAN_Feat: 0.742 G_ID: 0.144 G_Rec: 0.336 D_GP: 0.040 D_real: 0.762 D_fake: 1.118 +(epoch: 151, iters: 6000, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.819 G_ID: 0.194 G_Rec: 0.419 D_GP: 0.024 D_real: 1.200 D_fake: 0.529 +(epoch: 151, iters: 6400, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.703 G_ID: 0.160 G_Rec: 0.327 D_GP: 0.036 D_real: 1.093 D_fake: 0.761 +(epoch: 151, iters: 6800, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 0.867 G_ID: 0.200 G_Rec: 0.395 D_GP: 0.035 D_real: 1.136 D_fake: 0.533 +(epoch: 151, iters: 7200, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.708 G_ID: 0.132 G_Rec: 0.324 D_GP: 0.025 D_real: 1.014 D_fake: 0.871 +(epoch: 151, iters: 7600, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.837 G_ID: 0.181 G_Rec: 0.391 D_GP: 0.033 D_real: 1.183 D_fake: 0.543 +(epoch: 151, iters: 8000, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.727 G_ID: 0.150 G_Rec: 0.318 D_GP: 0.035 D_real: 0.896 D_fake: 0.856 +(epoch: 151, iters: 8400, time: 0.064) G_GAN: 0.535 G_GAN_Feat: 1.021 G_ID: 0.212 G_Rec: 0.420 D_GP: 0.053 D_real: 0.845 D_fake: 0.469 +(epoch: 152, iters: 192, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 1.190 G_ID: 0.145 G_Rec: 0.347 D_GP: 0.189 D_real: 0.698 D_fake: 0.662 +(epoch: 152, iters: 592, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.963 G_ID: 0.212 G_Rec: 0.378 D_GP: 0.041 D_real: 1.100 D_fake: 0.496 +(epoch: 152, iters: 992, time: 0.064) G_GAN: -0.044 G_GAN_Feat: 0.722 G_ID: 0.133 G_Rec: 0.342 D_GP: 0.021 D_real: 0.849 D_fake: 1.044 +(epoch: 152, iters: 1392, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 1.248 G_ID: 0.190 G_Rec: 0.459 D_GP: 0.140 D_real: 0.376 D_fake: 0.871 +(epoch: 152, iters: 1792, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.719 G_ID: 0.145 G_Rec: 0.330 D_GP: 0.026 D_real: 1.245 D_fake: 0.622 +(epoch: 152, iters: 2192, time: 0.064) G_GAN: 0.730 G_GAN_Feat: 0.953 G_ID: 0.200 G_Rec: 0.408 D_GP: 0.030 D_real: 1.307 D_fake: 0.333 +(epoch: 152, iters: 2592, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.838 G_ID: 0.161 G_Rec: 0.367 D_GP: 0.031 D_real: 0.930 D_fake: 0.605 +(epoch: 152, iters: 2992, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.749 G_ID: 0.215 G_Rec: 0.359 D_GP: 0.037 D_real: 1.196 D_fake: 0.619 +(epoch: 152, iters: 3392, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.732 G_ID: 0.135 G_Rec: 0.314 D_GP: 0.041 D_real: 0.985 D_fake: 0.879 +(epoch: 152, iters: 3792, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.849 G_ID: 0.211 G_Rec: 0.391 D_GP: 0.045 D_real: 1.121 D_fake: 0.561 +(epoch: 152, iters: 4192, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.896 G_ID: 0.146 G_Rec: 0.354 D_GP: 0.106 D_real: 0.788 D_fake: 0.827 +(epoch: 152, iters: 4592, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.900 G_ID: 0.201 G_Rec: 0.420 D_GP: 0.030 D_real: 1.236 D_fake: 0.509 +(epoch: 152, iters: 4992, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.758 G_ID: 0.172 G_Rec: 0.324 D_GP: 0.027 D_real: 1.080 D_fake: 0.794 +(epoch: 152, iters: 5392, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 0.954 G_ID: 0.188 G_Rec: 0.390 D_GP: 0.042 D_real: 1.064 D_fake: 0.511 +(epoch: 152, iters: 5792, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.691 G_ID: 0.160 G_Rec: 0.308 D_GP: 0.022 D_real: 1.181 D_fake: 0.674 +(epoch: 152, iters: 6192, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 1.061 G_ID: 0.218 G_Rec: 0.403 D_GP: 0.092 D_real: 1.075 D_fake: 0.556 +(epoch: 152, iters: 6592, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.648 G_ID: 0.152 G_Rec: 0.334 D_GP: 0.020 D_real: 1.377 D_fake: 0.613 +(epoch: 152, iters: 6992, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.838 G_ID: 0.200 G_Rec: 0.407 D_GP: 0.020 D_real: 1.181 D_fake: 0.538 +(epoch: 152, iters: 7392, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.749 G_ID: 0.186 G_Rec: 0.381 D_GP: 0.028 D_real: 0.936 D_fake: 0.881 +(epoch: 152, iters: 7792, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.834 G_ID: 0.235 G_Rec: 0.402 D_GP: 0.036 D_real: 0.883 D_fake: 0.782 +(epoch: 152, iters: 8192, time: 0.064) G_GAN: 0.063 G_GAN_Feat: 0.634 G_ID: 0.142 G_Rec: 0.317 D_GP: 0.026 D_real: 0.978 D_fake: 0.937 +(epoch: 152, iters: 8592, time: 0.064) G_GAN: 0.558 G_GAN_Feat: 0.985 G_ID: 0.197 G_Rec: 0.393 D_GP: 0.089 D_real: 0.898 D_fake: 0.460 +(epoch: 153, iters: 384, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.972 G_ID: 0.139 G_Rec: 0.324 D_GP: 0.064 D_real: 0.507 D_fake: 0.775 +(epoch: 153, iters: 784, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.805 G_ID: 0.221 G_Rec: 0.357 D_GP: 0.031 D_real: 1.110 D_fake: 0.734 +(epoch: 153, iters: 1184, time: 0.064) G_GAN: -0.074 G_GAN_Feat: 0.875 G_ID: 0.157 G_Rec: 0.355 D_GP: 0.050 D_real: 0.647 D_fake: 1.074 +(epoch: 153, iters: 1584, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 1.022 G_ID: 0.202 G_Rec: 0.433 D_GP: 0.041 D_real: 0.894 D_fake: 0.499 +(epoch: 153, iters: 1984, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.703 G_ID: 0.160 G_Rec: 0.316 D_GP: 0.025 D_real: 0.967 D_fake: 0.968 +(epoch: 153, iters: 2384, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.923 G_ID: 0.221 G_Rec: 0.402 D_GP: 0.031 D_real: 1.109 D_fake: 0.500 +(epoch: 153, iters: 2784, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.946 G_ID: 0.161 G_Rec: 0.346 D_GP: 0.198 D_real: 0.399 D_fake: 0.884 +(epoch: 153, iters: 3184, time: 0.064) G_GAN: 0.480 G_GAN_Feat: 0.847 G_ID: 0.181 G_Rec: 0.352 D_GP: 0.030 D_real: 1.187 D_fake: 0.533 +(epoch: 153, iters: 3584, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.951 G_ID: 0.177 G_Rec: 0.361 D_GP: 0.274 D_real: 0.389 D_fake: 0.771 +(epoch: 153, iters: 3984, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.884 G_ID: 0.208 G_Rec: 0.377 D_GP: 0.027 D_real: 1.107 D_fake: 0.666 +(epoch: 153, iters: 4384, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 0.917 G_ID: 0.139 G_Rec: 0.332 D_GP: 0.042 D_real: 0.688 D_fake: 0.506 +(epoch: 153, iters: 4784, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 1.066 G_ID: 0.199 G_Rec: 0.423 D_GP: 0.047 D_real: 0.345 D_fake: 0.798 +(epoch: 153, iters: 5184, time: 0.064) G_GAN: 0.030 G_GAN_Feat: 0.685 G_ID: 0.171 G_Rec: 0.325 D_GP: 0.022 D_real: 1.024 D_fake: 0.970 +(epoch: 153, iters: 5584, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 1.005 G_ID: 0.236 G_Rec: 0.425 D_GP: 0.040 D_real: 0.537 D_fake: 0.904 +(epoch: 153, iters: 5984, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.726 G_ID: 0.135 G_Rec: 0.333 D_GP: 0.025 D_real: 1.226 D_fake: 0.600 +(epoch: 153, iters: 6384, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.840 G_ID: 0.188 G_Rec: 0.473 D_GP: 0.021 D_real: 1.055 D_fake: 0.675 +(epoch: 153, iters: 6784, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.819 G_ID: 0.157 G_Rec: 0.328 D_GP: 0.026 D_real: 1.101 D_fake: 0.657 +(epoch: 153, iters: 7184, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.865 G_ID: 0.180 G_Rec: 0.407 D_GP: 0.021 D_real: 1.310 D_fake: 0.496 +(epoch: 153, iters: 7584, time: 0.064) G_GAN: 0.667 G_GAN_Feat: 1.064 G_ID: 0.148 G_Rec: 0.343 D_GP: 0.630 D_real: 0.558 D_fake: 0.568 +(epoch: 153, iters: 7984, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.869 G_ID: 0.205 G_Rec: 0.419 D_GP: 0.021 D_real: 0.821 D_fake: 0.885 +(epoch: 153, iters: 8384, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.745 G_ID: 0.119 G_Rec: 0.301 D_GP: 0.054 D_real: 0.797 D_fake: 0.948 +(epoch: 154, iters: 176, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.966 G_ID: 0.232 G_Rec: 0.418 D_GP: 0.046 D_real: 0.711 D_fake: 0.898 +(epoch: 154, iters: 576, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.872 G_ID: 0.136 G_Rec: 0.326 D_GP: 0.038 D_real: 0.861 D_fake: 0.724 +(epoch: 154, iters: 976, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.824 G_ID: 0.210 G_Rec: 0.408 D_GP: 0.022 D_real: 1.232 D_fake: 0.511 +(epoch: 154, iters: 1376, time: 0.064) G_GAN: 0.035 G_GAN_Feat: 0.681 G_ID: 0.167 G_Rec: 0.330 D_GP: 0.026 D_real: 0.951 D_fake: 0.965 +(epoch: 154, iters: 1776, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.846 G_ID: 0.190 G_Rec: 0.406 D_GP: 0.034 D_real: 0.915 D_fake: 0.656 +(epoch: 154, iters: 2176, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.759 G_ID: 0.139 G_Rec: 0.362 D_GP: 0.038 D_real: 1.043 D_fake: 0.799 +(epoch: 154, iters: 2576, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 0.831 G_ID: 0.210 G_Rec: 0.394 D_GP: 0.028 D_real: 1.110 D_fake: 0.593 +(epoch: 154, iters: 2976, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.729 G_ID: 0.160 G_Rec: 0.321 D_GP: 0.036 D_real: 0.952 D_fake: 0.914 +(epoch: 154, iters: 3376, time: 0.064) G_GAN: 0.682 G_GAN_Feat: 0.879 G_ID: 0.192 G_Rec: 0.408 D_GP: 0.028 D_real: 1.454 D_fake: 0.353 +(epoch: 154, iters: 3776, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.922 G_ID: 0.151 G_Rec: 0.358 D_GP: 0.047 D_real: 0.824 D_fake: 0.575 +(epoch: 154, iters: 4176, time: 0.064) G_GAN: 0.687 G_GAN_Feat: 0.928 G_ID: 0.196 G_Rec: 0.404 D_GP: 0.031 D_real: 1.212 D_fake: 0.346 +(epoch: 154, iters: 4576, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.596 G_ID: 0.143 G_Rec: 0.368 D_GP: 0.018 D_real: 1.198 D_fake: 0.794 +(epoch: 154, iters: 4976, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.827 G_ID: 0.214 G_Rec: 0.382 D_GP: 0.026 D_real: 1.071 D_fake: 0.609 +(epoch: 154, iters: 5376, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.665 G_ID: 0.132 G_Rec: 0.308 D_GP: 0.022 D_real: 1.092 D_fake: 0.802 +(epoch: 154, iters: 5776, time: 0.064) G_GAN: 0.613 G_GAN_Feat: 0.900 G_ID: 0.180 G_Rec: 0.439 D_GP: 0.025 D_real: 1.196 D_fake: 0.397 +(epoch: 154, iters: 6176, time: 0.064) G_GAN: -0.055 G_GAN_Feat: 0.713 G_ID: 0.167 G_Rec: 0.307 D_GP: 0.031 D_real: 0.806 D_fake: 1.055 +(epoch: 154, iters: 6576, time: 0.064) G_GAN: 0.787 G_GAN_Feat: 0.840 G_ID: 0.235 G_Rec: 0.386 D_GP: 0.024 D_real: 1.544 D_fake: 0.248 +(epoch: 154, iters: 6976, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.750 G_ID: 0.172 G_Rec: 0.319 D_GP: 0.036 D_real: 0.913 D_fake: 0.852 +(epoch: 154, iters: 7376, time: 0.064) G_GAN: 0.434 G_GAN_Feat: 0.869 G_ID: 0.194 G_Rec: 0.381 D_GP: 0.037 D_real: 1.064 D_fake: 0.567 +(epoch: 154, iters: 7776, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.867 G_ID: 0.176 G_Rec: 0.340 D_GP: 0.059 D_real: 0.782 D_fake: 0.819 +(epoch: 154, iters: 8176, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 1.054 G_ID: 0.232 G_Rec: 0.468 D_GP: 0.024 D_real: 1.043 D_fake: 0.544 +(epoch: 154, iters: 8576, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.689 G_ID: 0.143 G_Rec: 0.304 D_GP: 0.025 D_real: 1.174 D_fake: 0.714 +(epoch: 155, iters: 368, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 1.207 G_ID: 0.210 G_Rec: 0.407 D_GP: 0.257 D_real: 0.465 D_fake: 0.596 +(epoch: 155, iters: 768, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.651 G_ID: 0.132 G_Rec: 0.304 D_GP: 0.023 D_real: 1.111 D_fake: 0.778 +(epoch: 155, iters: 1168, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 1.075 G_ID: 0.184 G_Rec: 0.464 D_GP: 0.126 D_real: 0.487 D_fake: 0.790 +(epoch: 155, iters: 1568, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.642 G_ID: 0.126 G_Rec: 0.300 D_GP: 0.023 D_real: 1.223 D_fake: 0.736 +(epoch: 155, iters: 1968, time: 0.064) G_GAN: 0.768 G_GAN_Feat: 0.837 G_ID: 0.191 G_Rec: 0.409 D_GP: 0.026 D_real: 1.509 D_fake: 0.318 +(epoch: 155, iters: 2368, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.785 G_ID: 0.132 G_Rec: 0.321 D_GP: 0.035 D_real: 1.241 D_fake: 0.589 +(epoch: 155, iters: 2768, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 0.853 G_ID: 0.200 G_Rec: 0.382 D_GP: 0.024 D_real: 1.394 D_fake: 0.429 +(epoch: 155, iters: 3168, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.711 G_ID: 0.146 G_Rec: 0.341 D_GP: 0.024 D_real: 1.124 D_fake: 0.713 +(epoch: 155, iters: 3568, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.918 G_ID: 0.190 G_Rec: 0.388 D_GP: 0.029 D_real: 1.188 D_fake: 0.546 +(epoch: 155, iters: 3968, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.752 G_ID: 0.146 G_Rec: 0.332 D_GP: 0.031 D_real: 1.030 D_fake: 0.810 +(epoch: 155, iters: 4368, time: 0.064) G_GAN: 0.880 G_GAN_Feat: 0.833 G_ID: 0.180 G_Rec: 0.362 D_GP: 0.035 D_real: 1.615 D_fake: 0.232 +(epoch: 155, iters: 4768, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.677 G_ID: 0.155 G_Rec: 0.344 D_GP: 0.025 D_real: 1.360 D_fake: 0.580 +(epoch: 155, iters: 5168, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.920 G_ID: 0.221 G_Rec: 0.451 D_GP: 0.037 D_real: 0.766 D_fake: 0.810 +(epoch: 155, iters: 5568, time: 0.064) G_GAN: 0.557 G_GAN_Feat: 0.916 G_ID: 0.137 G_Rec: 0.356 D_GP: 0.440 D_real: 0.656 D_fake: 0.459 +(epoch: 155, iters: 5968, time: 0.064) G_GAN: 0.532 G_GAN_Feat: 0.871 G_ID: 0.191 G_Rec: 0.399 D_GP: 0.022 D_real: 1.177 D_fake: 0.471 +(epoch: 155, iters: 6368, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 1.064 G_ID: 0.146 G_Rec: 0.314 D_GP: 0.148 D_real: 0.409 D_fake: 0.751 +(epoch: 155, iters: 6768, time: 0.064) G_GAN: 0.480 G_GAN_Feat: 0.867 G_ID: 0.229 G_Rec: 0.437 D_GP: 0.021 D_real: 1.212 D_fake: 0.522 +(epoch: 155, iters: 7168, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.659 G_ID: 0.141 G_Rec: 0.297 D_GP: 0.025 D_real: 1.222 D_fake: 0.644 +(epoch: 155, iters: 7568, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.819 G_ID: 0.172 G_Rec: 0.391 D_GP: 0.025 D_real: 1.096 D_fake: 0.647 +(epoch: 155, iters: 7968, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.905 G_ID: 0.129 G_Rec: 0.336 D_GP: 0.123 D_real: 0.897 D_fake: 0.701 +(epoch: 155, iters: 8368, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.873 G_ID: 0.193 G_Rec: 0.395 D_GP: 0.027 D_real: 1.272 D_fake: 0.427 +(epoch: 156, iters: 160, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.854 G_ID: 0.183 G_Rec: 0.313 D_GP: 0.038 D_real: 0.600 D_fake: 0.916 +(epoch: 156, iters: 560, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 0.794 G_ID: 0.214 G_Rec: 0.398 D_GP: 0.023 D_real: 1.241 D_fake: 0.530 +(epoch: 156, iters: 960, time: 0.064) G_GAN: -0.086 G_GAN_Feat: 0.748 G_ID: 0.151 G_Rec: 0.336 D_GP: 0.051 D_real: 0.646 D_fake: 1.086 +(epoch: 156, iters: 1360, time: 0.064) G_GAN: 0.666 G_GAN_Feat: 0.907 G_ID: 0.197 G_Rec: 0.382 D_GP: 0.043 D_real: 1.275 D_fake: 0.351 +(epoch: 156, iters: 1760, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.803 G_ID: 0.140 G_Rec: 0.322 D_GP: 0.043 D_real: 0.886 D_fake: 0.783 +(epoch: 156, iters: 2160, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 1.125 G_ID: 0.216 G_Rec: 0.450 D_GP: 0.100 D_real: 0.434 D_fake: 0.681 +(epoch: 156, iters: 2560, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.792 G_ID: 0.130 G_Rec: 0.314 D_GP: 0.041 D_real: 1.025 D_fake: 0.520 +(epoch: 156, iters: 2960, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 0.742 G_ID: 0.230 G_Rec: 0.417 D_GP: 0.020 D_real: 1.377 D_fake: 0.388 +(epoch: 156, iters: 3360, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.577 G_ID: 0.166 G_Rec: 0.287 D_GP: 0.020 D_real: 1.224 D_fake: 0.732 +(epoch: 156, iters: 3760, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.792 G_ID: 0.189 G_Rec: 0.415 D_GP: 0.027 D_real: 1.045 D_fake: 0.681 +(epoch: 156, iters: 4160, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.594 G_ID: 0.166 G_Rec: 0.294 D_GP: 0.022 D_real: 1.065 D_fake: 0.879 +(epoch: 156, iters: 4560, time: 0.064) G_GAN: 0.434 G_GAN_Feat: 0.851 G_ID: 0.170 G_Rec: 0.405 D_GP: 0.052 D_real: 1.070 D_fake: 0.569 +(epoch: 156, iters: 4960, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.739 G_ID: 0.160 G_Rec: 0.369 D_GP: 0.030 D_real: 1.059 D_fake: 0.860 +(epoch: 156, iters: 5360, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.957 G_ID: 0.212 G_Rec: 0.437 D_GP: 0.047 D_real: 0.972 D_fake: 0.528 +(epoch: 156, iters: 5760, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.675 G_ID: 0.142 G_Rec: 0.321 D_GP: 0.020 D_real: 1.079 D_fake: 0.819 +(epoch: 156, iters: 6160, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.901 G_ID: 0.193 G_Rec: 0.389 D_GP: 0.045 D_real: 1.098 D_fake: 0.527 +(epoch: 156, iters: 6560, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.808 G_ID: 0.142 G_Rec: 0.306 D_GP: 0.039 D_real: 0.983 D_fake: 0.720 +(epoch: 156, iters: 6960, time: 0.064) G_GAN: 0.530 G_GAN_Feat: 0.784 G_ID: 0.174 G_Rec: 0.396 D_GP: 0.024 D_real: 1.288 D_fake: 0.470 +(epoch: 156, iters: 7360, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.914 G_ID: 0.183 G_Rec: 0.360 D_GP: 0.052 D_real: 0.926 D_fake: 0.686 +(epoch: 156, iters: 7760, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.914 G_ID: 0.189 G_Rec: 0.410 D_GP: 0.030 D_real: 1.085 D_fake: 0.587 +(epoch: 156, iters: 8160, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.820 G_ID: 0.148 G_Rec: 0.315 D_GP: 0.050 D_real: 1.025 D_fake: 0.578 +(epoch: 156, iters: 8560, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.920 G_ID: 0.177 G_Rec: 0.385 D_GP: 0.040 D_real: 1.086 D_fake: 0.519 +(epoch: 157, iters: 352, time: 0.064) G_GAN: 0.675 G_GAN_Feat: 0.856 G_ID: 0.147 G_Rec: 0.370 D_GP: 0.081 D_real: 1.232 D_fake: 0.350 +(epoch: 157, iters: 752, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.881 G_ID: 0.220 G_Rec: 0.393 D_GP: 0.046 D_real: 1.095 D_fake: 0.576 +(epoch: 157, iters: 1152, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 0.805 G_ID: 0.152 G_Rec: 0.317 D_GP: 0.034 D_real: 1.347 D_fake: 0.532 +(epoch: 157, iters: 1552, time: 0.064) G_GAN: 0.632 G_GAN_Feat: 1.200 G_ID: 0.186 G_Rec: 0.454 D_GP: 0.078 D_real: 0.388 D_fake: 0.423 +(epoch: 157, iters: 1952, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.819 G_ID: 0.141 G_Rec: 0.324 D_GP: 0.036 D_real: 1.124 D_fake: 0.645 +(epoch: 157, iters: 2352, time: 0.064) G_GAN: 0.528 G_GAN_Feat: 0.824 G_ID: 0.189 G_Rec: 0.395 D_GP: 0.035 D_real: 1.254 D_fake: 0.492 +(epoch: 157, iters: 2752, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.677 G_ID: 0.142 G_Rec: 0.305 D_GP: 0.031 D_real: 1.021 D_fake: 0.926 +(epoch: 157, iters: 3152, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 1.067 G_ID: 0.204 G_Rec: 0.426 D_GP: 0.048 D_real: 1.129 D_fake: 0.522 +(epoch: 157, iters: 3552, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.782 G_ID: 0.148 G_Rec: 0.325 D_GP: 0.036 D_real: 1.035 D_fake: 0.617 +(epoch: 157, iters: 3952, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.992 G_ID: 0.239 G_Rec: 0.408 D_GP: 0.091 D_real: 0.460 D_fake: 0.857 +(epoch: 157, iters: 4352, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.799 G_ID: 0.137 G_Rec: 0.331 D_GP: 0.030 D_real: 1.316 D_fake: 0.503 +(epoch: 157, iters: 4752, time: 0.064) G_GAN: 0.676 G_GAN_Feat: 1.242 G_ID: 0.234 G_Rec: 0.414 D_GP: 0.103 D_real: 0.703 D_fake: 0.338 +(epoch: 157, iters: 5152, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.835 G_ID: 0.135 G_Rec: 0.318 D_GP: 0.046 D_real: 1.068 D_fake: 0.620 +(epoch: 157, iters: 5552, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 1.192 G_ID: 0.199 G_Rec: 0.483 D_GP: 0.165 D_real: 0.447 D_fake: 0.585 +(epoch: 157, iters: 5952, time: 0.064) G_GAN: -0.126 G_GAN_Feat: 0.763 G_ID: 0.132 G_Rec: 0.333 D_GP: 0.026 D_real: 0.718 D_fake: 1.126 +(epoch: 157, iters: 6352, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.856 G_ID: 0.200 G_Rec: 0.397 D_GP: 0.027 D_real: 0.960 D_fake: 0.874 +(epoch: 157, iters: 6752, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.664 G_ID: 0.167 G_Rec: 0.324 D_GP: 0.023 D_real: 0.932 D_fake: 0.964 +(epoch: 157, iters: 7152, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 0.982 G_ID: 0.211 G_Rec: 0.438 D_GP: 0.045 D_real: 0.880 D_fake: 0.552 +(epoch: 157, iters: 7552, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.724 G_ID: 0.126 G_Rec: 0.314 D_GP: 0.022 D_real: 1.172 D_fake: 0.694 +(epoch: 157, iters: 7952, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.927 G_ID: 0.224 G_Rec: 0.401 D_GP: 0.028 D_real: 1.109 D_fake: 0.522 +(epoch: 157, iters: 8352, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.777 G_ID: 0.143 G_Rec: 0.317 D_GP: 0.052 D_real: 1.149 D_fake: 0.618 +(epoch: 158, iters: 144, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.938 G_ID: 0.215 G_Rec: 0.461 D_GP: 0.043 D_real: 0.928 D_fake: 0.677 +(epoch: 158, iters: 544, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.730 G_ID: 0.157 G_Rec: 0.337 D_GP: 0.070 D_real: 1.008 D_fake: 0.758 +(epoch: 158, iters: 944, time: 0.064) G_GAN: 0.568 G_GAN_Feat: 1.000 G_ID: 0.206 G_Rec: 0.409 D_GP: 0.114 D_real: 0.860 D_fake: 0.436 +(epoch: 158, iters: 1344, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.890 G_ID: 0.128 G_Rec: 0.351 D_GP: 0.112 D_real: 0.770 D_fake: 0.571 +(epoch: 158, iters: 1744, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.941 G_ID: 0.207 G_Rec: 0.411 D_GP: 0.033 D_real: 0.929 D_fake: 0.773 +(epoch: 158, iters: 2144, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.781 G_ID: 0.150 G_Rec: 0.315 D_GP: 0.028 D_real: 0.927 D_fake: 0.807 +(epoch: 158, iters: 2544, time: 0.064) G_GAN: 0.706 G_GAN_Feat: 1.024 G_ID: 0.218 G_Rec: 0.411 D_GP: 0.080 D_real: 0.984 D_fake: 0.371 +(epoch: 158, iters: 2944, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.664 G_ID: 0.140 G_Rec: 0.323 D_GP: 0.022 D_real: 1.002 D_fake: 0.987 +(epoch: 158, iters: 3344, time: 0.064) G_GAN: 0.579 G_GAN_Feat: 0.784 G_ID: 0.189 G_Rec: 0.380 D_GP: 0.025 D_real: 1.289 D_fake: 0.433 +(epoch: 158, iters: 3744, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.882 G_ID: 0.137 G_Rec: 0.389 D_GP: 0.113 D_real: 0.410 D_fake: 0.910 +(epoch: 158, iters: 4144, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 1.228 G_ID: 0.237 G_Rec: 0.451 D_GP: 0.064 D_real: 1.043 D_fake: 0.674 +(epoch: 158, iters: 4544, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.921 G_ID: 0.146 G_Rec: 0.350 D_GP: 0.119 D_real: 0.717 D_fake: 0.533 +(epoch: 158, iters: 4944, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.852 G_ID: 0.208 G_Rec: 0.411 D_GP: 0.025 D_real: 1.027 D_fake: 0.737 +(epoch: 158, iters: 5344, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.737 G_ID: 0.169 G_Rec: 0.359 D_GP: 0.030 D_real: 0.865 D_fake: 0.912 +(epoch: 158, iters: 5744, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.795 G_ID: 0.198 G_Rec: 0.374 D_GP: 0.023 D_real: 1.066 D_fake: 0.719 +(epoch: 158, iters: 6144, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.678 G_ID: 0.124 G_Rec: 0.321 D_GP: 0.037 D_real: 1.049 D_fake: 0.858 +(epoch: 158, iters: 6544, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.873 G_ID: 0.190 G_Rec: 0.390 D_GP: 0.029 D_real: 1.010 D_fake: 0.664 +(epoch: 158, iters: 6944, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.749 G_ID: 0.125 G_Rec: 0.335 D_GP: 0.030 D_real: 1.207 D_fake: 0.638 +(epoch: 158, iters: 7344, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.970 G_ID: 0.196 G_Rec: 0.376 D_GP: 0.059 D_real: 0.797 D_fake: 0.565 +(epoch: 158, iters: 7744, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.842 G_ID: 0.180 G_Rec: 0.326 D_GP: 0.039 D_real: 0.875 D_fake: 1.013 +(epoch: 158, iters: 8144, time: 0.064) G_GAN: 0.909 G_GAN_Feat: 1.059 G_ID: 0.198 G_Rec: 0.438 D_GP: 0.033 D_real: 1.252 D_fake: 0.171 +(epoch: 158, iters: 8544, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.716 G_ID: 0.152 G_Rec: 0.329 D_GP: 0.023 D_real: 1.149 D_fake: 0.661 +(epoch: 159, iters: 336, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.898 G_ID: 0.168 G_Rec: 0.435 D_GP: 0.031 D_real: 1.041 D_fake: 0.593 +(epoch: 159, iters: 736, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.678 G_ID: 0.137 G_Rec: 0.320 D_GP: 0.026 D_real: 1.175 D_fake: 0.637 +(epoch: 159, iters: 1136, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.773 G_ID: 0.209 G_Rec: 0.390 D_GP: 0.025 D_real: 1.171 D_fake: 0.576 +(epoch: 159, iters: 1536, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.628 G_ID: 0.140 G_Rec: 0.287 D_GP: 0.025 D_real: 1.123 D_fake: 0.803 +(epoch: 159, iters: 1936, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.819 G_ID: 0.207 G_Rec: 0.422 D_GP: 0.020 D_real: 1.111 D_fake: 0.624 +(epoch: 159, iters: 2336, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.679 G_ID: 0.148 G_Rec: 0.296 D_GP: 0.032 D_real: 0.957 D_fake: 0.822 +(epoch: 159, iters: 2736, time: 0.064) G_GAN: 0.601 G_GAN_Feat: 1.109 G_ID: 0.226 G_Rec: 0.442 D_GP: 0.041 D_real: 1.181 D_fake: 0.448 +(epoch: 159, iters: 3136, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.748 G_ID: 0.128 G_Rec: 0.305 D_GP: 0.032 D_real: 1.271 D_fake: 0.469 +(epoch: 159, iters: 3536, time: 0.064) G_GAN: 0.579 G_GAN_Feat: 1.004 G_ID: 0.214 G_Rec: 0.409 D_GP: 0.037 D_real: 1.034 D_fake: 0.434 +(epoch: 159, iters: 3936, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.904 G_ID: 0.154 G_Rec: 0.369 D_GP: 0.065 D_real: 0.794 D_fake: 0.656 +(epoch: 159, iters: 4336, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.752 G_ID: 0.227 G_Rec: 0.370 D_GP: 0.024 D_real: 1.097 D_fake: 0.720 +(epoch: 159, iters: 4736, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.643 G_ID: 0.152 G_Rec: 0.322 D_GP: 0.027 D_real: 1.091 D_fake: 0.814 +(epoch: 159, iters: 5136, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.796 G_ID: 0.202 G_Rec: 0.399 D_GP: 0.024 D_real: 1.010 D_fake: 0.781 +(epoch: 159, iters: 5536, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.680 G_ID: 0.139 G_Rec: 0.308 D_GP: 0.032 D_real: 0.965 D_fake: 0.963 +(epoch: 159, iters: 5936, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.750 G_ID: 0.188 G_Rec: 0.355 D_GP: 0.028 D_real: 1.144 D_fake: 0.689 +(epoch: 159, iters: 6336, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.758 G_ID: 0.136 G_Rec: 0.332 D_GP: 0.053 D_real: 0.860 D_fake: 0.941 +(epoch: 159, iters: 6736, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.906 G_ID: 0.202 G_Rec: 0.375 D_GP: 0.056 D_real: 0.990 D_fake: 0.820 +(epoch: 159, iters: 7136, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.663 G_ID: 0.123 G_Rec: 0.298 D_GP: 0.028 D_real: 1.061 D_fake: 0.874 +(epoch: 159, iters: 7536, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.910 G_ID: 0.208 G_Rec: 0.404 D_GP: 0.029 D_real: 1.137 D_fake: 0.609 +(epoch: 159, iters: 7936, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.676 G_ID: 0.133 G_Rec: 0.314 D_GP: 0.027 D_real: 1.100 D_fake: 0.793 +(epoch: 159, iters: 8336, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.852 G_ID: 0.211 G_Rec: 0.401 D_GP: 0.029 D_real: 1.118 D_fake: 0.580 +(epoch: 160, iters: 128, time: 0.064) G_GAN: 0.049 G_GAN_Feat: 0.768 G_ID: 0.123 G_Rec: 0.340 D_GP: 0.059 D_real: 0.899 D_fake: 0.951 +(epoch: 160, iters: 528, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.964 G_ID: 0.203 G_Rec: 0.455 D_GP: 0.046 D_real: 0.871 D_fake: 0.652 +(epoch: 160, iters: 928, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 1.082 G_ID: 0.139 G_Rec: 0.353 D_GP: 0.036 D_real: 0.824 D_fake: 0.798 +(epoch: 160, iters: 1328, time: 0.064) G_GAN: 0.600 G_GAN_Feat: 0.987 G_ID: 0.182 G_Rec: 0.382 D_GP: 0.031 D_real: 1.209 D_fake: 0.410 +(epoch: 160, iters: 1728, time: 0.064) G_GAN: 0.044 G_GAN_Feat: 0.917 G_ID: 0.153 G_Rec: 0.346 D_GP: 0.225 D_real: 0.468 D_fake: 0.956 +(epoch: 160, iters: 2128, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.874 G_ID: 0.194 G_Rec: 0.393 D_GP: 0.029 D_real: 0.924 D_fake: 0.724 +(epoch: 160, iters: 2528, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.737 G_ID: 0.116 G_Rec: 0.417 D_GP: 0.028 D_real: 0.971 D_fake: 0.749 +(epoch: 160, iters: 2928, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.890 G_ID: 0.191 G_Rec: 0.413 D_GP: 0.030 D_real: 1.123 D_fake: 0.591 +(epoch: 160, iters: 3328, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.703 G_ID: 0.162 G_Rec: 0.326 D_GP: 0.021 D_real: 1.214 D_fake: 0.719 +(epoch: 160, iters: 3728, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 0.842 G_ID: 0.193 G_Rec: 0.398 D_GP: 0.028 D_real: 1.179 D_fake: 0.560 +(epoch: 160, iters: 4128, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.739 G_ID: 0.138 G_Rec: 0.356 D_GP: 0.035 D_real: 1.221 D_fake: 0.611 +(epoch: 160, iters: 4528, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 1.166 G_ID: 0.198 G_Rec: 0.421 D_GP: 0.244 D_real: 0.302 D_fake: 0.709 +(epoch: 160, iters: 4928, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.845 G_ID: 0.143 G_Rec: 0.327 D_GP: 0.093 D_real: 0.785 D_fake: 0.706 +(epoch: 160, iters: 5328, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.835 G_ID: 0.201 G_Rec: 0.399 D_GP: 0.021 D_real: 1.108 D_fake: 0.713 +(epoch: 160, iters: 5728, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.645 G_ID: 0.142 G_Rec: 0.319 D_GP: 0.033 D_real: 1.092 D_fake: 0.749 +(epoch: 160, iters: 6128, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.858 G_ID: 0.205 G_Rec: 0.395 D_GP: 0.029 D_real: 0.995 D_fake: 0.782 +(epoch: 160, iters: 6528, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.768 G_ID: 0.136 G_Rec: 0.305 D_GP: 0.109 D_real: 0.837 D_fake: 0.873 +(epoch: 160, iters: 6928, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.996 G_ID: 0.198 G_Rec: 0.422 D_GP: 0.035 D_real: 1.105 D_fake: 0.492 +(epoch: 160, iters: 7328, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.849 G_ID: 0.137 G_Rec: 0.404 D_GP: 0.052 D_real: 0.714 D_fake: 0.908 +(epoch: 160, iters: 7728, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.981 G_ID: 0.179 G_Rec: 0.424 D_GP: 0.067 D_real: 0.784 D_fake: 0.616 +(epoch: 160, iters: 8128, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.850 G_ID: 0.134 G_Rec: 0.320 D_GP: 0.116 D_real: 0.848 D_fake: 0.679 +(epoch: 160, iters: 8528, time: 0.064) G_GAN: 0.583 G_GAN_Feat: 0.883 G_ID: 0.163 G_Rec: 0.410 D_GP: 0.025 D_real: 1.338 D_fake: 0.422 +(epoch: 161, iters: 320, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.741 G_ID: 0.147 G_Rec: 0.307 D_GP: 0.041 D_real: 0.940 D_fake: 0.787 +(epoch: 161, iters: 720, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.872 G_ID: 0.190 G_Rec: 0.376 D_GP: 0.039 D_real: 1.028 D_fake: 0.786 +(epoch: 161, iters: 1120, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.882 G_ID: 0.173 G_Rec: 0.340 D_GP: 0.029 D_real: 1.091 D_fake: 0.774 +(epoch: 161, iters: 1520, time: 0.064) G_GAN: 0.779 G_GAN_Feat: 1.058 G_ID: 0.190 G_Rec: 0.444 D_GP: 0.032 D_real: 1.155 D_fake: 0.251 +(epoch: 161, iters: 1920, time: 0.064) G_GAN: -0.040 G_GAN_Feat: 0.896 G_ID: 0.152 G_Rec: 0.338 D_GP: 0.048 D_real: 0.418 D_fake: 1.040 +(epoch: 161, iters: 2320, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.798 G_ID: 0.208 G_Rec: 0.406 D_GP: 0.023 D_real: 1.194 D_fake: 0.585 +(epoch: 161, iters: 2720, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.644 G_ID: 0.145 G_Rec: 0.321 D_GP: 0.026 D_real: 1.062 D_fake: 0.881 +(epoch: 161, iters: 3120, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.812 G_ID: 0.168 G_Rec: 0.393 D_GP: 0.031 D_real: 1.304 D_fake: 0.491 +(epoch: 161, iters: 3520, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.728 G_ID: 0.141 G_Rec: 0.329 D_GP: 0.046 D_real: 1.002 D_fake: 0.862 +(epoch: 161, iters: 3920, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.855 G_ID: 0.211 G_Rec: 0.411 D_GP: 0.027 D_real: 0.893 D_fake: 0.825 +(epoch: 161, iters: 4320, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.704 G_ID: 0.149 G_Rec: 0.302 D_GP: 0.025 D_real: 1.133 D_fake: 0.778 +(epoch: 161, iters: 4720, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.937 G_ID: 0.185 G_Rec: 0.454 D_GP: 0.055 D_real: 0.986 D_fake: 0.581 +(epoch: 161, iters: 5120, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.791 G_ID: 0.162 G_Rec: 0.361 D_GP: 0.081 D_real: 0.930 D_fake: 0.736 +(epoch: 161, iters: 5520, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.904 G_ID: 0.211 G_Rec: 0.393 D_GP: 0.036 D_real: 0.819 D_fake: 0.857 +(epoch: 161, iters: 5920, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.754 G_ID: 0.159 G_Rec: 0.370 D_GP: 0.039 D_real: 0.911 D_fake: 0.785 +(epoch: 161, iters: 6320, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.866 G_ID: 0.193 G_Rec: 0.408 D_GP: 0.052 D_real: 0.962 D_fake: 0.665 +(epoch: 161, iters: 6720, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.742 G_ID: 0.142 G_Rec: 0.345 D_GP: 0.025 D_real: 1.092 D_fake: 0.815 +(epoch: 161, iters: 7120, time: 0.064) G_GAN: 0.536 G_GAN_Feat: 0.951 G_ID: 0.247 G_Rec: 0.393 D_GP: 0.050 D_real: 0.966 D_fake: 0.478 +(epoch: 161, iters: 7520, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.881 G_ID: 0.155 G_Rec: 0.342 D_GP: 0.033 D_real: 0.731 D_fake: 0.916 +(epoch: 161, iters: 7920, time: 0.064) G_GAN: 0.656 G_GAN_Feat: 0.993 G_ID: 0.218 G_Rec: 0.435 D_GP: 0.039 D_real: 1.069 D_fake: 0.368 +(epoch: 161, iters: 8320, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.650 G_ID: 0.133 G_Rec: 0.290 D_GP: 0.020 D_real: 1.297 D_fake: 0.671 +(epoch: 162, iters: 112, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.976 G_ID: 0.225 G_Rec: 0.428 D_GP: 0.071 D_real: 0.720 D_fake: 0.655 +(epoch: 162, iters: 512, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.670 G_ID: 0.172 G_Rec: 0.299 D_GP: 0.021 D_real: 1.191 D_fake: 0.751 +(epoch: 162, iters: 912, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.839 G_ID: 0.240 G_Rec: 0.379 D_GP: 0.046 D_real: 1.001 D_fake: 0.640 +(epoch: 162, iters: 1312, time: 0.064) G_GAN: 0.697 G_GAN_Feat: 0.774 G_ID: 0.138 G_Rec: 0.342 D_GP: 0.025 D_real: 1.562 D_fake: 0.422 +(epoch: 162, iters: 1712, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 1.069 G_ID: 0.231 G_Rec: 0.405 D_GP: 0.065 D_real: 0.415 D_fake: 0.788 +(epoch: 162, iters: 2112, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.782 G_ID: 0.165 G_Rec: 0.333 D_GP: 0.057 D_real: 1.025 D_fake: 0.645 +(epoch: 162, iters: 2512, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.945 G_ID: 0.187 G_Rec: 0.427 D_GP: 0.049 D_real: 1.061 D_fake: 0.512 +(epoch: 162, iters: 2912, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.716 G_ID: 0.154 G_Rec: 0.351 D_GP: 0.021 D_real: 0.989 D_fake: 0.917 +(epoch: 162, iters: 3312, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.468 G_ID: 0.177 G_Rec: 0.454 D_GP: 0.124 D_real: 0.483 D_fake: 0.659 +(epoch: 162, iters: 3712, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.804 G_ID: 0.156 G_Rec: 0.323 D_GP: 0.085 D_real: 1.005 D_fake: 0.598 +(epoch: 162, iters: 4112, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.868 G_ID: 0.209 G_Rec: 0.429 D_GP: 0.035 D_real: 1.015 D_fake: 0.653 +(epoch: 162, iters: 4512, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.732 G_ID: 0.145 G_Rec: 0.296 D_GP: 0.067 D_real: 0.864 D_fake: 0.828 +(epoch: 162, iters: 4912, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 0.895 G_ID: 0.212 G_Rec: 0.444 D_GP: 0.032 D_real: 1.281 D_fake: 0.409 +(epoch: 162, iters: 5312, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.639 G_ID: 0.159 G_Rec: 0.312 D_GP: 0.023 D_real: 1.002 D_fake: 0.936 +(epoch: 162, iters: 5712, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.855 G_ID: 0.167 G_Rec: 0.362 D_GP: 0.046 D_real: 0.956 D_fake: 0.674 +(epoch: 162, iters: 6112, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.681 G_ID: 0.139 G_Rec: 0.317 D_GP: 0.033 D_real: 1.058 D_fake: 0.834 +(epoch: 162, iters: 6512, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 0.998 G_ID: 0.225 G_Rec: 0.422 D_GP: 0.135 D_real: 0.664 D_fake: 0.562 +(epoch: 162, iters: 6912, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.842 G_ID: 0.166 G_Rec: 0.340 D_GP: 0.057 D_real: 0.740 D_fake: 0.800 +(epoch: 162, iters: 7312, time: 0.064) G_GAN: 0.628 G_GAN_Feat: 0.977 G_ID: 0.198 G_Rec: 0.392 D_GP: 0.037 D_real: 1.004 D_fake: 0.377 +(epoch: 162, iters: 7712, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.609 G_ID: 0.138 G_Rec: 0.294 D_GP: 0.021 D_real: 1.123 D_fake: 0.836 +(epoch: 162, iters: 8112, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.829 G_ID: 0.199 G_Rec: 0.381 D_GP: 0.030 D_real: 0.842 D_fake: 0.789 +(epoch: 162, iters: 8512, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.802 G_ID: 0.144 G_Rec: 0.340 D_GP: 0.122 D_real: 0.763 D_fake: 0.879 +(epoch: 163, iters: 304, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.941 G_ID: 0.199 G_Rec: 0.406 D_GP: 0.044 D_real: 1.051 D_fake: 0.550 +(epoch: 163, iters: 704, time: 0.064) G_GAN: 0.068 G_GAN_Feat: 0.823 G_ID: 0.140 G_Rec: 0.360 D_GP: 0.047 D_real: 0.638 D_fake: 0.932 +(epoch: 163, iters: 1104, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.988 G_ID: 0.209 G_Rec: 0.444 D_GP: 0.079 D_real: 0.771 D_fake: 0.775 +(epoch: 163, iters: 1504, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.732 G_ID: 0.137 G_Rec: 0.319 D_GP: 0.025 D_real: 1.151 D_fake: 0.725 +(epoch: 163, iters: 1904, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.916 G_ID: 0.239 G_Rec: 0.438 D_GP: 0.022 D_real: 1.035 D_fake: 0.656 +(epoch: 163, iters: 2304, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.989 G_ID: 0.146 G_Rec: 0.342 D_GP: 0.054 D_real: 0.730 D_fake: 0.631 +(epoch: 163, iters: 2704, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.877 G_ID: 0.217 G_Rec: 0.433 D_GP: 0.029 D_real: 1.081 D_fake: 0.573 +(epoch: 163, iters: 3104, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.906 G_ID: 0.185 G_Rec: 0.353 D_GP: 0.053 D_real: 0.692 D_fake: 0.781 +(epoch: 163, iters: 3504, time: 0.064) G_GAN: 0.920 G_GAN_Feat: 0.931 G_ID: 0.190 G_Rec: 0.425 D_GP: 0.029 D_real: 1.655 D_fake: 0.167 +(epoch: 163, iters: 3904, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.771 G_ID: 0.143 G_Rec: 0.325 D_GP: 0.035 D_real: 1.061 D_fake: 0.857 +(epoch: 163, iters: 4304, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 1.005 G_ID: 0.193 G_Rec: 0.420 D_GP: 0.096 D_real: 0.856 D_fake: 0.495 +(epoch: 163, iters: 4704, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.895 G_ID: 0.163 G_Rec: 0.323 D_GP: 0.072 D_real: 1.021 D_fake: 0.573 +(epoch: 163, iters: 5104, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.840 G_ID: 0.207 G_Rec: 0.374 D_GP: 0.026 D_real: 1.133 D_fake: 0.701 +(epoch: 163, iters: 5504, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.765 G_ID: 0.133 G_Rec: 0.322 D_GP: 0.028 D_real: 0.945 D_fake: 0.893 +(epoch: 163, iters: 5904, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.924 G_ID: 0.201 G_Rec: 0.408 D_GP: 0.048 D_real: 1.060 D_fake: 0.552 +(epoch: 163, iters: 6304, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.694 G_ID: 0.153 G_Rec: 0.322 D_GP: 0.022 D_real: 1.087 D_fake: 0.776 +(epoch: 163, iters: 6704, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.931 G_ID: 0.196 G_Rec: 0.482 D_GP: 0.022 D_real: 1.107 D_fake: 0.619 +(epoch: 163, iters: 7104, time: 0.064) G_GAN: -0.076 G_GAN_Feat: 0.861 G_ID: 0.148 G_Rec: 0.348 D_GP: 0.085 D_real: 0.470 D_fake: 1.076 +(epoch: 163, iters: 7504, time: 0.064) G_GAN: 0.615 G_GAN_Feat: 0.941 G_ID: 0.213 G_Rec: 0.428 D_GP: 0.031 D_real: 1.325 D_fake: 0.425 +(epoch: 163, iters: 7904, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.805 G_ID: 0.126 G_Rec: 0.383 D_GP: 0.035 D_real: 0.805 D_fake: 0.893 +(epoch: 163, iters: 8304, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.980 G_ID: 0.188 G_Rec: 0.399 D_GP: 0.077 D_real: 1.070 D_fake: 0.607 +(epoch: 164, iters: 96, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.908 G_ID: 0.147 G_Rec: 0.361 D_GP: 0.088 D_real: 0.568 D_fake: 0.718 +(epoch: 164, iters: 496, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.834 G_ID: 0.206 G_Rec: 0.396 D_GP: 0.021 D_real: 1.184 D_fake: 0.554 +(epoch: 164, iters: 896, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.706 G_ID: 0.144 G_Rec: 0.302 D_GP: 0.030 D_real: 1.135 D_fake: 0.732 +(epoch: 164, iters: 1296, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.877 G_ID: 0.212 G_Rec: 0.385 D_GP: 0.026 D_real: 1.121 D_fake: 0.658 +(epoch: 164, iters: 1696, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.747 G_ID: 0.137 G_Rec: 0.292 D_GP: 0.039 D_real: 1.112 D_fake: 0.642 +(epoch: 164, iters: 2096, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.763 G_ID: 0.230 G_Rec: 0.386 D_GP: 0.023 D_real: 0.988 D_fake: 0.834 +(epoch: 164, iters: 2496, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.680 G_ID: 0.155 G_Rec: 0.324 D_GP: 0.035 D_real: 0.960 D_fake: 0.884 +(epoch: 164, iters: 2896, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.855 G_ID: 0.214 G_Rec: 0.412 D_GP: 0.034 D_real: 0.987 D_fake: 0.702 +(epoch: 164, iters: 3296, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.641 G_ID: 0.161 G_Rec: 0.285 D_GP: 0.025 D_real: 1.267 D_fake: 0.629 +(epoch: 164, iters: 3696, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 1.061 G_ID: 0.199 G_Rec: 0.520 D_GP: 0.031 D_real: 0.904 D_fake: 0.632 +(epoch: 164, iters: 4096, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.746 G_ID: 0.137 G_Rec: 0.311 D_GP: 0.034 D_real: 0.898 D_fake: 0.808 +(epoch: 164, iters: 4496, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.953 G_ID: 0.198 G_Rec: 0.392 D_GP: 0.044 D_real: 1.037 D_fake: 0.442 +(epoch: 164, iters: 4896, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.812 G_ID: 0.132 G_Rec: 0.337 D_GP: 0.026 D_real: 1.119 D_fake: 0.633 +(epoch: 164, iters: 5296, time: 0.064) G_GAN: 0.511 G_GAN_Feat: 0.982 G_ID: 0.193 G_Rec: 0.476 D_GP: 0.028 D_real: 1.001 D_fake: 0.497 +(epoch: 164, iters: 5696, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.657 G_ID: 0.124 G_Rec: 0.283 D_GP: 0.025 D_real: 1.066 D_fake: 0.815 +(epoch: 164, iters: 6096, time: 0.064) G_GAN: 0.669 G_GAN_Feat: 0.846 G_ID: 0.219 G_Rec: 0.402 D_GP: 0.026 D_real: 1.301 D_fake: 0.348 +(epoch: 164, iters: 6496, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.894 G_ID: 0.141 G_Rec: 0.343 D_GP: 0.047 D_real: 0.694 D_fake: 0.830 +(epoch: 164, iters: 6896, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.905 G_ID: 0.176 G_Rec: 0.423 D_GP: 0.030 D_real: 0.884 D_fake: 0.731 +(epoch: 164, iters: 7296, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.921 G_ID: 0.136 G_Rec: 0.373 D_GP: 0.159 D_real: 0.902 D_fake: 0.624 +(epoch: 164, iters: 7696, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 1.067 G_ID: 0.245 G_Rec: 0.389 D_GP: 0.061 D_real: 0.475 D_fake: 0.732 +(epoch: 164, iters: 8096, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.614 G_ID: 0.156 G_Rec: 0.301 D_GP: 0.020 D_real: 1.139 D_fake: 0.855 +(epoch: 164, iters: 8496, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.825 G_ID: 0.189 G_Rec: 0.401 D_GP: 0.026 D_real: 1.107 D_fake: 0.558 +(epoch: 165, iters: 288, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.696 G_ID: 0.144 G_Rec: 0.310 D_GP: 0.026 D_real: 0.993 D_fake: 0.902 +(epoch: 165, iters: 688, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.831 G_ID: 0.215 G_Rec: 0.382 D_GP: 0.026 D_real: 1.049 D_fake: 0.667 +(epoch: 165, iters: 1088, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.721 G_ID: 0.154 G_Rec: 0.315 D_GP: 0.021 D_real: 0.972 D_fake: 0.887 +(epoch: 165, iters: 1488, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.935 G_ID: 0.207 G_Rec: 0.443 D_GP: 0.124 D_real: 0.746 D_fake: 0.582 +(epoch: 165, iters: 1888, time: 0.064) G_GAN: -0.250 G_GAN_Feat: 0.733 G_ID: 0.161 G_Rec: 0.308 D_GP: 0.023 D_real: 0.736 D_fake: 1.250 +(epoch: 165, iters: 2288, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.915 G_ID: 0.204 G_Rec: 0.402 D_GP: 0.024 D_real: 1.191 D_fake: 0.470 +(epoch: 165, iters: 2688, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.726 G_ID: 0.143 G_Rec: 0.340 D_GP: 0.025 D_real: 1.331 D_fake: 0.655 +(epoch: 165, iters: 3088, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.829 G_ID: 0.200 G_Rec: 0.394 D_GP: 0.029 D_real: 1.091 D_fake: 0.603 +(epoch: 165, iters: 3488, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.865 G_ID: 0.156 G_Rec: 0.336 D_GP: 0.142 D_real: 0.523 D_fake: 0.851 +(epoch: 165, iters: 3888, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 1.203 G_ID: 0.202 G_Rec: 0.447 D_GP: 0.640 D_real: 0.301 D_fake: 0.725 +(epoch: 165, iters: 4288, time: 0.064) G_GAN: -0.151 G_GAN_Feat: 0.806 G_ID: 0.127 G_Rec: 0.357 D_GP: 0.042 D_real: 0.516 D_fake: 1.151 +(epoch: 165, iters: 4688, time: 0.064) G_GAN: 0.855 G_GAN_Feat: 1.045 G_ID: 0.210 G_Rec: 0.426 D_GP: 0.036 D_real: 1.361 D_fake: 0.221 +(epoch: 165, iters: 5088, time: 0.064) G_GAN: 0.044 G_GAN_Feat: 0.801 G_ID: 0.124 G_Rec: 0.336 D_GP: 0.050 D_real: 0.744 D_fake: 0.956 +(epoch: 165, iters: 5488, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.789 G_ID: 0.190 G_Rec: 0.382 D_GP: 0.022 D_real: 1.297 D_fake: 0.528 +(epoch: 165, iters: 5888, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.650 G_ID: 0.122 G_Rec: 0.318 D_GP: 0.027 D_real: 1.139 D_fake: 0.773 +(epoch: 165, iters: 6288, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.872 G_ID: 0.176 G_Rec: 0.382 D_GP: 0.027 D_real: 1.193 D_fake: 0.564 +(epoch: 165, iters: 6688, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.702 G_ID: 0.168 G_Rec: 0.311 D_GP: 0.030 D_real: 1.061 D_fake: 0.694 +(epoch: 165, iters: 7088, time: 0.064) G_GAN: 0.587 G_GAN_Feat: 1.025 G_ID: 0.191 G_Rec: 0.428 D_GP: 0.078 D_real: 0.824 D_fake: 0.431 +(epoch: 165, iters: 7488, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.937 G_ID: 0.165 G_Rec: 0.343 D_GP: 0.067 D_real: 0.960 D_fake: 0.741 +(epoch: 165, iters: 7888, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.908 G_ID: 0.206 G_Rec: 0.425 D_GP: 0.024 D_real: 1.050 D_fake: 0.649 +(epoch: 165, iters: 8288, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.756 G_ID: 0.138 G_Rec: 0.318 D_GP: 0.032 D_real: 1.050 D_fake: 0.769 +(epoch: 166, iters: 80, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 0.909 G_ID: 0.181 G_Rec: 0.429 D_GP: 0.054 D_real: 1.121 D_fake: 0.498 +(epoch: 166, iters: 480, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.740 G_ID: 0.158 G_Rec: 0.300 D_GP: 0.089 D_real: 1.035 D_fake: 0.677 +(epoch: 166, iters: 880, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 0.950 G_ID: 0.175 G_Rec: 0.431 D_GP: 0.072 D_real: 0.969 D_fake: 0.508 +(epoch: 166, iters: 1280, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.824 G_ID: 0.149 G_Rec: 0.341 D_GP: 0.039 D_real: 1.016 D_fake: 0.700 +(epoch: 166, iters: 1680, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.906 G_ID: 0.194 G_Rec: 0.381 D_GP: 0.036 D_real: 0.978 D_fake: 0.636 +(epoch: 166, iters: 2080, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.656 G_ID: 0.132 G_Rec: 0.297 D_GP: 0.022 D_real: 1.092 D_fake: 0.808 +(epoch: 166, iters: 2480, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.794 G_ID: 0.198 G_Rec: 0.384 D_GP: 0.027 D_real: 1.303 D_fake: 0.528 +(epoch: 166, iters: 2880, time: 0.064) G_GAN: -0.007 G_GAN_Feat: 0.819 G_ID: 0.131 G_Rec: 0.375 D_GP: 0.109 D_real: 0.701 D_fake: 1.007 +(epoch: 166, iters: 3280, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.963 G_ID: 0.186 G_Rec: 0.417 D_GP: 0.037 D_real: 0.992 D_fake: 0.639 +(epoch: 166, iters: 3680, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.816 G_ID: 0.149 G_Rec: 0.359 D_GP: 0.094 D_real: 0.782 D_fake: 0.874 +(epoch: 166, iters: 4080, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.831 G_ID: 0.186 G_Rec: 0.395 D_GP: 0.025 D_real: 1.160 D_fake: 0.551 +(epoch: 166, iters: 4480, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.665 G_ID: 0.124 G_Rec: 0.308 D_GP: 0.022 D_real: 1.064 D_fake: 0.792 +(epoch: 166, iters: 4880, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.973 G_ID: 0.227 G_Rec: 0.448 D_GP: 0.066 D_real: 0.888 D_fake: 0.565 +(epoch: 166, iters: 5280, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.815 G_ID: 0.142 G_Rec: 0.350 D_GP: 0.032 D_real: 1.007 D_fake: 0.718 +(epoch: 166, iters: 5680, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 1.040 G_ID: 0.171 G_Rec: 0.407 D_GP: 0.040 D_real: 0.676 D_fake: 0.607 +(epoch: 166, iters: 6080, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.714 G_ID: 0.166 G_Rec: 0.310 D_GP: 0.025 D_real: 1.099 D_fake: 0.816 +(epoch: 166, iters: 6480, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.928 G_ID: 0.216 G_Rec: 0.374 D_GP: 0.030 D_real: 1.000 D_fake: 0.585 +(epoch: 166, iters: 6880, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.724 G_ID: 0.131 G_Rec: 0.345 D_GP: 0.028 D_real: 1.212 D_fake: 0.705 +(epoch: 166, iters: 7280, time: 0.064) G_GAN: 0.765 G_GAN_Feat: 0.814 G_ID: 0.222 G_Rec: 0.382 D_GP: 0.022 D_real: 1.504 D_fake: 0.332 +(epoch: 166, iters: 7680, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.812 G_ID: 0.140 G_Rec: 0.323 D_GP: 0.070 D_real: 0.725 D_fake: 0.863 +(epoch: 166, iters: 8080, time: 0.064) G_GAN: 0.576 G_GAN_Feat: 1.225 G_ID: 0.184 G_Rec: 0.475 D_GP: 0.438 D_real: 0.440 D_fake: 0.445 +(epoch: 166, iters: 8480, time: 0.064) G_GAN: 0.039 G_GAN_Feat: 0.807 G_ID: 0.185 G_Rec: 0.309 D_GP: 0.025 D_real: 0.815 D_fake: 0.961 +(epoch: 167, iters: 272, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 1.132 G_ID: 0.198 G_Rec: 0.465 D_GP: 0.302 D_real: 0.469 D_fake: 0.487 +(epoch: 167, iters: 672, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.817 G_ID: 0.129 G_Rec: 0.325 D_GP: 0.022 D_real: 1.055 D_fake: 0.718 +(epoch: 167, iters: 1072, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.735 G_ID: 0.205 G_Rec: 0.405 D_GP: 0.019 D_real: 1.221 D_fake: 0.685 +(epoch: 167, iters: 1472, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.567 G_ID: 0.163 G_Rec: 0.279 D_GP: 0.021 D_real: 1.221 D_fake: 0.719 +(epoch: 167, iters: 1872, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.837 G_ID: 0.180 G_Rec: 0.384 D_GP: 0.023 D_real: 1.130 D_fake: 0.616 +(epoch: 167, iters: 2272, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.644 G_ID: 0.147 G_Rec: 0.336 D_GP: 0.025 D_real: 1.290 D_fake: 0.685 +(epoch: 167, iters: 2672, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 0.753 G_ID: 0.185 G_Rec: 0.375 D_GP: 0.026 D_real: 1.356 D_fake: 0.432 +(epoch: 167, iters: 3072, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.643 G_ID: 0.154 G_Rec: 0.320 D_GP: 0.025 D_real: 1.031 D_fake: 0.844 +(epoch: 167, iters: 3472, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.987 G_ID: 0.177 G_Rec: 0.420 D_GP: 0.051 D_real: 1.297 D_fake: 0.421 +(epoch: 167, iters: 3872, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.737 G_ID: 0.177 G_Rec: 0.320 D_GP: 0.047 D_real: 0.787 D_fake: 0.963 +(epoch: 167, iters: 4272, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.925 G_ID: 0.203 G_Rec: 0.417 D_GP: 0.044 D_real: 1.175 D_fake: 0.442 +(epoch: 167, iters: 4672, time: 0.064) G_GAN: -0.319 G_GAN_Feat: 1.039 G_ID: 0.137 G_Rec: 0.447 D_GP: 0.318 D_real: 0.343 D_fake: 1.319 +(epoch: 167, iters: 5072, time: 0.064) G_GAN: 0.035 G_GAN_Feat: 0.992 G_ID: 0.225 G_Rec: 0.448 D_GP: 0.041 D_real: 0.503 D_fake: 0.965 +(epoch: 167, iters: 5472, time: 0.064) G_GAN: -0.107 G_GAN_Feat: 0.858 G_ID: 0.132 G_Rec: 0.340 D_GP: 0.133 D_real: 0.507 D_fake: 1.107 +(epoch: 167, iters: 5872, time: 0.064) G_GAN: 0.637 G_GAN_Feat: 0.906 G_ID: 0.178 G_Rec: 0.382 D_GP: 0.083 D_real: 1.198 D_fake: 0.383 +(epoch: 167, iters: 6272, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.730 G_ID: 0.152 G_Rec: 0.344 D_GP: 0.030 D_real: 0.961 D_fake: 0.827 +(epoch: 167, iters: 6672, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 1.008 G_ID: 0.194 G_Rec: 0.457 D_GP: 0.099 D_real: 0.940 D_fake: 0.488 +(epoch: 167, iters: 7072, time: 0.064) G_GAN: -0.060 G_GAN_Feat: 0.796 G_ID: 0.135 G_Rec: 0.316 D_GP: 0.089 D_real: 0.590 D_fake: 1.060 +(epoch: 167, iters: 7472, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.908 G_ID: 0.206 G_Rec: 0.393 D_GP: 0.098 D_real: 0.854 D_fake: 0.837 +(epoch: 167, iters: 7872, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.829 G_ID: 0.151 G_Rec: 0.311 D_GP: 0.034 D_real: 0.749 D_fake: 0.884 +(epoch: 167, iters: 8272, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 1.158 G_ID: 0.196 G_Rec: 0.454 D_GP: 0.221 D_real: 0.555 D_fake: 0.522 +(epoch: 168, iters: 64, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.698 G_ID: 0.162 G_Rec: 0.300 D_GP: 0.029 D_real: 1.097 D_fake: 0.786 +(epoch: 168, iters: 464, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.964 G_ID: 0.215 G_Rec: 0.436 D_GP: 0.030 D_real: 0.848 D_fake: 0.775 +(epoch: 168, iters: 864, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.776 G_ID: 0.150 G_Rec: 0.341 D_GP: 0.075 D_real: 0.958 D_fake: 0.795 +(epoch: 168, iters: 1264, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.907 G_ID: 0.183 G_Rec: 0.378 D_GP: 0.029 D_real: 0.963 D_fake: 0.723 +(epoch: 168, iters: 1664, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.839 G_ID: 0.139 G_Rec: 0.331 D_GP: 0.045 D_real: 1.087 D_fake: 0.687 +(epoch: 168, iters: 2064, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.782 G_ID: 0.208 G_Rec: 0.393 D_GP: 0.021 D_real: 1.218 D_fake: 0.562 +(epoch: 168, iters: 2464, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 0.629 G_ID: 0.135 G_Rec: 0.369 D_GP: 0.025 D_real: 0.934 D_fake: 0.992 +(epoch: 168, iters: 2864, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.877 G_ID: 0.196 G_Rec: 0.420 D_GP: 0.034 D_real: 1.027 D_fake: 0.720 +(epoch: 168, iters: 3264, time: 0.064) G_GAN: 0.051 G_GAN_Feat: 0.622 G_ID: 0.150 G_Rec: 0.298 D_GP: 0.022 D_real: 0.958 D_fake: 0.949 +(epoch: 168, iters: 3664, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 0.757 G_ID: 0.195 G_Rec: 0.353 D_GP: 0.022 D_real: 1.337 D_fake: 0.454 +(epoch: 168, iters: 4064, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.683 G_ID: 0.154 G_Rec: 0.416 D_GP: 0.025 D_real: 1.252 D_fake: 0.659 +(epoch: 168, iters: 4464, time: 0.064) G_GAN: 0.612 G_GAN_Feat: 1.021 G_ID: 0.178 G_Rec: 0.409 D_GP: 0.174 D_real: 0.779 D_fake: 0.395 +(epoch: 168, iters: 4864, time: 0.064) G_GAN: 0.031 G_GAN_Feat: 0.772 G_ID: 0.137 G_Rec: 0.354 D_GP: 0.023 D_real: 0.858 D_fake: 0.970 +(epoch: 168, iters: 5264, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.860 G_ID: 0.193 G_Rec: 0.372 D_GP: 0.032 D_real: 1.087 D_fake: 0.617 +(epoch: 168, iters: 5664, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.801 G_ID: 0.177 G_Rec: 0.353 D_GP: 0.046 D_real: 0.852 D_fake: 0.782 +(epoch: 168, iters: 6064, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 0.989 G_ID: 0.212 G_Rec: 0.410 D_GP: 0.043 D_real: 0.955 D_fake: 0.403 +(epoch: 168, iters: 6464, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.783 G_ID: 0.163 G_Rec: 0.311 D_GP: 0.029 D_real: 1.070 D_fake: 0.724 +(epoch: 168, iters: 6864, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 0.967 G_ID: 0.195 G_Rec: 0.379 D_GP: 0.061 D_real: 0.926 D_fake: 0.354 +(epoch: 168, iters: 7264, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.738 G_ID: 0.149 G_Rec: 0.306 D_GP: 0.029 D_real: 1.103 D_fake: 0.713 +(epoch: 168, iters: 7664, time: 0.064) G_GAN: 0.614 G_GAN_Feat: 0.894 G_ID: 0.186 G_Rec: 0.452 D_GP: 0.024 D_real: 1.313 D_fake: 0.390 +(epoch: 168, iters: 8064, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.604 G_ID: 0.150 G_Rec: 0.289 D_GP: 0.018 D_real: 1.058 D_fake: 0.871 +(epoch: 168, iters: 8464, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.907 G_ID: 0.189 G_Rec: 0.386 D_GP: 0.036 D_real: 0.964 D_fake: 0.758 +(epoch: 169, iters: 256, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.711 G_ID: 0.153 G_Rec: 0.283 D_GP: 0.032 D_real: 1.018 D_fake: 0.787 +(epoch: 169, iters: 656, time: 0.064) G_GAN: 0.763 G_GAN_Feat: 0.899 G_ID: 0.202 G_Rec: 0.426 D_GP: 0.039 D_real: 1.352 D_fake: 0.281 +(epoch: 169, iters: 1056, time: 0.064) G_GAN: 0.065 G_GAN_Feat: 0.823 G_ID: 0.122 G_Rec: 0.308 D_GP: 0.055 D_real: 0.499 D_fake: 0.936 +(epoch: 169, iters: 1456, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 1.072 G_ID: 0.198 G_Rec: 0.405 D_GP: 0.041 D_real: 0.606 D_fake: 0.634 +(epoch: 169, iters: 1856, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 0.945 G_ID: 0.126 G_Rec: 0.328 D_GP: 0.166 D_real: 0.557 D_fake: 0.550 +(epoch: 169, iters: 2256, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.802 G_ID: 0.179 G_Rec: 0.406 D_GP: 0.017 D_real: 1.279 D_fake: 0.495 +(epoch: 169, iters: 2656, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.679 G_ID: 0.155 G_Rec: 0.310 D_GP: 0.036 D_real: 1.068 D_fake: 0.875 +(epoch: 169, iters: 3056, time: 0.064) G_GAN: 0.642 G_GAN_Feat: 1.058 G_ID: 0.178 G_Rec: 0.417 D_GP: 0.043 D_real: 1.171 D_fake: 0.380 +(epoch: 169, iters: 3456, time: 0.064) G_GAN: 0.165 G_GAN_Feat: 0.747 G_ID: 0.151 G_Rec: 0.324 D_GP: 0.029 D_real: 0.996 D_fake: 0.835 +(epoch: 169, iters: 3856, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.946 G_ID: 0.189 G_Rec: 0.428 D_GP: 0.026 D_real: 1.172 D_fake: 0.528 +(epoch: 169, iters: 4256, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.856 G_ID: 0.142 G_Rec: 0.348 D_GP: 0.096 D_real: 0.585 D_fake: 0.910 +(epoch: 169, iters: 4656, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 1.026 G_ID: 0.184 G_Rec: 0.416 D_GP: 0.043 D_real: 1.116 D_fake: 0.537 +(epoch: 169, iters: 5056, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.709 G_ID: 0.180 G_Rec: 0.316 D_GP: 0.025 D_real: 1.086 D_fake: 0.764 +(epoch: 169, iters: 5456, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.805 G_ID: 0.205 G_Rec: 0.416 D_GP: 0.028 D_real: 1.028 D_fake: 0.732 +(epoch: 169, iters: 5856, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.733 G_ID: 0.141 G_Rec: 0.303 D_GP: 0.051 D_real: 1.061 D_fake: 0.661 +(epoch: 169, iters: 6256, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.947 G_ID: 0.195 G_Rec: 0.365 D_GP: 0.032 D_real: 0.914 D_fake: 0.614 +(epoch: 169, iters: 6656, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 0.871 G_ID: 0.118 G_Rec: 0.320 D_GP: 0.133 D_real: 0.829 D_fake: 0.564 +(epoch: 169, iters: 7056, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.841 G_ID: 0.201 G_Rec: 0.407 D_GP: 0.025 D_real: 1.063 D_fake: 0.631 +(epoch: 169, iters: 7456, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.658 G_ID: 0.136 G_Rec: 0.316 D_GP: 0.025 D_real: 0.981 D_fake: 0.921 +(epoch: 169, iters: 7856, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 1.058 G_ID: 0.150 G_Rec: 0.451 D_GP: 0.091 D_real: 0.778 D_fake: 0.572 +(epoch: 169, iters: 8256, time: 0.064) G_GAN: -0.009 G_GAN_Feat: 0.785 G_ID: 0.134 G_Rec: 0.332 D_GP: 0.032 D_real: 0.737 D_fake: 1.009 +(epoch: 170, iters: 48, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 1.171 G_ID: 0.188 G_Rec: 0.447 D_GP: 0.108 D_real: 0.427 D_fake: 0.461 +(epoch: 170, iters: 448, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.774 G_ID: 0.123 G_Rec: 0.307 D_GP: 0.023 D_real: 0.984 D_fake: 0.908 +(epoch: 170, iters: 848, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.898 G_ID: 0.213 G_Rec: 0.393 D_GP: 0.045 D_real: 0.631 D_fake: 0.856 +(epoch: 170, iters: 1248, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.742 G_ID: 0.112 G_Rec: 0.320 D_GP: 0.018 D_real: 1.112 D_fake: 0.836 +(epoch: 170, iters: 1648, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.871 G_ID: 0.205 G_Rec: 0.426 D_GP: 0.025 D_real: 0.927 D_fake: 0.753 +(epoch: 170, iters: 2048, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.720 G_ID: 0.119 G_Rec: 0.301 D_GP: 0.048 D_real: 0.896 D_fake: 0.872 +(epoch: 170, iters: 2448, time: 0.064) G_GAN: 0.548 G_GAN_Feat: 0.929 G_ID: 0.197 G_Rec: 0.390 D_GP: 0.030 D_real: 1.132 D_fake: 0.464 +(epoch: 170, iters: 2848, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.947 G_ID: 0.123 G_Rec: 0.314 D_GP: 0.414 D_real: 0.492 D_fake: 0.692 +(epoch: 170, iters: 3248, time: 0.064) G_GAN: 0.651 G_GAN_Feat: 0.863 G_ID: 0.175 G_Rec: 0.388 D_GP: 0.024 D_real: 1.472 D_fake: 0.359 +(epoch: 170, iters: 3648, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.644 G_ID: 0.123 G_Rec: 0.321 D_GP: 0.019 D_real: 1.140 D_fake: 0.823 +(epoch: 170, iters: 4048, time: 0.064) G_GAN: 0.689 G_GAN_Feat: 0.834 G_ID: 0.187 G_Rec: 0.385 D_GP: 0.023 D_real: 1.458 D_fake: 0.335 +(epoch: 170, iters: 4448, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.644 G_ID: 0.140 G_Rec: 0.313 D_GP: 0.023 D_real: 1.118 D_fake: 0.772 +(epoch: 170, iters: 4848, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 1.055 G_ID: 0.209 G_Rec: 0.387 D_GP: 0.057 D_real: 1.147 D_fake: 0.518 +(epoch: 170, iters: 5248, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.769 G_ID: 0.163 G_Rec: 0.315 D_GP: 0.027 D_real: 1.274 D_fake: 0.554 +(epoch: 170, iters: 5648, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.802 G_ID: 0.200 G_Rec: 0.362 D_GP: 0.025 D_real: 1.034 D_fake: 0.741 +(epoch: 170, iters: 6048, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.830 G_ID: 0.146 G_Rec: 0.353 D_GP: 0.056 D_real: 0.758 D_fake: 0.852 +(epoch: 170, iters: 6448, time: 0.064) G_GAN: 0.709 G_GAN_Feat: 0.966 G_ID: 0.182 G_Rec: 0.410 D_GP: 0.027 D_real: 1.410 D_fake: 0.342 +(epoch: 170, iters: 6848, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.734 G_ID: 0.126 G_Rec: 0.317 D_GP: 0.024 D_real: 1.320 D_fake: 0.640 +(epoch: 170, iters: 7248, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.838 G_ID: 0.198 G_Rec: 0.389 D_GP: 0.029 D_real: 0.844 D_fake: 0.826 +(epoch: 170, iters: 7648, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.746 G_ID: 0.179 G_Rec: 0.331 D_GP: 0.033 D_real: 1.066 D_fake: 0.812 +(epoch: 170, iters: 8048, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.805 G_ID: 0.190 G_Rec: 0.362 D_GP: 0.023 D_real: 1.183 D_fake: 0.541 +(epoch: 170, iters: 8448, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.729 G_ID: 0.146 G_Rec: 0.371 D_GP: 0.033 D_real: 1.065 D_fake: 0.759 +(epoch: 171, iters: 240, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.947 G_ID: 0.176 G_Rec: 0.428 D_GP: 0.042 D_real: 0.984 D_fake: 0.617 +(epoch: 171, iters: 640, time: 0.064) G_GAN: -0.068 G_GAN_Feat: 1.016 G_ID: 0.152 G_Rec: 0.398 D_GP: 0.204 D_real: 0.111 D_fake: 1.070 +(epoch: 171, iters: 1040, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.767 G_ID: 0.218 G_Rec: 0.396 D_GP: 0.022 D_real: 1.103 D_fake: 0.687 +(epoch: 171, iters: 1440, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.642 G_ID: 0.131 G_Rec: 0.292 D_GP: 0.030 D_real: 1.242 D_fake: 0.653 +(epoch: 171, iters: 1840, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.868 G_ID: 0.219 G_Rec: 0.397 D_GP: 0.035 D_real: 1.080 D_fake: 0.618 +(epoch: 171, iters: 2240, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.826 G_ID: 0.129 G_Rec: 0.313 D_GP: 0.057 D_real: 0.897 D_fake: 0.697 +(epoch: 171, iters: 2640, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 0.773 G_ID: 0.189 G_Rec: 0.432 D_GP: 0.020 D_real: 1.365 D_fake: 0.389 +(epoch: 171, iters: 3040, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.679 G_ID: 0.129 G_Rec: 0.305 D_GP: 0.026 D_real: 1.340 D_fake: 0.534 +(epoch: 171, iters: 3440, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 1.036 G_ID: 0.213 G_Rec: 0.397 D_GP: 0.143 D_real: 0.361 D_fake: 0.828 +(epoch: 171, iters: 3840, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 1.031 G_ID: 0.170 G_Rec: 0.379 D_GP: 0.229 D_real: 0.372 D_fake: 0.917 +(epoch: 171, iters: 4240, time: 0.064) G_GAN: 0.746 G_GAN_Feat: 1.012 G_ID: 0.233 G_Rec: 0.421 D_GP: 0.170 D_real: 0.986 D_fake: 0.514 +(epoch: 171, iters: 4640, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.909 G_ID: 0.145 G_Rec: 0.359 D_GP: 0.067 D_real: 0.591 D_fake: 0.798 +(epoch: 171, iters: 5040, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 0.983 G_ID: 0.196 G_Rec: 0.407 D_GP: 0.032 D_real: 0.891 D_fake: 0.559 +(epoch: 171, iters: 5440, time: 0.064) G_GAN: -0.026 G_GAN_Feat: 0.800 G_ID: 0.142 G_Rec: 0.319 D_GP: 0.076 D_real: 0.763 D_fake: 1.026 +(epoch: 171, iters: 5840, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.928 G_ID: 0.178 G_Rec: 0.438 D_GP: 0.034 D_real: 0.989 D_fake: 0.588 +(epoch: 171, iters: 6240, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.929 G_ID: 0.133 G_Rec: 0.358 D_GP: 0.350 D_real: 0.380 D_fake: 0.714 +(epoch: 171, iters: 6640, time: 0.064) G_GAN: 0.684 G_GAN_Feat: 0.952 G_ID: 0.205 G_Rec: 0.426 D_GP: 0.032 D_real: 1.224 D_fake: 0.376 +(epoch: 171, iters: 7040, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.666 G_ID: 0.138 G_Rec: 0.341 D_GP: 0.021 D_real: 1.115 D_fake: 0.852 +(epoch: 171, iters: 7440, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.798 G_ID: 0.181 G_Rec: 0.387 D_GP: 0.026 D_real: 1.006 D_fake: 0.670 +(epoch: 171, iters: 7840, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.737 G_ID: 0.132 G_Rec: 0.337 D_GP: 0.062 D_real: 0.718 D_fake: 0.952 +(epoch: 171, iters: 8240, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.815 G_ID: 0.217 G_Rec: 0.374 D_GP: 0.034 D_real: 0.884 D_fake: 0.854 +(epoch: 172, iters: 32, time: 0.064) G_GAN: 0.070 G_GAN_Feat: 0.712 G_ID: 0.134 G_Rec: 0.323 D_GP: 0.032 D_real: 0.935 D_fake: 0.930 +(epoch: 172, iters: 432, time: 0.064) G_GAN: 0.543 G_GAN_Feat: 0.806 G_ID: 0.203 G_Rec: 0.402 D_GP: 0.024 D_real: 1.262 D_fake: 0.468 +(epoch: 172, iters: 832, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.708 G_ID: 0.154 G_Rec: 0.320 D_GP: 0.025 D_real: 1.170 D_fake: 0.730 +(epoch: 172, iters: 1232, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.932 G_ID: 0.179 G_Rec: 0.391 D_GP: 0.026 D_real: 0.826 D_fake: 0.871 +(epoch: 172, iters: 1632, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.678 G_ID: 0.163 G_Rec: 0.295 D_GP: 0.028 D_real: 0.936 D_fake: 0.947 +(epoch: 172, iters: 2032, time: 0.064) G_GAN: 0.626 G_GAN_Feat: 0.891 G_ID: 0.210 G_Rec: 0.398 D_GP: 0.028 D_real: 1.260 D_fake: 0.387 +(epoch: 172, iters: 2432, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.701 G_ID: 0.148 G_Rec: 0.299 D_GP: 0.022 D_real: 1.257 D_fake: 0.628 +(epoch: 172, iters: 2832, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.905 G_ID: 0.194 G_Rec: 0.383 D_GP: 0.034 D_real: 1.033 D_fake: 0.556 +(epoch: 172, iters: 3232, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.844 G_ID: 0.155 G_Rec: 0.338 D_GP: 0.058 D_real: 0.783 D_fake: 0.827 +(epoch: 172, iters: 3632, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.916 G_ID: 0.192 G_Rec: 0.405 D_GP: 0.039 D_real: 0.922 D_fake: 0.628 +(epoch: 172, iters: 4032, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.693 G_ID: 0.160 G_Rec: 0.306 D_GP: 0.024 D_real: 1.201 D_fake: 0.715 +(epoch: 172, iters: 4432, time: 0.064) G_GAN: 0.509 G_GAN_Feat: 0.889 G_ID: 0.187 G_Rec: 0.382 D_GP: 0.037 D_real: 1.189 D_fake: 0.525 +(epoch: 172, iters: 4832, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.681 G_ID: 0.152 G_Rec: 0.335 D_GP: 0.024 D_real: 1.104 D_fake: 0.844 +(epoch: 172, iters: 5232, time: 0.064) G_GAN: 0.764 G_GAN_Feat: 1.108 G_ID: 0.201 G_Rec: 0.424 D_GP: 0.061 D_real: 0.830 D_fake: 0.298 +(epoch: 172, iters: 5632, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.697 G_ID: 0.146 G_Rec: 0.310 D_GP: 0.024 D_real: 0.996 D_fake: 0.873 +(epoch: 172, iters: 6032, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 0.849 G_ID: 0.174 G_Rec: 0.389 D_GP: 0.037 D_real: 1.203 D_fake: 0.509 +(epoch: 172, iters: 6432, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.896 G_ID: 0.111 G_Rec: 0.341 D_GP: 0.133 D_real: 0.631 D_fake: 0.797 +(epoch: 172, iters: 6832, time: 0.064) G_GAN: 0.568 G_GAN_Feat: 0.974 G_ID: 0.195 G_Rec: 0.464 D_GP: 0.034 D_real: 1.096 D_fake: 0.436 +(epoch: 172, iters: 7232, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.817 G_ID: 0.136 G_Rec: 0.364 D_GP: 0.019 D_real: 1.249 D_fake: 0.664 +(epoch: 172, iters: 7632, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.832 G_ID: 0.170 G_Rec: 0.422 D_GP: 0.023 D_real: 1.176 D_fake: 0.594 +(epoch: 172, iters: 8032, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.643 G_ID: 0.143 G_Rec: 0.332 D_GP: 0.020 D_real: 0.832 D_fake: 1.030 +(epoch: 172, iters: 8432, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.814 G_ID: 0.194 G_Rec: 0.416 D_GP: 0.024 D_real: 0.854 D_fake: 0.881 +(epoch: 173, iters: 224, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.653 G_ID: 0.126 G_Rec: 0.322 D_GP: 0.029 D_real: 0.911 D_fake: 0.942 +(epoch: 173, iters: 624, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.897 G_ID: 0.177 G_Rec: 0.404 D_GP: 0.043 D_real: 0.821 D_fake: 0.715 +(epoch: 173, iters: 1024, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.656 G_ID: 0.142 G_Rec: 0.297 D_GP: 0.030 D_real: 1.071 D_fake: 0.847 +(epoch: 173, iters: 1424, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.893 G_ID: 0.177 G_Rec: 0.385 D_GP: 0.039 D_real: 1.088 D_fake: 0.482 +(epoch: 173, iters: 1824, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.768 G_ID: 0.146 G_Rec: 0.297 D_GP: 0.025 D_real: 1.131 D_fake: 0.725 +(epoch: 173, iters: 2224, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.837 G_ID: 0.190 G_Rec: 0.372 D_GP: 0.026 D_real: 1.048 D_fake: 0.652 +(epoch: 173, iters: 2624, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.722 G_ID: 0.137 G_Rec: 0.334 D_GP: 0.025 D_real: 0.946 D_fake: 0.941 +(epoch: 173, iters: 3024, time: 0.064) G_GAN: 0.587 G_GAN_Feat: 0.947 G_ID: 0.219 G_Rec: 0.399 D_GP: 0.031 D_real: 1.160 D_fake: 0.426 +(epoch: 173, iters: 3424, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.703 G_ID: 0.144 G_Rec: 0.310 D_GP: 0.028 D_real: 0.928 D_fake: 0.977 +(epoch: 173, iters: 3824, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.979 G_ID: 0.233 G_Rec: 0.429 D_GP: 0.068 D_real: 0.842 D_fake: 0.606 +(epoch: 173, iters: 4224, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.843 G_ID: 0.141 G_Rec: 0.315 D_GP: 0.032 D_real: 0.910 D_fake: 0.721 +(epoch: 173, iters: 4624, time: 0.064) G_GAN: 0.751 G_GAN_Feat: 0.874 G_ID: 0.177 G_Rec: 0.384 D_GP: 0.024 D_real: 1.467 D_fake: 0.351 +(epoch: 173, iters: 5024, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.585 G_ID: 0.120 G_Rec: 0.306 D_GP: 0.020 D_real: 1.068 D_fake: 0.856 +(epoch: 173, iters: 5424, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 0.710 G_ID: 0.187 G_Rec: 0.370 D_GP: 0.020 D_real: 1.285 D_fake: 0.535 +(epoch: 173, iters: 5824, time: 0.064) G_GAN: -0.010 G_GAN_Feat: 0.599 G_ID: 0.162 G_Rec: 0.324 D_GP: 0.024 D_real: 0.919 D_fake: 1.010 +(epoch: 173, iters: 6224, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.770 G_ID: 0.194 G_Rec: 0.383 D_GP: 0.031 D_real: 1.034 D_fake: 0.703 +(epoch: 173, iters: 6624, time: 0.064) G_GAN: -0.007 G_GAN_Feat: 0.685 G_ID: 0.139 G_Rec: 0.334 D_GP: 0.035 D_real: 0.813 D_fake: 1.007 +(epoch: 173, iters: 7024, time: 0.064) G_GAN: 0.045 G_GAN_Feat: 0.938 G_ID: 0.189 G_Rec: 0.416 D_GP: 0.184 D_real: 0.517 D_fake: 0.955 +(epoch: 173, iters: 7424, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.636 G_ID: 0.130 G_Rec: 0.317 D_GP: 0.066 D_real: 0.979 D_fake: 0.843 +(epoch: 173, iters: 7824, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.776 G_ID: 0.200 G_Rec: 0.360 D_GP: 0.041 D_real: 0.861 D_fake: 0.779 +(epoch: 173, iters: 8224, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.891 G_ID: 0.131 G_Rec: 0.340 D_GP: 0.083 D_real: 0.933 D_fake: 0.698 +(epoch: 174, iters: 16, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.873 G_ID: 0.204 G_Rec: 0.390 D_GP: 0.033 D_real: 0.904 D_fake: 0.754 +(epoch: 174, iters: 416, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.766 G_ID: 0.152 G_Rec: 0.320 D_GP: 0.040 D_real: 1.111 D_fake: 0.675 +(epoch: 174, iters: 816, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.836 G_ID: 0.177 G_Rec: 0.419 D_GP: 0.023 D_real: 1.195 D_fake: 0.628 +(epoch: 174, iters: 1216, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.610 G_ID: 0.158 G_Rec: 0.307 D_GP: 0.025 D_real: 1.149 D_fake: 0.747 +(epoch: 174, iters: 1616, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.782 G_ID: 0.197 G_Rec: 0.385 D_GP: 0.031 D_real: 1.158 D_fake: 0.578 +(epoch: 174, iters: 2016, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.721 G_ID: 0.116 G_Rec: 0.295 D_GP: 0.044 D_real: 0.926 D_fake: 0.872 +(epoch: 174, iters: 2416, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.961 G_ID: 0.172 G_Rec: 0.452 D_GP: 0.037 D_real: 1.031 D_fake: 0.494 +(epoch: 174, iters: 2816, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.908 G_ID: 0.109 G_Rec: 0.359 D_GP: 0.100 D_real: 0.757 D_fake: 0.623 +(epoch: 174, iters: 3216, time: 0.064) G_GAN: 0.644 G_GAN_Feat: 0.895 G_ID: 0.260 G_Rec: 0.424 D_GP: 0.031 D_real: 1.301 D_fake: 0.386 +(epoch: 174, iters: 3616, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.786 G_ID: 0.148 G_Rec: 0.304 D_GP: 0.066 D_real: 0.981 D_fake: 0.653 +(epoch: 174, iters: 4016, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.875 G_ID: 0.197 G_Rec: 0.387 D_GP: 0.041 D_real: 0.916 D_fake: 0.617 +(epoch: 174, iters: 4416, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.765 G_ID: 0.124 G_Rec: 0.325 D_GP: 0.066 D_real: 0.783 D_fake: 0.917 +(epoch: 174, iters: 4816, time: 0.064) G_GAN: 0.602 G_GAN_Feat: 0.995 G_ID: 0.190 G_Rec: 0.410 D_GP: 0.076 D_real: 0.934 D_fake: 0.414 +(epoch: 174, iters: 5216, time: 0.064) G_GAN: 0.597 G_GAN_Feat: 1.010 G_ID: 0.131 G_Rec: 0.406 D_GP: 0.870 D_real: 0.777 D_fake: 0.427 +(epoch: 174, iters: 5616, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.994 G_ID: 0.189 G_Rec: 0.498 D_GP: 0.061 D_real: 0.923 D_fake: 0.582 +(epoch: 174, iters: 6016, time: 0.064) G_GAN: 0.054 G_GAN_Feat: 0.733 G_ID: 0.122 G_Rec: 0.320 D_GP: 0.025 D_real: 1.036 D_fake: 0.946 +(epoch: 174, iters: 6416, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.882 G_ID: 0.190 G_Rec: 0.428 D_GP: 0.024 D_real: 1.183 D_fake: 0.501 +(epoch: 174, iters: 6816, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.911 G_ID: 0.155 G_Rec: 0.349 D_GP: 0.060 D_real: 0.715 D_fake: 0.634 +(epoch: 174, iters: 7216, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.786 G_ID: 0.186 G_Rec: 0.410 D_GP: 0.019 D_real: 1.014 D_fake: 0.731 +(epoch: 174, iters: 7616, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.691 G_ID: 0.145 G_Rec: 0.320 D_GP: 0.023 D_real: 1.227 D_fake: 0.766 +(epoch: 174, iters: 8016, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.833 G_ID: 0.204 G_Rec: 0.383 D_GP: 0.025 D_real: 0.948 D_fake: 0.735 +(epoch: 174, iters: 8416, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.709 G_ID: 0.146 G_Rec: 0.302 D_GP: 0.036 D_real: 1.084 D_fake: 0.741 +(epoch: 175, iters: 208, time: 0.064) G_GAN: 0.620 G_GAN_Feat: 0.880 G_ID: 0.203 G_Rec: 0.391 D_GP: 0.027 D_real: 1.312 D_fake: 0.403 +(epoch: 175, iters: 608, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.790 G_ID: 0.158 G_Rec: 0.339 D_GP: 0.037 D_real: 0.930 D_fake: 0.798 +(epoch: 175, iters: 1008, time: 0.064) G_GAN: 0.636 G_GAN_Feat: 1.072 G_ID: 0.181 G_Rec: 0.468 D_GP: 0.069 D_real: 0.840 D_fake: 0.395 +(epoch: 175, iters: 1408, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.751 G_ID: 0.143 G_Rec: 0.337 D_GP: 0.022 D_real: 1.155 D_fake: 0.704 +(epoch: 175, iters: 1808, time: 0.064) G_GAN: 0.615 G_GAN_Feat: 0.932 G_ID: 0.174 G_Rec: 0.406 D_GP: 0.038 D_real: 1.246 D_fake: 0.416 +(epoch: 175, iters: 2208, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.696 G_ID: 0.125 G_Rec: 0.315 D_GP: 0.025 D_real: 1.448 D_fake: 0.515 +(epoch: 175, iters: 2608, time: 0.064) G_GAN: 0.592 G_GAN_Feat: 0.977 G_ID: 0.201 G_Rec: 0.422 D_GP: 0.040 D_real: 0.978 D_fake: 0.420 +(epoch: 175, iters: 3008, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.686 G_ID: 0.130 G_Rec: 0.347 D_GP: 0.024 D_real: 1.213 D_fake: 0.716 +(epoch: 175, iters: 3408, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.886 G_ID: 0.222 G_Rec: 0.367 D_GP: 0.047 D_real: 0.817 D_fake: 0.817 +(epoch: 175, iters: 3808, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.680 G_ID: 0.130 G_Rec: 0.299 D_GP: 0.022 D_real: 1.295 D_fake: 0.625 +(epoch: 175, iters: 4208, time: 0.064) G_GAN: 0.562 G_GAN_Feat: 0.902 G_ID: 0.185 G_Rec: 0.378 D_GP: 0.035 D_real: 1.212 D_fake: 0.448 +(epoch: 175, iters: 4608, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.706 G_ID: 0.137 G_Rec: 0.295 D_GP: 0.022 D_real: 1.097 D_fake: 0.883 +(epoch: 175, iters: 5008, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.938 G_ID: 0.227 G_Rec: 0.409 D_GP: 0.049 D_real: 0.848 D_fake: 0.897 +(epoch: 175, iters: 5408, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.662 G_ID: 0.152 G_Rec: 0.284 D_GP: 0.026 D_real: 1.303 D_fake: 0.654 +(epoch: 175, iters: 5808, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.961 G_ID: 0.182 G_Rec: 0.428 D_GP: 0.072 D_real: 0.861 D_fake: 0.545 +(epoch: 175, iters: 6208, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.766 G_ID: 0.138 G_Rec: 0.305 D_GP: 0.031 D_real: 1.289 D_fake: 0.502 +(epoch: 175, iters: 6608, time: 0.064) G_GAN: 0.598 G_GAN_Feat: 0.806 G_ID: 0.206 G_Rec: 0.350 D_GP: 0.029 D_real: 1.389 D_fake: 0.419 +(epoch: 175, iters: 7008, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.707 G_ID: 0.149 G_Rec: 0.296 D_GP: 0.023 D_real: 1.465 D_fake: 0.489 +(epoch: 175, iters: 7408, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.780 G_ID: 0.181 G_Rec: 0.392 D_GP: 0.021 D_real: 1.218 D_fake: 0.611 +(epoch: 175, iters: 7808, time: 0.064) G_GAN: -0.001 G_GAN_Feat: 0.688 G_ID: 0.142 G_Rec: 0.340 D_GP: 0.029 D_real: 0.905 D_fake: 1.001 +(epoch: 175, iters: 8208, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.872 G_ID: 0.201 G_Rec: 0.403 D_GP: 0.043 D_real: 0.968 D_fake: 0.674 +(epoch: 175, iters: 8608, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.790 G_ID: 0.147 G_Rec: 0.286 D_GP: 0.026 D_real: 1.098 D_fake: 0.648 +(epoch: 176, iters: 400, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.862 G_ID: 0.182 G_Rec: 0.395 D_GP: 0.025 D_real: 1.133 D_fake: 0.657 +(epoch: 176, iters: 800, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.680 G_ID: 0.141 G_Rec: 0.277 D_GP: 0.027 D_real: 1.344 D_fake: 0.550 +(epoch: 176, iters: 1200, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.723 G_ID: 0.184 G_Rec: 0.376 D_GP: 0.022 D_real: 1.038 D_fake: 0.731 +(epoch: 176, iters: 1600, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.712 G_ID: 0.138 G_Rec: 0.334 D_GP: 0.040 D_real: 0.885 D_fake: 0.896 +(epoch: 176, iters: 2000, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.944 G_ID: 0.190 G_Rec: 0.419 D_GP: 0.271 D_real: 0.555 D_fake: 0.861 +(epoch: 176, iters: 2400, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.803 G_ID: 0.157 G_Rec: 0.312 D_GP: 0.043 D_real: 0.921 D_fake: 0.692 +(epoch: 176, iters: 2800, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.657 G_ID: 0.176 G_Rec: 0.366 D_GP: 0.021 D_real: 1.264 D_fake: 0.515 +(epoch: 176, iters: 3200, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.531 G_ID: 0.140 G_Rec: 0.307 D_GP: 0.019 D_real: 1.175 D_fake: 0.782 +(epoch: 176, iters: 3600, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.749 G_ID: 0.215 G_Rec: 0.403 D_GP: 0.026 D_real: 1.004 D_fake: 0.658 +(epoch: 176, iters: 4000, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.626 G_ID: 0.146 G_Rec: 0.347 D_GP: 0.034 D_real: 1.001 D_fake: 0.907 +(epoch: 176, iters: 4400, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 0.743 G_ID: 0.185 G_Rec: 0.371 D_GP: 0.029 D_real: 1.218 D_fake: 0.541 +(epoch: 176, iters: 4800, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.710 G_ID: 0.139 G_Rec: 0.314 D_GP: 0.071 D_real: 0.897 D_fake: 0.872 +(epoch: 176, iters: 5200, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.832 G_ID: 0.197 G_Rec: 0.385 D_GP: 0.039 D_real: 1.066 D_fake: 0.662 +(epoch: 176, iters: 5600, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.680 G_ID: 0.142 G_Rec: 0.274 D_GP: 0.044 D_real: 0.870 D_fake: 0.880 +(epoch: 176, iters: 6000, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.880 G_ID: 0.176 G_Rec: 0.382 D_GP: 0.036 D_real: 1.136 D_fake: 0.521 +(epoch: 176, iters: 6400, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.699 G_ID: 0.122 G_Rec: 0.301 D_GP: 0.024 D_real: 1.241 D_fake: 0.665 +(epoch: 176, iters: 6800, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.918 G_ID: 0.170 G_Rec: 0.432 D_GP: 0.030 D_real: 1.030 D_fake: 0.560 +(epoch: 176, iters: 7200, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.641 G_ID: 0.141 G_Rec: 0.305 D_GP: 0.028 D_real: 1.090 D_fake: 0.828 +(epoch: 176, iters: 7600, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 1.102 G_ID: 0.223 G_Rec: 0.416 D_GP: 0.100 D_real: 0.528 D_fake: 0.586 +(epoch: 176, iters: 8000, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.617 G_ID: 0.173 G_Rec: 0.311 D_GP: 0.018 D_real: 1.091 D_fake: 0.822 +(epoch: 176, iters: 8400, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.788 G_ID: 0.206 G_Rec: 0.380 D_GP: 0.028 D_real: 1.054 D_fake: 0.666 +(epoch: 177, iters: 192, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.785 G_ID: 0.130 G_Rec: 0.339 D_GP: 0.061 D_real: 1.077 D_fake: 0.590 +(epoch: 177, iters: 592, time: 0.064) G_GAN: 0.523 G_GAN_Feat: 1.016 G_ID: 0.173 G_Rec: 0.429 D_GP: 0.132 D_real: 0.780 D_fake: 0.485 +(epoch: 177, iters: 992, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.840 G_ID: 0.162 G_Rec: 0.343 D_GP: 0.043 D_real: 0.880 D_fake: 0.750 +(epoch: 177, iters: 1392, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.917 G_ID: 0.230 G_Rec: 0.392 D_GP: 0.042 D_real: 0.972 D_fake: 0.585 +(epoch: 177, iters: 1792, time: 0.064) G_GAN: 0.067 G_GAN_Feat: 0.696 G_ID: 0.150 G_Rec: 0.309 D_GP: 0.022 D_real: 1.012 D_fake: 0.935 +(epoch: 177, iters: 2192, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.858 G_ID: 0.185 G_Rec: 0.435 D_GP: 0.023 D_real: 0.973 D_fake: 0.732 +(epoch: 177, iters: 2592, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.740 G_ID: 0.152 G_Rec: 0.323 D_GP: 0.032 D_real: 1.063 D_fake: 0.758 +(epoch: 177, iters: 2992, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.796 G_ID: 0.191 G_Rec: 0.374 D_GP: 0.032 D_real: 1.078 D_fake: 0.680 +(epoch: 177, iters: 3392, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.665 G_ID: 0.151 G_Rec: 0.287 D_GP: 0.025 D_real: 1.047 D_fake: 0.840 +(epoch: 177, iters: 3792, time: 0.064) G_GAN: 0.139 G_GAN_Feat: 1.088 G_ID: 0.222 G_Rec: 0.419 D_GP: 0.379 D_real: 0.483 D_fake: 0.862 +(epoch: 177, iters: 4192, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.684 G_ID: 0.175 G_Rec: 0.335 D_GP: 0.024 D_real: 1.175 D_fake: 0.813 +(epoch: 177, iters: 4592, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.850 G_ID: 0.195 G_Rec: 0.357 D_GP: 0.028 D_real: 1.010 D_fake: 0.739 +(epoch: 177, iters: 4992, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.894 G_ID: 0.161 G_Rec: 0.353 D_GP: 0.044 D_real: 0.742 D_fake: 0.858 +(epoch: 177, iters: 5392, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.997 G_ID: 0.232 G_Rec: 0.415 D_GP: 0.058 D_real: 0.695 D_fake: 0.637 +(epoch: 177, iters: 5792, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.807 G_ID: 0.138 G_Rec: 0.298 D_GP: 0.030 D_real: 0.872 D_fake: 0.812 +(epoch: 177, iters: 6192, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.932 G_ID: 0.194 G_Rec: 0.390 D_GP: 0.030 D_real: 0.807 D_fake: 0.807 +(epoch: 177, iters: 6592, time: 0.064) G_GAN: -0.117 G_GAN_Feat: 0.692 G_ID: 0.140 G_Rec: 0.330 D_GP: 0.036 D_real: 0.827 D_fake: 1.117 +(epoch: 177, iters: 6992, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.866 G_ID: 0.191 G_Rec: 0.397 D_GP: 0.041 D_real: 0.881 D_fake: 0.825 +(epoch: 177, iters: 7392, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.718 G_ID: 0.137 G_Rec: 0.281 D_GP: 0.054 D_real: 1.141 D_fake: 0.735 +(epoch: 177, iters: 7792, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 1.102 G_ID: 0.183 G_Rec: 0.418 D_GP: 0.046 D_real: 1.263 D_fake: 0.679 +(epoch: 177, iters: 8192, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.747 G_ID: 0.139 G_Rec: 0.394 D_GP: 0.026 D_real: 1.099 D_fake: 0.742 +(epoch: 177, iters: 8592, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 1.002 G_ID: 0.188 G_Rec: 0.408 D_GP: 0.039 D_real: 0.746 D_fake: 0.818 +(epoch: 178, iters: 384, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.780 G_ID: 0.119 G_Rec: 0.312 D_GP: 0.293 D_real: 0.762 D_fake: 0.817 +(epoch: 178, iters: 784, time: 0.064) G_GAN: 0.616 G_GAN_Feat: 0.909 G_ID: 0.187 G_Rec: 0.393 D_GP: 0.046 D_real: 1.136 D_fake: 0.397 +(epoch: 178, iters: 1184, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.793 G_ID: 0.147 G_Rec: 0.304 D_GP: 0.033 D_real: 0.997 D_fake: 0.653 +(epoch: 178, iters: 1584, time: 0.064) G_GAN: 0.556 G_GAN_Feat: 1.062 G_ID: 0.175 G_Rec: 0.452 D_GP: 0.053 D_real: 0.782 D_fake: 0.448 +(epoch: 178, iters: 1984, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.703 G_ID: 0.164 G_Rec: 0.324 D_GP: 0.046 D_real: 0.920 D_fake: 0.898 +(epoch: 178, iters: 2384, time: 0.064) G_GAN: 0.657 G_GAN_Feat: 0.838 G_ID: 0.196 G_Rec: 0.395 D_GP: 0.046 D_real: 1.257 D_fake: 0.351 +(epoch: 178, iters: 2784, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.836 G_ID: 0.140 G_Rec: 0.338 D_GP: 0.173 D_real: 0.537 D_fake: 0.829 +(epoch: 178, iters: 3184, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.847 G_ID: 0.186 G_Rec: 0.416 D_GP: 0.022 D_real: 1.167 D_fake: 0.591 +(epoch: 178, iters: 3584, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.733 G_ID: 0.170 G_Rec: 0.327 D_GP: 0.024 D_real: 1.108 D_fake: 0.751 +(epoch: 178, iters: 3984, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.990 G_ID: 0.187 G_Rec: 0.431 D_GP: 0.055 D_real: 0.674 D_fake: 0.714 +(epoch: 178, iters: 4384, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.976 G_ID: 0.161 G_Rec: 0.360 D_GP: 0.075 D_real: 0.722 D_fake: 0.807 +(epoch: 178, iters: 4784, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.906 G_ID: 0.178 G_Rec: 0.389 D_GP: 0.032 D_real: 1.213 D_fake: 0.503 +(epoch: 178, iters: 5184, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.812 G_ID: 0.142 G_Rec: 0.338 D_GP: 0.048 D_real: 1.214 D_fake: 0.601 +(epoch: 178, iters: 5584, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 1.047 G_ID: 0.229 G_Rec: 0.412 D_GP: 0.132 D_real: 0.430 D_fake: 0.678 +(epoch: 178, iters: 5984, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 1.019 G_ID: 0.123 G_Rec: 0.319 D_GP: 0.385 D_real: 0.299 D_fake: 0.708 +(epoch: 178, iters: 6384, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.904 G_ID: 0.202 G_Rec: 0.387 D_GP: 0.034 D_real: 1.099 D_fake: 0.562 +(epoch: 178, iters: 6784, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.777 G_ID: 0.143 G_Rec: 0.322 D_GP: 0.030 D_real: 1.026 D_fake: 0.724 +(epoch: 178, iters: 7184, time: 0.064) G_GAN: 0.047 G_GAN_Feat: 0.896 G_ID: 0.202 G_Rec: 0.366 D_GP: 0.024 D_real: 0.848 D_fake: 0.953 +(epoch: 178, iters: 7584, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.859 G_ID: 0.134 G_Rec: 0.324 D_GP: 0.036 D_real: 1.000 D_fake: 0.661 +(epoch: 178, iters: 7984, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 1.125 G_ID: 0.192 G_Rec: 0.413 D_GP: 0.061 D_real: 0.676 D_fake: 0.461 +(epoch: 178, iters: 8384, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.760 G_ID: 0.199 G_Rec: 0.357 D_GP: 0.031 D_real: 1.028 D_fake: 0.758 +(epoch: 179, iters: 176, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 0.941 G_ID: 0.197 G_Rec: 0.414 D_GP: 0.054 D_real: 1.031 D_fake: 0.459 +(epoch: 179, iters: 576, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.706 G_ID: 0.129 G_Rec: 0.301 D_GP: 0.040 D_real: 1.228 D_fake: 0.581 +(epoch: 179, iters: 976, time: 0.064) G_GAN: 0.666 G_GAN_Feat: 0.821 G_ID: 0.192 G_Rec: 0.383 D_GP: 0.023 D_real: 1.358 D_fake: 0.345 +(epoch: 179, iters: 1376, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.775 G_ID: 0.141 G_Rec: 0.358 D_GP: 0.038 D_real: 0.977 D_fake: 0.715 +(epoch: 179, iters: 1776, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 1.099 G_ID: 0.238 G_Rec: 0.407 D_GP: 0.179 D_real: 0.642 D_fake: 0.589 +(epoch: 179, iters: 2176, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.891 G_ID: 0.132 G_Rec: 0.358 D_GP: 0.084 D_real: 0.737 D_fake: 0.692 +(epoch: 179, iters: 2576, time: 0.064) G_GAN: 0.572 G_GAN_Feat: 0.949 G_ID: 0.206 G_Rec: 0.394 D_GP: 0.045 D_real: 1.100 D_fake: 0.438 +(epoch: 179, iters: 2976, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.696 G_ID: 0.168 G_Rec: 0.308 D_GP: 0.023 D_real: 1.290 D_fake: 0.624 +(epoch: 179, iters: 3376, time: 0.064) G_GAN: 0.509 G_GAN_Feat: 0.890 G_ID: 0.217 G_Rec: 0.393 D_GP: 0.024 D_real: 1.210 D_fake: 0.492 +(epoch: 179, iters: 3776, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.639 G_ID: 0.154 G_Rec: 0.284 D_GP: 0.033 D_real: 1.173 D_fake: 0.740 +(epoch: 179, iters: 4176, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.878 G_ID: 0.204 G_Rec: 0.395 D_GP: 0.035 D_real: 1.008 D_fake: 0.739 +(epoch: 179, iters: 4576, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.833 G_ID: 0.122 G_Rec: 0.298 D_GP: 0.053 D_real: 1.117 D_fake: 0.443 +(epoch: 179, iters: 4976, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 0.908 G_ID: 0.173 G_Rec: 0.408 D_GP: 0.035 D_real: 1.105 D_fake: 0.534 +(epoch: 179, iters: 5376, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.789 G_ID: 0.138 G_Rec: 0.323 D_GP: 0.037 D_real: 1.164 D_fake: 0.651 +(epoch: 179, iters: 5776, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.732 G_ID: 0.193 G_Rec: 0.393 D_GP: 0.018 D_real: 1.068 D_fake: 0.807 +(epoch: 179, iters: 6176, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.619 G_ID: 0.140 G_Rec: 0.289 D_GP: 0.019 D_real: 1.191 D_fake: 0.784 +(epoch: 179, iters: 6576, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.771 G_ID: 0.193 G_Rec: 0.372 D_GP: 0.021 D_real: 1.173 D_fake: 0.619 +(epoch: 179, iters: 6976, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.726 G_ID: 0.161 G_Rec: 0.385 D_GP: 0.036 D_real: 1.074 D_fake: 0.811 +(epoch: 179, iters: 7376, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.804 G_ID: 0.200 G_Rec: 0.353 D_GP: 0.040 D_real: 1.026 D_fake: 0.767 +(epoch: 179, iters: 7776, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.755 G_ID: 0.181 G_Rec: 0.303 D_GP: 0.048 D_real: 0.981 D_fake: 0.707 +(epoch: 179, iters: 8176, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.917 G_ID: 0.174 G_Rec: 0.399 D_GP: 0.031 D_real: 1.099 D_fake: 0.588 +(epoch: 179, iters: 8576, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.852 G_ID: 0.134 G_Rec: 0.337 D_GP: 0.080 D_real: 0.974 D_fake: 0.877 +(epoch: 180, iters: 368, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.805 G_ID: 0.228 G_Rec: 0.378 D_GP: 0.024 D_real: 1.073 D_fake: 0.704 +(epoch: 180, iters: 768, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.731 G_ID: 0.151 G_Rec: 0.316 D_GP: 0.035 D_real: 1.009 D_fake: 0.843 +(epoch: 180, iters: 1168, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.914 G_ID: 0.177 G_Rec: 0.413 D_GP: 0.038 D_real: 1.157 D_fake: 0.514 +(epoch: 180, iters: 1568, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.736 G_ID: 0.160 G_Rec: 0.332 D_GP: 0.028 D_real: 1.023 D_fake: 0.827 +(epoch: 180, iters: 1968, time: 0.064) G_GAN: 0.957 G_GAN_Feat: 0.945 G_ID: 0.183 G_Rec: 0.377 D_GP: 0.168 D_real: 1.364 D_fake: 0.190 +(epoch: 180, iters: 2368, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.774 G_ID: 0.146 G_Rec: 0.375 D_GP: 0.034 D_real: 1.044 D_fake: 0.702 +(epoch: 180, iters: 2768, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 0.930 G_ID: 0.197 G_Rec: 0.371 D_GP: 0.034 D_real: 1.075 D_fake: 0.472 +(epoch: 180, iters: 3168, time: 0.064) G_GAN: 0.656 G_GAN_Feat: 0.855 G_ID: 0.147 G_Rec: 0.328 D_GP: 0.057 D_real: 1.220 D_fake: 0.363 +(epoch: 180, iters: 3568, time: 0.064) G_GAN: 0.815 G_GAN_Feat: 0.903 G_ID: 0.211 G_Rec: 0.378 D_GP: 0.100 D_real: 1.391 D_fake: 0.346 +(epoch: 180, iters: 3968, time: 0.064) G_GAN: -0.063 G_GAN_Feat: 0.669 G_ID: 0.130 G_Rec: 0.318 D_GP: 0.022 D_real: 0.933 D_fake: 1.063 +(epoch: 180, iters: 4368, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.822 G_ID: 0.194 G_Rec: 0.433 D_GP: 0.024 D_real: 0.942 D_fake: 0.772 +(epoch: 180, iters: 4768, time: 0.064) G_GAN: -0.006 G_GAN_Feat: 0.705 G_ID: 0.124 G_Rec: 0.322 D_GP: 0.037 D_real: 0.739 D_fake: 1.006 +(epoch: 180, iters: 5168, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.871 G_ID: 0.181 G_Rec: 0.409 D_GP: 0.025 D_real: 0.967 D_fake: 0.710 +(epoch: 180, iters: 5568, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.848 G_ID: 0.115 G_Rec: 0.309 D_GP: 0.032 D_real: 0.921 D_fake: 0.779 +(epoch: 180, iters: 5968, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.837 G_ID: 0.194 G_Rec: 0.390 D_GP: 0.024 D_real: 1.207 D_fake: 0.495 +(epoch: 180, iters: 6368, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.823 G_ID: 0.150 G_Rec: 0.314 D_GP: 0.039 D_real: 0.839 D_fake: 0.862 +(epoch: 180, iters: 6768, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.932 G_ID: 0.185 G_Rec: 0.395 D_GP: 0.030 D_real: 0.972 D_fake: 0.521 +(epoch: 180, iters: 7168, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.937 G_ID: 0.123 G_Rec: 0.329 D_GP: 0.120 D_real: 0.423 D_fake: 0.804 +(epoch: 180, iters: 7568, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.762 G_ID: 0.184 G_Rec: 0.411 D_GP: 0.022 D_real: 1.074 D_fake: 0.718 +(epoch: 180, iters: 7968, time: 0.064) G_GAN: -0.015 G_GAN_Feat: 0.647 G_ID: 0.163 G_Rec: 0.342 D_GP: 0.020 D_real: 0.956 D_fake: 1.015 +(epoch: 180, iters: 8368, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.794 G_ID: 0.204 G_Rec: 0.388 D_GP: 0.025 D_real: 0.908 D_fake: 0.834 +(epoch: 181, iters: 160, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.646 G_ID: 0.145 G_Rec: 0.287 D_GP: 0.025 D_real: 1.086 D_fake: 0.910 +(epoch: 181, iters: 560, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.972 G_ID: 0.203 G_Rec: 0.407 D_GP: 0.150 D_real: 0.424 D_fake: 0.913 +(epoch: 181, iters: 960, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.717 G_ID: 0.135 G_Rec: 0.311 D_GP: 0.022 D_real: 1.007 D_fake: 0.838 +(epoch: 181, iters: 1360, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.893 G_ID: 0.179 G_Rec: 0.428 D_GP: 0.034 D_real: 1.032 D_fake: 0.586 +(epoch: 181, iters: 1760, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.897 G_ID: 0.131 G_Rec: 0.331 D_GP: 0.109 D_real: 0.639 D_fake: 0.758 +(epoch: 181, iters: 2160, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.860 G_ID: 0.205 G_Rec: 0.396 D_GP: 0.036 D_real: 1.058 D_fake: 0.603 +(epoch: 181, iters: 2560, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 1.108 G_ID: 0.163 G_Rec: 0.373 D_GP: 0.217 D_real: 0.312 D_fake: 0.842 +(epoch: 181, iters: 2960, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.938 G_ID: 0.225 G_Rec: 0.421 D_GP: 0.077 D_real: 0.737 D_fake: 0.751 +(epoch: 181, iters: 3360, time: 0.064) G_GAN: -0.018 G_GAN_Feat: 1.023 G_ID: 0.130 G_Rec: 0.433 D_GP: 0.345 D_real: 0.386 D_fake: 1.018 +(epoch: 181, iters: 3760, time: 0.064) G_GAN: 0.689 G_GAN_Feat: 0.946 G_ID: 0.165 G_Rec: 0.407 D_GP: 0.029 D_real: 1.247 D_fake: 0.325 +(epoch: 181, iters: 4160, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.876 G_ID: 0.136 G_Rec: 0.333 D_GP: 0.104 D_real: 0.532 D_fake: 0.909 +(epoch: 181, iters: 4560, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 1.035 G_ID: 0.212 G_Rec: 0.394 D_GP: 0.037 D_real: 0.778 D_fake: 0.665 +(epoch: 181, iters: 4960, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.680 G_ID: 0.154 G_Rec: 0.300 D_GP: 0.023 D_real: 1.255 D_fake: 0.702 +(epoch: 181, iters: 5360, time: 0.064) G_GAN: 0.868 G_GAN_Feat: 0.791 G_ID: 0.201 G_Rec: 0.410 D_GP: 0.018 D_real: 1.709 D_fake: 0.236 +(epoch: 181, iters: 5760, time: 0.064) G_GAN: 0.101 G_GAN_Feat: 0.653 G_ID: 0.150 G_Rec: 0.338 D_GP: 0.028 D_real: 1.016 D_fake: 0.899 +(epoch: 181, iters: 6160, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.799 G_ID: 0.217 G_Rec: 0.365 D_GP: 0.027 D_real: 1.302 D_fake: 0.464 +(epoch: 181, iters: 6560, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.754 G_ID: 0.125 G_Rec: 0.343 D_GP: 0.036 D_real: 0.985 D_fake: 0.773 +(epoch: 181, iters: 6960, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.846 G_ID: 0.214 G_Rec: 0.387 D_GP: 0.024 D_real: 1.245 D_fake: 0.499 +(epoch: 181, iters: 7360, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 0.619 G_ID: 0.161 G_Rec: 0.279 D_GP: 0.020 D_real: 0.977 D_fake: 0.990 +(epoch: 181, iters: 7760, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.900 G_ID: 0.185 G_Rec: 0.374 D_GP: 0.069 D_real: 0.834 D_fake: 0.747 +(epoch: 181, iters: 8160, time: 0.064) G_GAN: 0.625 G_GAN_Feat: 0.666 G_ID: 0.117 G_Rec: 0.292 D_GP: 0.028 D_real: 1.524 D_fake: 0.397 +(epoch: 181, iters: 8560, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 1.080 G_ID: 0.196 G_Rec: 0.494 D_GP: 0.173 D_real: 0.622 D_fake: 0.709 +(epoch: 182, iters: 352, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.778 G_ID: 0.160 G_Rec: 0.305 D_GP: 0.050 D_real: 0.714 D_fake: 0.887 +(epoch: 182, iters: 752, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.943 G_ID: 0.178 G_Rec: 0.386 D_GP: 0.044 D_real: 0.938 D_fake: 0.641 +(epoch: 182, iters: 1152, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.766 G_ID: 0.180 G_Rec: 0.279 D_GP: 0.028 D_real: 1.113 D_fake: 0.647 +(epoch: 182, iters: 1552, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.730 G_ID: 0.229 G_Rec: 0.353 D_GP: 0.029 D_real: 1.126 D_fake: 0.684 +(epoch: 182, iters: 1952, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.763 G_ID: 0.139 G_Rec: 0.333 D_GP: 0.076 D_real: 0.778 D_fake: 0.913 +(epoch: 182, iters: 2352, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.967 G_ID: 0.178 G_Rec: 0.448 D_GP: 0.031 D_real: 0.879 D_fake: 0.634 +(epoch: 182, iters: 2752, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.794 G_ID: 0.117 G_Rec: 0.349 D_GP: 0.025 D_real: 1.043 D_fake: 0.827 +(epoch: 182, iters: 3152, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 0.819 G_ID: 0.184 G_Rec: 0.361 D_GP: 0.023 D_real: 1.250 D_fake: 0.534 +(epoch: 182, iters: 3552, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.691 G_ID: 0.155 G_Rec: 0.333 D_GP: 0.039 D_real: 1.091 D_fake: 0.776 +(epoch: 182, iters: 3952, time: 0.064) G_GAN: 0.867 G_GAN_Feat: 1.025 G_ID: 0.173 G_Rec: 0.411 D_GP: 0.051 D_real: 1.355 D_fake: 0.237 +(epoch: 182, iters: 4352, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.862 G_ID: 0.141 G_Rec: 0.323 D_GP: 0.065 D_real: 0.640 D_fake: 0.812 +(epoch: 182, iters: 4752, time: 0.064) G_GAN: 0.874 G_GAN_Feat: 0.961 G_ID: 0.190 G_Rec: 0.419 D_GP: 0.028 D_real: 1.382 D_fake: 0.238 +(epoch: 182, iters: 5152, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.688 G_ID: 0.152 G_Rec: 0.293 D_GP: 0.022 D_real: 1.409 D_fake: 0.558 +(epoch: 182, iters: 5552, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 1.106 G_ID: 0.185 G_Rec: 0.438 D_GP: 0.033 D_real: 0.913 D_fake: 0.517 +(epoch: 182, iters: 5952, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.911 G_ID: 0.138 G_Rec: 0.334 D_GP: 0.083 D_real: 0.710 D_fake: 0.744 +(epoch: 182, iters: 6352, time: 0.064) G_GAN: 0.683 G_GAN_Feat: 0.856 G_ID: 0.190 G_Rec: 0.388 D_GP: 0.022 D_real: 1.485 D_fake: 0.339 +(epoch: 182, iters: 6752, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.709 G_ID: 0.161 G_Rec: 0.314 D_GP: 0.029 D_real: 1.131 D_fake: 0.668 +(epoch: 182, iters: 7152, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.946 G_ID: 0.211 G_Rec: 0.353 D_GP: 0.028 D_real: 1.072 D_fake: 0.803 +(epoch: 182, iters: 7552, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.718 G_ID: 0.150 G_Rec: 0.320 D_GP: 0.032 D_real: 1.243 D_fake: 0.723 +(epoch: 182, iters: 7952, time: 0.064) G_GAN: 0.602 G_GAN_Feat: 1.042 G_ID: 0.207 G_Rec: 0.436 D_GP: 0.047 D_real: 1.072 D_fake: 0.417 +(epoch: 182, iters: 8352, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.608 G_ID: 0.131 G_Rec: 0.319 D_GP: 0.021 D_real: 1.326 D_fake: 0.674 +(epoch: 183, iters: 144, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.818 G_ID: 0.174 G_Rec: 0.418 D_GP: 0.025 D_real: 1.190 D_fake: 0.584 +(epoch: 183, iters: 544, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.679 G_ID: 0.147 G_Rec: 0.319 D_GP: 0.031 D_real: 1.037 D_fake: 0.827 +(epoch: 183, iters: 944, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.863 G_ID: 0.186 G_Rec: 0.423 D_GP: 0.026 D_real: 1.052 D_fake: 0.593 +(epoch: 183, iters: 1344, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.698 G_ID: 0.144 G_Rec: 0.294 D_GP: 0.056 D_real: 1.181 D_fake: 0.679 +(epoch: 183, iters: 1744, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 1.034 G_ID: 0.214 G_Rec: 0.417 D_GP: 0.222 D_real: 0.324 D_fake: 0.760 +(epoch: 183, iters: 2144, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.801 G_ID: 0.144 G_Rec: 0.328 D_GP: 0.048 D_real: 0.856 D_fake: 0.713 +(epoch: 183, iters: 2544, time: 0.064) G_GAN: 0.592 G_GAN_Feat: 0.784 G_ID: 0.189 G_Rec: 0.392 D_GP: 0.022 D_real: 1.374 D_fake: 0.414 +(epoch: 183, iters: 2944, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.769 G_ID: 0.144 G_Rec: 0.321 D_GP: 0.077 D_real: 0.926 D_fake: 0.644 +(epoch: 183, iters: 3344, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.906 G_ID: 0.186 G_Rec: 0.380 D_GP: 0.092 D_real: 0.761 D_fake: 0.758 +(epoch: 183, iters: 3744, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.704 G_ID: 0.144 G_Rec: 0.300 D_GP: 0.030 D_real: 1.136 D_fake: 0.657 +(epoch: 183, iters: 4144, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.893 G_ID: 0.196 G_Rec: 0.385 D_GP: 0.041 D_real: 0.757 D_fake: 0.814 +(epoch: 183, iters: 4544, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.818 G_ID: 0.133 G_Rec: 0.360 D_GP: 0.063 D_real: 0.918 D_fake: 0.771 +(epoch: 183, iters: 4944, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.802 G_ID: 0.178 G_Rec: 0.376 D_GP: 0.031 D_real: 0.873 D_fake: 0.790 +(epoch: 183, iters: 5344, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.765 G_ID: 0.116 G_Rec: 0.303 D_GP: 0.038 D_real: 1.133 D_fake: 0.693 +(epoch: 183, iters: 5744, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.842 G_ID: 0.196 G_Rec: 0.392 D_GP: 0.028 D_real: 1.080 D_fake: 0.602 +(epoch: 183, iters: 6144, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.882 G_ID: 0.140 G_Rec: 0.302 D_GP: 0.182 D_real: 0.450 D_fake: 0.803 +(epoch: 183, iters: 6544, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 1.015 G_ID: 0.189 G_Rec: 0.461 D_GP: 0.082 D_real: 0.826 D_fake: 0.717 +(epoch: 183, iters: 6944, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.663 G_ID: 0.139 G_Rec: 0.295 D_GP: 0.024 D_real: 1.310 D_fake: 0.585 +(epoch: 183, iters: 7344, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 1.111 G_ID: 0.207 G_Rec: 0.402 D_GP: 0.034 D_real: 0.453 D_fake: 0.649 +(epoch: 183, iters: 7744, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.665 G_ID: 0.133 G_Rec: 0.305 D_GP: 0.025 D_real: 1.173 D_fake: 0.762 +(epoch: 183, iters: 8144, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.876 G_ID: 0.168 G_Rec: 0.405 D_GP: 0.027 D_real: 1.012 D_fake: 0.651 +(epoch: 183, iters: 8544, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.763 G_ID: 0.148 G_Rec: 0.319 D_GP: 0.028 D_real: 1.080 D_fake: 0.838 +(epoch: 184, iters: 336, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 1.025 G_ID: 0.226 G_Rec: 0.448 D_GP: 0.086 D_real: 0.798 D_fake: 0.721 +(epoch: 184, iters: 736, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.872 G_ID: 0.147 G_Rec: 0.334 D_GP: 0.175 D_real: 0.732 D_fake: 0.840 +(epoch: 184, iters: 1136, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.971 G_ID: 0.195 G_Rec: 0.390 D_GP: 0.084 D_real: 0.661 D_fake: 0.697 +(epoch: 184, iters: 1536, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.776 G_ID: 0.133 G_Rec: 0.344 D_GP: 0.026 D_real: 1.175 D_fake: 0.652 +(epoch: 184, iters: 1936, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.943 G_ID: 0.211 G_Rec: 0.403 D_GP: 0.027 D_real: 1.019 D_fake: 0.677 +(epoch: 184, iters: 2336, time: 0.064) G_GAN: 0.006 G_GAN_Feat: 0.711 G_ID: 0.176 G_Rec: 0.352 D_GP: 0.037 D_real: 0.796 D_fake: 0.994 +(epoch: 184, iters: 2736, time: 0.064) G_GAN: 0.628 G_GAN_Feat: 0.851 G_ID: 0.172 G_Rec: 0.372 D_GP: 0.028 D_real: 1.333 D_fake: 0.384 +(epoch: 184, iters: 3136, time: 0.064) G_GAN: -0.093 G_GAN_Feat: 0.796 G_ID: 0.130 G_Rec: 0.330 D_GP: 0.025 D_real: 0.883 D_fake: 1.098 +(epoch: 184, iters: 3536, time: 0.064) G_GAN: 0.903 G_GAN_Feat: 0.948 G_ID: 0.152 G_Rec: 0.399 D_GP: 0.026 D_real: 1.482 D_fake: 0.152 +(epoch: 184, iters: 3936, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.912 G_ID: 0.151 G_Rec: 0.382 D_GP: 0.078 D_real: 1.024 D_fake: 0.714 +(epoch: 184, iters: 4336, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.799 G_ID: 0.214 G_Rec: 0.402 D_GP: 0.021 D_real: 1.016 D_fake: 0.754 +(epoch: 184, iters: 4736, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.766 G_ID: 0.138 G_Rec: 0.327 D_GP: 0.044 D_real: 0.879 D_fake: 0.797 +(epoch: 184, iters: 5136, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 0.933 G_ID: 0.186 G_Rec: 0.409 D_GP: 0.038 D_real: 1.036 D_fake: 0.457 +(epoch: 184, iters: 5536, time: 0.064) G_GAN: 0.664 G_GAN_Feat: 0.848 G_ID: 0.107 G_Rec: 0.358 D_GP: 0.167 D_real: 1.265 D_fake: 0.366 +(epoch: 184, iters: 5936, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 1.010 G_ID: 0.180 G_Rec: 0.433 D_GP: 0.104 D_real: 0.472 D_fake: 0.761 +(epoch: 184, iters: 6336, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.819 G_ID: 0.136 G_Rec: 0.322 D_GP: 0.038 D_real: 0.944 D_fake: 0.738 +(epoch: 184, iters: 6736, time: 0.064) G_GAN: 0.595 G_GAN_Feat: 1.113 G_ID: 0.198 G_Rec: 0.415 D_GP: 0.091 D_real: 0.572 D_fake: 0.414 +(epoch: 184, iters: 7136, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.830 G_ID: 0.168 G_Rec: 0.345 D_GP: 0.112 D_real: 0.739 D_fake: 0.844 +(epoch: 184, iters: 7536, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 1.130 G_ID: 0.165 G_Rec: 0.425 D_GP: 0.138 D_real: 0.556 D_fake: 0.652 +(epoch: 184, iters: 7936, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.743 G_ID: 0.129 G_Rec: 0.301 D_GP: 0.022 D_real: 1.015 D_fake: 0.797 +(epoch: 184, iters: 8336, time: 0.064) G_GAN: 0.548 G_GAN_Feat: 1.078 G_ID: 0.203 G_Rec: 0.454 D_GP: 0.031 D_real: 0.863 D_fake: 0.484 +(epoch: 185, iters: 128, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.727 G_ID: 0.131 G_Rec: 0.293 D_GP: 0.022 D_real: 1.396 D_fake: 0.538 +(epoch: 185, iters: 528, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.994 G_ID: 0.174 G_Rec: 0.414 D_GP: 0.040 D_real: 0.826 D_fake: 0.630 +(epoch: 185, iters: 928, time: 0.064) G_GAN: 0.560 G_GAN_Feat: 0.813 G_ID: 0.143 G_Rec: 0.340 D_GP: 0.026 D_real: 1.346 D_fake: 0.544 +(epoch: 185, iters: 1328, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 1.038 G_ID: 0.189 G_Rec: 0.418 D_GP: 0.024 D_real: 1.230 D_fake: 0.555 +(epoch: 185, iters: 1728, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.688 G_ID: 0.135 G_Rec: 0.311 D_GP: 0.027 D_real: 1.035 D_fake: 0.786 +(epoch: 185, iters: 2128, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 1.004 G_ID: 0.192 G_Rec: 0.401 D_GP: 0.133 D_real: 0.731 D_fake: 0.617 +(epoch: 185, iters: 2528, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.715 G_ID: 0.130 G_Rec: 0.308 D_GP: 0.021 D_real: 1.111 D_fake: 0.751 +(epoch: 185, iters: 2928, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 1.102 G_ID: 0.186 G_Rec: 0.451 D_GP: 0.035 D_real: 0.520 D_fake: 0.854 +(epoch: 185, iters: 3328, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.686 G_ID: 0.148 G_Rec: 0.325 D_GP: 0.022 D_real: 1.199 D_fake: 0.785 +(epoch: 185, iters: 3728, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 1.111 G_ID: 0.187 G_Rec: 0.442 D_GP: 0.086 D_real: 0.743 D_fake: 0.608 +(epoch: 185, iters: 4128, time: 0.064) G_GAN: 0.487 G_GAN_Feat: 0.746 G_ID: 0.146 G_Rec: 0.328 D_GP: 0.021 D_real: 1.467 D_fake: 0.516 +(epoch: 185, iters: 4528, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.818 G_ID: 0.182 G_Rec: 0.417 D_GP: 0.022 D_real: 1.043 D_fake: 0.731 +(epoch: 185, iters: 4928, time: 0.064) G_GAN: -0.084 G_GAN_Feat: 0.776 G_ID: 0.157 G_Rec: 0.329 D_GP: 0.059 D_real: 0.769 D_fake: 1.084 +(epoch: 185, iters: 5328, time: 0.064) G_GAN: 0.615 G_GAN_Feat: 0.827 G_ID: 0.176 G_Rec: 0.379 D_GP: 0.022 D_real: 1.353 D_fake: 0.398 +(epoch: 185, iters: 5728, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.688 G_ID: 0.142 G_Rec: 0.313 D_GP: 0.027 D_real: 1.156 D_fake: 0.740 +(epoch: 185, iters: 6128, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 1.114 G_ID: 0.200 G_Rec: 0.463 D_GP: 0.140 D_real: 0.476 D_fake: 0.485 +(epoch: 185, iters: 6528, time: 0.064) G_GAN: -0.015 G_GAN_Feat: 0.866 G_ID: 0.142 G_Rec: 0.328 D_GP: 0.131 D_real: 0.393 D_fake: 1.015 +(epoch: 185, iters: 6928, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.968 G_ID: 0.180 G_Rec: 0.443 D_GP: 0.027 D_real: 0.994 D_fake: 0.642 +(epoch: 185, iters: 7328, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.745 G_ID: 0.120 G_Rec: 0.291 D_GP: 0.025 D_real: 0.983 D_fake: 0.821 +(epoch: 185, iters: 7728, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.941 G_ID: 0.186 G_Rec: 0.404 D_GP: 0.027 D_real: 1.115 D_fake: 0.484 +(epoch: 185, iters: 8128, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.876 G_ID: 0.137 G_Rec: 0.311 D_GP: 0.073 D_real: 0.979 D_fake: 0.518 +(epoch: 185, iters: 8528, time: 0.064) G_GAN: 0.141 G_GAN_Feat: 0.929 G_ID: 0.209 G_Rec: 0.386 D_GP: 0.037 D_real: 0.877 D_fake: 0.859 +(epoch: 186, iters: 320, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.764 G_ID: 0.137 G_Rec: 0.309 D_GP: 0.048 D_real: 1.169 D_fake: 0.689 +(epoch: 186, iters: 720, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.949 G_ID: 0.218 G_Rec: 0.438 D_GP: 0.084 D_real: 0.808 D_fake: 0.800 +(epoch: 186, iters: 1120, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.757 G_ID: 0.139 G_Rec: 0.317 D_GP: 0.027 D_real: 0.988 D_fake: 0.857 +(epoch: 186, iters: 1520, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.898 G_ID: 0.157 G_Rec: 0.395 D_GP: 0.027 D_real: 1.188 D_fake: 0.452 +(epoch: 186, iters: 1920, time: 0.064) G_GAN: -0.094 G_GAN_Feat: 0.783 G_ID: 0.160 G_Rec: 0.302 D_GP: 0.021 D_real: 0.792 D_fake: 1.094 +(epoch: 186, iters: 2320, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 1.102 G_ID: 0.216 G_Rec: 0.406 D_GP: 0.564 D_real: 0.450 D_fake: 0.565 +(epoch: 186, iters: 2720, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.638 G_ID: 0.139 G_Rec: 0.322 D_GP: 0.020 D_real: 1.257 D_fake: 0.704 +(epoch: 186, iters: 3120, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 1.056 G_ID: 0.219 G_Rec: 0.432 D_GP: 0.117 D_real: 0.399 D_fake: 0.746 +(epoch: 186, iters: 3520, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.787 G_ID: 0.130 G_Rec: 0.305 D_GP: 0.040 D_real: 1.016 D_fake: 0.748 +(epoch: 186, iters: 3920, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 1.015 G_ID: 0.203 G_Rec: 0.417 D_GP: 0.050 D_real: 0.798 D_fake: 0.592 +(epoch: 186, iters: 4320, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 1.039 G_ID: 0.134 G_Rec: 0.372 D_GP: 0.089 D_real: 0.425 D_fake: 0.778 +(epoch: 186, iters: 4720, time: 0.064) G_GAN: 0.570 G_GAN_Feat: 0.802 G_ID: 0.171 G_Rec: 0.406 D_GP: 0.017 D_real: 1.396 D_fake: 0.437 +(epoch: 186, iters: 5120, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 0.720 G_ID: 0.148 G_Rec: 0.356 D_GP: 0.033 D_real: 0.936 D_fake: 0.904 +(epoch: 186, iters: 5520, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.990 G_ID: 0.235 G_Rec: 0.413 D_GP: 0.032 D_real: 0.922 D_fake: 0.537 +(epoch: 186, iters: 5920, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.840 G_ID: 0.130 G_Rec: 0.333 D_GP: 0.068 D_real: 0.865 D_fake: 0.832 +(epoch: 186, iters: 6320, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.922 G_ID: 0.192 G_Rec: 0.416 D_GP: 0.033 D_real: 0.870 D_fake: 0.699 +(epoch: 186, iters: 6720, time: 0.064) G_GAN: -0.011 G_GAN_Feat: 0.724 G_ID: 0.157 G_Rec: 0.315 D_GP: 0.027 D_real: 0.970 D_fake: 1.013 +(epoch: 186, iters: 7120, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 1.017 G_ID: 0.185 G_Rec: 0.403 D_GP: 0.043 D_real: 0.970 D_fake: 0.677 +(epoch: 186, iters: 7520, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.748 G_ID: 0.133 G_Rec: 0.303 D_GP: 0.018 D_real: 1.026 D_fake: 0.937 +(epoch: 186, iters: 7920, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.905 G_ID: 0.219 G_Rec: 0.416 D_GP: 0.022 D_real: 1.125 D_fake: 0.604 +(epoch: 186, iters: 8320, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.768 G_ID: 0.144 G_Rec: 0.316 D_GP: 0.038 D_real: 0.895 D_fake: 0.783 +(epoch: 187, iters: 112, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.849 G_ID: 0.163 G_Rec: 0.400 D_GP: 0.024 D_real: 1.166 D_fake: 0.635 +(epoch: 187, iters: 512, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.807 G_ID: 0.141 G_Rec: 0.346 D_GP: 0.025 D_real: 1.195 D_fake: 0.705 +(epoch: 187, iters: 912, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.885 G_ID: 0.172 G_Rec: 0.407 D_GP: 0.024 D_real: 1.124 D_fake: 0.706 +(epoch: 187, iters: 1312, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.705 G_ID: 0.138 G_Rec: 0.319 D_GP: 0.027 D_real: 1.029 D_fake: 0.828 +(epoch: 187, iters: 1712, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.995 G_ID: 0.208 G_Rec: 0.413 D_GP: 0.027 D_real: 1.005 D_fake: 0.633 +(epoch: 187, iters: 2112, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.718 G_ID: 0.122 G_Rec: 0.302 D_GP: 0.020 D_real: 1.023 D_fake: 0.847 +(epoch: 187, iters: 2512, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.953 G_ID: 0.192 G_Rec: 0.424 D_GP: 0.027 D_real: 1.064 D_fake: 0.635 +(epoch: 187, iters: 2912, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 1.027 G_ID: 0.140 G_Rec: 0.363 D_GP: 0.086 D_real: 0.948 D_fake: 0.807 +(epoch: 187, iters: 3312, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.845 G_ID: 0.208 G_Rec: 0.352 D_GP: 0.026 D_real: 1.101 D_fake: 0.651 +(epoch: 187, iters: 3712, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.709 G_ID: 0.165 G_Rec: 0.314 D_GP: 0.025 D_real: 1.249 D_fake: 0.699 +(epoch: 187, iters: 4112, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.828 G_ID: 0.192 G_Rec: 0.383 D_GP: 0.023 D_real: 1.207 D_fake: 0.584 +(epoch: 187, iters: 4512, time: 0.064) G_GAN: -0.122 G_GAN_Feat: 0.871 G_ID: 0.146 G_Rec: 0.326 D_GP: 0.164 D_real: 0.178 D_fake: 1.122 +(epoch: 187, iters: 4912, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.847 G_ID: 0.181 G_Rec: 0.469 D_GP: 0.025 D_real: 0.883 D_fake: 0.883 +(epoch: 187, iters: 5312, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.734 G_ID: 0.138 G_Rec: 0.300 D_GP: 0.033 D_real: 1.111 D_fake: 0.711 +(epoch: 187, iters: 5712, time: 0.064) G_GAN: 0.923 G_GAN_Feat: 0.989 G_ID: 0.183 G_Rec: 0.423 D_GP: 0.028 D_real: 1.405 D_fake: 0.190 +(epoch: 187, iters: 6112, time: 0.064) G_GAN: -0.082 G_GAN_Feat: 0.983 G_ID: 0.175 G_Rec: 0.340 D_GP: 0.118 D_real: 0.423 D_fake: 1.082 +(epoch: 187, iters: 6512, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.768 G_ID: 0.183 G_Rec: 0.384 D_GP: 0.019 D_real: 1.195 D_fake: 0.579 +(epoch: 187, iters: 6912, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.949 G_ID: 0.152 G_Rec: 0.333 D_GP: 0.172 D_real: 0.453 D_fake: 0.741 +(epoch: 187, iters: 7312, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 1.122 G_ID: 0.184 G_Rec: 0.456 D_GP: 0.080 D_real: 0.598 D_fake: 0.488 +(epoch: 187, iters: 7712, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.734 G_ID: 0.120 G_Rec: 0.285 D_GP: 0.023 D_real: 1.064 D_fake: 0.878 +(epoch: 187, iters: 8112, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 1.076 G_ID: 0.179 G_Rec: 0.396 D_GP: 0.031 D_real: 0.829 D_fake: 0.492 +(epoch: 187, iters: 8512, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.923 G_ID: 0.138 G_Rec: 0.333 D_GP: 0.051 D_real: 0.587 D_fake: 0.789 +(epoch: 188, iters: 304, time: 0.064) G_GAN: 0.878 G_GAN_Feat: 1.021 G_ID: 0.163 G_Rec: 0.429 D_GP: 0.027 D_real: 1.388 D_fake: 0.213 +(epoch: 188, iters: 704, time: 0.064) G_GAN: -0.213 G_GAN_Feat: 0.713 G_ID: 0.156 G_Rec: 0.291 D_GP: 0.025 D_real: 0.818 D_fake: 1.213 +(epoch: 188, iters: 1104, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.971 G_ID: 0.191 G_Rec: 0.400 D_GP: 0.027 D_real: 0.830 D_fake: 0.734 +(epoch: 188, iters: 1504, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.938 G_ID: 0.132 G_Rec: 0.327 D_GP: 0.101 D_real: 0.459 D_fake: 0.724 +(epoch: 188, iters: 1904, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.790 G_ID: 0.186 G_Rec: 0.409 D_GP: 0.019 D_real: 1.235 D_fake: 0.586 +(epoch: 188, iters: 2304, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.788 G_ID: 0.125 G_Rec: 0.305 D_GP: 0.031 D_real: 1.189 D_fake: 0.682 +(epoch: 188, iters: 2704, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.850 G_ID: 0.213 G_Rec: 0.396 D_GP: 0.024 D_real: 1.032 D_fake: 0.717 +(epoch: 188, iters: 3104, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.705 G_ID: 0.140 G_Rec: 0.330 D_GP: 0.022 D_real: 1.243 D_fake: 0.614 +(epoch: 188, iters: 3504, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.824 G_ID: 0.194 G_Rec: 0.362 D_GP: 0.027 D_real: 1.000 D_fake: 0.707 +(epoch: 188, iters: 3904, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.834 G_ID: 0.144 G_Rec: 0.317 D_GP: 0.110 D_real: 0.685 D_fake: 0.947 +(epoch: 188, iters: 4304, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.893 G_ID: 0.196 G_Rec: 0.380 D_GP: 0.037 D_real: 0.926 D_fake: 0.631 +(epoch: 188, iters: 4704, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.605 G_ID: 0.138 G_Rec: 0.288 D_GP: 0.023 D_real: 1.163 D_fake: 0.834 +(epoch: 188, iters: 5104, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 1.058 G_ID: 0.187 G_Rec: 0.427 D_GP: 0.129 D_real: 0.467 D_fake: 0.789 +(epoch: 188, iters: 5504, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.710 G_ID: 0.157 G_Rec: 0.333 D_GP: 0.023 D_real: 1.115 D_fake: 0.752 +(epoch: 188, iters: 5904, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.947 G_ID: 0.181 G_Rec: 0.373 D_GP: 0.066 D_real: 0.675 D_fake: 0.722 +(epoch: 188, iters: 6304, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.771 G_ID: 0.146 G_Rec: 0.292 D_GP: 0.031 D_real: 1.050 D_fake: 0.763 +(epoch: 188, iters: 6704, time: 0.064) G_GAN: 0.558 G_GAN_Feat: 1.002 G_ID: 0.177 G_Rec: 0.398 D_GP: 0.035 D_real: 1.052 D_fake: 0.449 +(epoch: 188, iters: 7104, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.808 G_ID: 0.147 G_Rec: 0.301 D_GP: 0.050 D_real: 0.912 D_fake: 0.740 +(epoch: 188, iters: 7504, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.969 G_ID: 0.172 G_Rec: 0.383 D_GP: 0.025 D_real: 1.005 D_fake: 0.602 +(epoch: 188, iters: 7904, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 0.635 G_ID: 0.125 G_Rec: 0.318 D_GP: 0.020 D_real: 1.458 D_fake: 0.543 +(epoch: 188, iters: 8304, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.882 G_ID: 0.207 G_Rec: 0.416 D_GP: 0.031 D_real: 0.777 D_fake: 0.854 +(epoch: 189, iters: 96, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.719 G_ID: 0.131 G_Rec: 0.298 D_GP: 0.040 D_real: 0.980 D_fake: 0.809 +(epoch: 189, iters: 496, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.860 G_ID: 0.199 G_Rec: 0.382 D_GP: 0.029 D_real: 1.188 D_fake: 0.501 +(epoch: 189, iters: 896, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.733 G_ID: 0.116 G_Rec: 0.308 D_GP: 0.042 D_real: 0.996 D_fake: 0.725 +(epoch: 189, iters: 1296, time: 0.064) G_GAN: 0.817 G_GAN_Feat: 1.011 G_ID: 0.197 G_Rec: 0.416 D_GP: 0.084 D_real: 1.229 D_fake: 0.455 +(epoch: 189, iters: 1696, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.733 G_ID: 0.133 G_Rec: 0.360 D_GP: 0.023 D_real: 1.203 D_fake: 0.683 +(epoch: 189, iters: 2096, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 1.021 G_ID: 0.186 G_Rec: 0.457 D_GP: 0.773 D_real: 0.586 D_fake: 0.707 +(epoch: 189, iters: 2496, time: 0.064) G_GAN: 0.042 G_GAN_Feat: 0.615 G_ID: 0.167 G_Rec: 0.295 D_GP: 0.020 D_real: 0.936 D_fake: 0.958 +(epoch: 189, iters: 2896, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 1.159 G_ID: 0.195 G_Rec: 0.457 D_GP: 0.404 D_real: 0.433 D_fake: 0.742 +(epoch: 189, iters: 3296, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.834 G_ID: 0.159 G_Rec: 0.388 D_GP: 0.102 D_real: 0.814 D_fake: 0.751 +(epoch: 189, iters: 3696, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.812 G_ID: 0.158 G_Rec: 0.398 D_GP: 0.027 D_real: 1.283 D_fake: 0.516 +(epoch: 189, iters: 4096, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.744 G_ID: 0.161 G_Rec: 0.299 D_GP: 0.032 D_real: 0.980 D_fake: 0.890 +(epoch: 189, iters: 4496, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.857 G_ID: 0.190 G_Rec: 0.393 D_GP: 0.026 D_real: 1.184 D_fake: 0.499 +(epoch: 189, iters: 4896, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.723 G_ID: 0.131 G_Rec: 0.323 D_GP: 0.023 D_real: 1.295 D_fake: 0.569 +(epoch: 189, iters: 5296, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.942 G_ID: 0.187 G_Rec: 0.394 D_GP: 0.029 D_real: 1.054 D_fake: 0.623 +(epoch: 189, iters: 5696, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.573 G_ID: 0.123 G_Rec: 0.293 D_GP: 0.026 D_real: 1.400 D_fake: 0.596 +(epoch: 189, iters: 6096, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.919 G_ID: 0.183 G_Rec: 0.457 D_GP: 0.105 D_real: 0.725 D_fake: 0.712 +(epoch: 189, iters: 6496, time: 0.064) G_GAN: -0.029 G_GAN_Feat: 0.661 G_ID: 0.127 G_Rec: 0.332 D_GP: 0.027 D_real: 0.880 D_fake: 1.029 +(epoch: 189, iters: 6896, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.844 G_ID: 0.192 G_Rec: 0.340 D_GP: 0.037 D_real: 1.176 D_fake: 0.554 +(epoch: 189, iters: 7296, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.774 G_ID: 0.177 G_Rec: 0.313 D_GP: 0.038 D_real: 1.083 D_fake: 0.690 +(epoch: 189, iters: 7696, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.969 G_ID: 0.197 G_Rec: 0.378 D_GP: 0.056 D_real: 0.580 D_fake: 0.719 +(epoch: 189, iters: 8096, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.714 G_ID: 0.172 G_Rec: 0.314 D_GP: 0.024 D_real: 1.172 D_fake: 0.773 +(epoch: 189, iters: 8496, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 1.146 G_ID: 0.202 G_Rec: 0.462 D_GP: 0.063 D_real: 0.324 D_fake: 0.554 +(epoch: 190, iters: 288, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.563 G_ID: 0.149 G_Rec: 0.298 D_GP: 0.019 D_real: 1.038 D_fake: 0.902 +(epoch: 190, iters: 688, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.760 G_ID: 0.181 G_Rec: 0.399 D_GP: 0.021 D_real: 1.152 D_fake: 0.569 +(epoch: 190, iters: 1088, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.573 G_ID: 0.132 G_Rec: 0.302 D_GP: 0.020 D_real: 1.110 D_fake: 0.845 +(epoch: 190, iters: 1488, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.807 G_ID: 0.200 G_Rec: 0.395 D_GP: 0.039 D_real: 1.115 D_fake: 0.598 +(epoch: 190, iters: 1888, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.659 G_ID: 0.125 G_Rec: 0.319 D_GP: 0.034 D_real: 1.003 D_fake: 0.888 +(epoch: 190, iters: 2288, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.786 G_ID: 0.182 G_Rec: 0.381 D_GP: 0.027 D_real: 0.986 D_fake: 0.776 +(epoch: 190, iters: 2688, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.671 G_ID: 0.117 G_Rec: 0.301 D_GP: 0.053 D_real: 0.988 D_fake: 0.828 +(epoch: 190, iters: 3088, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.803 G_ID: 0.208 G_Rec: 0.365 D_GP: 0.062 D_real: 0.823 D_fake: 0.816 +(epoch: 190, iters: 3488, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.736 G_ID: 0.139 G_Rec: 0.303 D_GP: 0.077 D_real: 0.947 D_fake: 0.692 +(epoch: 190, iters: 3888, time: 0.064) G_GAN: 0.660 G_GAN_Feat: 0.843 G_ID: 0.174 G_Rec: 0.380 D_GP: 0.025 D_real: 1.362 D_fake: 0.375 +(epoch: 190, iters: 4288, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.708 G_ID: 0.125 G_Rec: 0.306 D_GP: 0.034 D_real: 1.266 D_fake: 0.603 +(epoch: 190, iters: 4688, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.888 G_ID: 0.208 G_Rec: 0.372 D_GP: 0.032 D_real: 0.838 D_fake: 0.827 +(epoch: 190, iters: 5088, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.938 G_ID: 0.151 G_Rec: 0.303 D_GP: 0.060 D_real: 0.536 D_fake: 0.808 +(epoch: 190, iters: 5488, time: 0.064) G_GAN: 0.641 G_GAN_Feat: 0.881 G_ID: 0.190 G_Rec: 0.397 D_GP: 0.026 D_real: 1.291 D_fake: 0.373 +(epoch: 190, iters: 5888, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.986 G_ID: 0.172 G_Rec: 0.336 D_GP: 0.079 D_real: 0.994 D_fake: 0.697 +(epoch: 190, iters: 6288, time: 0.064) G_GAN: 0.885 G_GAN_Feat: 0.911 G_ID: 0.196 G_Rec: 0.406 D_GP: 0.029 D_real: 1.648 D_fake: 0.222 +(epoch: 190, iters: 6688, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 1.004 G_ID: 0.140 G_Rec: 0.363 D_GP: 0.085 D_real: 0.770 D_fake: 0.485 +(epoch: 190, iters: 7088, time: 0.064) G_GAN: 0.655 G_GAN_Feat: 0.926 G_ID: 0.176 G_Rec: 0.404 D_GP: 0.030 D_real: 1.265 D_fake: 0.354 +(epoch: 190, iters: 7488, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.823 G_ID: 0.144 G_Rec: 0.311 D_GP: 0.064 D_real: 0.724 D_fake: 0.846 +(epoch: 190, iters: 7888, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.848 G_ID: 0.206 G_Rec: 0.400 D_GP: 0.025 D_real: 1.144 D_fake: 0.557 +(epoch: 190, iters: 8288, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.775 G_ID: 0.124 G_Rec: 0.337 D_GP: 0.040 D_real: 1.012 D_fake: 0.727 +(epoch: 191, iters: 80, time: 0.064) G_GAN: 0.682 G_GAN_Feat: 1.216 G_ID: 0.200 G_Rec: 0.454 D_GP: 0.277 D_real: 0.460 D_fake: 0.374 +(epoch: 191, iters: 480, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.695 G_ID: 0.138 G_Rec: 0.369 D_GP: 0.021 D_real: 1.166 D_fake: 0.711 +(epoch: 191, iters: 880, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.843 G_ID: 0.185 G_Rec: 0.381 D_GP: 0.028 D_real: 1.138 D_fake: 0.556 +(epoch: 191, iters: 1280, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.824 G_ID: 0.142 G_Rec: 0.328 D_GP: 0.106 D_real: 0.468 D_fake: 0.948 +(epoch: 191, iters: 1680, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.829 G_ID: 0.169 G_Rec: 0.377 D_GP: 0.022 D_real: 1.153 D_fake: 0.609 +(epoch: 191, iters: 2080, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.831 G_ID: 0.136 G_Rec: 0.389 D_GP: 0.064 D_real: 0.936 D_fake: 0.765 +(epoch: 191, iters: 2480, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.817 G_ID: 0.202 G_Rec: 0.390 D_GP: 0.029 D_real: 1.281 D_fake: 0.492 +(epoch: 191, iters: 2880, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.895 G_ID: 0.144 G_Rec: 0.367 D_GP: 0.140 D_real: 0.548 D_fake: 0.655 +(epoch: 191, iters: 3280, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 1.096 G_ID: 0.157 G_Rec: 0.375 D_GP: 0.047 D_real: 0.571 D_fake: 0.627 +(epoch: 191, iters: 3680, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.826 G_ID: 0.131 G_Rec: 0.320 D_GP: 0.072 D_real: 1.161 D_fake: 0.672 +(epoch: 191, iters: 4080, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.936 G_ID: 0.193 G_Rec: 0.396 D_GP: 0.031 D_real: 1.106 D_fake: 0.568 +(epoch: 191, iters: 4480, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.794 G_ID: 0.124 G_Rec: 0.324 D_GP: 0.045 D_real: 0.640 D_fake: 0.915 +(epoch: 191, iters: 4880, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.117 G_ID: 0.195 G_Rec: 0.454 D_GP: 2.094 D_real: 0.499 D_fake: 0.652 +(epoch: 191, iters: 5280, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.601 G_ID: 0.137 G_Rec: 0.465 D_GP: 0.018 D_real: 1.230 D_fake: 0.753 +(epoch: 191, iters: 5680, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.764 G_ID: 0.198 G_Rec: 0.405 D_GP: 0.019 D_real: 1.091 D_fake: 0.621 +(epoch: 191, iters: 6080, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.693 G_ID: 0.140 G_Rec: 0.331 D_GP: 0.037 D_real: 0.940 D_fake: 0.829 +(epoch: 191, iters: 6480, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 1.013 G_ID: 0.186 G_Rec: 0.404 D_GP: 0.093 D_real: 0.552 D_fake: 0.769 +(epoch: 191, iters: 6880, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.746 G_ID: 0.143 G_Rec: 0.314 D_GP: 0.059 D_real: 0.927 D_fake: 0.715 +(epoch: 191, iters: 7280, time: 0.064) G_GAN: 0.656 G_GAN_Feat: 0.981 G_ID: 0.205 G_Rec: 0.426 D_GP: 0.059 D_real: 1.085 D_fake: 0.430 +(epoch: 191, iters: 7680, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.776 G_ID: 0.151 G_Rec: 0.351 D_GP: 0.028 D_real: 1.386 D_fake: 0.575 +(epoch: 191, iters: 8080, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.950 G_ID: 0.203 G_Rec: 0.371 D_GP: 0.076 D_real: 0.729 D_fake: 0.908 +(epoch: 191, iters: 8480, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.749 G_ID: 0.132 G_Rec: 0.310 D_GP: 0.040 D_real: 0.964 D_fake: 0.784 +(epoch: 192, iters: 272, time: 0.064) G_GAN: 0.495 G_GAN_Feat: 0.823 G_ID: 0.192 G_Rec: 0.391 D_GP: 0.022 D_real: 1.333 D_fake: 0.508 +(epoch: 192, iters: 672, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.803 G_ID: 0.146 G_Rec: 0.323 D_GP: 0.070 D_real: 1.019 D_fake: 0.593 +(epoch: 192, iters: 1072, time: 0.064) G_GAN: 0.557 G_GAN_Feat: 0.959 G_ID: 0.182 G_Rec: 0.418 D_GP: 0.037 D_real: 1.019 D_fake: 0.448 +(epoch: 192, iters: 1472, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.732 G_ID: 0.139 G_Rec: 0.336 D_GP: 0.040 D_real: 1.122 D_fake: 0.665 +(epoch: 192, iters: 1872, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.811 G_ID: 0.188 G_Rec: 0.396 D_GP: 0.025 D_real: 1.161 D_fake: 0.517 +(epoch: 192, iters: 2272, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.646 G_ID: 0.138 G_Rec: 0.285 D_GP: 0.028 D_real: 1.097 D_fake: 0.770 +(epoch: 192, iters: 2672, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 1.044 G_ID: 0.196 G_Rec: 0.443 D_GP: 0.112 D_real: 0.568 D_fake: 0.654 +(epoch: 192, iters: 3072, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.663 G_ID: 0.139 G_Rec: 0.330 D_GP: 0.042 D_real: 1.136 D_fake: 0.767 +(epoch: 192, iters: 3472, time: 0.064) G_GAN: 0.700 G_GAN_Feat: 0.928 G_ID: 0.185 G_Rec: 0.415 D_GP: 0.036 D_real: 1.219 D_fake: 0.334 +(epoch: 192, iters: 3872, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.757 G_ID: 0.144 G_Rec: 0.321 D_GP: 0.060 D_real: 1.014 D_fake: 0.715 +(epoch: 192, iters: 4272, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 1.098 G_ID: 0.204 G_Rec: 0.450 D_GP: 0.202 D_real: 0.338 D_fake: 0.638 +(epoch: 192, iters: 4672, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.683 G_ID: 0.126 G_Rec: 0.309 D_GP: 0.022 D_real: 1.166 D_fake: 0.744 +(epoch: 192, iters: 5072, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.862 G_ID: 0.235 G_Rec: 0.392 D_GP: 0.030 D_real: 1.022 D_fake: 0.613 +(epoch: 192, iters: 5472, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 0.721 G_ID: 0.128 G_Rec: 0.315 D_GP: 0.040 D_real: 1.260 D_fake: 0.559 +(epoch: 192, iters: 5872, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.873 G_ID: 0.185 G_Rec: 0.384 D_GP: 0.024 D_real: 0.961 D_fake: 0.669 +(epoch: 192, iters: 6272, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.777 G_ID: 0.139 G_Rec: 0.312 D_GP: 0.035 D_real: 1.029 D_fake: 0.739 +(epoch: 192, iters: 6672, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.987 G_ID: 0.216 G_Rec: 0.429 D_GP: 0.054 D_real: 0.924 D_fake: 0.517 +(epoch: 192, iters: 7072, time: 0.064) G_GAN: -0.049 G_GAN_Feat: 1.017 G_ID: 0.144 G_Rec: 0.431 D_GP: 0.291 D_real: 0.382 D_fake: 1.051 +(epoch: 192, iters: 7472, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.869 G_ID: 0.200 G_Rec: 0.402 D_GP: 0.024 D_real: 1.069 D_fake: 0.733 +(epoch: 192, iters: 7872, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.656 G_ID: 0.172 G_Rec: 0.281 D_GP: 0.027 D_real: 1.297 D_fake: 0.621 +(epoch: 192, iters: 8272, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 0.970 G_ID: 0.205 G_Rec: 0.434 D_GP: 0.037 D_real: 1.114 D_fake: 0.504 +(epoch: 193, iters: 64, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.708 G_ID: 0.136 G_Rec: 0.291 D_GP: 0.025 D_real: 1.148 D_fake: 0.736 +(epoch: 193, iters: 464, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.830 G_ID: 0.208 G_Rec: 0.350 D_GP: 0.026 D_real: 1.205 D_fake: 0.514 +(epoch: 193, iters: 864, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.630 G_ID: 0.137 G_Rec: 0.307 D_GP: 0.020 D_real: 1.211 D_fake: 0.794 +(epoch: 193, iters: 1264, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.794 G_ID: 0.195 G_Rec: 0.391 D_GP: 0.024 D_real: 1.067 D_fake: 0.689 +(epoch: 193, iters: 1664, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.664 G_ID: 0.130 G_Rec: 0.312 D_GP: 0.029 D_real: 1.048 D_fake: 0.855 +(epoch: 193, iters: 2064, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.869 G_ID: 0.180 G_Rec: 0.404 D_GP: 0.034 D_real: 0.943 D_fake: 0.634 +(epoch: 193, iters: 2464, time: 0.064) G_GAN: -0.166 G_GAN_Feat: 0.827 G_ID: 0.157 G_Rec: 0.367 D_GP: 0.051 D_real: 0.430 D_fake: 1.168 +(epoch: 193, iters: 2864, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.951 G_ID: 0.192 G_Rec: 0.383 D_GP: 0.057 D_real: 0.758 D_fake: 0.723 +(epoch: 193, iters: 3264, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 1.028 G_ID: 0.157 G_Rec: 0.322 D_GP: 0.047 D_real: 0.404 D_fake: 0.711 +(epoch: 193, iters: 3664, time: 0.064) G_GAN: 0.665 G_GAN_Feat: 0.891 G_ID: 0.192 G_Rec: 0.399 D_GP: 0.030 D_real: 1.254 D_fake: 0.347 +(epoch: 193, iters: 4064, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.757 G_ID: 0.136 G_Rec: 0.333 D_GP: 0.035 D_real: 0.960 D_fake: 0.769 +(epoch: 193, iters: 4464, time: 0.064) G_GAN: 0.729 G_GAN_Feat: 0.905 G_ID: 0.196 G_Rec: 0.387 D_GP: 0.027 D_real: 1.491 D_fake: 0.299 +(epoch: 193, iters: 4864, time: 0.064) G_GAN: -0.100 G_GAN_Feat: 0.749 G_ID: 0.156 G_Rec: 0.331 D_GP: 0.060 D_real: 0.627 D_fake: 1.100 +(epoch: 193, iters: 5264, time: 0.064) G_GAN: 0.562 G_GAN_Feat: 0.970 G_ID: 0.180 G_Rec: 0.382 D_GP: 0.096 D_real: 0.826 D_fake: 0.461 +(epoch: 193, iters: 5664, time: 0.064) G_GAN: 0.022 G_GAN_Feat: 0.742 G_ID: 0.157 G_Rec: 0.329 D_GP: 0.032 D_real: 0.911 D_fake: 0.978 +(epoch: 193, iters: 6064, time: 0.064) G_GAN: 0.579 G_GAN_Feat: 1.055 G_ID: 0.211 G_Rec: 0.417 D_GP: 0.067 D_real: 0.630 D_fake: 0.430 +(epoch: 193, iters: 6464, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.966 G_ID: 0.160 G_Rec: 0.373 D_GP: 0.441 D_real: 0.368 D_fake: 0.857 +(epoch: 193, iters: 6864, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 1.010 G_ID: 0.185 G_Rec: 0.420 D_GP: 0.074 D_real: 0.889 D_fake: 0.452 +(epoch: 193, iters: 7264, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.702 G_ID: 0.141 G_Rec: 0.341 D_GP: 0.022 D_real: 1.289 D_fake: 0.649 +(epoch: 193, iters: 7664, time: 0.064) G_GAN: 0.598 G_GAN_Feat: 0.860 G_ID: 0.165 G_Rec: 0.390 D_GP: 0.030 D_real: 1.335 D_fake: 0.420 +(epoch: 193, iters: 8064, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.715 G_ID: 0.128 G_Rec: 0.298 D_GP: 0.027 D_real: 0.934 D_fake: 0.848 +(epoch: 193, iters: 8464, time: 0.064) G_GAN: 0.663 G_GAN_Feat: 0.867 G_ID: 0.176 G_Rec: 0.401 D_GP: 0.023 D_real: 1.396 D_fake: 0.344 +(epoch: 194, iters: 256, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.686 G_ID: 0.155 G_Rec: 0.296 D_GP: 0.025 D_real: 1.140 D_fake: 0.800 +(epoch: 194, iters: 656, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.951 G_ID: 0.170 G_Rec: 0.381 D_GP: 0.088 D_real: 0.755 D_fake: 0.612 +(epoch: 194, iters: 1056, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.798 G_ID: 0.154 G_Rec: 0.326 D_GP: 0.090 D_real: 1.117 D_fake: 0.455 +(epoch: 194, iters: 1456, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 0.961 G_ID: 0.176 G_Rec: 0.403 D_GP: 0.040 D_real: 1.165 D_fake: 0.424 +(epoch: 194, iters: 1856, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.779 G_ID: 0.152 G_Rec: 0.323 D_GP: 0.071 D_real: 0.828 D_fake: 0.824 +(epoch: 194, iters: 2256, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.821 G_ID: 0.188 G_Rec: 0.374 D_GP: 0.023 D_real: 1.087 D_fake: 0.620 +(epoch: 194, iters: 2656, time: 0.064) G_GAN: 0.070 G_GAN_Feat: 0.754 G_ID: 0.135 G_Rec: 0.310 D_GP: 0.032 D_real: 0.790 D_fake: 0.930 +(epoch: 194, iters: 3056, time: 0.064) G_GAN: 0.575 G_GAN_Feat: 0.967 G_ID: 0.170 G_Rec: 0.447 D_GP: 0.043 D_real: 1.133 D_fake: 0.438 +(epoch: 194, iters: 3456, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.711 G_ID: 0.137 G_Rec: 0.307 D_GP: 0.029 D_real: 1.206 D_fake: 0.649 +(epoch: 194, iters: 3856, time: 0.064) G_GAN: 0.805 G_GAN_Feat: 0.961 G_ID: 0.178 G_Rec: 0.384 D_GP: 0.034 D_real: 1.444 D_fake: 0.251 +(epoch: 194, iters: 4256, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.832 G_ID: 0.155 G_Rec: 0.319 D_GP: 0.040 D_real: 0.771 D_fake: 0.897 +(epoch: 194, iters: 4656, time: 0.064) G_GAN: 0.609 G_GAN_Feat: 0.844 G_ID: 0.168 G_Rec: 0.350 D_GP: 0.032 D_real: 1.291 D_fake: 0.407 +(epoch: 194, iters: 5056, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.775 G_ID: 0.134 G_Rec: 0.298 D_GP: 0.038 D_real: 1.144 D_fake: 0.608 +(epoch: 194, iters: 5456, time: 0.064) G_GAN: 0.588 G_GAN_Feat: 1.154 G_ID: 0.172 G_Rec: 0.401 D_GP: 0.068 D_real: 0.476 D_fake: 0.425 +(epoch: 194, iters: 5856, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.891 G_ID: 0.137 G_Rec: 0.301 D_GP: 0.045 D_real: 0.978 D_fake: 0.572 +(epoch: 194, iters: 6256, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.832 G_ID: 0.198 G_Rec: 0.421 D_GP: 0.025 D_real: 1.128 D_fake: 0.573 +(epoch: 194, iters: 6656, time: 0.064) G_GAN: -0.079 G_GAN_Feat: 0.744 G_ID: 0.162 G_Rec: 0.333 D_GP: 0.050 D_real: 0.686 D_fake: 1.079 +(epoch: 194, iters: 7056, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.916 G_ID: 0.191 G_Rec: 0.397 D_GP: 0.046 D_real: 0.839 D_fake: 0.678 +(epoch: 194, iters: 7456, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.738 G_ID: 0.135 G_Rec: 0.311 D_GP: 0.053 D_real: 0.875 D_fake: 0.927 +(epoch: 194, iters: 7856, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 1.008 G_ID: 0.189 G_Rec: 0.414 D_GP: 0.037 D_real: 0.682 D_fake: 0.659 +(epoch: 194, iters: 8256, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.632 G_ID: 0.142 G_Rec: 0.268 D_GP: 0.026 D_real: 1.204 D_fake: 0.732 +(epoch: 195, iters: 48, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 1.022 G_ID: 0.181 G_Rec: 0.404 D_GP: 0.086 D_real: 0.542 D_fake: 0.732 +(epoch: 195, iters: 448, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.790 G_ID: 0.159 G_Rec: 0.312 D_GP: 0.034 D_real: 1.196 D_fake: 0.533 +(epoch: 195, iters: 848, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 1.128 G_ID: 0.199 G_Rec: 0.418 D_GP: 0.084 D_real: 0.372 D_fake: 0.558 +(epoch: 195, iters: 1248, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.783 G_ID: 0.165 G_Rec: 0.338 D_GP: 0.047 D_real: 1.064 D_fake: 0.645 +(epoch: 195, iters: 1648, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.997 G_ID: 0.200 G_Rec: 0.398 D_GP: 0.074 D_real: 0.450 D_fake: 0.762 +(epoch: 195, iters: 2048, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.667 G_ID: 0.143 G_Rec: 0.316 D_GP: 0.021 D_real: 1.393 D_fake: 0.574 +(epoch: 195, iters: 2448, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.892 G_ID: 0.180 G_Rec: 0.418 D_GP: 0.024 D_real: 1.006 D_fake: 0.785 +(epoch: 195, iters: 2848, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.746 G_ID: 0.137 G_Rec: 0.295 D_GP: 0.027 D_real: 1.059 D_fake: 0.808 +(epoch: 195, iters: 3248, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.898 G_ID: 0.185 G_Rec: 0.431 D_GP: 0.022 D_real: 1.198 D_fake: 0.537 +(epoch: 195, iters: 3648, time: 0.064) G_GAN: 0.020 G_GAN_Feat: 0.697 G_ID: 0.126 G_Rec: 0.288 D_GP: 0.025 D_real: 0.871 D_fake: 0.980 +(epoch: 195, iters: 4048, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.941 G_ID: 0.180 G_Rec: 0.392 D_GP: 0.074 D_real: 0.814 D_fake: 0.559 +(epoch: 195, iters: 4448, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.754 G_ID: 0.153 G_Rec: 0.311 D_GP: 0.028 D_real: 1.036 D_fake: 0.823 +(epoch: 195, iters: 4848, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.935 G_ID: 0.178 G_Rec: 0.358 D_GP: 0.045 D_real: 0.848 D_fake: 0.577 +(epoch: 195, iters: 5248, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.885 G_ID: 0.159 G_Rec: 0.341 D_GP: 0.028 D_real: 0.994 D_fake: 0.670 +(epoch: 195, iters: 5648, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 1.153 G_ID: 0.166 G_Rec: 0.399 D_GP: 0.481 D_real: 0.379 D_fake: 0.768 +(epoch: 195, iters: 6048, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.642 G_ID: 0.137 G_Rec: 0.350 D_GP: 0.020 D_real: 1.278 D_fake: 0.676 +(epoch: 195, iters: 6448, time: 0.064) G_GAN: 0.712 G_GAN_Feat: 1.001 G_ID: 0.192 G_Rec: 0.450 D_GP: 0.054 D_real: 1.007 D_fake: 0.311 +(epoch: 195, iters: 6848, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.909 G_ID: 0.140 G_Rec: 0.309 D_GP: 0.037 D_real: 0.472 D_fake: 0.861 +(epoch: 195, iters: 7248, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.881 G_ID: 0.168 G_Rec: 0.422 D_GP: 0.033 D_real: 1.044 D_fake: 0.593 +(epoch: 195, iters: 7648, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.907 G_ID: 0.147 G_Rec: 0.353 D_GP: 0.198 D_real: 0.858 D_fake: 0.716 +(epoch: 195, iters: 8048, time: 0.064) G_GAN: 0.656 G_GAN_Feat: 0.871 G_ID: 0.198 G_Rec: 0.390 D_GP: 0.030 D_real: 1.399 D_fake: 0.380 +(epoch: 195, iters: 8448, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.703 G_ID: 0.140 G_Rec: 0.275 D_GP: 0.027 D_real: 0.976 D_fake: 0.865 +(epoch: 196, iters: 240, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.827 G_ID: 0.195 G_Rec: 0.395 D_GP: 0.030 D_real: 1.175 D_fake: 0.651 +(epoch: 196, iters: 640, time: 0.064) G_GAN: -0.215 G_GAN_Feat: 0.917 G_ID: 0.164 G_Rec: 0.363 D_GP: 0.057 D_real: 0.903 D_fake: 1.215 +(epoch: 196, iters: 1040, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.923 G_ID: 0.182 G_Rec: 0.418 D_GP: 0.031 D_real: 1.123 D_fake: 0.596 +(epoch: 196, iters: 1440, time: 0.064) G_GAN: -0.116 G_GAN_Feat: 0.741 G_ID: 0.131 G_Rec: 0.333 D_GP: 0.110 D_real: 0.654 D_fake: 1.116 +(epoch: 196, iters: 1840, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.935 G_ID: 0.194 G_Rec: 0.400 D_GP: 0.031 D_real: 1.093 D_fake: 0.769 +(epoch: 196, iters: 2240, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.818 G_ID: 0.118 G_Rec: 0.337 D_GP: 0.054 D_real: 1.179 D_fake: 0.697 +(epoch: 196, iters: 2640, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.846 G_ID: 0.180 G_Rec: 0.429 D_GP: 0.026 D_real: 0.956 D_fake: 0.694 +(epoch: 196, iters: 3040, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.633 G_ID: 0.137 G_Rec: 0.317 D_GP: 0.024 D_real: 1.079 D_fake: 0.842 +(epoch: 196, iters: 3440, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 1.062 G_ID: 0.192 G_Rec: 0.460 D_GP: 0.167 D_real: 0.597 D_fake: 0.732 +(epoch: 196, iters: 3840, time: 0.064) G_GAN: -0.087 G_GAN_Feat: 0.767 G_ID: 0.139 G_Rec: 0.333 D_GP: 0.044 D_real: 0.730 D_fake: 1.087 +(epoch: 196, iters: 4240, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.780 G_ID: 0.154 G_Rec: 0.384 D_GP: 0.020 D_real: 1.366 D_fake: 0.514 +(epoch: 196, iters: 4640, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.744 G_ID: 0.159 G_Rec: 0.316 D_GP: 0.027 D_real: 0.972 D_fake: 0.841 +(epoch: 196, iters: 5040, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 0.893 G_ID: 0.179 G_Rec: 0.419 D_GP: 0.026 D_real: 1.285 D_fake: 0.517 +(epoch: 196, iters: 5440, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.850 G_ID: 0.139 G_Rec: 0.341 D_GP: 0.057 D_real: 0.727 D_fake: 0.752 +(epoch: 196, iters: 5840, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 1.166 G_ID: 0.192 G_Rec: 0.420 D_GP: 0.074 D_real: 0.659 D_fake: 0.616 +(epoch: 196, iters: 6240, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.593 G_ID: 0.158 G_Rec: 0.289 D_GP: 0.018 D_real: 1.159 D_fake: 0.787 +(epoch: 196, iters: 6640, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.857 G_ID: 0.173 G_Rec: 0.431 D_GP: 0.023 D_real: 0.989 D_fake: 0.726 +(epoch: 196, iters: 7040, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 1.115 G_ID: 0.130 G_Rec: 0.378 D_GP: 0.054 D_real: 1.326 D_fake: 0.875 +(epoch: 196, iters: 7440, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.805 G_ID: 0.184 G_Rec: 0.404 D_GP: 0.022 D_real: 0.936 D_fake: 0.879 +(epoch: 196, iters: 7840, time: 0.064) G_GAN: -0.015 G_GAN_Feat: 0.700 G_ID: 0.139 G_Rec: 0.304 D_GP: 0.037 D_real: 0.829 D_fake: 1.016 +(epoch: 196, iters: 8240, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.808 G_ID: 0.190 G_Rec: 0.358 D_GP: 0.035 D_real: 1.111 D_fake: 0.688 +(epoch: 197, iters: 32, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.973 G_ID: 0.152 G_Rec: 0.323 D_GP: 0.126 D_real: 1.055 D_fake: 0.755 +(epoch: 197, iters: 432, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 0.844 G_ID: 0.221 G_Rec: 0.374 D_GP: 0.023 D_real: 1.237 D_fake: 0.534 +(epoch: 197, iters: 832, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.814 G_ID: 0.151 G_Rec: 0.300 D_GP: 0.071 D_real: 0.671 D_fake: 0.805 +(epoch: 197, iters: 1232, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.824 G_ID: 0.220 G_Rec: 0.410 D_GP: 0.024 D_real: 1.064 D_fake: 0.673 +(epoch: 197, iters: 1632, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.668 G_ID: 0.116 G_Rec: 0.333 D_GP: 0.020 D_real: 0.973 D_fake: 0.938 +(epoch: 197, iters: 2032, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.863 G_ID: 0.190 G_Rec: 0.388 D_GP: 0.046 D_real: 0.882 D_fake: 0.764 +(epoch: 197, iters: 2432, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.827 G_ID: 0.146 G_Rec: 0.332 D_GP: 0.145 D_real: 0.507 D_fake: 0.977 +(epoch: 197, iters: 2832, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.968 G_ID: 0.185 G_Rec: 0.427 D_GP: 0.033 D_real: 0.836 D_fake: 0.701 +(epoch: 197, iters: 3232, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.655 G_ID: 0.129 G_Rec: 0.354 D_GP: 0.028 D_real: 1.331 D_fake: 0.563 +(epoch: 197, iters: 3632, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 1.025 G_ID: 0.206 G_Rec: 0.414 D_GP: 0.058 D_real: 0.803 D_fake: 0.402 +(epoch: 197, iters: 4032, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.744 G_ID: 0.146 G_Rec: 0.308 D_GP: 0.025 D_real: 1.326 D_fake: 0.582 +(epoch: 197, iters: 4432, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.924 G_ID: 0.184 G_Rec: 0.413 D_GP: 0.027 D_real: 0.996 D_fake: 0.684 +(epoch: 197, iters: 4832, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.712 G_ID: 0.129 G_Rec: 0.312 D_GP: 0.027 D_real: 1.289 D_fake: 0.617 +(epoch: 197, iters: 5232, time: 0.064) G_GAN: 0.691 G_GAN_Feat: 0.862 G_ID: 0.203 G_Rec: 0.380 D_GP: 0.027 D_real: 1.338 D_fake: 0.347 +(epoch: 197, iters: 5632, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.677 G_ID: 0.168 G_Rec: 0.350 D_GP: 0.030 D_real: 1.035 D_fake: 0.844 +(epoch: 197, iters: 6032, time: 0.064) G_GAN: 0.700 G_GAN_Feat: 0.786 G_ID: 0.179 G_Rec: 0.381 D_GP: 0.025 D_real: 1.423 D_fake: 0.349 +(epoch: 197, iters: 6432, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.674 G_ID: 0.129 G_Rec: 0.314 D_GP: 0.020 D_real: 1.254 D_fake: 0.658 +(epoch: 197, iters: 6832, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.787 G_ID: 0.211 G_Rec: 0.420 D_GP: 0.033 D_real: 0.888 D_fake: 0.866 +(epoch: 197, iters: 7232, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.682 G_ID: 0.136 G_Rec: 0.305 D_GP: 0.043 D_real: 1.130 D_fake: 0.743 +(epoch: 197, iters: 7632, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.959 G_ID: 0.187 G_Rec: 0.402 D_GP: 0.159 D_real: 0.802 D_fake: 0.598 +(epoch: 197, iters: 8032, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.810 G_ID: 0.152 G_Rec: 0.321 D_GP: 0.043 D_real: 0.793 D_fake: 0.879 +(epoch: 197, iters: 8432, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.962 G_ID: 0.238 G_Rec: 0.389 D_GP: 0.043 D_real: 0.778 D_fake: 0.605 +(epoch: 198, iters: 224, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.866 G_ID: 0.128 G_Rec: 0.343 D_GP: 0.052 D_real: 0.672 D_fake: 0.921 +(epoch: 198, iters: 624, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.873 G_ID: 0.184 G_Rec: 0.393 D_GP: 0.035 D_real: 0.705 D_fake: 0.964 +(epoch: 198, iters: 1024, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.953 G_ID: 0.124 G_Rec: 0.365 D_GP: 0.980 D_real: 0.376 D_fake: 0.885 +(epoch: 198, iters: 1424, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 0.777 G_ID: 0.170 G_Rec: 0.363 D_GP: 0.021 D_real: 1.330 D_fake: 0.460 +(epoch: 198, iters: 1824, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.633 G_ID: 0.121 G_Rec: 0.278 D_GP: 0.028 D_real: 1.307 D_fake: 0.601 +(epoch: 198, iters: 2224, time: 0.064) G_GAN: 0.444 G_GAN_Feat: 0.824 G_ID: 0.182 G_Rec: 0.369 D_GP: 0.035 D_real: 1.177 D_fake: 0.557 +(epoch: 198, iters: 2624, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.765 G_ID: 0.144 G_Rec: 0.304 D_GP: 0.098 D_real: 1.102 D_fake: 0.586 +(epoch: 198, iters: 3024, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 1.047 G_ID: 0.176 G_Rec: 0.426 D_GP: 0.162 D_real: 0.896 D_fake: 0.634 +(epoch: 198, iters: 3424, time: 0.064) G_GAN: -0.074 G_GAN_Feat: 0.803 G_ID: 0.121 G_Rec: 0.377 D_GP: 0.024 D_real: 0.800 D_fake: 1.074 +(epoch: 198, iters: 3824, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 1.107 G_ID: 0.176 G_Rec: 0.391 D_GP: 0.246 D_real: 0.369 D_fake: 0.576 +(epoch: 198, iters: 4224, time: 0.064) G_GAN: 0.133 G_GAN_Feat: 0.746 G_ID: 0.132 G_Rec: 0.314 D_GP: 0.029 D_real: 1.013 D_fake: 0.867 +(epoch: 198, iters: 4624, time: 0.064) G_GAN: 0.563 G_GAN_Feat: 0.974 G_ID: 0.207 G_Rec: 0.408 D_GP: 0.031 D_real: 1.174 D_fake: 0.443 +(epoch: 198, iters: 5024, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.671 G_ID: 0.130 G_Rec: 0.298 D_GP: 0.021 D_real: 1.173 D_fake: 0.761 +(epoch: 198, iters: 5424, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.895 G_ID: 0.199 G_Rec: 0.437 D_GP: 0.080 D_real: 0.829 D_fake: 0.750 +(epoch: 198, iters: 5824, time: 0.064) G_GAN: 0.060 G_GAN_Feat: 0.742 G_ID: 0.115 G_Rec: 0.305 D_GP: 0.052 D_real: 0.847 D_fake: 0.940 +(epoch: 198, iters: 6224, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.807 G_ID: 0.161 G_Rec: 0.375 D_GP: 0.023 D_real: 1.242 D_fake: 0.489 +(epoch: 198, iters: 6624, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.683 G_ID: 0.137 G_Rec: 0.305 D_GP: 0.023 D_real: 1.110 D_fake: 0.782 +(epoch: 198, iters: 7024, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.869 G_ID: 0.191 G_Rec: 0.388 D_GP: 0.024 D_real: 1.021 D_fake: 0.733 +(epoch: 198, iters: 7424, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.847 G_ID: 0.140 G_Rec: 0.297 D_GP: 0.050 D_real: 0.775 D_fake: 0.684 +(epoch: 198, iters: 7824, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.768 G_ID: 0.190 G_Rec: 0.401 D_GP: 0.022 D_real: 1.049 D_fake: 0.735 +(epoch: 198, iters: 8224, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.586 G_ID: 0.134 G_Rec: 0.313 D_GP: 0.021 D_real: 1.250 D_fake: 0.755 +(epoch: 199, iters: 16, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.712 G_ID: 0.163 G_Rec: 0.360 D_GP: 0.022 D_real: 1.104 D_fake: 0.749 +(epoch: 199, iters: 416, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.619 G_ID: 0.198 G_Rec: 0.307 D_GP: 0.025 D_real: 1.056 D_fake: 0.942 +(epoch: 199, iters: 816, time: 0.064) G_GAN: 0.608 G_GAN_Feat: 0.807 G_ID: 0.166 G_Rec: 0.416 D_GP: 0.021 D_real: 1.313 D_fake: 0.423 +(epoch: 199, iters: 1216, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.747 G_ID: 0.117 G_Rec: 0.288 D_GP: 0.030 D_real: 1.139 D_fake: 0.779 +(epoch: 199, iters: 1616, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.884 G_ID: 0.188 G_Rec: 0.433 D_GP: 0.029 D_real: 0.988 D_fake: 0.747 +(epoch: 199, iters: 2016, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.788 G_ID: 0.162 G_Rec: 0.307 D_GP: 0.049 D_real: 0.703 D_fake: 0.854 +(epoch: 199, iters: 2416, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 0.714 G_ID: 0.195 G_Rec: 0.364 D_GP: 0.021 D_real: 1.217 D_fake: 0.539 +(epoch: 199, iters: 2816, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.628 G_ID: 0.136 G_Rec: 0.297 D_GP: 0.030 D_real: 1.049 D_fake: 0.947 +(epoch: 199, iters: 3216, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.793 G_ID: 0.188 G_Rec: 0.400 D_GP: 0.027 D_real: 1.076 D_fake: 0.718 +(epoch: 199, iters: 3616, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.767 G_ID: 0.143 G_Rec: 0.349 D_GP: 0.040 D_real: 1.246 D_fake: 0.568 +(epoch: 199, iters: 4016, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.855 G_ID: 0.182 G_Rec: 0.385 D_GP: 0.029 D_real: 0.842 D_fake: 0.833 +(epoch: 199, iters: 4416, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.717 G_ID: 0.136 G_Rec: 0.311 D_GP: 0.044 D_real: 1.147 D_fake: 0.720 +(epoch: 199, iters: 4816, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 1.011 G_ID: 0.185 G_Rec: 0.422 D_GP: 0.052 D_real: 0.899 D_fake: 0.527 +(epoch: 199, iters: 5216, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.730 G_ID: 0.128 G_Rec: 0.307 D_GP: 0.028 D_real: 1.075 D_fake: 0.751 +(epoch: 199, iters: 5616, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.830 G_ID: 0.180 G_Rec: 0.400 D_GP: 0.020 D_real: 1.123 D_fake: 0.603 +(epoch: 199, iters: 6016, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.602 G_ID: 0.133 G_Rec: 0.278 D_GP: 0.022 D_real: 1.254 D_fake: 0.688 +(epoch: 199, iters: 6416, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.809 G_ID: 0.193 G_Rec: 0.396 D_GP: 0.036 D_real: 1.038 D_fake: 0.670 +(epoch: 199, iters: 6816, time: 0.064) G_GAN: 0.133 G_GAN_Feat: 0.749 G_ID: 0.138 G_Rec: 0.305 D_GP: 0.054 D_real: 1.007 D_fake: 0.867 +(epoch: 199, iters: 7216, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 0.837 G_ID: 0.205 G_Rec: 0.388 D_GP: 0.020 D_real: 1.287 D_fake: 0.456 +(epoch: 199, iters: 7616, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.701 G_ID: 0.128 G_Rec: 0.308 D_GP: 0.038 D_real: 1.019 D_fake: 0.882 +(epoch: 199, iters: 8016, time: 0.064) G_GAN: 0.615 G_GAN_Feat: 1.115 G_ID: 0.191 G_Rec: 0.451 D_GP: 0.090 D_real: 0.596 D_fake: 0.407 +(epoch: 199, iters: 8416, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.685 G_ID: 0.140 G_Rec: 0.313 D_GP: 0.022 D_real: 1.228 D_fake: 0.709 +(epoch: 200, iters: 208, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 1.043 G_ID: 0.197 G_Rec: 0.411 D_GP: 0.054 D_real: 0.665 D_fake: 0.438 +(epoch: 200, iters: 608, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.598 G_ID: 0.137 G_Rec: 0.277 D_GP: 0.021 D_real: 1.199 D_fake: 0.764 +(epoch: 200, iters: 1008, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.796 G_ID: 0.209 G_Rec: 0.388 D_GP: 0.027 D_real: 1.000 D_fake: 0.723 +(epoch: 200, iters: 1408, time: 0.064) G_GAN: 0.014 G_GAN_Feat: 0.772 G_ID: 0.142 G_Rec: 0.338 D_GP: 0.053 D_real: 0.666 D_fake: 0.987 +(epoch: 200, iters: 1808, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.997 G_ID: 0.189 G_Rec: 0.450 D_GP: 0.083 D_real: 0.664 D_fake: 0.575 +(epoch: 200, iters: 2208, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.856 G_ID: 0.145 G_Rec: 0.312 D_GP: 0.061 D_real: 0.683 D_fake: 0.697 +(epoch: 200, iters: 2608, time: 0.064) G_GAN: 0.444 G_GAN_Feat: 0.866 G_ID: 0.183 G_Rec: 0.398 D_GP: 0.026 D_real: 1.174 D_fake: 0.567 +(epoch: 200, iters: 3008, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.693 G_ID: 0.126 G_Rec: 0.308 D_GP: 0.024 D_real: 0.956 D_fake: 0.918 +(epoch: 200, iters: 3408, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.992 G_ID: 0.183 G_Rec: 0.435 D_GP: 0.072 D_real: 0.699 D_fake: 0.727 +(epoch: 200, iters: 3808, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.734 G_ID: 0.148 G_Rec: 0.291 D_GP: 0.025 D_real: 1.273 D_fake: 0.595 +(epoch: 200, iters: 4208, time: 0.064) G_GAN: 0.550 G_GAN_Feat: 0.900 G_ID: 0.188 G_Rec: 0.421 D_GP: 0.030 D_real: 1.239 D_fake: 0.465 +(epoch: 200, iters: 4608, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 1.008 G_ID: 0.149 G_Rec: 0.322 D_GP: 0.214 D_real: 0.368 D_fake: 0.732 +(epoch: 200, iters: 5008, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.969 G_ID: 0.193 G_Rec: 0.485 D_GP: 0.025 D_real: 1.216 D_fake: 0.453 +(epoch: 200, iters: 5408, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.648 G_ID: 0.128 G_Rec: 0.310 D_GP: 0.026 D_real: 1.082 D_fake: 0.888 +(epoch: 200, iters: 5808, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.784 G_ID: 0.189 G_Rec: 0.376 D_GP: 0.029 D_real: 1.253 D_fake: 0.567 +(epoch: 200, iters: 6208, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.718 G_ID: 0.125 G_Rec: 0.323 D_GP: 0.038 D_real: 0.946 D_fake: 0.783 +(epoch: 200, iters: 6608, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 1.081 G_ID: 0.190 G_Rec: 0.442 D_GP: 0.061 D_real: 0.627 D_fake: 0.594 +(epoch: 200, iters: 7008, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.777 G_ID: 0.152 G_Rec: 0.339 D_GP: 0.031 D_real: 0.959 D_fake: 0.947 +(epoch: 200, iters: 7408, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.910 G_ID: 0.166 G_Rec: 0.411 D_GP: 0.037 D_real: 1.197 D_fake: 0.493 +(epoch: 200, iters: 7808, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.676 G_ID: 0.144 G_Rec: 0.283 D_GP: 0.025 D_real: 1.123 D_fake: 0.741 +(epoch: 200, iters: 8208, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.860 G_ID: 0.184 G_Rec: 0.391 D_GP: 0.023 D_real: 1.351 D_fake: 0.394 +(epoch: 200, iters: 8608, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.798 G_ID: 0.133 G_Rec: 0.322 D_GP: 0.026 D_real: 1.020 D_fake: 0.697 +(epoch: 201, iters: 400, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.898 G_ID: 0.174 G_Rec: 0.368 D_GP: 0.033 D_real: 0.942 D_fake: 0.575 +(epoch: 201, iters: 800, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.745 G_ID: 0.141 G_Rec: 0.308 D_GP: 0.029 D_real: 1.020 D_fake: 0.725 +(epoch: 201, iters: 1200, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 1.131 G_ID: 0.170 G_Rec: 0.414 D_GP: 0.209 D_real: 0.425 D_fake: 0.489 +(epoch: 201, iters: 1600, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.775 G_ID: 0.132 G_Rec: 0.301 D_GP: 0.027 D_real: 1.152 D_fake: 0.713 +(epoch: 201, iters: 2000, time: 0.064) G_GAN: 0.602 G_GAN_Feat: 0.732 G_ID: 0.166 G_Rec: 0.387 D_GP: 0.020 D_real: 1.479 D_fake: 0.423 +(epoch: 201, iters: 2400, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.649 G_ID: 0.147 G_Rec: 0.306 D_GP: 0.020 D_real: 1.026 D_fake: 0.882 +(epoch: 201, iters: 2800, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.796 G_ID: 0.194 G_Rec: 0.391 D_GP: 0.027 D_real: 1.161 D_fake: 0.556 +(epoch: 201, iters: 3200, time: 0.064) G_GAN: 0.089 G_GAN_Feat: 0.625 G_ID: 0.138 G_Rec: 0.281 D_GP: 0.025 D_real: 1.016 D_fake: 0.914 +(epoch: 201, iters: 3600, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.970 G_ID: 0.165 G_Rec: 0.402 D_GP: 0.085 D_real: 0.571 D_fake: 0.756 +(epoch: 201, iters: 4000, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.649 G_ID: 0.142 G_Rec: 0.268 D_GP: 0.028 D_real: 1.034 D_fake: 0.856 +(epoch: 201, iters: 4400, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.977 G_ID: 0.199 G_Rec: 0.449 D_GP: 0.033 D_real: 0.995 D_fake: 0.535 +(epoch: 201, iters: 4800, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.690 G_ID: 0.136 G_Rec: 0.296 D_GP: 0.026 D_real: 1.061 D_fake: 0.921 +(epoch: 201, iters: 5200, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.950 G_ID: 0.179 G_Rec: 0.406 D_GP: 0.027 D_real: 1.081 D_fake: 0.486 +(epoch: 201, iters: 5600, time: 0.064) G_GAN: 0.075 G_GAN_Feat: 0.920 G_ID: 0.141 G_Rec: 0.321 D_GP: 0.104 D_real: 0.559 D_fake: 0.925 +(epoch: 201, iters: 6000, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 0.813 G_ID: 0.166 G_Rec: 0.369 D_GP: 0.020 D_real: 1.220 D_fake: 0.591 +(epoch: 201, iters: 6400, time: 0.064) G_GAN: -0.013 G_GAN_Feat: 0.845 G_ID: 0.136 G_Rec: 0.302 D_GP: 0.116 D_real: 0.501 D_fake: 1.013 +(epoch: 201, iters: 6800, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.863 G_ID: 0.172 G_Rec: 0.405 D_GP: 0.023 D_real: 1.341 D_fake: 0.430 +(epoch: 201, iters: 7200, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.706 G_ID: 0.133 G_Rec: 0.293 D_GP: 0.025 D_real: 1.230 D_fake: 0.632 +(epoch: 201, iters: 7600, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.857 G_ID: 0.193 G_Rec: 0.397 D_GP: 0.028 D_real: 1.075 D_fake: 0.550 +(epoch: 201, iters: 8000, time: 0.064) G_GAN: 0.635 G_GAN_Feat: 0.964 G_ID: 0.126 G_Rec: 0.325 D_GP: 0.037 D_real: 1.354 D_fake: 0.528 +(epoch: 201, iters: 8400, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.789 G_ID: 0.195 G_Rec: 0.401 D_GP: 0.026 D_real: 1.257 D_fake: 0.525 +(epoch: 202, iters: 192, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.551 G_ID: 0.139 G_Rec: 0.345 D_GP: 0.017 D_real: 1.048 D_fake: 0.903 +(epoch: 202, iters: 592, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.679 G_ID: 0.176 G_Rec: 0.337 D_GP: 0.023 D_real: 1.172 D_fake: 0.672 +(epoch: 202, iters: 992, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.602 G_ID: 0.154 G_Rec: 0.307 D_GP: 0.024 D_real: 1.023 D_fake: 0.845 +(epoch: 202, iters: 1392, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.773 G_ID: 0.181 G_Rec: 0.382 D_GP: 0.026 D_real: 0.926 D_fake: 0.840 +(epoch: 202, iters: 1792, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.617 G_ID: 0.126 G_Rec: 0.289 D_GP: 0.024 D_real: 1.005 D_fake: 0.859 +(epoch: 202, iters: 2192, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.927 G_ID: 0.178 G_Rec: 0.429 D_GP: 0.116 D_real: 0.768 D_fake: 0.654 +(epoch: 202, iters: 2592, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.637 G_ID: 0.133 G_Rec: 0.337 D_GP: 0.020 D_real: 1.242 D_fake: 0.704 +(epoch: 202, iters: 2992, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.915 G_ID: 0.183 G_Rec: 0.474 D_GP: 0.026 D_real: 1.348 D_fake: 0.480 +(epoch: 202, iters: 3392, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.593 G_ID: 0.137 G_Rec: 0.304 D_GP: 0.021 D_real: 1.242 D_fake: 0.717 +(epoch: 202, iters: 3792, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.842 G_ID: 0.189 G_Rec: 0.380 D_GP: 0.044 D_real: 1.040 D_fake: 0.550 +(epoch: 202, iters: 4192, time: 0.064) G_GAN: -0.026 G_GAN_Feat: 0.750 G_ID: 0.150 G_Rec: 0.312 D_GP: 0.069 D_real: 0.667 D_fake: 1.026 +(epoch: 202, iters: 4592, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 1.014 G_ID: 0.183 G_Rec: 0.443 D_GP: 0.517 D_real: 0.659 D_fake: 0.592 +(epoch: 202, iters: 4992, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.745 G_ID: 0.139 G_Rec: 0.335 D_GP: 0.036 D_real: 0.963 D_fake: 0.822 +(epoch: 202, iters: 5392, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.866 G_ID: 0.177 G_Rec: 0.399 D_GP: 0.023 D_real: 1.024 D_fake: 0.672 +(epoch: 202, iters: 5792, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.885 G_ID: 0.118 G_Rec: 0.324 D_GP: 0.453 D_real: 0.587 D_fake: 0.684 +(epoch: 202, iters: 6192, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.859 G_ID: 0.192 G_Rec: 0.389 D_GP: 0.034 D_real: 1.080 D_fake: 0.596 +(epoch: 202, iters: 6592, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.703 G_ID: 0.127 G_Rec: 0.286 D_GP: 0.040 D_real: 1.199 D_fake: 0.581 +(epoch: 202, iters: 6992, time: 0.064) G_GAN: -0.206 G_GAN_Feat: 1.055 G_ID: 0.224 G_Rec: 0.423 D_GP: 0.113 D_real: 0.079 D_fake: 1.206 +(epoch: 202, iters: 7392, time: 0.064) G_GAN: 0.067 G_GAN_Feat: 0.848 G_ID: 0.170 G_Rec: 0.308 D_GP: 0.030 D_real: 0.723 D_fake: 0.933 +(epoch: 202, iters: 7792, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.840 G_ID: 0.172 G_Rec: 0.368 D_GP: 0.024 D_real: 1.134 D_fake: 0.628 +(epoch: 202, iters: 8192, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.763 G_ID: 0.137 G_Rec: 0.316 D_GP: 0.045 D_real: 0.908 D_fake: 0.760 +(epoch: 202, iters: 8592, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 1.020 G_ID: 0.174 G_Rec: 0.399 D_GP: 0.052 D_real: 0.968 D_fake: 0.727 +(epoch: 203, iters: 384, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.914 G_ID: 0.131 G_Rec: 0.365 D_GP: 0.033 D_real: 0.498 D_fake: 0.810 +(epoch: 203, iters: 784, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.923 G_ID: 0.153 G_Rec: 0.367 D_GP: 0.072 D_real: 0.959 D_fake: 0.498 +(epoch: 203, iters: 1184, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.759 G_ID: 0.138 G_Rec: 0.328 D_GP: 0.031 D_real: 1.097 D_fake: 0.733 +(epoch: 203, iters: 1584, time: 0.064) G_GAN: 0.693 G_GAN_Feat: 0.821 G_ID: 0.169 G_Rec: 0.370 D_GP: 0.022 D_real: 1.442 D_fake: 0.322 +(epoch: 203, iters: 1984, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.647 G_ID: 0.138 G_Rec: 0.280 D_GP: 0.024 D_real: 1.229 D_fake: 0.660 +(epoch: 203, iters: 2384, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.861 G_ID: 0.183 G_Rec: 0.387 D_GP: 0.022 D_real: 1.161 D_fake: 0.564 +(epoch: 203, iters: 2784, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.850 G_ID: 0.119 G_Rec: 0.327 D_GP: 0.077 D_real: 0.649 D_fake: 0.659 +(epoch: 203, iters: 3184, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 1.064 G_ID: 0.216 G_Rec: 0.419 D_GP: 0.048 D_real: 0.579 D_fake: 0.533 +(epoch: 203, iters: 3584, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.699 G_ID: 0.116 G_Rec: 0.321 D_GP: 0.021 D_real: 1.356 D_fake: 0.550 +(epoch: 203, iters: 3984, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.914 G_ID: 0.204 G_Rec: 0.436 D_GP: 0.033 D_real: 0.904 D_fake: 0.769 +(epoch: 203, iters: 4384, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.640 G_ID: 0.127 G_Rec: 0.361 D_GP: 0.021 D_real: 1.214 D_fake: 0.710 +(epoch: 203, iters: 4784, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.924 G_ID: 0.196 G_Rec: 0.445 D_GP: 0.052 D_real: 0.802 D_fake: 0.650 +(epoch: 203, iters: 5184, time: 0.064) G_GAN: 0.045 G_GAN_Feat: 0.778 G_ID: 0.133 G_Rec: 0.291 D_GP: 0.065 D_real: 0.618 D_fake: 0.956 +(epoch: 203, iters: 5584, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 1.057 G_ID: 0.197 G_Rec: 0.475 D_GP: 0.046 D_real: 0.753 D_fake: 0.526 +(epoch: 203, iters: 5984, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.576 G_ID: 0.141 G_Rec: 0.314 D_GP: 0.018 D_real: 1.503 D_fake: 0.513 +(epoch: 203, iters: 6384, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.814 G_ID: 0.221 G_Rec: 0.447 D_GP: 0.022 D_real: 1.048 D_fake: 0.636 +(epoch: 203, iters: 6784, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.623 G_ID: 0.136 G_Rec: 0.312 D_GP: 0.022 D_real: 1.182 D_fake: 0.777 +(epoch: 203, iters: 7184, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.697 G_ID: 0.176 G_Rec: 0.403 D_GP: 0.022 D_real: 1.184 D_fake: 0.551 +(epoch: 203, iters: 7584, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.624 G_ID: 0.194 G_Rec: 0.319 D_GP: 0.027 D_real: 0.939 D_fake: 0.988 +(epoch: 203, iters: 7984, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.788 G_ID: 0.192 G_Rec: 0.384 D_GP: 0.022 D_real: 1.114 D_fake: 0.728 +(epoch: 203, iters: 8384, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.552 G_ID: 0.138 G_Rec: 0.285 D_GP: 0.020 D_real: 1.219 D_fake: 0.728 +(epoch: 204, iters: 176, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.803 G_ID: 0.183 G_Rec: 0.399 D_GP: 0.026 D_real: 1.202 D_fake: 0.491 +(epoch: 204, iters: 576, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.647 G_ID: 0.124 G_Rec: 0.306 D_GP: 0.045 D_real: 1.149 D_fake: 0.760 +(epoch: 204, iters: 976, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.828 G_ID: 0.221 G_Rec: 0.402 D_GP: 0.023 D_real: 0.999 D_fake: 0.766 +(epoch: 204, iters: 1376, time: 0.064) G_GAN: 0.124 G_GAN_Feat: 0.661 G_ID: 0.137 G_Rec: 0.313 D_GP: 0.042 D_real: 1.022 D_fake: 0.876 +(epoch: 204, iters: 1776, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.783 G_ID: 0.239 G_Rec: 0.404 D_GP: 0.028 D_real: 1.106 D_fake: 0.671 +(epoch: 204, iters: 2176, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.705 G_ID: 0.144 G_Rec: 0.309 D_GP: 0.064 D_real: 1.002 D_fake: 0.890 +(epoch: 204, iters: 2576, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.865 G_ID: 0.179 G_Rec: 0.405 D_GP: 0.035 D_real: 1.060 D_fake: 0.662 +(epoch: 204, iters: 2976, time: 0.064) G_GAN: -0.006 G_GAN_Feat: 0.838 G_ID: 0.124 G_Rec: 0.364 D_GP: 0.108 D_real: 0.773 D_fake: 1.007 +(epoch: 204, iters: 3376, time: 0.064) G_GAN: 0.656 G_GAN_Feat: 0.855 G_ID: 0.168 G_Rec: 0.418 D_GP: 0.030 D_real: 1.312 D_fake: 0.373 +(epoch: 204, iters: 3776, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.668 G_ID: 0.144 G_Rec: 0.296 D_GP: 0.021 D_real: 1.192 D_fake: 0.737 +(epoch: 204, iters: 4176, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.743 G_ID: 0.166 G_Rec: 0.358 D_GP: 0.026 D_real: 1.068 D_fake: 0.656 +(epoch: 204, iters: 4576, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.689 G_ID: 0.126 G_Rec: 0.293 D_GP: 0.027 D_real: 1.165 D_fake: 0.636 +(epoch: 204, iters: 4976, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.727 G_ID: 0.181 G_Rec: 0.371 D_GP: 0.023 D_real: 1.170 D_fake: 0.612 +(epoch: 204, iters: 5376, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.639 G_ID: 0.122 G_Rec: 0.287 D_GP: 0.025 D_real: 1.289 D_fake: 0.631 +(epoch: 204, iters: 5776, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.799 G_ID: 0.212 G_Rec: 0.412 D_GP: 0.025 D_real: 1.150 D_fake: 0.658 +(epoch: 204, iters: 6176, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.734 G_ID: 0.141 G_Rec: 0.325 D_GP: 0.033 D_real: 0.883 D_fake: 0.927 +(epoch: 204, iters: 6576, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 0.809 G_ID: 0.194 G_Rec: 0.395 D_GP: 0.023 D_real: 1.271 D_fake: 0.530 +(epoch: 204, iters: 6976, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.612 G_ID: 0.145 G_Rec: 0.269 D_GP: 0.025 D_real: 1.408 D_fake: 0.509 +(epoch: 204, iters: 7376, time: 0.064) G_GAN: 0.694 G_GAN_Feat: 0.911 G_ID: 0.173 G_Rec: 0.465 D_GP: 0.028 D_real: 1.416 D_fake: 0.329 +(epoch: 204, iters: 7776, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.802 G_ID: 0.143 G_Rec: 0.316 D_GP: 0.039 D_real: 1.153 D_fake: 0.564 +(epoch: 204, iters: 8176, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 0.817 G_ID: 0.160 G_Rec: 0.395 D_GP: 0.020 D_real: 1.343 D_fake: 0.431 +(epoch: 204, iters: 8576, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.657 G_ID: 0.141 G_Rec: 0.308 D_GP: 0.029 D_real: 1.057 D_fake: 0.798 +(epoch: 205, iters: 368, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.954 G_ID: 0.154 G_Rec: 0.459 D_GP: 0.041 D_real: 0.876 D_fake: 0.624 +(epoch: 205, iters: 768, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.764 G_ID: 0.123 G_Rec: 0.348 D_GP: 0.033 D_real: 1.044 D_fake: 0.730 +(epoch: 205, iters: 1168, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 1.158 G_ID: 0.173 G_Rec: 0.462 D_GP: 0.134 D_real: 1.037 D_fake: 0.471 +(epoch: 205, iters: 1568, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.710 G_ID: 0.134 G_Rec: 0.296 D_GP: 0.029 D_real: 1.250 D_fake: 0.653 +(epoch: 205, iters: 1968, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.951 G_ID: 0.154 G_Rec: 0.415 D_GP: 0.044 D_real: 0.848 D_fake: 0.591 +(epoch: 205, iters: 2368, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.725 G_ID: 0.125 G_Rec: 0.317 D_GP: 0.037 D_real: 1.121 D_fake: 0.681 +(epoch: 205, iters: 2768, time: 0.064) G_GAN: 0.865 G_GAN_Feat: 0.880 G_ID: 0.175 G_Rec: 0.375 D_GP: 0.029 D_real: 1.547 D_fake: 0.206 +(epoch: 205, iters: 3168, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.771 G_ID: 0.124 G_Rec: 0.320 D_GP: 0.035 D_real: 1.136 D_fake: 0.662 +(epoch: 205, iters: 3568, time: 0.064) G_GAN: 0.864 G_GAN_Feat: 1.269 G_ID: 0.172 G_Rec: 0.440 D_GP: 0.133 D_real: 0.624 D_fake: 0.254 +(epoch: 205, iters: 3968, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.737 G_ID: 0.143 G_Rec: 0.327 D_GP: 0.023 D_real: 1.325 D_fake: 0.590 +(epoch: 205, iters: 4368, time: 0.064) G_GAN: 0.444 G_GAN_Feat: 1.039 G_ID: 0.188 G_Rec: 0.402 D_GP: 0.028 D_real: 0.778 D_fake: 0.561 +(epoch: 205, iters: 4768, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.617 G_ID: 0.117 G_Rec: 0.288 D_GP: 0.023 D_real: 1.318 D_fake: 0.619 +(epoch: 205, iters: 5168, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.892 G_ID: 0.181 G_Rec: 0.456 D_GP: 0.035 D_real: 1.023 D_fake: 0.617 +(epoch: 205, iters: 5568, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.676 G_ID: 0.131 G_Rec: 0.307 D_GP: 0.033 D_real: 1.099 D_fake: 0.718 +(epoch: 205, iters: 5968, time: 0.064) G_GAN: 0.396 G_GAN_Feat: 0.856 G_ID: 0.167 G_Rec: 0.445 D_GP: 0.026 D_real: 1.003 D_fake: 0.608 +(epoch: 205, iters: 6368, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.608 G_ID: 0.149 G_Rec: 0.270 D_GP: 0.023 D_real: 1.137 D_fake: 0.797 +(epoch: 205, iters: 6768, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.003 G_ID: 0.175 G_Rec: 0.406 D_GP: 0.048 D_real: 0.760 D_fake: 0.652 +(epoch: 205, iters: 7168, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.696 G_ID: 0.144 G_Rec: 0.280 D_GP: 0.022 D_real: 1.067 D_fake: 0.768 +(epoch: 205, iters: 7568, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 1.145 G_ID: 0.173 G_Rec: 0.439 D_GP: 0.106 D_real: 0.507 D_fake: 0.526 +(epoch: 205, iters: 7968, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.743 G_ID: 0.148 G_Rec: 0.317 D_GP: 0.020 D_real: 0.997 D_fake: 0.881 +(epoch: 205, iters: 8368, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 1.257 G_ID: 0.207 G_Rec: 0.499 D_GP: 0.362 D_real: 0.349 D_fake: 0.502 +(epoch: 206, iters: 160, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.752 G_ID: 0.137 G_Rec: 0.374 D_GP: 0.035 D_real: 0.779 D_fake: 0.902 +(epoch: 206, iters: 560, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.921 G_ID: 0.209 G_Rec: 0.427 D_GP: 0.024 D_real: 1.107 D_fake: 0.528 +(epoch: 206, iters: 960, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.673 G_ID: 0.172 G_Rec: 0.279 D_GP: 0.029 D_real: 1.174 D_fake: 0.856 +(epoch: 206, iters: 1360, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 0.933 G_ID: 0.185 G_Rec: 0.365 D_GP: 0.030 D_real: 0.996 D_fake: 0.540 +(epoch: 206, iters: 1760, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.764 G_ID: 0.131 G_Rec: 0.324 D_GP: 0.039 D_real: 0.962 D_fake: 0.781 +(epoch: 206, iters: 2160, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.834 G_ID: 0.202 G_Rec: 0.415 D_GP: 0.027 D_real: 0.960 D_fake: 0.756 +(epoch: 206, iters: 2560, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.704 G_ID: 0.172 G_Rec: 0.283 D_GP: 0.027 D_real: 1.186 D_fake: 0.679 +(epoch: 206, iters: 2960, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.785 G_ID: 0.182 G_Rec: 0.324 D_GP: 0.022 D_real: 1.022 D_fake: 0.726 +(epoch: 206, iters: 3360, time: 0.064) G_GAN: 0.650 G_GAN_Feat: 0.748 G_ID: 0.131 G_Rec: 0.333 D_GP: 0.024 D_real: 1.523 D_fake: 0.367 +(epoch: 206, iters: 3760, time: 0.064) G_GAN: 0.556 G_GAN_Feat: 0.852 G_ID: 0.155 G_Rec: 0.394 D_GP: 0.026 D_real: 1.312 D_fake: 0.448 +(epoch: 206, iters: 4160, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.577 G_ID: 0.171 G_Rec: 0.311 D_GP: 0.016 D_real: 1.214 D_fake: 0.729 +(epoch: 206, iters: 4560, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.696 G_ID: 0.187 G_Rec: 0.332 D_GP: 0.021 D_real: 1.214 D_fake: 0.594 +(epoch: 206, iters: 4960, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.667 G_ID: 0.147 G_Rec: 0.332 D_GP: 0.035 D_real: 1.052 D_fake: 0.799 +(epoch: 206, iters: 5360, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.880 G_ID: 0.171 G_Rec: 0.378 D_GP: 0.028 D_real: 1.137 D_fake: 0.582 +(epoch: 206, iters: 5760, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.873 G_ID: 0.165 G_Rec: 0.312 D_GP: 0.197 D_real: 0.482 D_fake: 0.818 +(epoch: 206, iters: 6160, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.907 G_ID: 0.166 G_Rec: 0.389 D_GP: 0.038 D_real: 1.030 D_fake: 0.537 +(epoch: 206, iters: 6560, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.640 G_ID: 0.123 G_Rec: 0.288 D_GP: 0.023 D_real: 1.384 D_fake: 0.547 +(epoch: 206, iters: 6960, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 1.217 G_ID: 0.170 G_Rec: 0.410 D_GP: 0.488 D_real: 0.362 D_fake: 0.664 +(epoch: 206, iters: 7360, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.723 G_ID: 0.139 G_Rec: 0.284 D_GP: 0.026 D_real: 1.040 D_fake: 0.854 +(epoch: 206, iters: 7760, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 0.935 G_ID: 0.169 G_Rec: 0.392 D_GP: 0.043 D_real: 1.218 D_fake: 0.481 +(epoch: 206, iters: 8160, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.893 G_ID: 0.165 G_Rec: 0.353 D_GP: 0.060 D_real: 0.652 D_fake: 0.756 +(epoch: 206, iters: 8560, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.662 G_ID: 0.190 G_Rec: 0.368 D_GP: 0.018 D_real: 1.103 D_fake: 0.740 +(epoch: 207, iters: 352, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.533 G_ID: 0.113 G_Rec: 0.289 D_GP: 0.016 D_real: 1.356 D_fake: 0.615 +(epoch: 207, iters: 752, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.721 G_ID: 0.177 G_Rec: 0.422 D_GP: 0.017 D_real: 1.181 D_fake: 0.659 +(epoch: 207, iters: 1152, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.529 G_ID: 0.175 G_Rec: 0.295 D_GP: 0.018 D_real: 1.017 D_fake: 0.959 +(epoch: 207, iters: 1552, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.750 G_ID: 0.162 G_Rec: 0.452 D_GP: 0.022 D_real: 1.067 D_fake: 0.647 +(epoch: 207, iters: 1952, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.561 G_ID: 0.149 G_Rec: 0.298 D_GP: 0.020 D_real: 1.011 D_fake: 0.938 +(epoch: 207, iters: 2352, time: 0.064) G_GAN: 0.040 G_GAN_Feat: 0.753 G_ID: 0.163 G_Rec: 0.391 D_GP: 0.024 D_real: 0.751 D_fake: 0.960 +(epoch: 207, iters: 2752, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.568 G_ID: 0.148 G_Rec: 0.315 D_GP: 0.020 D_real: 1.072 D_fake: 0.879 +(epoch: 207, iters: 3152, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.772 G_ID: 0.191 G_Rec: 0.375 D_GP: 0.028 D_real: 0.918 D_fake: 0.850 +(epoch: 207, iters: 3552, time: 0.064) G_GAN: -0.010 G_GAN_Feat: 0.667 G_ID: 0.128 G_Rec: 0.334 D_GP: 0.055 D_real: 0.905 D_fake: 1.010 +(epoch: 207, iters: 3952, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.936 G_ID: 0.186 G_Rec: 0.417 D_GP: 0.050 D_real: 0.720 D_fake: 0.756 +(epoch: 207, iters: 4352, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.621 G_ID: 0.145 G_Rec: 0.285 D_GP: 0.026 D_real: 1.088 D_fake: 0.855 +(epoch: 207, iters: 4752, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.887 G_ID: 0.165 G_Rec: 0.398 D_GP: 0.049 D_real: 1.079 D_fake: 0.539 +(epoch: 207, iters: 5152, time: 0.064) G_GAN: -0.056 G_GAN_Feat: 0.733 G_ID: 0.121 G_Rec: 0.313 D_GP: 0.083 D_real: 0.651 D_fake: 1.056 +(epoch: 207, iters: 5552, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.872 G_ID: 0.198 G_Rec: 0.393 D_GP: 0.030 D_real: 0.907 D_fake: 0.762 +(epoch: 207, iters: 5952, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.757 G_ID: 0.158 G_Rec: 0.334 D_GP: 0.030 D_real: 0.990 D_fake: 0.885 +(epoch: 207, iters: 6352, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.825 G_ID: 0.162 G_Rec: 0.379 D_GP: 0.024 D_real: 1.218 D_fake: 0.509 +(epoch: 207, iters: 6752, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.684 G_ID: 0.136 G_Rec: 0.317 D_GP: 0.030 D_real: 0.965 D_fake: 0.853 +(epoch: 207, iters: 7152, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 1.046 G_ID: 0.165 G_Rec: 0.457 D_GP: 0.107 D_real: 0.657 D_fake: 0.654 +(epoch: 207, iters: 7552, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.765 G_ID: 0.144 G_Rec: 0.324 D_GP: 0.041 D_real: 0.941 D_fake: 0.723 +(epoch: 207, iters: 7952, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.769 G_ID: 0.205 G_Rec: 0.386 D_GP: 0.023 D_real: 1.103 D_fake: 0.687 +(epoch: 207, iters: 8352, time: 0.064) G_GAN: 0.038 G_GAN_Feat: 0.779 G_ID: 0.152 G_Rec: 0.345 D_GP: 0.058 D_real: 0.823 D_fake: 0.962 +(epoch: 208, iters: 144, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 1.182 G_ID: 0.170 G_Rec: 0.414 D_GP: 0.101 D_real: 0.458 D_fake: 0.602 +(epoch: 208, iters: 544, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.742 G_ID: 0.139 G_Rec: 0.311 D_GP: 0.053 D_real: 0.832 D_fake: 0.787 +(epoch: 208, iters: 944, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.963 G_ID: 0.193 G_Rec: 0.446 D_GP: 0.060 D_real: 0.924 D_fake: 0.622 +(epoch: 208, iters: 1344, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.791 G_ID: 0.131 G_Rec: 0.352 D_GP: 0.034 D_real: 0.956 D_fake: 0.726 +(epoch: 208, iters: 1744, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.951 G_ID: 0.193 G_Rec: 0.402 D_GP: 0.044 D_real: 0.589 D_fake: 0.801 +(epoch: 208, iters: 2144, time: 0.064) G_GAN: -0.002 G_GAN_Feat: 0.654 G_ID: 0.154 G_Rec: 0.286 D_GP: 0.024 D_real: 0.962 D_fake: 1.002 +(epoch: 208, iters: 2544, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.897 G_ID: 0.188 G_Rec: 0.379 D_GP: 0.023 D_real: 0.961 D_fake: 0.730 +(epoch: 208, iters: 2944, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.856 G_ID: 0.119 G_Rec: 0.322 D_GP: 0.108 D_real: 1.013 D_fake: 0.714 +(epoch: 208, iters: 3344, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.818 G_ID: 0.189 G_Rec: 0.354 D_GP: 0.028 D_real: 1.155 D_fake: 0.572 +(epoch: 208, iters: 3744, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.812 G_ID: 0.163 G_Rec: 0.335 D_GP: 0.073 D_real: 0.726 D_fake: 0.947 +(epoch: 208, iters: 4144, time: 0.064) G_GAN: 0.925 G_GAN_Feat: 1.102 G_ID: 0.203 G_Rec: 0.418 D_GP: 0.047 D_real: 1.338 D_fake: 0.455 +(epoch: 208, iters: 4544, time: 0.064) G_GAN: -0.071 G_GAN_Feat: 0.645 G_ID: 0.152 G_Rec: 0.291 D_GP: 0.025 D_real: 0.862 D_fake: 1.071 +(epoch: 208, iters: 4944, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.991 G_ID: 0.180 G_Rec: 0.414 D_GP: 0.057 D_real: 0.725 D_fake: 0.624 +(epoch: 208, iters: 5344, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 0.758 G_ID: 0.141 G_Rec: 0.311 D_GP: 0.035 D_real: 1.423 D_fake: 0.466 +(epoch: 208, iters: 5744, time: 0.064) G_GAN: 0.204 G_GAN_Feat: 0.831 G_ID: 0.172 G_Rec: 0.400 D_GP: 0.025 D_real: 0.920 D_fake: 0.797 +(epoch: 208, iters: 6144, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.816 G_ID: 0.189 G_Rec: 0.298 D_GP: 0.043 D_real: 0.740 D_fake: 0.875 +(epoch: 208, iters: 6544, time: 0.064) G_GAN: 0.783 G_GAN_Feat: 1.110 G_ID: 0.203 G_Rec: 0.459 D_GP: 0.407 D_real: 0.805 D_fake: 0.329 +(epoch: 208, iters: 6944, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.706 G_ID: 0.143 G_Rec: 0.295 D_GP: 0.034 D_real: 1.167 D_fake: 0.671 +(epoch: 208, iters: 7344, time: 0.064) G_GAN: 0.529 G_GAN_Feat: 0.970 G_ID: 0.167 G_Rec: 0.411 D_GP: 0.080 D_real: 0.939 D_fake: 0.476 +(epoch: 208, iters: 7744, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.774 G_ID: 0.138 G_Rec: 0.351 D_GP: 0.067 D_real: 0.679 D_fake: 0.964 +(epoch: 208, iters: 8144, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 0.892 G_ID: 0.154 G_Rec: 0.408 D_GP: 0.025 D_real: 1.096 D_fake: 0.519 +(epoch: 208, iters: 8544, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.849 G_ID: 0.133 G_Rec: 0.319 D_GP: 0.029 D_real: 0.849 D_fake: 0.808 +(epoch: 209, iters: 336, time: 0.064) G_GAN: 0.592 G_GAN_Feat: 0.878 G_ID: 0.180 G_Rec: 0.431 D_GP: 0.027 D_real: 1.293 D_fake: 0.429 +(epoch: 209, iters: 736, time: 0.064) G_GAN: 0.068 G_GAN_Feat: 0.612 G_ID: 0.144 G_Rec: 0.312 D_GP: 0.023 D_real: 1.019 D_fake: 0.932 +(epoch: 209, iters: 1136, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.853 G_ID: 0.190 G_Rec: 0.423 D_GP: 0.035 D_real: 0.870 D_fake: 0.804 +(epoch: 209, iters: 1536, time: 0.064) G_GAN: -0.025 G_GAN_Feat: 0.722 G_ID: 0.156 G_Rec: 0.300 D_GP: 0.055 D_real: 0.671 D_fake: 1.026 +(epoch: 209, iters: 1936, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.919 G_ID: 0.163 G_Rec: 0.387 D_GP: 0.279 D_real: 0.689 D_fake: 0.653 +(epoch: 209, iters: 2336, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.696 G_ID: 0.118 G_Rec: 0.316 D_GP: 0.036 D_real: 0.968 D_fake: 0.814 +(epoch: 209, iters: 2736, time: 0.064) G_GAN: 0.475 G_GAN_Feat: 0.778 G_ID: 0.192 G_Rec: 0.329 D_GP: 0.023 D_real: 1.316 D_fake: 0.535 +(epoch: 209, iters: 3136, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.661 G_ID: 0.130 G_Rec: 0.279 D_GP: 0.023 D_real: 1.242 D_fake: 0.701 +(epoch: 209, iters: 3536, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.780 G_ID: 0.179 G_Rec: 0.354 D_GP: 0.021 D_real: 1.033 D_fake: 0.887 +(epoch: 209, iters: 3936, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.688 G_ID: 0.136 G_Rec: 0.344 D_GP: 0.025 D_real: 1.143 D_fake: 0.756 +(epoch: 209, iters: 4336, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.891 G_ID: 0.173 G_Rec: 0.370 D_GP: 0.037 D_real: 0.937 D_fake: 0.719 +(epoch: 209, iters: 4736, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.755 G_ID: 0.126 G_Rec: 0.287 D_GP: 0.049 D_real: 0.902 D_fake: 0.696 +(epoch: 209, iters: 5136, time: 0.064) G_GAN: 0.554 G_GAN_Feat: 1.060 G_ID: 0.169 G_Rec: 0.438 D_GP: 0.059 D_real: 0.889 D_fake: 0.462 +(epoch: 209, iters: 5536, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.713 G_ID: 0.135 G_Rec: 0.282 D_GP: 0.026 D_real: 1.240 D_fake: 0.640 +(epoch: 209, iters: 5936, time: 0.064) G_GAN: 0.562 G_GAN_Feat: 0.965 G_ID: 0.156 G_Rec: 0.430 D_GP: 0.028 D_real: 1.194 D_fake: 0.446 +(epoch: 209, iters: 6336, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.783 G_ID: 0.128 G_Rec: 0.317 D_GP: 0.027 D_real: 0.919 D_fake: 0.812 +(epoch: 209, iters: 6736, time: 0.064) G_GAN: 0.470 G_GAN_Feat: 0.834 G_ID: 0.162 G_Rec: 0.368 D_GP: 0.022 D_real: 1.328 D_fake: 0.533 +(epoch: 209, iters: 7136, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 0.749 G_ID: 0.134 G_Rec: 0.297 D_GP: 0.042 D_real: 0.863 D_fake: 0.988 +(epoch: 209, iters: 7536, time: 0.064) G_GAN: 0.634 G_GAN_Feat: 0.911 G_ID: 0.155 G_Rec: 0.449 D_GP: 0.026 D_real: 1.351 D_fake: 0.378 +(epoch: 209, iters: 7936, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.772 G_ID: 0.132 G_Rec: 0.385 D_GP: 0.132 D_real: 0.912 D_fake: 0.712 +(epoch: 209, iters: 8336, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 1.038 G_ID: 0.222 G_Rec: 0.410 D_GP: 0.039 D_real: 0.653 D_fake: 0.590 +(epoch: 210, iters: 128, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.967 G_ID: 0.128 G_Rec: 0.361 D_GP: 0.224 D_real: 0.536 D_fake: 0.897 +(epoch: 210, iters: 528, time: 0.064) G_GAN: 0.654 G_GAN_Feat: 0.975 G_ID: 0.147 G_Rec: 0.394 D_GP: 0.026 D_real: 1.238 D_fake: 0.371 +(epoch: 210, iters: 928, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.774 G_ID: 0.139 G_Rec: 0.314 D_GP: 0.025 D_real: 1.169 D_fake: 0.640 +(epoch: 210, iters: 1328, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.933 G_ID: 0.179 G_Rec: 0.420 D_GP: 0.036 D_real: 1.015 D_fake: 0.540 +(epoch: 210, iters: 1728, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.723 G_ID: 0.157 G_Rec: 0.347 D_GP: 0.025 D_real: 0.936 D_fake: 0.942 +(epoch: 210, iters: 2128, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.879 G_ID: 0.181 G_Rec: 0.395 D_GP: 0.056 D_real: 0.885 D_fake: 0.633 +(epoch: 210, iters: 2528, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.698 G_ID: 0.141 G_Rec: 0.316 D_GP: 0.023 D_real: 1.378 D_fake: 0.491 +(epoch: 210, iters: 2928, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.840 G_ID: 0.193 G_Rec: 0.426 D_GP: 0.021 D_real: 1.229 D_fake: 0.505 +(epoch: 210, iters: 3328, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.683 G_ID: 0.132 G_Rec: 0.307 D_GP: 0.048 D_real: 1.212 D_fake: 0.596 +(epoch: 210, iters: 3728, time: 0.064) G_GAN: 0.627 G_GAN_Feat: 0.919 G_ID: 0.180 G_Rec: 0.409 D_GP: 0.030 D_real: 1.349 D_fake: 0.401 +(epoch: 210, iters: 4128, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.740 G_ID: 0.125 G_Rec: 0.285 D_GP: 0.027 D_real: 1.051 D_fake: 0.799 +(epoch: 210, iters: 4528, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 1.167 G_ID: 0.228 G_Rec: 0.434 D_GP: 0.544 D_real: 0.306 D_fake: 0.823 +(epoch: 210, iters: 4928, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.940 G_ID: 0.111 G_Rec: 0.318 D_GP: 0.057 D_real: 0.473 D_fake: 0.683 +(epoch: 210, iters: 5328, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.759 G_ID: 0.172 G_Rec: 0.371 D_GP: 0.023 D_real: 1.277 D_fake: 0.515 +(epoch: 210, iters: 5728, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.654 G_ID: 0.163 G_Rec: 0.330 D_GP: 0.025 D_real: 0.973 D_fake: 0.923 +(epoch: 210, iters: 6128, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.801 G_ID: 0.209 G_Rec: 0.357 D_GP: 0.027 D_real: 0.874 D_fake: 0.833 +(epoch: 210, iters: 6528, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.648 G_ID: 0.143 G_Rec: 0.339 D_GP: 0.021 D_real: 1.148 D_fake: 0.759 +(epoch: 210, iters: 6928, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 1.020 G_ID: 0.211 G_Rec: 0.417 D_GP: 0.212 D_real: 0.192 D_fake: 0.992 +(epoch: 210, iters: 7328, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.642 G_ID: 0.158 G_Rec: 0.279 D_GP: 0.022 D_real: 1.237 D_fake: 0.693 +(epoch: 210, iters: 7728, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.842 G_ID: 0.192 G_Rec: 0.398 D_GP: 0.025 D_real: 1.186 D_fake: 0.611 +(epoch: 210, iters: 8128, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.820 G_ID: 0.142 G_Rec: 0.310 D_GP: 0.027 D_real: 0.997 D_fake: 0.673 +(epoch: 210, iters: 8528, time: 0.064) G_GAN: 0.688 G_GAN_Feat: 0.952 G_ID: 0.165 G_Rec: 0.440 D_GP: 0.021 D_real: 1.403 D_fake: 0.348 +(epoch: 211, iters: 320, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.594 G_ID: 0.142 G_Rec: 0.285 D_GP: 0.019 D_real: 1.238 D_fake: 0.692 +(epoch: 211, iters: 720, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 1.014 G_ID: 0.181 G_Rec: 0.440 D_GP: 0.058 D_real: 0.849 D_fake: 0.538 +(epoch: 211, iters: 1120, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.710 G_ID: 0.154 G_Rec: 0.304 D_GP: 0.023 D_real: 1.032 D_fake: 0.790 +(epoch: 211, iters: 1520, time: 0.064) G_GAN: 0.601 G_GAN_Feat: 0.863 G_ID: 0.190 G_Rec: 0.406 D_GP: 0.027 D_real: 1.422 D_fake: 0.426 +(epoch: 211, iters: 1920, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.744 G_ID: 0.142 G_Rec: 0.342 D_GP: 0.053 D_real: 0.993 D_fake: 0.751 +(epoch: 211, iters: 2320, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.865 G_ID: 0.164 G_Rec: 0.363 D_GP: 0.028 D_real: 1.122 D_fake: 0.573 +(epoch: 211, iters: 2720, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.675 G_ID: 0.130 G_Rec: 0.286 D_GP: 0.022 D_real: 1.065 D_fake: 0.873 +(epoch: 211, iters: 3120, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.824 G_ID: 0.179 G_Rec: 0.401 D_GP: 0.019 D_real: 1.092 D_fake: 0.616 +(epoch: 211, iters: 3520, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.710 G_ID: 0.133 G_Rec: 0.292 D_GP: 0.025 D_real: 1.300 D_fake: 0.576 +(epoch: 211, iters: 3920, time: 0.064) G_GAN: 0.552 G_GAN_Feat: 1.065 G_ID: 0.183 G_Rec: 0.385 D_GP: 0.044 D_real: 0.547 D_fake: 0.451 +(epoch: 211, iters: 4320, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.643 G_ID: 0.122 G_Rec: 0.280 D_GP: 0.024 D_real: 1.109 D_fake: 0.813 +(epoch: 211, iters: 4720, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.928 G_ID: 0.184 G_Rec: 0.393 D_GP: 0.060 D_real: 1.055 D_fake: 0.484 +(epoch: 211, iters: 5120, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 0.786 G_ID: 0.138 G_Rec: 0.315 D_GP: 0.035 D_real: 1.068 D_fake: 0.534 +(epoch: 211, iters: 5520, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 1.029 G_ID: 0.175 G_Rec: 0.410 D_GP: 0.033 D_real: 0.757 D_fake: 0.512 +(epoch: 211, iters: 5920, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.652 G_ID: 0.122 G_Rec: 0.284 D_GP: 0.021 D_real: 1.306 D_fake: 0.615 +(epoch: 211, iters: 6320, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.905 G_ID: 0.192 G_Rec: 0.420 D_GP: 0.024 D_real: 1.024 D_fake: 0.634 +(epoch: 211, iters: 6720, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 0.754 G_ID: 0.141 G_Rec: 0.337 D_GP: 0.032 D_real: 0.830 D_fake: 0.992 +(epoch: 211, iters: 7120, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.958 G_ID: 0.157 G_Rec: 0.424 D_GP: 0.047 D_real: 0.817 D_fake: 0.690 +(epoch: 211, iters: 7520, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.884 G_ID: 0.138 G_Rec: 0.347 D_GP: 0.090 D_real: 0.571 D_fake: 0.797 +(epoch: 211, iters: 7920, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.937 G_ID: 0.205 G_Rec: 0.392 D_GP: 0.029 D_real: 0.984 D_fake: 0.633 +(epoch: 211, iters: 8320, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.718 G_ID: 0.140 G_Rec: 0.331 D_GP: 0.021 D_real: 1.085 D_fake: 0.863 +(epoch: 212, iters: 112, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.814 G_ID: 0.165 G_Rec: 0.410 D_GP: 0.023 D_real: 1.093 D_fake: 0.634 +(epoch: 212, iters: 512, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.608 G_ID: 0.139 G_Rec: 0.292 D_GP: 0.021 D_real: 1.160 D_fake: 0.863 +(epoch: 212, iters: 912, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.787 G_ID: 0.147 G_Rec: 0.401 D_GP: 0.019 D_real: 1.219 D_fake: 0.576 +(epoch: 212, iters: 1312, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.680 G_ID: 0.123 G_Rec: 0.310 D_GP: 0.025 D_real: 1.085 D_fake: 0.797 +(epoch: 212, iters: 1712, time: 0.064) G_GAN: 0.747 G_GAN_Feat: 0.956 G_ID: 0.181 G_Rec: 0.427 D_GP: 0.025 D_real: 1.476 D_fake: 0.276 +(epoch: 212, iters: 2112, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.733 G_ID: 0.128 G_Rec: 0.316 D_GP: 0.028 D_real: 1.220 D_fake: 0.657 +(epoch: 212, iters: 2512, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.823 G_ID: 0.168 G_Rec: 0.378 D_GP: 0.025 D_real: 1.085 D_fake: 0.562 +(epoch: 212, iters: 2912, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.688 G_ID: 0.122 G_Rec: 0.288 D_GP: 0.034 D_real: 1.048 D_fake: 0.745 +(epoch: 212, iters: 3312, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.850 G_ID: 0.173 G_Rec: 0.395 D_GP: 0.026 D_real: 1.307 D_fake: 0.504 +(epoch: 212, iters: 3712, time: 0.064) G_GAN: 0.038 G_GAN_Feat: 0.593 G_ID: 0.135 G_Rec: 0.289 D_GP: 0.019 D_real: 1.005 D_fake: 0.962 +(epoch: 212, iters: 4112, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.821 G_ID: 0.185 G_Rec: 0.450 D_GP: 0.027 D_real: 0.909 D_fake: 0.779 +(epoch: 212, iters: 4512, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.670 G_ID: 0.119 G_Rec: 0.307 D_GP: 0.072 D_real: 0.901 D_fake: 0.887 +(epoch: 212, iters: 4912, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.852 G_ID: 0.185 G_Rec: 0.401 D_GP: 0.031 D_real: 1.119 D_fake: 0.573 +(epoch: 212, iters: 5312, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.812 G_ID: 0.125 G_Rec: 0.324 D_GP: 0.056 D_real: 0.871 D_fake: 0.709 +(epoch: 212, iters: 5712, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.930 G_ID: 0.166 G_Rec: 0.394 D_GP: 0.058 D_real: 0.764 D_fake: 0.885 +(epoch: 212, iters: 6112, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.788 G_ID: 0.151 G_Rec: 0.316 D_GP: 0.064 D_real: 0.773 D_fake: 0.789 +(epoch: 212, iters: 6512, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 0.889 G_ID: 0.187 G_Rec: 0.403 D_GP: 0.025 D_real: 1.197 D_fake: 0.493 +(epoch: 212, iters: 6912, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.871 G_ID: 0.121 G_Rec: 0.308 D_GP: 0.102 D_real: 0.618 D_fake: 0.617 +(epoch: 212, iters: 7312, time: 0.064) G_GAN: 0.669 G_GAN_Feat: 0.813 G_ID: 0.158 G_Rec: 0.460 D_GP: 0.019 D_real: 1.467 D_fake: 0.366 +(epoch: 212, iters: 7712, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.624 G_ID: 0.133 G_Rec: 0.313 D_GP: 0.026 D_real: 1.094 D_fake: 0.831 +(epoch: 212, iters: 8112, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.925 G_ID: 0.155 G_Rec: 0.409 D_GP: 0.064 D_real: 0.956 D_fake: 0.569 +(epoch: 212, iters: 8512, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.757 G_ID: 0.144 G_Rec: 0.297 D_GP: 0.041 D_real: 1.132 D_fake: 0.597 +(epoch: 213, iters: 304, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.830 G_ID: 0.183 G_Rec: 0.387 D_GP: 0.025 D_real: 1.154 D_fake: 0.548 +(epoch: 213, iters: 704, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.732 G_ID: 0.122 G_Rec: 0.301 D_GP: 0.042 D_real: 1.067 D_fake: 0.728 +(epoch: 213, iters: 1104, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.976 G_ID: 0.162 G_Rec: 0.447 D_GP: 0.025 D_real: 0.893 D_fake: 0.702 +(epoch: 213, iters: 1504, time: 0.064) G_GAN: -0.027 G_GAN_Feat: 0.676 G_ID: 0.132 G_Rec: 0.298 D_GP: 0.022 D_real: 0.873 D_fake: 1.027 +(epoch: 213, iters: 1904, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 1.053 G_ID: 0.194 G_Rec: 0.446 D_GP: 0.159 D_real: 0.492 D_fake: 0.630 +(epoch: 213, iters: 2304, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.816 G_ID: 0.139 G_Rec: 0.350 D_GP: 0.048 D_real: 0.892 D_fake: 0.686 +(epoch: 213, iters: 2704, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 0.784 G_ID: 0.179 G_Rec: 0.399 D_GP: 0.019 D_real: 1.392 D_fake: 0.372 +(epoch: 213, iters: 3104, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.592 G_ID: 0.134 G_Rec: 0.312 D_GP: 0.018 D_real: 1.097 D_fake: 0.852 +(epoch: 213, iters: 3504, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.710 G_ID: 0.166 G_Rec: 0.350 D_GP: 0.026 D_real: 1.213 D_fake: 0.669 +(epoch: 213, iters: 3904, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.599 G_ID: 0.106 G_Rec: 0.291 D_GP: 0.028 D_real: 1.143 D_fake: 0.776 +(epoch: 213, iters: 4304, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.818 G_ID: 0.174 G_Rec: 0.408 D_GP: 0.046 D_real: 1.057 D_fake: 0.657 +(epoch: 213, iters: 4704, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.768 G_ID: 0.135 G_Rec: 0.327 D_GP: 0.027 D_real: 1.111 D_fake: 0.723 +(epoch: 213, iters: 5104, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.854 G_ID: 0.180 G_Rec: 0.400 D_GP: 0.039 D_real: 1.175 D_fake: 0.490 +(epoch: 213, iters: 5504, time: 0.064) G_GAN: 0.043 G_GAN_Feat: 0.741 G_ID: 0.124 G_Rec: 0.329 D_GP: 0.073 D_real: 0.856 D_fake: 0.957 +(epoch: 213, iters: 5904, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.942 G_ID: 0.179 G_Rec: 0.399 D_GP: 0.139 D_real: 0.592 D_fake: 0.733 +(epoch: 213, iters: 6304, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.830 G_ID: 0.158 G_Rec: 0.341 D_GP: 0.054 D_real: 0.912 D_fake: 0.694 +(epoch: 213, iters: 6704, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.824 G_ID: 0.207 G_Rec: 0.355 D_GP: 0.026 D_real: 0.957 D_fake: 0.742 +(epoch: 213, iters: 7104, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.704 G_ID: 0.125 G_Rec: 0.327 D_GP: 0.025 D_real: 1.228 D_fake: 0.674 +(epoch: 213, iters: 7504, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.935 G_ID: 0.186 G_Rec: 0.386 D_GP: 0.043 D_real: 0.859 D_fake: 0.599 +(epoch: 213, iters: 7904, time: 0.064) G_GAN: 0.555 G_GAN_Feat: 0.793 G_ID: 0.121 G_Rec: 0.318 D_GP: 0.077 D_real: 1.223 D_fake: 0.474 +(epoch: 213, iters: 8304, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.852 G_ID: 0.156 G_Rec: 0.413 D_GP: 0.026 D_real: 0.943 D_fake: 0.777 +(epoch: 214, iters: 96, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.713 G_ID: 0.131 G_Rec: 0.316 D_GP: 0.050 D_real: 1.050 D_fake: 0.775 +(epoch: 214, iters: 496, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.974 G_ID: 0.168 G_Rec: 0.386 D_GP: 0.025 D_real: 1.043 D_fake: 0.672 +(epoch: 214, iters: 896, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.809 G_ID: 0.136 G_Rec: 0.319 D_GP: 0.038 D_real: 0.840 D_fake: 0.787 +(epoch: 214, iters: 1296, time: 0.064) G_GAN: 0.762 G_GAN_Feat: 0.903 G_ID: 0.190 G_Rec: 0.376 D_GP: 0.038 D_real: 1.428 D_fake: 0.347 +(epoch: 214, iters: 1696, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.785 G_ID: 0.120 G_Rec: 0.278 D_GP: 0.070 D_real: 1.051 D_fake: 0.882 +(epoch: 214, iters: 2096, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.995 G_ID: 0.171 G_Rec: 0.469 D_GP: 0.071 D_real: 0.617 D_fake: 0.864 +(epoch: 214, iters: 2496, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 0.848 G_ID: 0.161 G_Rec: 0.332 D_GP: 0.141 D_real: 0.417 D_fake: 0.991 +(epoch: 214, iters: 2896, time: 0.064) G_GAN: 0.879 G_GAN_Feat: 0.807 G_ID: 0.166 G_Rec: 0.371 D_GP: 0.025 D_real: 1.612 D_fake: 0.220 +(epoch: 214, iters: 3296, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.666 G_ID: 0.189 G_Rec: 0.288 D_GP: 0.025 D_real: 1.022 D_fake: 0.863 +(epoch: 214, iters: 3696, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.951 G_ID: 0.198 G_Rec: 0.443 D_GP: 0.059 D_real: 1.032 D_fake: 0.647 +(epoch: 214, iters: 4096, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.690 G_ID: 0.134 G_Rec: 0.269 D_GP: 0.023 D_real: 1.154 D_fake: 0.732 +(epoch: 214, iters: 4496, time: 0.064) G_GAN: 0.722 G_GAN_Feat: 0.771 G_ID: 0.148 G_Rec: 0.349 D_GP: 0.026 D_real: 1.522 D_fake: 0.291 +(epoch: 214, iters: 4896, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.899 G_ID: 0.128 G_Rec: 0.324 D_GP: 0.622 D_real: 0.450 D_fake: 0.767 +(epoch: 214, iters: 5296, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.840 G_ID: 0.180 G_Rec: 0.382 D_GP: 0.028 D_real: 1.162 D_fake: 0.548 +(epoch: 214, iters: 5696, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.781 G_ID: 0.144 G_Rec: 0.335 D_GP: 0.054 D_real: 0.885 D_fake: 0.694 +(epoch: 214, iters: 6096, time: 0.064) G_GAN: 0.672 G_GAN_Feat: 0.883 G_ID: 0.171 G_Rec: 0.412 D_GP: 0.036 D_real: 1.309 D_fake: 0.364 +(epoch: 214, iters: 6496, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.908 G_ID: 0.143 G_Rec: 0.341 D_GP: 0.092 D_real: 0.485 D_fake: 0.792 +(epoch: 214, iters: 6896, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.828 G_ID: 0.184 G_Rec: 0.447 D_GP: 0.020 D_real: 1.188 D_fake: 0.584 +(epoch: 214, iters: 7296, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.796 G_ID: 0.128 G_Rec: 0.318 D_GP: 0.043 D_real: 1.055 D_fake: 0.665 +(epoch: 214, iters: 7696, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.804 G_ID: 0.161 G_Rec: 0.361 D_GP: 0.022 D_real: 1.236 D_fake: 0.583 +(epoch: 214, iters: 8096, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.655 G_ID: 0.131 G_Rec: 0.283 D_GP: 0.024 D_real: 1.215 D_fake: 0.674 +(epoch: 214, iters: 8496, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.957 G_ID: 0.171 G_Rec: 0.403 D_GP: 0.024 D_real: 1.106 D_fake: 0.473 +(epoch: 215, iters: 288, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.610 G_ID: 0.129 G_Rec: 0.308 D_GP: 0.020 D_real: 0.972 D_fake: 0.959 +(epoch: 215, iters: 688, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.835 G_ID: 0.168 G_Rec: 0.374 D_GP: 0.026 D_real: 0.958 D_fake: 0.755 +(epoch: 215, iters: 1088, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.687 G_ID: 0.128 G_Rec: 0.300 D_GP: 0.025 D_real: 1.063 D_fake: 0.894 +(epoch: 215, iters: 1488, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.950 G_ID: 0.160 G_Rec: 0.380 D_GP: 0.034 D_real: 0.982 D_fake: 0.489 +(epoch: 215, iters: 1888, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.708 G_ID: 0.133 G_Rec: 0.281 D_GP: 0.030 D_real: 1.196 D_fake: 0.605 +(epoch: 215, iters: 2288, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.986 G_ID: 0.199 G_Rec: 0.430 D_GP: 0.038 D_real: 0.963 D_fake: 0.534 +(epoch: 215, iters: 2688, time: 0.064) G_GAN: -0.024 G_GAN_Feat: 0.772 G_ID: 0.119 G_Rec: 0.344 D_GP: 0.085 D_real: 0.585 D_fake: 1.024 +(epoch: 215, iters: 3088, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 1.141 G_ID: 0.162 G_Rec: 0.438 D_GP: 0.505 D_real: 0.312 D_fake: 0.902 +(epoch: 215, iters: 3488, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.636 G_ID: 0.151 G_Rec: 0.287 D_GP: 0.027 D_real: 1.272 D_fake: 0.661 +(epoch: 215, iters: 3888, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 1.068 G_ID: 0.174 G_Rec: 0.476 D_GP: 0.104 D_real: 0.723 D_fake: 0.412 +(epoch: 215, iters: 4288, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.654 G_ID: 0.128 G_Rec: 0.286 D_GP: 0.028 D_real: 1.042 D_fake: 0.872 +(epoch: 215, iters: 4688, time: 0.064) G_GAN: 0.695 G_GAN_Feat: 0.927 G_ID: 0.171 G_Rec: 0.406 D_GP: 0.049 D_real: 1.248 D_fake: 0.323 +(epoch: 215, iters: 5088, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.803 G_ID: 0.147 G_Rec: 0.312 D_GP: 0.076 D_real: 0.690 D_fake: 0.741 +(epoch: 215, iters: 5488, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.843 G_ID: 0.190 G_Rec: 0.370 D_GP: 0.020 D_real: 1.005 D_fake: 0.802 +(epoch: 215, iters: 5888, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.626 G_ID: 0.120 G_Rec: 0.282 D_GP: 0.022 D_real: 1.093 D_fake: 0.817 +(epoch: 215, iters: 6288, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.923 G_ID: 0.197 G_Rec: 0.431 D_GP: 0.082 D_real: 0.754 D_fake: 0.693 +(epoch: 215, iters: 6688, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.835 G_ID: 0.156 G_Rec: 0.353 D_GP: 0.029 D_real: 0.710 D_fake: 0.921 +(epoch: 215, iters: 7088, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.981 G_ID: 0.187 G_Rec: 0.418 D_GP: 0.040 D_real: 0.910 D_fake: 0.483 +(epoch: 215, iters: 7488, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.857 G_ID: 0.136 G_Rec: 0.290 D_GP: 0.052 D_real: 0.609 D_fake: 0.843 +(epoch: 215, iters: 7888, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.869 G_ID: 0.192 G_Rec: 0.388 D_GP: 0.020 D_real: 0.913 D_fake: 0.855 +(epoch: 215, iters: 8288, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.738 G_ID: 0.135 G_Rec: 0.298 D_GP: 0.022 D_real: 1.140 D_fake: 0.746 +(epoch: 216, iters: 80, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.889 G_ID: 0.169 G_Rec: 0.349 D_GP: 0.021 D_real: 0.768 D_fake: 0.853 +(epoch: 216, iters: 480, time: 0.064) G_GAN: 0.020 G_GAN_Feat: 0.748 G_ID: 0.114 G_Rec: 0.316 D_GP: 0.028 D_real: 0.859 D_fake: 0.980 +(epoch: 216, iters: 880, time: 0.064) G_GAN: 0.768 G_GAN_Feat: 1.086 G_ID: 0.179 G_Rec: 0.445 D_GP: 0.233 D_real: 0.697 D_fake: 0.405 +(epoch: 216, iters: 1280, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.684 G_ID: 0.133 G_Rec: 0.344 D_GP: 0.027 D_real: 1.138 D_fake: 0.791 +(epoch: 216, iters: 1680, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.852 G_ID: 0.165 G_Rec: 0.403 D_GP: 0.033 D_real: 0.939 D_fake: 0.769 +(epoch: 216, iters: 2080, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.608 G_ID: 0.143 G_Rec: 0.278 D_GP: 0.020 D_real: 1.248 D_fake: 0.709 +(epoch: 216, iters: 2480, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.885 G_ID: 0.162 G_Rec: 0.389 D_GP: 0.029 D_real: 1.069 D_fake: 0.615 +(epoch: 216, iters: 2880, time: 0.064) G_GAN: -0.017 G_GAN_Feat: 0.648 G_ID: 0.133 G_Rec: 0.273 D_GP: 0.033 D_real: 0.867 D_fake: 1.017 +(epoch: 216, iters: 3280, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.771 G_ID: 0.170 G_Rec: 0.391 D_GP: 0.022 D_real: 1.268 D_fake: 0.562 +(epoch: 216, iters: 3680, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.580 G_ID: 0.173 G_Rec: 0.281 D_GP: 0.019 D_real: 1.041 D_fake: 0.952 +(epoch: 216, iters: 4080, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.795 G_ID: 0.169 G_Rec: 0.388 D_GP: 0.022 D_real: 1.091 D_fake: 0.661 +(epoch: 216, iters: 4480, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.668 G_ID: 0.143 G_Rec: 0.320 D_GP: 0.034 D_real: 1.115 D_fake: 0.714 +(epoch: 216, iters: 4880, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.781 G_ID: 0.182 G_Rec: 0.365 D_GP: 0.030 D_real: 0.990 D_fake: 0.720 +(epoch: 216, iters: 5280, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.645 G_ID: 0.129 G_Rec: 0.320 D_GP: 0.025 D_real: 1.024 D_fake: 0.831 +(epoch: 216, iters: 5680, time: 0.064) G_GAN: 0.610 G_GAN_Feat: 0.873 G_ID: 0.171 G_Rec: 0.403 D_GP: 0.041 D_real: 1.213 D_fake: 0.421 +(epoch: 216, iters: 6080, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.753 G_ID: 0.128 G_Rec: 0.303 D_GP: 0.048 D_real: 0.899 D_fake: 0.765 +(epoch: 216, iters: 6480, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.894 G_ID: 0.180 G_Rec: 0.427 D_GP: 0.031 D_real: 0.879 D_fake: 0.765 +(epoch: 216, iters: 6880, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.608 G_ID: 0.136 G_Rec: 0.257 D_GP: 0.027 D_real: 1.038 D_fake: 0.857 +(epoch: 216, iters: 7280, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.941 G_ID: 0.170 G_Rec: 0.439 D_GP: 0.040 D_real: 0.794 D_fake: 0.698 +(epoch: 216, iters: 7680, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.840 G_ID: 0.128 G_Rec: 0.319 D_GP: 0.088 D_real: 0.680 D_fake: 0.712 +(epoch: 216, iters: 8080, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.807 G_ID: 0.157 G_Rec: 0.384 D_GP: 0.022 D_real: 1.261 D_fake: 0.588 +(epoch: 216, iters: 8480, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.707 G_ID: 0.149 G_Rec: 0.294 D_GP: 0.043 D_real: 0.941 D_fake: 0.941 +(epoch: 217, iters: 272, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.879 G_ID: 0.176 G_Rec: 0.406 D_GP: 0.076 D_real: 1.063 D_fake: 0.726 +(epoch: 217, iters: 672, time: 0.064) G_GAN: 0.076 G_GAN_Feat: 0.719 G_ID: 0.122 G_Rec: 0.299 D_GP: 0.035 D_real: 0.935 D_fake: 0.924 +(epoch: 217, iters: 1072, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.872 G_ID: 0.183 G_Rec: 0.367 D_GP: 0.030 D_real: 0.785 D_fake: 0.816 +(epoch: 217, iters: 1472, time: 0.064) G_GAN: 0.044 G_GAN_Feat: 0.720 G_ID: 0.166 G_Rec: 0.296 D_GP: 0.022 D_real: 0.942 D_fake: 0.956 +(epoch: 217, iters: 1872, time: 0.064) G_GAN: 0.832 G_GAN_Feat: 0.938 G_ID: 0.183 G_Rec: 0.426 D_GP: 0.024 D_real: 1.501 D_fake: 0.229 +(epoch: 217, iters: 2272, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.704 G_ID: 0.120 G_Rec: 0.336 D_GP: 0.020 D_real: 0.933 D_fake: 0.898 +(epoch: 217, iters: 2672, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.903 G_ID: 0.151 G_Rec: 0.395 D_GP: 0.047 D_real: 0.892 D_fake: 0.651 +(epoch: 217, iters: 3072, time: 0.064) G_GAN: -0.176 G_GAN_Feat: 0.802 G_ID: 0.139 G_Rec: 0.308 D_GP: 0.061 D_real: 0.617 D_fake: 1.176 +(epoch: 217, iters: 3472, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.852 G_ID: 0.201 G_Rec: 0.401 D_GP: 0.024 D_real: 1.210 D_fake: 0.596 +(epoch: 217, iters: 3872, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.887 G_ID: 0.194 G_Rec: 0.336 D_GP: 0.110 D_real: 0.807 D_fake: 0.556 +(epoch: 217, iters: 4272, time: 0.064) G_GAN: 0.669 G_GAN_Feat: 0.911 G_ID: 0.175 G_Rec: 0.409 D_GP: 0.091 D_real: 1.361 D_fake: 0.381 +(epoch: 217, iters: 4672, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.742 G_ID: 0.168 G_Rec: 0.332 D_GP: 0.057 D_real: 0.836 D_fake: 0.938 +(epoch: 217, iters: 5072, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.774 G_ID: 0.181 G_Rec: 0.395 D_GP: 0.022 D_real: 1.257 D_fake: 0.611 +(epoch: 217, iters: 5472, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.618 G_ID: 0.148 G_Rec: 0.281 D_GP: 0.022 D_real: 1.129 D_fake: 0.788 +(epoch: 217, iters: 5872, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.955 G_ID: 0.194 G_Rec: 0.411 D_GP: 0.037 D_real: 0.718 D_fake: 0.658 +(epoch: 217, iters: 6272, time: 0.064) G_GAN: 0.050 G_GAN_Feat: 0.741 G_ID: 0.159 G_Rec: 0.322 D_GP: 0.025 D_real: 0.896 D_fake: 0.950 +(epoch: 217, iters: 6672, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.918 G_ID: 0.172 G_Rec: 0.401 D_GP: 0.031 D_real: 1.166 D_fake: 0.492 +(epoch: 217, iters: 7072, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.741 G_ID: 0.119 G_Rec: 0.302 D_GP: 0.048 D_real: 0.969 D_fake: 0.798 +(epoch: 217, iters: 7472, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.830 G_ID: 0.212 G_Rec: 0.387 D_GP: 0.028 D_real: 1.066 D_fake: 0.682 +(epoch: 217, iters: 7872, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.681 G_ID: 0.142 G_Rec: 0.318 D_GP: 0.022 D_real: 1.124 D_fake: 0.782 +(epoch: 217, iters: 8272, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 1.053 G_ID: 0.183 G_Rec: 0.411 D_GP: 0.218 D_real: 0.554 D_fake: 0.583 +(epoch: 218, iters: 64, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.600 G_ID: 0.135 G_Rec: 0.316 D_GP: 0.019 D_real: 1.186 D_fake: 0.793 +(epoch: 218, iters: 464, time: 0.064) G_GAN: 0.620 G_GAN_Feat: 0.807 G_ID: 0.142 G_Rec: 0.398 D_GP: 0.019 D_real: 1.382 D_fake: 0.396 +(epoch: 218, iters: 864, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.643 G_ID: 0.119 G_Rec: 0.311 D_GP: 0.028 D_real: 1.129 D_fake: 0.781 +(epoch: 218, iters: 1264, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.801 G_ID: 0.219 G_Rec: 0.390 D_GP: 0.031 D_real: 1.148 D_fake: 0.608 +(epoch: 218, iters: 1664, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.727 G_ID: 0.140 G_Rec: 0.308 D_GP: 0.033 D_real: 1.009 D_fake: 0.738 +(epoch: 218, iters: 2064, time: 0.064) G_GAN: 0.697 G_GAN_Feat: 0.853 G_ID: 0.166 G_Rec: 0.402 D_GP: 0.025 D_real: 1.369 D_fake: 0.330 +(epoch: 218, iters: 2464, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.725 G_ID: 0.151 G_Rec: 0.310 D_GP: 0.029 D_real: 1.161 D_fake: 0.682 +(epoch: 218, iters: 2864, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.974 G_ID: 0.191 G_Rec: 0.417 D_GP: 0.028 D_real: 0.820 D_fake: 0.647 +(epoch: 218, iters: 3264, time: 0.064) G_GAN: -0.050 G_GAN_Feat: 0.750 G_ID: 0.125 G_Rec: 0.299 D_GP: 0.026 D_real: 0.791 D_fake: 1.050 +(epoch: 218, iters: 3664, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.806 G_ID: 0.212 G_Rec: 0.392 D_GP: 0.027 D_real: 1.033 D_fake: 0.801 +(epoch: 218, iters: 4064, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 0.661 G_ID: 0.132 G_Rec: 0.311 D_GP: 0.028 D_real: 1.012 D_fake: 0.893 +(epoch: 218, iters: 4464, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.781 G_ID: 0.186 G_Rec: 0.370 D_GP: 0.020 D_real: 1.147 D_fake: 0.664 +(epoch: 218, iters: 4864, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.610 G_ID: 0.136 G_Rec: 0.263 D_GP: 0.021 D_real: 1.173 D_fake: 0.745 +(epoch: 218, iters: 5264, time: 0.064) G_GAN: 0.797 G_GAN_Feat: 1.289 G_ID: 0.185 G_Rec: 0.441 D_GP: 0.033 D_real: 1.469 D_fake: 0.291 +(epoch: 218, iters: 5664, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 0.646 G_ID: 0.141 G_Rec: 0.318 D_GP: 0.018 D_real: 0.958 D_fake: 0.992 +(epoch: 218, iters: 6064, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.818 G_ID: 0.199 G_Rec: 0.394 D_GP: 0.030 D_real: 0.848 D_fake: 0.922 +(epoch: 218, iters: 6464, time: 0.064) G_GAN: -0.068 G_GAN_Feat: 0.669 G_ID: 0.140 G_Rec: 0.327 D_GP: 0.028 D_real: 0.804 D_fake: 1.069 +(epoch: 218, iters: 6864, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.935 G_ID: 0.177 G_Rec: 0.424 D_GP: 0.060 D_real: 0.922 D_fake: 0.589 +(epoch: 218, iters: 7264, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.680 G_ID: 0.162 G_Rec: 0.271 D_GP: 0.024 D_real: 1.147 D_fake: 0.714 +(epoch: 218, iters: 7664, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.938 G_ID: 0.183 G_Rec: 0.411 D_GP: 0.026 D_real: 1.200 D_fake: 0.575 +(epoch: 218, iters: 8064, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.722 G_ID: 0.145 G_Rec: 0.325 D_GP: 0.037 D_real: 1.023 D_fake: 0.825 +(epoch: 218, iters: 8464, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.876 G_ID: 0.163 G_Rec: 0.404 D_GP: 0.022 D_real: 1.065 D_fake: 0.617 +(epoch: 219, iters: 256, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.846 G_ID: 0.152 G_Rec: 0.319 D_GP: 0.097 D_real: 0.847 D_fake: 0.639 +(epoch: 219, iters: 656, time: 0.064) G_GAN: 0.644 G_GAN_Feat: 1.005 G_ID: 0.158 G_Rec: 0.409 D_GP: 0.037 D_real: 1.431 D_fake: 0.371 +(epoch: 219, iters: 1056, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.675 G_ID: 0.152 G_Rec: 0.319 D_GP: 0.018 D_real: 1.131 D_fake: 0.779 +(epoch: 219, iters: 1456, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.903 G_ID: 0.149 G_Rec: 0.382 D_GP: 0.046 D_real: 1.016 D_fake: 0.542 +(epoch: 219, iters: 1856, time: 0.064) G_GAN: -0.294 G_GAN_Feat: 0.723 G_ID: 0.126 G_Rec: 0.299 D_GP: 0.042 D_real: 0.592 D_fake: 1.294 +(epoch: 219, iters: 2256, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 1.016 G_ID: 0.183 G_Rec: 0.458 D_GP: 0.067 D_real: 0.621 D_fake: 0.614 +(epoch: 219, iters: 2656, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.715 G_ID: 0.142 G_Rec: 0.313 D_GP: 0.029 D_real: 1.025 D_fake: 0.919 +(epoch: 219, iters: 3056, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.916 G_ID: 0.159 G_Rec: 0.394 D_GP: 0.027 D_real: 1.070 D_fake: 0.654 +(epoch: 219, iters: 3456, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.732 G_ID: 0.130 G_Rec: 0.295 D_GP: 0.038 D_real: 1.120 D_fake: 0.640 +(epoch: 219, iters: 3856, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.899 G_ID: 0.163 G_Rec: 0.387 D_GP: 0.042 D_real: 0.922 D_fake: 0.771 +(epoch: 219, iters: 4256, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.799 G_ID: 0.143 G_Rec: 0.326 D_GP: 0.040 D_real: 1.075 D_fake: 0.725 +(epoch: 219, iters: 4656, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 1.211 G_ID: 0.200 G_Rec: 0.436 D_GP: 0.584 D_real: 0.359 D_fake: 0.577 +(epoch: 219, iters: 5056, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.715 G_ID: 0.134 G_Rec: 0.290 D_GP: 0.028 D_real: 1.048 D_fake: 0.822 +(epoch: 219, iters: 5456, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.884 G_ID: 0.196 G_Rec: 0.408 D_GP: 0.021 D_real: 1.036 D_fake: 0.637 +(epoch: 219, iters: 5856, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.751 G_ID: 0.126 G_Rec: 0.293 D_GP: 0.025 D_real: 1.111 D_fake: 0.682 +(epoch: 219, iters: 6256, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.905 G_ID: 0.167 G_Rec: 0.395 D_GP: 0.025 D_real: 1.149 D_fake: 0.572 +(epoch: 219, iters: 6656, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.621 G_ID: 0.148 G_Rec: 0.275 D_GP: 0.023 D_real: 1.264 D_fake: 0.731 +(epoch: 219, iters: 7056, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.858 G_ID: 0.194 G_Rec: 0.372 D_GP: 0.029 D_real: 0.947 D_fake: 0.763 +(epoch: 219, iters: 7456, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.845 G_ID: 0.146 G_Rec: 0.355 D_GP: 0.077 D_real: 0.733 D_fake: 0.798 +(epoch: 219, iters: 7856, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 1.020 G_ID: 0.204 G_Rec: 0.439 D_GP: 0.258 D_real: 0.634 D_fake: 0.591 +(epoch: 219, iters: 8256, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.737 G_ID: 0.114 G_Rec: 0.282 D_GP: 0.035 D_real: 1.148 D_fake: 0.736 +(epoch: 220, iters: 48, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.929 G_ID: 0.187 G_Rec: 0.410 D_GP: 0.048 D_real: 0.815 D_fake: 0.665 +(epoch: 220, iters: 448, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.884 G_ID: 0.134 G_Rec: 0.330 D_GP: 0.062 D_real: 0.493 D_fake: 0.915 +(epoch: 220, iters: 848, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 1.007 G_ID: 0.208 G_Rec: 0.390 D_GP: 0.043 D_real: 0.417 D_fake: 0.794 +(epoch: 220, iters: 1248, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.695 G_ID: 0.171 G_Rec: 0.279 D_GP: 0.026 D_real: 1.197 D_fake: 0.746 +(epoch: 220, iters: 1648, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.954 G_ID: 0.160 G_Rec: 0.423 D_GP: 0.041 D_real: 1.161 D_fake: 0.472 +(epoch: 220, iters: 2048, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.739 G_ID: 0.134 G_Rec: 0.341 D_GP: 0.023 D_real: 1.026 D_fake: 0.821 +(epoch: 220, iters: 2448, time: 0.064) G_GAN: 0.556 G_GAN_Feat: 0.940 G_ID: 0.180 G_Rec: 0.398 D_GP: 0.025 D_real: 1.193 D_fake: 0.449 +(epoch: 220, iters: 2848, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.692 G_ID: 0.126 G_Rec: 0.307 D_GP: 0.050 D_real: 0.904 D_fake: 0.942 +(epoch: 220, iters: 3248, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.749 G_ID: 0.175 G_Rec: 0.375 D_GP: 0.019 D_real: 1.188 D_fake: 0.631 +(epoch: 220, iters: 3648, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.640 G_ID: 0.145 G_Rec: 0.326 D_GP: 0.028 D_real: 1.084 D_fake: 0.805 +(epoch: 220, iters: 4048, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.799 G_ID: 0.194 G_Rec: 0.387 D_GP: 0.031 D_real: 1.038 D_fake: 0.676 +(epoch: 220, iters: 4448, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.638 G_ID: 0.125 G_Rec: 0.263 D_GP: 0.039 D_real: 0.998 D_fake: 0.834 +(epoch: 220, iters: 4848, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.932 G_ID: 0.177 G_Rec: 0.422 D_GP: 0.063 D_real: 0.866 D_fake: 0.683 +(epoch: 220, iters: 5248, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.809 G_ID: 0.133 G_Rec: 0.324 D_GP: 0.082 D_real: 0.717 D_fake: 0.819 +(epoch: 220, iters: 5648, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.996 G_ID: 0.190 G_Rec: 0.454 D_GP: 0.126 D_real: 0.699 D_fake: 0.658 +(epoch: 220, iters: 6048, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.875 G_ID: 0.139 G_Rec: 0.342 D_GP: 0.028 D_real: 1.088 D_fake: 0.623 +(epoch: 220, iters: 6448, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.832 G_ID: 0.205 G_Rec: 0.348 D_GP: 0.036 D_real: 1.212 D_fake: 0.562 +(epoch: 220, iters: 6848, time: 0.064) G_GAN: -0.083 G_GAN_Feat: 0.882 G_ID: 0.195 G_Rec: 0.318 D_GP: 0.225 D_real: 0.294 D_fake: 1.084 +(epoch: 220, iters: 7248, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 1.002 G_ID: 0.189 G_Rec: 0.388 D_GP: 0.031 D_real: 0.737 D_fake: 0.795 +(epoch: 220, iters: 7648, time: 0.064) G_GAN: 0.768 G_GAN_Feat: 1.046 G_ID: 0.151 G_Rec: 0.345 D_GP: 0.057 D_real: 1.100 D_fake: 0.655 +(epoch: 220, iters: 8048, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 0.997 G_ID: 0.165 G_Rec: 0.442 D_GP: 0.022 D_real: 1.171 D_fake: 0.558 +(epoch: 220, iters: 8448, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.786 G_ID: 0.122 G_Rec: 0.334 D_GP: 0.084 D_real: 0.678 D_fake: 0.921 +(epoch: 221, iters: 240, time: 0.064) G_GAN: 0.629 G_GAN_Feat: 0.899 G_ID: 0.164 G_Rec: 0.390 D_GP: 0.043 D_real: 1.293 D_fake: 0.383 +(epoch: 221, iters: 640, time: 0.064) G_GAN: 0.165 G_GAN_Feat: 0.627 G_ID: 0.146 G_Rec: 0.309 D_GP: 0.023 D_real: 1.128 D_fake: 0.835 +(epoch: 221, iters: 1040, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 0.825 G_ID: 0.162 G_Rec: 0.362 D_GP: 0.026 D_real: 1.243 D_fake: 0.480 +(epoch: 221, iters: 1440, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.643 G_ID: 0.133 G_Rec: 0.277 D_GP: 0.020 D_real: 1.365 D_fake: 0.582 +(epoch: 221, iters: 1840, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 1.092 G_ID: 0.159 G_Rec: 0.468 D_GP: 0.046 D_real: 0.557 D_fake: 0.580 +(epoch: 221, iters: 2240, time: 0.064) G_GAN: -0.043 G_GAN_Feat: 0.687 G_ID: 0.161 G_Rec: 0.314 D_GP: 0.028 D_real: 0.801 D_fake: 1.044 +(epoch: 221, iters: 2640, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.937 G_ID: 0.181 G_Rec: 0.439 D_GP: 0.033 D_real: 0.997 D_fake: 0.626 +(epoch: 221, iters: 3040, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.750 G_ID: 0.139 G_Rec: 0.339 D_GP: 0.032 D_real: 1.172 D_fake: 0.686 +(epoch: 221, iters: 3440, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.882 G_ID: 0.164 G_Rec: 0.396 D_GP: 0.023 D_real: 1.219 D_fake: 0.511 +(epoch: 221, iters: 3840, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.642 G_ID: 0.142 G_Rec: 0.292 D_GP: 0.022 D_real: 1.221 D_fake: 0.717 +(epoch: 221, iters: 4240, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.863 G_ID: 0.152 G_Rec: 0.368 D_GP: 0.024 D_real: 1.302 D_fake: 0.463 +(epoch: 221, iters: 4640, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.537 G_ID: 0.130 G_Rec: 0.299 D_GP: 0.018 D_real: 1.211 D_fake: 0.782 +(epoch: 221, iters: 5040, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.713 G_ID: 0.180 G_Rec: 0.393 D_GP: 0.020 D_real: 1.145 D_fake: 0.725 +(epoch: 221, iters: 5440, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.569 G_ID: 0.136 G_Rec: 0.315 D_GP: 0.017 D_real: 1.119 D_fake: 0.829 +(epoch: 221, iters: 5840, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.775 G_ID: 0.170 G_Rec: 0.455 D_GP: 0.022 D_real: 1.142 D_fake: 0.608 +(epoch: 221, iters: 6240, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.589 G_ID: 0.156 G_Rec: 0.317 D_GP: 0.027 D_real: 1.019 D_fake: 0.885 +(epoch: 221, iters: 6640, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.717 G_ID: 0.194 G_Rec: 0.381 D_GP: 0.026 D_real: 0.997 D_fake: 0.809 +(epoch: 221, iters: 7040, time: 0.064) G_GAN: -0.034 G_GAN_Feat: 0.538 G_ID: 0.132 G_Rec: 0.306 D_GP: 0.022 D_real: 0.939 D_fake: 1.034 +(epoch: 221, iters: 7440, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.725 G_ID: 0.177 G_Rec: 0.401 D_GP: 0.027 D_real: 0.860 D_fake: 0.889 +(epoch: 221, iters: 7840, time: 0.064) G_GAN: 0.016 G_GAN_Feat: 0.627 G_ID: 0.175 G_Rec: 0.306 D_GP: 0.038 D_real: 0.883 D_fake: 0.984 +(epoch: 221, iters: 8240, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.757 G_ID: 0.195 G_Rec: 0.365 D_GP: 0.037 D_real: 0.853 D_fake: 0.867 +(epoch: 222, iters: 32, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.610 G_ID: 0.119 G_Rec: 0.299 D_GP: 0.027 D_real: 1.126 D_fake: 0.905 +(epoch: 222, iters: 432, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.787 G_ID: 0.190 G_Rec: 0.408 D_GP: 0.025 D_real: 0.915 D_fake: 0.881 +(epoch: 222, iters: 832, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 0.581 G_ID: 0.115 G_Rec: 0.268 D_GP: 0.026 D_real: 1.005 D_fake: 0.943 +(epoch: 222, iters: 1232, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.740 G_ID: 0.163 G_Rec: 0.367 D_GP: 0.027 D_real: 1.248 D_fake: 0.580 +(epoch: 222, iters: 1632, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.611 G_ID: 0.153 G_Rec: 0.308 D_GP: 0.039 D_real: 0.946 D_fake: 0.927 +(epoch: 222, iters: 2032, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.801 G_ID: 0.170 G_Rec: 0.402 D_GP: 0.023 D_real: 1.066 D_fake: 0.718 +(epoch: 222, iters: 2432, time: 0.064) G_GAN: 0.022 G_GAN_Feat: 0.689 G_ID: 0.129 G_Rec: 0.293 D_GP: 0.039 D_real: 0.831 D_fake: 0.978 +(epoch: 222, iters: 2832, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.814 G_ID: 0.189 G_Rec: 0.372 D_GP: 0.037 D_real: 0.927 D_fake: 0.772 +(epoch: 222, iters: 3232, time: 0.064) G_GAN: -0.181 G_GAN_Feat: 0.711 G_ID: 0.165 G_Rec: 0.336 D_GP: 0.035 D_real: 0.814 D_fake: 1.182 +(epoch: 222, iters: 3632, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.790 G_ID: 0.180 G_Rec: 0.400 D_GP: 0.025 D_real: 1.082 D_fake: 0.656 +(epoch: 222, iters: 4032, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.601 G_ID: 0.121 G_Rec: 0.288 D_GP: 0.023 D_real: 1.273 D_fake: 0.675 +(epoch: 222, iters: 4432, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.831 G_ID: 0.173 G_Rec: 0.461 D_GP: 0.042 D_real: 1.090 D_fake: 0.543 +(epoch: 222, iters: 4832, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.553 G_ID: 0.122 G_Rec: 0.261 D_GP: 0.022 D_real: 1.127 D_fake: 0.804 +(epoch: 222, iters: 5232, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 1.118 G_ID: 0.182 G_Rec: 0.512 D_GP: 1.347 D_real: 0.623 D_fake: 0.578 +(epoch: 222, iters: 5632, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.577 G_ID: 0.129 G_Rec: 0.279 D_GP: 0.021 D_real: 1.176 D_fake: 0.759 +(epoch: 222, iters: 6032, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 0.838 G_ID: 0.154 G_Rec: 0.382 D_GP: 0.041 D_real: 1.107 D_fake: 0.498 +(epoch: 222, iters: 6432, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.650 G_ID: 0.128 G_Rec: 0.291 D_GP: 0.031 D_real: 1.142 D_fake: 0.795 +(epoch: 222, iters: 6832, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.895 G_ID: 0.184 G_Rec: 0.432 D_GP: 0.053 D_real: 0.800 D_fake: 0.740 +(epoch: 222, iters: 7232, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.618 G_ID: 0.140 G_Rec: 0.290 D_GP: 0.022 D_real: 1.065 D_fake: 0.803 +(epoch: 222, iters: 7632, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.773 G_ID: 0.175 G_Rec: 0.380 D_GP: 0.026 D_real: 1.204 D_fake: 0.559 +(epoch: 222, iters: 8032, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.591 G_ID: 0.120 G_Rec: 0.277 D_GP: 0.022 D_real: 1.326 D_fake: 0.635 +(epoch: 222, iters: 8432, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.895 G_ID: 0.199 G_Rec: 0.370 D_GP: 0.062 D_real: 0.908 D_fake: 0.496 +(epoch: 223, iters: 224, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.676 G_ID: 0.141 G_Rec: 0.301 D_GP: 0.032 D_real: 0.958 D_fake: 0.817 +(epoch: 223, iters: 624, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.817 G_ID: 0.179 G_Rec: 0.460 D_GP: 0.020 D_real: 1.241 D_fake: 0.570 +(epoch: 223, iters: 1024, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.730 G_ID: 0.166 G_Rec: 0.315 D_GP: 0.035 D_real: 0.971 D_fake: 0.841 +(epoch: 223, iters: 1424, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.811 G_ID: 0.169 G_Rec: 0.450 D_GP: 0.024 D_real: 1.189 D_fake: 0.506 +(epoch: 223, iters: 1824, time: 0.064) G_GAN: -0.041 G_GAN_Feat: 0.662 G_ID: 0.156 G_Rec: 0.301 D_GP: 0.042 D_real: 0.776 D_fake: 1.041 +(epoch: 223, iters: 2224, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.940 G_ID: 0.166 G_Rec: 0.431 D_GP: 0.035 D_real: 0.898 D_fake: 0.702 +(epoch: 223, iters: 2624, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.731 G_ID: 0.126 G_Rec: 0.333 D_GP: 0.101 D_real: 0.770 D_fake: 0.921 +(epoch: 223, iters: 3024, time: 0.064) G_GAN: 0.572 G_GAN_Feat: 0.841 G_ID: 0.170 G_Rec: 0.382 D_GP: 0.024 D_real: 1.228 D_fake: 0.452 +(epoch: 223, iters: 3424, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.755 G_ID: 0.141 G_Rec: 0.324 D_GP: 0.058 D_real: 0.958 D_fake: 0.798 +(epoch: 223, iters: 3824, time: 0.064) G_GAN: 0.568 G_GAN_Feat: 1.018 G_ID: 0.163 G_Rec: 0.425 D_GP: 0.157 D_real: 0.682 D_fake: 0.440 +(epoch: 223, iters: 4224, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.702 G_ID: 0.144 G_Rec: 0.374 D_GP: 0.029 D_real: 1.098 D_fake: 0.829 +(epoch: 223, iters: 4624, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.838 G_ID: 0.168 G_Rec: 0.390 D_GP: 0.020 D_real: 1.330 D_fake: 0.419 +(epoch: 223, iters: 5024, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.718 G_ID: 0.134 G_Rec: 0.301 D_GP: 0.057 D_real: 0.790 D_fake: 0.926 +(epoch: 223, iters: 5424, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.883 G_ID: 0.194 G_Rec: 0.404 D_GP: 0.025 D_real: 1.245 D_fake: 0.440 +(epoch: 223, iters: 5824, time: 0.064) G_GAN: 0.571 G_GAN_Feat: 0.788 G_ID: 0.136 G_Rec: 0.321 D_GP: 0.036 D_real: 1.409 D_fake: 0.539 +(epoch: 223, iters: 6224, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.952 G_ID: 0.160 G_Rec: 0.373 D_GP: 0.161 D_real: 0.397 D_fake: 0.874 +(epoch: 223, iters: 6624, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.729 G_ID: 0.126 G_Rec: 0.357 D_GP: 0.033 D_real: 1.132 D_fake: 0.729 +(epoch: 223, iters: 7024, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.764 G_ID: 0.194 G_Rec: 0.421 D_GP: 0.020 D_real: 1.110 D_fake: 0.750 +(epoch: 223, iters: 7424, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.836 G_ID: 0.134 G_Rec: 0.356 D_GP: 0.021 D_real: 0.933 D_fake: 0.907 +(epoch: 223, iters: 7824, time: 0.064) G_GAN: 0.025 G_GAN_Feat: 0.752 G_ID: 0.163 G_Rec: 0.359 D_GP: 0.020 D_real: 0.878 D_fake: 0.978 +(epoch: 223, iters: 8224, time: 0.064) G_GAN: 0.024 G_GAN_Feat: 0.654 G_ID: 0.138 G_Rec: 0.301 D_GP: 0.030 D_real: 0.956 D_fake: 0.976 +(epoch: 224, iters: 16, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 1.031 G_ID: 0.151 G_Rec: 0.429 D_GP: 0.085 D_real: 0.633 D_fake: 0.595 +(epoch: 224, iters: 416, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.587 G_ID: 0.124 G_Rec: 0.251 D_GP: 0.025 D_real: 1.350 D_fake: 0.614 +(epoch: 224, iters: 816, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.698 G_ID: 0.172 G_Rec: 0.334 D_GP: 0.021 D_real: 1.019 D_fake: 0.759 +(epoch: 224, iters: 1216, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.628 G_ID: 0.143 G_Rec: 0.295 D_GP: 0.024 D_real: 0.940 D_fake: 0.965 +(epoch: 224, iters: 1616, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.831 G_ID: 0.170 G_Rec: 0.377 D_GP: 0.035 D_real: 0.826 D_fake: 0.886 +(epoch: 224, iters: 2016, time: 0.064) G_GAN: -0.224 G_GAN_Feat: 0.790 G_ID: 0.118 G_Rec: 0.325 D_GP: 0.100 D_real: 0.361 D_fake: 1.224 +(epoch: 224, iters: 2416, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.917 G_ID: 0.164 G_Rec: 0.399 D_GP: 0.088 D_real: 0.913 D_fake: 0.489 +(epoch: 224, iters: 2816, time: 0.064) G_GAN: 0.068 G_GAN_Feat: 0.905 G_ID: 0.122 G_Rec: 0.328 D_GP: 0.085 D_real: 0.513 D_fake: 0.933 +(epoch: 224, iters: 3216, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.926 G_ID: 0.195 G_Rec: 0.401 D_GP: 0.037 D_real: 1.107 D_fake: 0.684 +(epoch: 224, iters: 3616, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.707 G_ID: 0.154 G_Rec: 0.300 D_GP: 0.034 D_real: 1.044 D_fake: 0.744 +(epoch: 224, iters: 4016, time: 0.064) G_GAN: 0.694 G_GAN_Feat: 0.831 G_ID: 0.173 G_Rec: 0.394 D_GP: 0.023 D_real: 1.421 D_fake: 0.330 +(epoch: 224, iters: 4416, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.743 G_ID: 0.130 G_Rec: 0.288 D_GP: 0.041 D_real: 1.111 D_fake: 0.605 +(epoch: 224, iters: 4816, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.797 G_ID: 0.167 G_Rec: 0.366 D_GP: 0.025 D_real: 1.356 D_fake: 0.441 +(epoch: 224, iters: 5216, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.682 G_ID: 0.133 G_Rec: 0.298 D_GP: 0.030 D_real: 1.239 D_fake: 0.652 +(epoch: 224, iters: 5616, time: 0.064) G_GAN: 0.833 G_GAN_Feat: 0.994 G_ID: 0.173 G_Rec: 0.440 D_GP: 0.071 D_real: 1.141 D_fake: 0.389 +(epoch: 224, iters: 6016, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.909 G_ID: 0.134 G_Rec: 0.354 D_GP: 0.140 D_real: 0.478 D_fake: 0.684 +(epoch: 224, iters: 6416, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 1.206 G_ID: 0.179 G_Rec: 0.435 D_GP: 0.369 D_real: 0.270 D_fake: 0.611 +(epoch: 224, iters: 6816, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.877 G_ID: 0.118 G_Rec: 0.308 D_GP: 0.050 D_real: 1.008 D_fake: 0.726 +(epoch: 224, iters: 7216, time: 0.064) G_GAN: 0.607 G_GAN_Feat: 0.878 G_ID: 0.186 G_Rec: 0.412 D_GP: 0.020 D_real: 1.313 D_fake: 0.403 +(epoch: 224, iters: 7616, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.643 G_ID: 0.152 G_Rec: 0.287 D_GP: 0.023 D_real: 1.190 D_fake: 0.752 +(epoch: 224, iters: 8016, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 1.062 G_ID: 0.188 G_Rec: 0.451 D_GP: 0.315 D_real: 0.377 D_fake: 0.707 +(epoch: 224, iters: 8416, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.744 G_ID: 0.127 G_Rec: 0.299 D_GP: 0.030 D_real: 1.035 D_fake: 0.730 +(epoch: 225, iters: 208, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.946 G_ID: 0.174 G_Rec: 0.413 D_GP: 0.057 D_real: 0.889 D_fake: 0.661 +(epoch: 225, iters: 608, time: 0.064) G_GAN: -0.008 G_GAN_Feat: 0.724 G_ID: 0.149 G_Rec: 0.306 D_GP: 0.021 D_real: 0.926 D_fake: 1.008 +(epoch: 225, iters: 1008, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.847 G_ID: 0.162 G_Rec: 0.368 D_GP: 0.034 D_real: 1.212 D_fake: 0.495 +(epoch: 225, iters: 1408, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.655 G_ID: 0.138 G_Rec: 0.294 D_GP: 0.020 D_real: 1.290 D_fake: 0.655 +(epoch: 225, iters: 1808, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.838 G_ID: 0.173 G_Rec: 0.373 D_GP: 0.021 D_real: 0.885 D_fake: 0.896 +(epoch: 225, iters: 2208, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.876 G_ID: 0.156 G_Rec: 0.325 D_GP: 0.098 D_real: 0.539 D_fake: 0.618 +(epoch: 225, iters: 2608, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.822 G_ID: 0.165 G_Rec: 0.411 D_GP: 0.024 D_real: 0.964 D_fake: 0.771 +(epoch: 225, iters: 3008, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.631 G_ID: 0.121 G_Rec: 0.295 D_GP: 0.023 D_real: 1.177 D_fake: 0.783 +(epoch: 225, iters: 3408, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.938 G_ID: 0.169 G_Rec: 0.432 D_GP: 0.036 D_real: 0.976 D_fake: 0.581 +(epoch: 225, iters: 3808, time: 0.064) G_GAN: -0.226 G_GAN_Feat: 0.563 G_ID: 0.122 G_Rec: 0.317 D_GP: 0.016 D_real: 0.738 D_fake: 1.227 +(epoch: 225, iters: 4208, time: 0.064) G_GAN: -0.061 G_GAN_Feat: 0.753 G_ID: 0.200 G_Rec: 0.384 D_GP: 0.021 D_real: 0.740 D_fake: 1.062 +(epoch: 225, iters: 4608, time: 0.064) G_GAN: -0.231 G_GAN_Feat: 0.730 G_ID: 0.123 G_Rec: 0.355 D_GP: 0.041 D_real: 0.633 D_fake: 1.232 +(epoch: 225, iters: 5008, time: 0.064) G_GAN: -0.018 G_GAN_Feat: 0.769 G_ID: 0.220 G_Rec: 0.374 D_GP: 0.026 D_real: 0.779 D_fake: 1.018 +(epoch: 225, iters: 5408, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.621 G_ID: 0.127 G_Rec: 0.286 D_GP: 0.029 D_real: 0.937 D_fake: 0.987 +(epoch: 225, iters: 5808, time: 0.064) G_GAN: 0.495 G_GAN_Feat: 0.782 G_ID: 0.159 G_Rec: 0.377 D_GP: 0.025 D_real: 1.241 D_fake: 0.520 +(epoch: 225, iters: 6208, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.898 G_ID: 0.120 G_Rec: 0.335 D_GP: 0.159 D_real: 0.549 D_fake: 0.767 +(epoch: 225, iters: 6608, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.890 G_ID: 0.190 G_Rec: 0.375 D_GP: 0.045 D_real: 0.858 D_fake: 0.672 +(epoch: 225, iters: 7008, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.688 G_ID: 0.152 G_Rec: 0.365 D_GP: 0.020 D_real: 1.254 D_fake: 0.726 +(epoch: 225, iters: 7408, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.819 G_ID: 0.168 G_Rec: 0.413 D_GP: 0.023 D_real: 1.159 D_fake: 0.623 +(epoch: 225, iters: 7808, time: 0.064) G_GAN: -0.070 G_GAN_Feat: 0.702 G_ID: 0.143 G_Rec: 0.303 D_GP: 0.034 D_real: 0.831 D_fake: 1.070 +(epoch: 225, iters: 8208, time: 0.064) G_GAN: 0.590 G_GAN_Feat: 0.835 G_ID: 0.165 G_Rec: 0.371 D_GP: 0.022 D_real: 1.268 D_fake: 0.417 +(epoch: 225, iters: 8608, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.714 G_ID: 0.129 G_Rec: 0.290 D_GP: 0.032 D_real: 1.042 D_fake: 0.814 +(epoch: 226, iters: 400, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.809 G_ID: 0.168 G_Rec: 0.412 D_GP: 0.024 D_real: 1.131 D_fake: 0.608 +(epoch: 226, iters: 800, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.773 G_ID: 0.113 G_Rec: 0.314 D_GP: 0.100 D_real: 0.778 D_fake: 0.831 +(epoch: 226, iters: 1200, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.873 G_ID: 0.211 G_Rec: 0.387 D_GP: 0.024 D_real: 1.095 D_fake: 0.575 +(epoch: 226, iters: 1600, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.857 G_ID: 0.156 G_Rec: 0.319 D_GP: 0.159 D_real: 0.505 D_fake: 0.819 +(epoch: 226, iters: 2000, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.812 G_ID: 0.224 G_Rec: 0.338 D_GP: 0.028 D_real: 1.146 D_fake: 0.565 +(epoch: 226, iters: 2400, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.906 G_ID: 0.134 G_Rec: 0.317 D_GP: 0.032 D_real: 0.708 D_fake: 0.707 +(epoch: 226, iters: 2800, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.717 G_ID: 0.178 G_Rec: 0.385 D_GP: 0.021 D_real: 1.179 D_fake: 0.679 +(epoch: 226, iters: 3200, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.568 G_ID: 0.144 G_Rec: 0.310 D_GP: 0.019 D_real: 1.284 D_fake: 0.675 +(epoch: 226, iters: 3600, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.701 G_ID: 0.172 G_Rec: 0.353 D_GP: 0.021 D_real: 1.209 D_fake: 0.662 +(epoch: 226, iters: 4000, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.652 G_ID: 0.134 G_Rec: 0.322 D_GP: 0.028 D_real: 1.016 D_fake: 0.834 +(epoch: 226, iters: 4400, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 0.791 G_ID: 0.173 G_Rec: 0.391 D_GP: 0.037 D_real: 0.805 D_fake: 0.928 +(epoch: 226, iters: 4800, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.617 G_ID: 0.138 G_Rec: 0.313 D_GP: 0.028 D_real: 1.018 D_fake: 0.909 +(epoch: 226, iters: 5200, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.827 G_ID: 0.164 G_Rec: 0.396 D_GP: 0.033 D_real: 1.185 D_fake: 0.651 +(epoch: 226, iters: 5600, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.611 G_ID: 0.120 G_Rec: 0.299 D_GP: 0.031 D_real: 1.165 D_fake: 0.763 +(epoch: 226, iters: 6000, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.953 G_ID: 0.190 G_Rec: 0.438 D_GP: 0.049 D_real: 1.021 D_fake: 0.534 +(epoch: 226, iters: 6400, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.703 G_ID: 0.142 G_Rec: 0.329 D_GP: 0.053 D_real: 0.948 D_fake: 0.812 +(epoch: 226, iters: 6800, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.767 G_ID: 0.151 G_Rec: 0.381 D_GP: 0.025 D_real: 1.168 D_fake: 0.572 +(epoch: 226, iters: 7200, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.644 G_ID: 0.136 G_Rec: 0.310 D_GP: 0.034 D_real: 1.257 D_fake: 0.687 +(epoch: 226, iters: 7600, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.898 G_ID: 0.173 G_Rec: 0.402 D_GP: 0.038 D_real: 1.109 D_fake: 0.563 +(epoch: 226, iters: 8000, time: 0.064) G_GAN: -0.020 G_GAN_Feat: 0.722 G_ID: 0.154 G_Rec: 0.305 D_GP: 0.039 D_real: 0.684 D_fake: 1.020 +(epoch: 226, iters: 8400, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 1.019 G_ID: 0.180 G_Rec: 0.413 D_GP: 0.124 D_real: 0.449 D_fake: 0.799 +(epoch: 227, iters: 192, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.639 G_ID: 0.130 G_Rec: 0.314 D_GP: 0.017 D_real: 1.214 D_fake: 0.769 +(epoch: 227, iters: 592, time: 0.064) G_GAN: 0.480 G_GAN_Feat: 0.777 G_ID: 0.161 G_Rec: 0.400 D_GP: 0.020 D_real: 1.218 D_fake: 0.522 +(epoch: 227, iters: 992, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.706 G_ID: 0.131 G_Rec: 0.350 D_GP: 0.040 D_real: 0.977 D_fake: 0.817 +(epoch: 227, iters: 1392, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.759 G_ID: 0.167 G_Rec: 0.384 D_GP: 0.029 D_real: 1.296 D_fake: 0.511 +(epoch: 227, iters: 1792, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.729 G_ID: 0.130 G_Rec: 0.362 D_GP: 0.052 D_real: 1.129 D_fake: 0.749 +(epoch: 227, iters: 2192, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.861 G_ID: 0.190 G_Rec: 0.446 D_GP: 0.029 D_real: 1.008 D_fake: 0.597 +(epoch: 227, iters: 2592, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.632 G_ID: 0.114 G_Rec: 0.295 D_GP: 0.031 D_real: 1.181 D_fake: 0.722 +(epoch: 227, iters: 2992, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 1.011 G_ID: 0.182 G_Rec: 0.420 D_GP: 0.088 D_real: 0.805 D_fake: 0.632 +(epoch: 227, iters: 3392, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.804 G_ID: 0.127 G_Rec: 0.306 D_GP: 0.040 D_real: 0.695 D_fake: 0.915 +(epoch: 227, iters: 3792, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.897 G_ID: 0.150 G_Rec: 0.416 D_GP: 0.023 D_real: 0.877 D_fake: 0.878 +(epoch: 227, iters: 4192, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.695 G_ID: 0.139 G_Rec: 0.287 D_GP: 0.026 D_real: 1.176 D_fake: 0.729 +(epoch: 227, iters: 4592, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 1.122 G_ID: 0.228 G_Rec: 0.439 D_GP: 0.090 D_real: 0.423 D_fake: 0.489 +(epoch: 227, iters: 4992, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.695 G_ID: 0.132 G_Rec: 0.331 D_GP: 0.031 D_real: 1.121 D_fake: 0.691 +(epoch: 227, iters: 5392, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.869 G_ID: 0.174 G_Rec: 0.379 D_GP: 0.042 D_real: 0.942 D_fake: 0.646 +(epoch: 227, iters: 5792, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.881 G_ID: 0.141 G_Rec: 0.322 D_GP: 0.067 D_real: 1.356 D_fake: 0.721 +(epoch: 227, iters: 6192, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.854 G_ID: 0.167 G_Rec: 0.409 D_GP: 0.028 D_real: 0.962 D_fake: 0.653 +(epoch: 227, iters: 6592, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.630 G_ID: 0.111 G_Rec: 0.302 D_GP: 0.031 D_real: 1.082 D_fake: 0.868 +(epoch: 227, iters: 6992, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 1.008 G_ID: 0.164 G_Rec: 0.454 D_GP: 0.057 D_real: 0.789 D_fake: 0.638 +(epoch: 227, iters: 7392, time: 0.064) G_GAN: 0.076 G_GAN_Feat: 0.681 G_ID: 0.129 G_Rec: 0.327 D_GP: 0.037 D_real: 1.011 D_fake: 0.925 +(epoch: 227, iters: 7792, time: 0.064) G_GAN: 0.529 G_GAN_Feat: 0.942 G_ID: 0.147 G_Rec: 0.395 D_GP: 0.066 D_real: 0.956 D_fake: 0.478 +(epoch: 227, iters: 8192, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.596 G_ID: 0.143 G_Rec: 0.252 D_GP: 0.022 D_real: 1.034 D_fake: 0.887 +(epoch: 227, iters: 8592, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.824 G_ID: 0.169 G_Rec: 0.403 D_GP: 0.031 D_real: 1.077 D_fake: 0.610 +(epoch: 228, iters: 384, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.652 G_ID: 0.153 G_Rec: 0.331 D_GP: 0.028 D_real: 1.130 D_fake: 0.813 +(epoch: 228, iters: 784, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.885 G_ID: 0.158 G_Rec: 0.390 D_GP: 0.063 D_real: 0.848 D_fake: 0.832 +(epoch: 228, iters: 1184, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 0.741 G_ID: 0.130 G_Rec: 0.308 D_GP: 0.044 D_real: 0.797 D_fake: 0.974 +(epoch: 228, iters: 1584, time: 0.064) G_GAN: 0.647 G_GAN_Feat: 1.097 G_ID: 0.160 G_Rec: 0.422 D_GP: 0.039 D_real: 1.221 D_fake: 0.408 +(epoch: 228, iters: 1984, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.825 G_ID: 0.142 G_Rec: 0.313 D_GP: 0.124 D_real: 0.709 D_fake: 0.907 +(epoch: 228, iters: 2384, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.851 G_ID: 0.198 G_Rec: 0.413 D_GP: 0.028 D_real: 1.116 D_fake: 0.570 +(epoch: 228, iters: 2784, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.630 G_ID: 0.128 G_Rec: 0.273 D_GP: 0.026 D_real: 1.027 D_fake: 0.852 +(epoch: 228, iters: 3184, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.801 G_ID: 0.163 G_Rec: 0.368 D_GP: 0.033 D_real: 1.074 D_fake: 0.671 +(epoch: 228, iters: 3584, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 1.051 G_ID: 0.117 G_Rec: 0.328 D_GP: 0.169 D_real: 0.537 D_fake: 0.945 +(epoch: 228, iters: 3984, time: 0.064) G_GAN: 0.720 G_GAN_Feat: 0.966 G_ID: 0.198 G_Rec: 0.409 D_GP: 0.036 D_real: 1.287 D_fake: 0.359 +(epoch: 228, iters: 4384, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 1.016 G_ID: 0.126 G_Rec: 0.327 D_GP: 0.316 D_real: 0.385 D_fake: 0.792 +(epoch: 228, iters: 4784, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.908 G_ID: 0.203 G_Rec: 0.372 D_GP: 0.050 D_real: 0.856 D_fake: 0.648 +(epoch: 228, iters: 5184, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.720 G_ID: 0.155 G_Rec: 0.307 D_GP: 0.023 D_real: 1.047 D_fake: 0.838 +(epoch: 228, iters: 5584, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.873 G_ID: 0.202 G_Rec: 0.398 D_GP: 0.026 D_real: 0.913 D_fake: 0.750 +(epoch: 228, iters: 5984, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.699 G_ID: 0.147 G_Rec: 0.305 D_GP: 0.020 D_real: 0.993 D_fake: 0.917 +(epoch: 228, iters: 6384, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.874 G_ID: 0.176 G_Rec: 0.353 D_GP: 0.062 D_real: 0.839 D_fake: 0.618 +(epoch: 228, iters: 6784, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.600 G_ID: 0.123 G_Rec: 0.285 D_GP: 0.022 D_real: 1.159 D_fake: 0.775 +(epoch: 228, iters: 7184, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.872 G_ID: 0.183 G_Rec: 0.365 D_GP: 0.026 D_real: 1.190 D_fake: 0.544 +(epoch: 228, iters: 7584, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.892 G_ID: 0.141 G_Rec: 0.308 D_GP: 0.047 D_real: 0.616 D_fake: 0.801 +(epoch: 228, iters: 7984, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 1.145 G_ID: 0.180 G_Rec: 0.421 D_GP: 0.243 D_real: 0.421 D_fake: 0.552 +(epoch: 228, iters: 8384, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.842 G_ID: 0.126 G_Rec: 0.310 D_GP: 0.077 D_real: 0.751 D_fake: 0.695 +(epoch: 229, iters: 176, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.838 G_ID: 0.144 G_Rec: 0.366 D_GP: 0.027 D_real: 1.103 D_fake: 0.669 +(epoch: 229, iters: 576, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.779 G_ID: 0.116 G_Rec: 0.354 D_GP: 0.023 D_real: 1.099 D_fake: 0.725 +(epoch: 229, iters: 976, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.904 G_ID: 0.162 G_Rec: 0.421 D_GP: 0.024 D_real: 1.211 D_fake: 0.581 +(epoch: 229, iters: 1376, time: 0.064) G_GAN: -0.210 G_GAN_Feat: 0.656 G_ID: 0.143 G_Rec: 0.304 D_GP: 0.020 D_real: 0.745 D_fake: 1.210 +(epoch: 229, iters: 1776, time: 0.064) G_GAN: -0.130 G_GAN_Feat: 0.750 G_ID: 0.170 G_Rec: 0.389 D_GP: 0.017 D_real: 0.675 D_fake: 1.131 +(epoch: 229, iters: 2176, time: 0.064) G_GAN: -0.192 G_GAN_Feat: 0.564 G_ID: 0.112 G_Rec: 0.267 D_GP: 0.019 D_real: 0.803 D_fake: 1.192 +(epoch: 229, iters: 2576, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.808 G_ID: 0.171 G_Rec: 0.386 D_GP: 0.030 D_real: 0.781 D_fake: 0.953 +(epoch: 229, iters: 2976, time: 0.064) G_GAN: -0.275 G_GAN_Feat: 0.742 G_ID: 0.122 G_Rec: 0.326 D_GP: 0.060 D_real: 0.403 D_fake: 1.275 +(epoch: 229, iters: 3376, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 0.971 G_ID: 0.166 G_Rec: 0.444 D_GP: 0.035 D_real: 1.150 D_fake: 0.550 +(epoch: 229, iters: 3776, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.784 G_ID: 0.125 G_Rec: 0.315 D_GP: 0.038 D_real: 0.920 D_fake: 0.721 +(epoch: 229, iters: 4176, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.969 G_ID: 0.184 G_Rec: 0.368 D_GP: 0.102 D_real: 0.348 D_fake: 0.882 +(epoch: 229, iters: 4576, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.587 G_ID: 0.110 G_Rec: 0.295 D_GP: 0.020 D_real: 1.260 D_fake: 0.714 +(epoch: 229, iters: 4976, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.718 G_ID: 0.158 G_Rec: 0.413 D_GP: 0.017 D_real: 1.136 D_fake: 0.676 +(epoch: 229, iters: 5376, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.607 G_ID: 0.131 G_Rec: 0.330 D_GP: 0.022 D_real: 1.136 D_fake: 0.810 +(epoch: 229, iters: 5776, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.695 G_ID: 0.181 G_Rec: 0.339 D_GP: 0.023 D_real: 0.937 D_fake: 0.850 +(epoch: 229, iters: 6176, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.584 G_ID: 0.137 G_Rec: 0.342 D_GP: 0.026 D_real: 0.994 D_fake: 0.932 +(epoch: 229, iters: 6576, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.827 G_ID: 0.167 G_Rec: 0.480 D_GP: 0.035 D_real: 0.750 D_fake: 0.878 +(epoch: 229, iters: 6976, time: 0.064) G_GAN: 0.046 G_GAN_Feat: 0.589 G_ID: 0.162 G_Rec: 0.283 D_GP: 0.028 D_real: 0.998 D_fake: 0.954 +(epoch: 229, iters: 7376, time: 0.064) G_GAN: 0.204 G_GAN_Feat: 0.816 G_ID: 0.189 G_Rec: 0.392 D_GP: 0.064 D_real: 0.777 D_fake: 0.799 +(epoch: 229, iters: 7776, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.622 G_ID: 0.142 G_Rec: 0.314 D_GP: 0.036 D_real: 1.002 D_fake: 0.889 +(epoch: 229, iters: 8176, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.844 G_ID: 0.173 G_Rec: 0.406 D_GP: 0.056 D_real: 0.785 D_fake: 0.736 +(epoch: 229, iters: 8576, time: 0.064) G_GAN: 0.109 G_GAN_Feat: 0.591 G_ID: 0.136 G_Rec: 0.303 D_GP: 0.021 D_real: 1.033 D_fake: 0.891 +(epoch: 230, iters: 368, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.805 G_ID: 0.155 G_Rec: 0.379 D_GP: 0.032 D_real: 0.862 D_fake: 0.830 +(epoch: 230, iters: 768, time: 0.064) G_GAN: -0.028 G_GAN_Feat: 0.709 G_ID: 0.122 G_Rec: 0.328 D_GP: 0.029 D_real: 0.930 D_fake: 1.028 +(epoch: 230, iters: 1168, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.825 G_ID: 0.166 G_Rec: 0.424 D_GP: 0.023 D_real: 1.162 D_fake: 0.566 +(epoch: 230, iters: 1568, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.646 G_ID: 0.130 G_Rec: 0.449 D_GP: 0.025 D_real: 1.048 D_fake: 0.856 +(epoch: 230, iters: 1968, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.804 G_ID: 0.171 G_Rec: 0.396 D_GP: 0.028 D_real: 1.142 D_fake: 0.631 +(epoch: 230, iters: 2368, time: 0.064) G_GAN: -0.019 G_GAN_Feat: 0.718 G_ID: 0.145 G_Rec: 0.301 D_GP: 0.074 D_real: 0.715 D_fake: 1.019 +(epoch: 230, iters: 2768, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.812 G_ID: 0.193 G_Rec: 0.386 D_GP: 0.029 D_real: 1.040 D_fake: 0.638 +(epoch: 230, iters: 3168, time: 0.064) G_GAN: -0.105 G_GAN_Feat: 0.639 G_ID: 0.160 G_Rec: 0.325 D_GP: 0.022 D_real: 0.921 D_fake: 1.105 +(epoch: 230, iters: 3568, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.842 G_ID: 0.169 G_Rec: 0.395 D_GP: 0.025 D_real: 0.842 D_fake: 0.901 +(epoch: 230, iters: 3968, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.691 G_ID: 0.112 G_Rec: 0.303 D_GP: 0.035 D_real: 1.258 D_fake: 0.572 +(epoch: 230, iters: 4368, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.722 G_ID: 0.166 G_Rec: 0.362 D_GP: 0.019 D_real: 1.082 D_fake: 0.762 +(epoch: 230, iters: 4768, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.579 G_ID: 0.116 G_Rec: 0.295 D_GP: 0.020 D_real: 1.372 D_fake: 0.598 +(epoch: 230, iters: 5168, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.794 G_ID: 0.184 G_Rec: 0.363 D_GP: 0.030 D_real: 1.169 D_fake: 0.548 +(epoch: 230, iters: 5568, time: 0.064) G_GAN: 0.054 G_GAN_Feat: 0.679 G_ID: 0.137 G_Rec: 0.310 D_GP: 0.030 D_real: 1.036 D_fake: 0.946 +(epoch: 230, iters: 5968, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.871 G_ID: 0.175 G_Rec: 0.380 D_GP: 0.036 D_real: 1.011 D_fake: 0.587 +(epoch: 230, iters: 6368, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.674 G_ID: 0.113 G_Rec: 0.313 D_GP: 0.041 D_real: 1.029 D_fake: 0.829 +(epoch: 230, iters: 6768, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.946 G_ID: 0.180 G_Rec: 0.452 D_GP: 0.041 D_real: 0.887 D_fake: 0.614 +(epoch: 230, iters: 7168, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.857 G_ID: 0.138 G_Rec: 0.310 D_GP: 0.132 D_real: 0.931 D_fake: 0.536 +(epoch: 230, iters: 7568, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.871 G_ID: 0.176 G_Rec: 0.479 D_GP: 0.019 D_real: 1.257 D_fake: 0.669 +(epoch: 230, iters: 7968, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.622 G_ID: 0.129 G_Rec: 0.312 D_GP: 0.020 D_real: 0.984 D_fake: 0.916 +(epoch: 230, iters: 8368, time: 0.064) G_GAN: -0.020 G_GAN_Feat: 0.805 G_ID: 0.172 G_Rec: 0.363 D_GP: 0.031 D_real: 0.738 D_fake: 1.020 +(epoch: 231, iters: 160, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.620 G_ID: 0.114 G_Rec: 0.341 D_GP: 0.028 D_real: 1.038 D_fake: 0.839 +(epoch: 231, iters: 560, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.808 G_ID: 0.154 G_Rec: 0.360 D_GP: 0.037 D_real: 1.091 D_fake: 0.610 +(epoch: 231, iters: 960, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.696 G_ID: 0.138 G_Rec: 0.337 D_GP: 0.033 D_real: 1.008 D_fake: 0.849 +(epoch: 231, iters: 1360, time: 0.064) G_GAN: 0.830 G_GAN_Feat: 0.911 G_ID: 0.166 G_Rec: 0.408 D_GP: 0.057 D_real: 1.468 D_fake: 0.211 +(epoch: 231, iters: 1760, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.706 G_ID: 0.135 G_Rec: 0.334 D_GP: 0.025 D_real: 1.193 D_fake: 0.643 +(epoch: 231, iters: 2160, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 0.778 G_ID: 0.178 G_Rec: 0.379 D_GP: 0.021 D_real: 1.210 D_fake: 0.532 +(epoch: 231, iters: 2560, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.677 G_ID: 0.138 G_Rec: 0.309 D_GP: 0.024 D_real: 0.919 D_fake: 0.938 +(epoch: 231, iters: 2960, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.984 G_ID: 0.184 G_Rec: 0.424 D_GP: 0.039 D_real: 0.939 D_fake: 0.522 +(epoch: 231, iters: 3360, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.662 G_ID: 0.130 G_Rec: 0.308 D_GP: 0.025 D_real: 1.110 D_fake: 0.808 +(epoch: 231, iters: 3760, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.739 G_ID: 0.173 G_Rec: 0.351 D_GP: 0.032 D_real: 1.271 D_fake: 0.477 +(epoch: 231, iters: 4160, time: 0.064) G_GAN: 0.025 G_GAN_Feat: 0.731 G_ID: 0.135 G_Rec: 0.308 D_GP: 0.049 D_real: 0.808 D_fake: 0.975 +(epoch: 231, iters: 4560, time: 0.064) G_GAN: -0.008 G_GAN_Feat: 1.070 G_ID: 0.212 G_Rec: 0.413 D_GP: 0.069 D_real: 0.145 D_fake: 1.013 +(epoch: 231, iters: 4960, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.715 G_ID: 0.141 G_Rec: 0.299 D_GP: 0.039 D_real: 0.924 D_fake: 0.826 +(epoch: 231, iters: 5360, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.950 G_ID: 0.189 G_Rec: 0.415 D_GP: 0.057 D_real: 0.715 D_fake: 0.658 +(epoch: 231, iters: 5760, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.784 G_ID: 0.108 G_Rec: 0.287 D_GP: 0.072 D_real: 0.905 D_fake: 0.779 +(epoch: 231, iters: 6160, time: 0.064) G_GAN: 0.675 G_GAN_Feat: 0.865 G_ID: 0.193 G_Rec: 0.357 D_GP: 0.029 D_real: 1.363 D_fake: 0.337 +(epoch: 231, iters: 6560, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.724 G_ID: 0.138 G_Rec: 0.299 D_GP: 0.042 D_real: 0.877 D_fake: 0.885 +(epoch: 231, iters: 6960, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.921 G_ID: 0.179 G_Rec: 0.401 D_GP: 0.033 D_real: 0.998 D_fake: 0.640 +(epoch: 231, iters: 7360, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.742 G_ID: 0.133 G_Rec: 0.305 D_GP: 0.026 D_real: 1.179 D_fake: 0.646 +(epoch: 231, iters: 7760, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 0.729 G_ID: 0.187 G_Rec: 0.379 D_GP: 0.021 D_real: 1.303 D_fake: 0.593 +(epoch: 231, iters: 8160, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.591 G_ID: 0.124 G_Rec: 0.307 D_GP: 0.018 D_real: 1.212 D_fake: 0.773 +(epoch: 231, iters: 8560, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.798 G_ID: 0.193 G_Rec: 0.433 D_GP: 0.018 D_real: 1.063 D_fake: 0.697 +(epoch: 232, iters: 352, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.558 G_ID: 0.132 G_Rec: 0.279 D_GP: 0.025 D_real: 1.052 D_fake: 0.898 +(epoch: 232, iters: 752, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.818 G_ID: 0.174 G_Rec: 0.407 D_GP: 0.028 D_real: 0.989 D_fake: 0.686 +(epoch: 232, iters: 1152, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.686 G_ID: 0.142 G_Rec: 0.324 D_GP: 0.035 D_real: 0.873 D_fake: 0.942 +(epoch: 232, iters: 1552, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.871 G_ID: 0.193 G_Rec: 0.432 D_GP: 0.056 D_real: 0.855 D_fake: 0.729 +(epoch: 232, iters: 1952, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.669 G_ID: 0.131 G_Rec: 0.291 D_GP: 0.044 D_real: 0.994 D_fake: 0.807 +(epoch: 232, iters: 2352, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.896 G_ID: 0.214 G_Rec: 0.430 D_GP: 0.045 D_real: 0.888 D_fake: 0.711 +(epoch: 232, iters: 2752, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.667 G_ID: 0.132 G_Rec: 0.310 D_GP: 0.052 D_real: 0.937 D_fake: 0.942 +(epoch: 232, iters: 3152, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.864 G_ID: 0.181 G_Rec: 0.436 D_GP: 0.022 D_real: 1.218 D_fake: 0.550 +(epoch: 232, iters: 3552, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.656 G_ID: 0.153 G_Rec: 0.272 D_GP: 0.043 D_real: 1.119 D_fake: 0.736 +(epoch: 232, iters: 3952, time: 0.064) G_GAN: 0.571 G_GAN_Feat: 0.900 G_ID: 0.191 G_Rec: 0.434 D_GP: 0.027 D_real: 1.199 D_fake: 0.445 +(epoch: 232, iters: 4352, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.669 G_ID: 0.130 G_Rec: 0.300 D_GP: 0.026 D_real: 1.215 D_fake: 0.763 +(epoch: 232, iters: 4752, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.914 G_ID: 0.172 G_Rec: 0.410 D_GP: 0.025 D_real: 1.063 D_fake: 0.587 +(epoch: 232, iters: 5152, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.662 G_ID: 0.124 G_Rec: 0.275 D_GP: 0.042 D_real: 0.907 D_fake: 0.947 +(epoch: 232, iters: 5552, time: 0.064) G_GAN: 0.530 G_GAN_Feat: 0.754 G_ID: 0.174 G_Rec: 0.381 D_GP: 0.020 D_real: 1.321 D_fake: 0.475 +(epoch: 232, iters: 5952, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.666 G_ID: 0.165 G_Rec: 0.296 D_GP: 0.025 D_real: 1.130 D_fake: 0.757 +(epoch: 232, iters: 6352, time: 0.064) G_GAN: 0.585 G_GAN_Feat: 0.785 G_ID: 0.178 G_Rec: 0.383 D_GP: 0.026 D_real: 1.300 D_fake: 0.437 +(epoch: 232, iters: 6752, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 0.788 G_ID: 0.142 G_Rec: 0.333 D_GP: 0.058 D_real: 0.663 D_fake: 0.943 +(epoch: 232, iters: 7152, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.913 G_ID: 0.177 G_Rec: 0.408 D_GP: 0.050 D_real: 0.960 D_fake: 0.437 +(epoch: 232, iters: 7552, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.672 G_ID: 0.128 G_Rec: 0.315 D_GP: 0.024 D_real: 1.200 D_fake: 0.682 +(epoch: 232, iters: 7952, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 0.982 G_ID: 0.179 G_Rec: 0.449 D_GP: 0.037 D_real: 1.156 D_fake: 0.413 +(epoch: 232, iters: 8352, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.954 G_ID: 0.145 G_Rec: 0.296 D_GP: 0.080 D_real: 0.390 D_fake: 0.802 +(epoch: 233, iters: 144, time: 0.064) G_GAN: 0.682 G_GAN_Feat: 1.001 G_ID: 0.170 G_Rec: 0.425 D_GP: 0.021 D_real: 1.414 D_fake: 0.394 +(epoch: 233, iters: 544, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.631 G_ID: 0.139 G_Rec: 0.297 D_GP: 0.018 D_real: 1.122 D_fake: 0.819 +(epoch: 233, iters: 944, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.848 G_ID: 0.179 G_Rec: 0.424 D_GP: 0.023 D_real: 1.001 D_fake: 0.733 +(epoch: 233, iters: 1344, time: 0.064) G_GAN: 0.030 G_GAN_Feat: 0.598 G_ID: 0.149 G_Rec: 0.287 D_GP: 0.022 D_real: 0.992 D_fake: 0.970 +(epoch: 233, iters: 1744, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.874 G_ID: 0.175 G_Rec: 0.402 D_GP: 0.045 D_real: 0.870 D_fake: 0.667 +(epoch: 233, iters: 2144, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.721 G_ID: 0.169 G_Rec: 0.311 D_GP: 0.025 D_real: 1.206 D_fake: 0.693 +(epoch: 233, iters: 2544, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 1.023 G_ID: 0.184 G_Rec: 0.421 D_GP: 0.167 D_real: 0.560 D_fake: 0.578 +(epoch: 233, iters: 2944, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.673 G_ID: 0.130 G_Rec: 0.321 D_GP: 0.022 D_real: 0.973 D_fake: 0.919 +(epoch: 233, iters: 3344, time: 0.064) G_GAN: 0.750 G_GAN_Feat: 0.787 G_ID: 0.178 G_Rec: 0.367 D_GP: 0.026 D_real: 1.502 D_fake: 0.315 +(epoch: 233, iters: 3744, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.780 G_ID: 0.155 G_Rec: 0.324 D_GP: 0.038 D_real: 1.016 D_fake: 0.644 +(epoch: 233, iters: 4144, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.061 G_ID: 0.167 G_Rec: 0.428 D_GP: 0.065 D_real: 0.470 D_fake: 0.652 +(epoch: 233, iters: 4544, time: 0.064) G_GAN: -0.002 G_GAN_Feat: 0.683 G_ID: 0.131 G_Rec: 0.303 D_GP: 0.030 D_real: 0.947 D_fake: 1.002 +(epoch: 233, iters: 4944, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.923 G_ID: 0.172 G_Rec: 0.402 D_GP: 0.039 D_real: 1.091 D_fake: 0.499 +(epoch: 233, iters: 5344, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.646 G_ID: 0.145 G_Rec: 0.306 D_GP: 0.020 D_real: 1.375 D_fake: 0.632 +(epoch: 233, iters: 5744, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.869 G_ID: 0.210 G_Rec: 0.430 D_GP: 0.021 D_real: 1.006 D_fake: 0.711 +(epoch: 233, iters: 6144, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.648 G_ID: 0.133 G_Rec: 0.335 D_GP: 0.023 D_real: 0.997 D_fake: 0.939 +(epoch: 233, iters: 6544, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 0.771 G_ID: 0.148 G_Rec: 0.370 D_GP: 0.025 D_real: 1.252 D_fake: 0.553 +(epoch: 233, iters: 6944, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.667 G_ID: 0.110 G_Rec: 0.289 D_GP: 0.036 D_real: 1.065 D_fake: 0.780 +(epoch: 233, iters: 7344, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.904 G_ID: 0.172 G_Rec: 0.393 D_GP: 0.030 D_real: 1.111 D_fake: 0.577 +(epoch: 233, iters: 7744, time: 0.064) G_GAN: -0.033 G_GAN_Feat: 0.935 G_ID: 0.131 G_Rec: 0.340 D_GP: 0.172 D_real: 0.608 D_fake: 1.034 +(epoch: 233, iters: 8144, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.906 G_ID: 0.186 G_Rec: 0.375 D_GP: 0.034 D_real: 1.043 D_fake: 0.658 +(epoch: 233, iters: 8544, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.656 G_ID: 0.123 G_Rec: 0.297 D_GP: 0.020 D_real: 1.205 D_fake: 0.737 +(epoch: 234, iters: 336, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.953 G_ID: 0.164 G_Rec: 0.427 D_GP: 0.051 D_real: 0.704 D_fake: 0.671 +(epoch: 234, iters: 736, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.670 G_ID: 0.115 G_Rec: 0.295 D_GP: 0.026 D_real: 1.217 D_fake: 0.683 +(epoch: 234, iters: 1136, time: 0.064) G_GAN: 0.787 G_GAN_Feat: 0.919 G_ID: 0.163 G_Rec: 0.375 D_GP: 0.323 D_real: 1.141 D_fake: 0.281 +(epoch: 234, iters: 1536, time: 0.064) G_GAN: -0.271 G_GAN_Feat: 0.589 G_ID: 0.121 G_Rec: 0.281 D_GP: 0.017 D_real: 0.694 D_fake: 1.271 +(epoch: 234, iters: 1936, time: 0.064) G_GAN: -0.093 G_GAN_Feat: 0.774 G_ID: 0.182 G_Rec: 0.381 D_GP: 0.019 D_real: 0.740 D_fake: 1.094 +(epoch: 234, iters: 2336, time: 0.064) G_GAN: -0.090 G_GAN_Feat: 0.605 G_ID: 0.127 G_Rec: 0.288 D_GP: 0.023 D_real: 0.831 D_fake: 1.090 +(epoch: 234, iters: 2736, time: 0.064) G_GAN: -0.038 G_GAN_Feat: 0.816 G_ID: 0.161 G_Rec: 0.396 D_GP: 0.026 D_real: 0.711 D_fake: 1.038 +(epoch: 234, iters: 3136, time: 0.064) G_GAN: -0.005 G_GAN_Feat: 0.645 G_ID: 0.129 G_Rec: 0.282 D_GP: 0.030 D_real: 0.887 D_fake: 1.007 +(epoch: 234, iters: 3536, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 0.933 G_ID: 0.160 G_Rec: 0.428 D_GP: 0.043 D_real: 1.107 D_fake: 0.445 +(epoch: 234, iters: 3936, time: 0.064) G_GAN: -0.069 G_GAN_Feat: 0.788 G_ID: 0.111 G_Rec: 0.300 D_GP: 0.115 D_real: 0.518 D_fake: 1.069 +(epoch: 234, iters: 4336, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 1.121 G_ID: 0.148 G_Rec: 0.413 D_GP: 0.209 D_real: 0.506 D_fake: 0.537 +(epoch: 234, iters: 4736, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.674 G_ID: 0.139 G_Rec: 0.261 D_GP: 0.029 D_real: 1.061 D_fake: 0.830 +(epoch: 234, iters: 5136, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.972 G_ID: 0.182 G_Rec: 0.392 D_GP: 0.071 D_real: 0.719 D_fake: 0.556 +(epoch: 234, iters: 5536, time: 0.064) G_GAN: 0.564 G_GAN_Feat: 0.738 G_ID: 0.124 G_Rec: 0.285 D_GP: 0.025 D_real: 1.436 D_fake: 0.481 +(epoch: 234, iters: 5936, time: 0.064) G_GAN: 0.557 G_GAN_Feat: 0.920 G_ID: 0.157 G_Rec: 0.400 D_GP: 0.032 D_real: 1.108 D_fake: 0.460 +(epoch: 234, iters: 6336, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 0.992 G_ID: 0.125 G_Rec: 0.324 D_GP: 0.157 D_real: 0.590 D_fake: 0.488 +(epoch: 234, iters: 6736, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.859 G_ID: 0.185 G_Rec: 0.413 D_GP: 0.050 D_real: 1.168 D_fake: 0.662 +(epoch: 234, iters: 7136, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.731 G_ID: 0.115 G_Rec: 0.317 D_GP: 0.024 D_real: 1.142 D_fake: 0.713 +(epoch: 234, iters: 7536, time: 0.064) G_GAN: 0.068 G_GAN_Feat: 0.877 G_ID: 0.165 G_Rec: 0.396 D_GP: 0.040 D_real: 0.797 D_fake: 0.933 +(epoch: 234, iters: 7936, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.625 G_ID: 0.153 G_Rec: 0.298 D_GP: 0.024 D_real: 1.026 D_fake: 0.879 +(epoch: 234, iters: 8336, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.821 G_ID: 0.179 G_Rec: 0.381 D_GP: 0.024 D_real: 1.254 D_fake: 0.567 +(epoch: 235, iters: 128, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.670 G_ID: 0.128 G_Rec: 0.291 D_GP: 0.035 D_real: 0.978 D_fake: 0.886 +(epoch: 235, iters: 528, time: 0.064) G_GAN: 0.754 G_GAN_Feat: 1.018 G_ID: 0.176 G_Rec: 0.402 D_GP: 0.059 D_real: 1.222 D_fake: 0.315 +(epoch: 235, iters: 928, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.813 G_ID: 0.138 G_Rec: 0.318 D_GP: 0.032 D_real: 1.006 D_fake: 0.657 +(epoch: 235, iters: 1328, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 1.059 G_ID: 0.200 G_Rec: 0.434 D_GP: 0.069 D_real: 0.537 D_fake: 0.528 +(epoch: 235, iters: 1728, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.933 G_ID: 0.122 G_Rec: 0.335 D_GP: 0.035 D_real: 0.925 D_fake: 0.516 +(epoch: 235, iters: 2128, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.848 G_ID: 0.168 G_Rec: 0.364 D_GP: 0.024 D_real: 1.089 D_fake: 0.608 +(epoch: 235, iters: 2528, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.781 G_ID: 0.123 G_Rec: 0.331 D_GP: 0.050 D_real: 0.828 D_fake: 0.817 +(epoch: 235, iters: 2928, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.894 G_ID: 0.178 G_Rec: 0.385 D_GP: 0.036 D_real: 1.134 D_fake: 0.548 +(epoch: 235, iters: 3328, time: 0.064) G_GAN: 0.028 G_GAN_Feat: 0.790 G_ID: 0.156 G_Rec: 0.324 D_GP: 0.041 D_real: 0.996 D_fake: 0.973 +(epoch: 235, iters: 3728, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 1.083 G_ID: 0.176 G_Rec: 0.412 D_GP: 0.100 D_real: 0.804 D_fake: 0.707 +(epoch: 235, iters: 4128, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.663 G_ID: 0.131 G_Rec: 0.314 D_GP: 0.024 D_real: 1.352 D_fake: 0.628 +(epoch: 235, iters: 4528, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.909 G_ID: 0.177 G_Rec: 0.410 D_GP: 0.024 D_real: 1.064 D_fake: 0.731 +(epoch: 235, iters: 4928, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.670 G_ID: 0.114 G_Rec: 0.318 D_GP: 0.116 D_real: 0.825 D_fake: 0.927 +(epoch: 235, iters: 5328, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.861 G_ID: 0.164 G_Rec: 0.420 D_GP: 0.022 D_real: 1.157 D_fake: 0.582 +(epoch: 235, iters: 5728, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.674 G_ID: 0.154 G_Rec: 0.332 D_GP: 0.043 D_real: 0.939 D_fake: 0.888 +(epoch: 235, iters: 6128, time: 0.064) G_GAN: -0.043 G_GAN_Feat: 1.048 G_ID: 0.172 G_Rec: 0.396 D_GP: 0.305 D_real: 0.430 D_fake: 1.043 +(epoch: 235, iters: 6528, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.734 G_ID: 0.136 G_Rec: 0.288 D_GP: 0.039 D_real: 1.120 D_fake: 0.712 +(epoch: 235, iters: 6928, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.834 G_ID: 0.177 G_Rec: 0.366 D_GP: 0.027 D_real: 1.115 D_fake: 0.649 +(epoch: 235, iters: 7328, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.671 G_ID: 0.122 G_Rec: 0.296 D_GP: 0.033 D_real: 1.149 D_fake: 0.711 +(epoch: 235, iters: 7728, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.858 G_ID: 0.170 G_Rec: 0.386 D_GP: 0.025 D_real: 0.891 D_fake: 0.783 +(epoch: 235, iters: 8128, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.633 G_ID: 0.119 G_Rec: 0.284 D_GP: 0.022 D_real: 1.070 D_fake: 0.805 +(epoch: 235, iters: 8528, time: 0.064) G_GAN: 0.434 G_GAN_Feat: 1.078 G_ID: 0.185 G_Rec: 0.436 D_GP: 0.153 D_real: 0.542 D_fake: 0.569 +(epoch: 236, iters: 320, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.788 G_ID: 0.121 G_Rec: 0.287 D_GP: 0.049 D_real: 0.944 D_fake: 0.637 +(epoch: 236, iters: 720, time: 0.064) G_GAN: 0.799 G_GAN_Feat: 0.943 G_ID: 0.186 G_Rec: 0.420 D_GP: 0.027 D_real: 1.425 D_fake: 0.246 +(epoch: 236, iters: 1120, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.687 G_ID: 0.136 G_Rec: 0.289 D_GP: 0.024 D_real: 1.157 D_fake: 0.767 +(epoch: 236, iters: 1520, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 1.033 G_ID: 0.174 G_Rec: 0.434 D_GP: 0.080 D_real: 0.516 D_fake: 0.604 +(epoch: 236, iters: 1920, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.709 G_ID: 0.154 G_Rec: 0.291 D_GP: 0.022 D_real: 1.042 D_fake: 0.902 +(epoch: 236, iters: 2320, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 1.039 G_ID: 0.168 G_Rec: 0.405 D_GP: 0.026 D_real: 0.862 D_fake: 0.565 +(epoch: 236, iters: 2720, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.734 G_ID: 0.139 G_Rec: 0.346 D_GP: 0.028 D_real: 1.057 D_fake: 0.824 +(epoch: 236, iters: 3120, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 1.226 G_ID: 0.186 G_Rec: 0.498 D_GP: 0.044 D_real: 0.292 D_fake: 0.453 +(epoch: 236, iters: 3520, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 1.003 G_ID: 0.138 G_Rec: 0.351 D_GP: 0.041 D_real: 0.410 D_fake: 0.764 +(epoch: 236, iters: 3920, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.804 G_ID: 0.161 G_Rec: 0.411 D_GP: 0.020 D_real: 1.031 D_fake: 0.754 +(epoch: 236, iters: 4320, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.691 G_ID: 0.144 G_Rec: 0.295 D_GP: 0.034 D_real: 0.877 D_fake: 0.919 +(epoch: 236, iters: 4720, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.947 G_ID: 0.161 G_Rec: 0.420 D_GP: 0.033 D_real: 1.164 D_fake: 0.738 +(epoch: 236, iters: 5120, time: 0.064) G_GAN: 0.141 G_GAN_Feat: 0.620 G_ID: 0.113 G_Rec: 0.302 D_GP: 0.019 D_real: 1.143 D_fake: 0.859 +(epoch: 236, iters: 5520, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.862 G_ID: 0.154 G_Rec: 0.382 D_GP: 0.024 D_real: 1.158 D_fake: 0.594 +(epoch: 236, iters: 5920, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.882 G_ID: 0.123 G_Rec: 0.313 D_GP: 0.066 D_real: 0.480 D_fake: 0.903 +(epoch: 236, iters: 6320, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.887 G_ID: 0.200 G_Rec: 0.407 D_GP: 0.023 D_real: 1.097 D_fake: 0.644 +(epoch: 236, iters: 6720, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.780 G_ID: 0.126 G_Rec: 0.303 D_GP: 0.027 D_real: 1.135 D_fake: 0.648 +(epoch: 236, iters: 7120, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.897 G_ID: 0.168 G_Rec: 0.405 D_GP: 0.029 D_real: 1.173 D_fake: 0.479 +(epoch: 236, iters: 7520, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.721 G_ID: 0.118 G_Rec: 0.294 D_GP: 0.040 D_real: 1.154 D_fake: 0.679 +(epoch: 236, iters: 7920, time: 0.064) G_GAN: 0.556 G_GAN_Feat: 0.865 G_ID: 0.192 G_Rec: 0.402 D_GP: 0.027 D_real: 1.231 D_fake: 0.451 +(epoch: 236, iters: 8320, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.762 G_ID: 0.131 G_Rec: 0.315 D_GP: 0.023 D_real: 0.977 D_fake: 0.816 +(epoch: 237, iters: 112, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 0.941 G_ID: 0.189 G_Rec: 0.370 D_GP: 0.085 D_real: 0.743 D_fake: 0.500 +(epoch: 237, iters: 512, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.961 G_ID: 0.116 G_Rec: 0.296 D_GP: 0.394 D_real: 0.523 D_fake: 0.705 +(epoch: 237, iters: 912, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 1.026 G_ID: 0.158 G_Rec: 0.398 D_GP: 0.069 D_real: 0.564 D_fake: 0.690 +(epoch: 237, iters: 1312, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.755 G_ID: 0.151 G_Rec: 0.292 D_GP: 0.031 D_real: 1.116 D_fake: 0.668 +(epoch: 237, iters: 1712, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.728 G_ID: 0.157 G_Rec: 0.422 D_GP: 0.019 D_real: 1.298 D_fake: 0.550 +(epoch: 237, iters: 2112, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.557 G_ID: 0.117 G_Rec: 0.269 D_GP: 0.019 D_real: 1.303 D_fake: 0.669 +(epoch: 237, iters: 2512, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.953 G_ID: 0.166 G_Rec: 0.448 D_GP: 0.036 D_real: 0.949 D_fake: 0.663 +(epoch: 237, iters: 2912, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.798 G_ID: 0.130 G_Rec: 0.307 D_GP: 0.026 D_real: 0.835 D_fake: 0.826 +(epoch: 237, iters: 3312, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.774 G_ID: 0.188 G_Rec: 0.376 D_GP: 0.021 D_real: 1.032 D_fake: 0.784 +(epoch: 237, iters: 3712, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.710 G_ID: 0.128 G_Rec: 0.308 D_GP: 0.064 D_real: 0.951 D_fake: 0.790 +(epoch: 237, iters: 4112, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.876 G_ID: 0.150 G_Rec: 0.384 D_GP: 0.035 D_real: 1.041 D_fake: 0.593 +(epoch: 237, iters: 4512, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.719 G_ID: 0.122 G_Rec: 0.301 D_GP: 0.024 D_real: 1.123 D_fake: 0.796 +(epoch: 237, iters: 4912, time: 0.064) G_GAN: 0.610 G_GAN_Feat: 0.833 G_ID: 0.149 G_Rec: 0.446 D_GP: 0.020 D_real: 1.357 D_fake: 0.398 +(epoch: 237, iters: 5312, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.619 G_ID: 0.106 G_Rec: 0.292 D_GP: 0.021 D_real: 1.336 D_fake: 0.714 +(epoch: 237, iters: 5712, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.804 G_ID: 0.189 G_Rec: 0.396 D_GP: 0.024 D_real: 1.128 D_fake: 0.627 +(epoch: 237, iters: 6112, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.615 G_ID: 0.137 G_Rec: 0.313 D_GP: 0.028 D_real: 1.141 D_fake: 0.780 +(epoch: 237, iters: 6512, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.750 G_ID: 0.165 G_Rec: 0.358 D_GP: 0.030 D_real: 1.137 D_fake: 0.637 +(epoch: 237, iters: 6912, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.648 G_ID: 0.143 G_Rec: 0.294 D_GP: 0.035 D_real: 0.930 D_fake: 0.916 +(epoch: 237, iters: 7312, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.855 G_ID: 0.200 G_Rec: 0.395 D_GP: 0.104 D_real: 1.030 D_fake: 0.592 +(epoch: 237, iters: 7712, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.705 G_ID: 0.135 G_Rec: 0.302 D_GP: 0.039 D_real: 0.921 D_fake: 0.836 +(epoch: 237, iters: 8112, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.882 G_ID: 0.173 G_Rec: 0.377 D_GP: 0.098 D_real: 0.844 D_fake: 0.673 +(epoch: 237, iters: 8512, time: 0.064) G_GAN: -0.053 G_GAN_Feat: 0.684 G_ID: 0.156 G_Rec: 0.292 D_GP: 0.033 D_real: 0.855 D_fake: 1.053 +(epoch: 238, iters: 304, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.787 G_ID: 0.163 G_Rec: 0.345 D_GP: 0.036 D_real: 1.160 D_fake: 0.574 +(epoch: 238, iters: 704, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.680 G_ID: 0.137 G_Rec: 0.289 D_GP: 0.026 D_real: 0.982 D_fake: 0.878 +(epoch: 238, iters: 1104, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.810 G_ID: 0.186 G_Rec: 0.366 D_GP: 0.033 D_real: 1.053 D_fake: 0.721 +(epoch: 238, iters: 1504, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.706 G_ID: 0.143 G_Rec: 0.324 D_GP: 0.027 D_real: 1.059 D_fake: 0.814 +(epoch: 238, iters: 1904, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 1.064 G_ID: 0.162 G_Rec: 0.422 D_GP: 0.121 D_real: 0.578 D_fake: 0.472 +(epoch: 238, iters: 2304, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.613 G_ID: 0.109 G_Rec: 0.284 D_GP: 0.019 D_real: 1.317 D_fake: 0.626 +(epoch: 238, iters: 2704, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.811 G_ID: 0.173 G_Rec: 0.377 D_GP: 0.028 D_real: 1.151 D_fake: 0.553 +(epoch: 238, iters: 3104, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.697 G_ID: 0.125 G_Rec: 0.296 D_GP: 0.059 D_real: 0.989 D_fake: 0.866 +(epoch: 238, iters: 3504, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.784 G_ID: 0.160 G_Rec: 0.363 D_GP: 0.023 D_real: 1.157 D_fake: 0.641 +(epoch: 238, iters: 3904, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.676 G_ID: 0.123 G_Rec: 0.296 D_GP: 0.028 D_real: 1.003 D_fake: 0.829 +(epoch: 238, iters: 4304, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.819 G_ID: 0.199 G_Rec: 0.358 D_GP: 0.026 D_real: 1.089 D_fake: 0.661 +(epoch: 238, iters: 4704, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.721 G_ID: 0.136 G_Rec: 0.298 D_GP: 0.041 D_real: 1.179 D_fake: 0.589 +(epoch: 238, iters: 5104, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.765 G_ID: 0.154 G_Rec: 0.326 D_GP: 0.026 D_real: 1.267 D_fake: 0.540 +(epoch: 238, iters: 5504, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.725 G_ID: 0.127 G_Rec: 0.302 D_GP: 0.022 D_real: 1.018 D_fake: 0.872 +(epoch: 238, iters: 5904, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 0.927 G_ID: 0.208 G_Rec: 0.402 D_GP: 0.031 D_real: 1.127 D_fake: 0.449 +(epoch: 238, iters: 6304, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.842 G_ID: 0.122 G_Rec: 0.299 D_GP: 0.065 D_real: 0.705 D_fake: 0.604 +(epoch: 238, iters: 6704, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.747 G_ID: 0.195 G_Rec: 0.399 D_GP: 0.019 D_real: 1.182 D_fake: 0.653 +(epoch: 238, iters: 7104, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.657 G_ID: 0.179 G_Rec: 0.357 D_GP: 0.022 D_real: 1.079 D_fake: 0.876 +(epoch: 238, iters: 7504, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.766 G_ID: 0.166 G_Rec: 0.375 D_GP: 0.022 D_real: 1.099 D_fake: 0.661 +(epoch: 238, iters: 7904, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.575 G_ID: 0.126 G_Rec: 0.276 D_GP: 0.024 D_real: 1.004 D_fake: 0.931 +(epoch: 238, iters: 8304, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.940 G_ID: 0.187 G_Rec: 0.441 D_GP: 0.044 D_real: 0.653 D_fake: 0.671 +(epoch: 239, iters: 96, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.839 G_ID: 0.126 G_Rec: 0.311 D_GP: 0.216 D_real: 0.646 D_fake: 0.883 +(epoch: 239, iters: 496, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.965 G_ID: 0.193 G_Rec: 0.367 D_GP: 0.079 D_real: 0.306 D_fake: 0.921 +(epoch: 239, iters: 896, time: 0.064) G_GAN: 0.075 G_GAN_Feat: 0.640 G_ID: 0.140 G_Rec: 0.285 D_GP: 0.021 D_real: 1.012 D_fake: 0.925 +(epoch: 239, iters: 1296, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.821 G_ID: 0.175 G_Rec: 0.405 D_GP: 0.032 D_real: 1.038 D_fake: 0.647 +(epoch: 239, iters: 1696, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.657 G_ID: 0.127 G_Rec: 0.292 D_GP: 0.024 D_real: 0.908 D_fake: 0.977 +(epoch: 239, iters: 2096, time: 0.064) G_GAN: 0.610 G_GAN_Feat: 0.848 G_ID: 0.174 G_Rec: 0.387 D_GP: 0.038 D_real: 1.152 D_fake: 0.425 +(epoch: 239, iters: 2496, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.776 G_ID: 0.115 G_Rec: 0.320 D_GP: 0.042 D_real: 1.153 D_fake: 0.722 +(epoch: 239, iters: 2896, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 1.128 G_ID: 0.176 G_Rec: 0.440 D_GP: 0.061 D_real: 0.416 D_fake: 0.601 +(epoch: 239, iters: 3296, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.738 G_ID: 0.134 G_Rec: 0.300 D_GP: 0.056 D_real: 1.105 D_fake: 0.622 +(epoch: 239, iters: 3696, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.826 G_ID: 0.215 G_Rec: 0.369 D_GP: 0.027 D_real: 1.034 D_fake: 0.803 +(epoch: 239, iters: 4096, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.833 G_ID: 0.111 G_Rec: 0.311 D_GP: 0.031 D_real: 0.877 D_fake: 0.708 +(epoch: 239, iters: 4496, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.832 G_ID: 0.158 G_Rec: 0.420 D_GP: 0.021 D_real: 0.842 D_fake: 0.947 +(epoch: 239, iters: 4896, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.691 G_ID: 0.133 G_Rec: 0.307 D_GP: 0.022 D_real: 1.133 D_fake: 0.798 +(epoch: 239, iters: 5296, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 1.122 G_ID: 0.164 G_Rec: 0.465 D_GP: 0.192 D_real: 0.372 D_fake: 0.511 +(epoch: 239, iters: 5696, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.896 G_ID: 0.127 G_Rec: 0.342 D_GP: 0.057 D_real: 0.528 D_fake: 0.769 +(epoch: 239, iters: 6096, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.885 G_ID: 0.167 G_Rec: 0.409 D_GP: 0.022 D_real: 1.182 D_fake: 0.565 +(epoch: 239, iters: 6496, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.656 G_ID: 0.154 G_Rec: 0.307 D_GP: 0.019 D_real: 1.105 D_fake: 0.823 +(epoch: 239, iters: 6896, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.769 G_ID: 0.174 G_Rec: 0.415 D_GP: 0.019 D_real: 1.141 D_fake: 0.632 +(epoch: 239, iters: 7296, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.537 G_ID: 0.131 G_Rec: 0.285 D_GP: 0.016 D_real: 1.191 D_fake: 0.761 +(epoch: 239, iters: 7696, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.781 G_ID: 0.184 G_Rec: 0.410 D_GP: 0.025 D_real: 1.233 D_fake: 0.542 +(epoch: 239, iters: 8096, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.597 G_ID: 0.130 G_Rec: 0.288 D_GP: 0.024 D_real: 1.082 D_fake: 0.800 +(epoch: 239, iters: 8496, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.775 G_ID: 0.172 G_Rec: 0.362 D_GP: 0.029 D_real: 1.124 D_fake: 0.641 +(epoch: 240, iters: 288, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.614 G_ID: 0.123 G_Rec: 0.273 D_GP: 0.025 D_real: 1.116 D_fake: 0.818 +(epoch: 240, iters: 688, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.943 G_ID: 0.175 G_Rec: 0.412 D_GP: 0.037 D_real: 0.933 D_fake: 0.574 +(epoch: 240, iters: 1088, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.732 G_ID: 0.144 G_Rec: 0.327 D_GP: 0.043 D_real: 0.801 D_fake: 0.948 +(epoch: 240, iters: 1488, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.811 G_ID: 0.169 G_Rec: 0.355 D_GP: 0.022 D_real: 1.336 D_fake: 0.513 +(epoch: 240, iters: 1888, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.710 G_ID: 0.146 G_Rec: 0.333 D_GP: 0.029 D_real: 0.975 D_fake: 0.819 +(epoch: 240, iters: 2288, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 1.051 G_ID: 0.182 G_Rec: 0.429 D_GP: 0.155 D_real: 0.514 D_fake: 0.640 +(epoch: 240, iters: 2688, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.722 G_ID: 0.126 G_Rec: 0.314 D_GP: 0.021 D_real: 1.128 D_fake: 0.710 +(epoch: 240, iters: 3088, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.784 G_ID: 0.185 G_Rec: 0.372 D_GP: 0.034 D_real: 1.069 D_fake: 0.690 +(epoch: 240, iters: 3488, time: 0.064) G_GAN: 0.070 G_GAN_Feat: 0.730 G_ID: 0.118 G_Rec: 0.271 D_GP: 0.042 D_real: 0.800 D_fake: 0.930 +(epoch: 240, iters: 3888, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.882 G_ID: 0.205 G_Rec: 0.378 D_GP: 0.103 D_real: 0.809 D_fake: 0.548 +(epoch: 240, iters: 4288, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.923 G_ID: 0.123 G_Rec: 0.320 D_GP: 0.129 D_real: 0.514 D_fake: 0.744 +(epoch: 240, iters: 4688, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.845 G_ID: 0.168 G_Rec: 0.367 D_GP: 0.022 D_real: 1.207 D_fake: 0.573 +(epoch: 240, iters: 5088, time: 0.064) G_GAN: -0.036 G_GAN_Feat: 0.680 G_ID: 0.146 G_Rec: 0.298 D_GP: 0.020 D_real: 0.913 D_fake: 1.036 +(epoch: 240, iters: 5488, time: 0.064) G_GAN: 0.645 G_GAN_Feat: 0.819 G_ID: 0.163 G_Rec: 0.432 D_GP: 0.027 D_real: 1.316 D_fake: 0.366 +(epoch: 240, iters: 5888, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.650 G_ID: 0.143 G_Rec: 0.291 D_GP: 0.021 D_real: 1.073 D_fake: 0.869 +(epoch: 240, iters: 6288, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.931 G_ID: 0.153 G_Rec: 0.410 D_GP: 0.063 D_real: 0.977 D_fake: 0.544 +(epoch: 240, iters: 6688, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.808 G_ID: 0.157 G_Rec: 0.337 D_GP: 0.083 D_real: 0.524 D_fake: 0.888 +(epoch: 240, iters: 7088, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.950 G_ID: 0.175 G_Rec: 0.366 D_GP: 0.098 D_real: 0.522 D_fake: 0.612 +(epoch: 240, iters: 7488, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.726 G_ID: 0.129 G_Rec: 0.298 D_GP: 0.026 D_real: 1.073 D_fake: 0.765 +(epoch: 240, iters: 7888, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.927 G_ID: 0.185 G_Rec: 0.390 D_GP: 0.112 D_real: 0.798 D_fake: 0.762 +(epoch: 240, iters: 8288, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.761 G_ID: 0.139 G_Rec: 0.309 D_GP: 0.025 D_real: 1.402 D_fake: 0.524 +(epoch: 241, iters: 80, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 1.083 G_ID: 0.187 G_Rec: 0.426 D_GP: 0.943 D_real: 0.430 D_fake: 0.889 +(epoch: 241, iters: 480, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.687 G_ID: 0.110 G_Rec: 0.290 D_GP: 0.021 D_real: 1.105 D_fake: 0.795 +(epoch: 241, iters: 880, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.945 G_ID: 0.170 G_Rec: 0.410 D_GP: 0.023 D_real: 1.086 D_fake: 0.553 +(epoch: 241, iters: 1280, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.827 G_ID: 0.147 G_Rec: 0.339 D_GP: 0.088 D_real: 0.568 D_fake: 0.814 +(epoch: 241, iters: 1680, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.979 G_ID: 0.191 G_Rec: 0.378 D_GP: 0.036 D_real: 0.856 D_fake: 0.508 +(epoch: 241, iters: 2080, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.733 G_ID: 0.134 G_Rec: 0.287 D_GP: 0.035 D_real: 1.049 D_fake: 0.733 +(epoch: 241, iters: 2480, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.903 G_ID: 0.163 G_Rec: 0.407 D_GP: 0.104 D_real: 0.819 D_fake: 0.691 +(epoch: 241, iters: 2880, time: 0.064) G_GAN: -0.060 G_GAN_Feat: 0.719 G_ID: 0.140 G_Rec: 0.297 D_GP: 0.032 D_real: 0.749 D_fake: 1.060 +(epoch: 241, iters: 3280, time: 0.064) G_GAN: 0.814 G_GAN_Feat: 1.002 G_ID: 0.160 G_Rec: 0.441 D_GP: 0.039 D_real: 1.226 D_fake: 0.256 +(epoch: 241, iters: 3680, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.845 G_ID: 0.124 G_Rec: 0.319 D_GP: 0.068 D_real: 0.784 D_fake: 0.824 +(epoch: 241, iters: 4080, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.749 G_ID: 0.173 G_Rec: 0.368 D_GP: 0.018 D_real: 1.359 D_fake: 0.634 +(epoch: 241, iters: 4480, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.619 G_ID: 0.124 G_Rec: 0.318 D_GP: 0.022 D_real: 1.187 D_fake: 0.774 +(epoch: 241, iters: 4880, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.697 G_ID: 0.129 G_Rec: 0.346 D_GP: 0.015 D_real: 1.326 D_fake: 0.539 +(epoch: 241, iters: 5280, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.649 G_ID: 0.126 G_Rec: 0.319 D_GP: 0.036 D_real: 1.064 D_fake: 0.839 +(epoch: 241, iters: 5680, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.782 G_ID: 0.168 G_Rec: 0.380 D_GP: 0.024 D_real: 1.198 D_fake: 0.554 +(epoch: 241, iters: 6080, time: 0.064) G_GAN: 0.049 G_GAN_Feat: 0.633 G_ID: 0.133 G_Rec: 0.288 D_GP: 0.044 D_real: 0.923 D_fake: 0.951 +(epoch: 241, iters: 6480, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.732 G_ID: 0.172 G_Rec: 0.364 D_GP: 0.020 D_real: 1.175 D_fake: 0.631 +(epoch: 241, iters: 6880, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.622 G_ID: 0.112 G_Rec: 0.306 D_GP: 0.039 D_real: 1.018 D_fake: 0.855 +(epoch: 241, iters: 7280, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.924 G_ID: 0.163 G_Rec: 0.411 D_GP: 0.026 D_real: 1.018 D_fake: 0.610 +(epoch: 241, iters: 7680, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.592 G_ID: 0.132 G_Rec: 0.263 D_GP: 0.022 D_real: 1.119 D_fake: 0.798 +(epoch: 241, iters: 8080, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.872 G_ID: 0.154 G_Rec: 0.436 D_GP: 0.041 D_real: 1.032 D_fake: 0.658 +(epoch: 241, iters: 8480, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.690 G_ID: 0.106 G_Rec: 0.293 D_GP: 0.128 D_real: 0.817 D_fake: 0.952 +(epoch: 242, iters: 272, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.777 G_ID: 0.152 G_Rec: 0.378 D_GP: 0.022 D_real: 1.000 D_fake: 0.788 +(epoch: 242, iters: 672, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.635 G_ID: 0.107 G_Rec: 0.319 D_GP: 0.028 D_real: 1.095 D_fake: 0.903 +(epoch: 242, iters: 1072, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.815 G_ID: 0.155 G_Rec: 0.391 D_GP: 0.027 D_real: 1.101 D_fake: 0.654 +(epoch: 242, iters: 1472, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.818 G_ID: 0.118 G_Rec: 0.322 D_GP: 0.457 D_real: 0.715 D_fake: 0.886 +(epoch: 242, iters: 1872, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.813 G_ID: 0.166 G_Rec: 0.360 D_GP: 0.023 D_real: 0.931 D_fake: 0.761 +(epoch: 242, iters: 2272, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.683 G_ID: 0.125 G_Rec: 0.298 D_GP: 0.034 D_real: 1.034 D_fake: 0.761 +(epoch: 242, iters: 2672, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.922 G_ID: 0.187 G_Rec: 0.425 D_GP: 0.028 D_real: 1.065 D_fake: 0.618 +(epoch: 242, iters: 3072, time: 0.064) G_GAN: -0.087 G_GAN_Feat: 0.760 G_ID: 0.133 G_Rec: 0.301 D_GP: 0.102 D_real: 0.521 D_fake: 1.087 +(epoch: 242, iters: 3472, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.756 G_ID: 0.174 G_Rec: 0.356 D_GP: 0.022 D_real: 1.235 D_fake: 0.606 +(epoch: 242, iters: 3872, time: 0.064) G_GAN: 0.021 G_GAN_Feat: 0.707 G_ID: 0.130 G_Rec: 0.328 D_GP: 0.022 D_real: 0.913 D_fake: 0.979 +(epoch: 242, iters: 4272, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.841 G_ID: 0.168 G_Rec: 0.409 D_GP: 0.026 D_real: 0.993 D_fake: 0.787 +(epoch: 242, iters: 4672, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.769 G_ID: 0.139 G_Rec: 0.342 D_GP: 0.083 D_real: 0.739 D_fake: 0.896 +(epoch: 242, iters: 5072, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.807 G_ID: 0.194 G_Rec: 0.403 D_GP: 0.024 D_real: 1.163 D_fake: 0.632 +(epoch: 242, iters: 5472, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.597 G_ID: 0.130 G_Rec: 0.279 D_GP: 0.019 D_real: 1.165 D_fake: 0.778 +(epoch: 242, iters: 5872, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.807 G_ID: 0.180 G_Rec: 0.379 D_GP: 0.026 D_real: 1.073 D_fake: 0.670 +(epoch: 242, iters: 6272, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.734 G_ID: 0.132 G_Rec: 0.269 D_GP: 0.035 D_real: 1.028 D_fake: 0.736 +(epoch: 242, iters: 6672, time: 0.064) G_GAN: 0.647 G_GAN_Feat: 1.153 G_ID: 0.170 G_Rec: 0.455 D_GP: 0.033 D_real: 0.958 D_fake: 0.402 +(epoch: 242, iters: 7072, time: 0.064) G_GAN: -0.231 G_GAN_Feat: 0.743 G_ID: 0.126 G_Rec: 0.285 D_GP: 0.036 D_real: 0.517 D_fake: 1.231 +(epoch: 242, iters: 7472, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.941 G_ID: 0.196 G_Rec: 0.418 D_GP: 0.059 D_real: 0.823 D_fake: 0.580 +(epoch: 242, iters: 7872, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.571 G_ID: 0.116 G_Rec: 0.282 D_GP: 0.018 D_real: 1.270 D_fake: 0.713 +(epoch: 242, iters: 8272, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.874 G_ID: 0.169 G_Rec: 0.432 D_GP: 0.034 D_real: 0.937 D_fake: 0.748 +(epoch: 243, iters: 64, time: 0.064) G_GAN: 0.001 G_GAN_Feat: 0.671 G_ID: 0.140 G_Rec: 0.292 D_GP: 0.035 D_real: 0.863 D_fake: 0.999 +(epoch: 243, iters: 464, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.936 G_ID: 0.186 G_Rec: 0.406 D_GP: 0.037 D_real: 0.855 D_fake: 0.601 +(epoch: 243, iters: 864, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.641 G_ID: 0.129 G_Rec: 0.271 D_GP: 0.025 D_real: 1.405 D_fake: 0.484 +(epoch: 243, iters: 1264, time: 0.064) G_GAN: 0.070 G_GAN_Feat: 0.857 G_ID: 0.177 G_Rec: 0.442 D_GP: 0.020 D_real: 0.765 D_fake: 0.930 +(epoch: 243, iters: 1664, time: 0.064) G_GAN: -0.112 G_GAN_Feat: 0.659 G_ID: 0.137 G_Rec: 0.312 D_GP: 0.026 D_real: 0.765 D_fake: 1.112 +(epoch: 243, iters: 2064, time: 0.064) G_GAN: -0.069 G_GAN_Feat: 0.976 G_ID: 0.185 G_Rec: 0.452 D_GP: 0.098 D_real: 0.404 D_fake: 1.069 +(epoch: 243, iters: 2464, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.672 G_ID: 0.121 G_Rec: 0.283 D_GP: 0.021 D_real: 1.061 D_fake: 0.868 +(epoch: 243, iters: 2864, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 0.847 G_ID: 0.189 G_Rec: 0.376 D_GP: 0.033 D_real: 1.224 D_fake: 0.481 +(epoch: 243, iters: 3264, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.690 G_ID: 0.107 G_Rec: 0.283 D_GP: 0.024 D_real: 1.189 D_fake: 0.699 +(epoch: 243, iters: 3664, time: 0.064) G_GAN: 0.716 G_GAN_Feat: 0.934 G_ID: 0.177 G_Rec: 0.391 D_GP: 0.022 D_real: 1.452 D_fake: 0.374 +(epoch: 243, iters: 4064, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.640 G_ID: 0.132 G_Rec: 0.290 D_GP: 0.023 D_real: 1.431 D_fake: 0.532 +(epoch: 243, iters: 4464, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.980 G_ID: 0.164 G_Rec: 0.429 D_GP: 0.033 D_real: 1.093 D_fake: 0.407 +(epoch: 243, iters: 4864, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.556 G_ID: 0.140 G_Rec: 0.287 D_GP: 0.015 D_real: 1.163 D_fake: 0.799 +(epoch: 243, iters: 5264, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.722 G_ID: 0.187 G_Rec: 0.373 D_GP: 0.018 D_real: 1.174 D_fake: 0.630 +(epoch: 243, iters: 5664, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.540 G_ID: 0.138 G_Rec: 0.278 D_GP: 0.016 D_real: 1.190 D_fake: 0.782 +(epoch: 243, iters: 6064, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.768 G_ID: 0.163 G_Rec: 0.375 D_GP: 0.029 D_real: 0.988 D_fake: 0.831 +(epoch: 243, iters: 6464, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.546 G_ID: 0.127 G_Rec: 0.265 D_GP: 0.022 D_real: 1.032 D_fake: 0.898 +(epoch: 243, iters: 6864, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.868 G_ID: 0.150 G_Rec: 0.421 D_GP: 0.048 D_real: 0.824 D_fake: 0.761 +(epoch: 243, iters: 7264, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.720 G_ID: 0.124 G_Rec: 0.325 D_GP: 0.043 D_real: 0.885 D_fake: 0.858 +(epoch: 243, iters: 7664, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.909 G_ID: 0.173 G_Rec: 0.433 D_GP: 0.052 D_real: 1.008 D_fake: 0.617 +(epoch: 243, iters: 8064, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.783 G_ID: 0.124 G_Rec: 0.326 D_GP: 0.073 D_real: 0.922 D_fake: 0.726 +(epoch: 243, iters: 8464, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.801 G_ID: 0.175 G_Rec: 0.414 D_GP: 0.021 D_real: 1.079 D_fake: 0.631 +(epoch: 244, iters: 256, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.731 G_ID: 0.127 G_Rec: 0.312 D_GP: 0.077 D_real: 0.782 D_fake: 0.890 +(epoch: 244, iters: 656, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.886 G_ID: 0.157 G_Rec: 0.392 D_GP: 0.043 D_real: 0.929 D_fake: 0.575 +(epoch: 244, iters: 1056, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.691 G_ID: 0.133 G_Rec: 0.312 D_GP: 0.024 D_real: 1.269 D_fake: 0.609 +(epoch: 244, iters: 1456, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 0.868 G_ID: 0.183 G_Rec: 0.400 D_GP: 0.024 D_real: 1.116 D_fake: 0.507 +(epoch: 244, iters: 1856, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.620 G_ID: 0.147 G_Rec: 0.310 D_GP: 0.024 D_real: 0.997 D_fake: 0.901 +(epoch: 244, iters: 2256, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.755 G_ID: 0.162 G_Rec: 0.358 D_GP: 0.031 D_real: 1.262 D_fake: 0.523 +(epoch: 244, iters: 2656, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.689 G_ID: 0.116 G_Rec: 0.309 D_GP: 0.039 D_real: 1.078 D_fake: 0.653 +(epoch: 244, iters: 3056, time: 0.064) G_GAN: 0.671 G_GAN_Feat: 0.905 G_ID: 0.183 G_Rec: 0.411 D_GP: 0.039 D_real: 1.187 D_fake: 0.357 +(epoch: 244, iters: 3456, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.651 G_ID: 0.108 G_Rec: 0.260 D_GP: 0.023 D_real: 1.212 D_fake: 0.740 +(epoch: 244, iters: 3856, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 1.079 G_ID: 0.175 G_Rec: 0.422 D_GP: 0.046 D_real: 0.255 D_fake: 0.831 +(epoch: 244, iters: 4256, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.689 G_ID: 0.164 G_Rec: 0.319 D_GP: 0.021 D_real: 0.931 D_fake: 0.913 +(epoch: 244, iters: 4656, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.852 G_ID: 0.147 G_Rec: 0.396 D_GP: 0.028 D_real: 0.993 D_fake: 0.668 +(epoch: 244, iters: 5056, time: 0.064) G_GAN: 0.016 G_GAN_Feat: 0.606 G_ID: 0.119 G_Rec: 0.266 D_GP: 0.023 D_real: 0.962 D_fake: 0.984 +(epoch: 244, iters: 5456, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.919 G_ID: 0.163 G_Rec: 0.378 D_GP: 0.032 D_real: 1.177 D_fake: 0.556 +(epoch: 244, iters: 5856, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.737 G_ID: 0.131 G_Rec: 0.329 D_GP: 0.025 D_real: 1.130 D_fake: 0.761 +(epoch: 244, iters: 6256, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.891 G_ID: 0.186 G_Rec: 0.377 D_GP: 0.035 D_real: 1.017 D_fake: 0.586 +(epoch: 244, iters: 6656, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.687 G_ID: 0.132 G_Rec: 0.288 D_GP: 0.051 D_real: 0.873 D_fake: 0.869 +(epoch: 244, iters: 7056, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 0.934 G_ID: 0.160 G_Rec: 0.430 D_GP: 0.099 D_real: 0.856 D_fake: 0.544 +(epoch: 244, iters: 7456, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.621 G_ID: 0.145 G_Rec: 0.287 D_GP: 0.023 D_real: 1.196 D_fake: 0.638 +(epoch: 244, iters: 7856, time: 0.064) G_GAN: 0.767 G_GAN_Feat: 0.826 G_ID: 0.162 G_Rec: 0.388 D_GP: 0.023 D_real: 1.565 D_fake: 0.265 +(epoch: 244, iters: 8256, time: 0.064) G_GAN: -0.168 G_GAN_Feat: 0.932 G_ID: 0.120 G_Rec: 0.308 D_GP: 0.740 D_real: 0.328 D_fake: 1.170 +(epoch: 245, iters: 48, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.863 G_ID: 0.157 G_Rec: 0.379 D_GP: 0.024 D_real: 1.127 D_fake: 0.609 +(epoch: 245, iters: 448, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.799 G_ID: 0.157 G_Rec: 0.366 D_GP: 0.056 D_real: 0.774 D_fake: 0.823 +(epoch: 245, iters: 848, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.968 G_ID: 0.183 G_Rec: 0.405 D_GP: 0.052 D_real: 0.848 D_fake: 0.519 +(epoch: 245, iters: 1248, time: 0.064) G_GAN: -0.267 G_GAN_Feat: 0.670 G_ID: 0.122 G_Rec: 0.370 D_GP: 0.025 D_real: 0.693 D_fake: 1.267 +(epoch: 245, iters: 1648, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.903 G_ID: 0.156 G_Rec: 0.389 D_GP: 0.031 D_real: 1.088 D_fake: 0.606 +(epoch: 245, iters: 2048, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.661 G_ID: 0.134 G_Rec: 0.276 D_GP: 0.025 D_real: 1.450 D_fake: 0.510 +(epoch: 245, iters: 2448, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 1.027 G_ID: 0.173 G_Rec: 0.450 D_GP: 0.033 D_real: 0.854 D_fake: 0.677 +(epoch: 245, iters: 2848, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.679 G_ID: 0.124 G_Rec: 0.314 D_GP: 0.022 D_real: 1.148 D_fake: 0.777 +(epoch: 245, iters: 3248, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.822 G_ID: 0.167 G_Rec: 0.379 D_GP: 0.023 D_real: 1.182 D_fake: 0.597 +(epoch: 245, iters: 3648, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.720 G_ID: 0.134 G_Rec: 0.311 D_GP: 0.026 D_real: 0.991 D_fake: 0.860 +(epoch: 245, iters: 4048, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.812 G_ID: 0.188 G_Rec: 0.372 D_GP: 0.022 D_real: 1.026 D_fake: 0.737 +(epoch: 245, iters: 4448, time: 0.064) G_GAN: -0.289 G_GAN_Feat: 0.867 G_ID: 0.134 G_Rec: 0.331 D_GP: 0.644 D_real: 0.332 D_fake: 1.289 +(epoch: 245, iters: 4848, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.957 G_ID: 0.167 G_Rec: 0.411 D_GP: 0.057 D_real: 0.763 D_fake: 0.643 +(epoch: 245, iters: 5248, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.691 G_ID: 0.115 G_Rec: 0.283 D_GP: 0.035 D_real: 1.135 D_fake: 0.674 +(epoch: 245, iters: 5648, time: 0.064) G_GAN: 0.587 G_GAN_Feat: 0.883 G_ID: 0.171 G_Rec: 0.394 D_GP: 0.027 D_real: 1.333 D_fake: 0.462 +(epoch: 245, iters: 6048, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.759 G_ID: 0.131 G_Rec: 0.300 D_GP: 0.031 D_real: 1.080 D_fake: 0.690 +(epoch: 245, iters: 6448, time: 0.064) G_GAN: 0.846 G_GAN_Feat: 0.907 G_ID: 0.174 G_Rec: 0.404 D_GP: 0.024 D_real: 1.545 D_fake: 0.217 +(epoch: 245, iters: 6848, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.599 G_ID: 0.119 G_Rec: 0.280 D_GP: 0.019 D_real: 1.132 D_fake: 0.765 +(epoch: 245, iters: 7248, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.839 G_ID: 0.208 G_Rec: 0.400 D_GP: 0.026 D_real: 0.928 D_fake: 0.808 +(epoch: 245, iters: 7648, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.809 G_ID: 0.120 G_Rec: 0.336 D_GP: 0.378 D_real: 0.736 D_fake: 0.821 +(epoch: 245, iters: 8048, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 1.014 G_ID: 0.166 G_Rec: 0.450 D_GP: 0.090 D_real: 0.621 D_fake: 0.548 +(epoch: 245, iters: 8448, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.872 G_ID: 0.126 G_Rec: 0.290 D_GP: 0.059 D_real: 0.510 D_fake: 0.785 +(epoch: 246, iters: 240, time: 0.064) G_GAN: 0.641 G_GAN_Feat: 0.854 G_ID: 0.181 G_Rec: 0.403 D_GP: 0.021 D_real: 1.361 D_fake: 0.419 +(epoch: 246, iters: 640, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.758 G_ID: 0.121 G_Rec: 0.324 D_GP: 0.026 D_real: 0.901 D_fake: 0.890 +(epoch: 246, iters: 1040, time: 0.064) G_GAN: 0.800 G_GAN_Feat: 1.047 G_ID: 0.160 G_Rec: 0.438 D_GP: 0.065 D_real: 0.601 D_fake: 0.321 +(epoch: 246, iters: 1440, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 0.626 G_ID: 0.127 G_Rec: 0.311 D_GP: 0.017 D_real: 1.193 D_fake: 0.806 +(epoch: 246, iters: 1840, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.857 G_ID: 0.175 G_Rec: 0.411 D_GP: 0.021 D_real: 1.018 D_fake: 0.741 +(epoch: 246, iters: 2240, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.722 G_ID: 0.117 G_Rec: 0.318 D_GP: 0.029 D_real: 1.055 D_fake: 0.894 +(epoch: 246, iters: 2640, time: 0.064) G_GAN: 0.751 G_GAN_Feat: 0.856 G_ID: 0.208 G_Rec: 0.384 D_GP: 0.025 D_real: 1.545 D_fake: 0.295 +(epoch: 246, iters: 3040, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.780 G_ID: 0.118 G_Rec: 0.283 D_GP: 0.114 D_real: 0.746 D_fake: 0.702 +(epoch: 246, iters: 3440, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.925 G_ID: 0.168 G_Rec: 0.463 D_GP: 0.022 D_real: 1.104 D_fake: 0.537 +(epoch: 246, iters: 3840, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.638 G_ID: 0.122 G_Rec: 0.308 D_GP: 0.022 D_real: 1.241 D_fake: 0.740 +(epoch: 246, iters: 4240, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.700 G_ID: 0.190 G_Rec: 0.346 D_GP: 0.018 D_real: 1.278 D_fake: 0.587 +(epoch: 246, iters: 4640, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.557 G_ID: 0.129 G_Rec: 0.274 D_GP: 0.019 D_real: 1.178 D_fake: 0.774 +(epoch: 246, iters: 5040, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.807 G_ID: 0.164 G_Rec: 0.381 D_GP: 0.030 D_real: 1.069 D_fake: 0.709 +(epoch: 246, iters: 5440, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.707 G_ID: 0.116 G_Rec: 0.301 D_GP: 0.032 D_real: 1.005 D_fake: 0.844 +(epoch: 246, iters: 5840, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.898 G_ID: 0.206 G_Rec: 0.389 D_GP: 0.026 D_real: 1.117 D_fake: 0.519 +(epoch: 246, iters: 6240, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.600 G_ID: 0.159 G_Rec: 0.295 D_GP: 0.023 D_real: 1.166 D_fake: 0.808 +(epoch: 246, iters: 6640, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.840 G_ID: 0.174 G_Rec: 0.360 D_GP: 0.029 D_real: 1.183 D_fake: 0.672 +(epoch: 246, iters: 7040, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.702 G_ID: 0.106 G_Rec: 0.297 D_GP: 0.032 D_real: 1.136 D_fake: 0.672 +(epoch: 246, iters: 7440, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.864 G_ID: 0.192 G_Rec: 0.358 D_GP: 0.055 D_real: 0.698 D_fake: 0.849 +(epoch: 246, iters: 7840, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.752 G_ID: 0.123 G_Rec: 0.283 D_GP: 0.083 D_real: 0.917 D_fake: 0.735 +(epoch: 246, iters: 8240, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.753 G_ID: 0.163 G_Rec: 0.356 D_GP: 0.023 D_real: 1.186 D_fake: 0.593 +(epoch: 247, iters: 32, time: 0.064) G_GAN: 0.141 G_GAN_Feat: 0.875 G_ID: 0.144 G_Rec: 0.340 D_GP: 0.335 D_real: 0.523 D_fake: 0.861 +(epoch: 247, iters: 432, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 1.140 G_ID: 0.184 G_Rec: 0.411 D_GP: 0.155 D_real: 0.488 D_fake: 0.475 +(epoch: 247, iters: 832, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.586 G_ID: 0.154 G_Rec: 0.326 D_GP: 0.020 D_real: 1.250 D_fake: 0.717 +(epoch: 247, iters: 1232, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.805 G_ID: 0.169 G_Rec: 0.387 D_GP: 0.020 D_real: 1.230 D_fake: 0.529 +(epoch: 247, iters: 1632, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.611 G_ID: 0.109 G_Rec: 0.278 D_GP: 0.026 D_real: 1.059 D_fake: 0.840 +(epoch: 247, iters: 2032, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.793 G_ID: 0.162 G_Rec: 0.385 D_GP: 0.024 D_real: 0.977 D_fake: 0.736 +(epoch: 247, iters: 2432, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.610 G_ID: 0.158 G_Rec: 0.290 D_GP: 0.025 D_real: 1.096 D_fake: 0.776 +(epoch: 247, iters: 2832, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.865 G_ID: 0.178 G_Rec: 0.384 D_GP: 0.042 D_real: 1.050 D_fake: 0.600 +(epoch: 247, iters: 3232, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 0.666 G_ID: 0.141 G_Rec: 0.294 D_GP: 0.028 D_real: 0.932 D_fake: 0.904 +(epoch: 247, iters: 3632, time: 0.064) G_GAN: 0.651 G_GAN_Feat: 0.852 G_ID: 0.153 G_Rec: 0.375 D_GP: 0.027 D_real: 1.382 D_fake: 0.365 +(epoch: 247, iters: 4032, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.747 G_ID: 0.115 G_Rec: 0.290 D_GP: 0.051 D_real: 1.002 D_fake: 0.755 +(epoch: 247, iters: 4432, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.977 G_ID: 0.183 G_Rec: 0.440 D_GP: 0.044 D_real: 0.863 D_fake: 0.527 +(epoch: 247, iters: 4832, time: 0.064) G_GAN: -0.109 G_GAN_Feat: 1.017 G_ID: 0.130 G_Rec: 0.356 D_GP: 3.370 D_real: 0.390 D_fake: 1.110 +(epoch: 247, iters: 5232, time: 0.064) G_GAN: 0.629 G_GAN_Feat: 0.817 G_ID: 0.138 G_Rec: 0.421 D_GP: 0.028 D_real: 1.368 D_fake: 0.391 +(epoch: 247, iters: 5632, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.664 G_ID: 0.130 G_Rec: 0.335 D_GP: 0.034 D_real: 1.040 D_fake: 0.847 +(epoch: 247, iters: 6032, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.872 G_ID: 0.170 G_Rec: 0.448 D_GP: 0.046 D_real: 0.818 D_fake: 0.788 +(epoch: 247, iters: 6432, time: 0.064) G_GAN: 0.025 G_GAN_Feat: 0.599 G_ID: 0.119 G_Rec: 0.303 D_GP: 0.047 D_real: 0.868 D_fake: 0.975 +(epoch: 247, iters: 6832, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.806 G_ID: 0.154 G_Rec: 0.393 D_GP: 0.030 D_real: 1.026 D_fake: 0.725 +(epoch: 247, iters: 7232, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.601 G_ID: 0.131 G_Rec: 0.289 D_GP: 0.022 D_real: 1.112 D_fake: 0.839 +(epoch: 247, iters: 7632, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.842 G_ID: 0.185 G_Rec: 0.372 D_GP: 0.057 D_real: 0.741 D_fake: 0.813 +(epoch: 247, iters: 8032, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.598 G_ID: 0.137 G_Rec: 0.304 D_GP: 0.020 D_real: 1.194 D_fake: 0.715 +(epoch: 247, iters: 8432, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.999 G_ID: 0.183 G_Rec: 0.390 D_GP: 0.396 D_real: 0.516 D_fake: 0.698 +(epoch: 248, iters: 224, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.637 G_ID: 0.120 G_Rec: 0.283 D_GP: 0.026 D_real: 1.126 D_fake: 0.794 +(epoch: 248, iters: 624, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 0.959 G_ID: 0.159 G_Rec: 0.407 D_GP: 0.089 D_real: 0.940 D_fake: 0.496 +(epoch: 248, iters: 1024, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.933 G_ID: 0.137 G_Rec: 0.337 D_GP: 0.128 D_real: 0.502 D_fake: 0.942 +(epoch: 248, iters: 1424, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.824 G_ID: 0.189 G_Rec: 0.385 D_GP: 0.029 D_real: 1.000 D_fake: 0.763 +(epoch: 248, iters: 1824, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.766 G_ID: 0.134 G_Rec: 0.311 D_GP: 0.146 D_real: 0.979 D_fake: 0.959 +(epoch: 248, iters: 2224, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.869 G_ID: 0.170 G_Rec: 0.390 D_GP: 0.037 D_real: 1.117 D_fake: 0.497 +(epoch: 248, iters: 2624, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.684 G_ID: 0.149 G_Rec: 0.277 D_GP: 0.025 D_real: 1.279 D_fake: 0.665 +(epoch: 248, iters: 3024, time: 0.064) G_GAN: 0.596 G_GAN_Feat: 1.048 G_ID: 0.158 G_Rec: 0.456 D_GP: 0.101 D_real: 1.093 D_fake: 0.428 +(epoch: 248, iters: 3424, time: 0.064) G_GAN: -0.177 G_GAN_Feat: 0.846 G_ID: 0.153 G_Rec: 0.308 D_GP: 0.086 D_real: 0.816 D_fake: 1.177 +(epoch: 248, iters: 3824, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.853 G_ID: 0.177 G_Rec: 0.391 D_GP: 0.021 D_real: 1.107 D_fake: 0.613 +(epoch: 248, iters: 4224, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.771 G_ID: 0.131 G_Rec: 0.303 D_GP: 0.037 D_real: 0.821 D_fake: 0.920 +(epoch: 248, iters: 4624, time: 0.064) G_GAN: 0.667 G_GAN_Feat: 0.895 G_ID: 0.159 G_Rec: 0.408 D_GP: 0.023 D_real: 1.340 D_fake: 0.343 +(epoch: 248, iters: 5024, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.645 G_ID: 0.124 G_Rec: 0.262 D_GP: 0.022 D_real: 1.147 D_fake: 0.766 +(epoch: 248, iters: 5424, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.856 G_ID: 0.173 G_Rec: 0.390 D_GP: 0.034 D_real: 0.885 D_fake: 0.722 +(epoch: 248, iters: 5824, time: 0.064) G_GAN: -0.004 G_GAN_Feat: 0.763 G_ID: 0.129 G_Rec: 0.313 D_GP: 0.048 D_real: 0.760 D_fake: 1.005 +(epoch: 248, iters: 6224, time: 0.064) G_GAN: 0.535 G_GAN_Feat: 1.123 G_ID: 0.169 G_Rec: 0.400 D_GP: 0.134 D_real: 0.492 D_fake: 0.467 +(epoch: 248, iters: 6624, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.647 G_ID: 0.142 G_Rec: 0.306 D_GP: 0.024 D_real: 1.228 D_fake: 0.681 +(epoch: 248, iters: 7024, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.875 G_ID: 0.172 G_Rec: 0.377 D_GP: 0.025 D_real: 1.109 D_fake: 0.517 +(epoch: 248, iters: 7424, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.829 G_ID: 0.133 G_Rec: 0.308 D_GP: 0.034 D_real: 0.841 D_fake: 0.785 +(epoch: 248, iters: 7824, time: 0.064) G_GAN: 0.613 G_GAN_Feat: 0.946 G_ID: 0.162 G_Rec: 0.388 D_GP: 0.034 D_real: 1.143 D_fake: 0.409 +(epoch: 248, iters: 8224, time: 0.064) G_GAN: -0.272 G_GAN_Feat: 0.981 G_ID: 0.135 G_Rec: 0.338 D_GP: 0.176 D_real: 0.135 D_fake: 1.272 +(epoch: 249, iters: 16, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 0.888 G_ID: 0.175 G_Rec: 0.448 D_GP: 0.026 D_real: 1.283 D_fake: 0.463 +(epoch: 249, iters: 416, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.651 G_ID: 0.116 G_Rec: 0.374 D_GP: 0.071 D_real: 1.020 D_fake: 0.801 +(epoch: 249, iters: 816, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.793 G_ID: 0.143 G_Rec: 0.391 D_GP: 0.022 D_real: 1.195 D_fake: 0.488 +(epoch: 249, iters: 1216, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.632 G_ID: 0.104 G_Rec: 0.289 D_GP: 0.023 D_real: 1.113 D_fake: 0.777 +(epoch: 249, iters: 1616, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.827 G_ID: 0.161 G_Rec: 0.358 D_GP: 0.044 D_real: 1.003 D_fake: 0.712 +(epoch: 249, iters: 2016, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.635 G_ID: 0.149 G_Rec: 0.296 D_GP: 0.022 D_real: 1.173 D_fake: 0.785 +(epoch: 249, iters: 2416, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.706 G_ID: 0.171 G_Rec: 0.367 D_GP: 0.018 D_real: 1.122 D_fake: 0.846 +(epoch: 249, iters: 2816, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.568 G_ID: 0.126 G_Rec: 0.317 D_GP: 0.021 D_real: 1.181 D_fake: 0.801 +(epoch: 249, iters: 3216, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.725 G_ID: 0.162 G_Rec: 0.367 D_GP: 0.019 D_real: 1.098 D_fake: 0.686 +(epoch: 249, iters: 3616, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.561 G_ID: 0.148 G_Rec: 0.259 D_GP: 0.029 D_real: 1.006 D_fake: 0.901 +(epoch: 249, iters: 4016, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.741 G_ID: 0.164 G_Rec: 0.365 D_GP: 0.021 D_real: 1.301 D_fake: 0.491 +(epoch: 249, iters: 4416, time: 0.064) G_GAN: -0.059 G_GAN_Feat: 0.562 G_ID: 0.143 G_Rec: 0.321 D_GP: 0.022 D_real: 0.850 D_fake: 1.059 +(epoch: 249, iters: 4816, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.792 G_ID: 0.143 G_Rec: 0.387 D_GP: 0.024 D_real: 1.122 D_fake: 0.637 +(epoch: 249, iters: 5216, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.648 G_ID: 0.156 G_Rec: 0.289 D_GP: 0.024 D_real: 1.206 D_fake: 0.757 +(epoch: 249, iters: 5616, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.835 G_ID: 0.159 G_Rec: 0.398 D_GP: 0.024 D_real: 1.162 D_fake: 0.578 +(epoch: 249, iters: 6016, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.719 G_ID: 0.136 G_Rec: 0.306 D_GP: 0.052 D_real: 0.858 D_fake: 0.867 +(epoch: 249, iters: 6416, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.878 G_ID: 0.196 G_Rec: 0.414 D_GP: 0.025 D_real: 1.266 D_fake: 0.511 +(epoch: 249, iters: 6816, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.752 G_ID: 0.134 G_Rec: 0.340 D_GP: 0.054 D_real: 0.975 D_fake: 0.710 +(epoch: 249, iters: 7216, time: 0.064) G_GAN: 0.841 G_GAN_Feat: 0.894 G_ID: 0.236 G_Rec: 0.450 D_GP: 0.035 D_real: 1.402 D_fake: 0.276 +(epoch: 249, iters: 7616, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.711 G_ID: 0.120 G_Rec: 0.325 D_GP: 0.039 D_real: 0.899 D_fake: 0.844 +(epoch: 249, iters: 8016, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.865 G_ID: 0.189 G_Rec: 0.392 D_GP: 0.028 D_real: 0.954 D_fake: 0.764 +(epoch: 249, iters: 8416, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.820 G_ID: 0.140 G_Rec: 0.302 D_GP: 0.084 D_real: 0.597 D_fake: 0.894 +(epoch: 250, iters: 208, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.926 G_ID: 0.163 G_Rec: 0.362 D_GP: 0.063 D_real: 0.723 D_fake: 0.730 +(epoch: 250, iters: 608, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.612 G_ID: 0.150 G_Rec: 0.288 D_GP: 0.019 D_real: 1.068 D_fake: 0.908 +(epoch: 250, iters: 1008, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.808 G_ID: 0.177 G_Rec: 0.382 D_GP: 0.033 D_real: 0.971 D_fake: 0.746 +(epoch: 250, iters: 1408, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.691 G_ID: 0.121 G_Rec: 0.304 D_GP: 0.041 D_real: 0.967 D_fake: 0.910 +(epoch: 250, iters: 1808, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.949 G_ID: 0.173 G_Rec: 0.476 D_GP: 0.031 D_real: 1.224 D_fake: 0.550 +(epoch: 250, iters: 2208, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.710 G_ID: 0.123 G_Rec: 0.296 D_GP: 0.028 D_real: 1.008 D_fake: 0.875 +(epoch: 250, iters: 2608, time: 0.064) G_GAN: 0.543 G_GAN_Feat: 0.763 G_ID: 0.146 G_Rec: 0.340 D_GP: 0.021 D_real: 1.310 D_fake: 0.470 +(epoch: 250, iters: 3008, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.603 G_ID: 0.126 G_Rec: 0.260 D_GP: 0.022 D_real: 1.252 D_fake: 0.702 +(epoch: 250, iters: 3408, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.939 G_ID: 0.194 G_Rec: 0.394 D_GP: 0.029 D_real: 0.774 D_fake: 0.878 +(epoch: 250, iters: 3808, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.711 G_ID: 0.126 G_Rec: 0.310 D_GP: 0.033 D_real: 0.957 D_fake: 0.808 +(epoch: 250, iters: 4208, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.788 G_ID: 0.179 G_Rec: 0.341 D_GP: 0.028 D_real: 1.112 D_fake: 0.661 +(epoch: 250, iters: 4608, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.785 G_ID: 0.125 G_Rec: 0.313 D_GP: 0.044 D_real: 0.881 D_fake: 0.718 +(epoch: 250, iters: 5008, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.888 G_ID: 0.203 G_Rec: 0.422 D_GP: 0.029 D_real: 1.167 D_fake: 0.542 +(epoch: 250, iters: 5408, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 1.047 G_ID: 0.138 G_Rec: 0.352 D_GP: 0.948 D_real: 0.578 D_fake: 0.568 +(epoch: 250, iters: 5808, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.826 G_ID: 0.178 G_Rec: 0.369 D_GP: 0.027 D_real: 1.170 D_fake: 0.555 +(epoch: 250, iters: 6208, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.656 G_ID: 0.132 G_Rec: 0.291 D_GP: 0.027 D_real: 1.212 D_fake: 0.686 +(epoch: 250, iters: 6608, time: 0.064) G_GAN: 0.701 G_GAN_Feat: 0.881 G_ID: 0.156 G_Rec: 0.441 D_GP: 0.029 D_real: 1.397 D_fake: 0.315 +(epoch: 250, iters: 7008, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.569 G_ID: 0.130 G_Rec: 0.287 D_GP: 0.018 D_real: 1.049 D_fake: 0.918 +(epoch: 250, iters: 7408, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.877 G_ID: 0.139 G_Rec: 0.462 D_GP: 0.032 D_real: 0.743 D_fake: 0.844 +(epoch: 250, iters: 7808, time: 0.064) G_GAN: -0.004 G_GAN_Feat: 0.680 G_ID: 0.153 G_Rec: 0.326 D_GP: 0.027 D_real: 0.847 D_fake: 1.004 +(epoch: 250, iters: 8208, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.867 G_ID: 0.211 G_Rec: 0.397 D_GP: 0.025 D_real: 0.913 D_fake: 0.712 +(epoch: 250, iters: 8608, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.728 G_ID: 0.144 G_Rec: 0.294 D_GP: 0.037 D_real: 1.070 D_fake: 0.763 +(epoch: 251, iters: 400, time: 0.064) G_GAN: 0.701 G_GAN_Feat: 0.823 G_ID: 0.168 G_Rec: 0.358 D_GP: 0.024 D_real: 1.507 D_fake: 0.319 +(epoch: 251, iters: 800, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.560 G_ID: 0.137 G_Rec: 0.289 D_GP: 0.019 D_real: 1.241 D_fake: 0.745 +(epoch: 251, iters: 1200, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.883 G_ID: 0.143 G_Rec: 0.415 D_GP: 0.031 D_real: 1.199 D_fake: 0.452 +(epoch: 251, iters: 1600, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.586 G_ID: 0.123 G_Rec: 0.286 D_GP: 0.022 D_real: 1.041 D_fake: 0.918 +(epoch: 251, iters: 2000, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.895 G_ID: 0.169 G_Rec: 0.401 D_GP: 0.030 D_real: 1.086 D_fake: 0.543 +(epoch: 251, iters: 2400, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.705 G_ID: 0.120 G_Rec: 0.323 D_GP: 0.033 D_real: 1.065 D_fake: 0.714 +(epoch: 251, iters: 2800, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.928 G_ID: 0.165 G_Rec: 0.401 D_GP: 0.176 D_real: 0.709 D_fake: 0.864 +(epoch: 251, iters: 3200, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.751 G_ID: 0.120 G_Rec: 0.303 D_GP: 0.036 D_real: 0.965 D_fake: 0.742 +(epoch: 251, iters: 3600, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.866 G_ID: 0.161 G_Rec: 0.404 D_GP: 0.025 D_real: 1.068 D_fake: 0.625 +(epoch: 251, iters: 4000, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.797 G_ID: 0.128 G_Rec: 0.335 D_GP: 0.295 D_real: 0.631 D_fake: 0.887 +(epoch: 251, iters: 4400, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.835 G_ID: 0.170 G_Rec: 0.378 D_GP: 0.030 D_real: 1.072 D_fake: 0.609 +(epoch: 251, iters: 4800, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.696 G_ID: 0.130 G_Rec: 0.289 D_GP: 0.030 D_real: 0.952 D_fake: 0.880 +(epoch: 251, iters: 5200, time: 0.064) G_GAN: 0.750 G_GAN_Feat: 0.782 G_ID: 0.155 G_Rec: 0.347 D_GP: 0.022 D_real: 1.556 D_fake: 0.348 +(epoch: 251, iters: 5600, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.680 G_ID: 0.140 G_Rec: 0.281 D_GP: 0.022 D_real: 1.230 D_fake: 0.656 +(epoch: 251, iters: 6000, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.955 G_ID: 0.184 G_Rec: 0.387 D_GP: 0.027 D_real: 0.717 D_fake: 0.702 +(epoch: 251, iters: 6400, time: 0.064) G_GAN: 0.063 G_GAN_Feat: 0.775 G_ID: 0.147 G_Rec: 0.325 D_GP: 0.070 D_real: 0.826 D_fake: 0.937 +(epoch: 251, iters: 6800, time: 0.064) G_GAN: -0.008 G_GAN_Feat: 1.032 G_ID: 0.201 G_Rec: 0.402 D_GP: 0.378 D_real: 0.406 D_fake: 1.008 +(epoch: 251, iters: 7200, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.696 G_ID: 0.150 G_Rec: 0.297 D_GP: 0.030 D_real: 1.046 D_fake: 0.838 +(epoch: 251, iters: 7600, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.862 G_ID: 0.199 G_Rec: 0.412 D_GP: 0.025 D_real: 1.177 D_fake: 0.430 +(epoch: 251, iters: 8000, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.646 G_ID: 0.132 G_Rec: 0.281 D_GP: 0.022 D_real: 1.250 D_fake: 0.685 +(epoch: 251, iters: 8400, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.975 G_ID: 0.199 G_Rec: 0.418 D_GP: 0.118 D_real: 0.709 D_fake: 0.650 +(epoch: 252, iters: 192, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.687 G_ID: 0.136 G_Rec: 0.284 D_GP: 0.025 D_real: 1.072 D_fake: 0.755 +(epoch: 252, iters: 592, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 0.834 G_ID: 0.157 G_Rec: 0.387 D_GP: 0.022 D_real: 1.297 D_fake: 0.491 +(epoch: 252, iters: 992, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.670 G_ID: 0.124 G_Rec: 0.251 D_GP: 0.026 D_real: 1.064 D_fake: 0.830 +(epoch: 252, iters: 1392, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.782 G_ID: 0.176 G_Rec: 0.361 D_GP: 0.024 D_real: 1.070 D_fake: 0.724 +(epoch: 252, iters: 1792, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.603 G_ID: 0.155 G_Rec: 0.254 D_GP: 0.028 D_real: 1.020 D_fake: 0.900 +(epoch: 252, iters: 2192, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.981 G_ID: 0.155 G_Rec: 0.418 D_GP: 0.042 D_real: 0.917 D_fake: 0.430 +(epoch: 252, iters: 2592, time: 0.064) G_GAN: -0.129 G_GAN_Feat: 0.894 G_ID: 0.132 G_Rec: 0.348 D_GP: 1.274 D_real: 0.261 D_fake: 1.129 +(epoch: 252, iters: 2992, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.842 G_ID: 0.180 G_Rec: 0.371 D_GP: 0.030 D_real: 1.075 D_fake: 0.618 +(epoch: 252, iters: 3392, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.706 G_ID: 0.126 G_Rec: 0.299 D_GP: 0.020 D_real: 1.357 D_fake: 0.583 +(epoch: 252, iters: 3792, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 1.019 G_ID: 0.171 G_Rec: 0.438 D_GP: 0.056 D_real: 0.731 D_fake: 0.592 +(epoch: 252, iters: 4192, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.896 G_ID: 0.113 G_Rec: 0.298 D_GP: 0.037 D_real: 0.733 D_fake: 0.711 +(epoch: 252, iters: 4592, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.934 G_ID: 0.159 G_Rec: 0.384 D_GP: 0.023 D_real: 1.040 D_fake: 0.589 +(epoch: 252, iters: 4992, time: 0.064) G_GAN: -0.270 G_GAN_Feat: 0.900 G_ID: 0.124 G_Rec: 0.360 D_GP: 0.214 D_real: 0.575 D_fake: 1.270 +(epoch: 252, iters: 5392, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.876 G_ID: 0.176 G_Rec: 0.384 D_GP: 0.047 D_real: 0.913 D_fake: 0.660 +(epoch: 252, iters: 5792, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.905 G_ID: 0.142 G_Rec: 0.330 D_GP: 0.174 D_real: 0.712 D_fake: 0.627 +(epoch: 252, iters: 6192, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 1.006 G_ID: 0.173 G_Rec: 0.407 D_GP: 0.031 D_real: 0.970 D_fake: 0.405 +(epoch: 252, iters: 6592, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.696 G_ID: 0.123 G_Rec: 0.297 D_GP: 0.024 D_real: 1.104 D_fake: 0.849 +(epoch: 252, iters: 6992, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 1.005 G_ID: 0.162 G_Rec: 0.421 D_GP: 0.078 D_real: 1.027 D_fake: 0.533 +(epoch: 252, iters: 7392, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.994 G_ID: 0.139 G_Rec: 0.342 D_GP: 1.859 D_real: 0.348 D_fake: 0.718 +(epoch: 252, iters: 7792, time: 0.064) G_GAN: 0.640 G_GAN_Feat: 0.752 G_ID: 0.161 G_Rec: 0.346 D_GP: 0.023 D_real: 1.411 D_fake: 0.425 +(epoch: 252, iters: 8192, time: 0.064) G_GAN: 0.039 G_GAN_Feat: 0.658 G_ID: 0.113 G_Rec: 0.319 D_GP: 0.026 D_real: 0.901 D_fake: 0.961 +(epoch: 252, iters: 8592, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 1.051 G_ID: 0.178 G_Rec: 0.452 D_GP: 0.072 D_real: 0.979 D_fake: 0.826 +(epoch: 253, iters: 384, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.665 G_ID: 0.134 G_Rec: 0.326 D_GP: 0.045 D_real: 1.004 D_fake: 0.823 +(epoch: 253, iters: 784, time: 0.064) G_GAN: 0.487 G_GAN_Feat: 0.953 G_ID: 0.149 G_Rec: 0.381 D_GP: 0.038 D_real: 0.968 D_fake: 0.517 +(epoch: 253, iters: 1184, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.676 G_ID: 0.118 G_Rec: 0.285 D_GP: 0.019 D_real: 1.128 D_fake: 0.831 +(epoch: 253, iters: 1584, time: 0.064) G_GAN: 0.669 G_GAN_Feat: 0.953 G_ID: 0.148 G_Rec: 0.419 D_GP: 0.029 D_real: 1.156 D_fake: 0.375 +(epoch: 253, iters: 1984, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.829 G_ID: 0.120 G_Rec: 0.358 D_GP: 0.038 D_real: 1.042 D_fake: 0.675 +(epoch: 253, iters: 2384, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 1.011 G_ID: 0.144 G_Rec: 0.422 D_GP: 0.022 D_real: 0.963 D_fake: 0.582 +(epoch: 253, iters: 2784, time: 0.064) G_GAN: -0.106 G_GAN_Feat: 0.732 G_ID: 0.145 G_Rec: 0.287 D_GP: 0.027 D_real: 0.689 D_fake: 1.106 +(epoch: 253, iters: 3184, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.849 G_ID: 0.180 G_Rec: 0.409 D_GP: 0.022 D_real: 1.034 D_fake: 0.736 +(epoch: 253, iters: 3584, time: 0.064) G_GAN: -0.003 G_GAN_Feat: 0.740 G_ID: 0.130 G_Rec: 0.319 D_GP: 0.032 D_real: 0.780 D_fake: 1.004 +(epoch: 253, iters: 3984, time: 0.064) G_GAN: 0.708 G_GAN_Feat: 0.823 G_ID: 0.164 G_Rec: 0.369 D_GP: 0.021 D_real: 1.509 D_fake: 0.312 +(epoch: 253, iters: 4384, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.644 G_ID: 0.130 G_Rec: 0.308 D_GP: 0.022 D_real: 1.219 D_fake: 0.702 +(epoch: 253, iters: 4784, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.972 G_ID: 0.155 G_Rec: 0.415 D_GP: 0.084 D_real: 0.697 D_fake: 0.932 +(epoch: 253, iters: 5184, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.891 G_ID: 0.141 G_Rec: 0.337 D_GP: 0.105 D_real: 1.200 D_fake: 0.657 +(epoch: 253, iters: 5584, time: 0.064) G_GAN: 0.556 G_GAN_Feat: 0.963 G_ID: 0.184 G_Rec: 0.416 D_GP: 0.046 D_real: 0.880 D_fake: 0.455 +(epoch: 253, iters: 5984, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.747 G_ID: 0.132 G_Rec: 0.302 D_GP: 0.021 D_real: 1.131 D_fake: 0.807 +(epoch: 253, iters: 6384, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.765 G_ID: 0.145 G_Rec: 0.347 D_GP: 0.022 D_real: 1.061 D_fake: 0.823 +(epoch: 253, iters: 6784, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.834 G_ID: 0.127 G_Rec: 0.324 D_GP: 0.045 D_real: 1.059 D_fake: 0.687 +(epoch: 253, iters: 7184, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.875 G_ID: 0.161 G_Rec: 0.400 D_GP: 0.046 D_real: 0.741 D_fake: 0.832 +(epoch: 253, iters: 7584, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.665 G_ID: 0.130 G_Rec: 0.317 D_GP: 0.023 D_real: 1.164 D_fake: 0.756 +(epoch: 253, iters: 7984, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.873 G_ID: 0.165 G_Rec: 0.441 D_GP: 0.024 D_real: 1.042 D_fake: 0.688 +(epoch: 253, iters: 8384, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.724 G_ID: 0.116 G_Rec: 0.306 D_GP: 0.039 D_real: 0.925 D_fake: 0.777 +(epoch: 254, iters: 176, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.980 G_ID: 0.180 G_Rec: 0.387 D_GP: 0.035 D_real: 0.634 D_fake: 0.688 +(epoch: 254, iters: 576, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.756 G_ID: 0.139 G_Rec: 0.311 D_GP: 0.030 D_real: 0.992 D_fake: 0.687 +(epoch: 254, iters: 976, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.866 G_ID: 0.180 G_Rec: 0.332 D_GP: 0.027 D_real: 1.280 D_fake: 0.515 +(epoch: 254, iters: 1376, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.729 G_ID: 0.117 G_Rec: 0.305 D_GP: 0.036 D_real: 1.297 D_fake: 0.485 +(epoch: 254, iters: 1776, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 0.801 G_ID: 0.195 G_Rec: 0.386 D_GP: 0.019 D_real: 1.445 D_fake: 0.480 +(epoch: 254, iters: 2176, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.772 G_ID: 0.160 G_Rec: 0.333 D_GP: 0.039 D_real: 1.009 D_fake: 0.769 +(epoch: 254, iters: 2576, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.821 G_ID: 0.150 G_Rec: 0.381 D_GP: 0.021 D_real: 1.024 D_fake: 0.739 +(epoch: 254, iters: 2976, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.831 G_ID: 0.136 G_Rec: 0.332 D_GP: 0.074 D_real: 0.586 D_fake: 0.743 +(epoch: 254, iters: 3376, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.909 G_ID: 0.169 G_Rec: 0.409 D_GP: 0.032 D_real: 1.176 D_fake: 0.497 +(epoch: 254, iters: 3776, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.895 G_ID: 0.126 G_Rec: 0.315 D_GP: 0.125 D_real: 0.434 D_fake: 0.959 +(epoch: 254, iters: 4176, time: 0.064) G_GAN: 0.877 G_GAN_Feat: 1.115 G_ID: 0.180 G_Rec: 0.425 D_GP: 0.098 D_real: 1.177 D_fake: 0.408 +(epoch: 254, iters: 4576, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.701 G_ID: 0.128 G_Rec: 0.300 D_GP: 0.031 D_real: 1.119 D_fake: 0.768 +(epoch: 254, iters: 4976, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.810 G_ID: 0.174 G_Rec: 0.407 D_GP: 0.020 D_real: 1.239 D_fake: 0.530 +(epoch: 254, iters: 5376, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.621 G_ID: 0.119 G_Rec: 0.294 D_GP: 0.024 D_real: 1.268 D_fake: 0.729 +(epoch: 254, iters: 5776, time: 0.064) G_GAN: 0.773 G_GAN_Feat: 1.024 G_ID: 0.177 G_Rec: 0.438 D_GP: 0.046 D_real: 1.067 D_fake: 0.368 +(epoch: 254, iters: 6176, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.684 G_ID: 0.138 G_Rec: 0.344 D_GP: 0.018 D_real: 1.153 D_fake: 0.754 +(epoch: 254, iters: 6576, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.784 G_ID: 0.146 G_Rec: 0.370 D_GP: 0.023 D_real: 1.307 D_fake: 0.464 +(epoch: 254, iters: 6976, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.590 G_ID: 0.141 G_Rec: 0.340 D_GP: 0.020 D_real: 1.228 D_fake: 0.769 +(epoch: 254, iters: 7376, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.793 G_ID: 0.168 G_Rec: 0.387 D_GP: 0.027 D_real: 1.169 D_fake: 0.546 +(epoch: 254, iters: 7776, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.601 G_ID: 0.113 G_Rec: 0.289 D_GP: 0.024 D_real: 1.148 D_fake: 0.731 +(epoch: 254, iters: 8176, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.815 G_ID: 0.162 G_Rec: 0.422 D_GP: 0.029 D_real: 1.164 D_fake: 0.527 +(epoch: 254, iters: 8576, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.736 G_ID: 0.127 G_Rec: 0.306 D_GP: 0.026 D_real: 1.053 D_fake: 0.805 +(epoch: 255, iters: 368, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.873 G_ID: 0.159 G_Rec: 0.364 D_GP: 0.029 D_real: 0.874 D_fake: 0.640 +(epoch: 255, iters: 768, time: 0.064) G_GAN: 0.027 G_GAN_Feat: 0.662 G_ID: 0.170 G_Rec: 0.283 D_GP: 0.038 D_real: 0.812 D_fake: 0.974 +(epoch: 255, iters: 1168, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.924 G_ID: 0.186 G_Rec: 0.393 D_GP: 0.068 D_real: 0.858 D_fake: 0.657 +(epoch: 255, iters: 1568, time: 0.064) G_GAN: 0.060 G_GAN_Feat: 0.751 G_ID: 0.123 G_Rec: 0.306 D_GP: 0.071 D_real: 0.651 D_fake: 0.941 +(epoch: 255, iters: 1968, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.905 G_ID: 0.179 G_Rec: 0.366 D_GP: 0.053 D_real: 0.744 D_fake: 0.700 +(epoch: 255, iters: 2368, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 0.714 G_ID: 0.120 G_Rec: 0.300 D_GP: 0.028 D_real: 1.241 D_fake: 0.547 +(epoch: 255, iters: 2768, time: 0.064) G_GAN: 0.742 G_GAN_Feat: 1.026 G_ID: 0.193 G_Rec: 0.520 D_GP: 0.081 D_real: 1.132 D_fake: 0.512 +(epoch: 255, iters: 3168, time: 0.064) G_GAN: 0.076 G_GAN_Feat: 0.638 G_ID: 0.148 G_Rec: 0.276 D_GP: 0.021 D_real: 0.994 D_fake: 0.924 +(epoch: 255, iters: 3568, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.865 G_ID: 0.168 G_Rec: 0.394 D_GP: 0.022 D_real: 1.102 D_fake: 0.566 +(epoch: 255, iters: 3968, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.642 G_ID: 0.170 G_Rec: 0.272 D_GP: 0.021 D_real: 1.055 D_fake: 0.915 +(epoch: 255, iters: 4368, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.998 G_ID: 0.182 G_Rec: 0.439 D_GP: 0.081 D_real: 0.579 D_fake: 0.745 +(epoch: 255, iters: 4768, time: 0.064) G_GAN: 0.075 G_GAN_Feat: 0.612 G_ID: 0.132 G_Rec: 0.271 D_GP: 0.023 D_real: 1.116 D_fake: 0.925 +(epoch: 255, iters: 5168, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.938 G_ID: 0.166 G_Rec: 0.427 D_GP: 0.046 D_real: 0.736 D_fake: 0.805 +(epoch: 255, iters: 5568, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.692 G_ID: 0.117 G_Rec: 0.282 D_GP: 0.038 D_real: 0.896 D_fake: 0.898 +(epoch: 255, iters: 5968, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 0.904 G_ID: 0.197 G_Rec: 0.378 D_GP: 0.026 D_real: 1.283 D_fake: 0.390 +(epoch: 255, iters: 6368, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.748 G_ID: 0.157 G_Rec: 0.294 D_GP: 0.033 D_real: 1.039 D_fake: 0.751 +(epoch: 255, iters: 6768, time: 0.064) G_GAN: 0.616 G_GAN_Feat: 0.956 G_ID: 0.176 G_Rec: 0.386 D_GP: 0.025 D_real: 1.307 D_fake: 0.387 +(epoch: 255, iters: 7168, time: 0.064) G_GAN: 0.101 G_GAN_Feat: 0.959 G_ID: 0.106 G_Rec: 0.350 D_GP: 0.049 D_real: 0.576 D_fake: 0.900 +(epoch: 255, iters: 7568, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.973 G_ID: 0.151 G_Rec: 0.413 D_GP: 0.033 D_real: 1.086 D_fake: 0.547 +(epoch: 255, iters: 7968, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.940 G_ID: 0.131 G_Rec: 0.304 D_GP: 0.188 D_real: 0.388 D_fake: 0.768 +(epoch: 255, iters: 8368, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 0.841 G_ID: 0.175 G_Rec: 0.402 D_GP: 0.022 D_real: 1.380 D_fake: 0.388 +(epoch: 256, iters: 160, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.924 G_ID: 0.137 G_Rec: 0.323 D_GP: 0.034 D_real: 0.539 D_fake: 0.803 +(epoch: 256, iters: 560, time: 0.064) G_GAN: 0.530 G_GAN_Feat: 0.837 G_ID: 0.155 G_Rec: 0.408 D_GP: 0.033 D_real: 1.351 D_fake: 0.499 +(epoch: 256, iters: 960, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 0.561 G_ID: 0.125 G_Rec: 0.288 D_GP: 0.018 D_real: 0.976 D_fake: 0.988 +(epoch: 256, iters: 1360, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.717 G_ID: 0.169 G_Rec: 0.383 D_GP: 0.021 D_real: 1.057 D_fake: 0.788 +(epoch: 256, iters: 1760, time: 0.064) G_GAN: 0.070 G_GAN_Feat: 0.603 G_ID: 0.144 G_Rec: 0.347 D_GP: 0.018 D_real: 1.017 D_fake: 0.930 +(epoch: 256, iters: 2160, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.853 G_ID: 0.147 G_Rec: 0.435 D_GP: 0.039 D_real: 0.944 D_fake: 0.822 +(epoch: 256, iters: 2560, time: 0.064) G_GAN: 0.027 G_GAN_Feat: 0.653 G_ID: 0.138 G_Rec: 0.315 D_GP: 0.032 D_real: 0.856 D_fake: 0.973 +(epoch: 256, iters: 2960, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.743 G_ID: 0.167 G_Rec: 0.346 D_GP: 0.026 D_real: 1.061 D_fake: 0.761 +(epoch: 256, iters: 3360, time: 0.064) G_GAN: -0.107 G_GAN_Feat: 0.659 G_ID: 0.108 G_Rec: 0.315 D_GP: 0.042 D_real: 0.711 D_fake: 1.107 +(epoch: 256, iters: 3760, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.899 G_ID: 0.172 G_Rec: 0.434 D_GP: 0.040 D_real: 0.755 D_fake: 0.847 +(epoch: 256, iters: 4160, time: 0.064) G_GAN: 0.002 G_GAN_Feat: 0.687 G_ID: 0.121 G_Rec: 0.298 D_GP: 0.064 D_real: 0.812 D_fake: 0.998 +(epoch: 256, iters: 4560, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.851 G_ID: 0.204 G_Rec: 0.395 D_GP: 0.034 D_real: 0.785 D_fake: 0.908 +(epoch: 256, iters: 4960, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.605 G_ID: 0.129 G_Rec: 0.264 D_GP: 0.021 D_real: 1.341 D_fake: 0.572 +(epoch: 256, iters: 5360, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.871 G_ID: 0.159 G_Rec: 0.359 D_GP: 0.028 D_real: 1.068 D_fake: 0.575 +(epoch: 256, iters: 5760, time: 0.064) G_GAN: 0.027 G_GAN_Feat: 0.726 G_ID: 0.130 G_Rec: 0.313 D_GP: 0.026 D_real: 0.803 D_fake: 0.973 +(epoch: 256, iters: 6160, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.903 G_ID: 0.176 G_Rec: 0.402 D_GP: 0.040 D_real: 0.989 D_fake: 0.542 +(epoch: 256, iters: 6560, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.615 G_ID: 0.161 G_Rec: 0.303 D_GP: 0.022 D_real: 1.309 D_fake: 0.668 +(epoch: 256, iters: 6960, time: 0.064) G_GAN: 0.444 G_GAN_Feat: 0.858 G_ID: 0.173 G_Rec: 0.397 D_GP: 0.033 D_real: 1.068 D_fake: 0.558 +(epoch: 256, iters: 7360, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.574 G_ID: 0.140 G_Rec: 0.255 D_GP: 0.019 D_real: 1.418 D_fake: 0.654 +(epoch: 256, iters: 7760, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.873 G_ID: 0.182 G_Rec: 0.415 D_GP: 0.028 D_real: 1.126 D_fake: 0.573 +(epoch: 256, iters: 8160, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.758 G_ID: 0.134 G_Rec: 0.318 D_GP: 0.065 D_real: 0.909 D_fake: 0.725 +(epoch: 256, iters: 8560, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.834 G_ID: 0.167 G_Rec: 0.367 D_GP: 0.028 D_real: 1.103 D_fake: 0.614 +(epoch: 257, iters: 352, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.684 G_ID: 0.131 G_Rec: 0.275 D_GP: 0.021 D_real: 1.125 D_fake: 0.763 +(epoch: 257, iters: 752, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.848 G_ID: 0.158 G_Rec: 0.375 D_GP: 0.023 D_real: 1.257 D_fake: 0.511 +(epoch: 257, iters: 1152, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.785 G_ID: 0.155 G_Rec: 0.354 D_GP: 0.043 D_real: 1.104 D_fake: 0.595 +(epoch: 257, iters: 1552, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.999 G_ID: 0.177 G_Rec: 0.427 D_GP: 0.023 D_real: 0.785 D_fake: 0.692 +(epoch: 257, iters: 1952, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.805 G_ID: 0.142 G_Rec: 0.297 D_GP: 0.066 D_real: 0.611 D_fake: 0.736 +(epoch: 257, iters: 2352, time: 0.064) G_GAN: 0.720 G_GAN_Feat: 1.041 G_ID: 0.157 G_Rec: 0.412 D_GP: 0.033 D_real: 1.179 D_fake: 0.351 +(epoch: 257, iters: 2752, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.761 G_ID: 0.129 G_Rec: 0.313 D_GP: 0.037 D_real: 0.887 D_fake: 0.860 +(epoch: 257, iters: 3152, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.789 G_ID: 0.157 G_Rec: 0.376 D_GP: 0.017 D_real: 0.963 D_fake: 0.892 +(epoch: 257, iters: 3552, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.644 G_ID: 0.116 G_Rec: 0.284 D_GP: 0.027 D_real: 1.049 D_fake: 0.852 +(epoch: 257, iters: 3952, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.812 G_ID: 0.177 G_Rec: 0.384 D_GP: 0.024 D_real: 1.200 D_fake: 0.615 +(epoch: 257, iters: 4352, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.728 G_ID: 0.109 G_Rec: 0.290 D_GP: 0.033 D_real: 1.247 D_fake: 0.572 +(epoch: 257, iters: 4752, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 0.846 G_ID: 0.190 G_Rec: 0.391 D_GP: 0.034 D_real: 1.251 D_fake: 0.461 +(epoch: 257, iters: 5152, time: 0.064) G_GAN: -0.062 G_GAN_Feat: 0.698 G_ID: 0.105 G_Rec: 0.296 D_GP: 0.030 D_real: 0.755 D_fake: 1.062 +(epoch: 257, iters: 5552, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.921 G_ID: 0.170 G_Rec: 0.413 D_GP: 0.046 D_real: 0.697 D_fake: 0.785 +(epoch: 257, iters: 5952, time: 0.064) G_GAN: -0.057 G_GAN_Feat: 0.835 G_ID: 0.132 G_Rec: 0.307 D_GP: 0.036 D_real: 1.008 D_fake: 1.060 +(epoch: 257, iters: 6352, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.977 G_ID: 0.157 G_Rec: 0.424 D_GP: 0.039 D_real: 0.931 D_fake: 0.500 +(epoch: 257, iters: 6752, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.646 G_ID: 0.131 G_Rec: 0.328 D_GP: 0.018 D_real: 1.129 D_fake: 0.858 +(epoch: 257, iters: 7152, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.840 G_ID: 0.181 G_Rec: 0.407 D_GP: 0.021 D_real: 1.089 D_fake: 0.675 +(epoch: 257, iters: 7552, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.678 G_ID: 0.113 G_Rec: 0.305 D_GP: 0.020 D_real: 1.227 D_fake: 0.691 +(epoch: 257, iters: 7952, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.916 G_ID: 0.169 G_Rec: 0.404 D_GP: 0.047 D_real: 0.925 D_fake: 0.547 +(epoch: 257, iters: 8352, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.763 G_ID: 0.123 G_Rec: 0.335 D_GP: 0.051 D_real: 0.914 D_fake: 0.853 +(epoch: 258, iters: 144, time: 0.064) G_GAN: 0.988 G_GAN_Feat: 1.237 G_ID: 0.168 G_Rec: 0.407 D_GP: 0.028 D_real: 1.428 D_fake: 0.232 +(epoch: 258, iters: 544, time: 0.064) G_GAN: -0.014 G_GAN_Feat: 0.724 G_ID: 0.148 G_Rec: 0.304 D_GP: 0.031 D_real: 0.816 D_fake: 1.014 +(epoch: 258, iters: 944, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.978 G_ID: 0.170 G_Rec: 0.422 D_GP: 0.075 D_real: 0.904 D_fake: 0.577 +(epoch: 258, iters: 1344, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.712 G_ID: 0.118 G_Rec: 0.307 D_GP: 0.020 D_real: 1.128 D_fake: 0.712 +(epoch: 258, iters: 1744, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.939 G_ID: 0.179 G_Rec: 0.380 D_GP: 0.066 D_real: 0.840 D_fake: 0.522 +(epoch: 258, iters: 2144, time: 0.064) G_GAN: -0.038 G_GAN_Feat: 0.841 G_ID: 0.138 G_Rec: 0.301 D_GP: 0.099 D_real: 0.392 D_fake: 1.038 +(epoch: 258, iters: 2544, time: 0.064) G_GAN: 0.495 G_GAN_Feat: 0.882 G_ID: 0.144 G_Rec: 0.401 D_GP: 0.021 D_real: 1.239 D_fake: 0.507 +(epoch: 258, iters: 2944, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.831 G_ID: 0.176 G_Rec: 0.314 D_GP: 0.035 D_real: 0.945 D_fake: 0.652 +(epoch: 258, iters: 3344, time: 0.064) G_GAN: 0.743 G_GAN_Feat: 0.986 G_ID: 0.170 G_Rec: 0.395 D_GP: 0.066 D_real: 1.195 D_fake: 0.299 +(epoch: 258, iters: 3744, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.793 G_ID: 0.124 G_Rec: 0.306 D_GP: 0.040 D_real: 1.041 D_fake: 0.615 +(epoch: 258, iters: 4144, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.787 G_ID: 0.162 G_Rec: 0.385 D_GP: 0.019 D_real: 1.194 D_fake: 0.644 +(epoch: 258, iters: 4544, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.629 G_ID: 0.125 G_Rec: 0.315 D_GP: 0.019 D_real: 1.087 D_fake: 0.889 +(epoch: 258, iters: 4944, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.816 G_ID: 0.186 G_Rec: 0.388 D_GP: 0.024 D_real: 0.961 D_fake: 0.739 +(epoch: 258, iters: 5344, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.645 G_ID: 0.135 G_Rec: 0.297 D_GP: 0.024 D_real: 1.021 D_fake: 0.948 +(epoch: 258, iters: 5744, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.960 G_ID: 0.161 G_Rec: 0.443 D_GP: 0.106 D_real: 0.957 D_fake: 0.558 +(epoch: 258, iters: 6144, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.678 G_ID: 0.114 G_Rec: 0.319 D_GP: 0.026 D_real: 0.985 D_fake: 0.888 +(epoch: 258, iters: 6544, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.788 G_ID: 0.169 G_Rec: 0.400 D_GP: 0.023 D_real: 1.094 D_fake: 0.645 +(epoch: 258, iters: 6944, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.781 G_ID: 0.158 G_Rec: 0.318 D_GP: 0.105 D_real: 0.717 D_fake: 0.834 +(epoch: 258, iters: 7344, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.819 G_ID: 0.157 G_Rec: 0.359 D_GP: 0.031 D_real: 1.080 D_fake: 0.647 +(epoch: 258, iters: 7744, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.777 G_ID: 0.121 G_Rec: 0.316 D_GP: 0.025 D_real: 1.255 D_fake: 0.890 +(epoch: 258, iters: 8144, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.777 G_ID: 0.167 G_Rec: 0.410 D_GP: 0.018 D_real: 1.153 D_fake: 0.638 +(epoch: 258, iters: 8544, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.535 G_ID: 0.125 G_Rec: 0.270 D_GP: 0.019 D_real: 1.224 D_fake: 0.730 +(epoch: 259, iters: 336, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.771 G_ID: 0.164 G_Rec: 0.410 D_GP: 0.025 D_real: 1.174 D_fake: 0.572 +(epoch: 259, iters: 736, time: 0.064) G_GAN: 0.109 G_GAN_Feat: 0.629 G_ID: 0.131 G_Rec: 0.317 D_GP: 0.030 D_real: 1.038 D_fake: 0.892 +(epoch: 259, iters: 1136, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.771 G_ID: 0.186 G_Rec: 0.357 D_GP: 0.035 D_real: 1.058 D_fake: 0.652 +(epoch: 259, iters: 1536, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.673 G_ID: 0.126 G_Rec: 0.301 D_GP: 0.049 D_real: 0.893 D_fake: 0.931 +(epoch: 259, iters: 1936, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.863 G_ID: 0.168 G_Rec: 0.383 D_GP: 0.024 D_real: 1.025 D_fake: 0.743 +(epoch: 259, iters: 2336, time: 0.064) G_GAN: 0.101 G_GAN_Feat: 0.625 G_ID: 0.130 G_Rec: 0.254 D_GP: 0.033 D_real: 1.013 D_fake: 0.899 +(epoch: 259, iters: 2736, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.753 G_ID: 0.172 G_Rec: 0.351 D_GP: 0.025 D_real: 1.052 D_fake: 0.783 +(epoch: 259, iters: 3136, time: 0.064) G_GAN: 0.045 G_GAN_Feat: 0.705 G_ID: 0.135 G_Rec: 0.310 D_GP: 0.024 D_real: 0.908 D_fake: 0.955 +(epoch: 259, iters: 3536, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.840 G_ID: 0.175 G_Rec: 0.368 D_GP: 0.030 D_real: 1.090 D_fake: 0.640 +(epoch: 259, iters: 3936, time: 0.064) G_GAN: 0.133 G_GAN_Feat: 0.737 G_ID: 0.109 G_Rec: 0.312 D_GP: 0.059 D_real: 0.897 D_fake: 0.868 +(epoch: 259, iters: 4336, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.934 G_ID: 0.151 G_Rec: 0.391 D_GP: 0.025 D_real: 1.088 D_fake: 0.587 +(epoch: 259, iters: 4736, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.678 G_ID: 0.125 G_Rec: 0.312 D_GP: 0.026 D_real: 0.923 D_fake: 0.959 +(epoch: 259, iters: 5136, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.770 G_ID: 0.160 G_Rec: 0.342 D_GP: 0.024 D_real: 1.168 D_fake: 0.608 +(epoch: 259, iters: 5536, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.859 G_ID: 0.136 G_Rec: 0.323 D_GP: 0.055 D_real: 0.537 D_fake: 0.862 +(epoch: 259, iters: 5936, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.958 G_ID: 0.161 G_Rec: 0.369 D_GP: 0.039 D_real: 0.743 D_fake: 0.678 +(epoch: 259, iters: 6336, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.751 G_ID: 0.116 G_Rec: 0.332 D_GP: 0.036 D_real: 1.049 D_fake: 0.737 +(epoch: 259, iters: 6736, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 1.081 G_ID: 0.173 G_Rec: 0.493 D_GP: 0.209 D_real: 0.368 D_fake: 0.777 +(epoch: 259, iters: 7136, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.647 G_ID: 0.125 G_Rec: 0.277 D_GP: 0.018 D_real: 1.166 D_fake: 0.763 +(epoch: 259, iters: 7536, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.885 G_ID: 0.161 G_Rec: 0.404 D_GP: 0.038 D_real: 1.173 D_fake: 0.539 +(epoch: 259, iters: 7936, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.721 G_ID: 0.130 G_Rec: 0.302 D_GP: 0.029 D_real: 1.130 D_fake: 0.652 +(epoch: 259, iters: 8336, time: 0.064) G_GAN: 0.852 G_GAN_Feat: 1.092 G_ID: 0.154 G_Rec: 0.452 D_GP: 0.084 D_real: 1.379 D_fake: 0.309 +(epoch: 260, iters: 128, time: 0.064) G_GAN: -0.148 G_GAN_Feat: 0.699 G_ID: 0.131 G_Rec: 0.286 D_GP: 0.021 D_real: 0.904 D_fake: 1.149 +(epoch: 260, iters: 528, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.842 G_ID: 0.154 G_Rec: 0.488 D_GP: 0.028 D_real: 1.133 D_fake: 0.502 +(epoch: 260, iters: 928, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.911 G_ID: 0.146 G_Rec: 0.322 D_GP: 0.298 D_real: 0.357 D_fake: 0.768 +(epoch: 260, iters: 1328, time: 0.064) G_GAN: 0.813 G_GAN_Feat: 1.025 G_ID: 0.159 G_Rec: 0.402 D_GP: 0.039 D_real: 1.064 D_fake: 0.234 +(epoch: 260, iters: 1728, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.544 G_ID: 0.130 G_Rec: 0.256 D_GP: 0.019 D_real: 1.085 D_fake: 0.900 +(epoch: 260, iters: 2128, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.784 G_ID: 0.194 G_Rec: 0.383 D_GP: 0.019 D_real: 1.239 D_fake: 0.578 +(epoch: 260, iters: 2528, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.577 G_ID: 0.137 G_Rec: 0.286 D_GP: 0.020 D_real: 1.046 D_fake: 0.849 +(epoch: 260, iters: 2928, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.835 G_ID: 0.130 G_Rec: 0.403 D_GP: 0.030 D_real: 1.279 D_fake: 0.494 +(epoch: 260, iters: 3328, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.568 G_ID: 0.125 G_Rec: 0.312 D_GP: 0.028 D_real: 1.052 D_fake: 0.881 +(epoch: 260, iters: 3728, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.843 G_ID: 0.156 G_Rec: 0.418 D_GP: 0.031 D_real: 0.989 D_fake: 0.649 +(epoch: 260, iters: 4128, time: 0.064) G_GAN: -0.029 G_GAN_Feat: 0.738 G_ID: 0.135 G_Rec: 0.304 D_GP: 0.066 D_real: 0.706 D_fake: 1.029 +(epoch: 260, iters: 4528, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.890 G_ID: 0.144 G_Rec: 0.439 D_GP: 0.030 D_real: 1.088 D_fake: 0.491 +(epoch: 260, iters: 4928, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.639 G_ID: 0.135 G_Rec: 0.266 D_GP: 0.026 D_real: 1.089 D_fake: 0.816 +(epoch: 260, iters: 5328, time: 0.064) G_GAN: 0.558 G_GAN_Feat: 0.925 G_ID: 0.151 G_Rec: 0.404 D_GP: 0.031 D_real: 1.156 D_fake: 0.446 +(epoch: 260, iters: 5728, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.673 G_ID: 0.113 G_Rec: 0.282 D_GP: 0.040 D_real: 1.025 D_fake: 0.841 +(epoch: 260, iters: 6128, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.975 G_ID: 0.162 G_Rec: 0.368 D_GP: 0.101 D_real: 0.872 D_fake: 0.483 +(epoch: 260, iters: 6528, time: 0.064) G_GAN: -0.072 G_GAN_Feat: 0.804 G_ID: 0.151 G_Rec: 0.341 D_GP: 0.188 D_real: 0.485 D_fake: 1.073 +(epoch: 260, iters: 6928, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.912 G_ID: 0.165 G_Rec: 0.404 D_GP: 0.037 D_real: 1.088 D_fake: 0.572 +(epoch: 260, iters: 7328, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.705 G_ID: 0.154 G_Rec: 0.299 D_GP: 0.029 D_real: 0.948 D_fake: 0.909 +(epoch: 260, iters: 7728, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.952 G_ID: 0.173 G_Rec: 0.415 D_GP: 0.068 D_real: 0.863 D_fake: 0.621 +(epoch: 260, iters: 8128, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.637 G_ID: 0.119 G_Rec: 0.265 D_GP: 0.022 D_real: 1.161 D_fake: 0.808 +(epoch: 260, iters: 8528, time: 0.064) G_GAN: 0.560 G_GAN_Feat: 0.947 G_ID: 0.168 G_Rec: 0.430 D_GP: 0.032 D_real: 1.043 D_fake: 0.445 +(epoch: 261, iters: 320, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.850 G_ID: 0.128 G_Rec: 0.289 D_GP: 0.033 D_real: 0.825 D_fake: 0.676 +(epoch: 261, iters: 720, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.864 G_ID: 0.187 G_Rec: 0.379 D_GP: 0.029 D_real: 1.028 D_fake: 0.609 +(epoch: 261, iters: 1120, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.677 G_ID: 0.125 G_Rec: 0.298 D_GP: 0.026 D_real: 1.143 D_fake: 0.779 +(epoch: 261, iters: 1520, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.848 G_ID: 0.178 G_Rec: 0.403 D_GP: 0.022 D_real: 1.007 D_fake: 0.743 +(epoch: 261, iters: 1920, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.864 G_ID: 0.123 G_Rec: 0.339 D_GP: 0.068 D_real: 0.935 D_fake: 0.723 +(epoch: 261, iters: 2320, time: 0.064) G_GAN: 0.529 G_GAN_Feat: 0.919 G_ID: 0.168 G_Rec: 0.359 D_GP: 0.052 D_real: 0.871 D_fake: 0.476 +(epoch: 261, iters: 2720, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.636 G_ID: 0.121 G_Rec: 0.311 D_GP: 0.019 D_real: 1.090 D_fake: 0.907 +(epoch: 261, iters: 3120, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.816 G_ID: 0.172 G_Rec: 0.397 D_GP: 0.021 D_real: 1.130 D_fake: 0.666 +(epoch: 261, iters: 3520, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.586 G_ID: 0.126 G_Rec: 0.289 D_GP: 0.018 D_real: 1.201 D_fake: 0.729 +(epoch: 261, iters: 3920, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.728 G_ID: 0.169 G_Rec: 0.350 D_GP: 0.019 D_real: 0.980 D_fake: 0.820 +(epoch: 261, iters: 4320, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.624 G_ID: 0.119 G_Rec: 0.301 D_GP: 0.024 D_real: 1.077 D_fake: 0.827 +(epoch: 261, iters: 4720, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.848 G_ID: 0.152 G_Rec: 0.396 D_GP: 0.025 D_real: 1.189 D_fake: 0.502 +(epoch: 261, iters: 5120, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.665 G_ID: 0.132 G_Rec: 0.288 D_GP: 0.021 D_real: 1.138 D_fake: 0.733 +(epoch: 261, iters: 5520, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.948 G_ID: 0.162 G_Rec: 0.415 D_GP: 0.100 D_real: 0.753 D_fake: 0.570 +(epoch: 261, iters: 5920, time: 0.064) G_GAN: 0.004 G_GAN_Feat: 0.837 G_ID: 0.147 G_Rec: 0.330 D_GP: 0.234 D_real: 0.456 D_fake: 0.996 +(epoch: 261, iters: 6320, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.913 G_ID: 0.156 G_Rec: 0.425 D_GP: 0.021 D_real: 1.054 D_fake: 0.655 +(epoch: 261, iters: 6720, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.709 G_ID: 0.107 G_Rec: 0.275 D_GP: 0.030 D_real: 0.886 D_fake: 0.856 +(epoch: 261, iters: 7120, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 1.020 G_ID: 0.161 G_Rec: 0.442 D_GP: 0.032 D_real: 0.889 D_fake: 0.462 +(epoch: 261, iters: 7520, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.657 G_ID: 0.124 G_Rec: 0.272 D_GP: 0.022 D_real: 1.227 D_fake: 0.683 +(epoch: 261, iters: 7920, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 1.138 G_ID: 0.191 G_Rec: 0.450 D_GP: 0.039 D_real: 0.959 D_fake: 0.800 +(epoch: 261, iters: 8320, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.613 G_ID: 0.117 G_Rec: 0.261 D_GP: 0.023 D_real: 1.207 D_fake: 0.800 +(epoch: 262, iters: 112, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.878 G_ID: 0.169 G_Rec: 0.354 D_GP: 0.022 D_real: 1.063 D_fake: 0.629 +(epoch: 262, iters: 512, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.867 G_ID: 0.121 G_Rec: 0.284 D_GP: 0.055 D_real: 0.545 D_fake: 0.837 +(epoch: 262, iters: 912, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.897 G_ID: 0.180 G_Rec: 0.423 D_GP: 0.026 D_real: 1.007 D_fake: 0.675 +(epoch: 262, iters: 1312, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.677 G_ID: 0.143 G_Rec: 0.308 D_GP: 0.038 D_real: 0.919 D_fake: 0.923 +(epoch: 262, iters: 1712, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.886 G_ID: 0.158 G_Rec: 0.383 D_GP: 0.037 D_real: 0.931 D_fake: 0.632 +(epoch: 262, iters: 2112, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.737 G_ID: 0.128 G_Rec: 0.291 D_GP: 0.032 D_real: 1.012 D_fake: 0.696 +(epoch: 262, iters: 2512, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 0.918 G_ID: 0.164 G_Rec: 0.388 D_GP: 0.040 D_real: 1.170 D_fake: 0.408 +(epoch: 262, iters: 2912, time: 0.064) G_GAN: 0.124 G_GAN_Feat: 0.789 G_ID: 0.150 G_Rec: 0.369 D_GP: 0.029 D_real: 0.816 D_fake: 0.876 +(epoch: 262, iters: 3312, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 1.124 G_ID: 0.149 G_Rec: 0.413 D_GP: 0.028 D_real: 1.403 D_fake: 0.475 +(epoch: 262, iters: 3712, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.677 G_ID: 0.151 G_Rec: 0.312 D_GP: 0.019 D_real: 0.985 D_fake: 0.964 +(epoch: 262, iters: 4112, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.769 G_ID: 0.207 G_Rec: 0.331 D_GP: 0.033 D_real: 0.884 D_fake: 0.824 +(epoch: 262, iters: 4512, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.668 G_ID: 0.121 G_Rec: 0.262 D_GP: 0.031 D_real: 1.094 D_fake: 0.764 +(epoch: 262, iters: 4912, time: 0.064) G_GAN: 0.613 G_GAN_Feat: 0.970 G_ID: 0.149 G_Rec: 0.382 D_GP: 0.027 D_real: 1.334 D_fake: 0.402 +(epoch: 262, iters: 5312, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.749 G_ID: 0.133 G_Rec: 0.274 D_GP: 0.025 D_real: 0.938 D_fake: 0.805 +(epoch: 262, iters: 5712, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.915 G_ID: 0.167 G_Rec: 0.372 D_GP: 0.044 D_real: 0.823 D_fake: 0.731 +(epoch: 262, iters: 6112, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.603 G_ID: 0.141 G_Rec: 0.280 D_GP: 0.021 D_real: 1.300 D_fake: 0.698 +(epoch: 262, iters: 6512, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.894 G_ID: 0.194 G_Rec: 0.396 D_GP: 0.033 D_real: 0.859 D_fake: 0.792 +(epoch: 262, iters: 6912, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.833 G_ID: 0.135 G_Rec: 0.324 D_GP: 0.022 D_real: 1.019 D_fake: 0.789 +(epoch: 262, iters: 7312, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.839 G_ID: 0.193 G_Rec: 0.390 D_GP: 0.024 D_real: 1.083 D_fake: 0.694 +(epoch: 262, iters: 7712, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.823 G_ID: 0.121 G_Rec: 0.296 D_GP: 0.070 D_real: 0.670 D_fake: 0.752 +(epoch: 262, iters: 8112, time: 0.064) G_GAN: 0.596 G_GAN_Feat: 1.346 G_ID: 0.161 G_Rec: 0.447 D_GP: 0.109 D_real: 1.281 D_fake: 0.582 +(epoch: 262, iters: 8512, time: 0.064) G_GAN: -0.011 G_GAN_Feat: 0.657 G_ID: 0.132 G_Rec: 0.287 D_GP: 0.021 D_real: 0.953 D_fake: 1.011 +(epoch: 263, iters: 304, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.898 G_ID: 0.151 G_Rec: 0.398 D_GP: 0.032 D_real: 0.829 D_fake: 0.792 +(epoch: 263, iters: 704, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.714 G_ID: 0.105 G_Rec: 0.281 D_GP: 0.027 D_real: 1.139 D_fake: 0.665 +(epoch: 263, iters: 1104, time: 0.064) G_GAN: 0.597 G_GAN_Feat: 0.802 G_ID: 0.158 G_Rec: 0.417 D_GP: 0.021 D_real: 1.319 D_fake: 0.413 +(epoch: 263, iters: 1504, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.614 G_ID: 0.110 G_Rec: 0.311 D_GP: 0.026 D_real: 1.047 D_fake: 0.872 +(epoch: 263, iters: 1904, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.775 G_ID: 0.169 G_Rec: 0.385 D_GP: 0.032 D_real: 1.136 D_fake: 0.682 +(epoch: 263, iters: 2304, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.578 G_ID: 0.123 G_Rec: 0.250 D_GP: 0.022 D_real: 1.079 D_fake: 0.872 +(epoch: 263, iters: 2704, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.770 G_ID: 0.166 G_Rec: 0.384 D_GP: 0.025 D_real: 1.264 D_fake: 0.530 +(epoch: 263, iters: 3104, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.575 G_ID: 0.123 G_Rec: 0.261 D_GP: 0.019 D_real: 1.229 D_fake: 0.723 +(epoch: 263, iters: 3504, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.912 G_ID: 0.173 G_Rec: 0.417 D_GP: 0.049 D_real: 0.951 D_fake: 0.762 +(epoch: 263, iters: 3904, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.614 G_ID: 0.124 G_Rec: 0.278 D_GP: 0.019 D_real: 1.216 D_fake: 0.769 +(epoch: 263, iters: 4304, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.984 G_ID: 0.159 G_Rec: 0.426 D_GP: 0.142 D_real: 0.492 D_fake: 0.865 +(epoch: 263, iters: 4704, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.695 G_ID: 0.130 G_Rec: 0.306 D_GP: 0.033 D_real: 1.065 D_fake: 0.768 +(epoch: 263, iters: 5104, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.795 G_ID: 0.172 G_Rec: 0.358 D_GP: 0.022 D_real: 1.135 D_fake: 0.652 +(epoch: 263, iters: 5504, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.676 G_ID: 0.120 G_Rec: 0.292 D_GP: 0.073 D_real: 0.927 D_fake: 0.832 +(epoch: 263, iters: 5904, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.793 G_ID: 0.156 G_Rec: 0.330 D_GP: 0.028 D_real: 1.117 D_fake: 0.661 +(epoch: 263, iters: 6304, time: 0.064) G_GAN: 0.016 G_GAN_Feat: 0.752 G_ID: 0.137 G_Rec: 0.300 D_GP: 0.052 D_real: 0.667 D_fake: 0.984 +(epoch: 263, iters: 6704, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.889 G_ID: 0.174 G_Rec: 0.372 D_GP: 0.041 D_real: 0.895 D_fake: 0.752 +(epoch: 263, iters: 7104, time: 0.064) G_GAN: -0.069 G_GAN_Feat: 0.759 G_ID: 0.152 G_Rec: 0.308 D_GP: 0.042 D_real: 0.666 D_fake: 1.070 +(epoch: 263, iters: 7504, time: 0.064) G_GAN: 0.707 G_GAN_Feat: 0.918 G_ID: 0.157 G_Rec: 0.427 D_GP: 0.022 D_real: 1.333 D_fake: 0.333 +(epoch: 263, iters: 7904, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.651 G_ID: 0.114 G_Rec: 0.276 D_GP: 0.029 D_real: 1.184 D_fake: 0.819 +(epoch: 263, iters: 8304, time: 0.064) G_GAN: 0.629 G_GAN_Feat: 0.811 G_ID: 0.158 G_Rec: 0.367 D_GP: 0.024 D_real: 1.411 D_fake: 0.385 +(epoch: 264, iters: 96, time: 0.064) G_GAN: 0.139 G_GAN_Feat: 0.649 G_ID: 0.141 G_Rec: 0.272 D_GP: 0.023 D_real: 1.111 D_fake: 0.862 +(epoch: 264, iters: 496, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.754 G_ID: 0.164 G_Rec: 0.332 D_GP: 0.026 D_real: 1.235 D_fake: 0.622 +(epoch: 264, iters: 896, time: 0.064) G_GAN: -0.013 G_GAN_Feat: 0.707 G_ID: 0.114 G_Rec: 0.308 D_GP: 0.051 D_real: 0.826 D_fake: 1.013 +(epoch: 264, iters: 1296, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.832 G_ID: 0.152 G_Rec: 0.436 D_GP: 0.035 D_real: 1.044 D_fake: 0.650 +(epoch: 264, iters: 1696, time: 0.064) G_GAN: 0.065 G_GAN_Feat: 0.720 G_ID: 0.120 G_Rec: 0.315 D_GP: 0.034 D_real: 0.828 D_fake: 0.935 +(epoch: 264, iters: 2096, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.878 G_ID: 0.173 G_Rec: 0.412 D_GP: 0.045 D_real: 0.993 D_fake: 0.649 +(epoch: 264, iters: 2496, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.726 G_ID: 0.114 G_Rec: 0.281 D_GP: 0.053 D_real: 1.063 D_fake: 0.674 +(epoch: 264, iters: 2896, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 0.955 G_ID: 0.173 G_Rec: 0.388 D_GP: 0.034 D_real: 0.984 D_fake: 0.936 +(epoch: 264, iters: 3296, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.869 G_ID: 0.132 G_Rec: 0.362 D_GP: 0.061 D_real: 0.972 D_fake: 0.575 +(epoch: 264, iters: 3696, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.827 G_ID: 0.176 G_Rec: 0.379 D_GP: 0.023 D_real: 1.225 D_fake: 0.567 +(epoch: 264, iters: 4096, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.643 G_ID: 0.115 G_Rec: 0.273 D_GP: 0.024 D_real: 1.249 D_fake: 0.706 +(epoch: 264, iters: 4496, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 1.000 G_ID: 0.154 G_Rec: 0.404 D_GP: 0.070 D_real: 0.906 D_fake: 0.515 +(epoch: 264, iters: 4896, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.784 G_ID: 0.133 G_Rec: 0.318 D_GP: 0.048 D_real: 0.678 D_fake: 0.893 +(epoch: 264, iters: 5296, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.831 G_ID: 0.159 G_Rec: 0.408 D_GP: 0.020 D_real: 1.141 D_fake: 0.603 +(epoch: 264, iters: 5696, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.660 G_ID: 0.128 G_Rec: 0.285 D_GP: 0.030 D_real: 0.982 D_fake: 0.883 +(epoch: 264, iters: 6096, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.900 G_ID: 0.191 G_Rec: 0.390 D_GP: 0.036 D_real: 1.168 D_fake: 0.540 +(epoch: 264, iters: 6496, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.697 G_ID: 0.125 G_Rec: 0.267 D_GP: 0.033 D_real: 0.986 D_fake: 0.777 +(epoch: 264, iters: 6896, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.994 G_ID: 0.169 G_Rec: 0.410 D_GP: 0.047 D_real: 0.749 D_fake: 0.599 +(epoch: 264, iters: 7296, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.931 G_ID: 0.137 G_Rec: 0.331 D_GP: 0.496 D_real: 0.554 D_fake: 0.668 +(epoch: 264, iters: 7696, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.933 G_ID: 0.168 G_Rec: 0.402 D_GP: 0.023 D_real: 1.188 D_fake: 0.440 +(epoch: 264, iters: 8096, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.671 G_ID: 0.122 G_Rec: 0.309 D_GP: 0.022 D_real: 1.377 D_fake: 0.614 +(epoch: 264, iters: 8496, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 1.106 G_ID: 0.151 G_Rec: 0.432 D_GP: 0.130 D_real: 0.797 D_fake: 0.492 +(epoch: 265, iters: 288, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.716 G_ID: 0.134 G_Rec: 0.318 D_GP: 0.024 D_real: 1.207 D_fake: 0.758 +(epoch: 265, iters: 688, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.835 G_ID: 0.156 G_Rec: 0.356 D_GP: 0.055 D_real: 1.123 D_fake: 0.543 +(epoch: 265, iters: 1088, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.869 G_ID: 0.113 G_Rec: 0.293 D_GP: 0.147 D_real: 0.409 D_fake: 0.772 +(epoch: 265, iters: 1488, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.951 G_ID: 0.157 G_Rec: 0.388 D_GP: 0.029 D_real: 0.985 D_fake: 0.519 +(epoch: 265, iters: 1888, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.703 G_ID: 0.108 G_Rec: 0.296 D_GP: 0.026 D_real: 1.352 D_fake: 0.641 +(epoch: 265, iters: 2288, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 1.019 G_ID: 0.183 G_Rec: 0.437 D_GP: 0.082 D_real: 0.808 D_fake: 0.584 +(epoch: 265, iters: 2688, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.937 G_ID: 0.132 G_Rec: 0.322 D_GP: 0.063 D_real: 0.468 D_fake: 0.727 +(epoch: 265, iters: 3088, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.796 G_ID: 0.165 G_Rec: 0.384 D_GP: 0.019 D_real: 1.231 D_fake: 0.527 +(epoch: 265, iters: 3488, time: 0.064) G_GAN: -0.013 G_GAN_Feat: 0.647 G_ID: 0.122 G_Rec: 0.294 D_GP: 0.018 D_real: 0.928 D_fake: 1.013 +(epoch: 265, iters: 3888, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.965 G_ID: 0.165 G_Rec: 0.406 D_GP: 0.039 D_real: 0.924 D_fake: 0.535 +(epoch: 265, iters: 4288, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.729 G_ID: 0.111 G_Rec: 0.300 D_GP: 0.028 D_real: 1.190 D_fake: 0.608 +(epoch: 265, iters: 4688, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.827 G_ID: 0.158 G_Rec: 0.349 D_GP: 0.022 D_real: 1.249 D_fake: 0.487 +(epoch: 265, iters: 5088, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.624 G_ID: 0.140 G_Rec: 0.332 D_GP: 0.019 D_real: 1.245 D_fake: 0.723 +(epoch: 265, iters: 5488, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.666 G_ID: 0.165 G_Rec: 0.350 D_GP: 0.020 D_real: 1.261 D_fake: 0.562 +(epoch: 265, iters: 5888, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.576 G_ID: 0.108 G_Rec: 0.282 D_GP: 0.023 D_real: 1.131 D_fake: 0.821 +(epoch: 265, iters: 6288, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.814 G_ID: 0.159 G_Rec: 0.435 D_GP: 0.034 D_real: 1.143 D_fake: 0.576 +(epoch: 265, iters: 6688, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.716 G_ID: 0.119 G_Rec: 0.309 D_GP: 0.052 D_real: 0.839 D_fake: 0.927 +(epoch: 265, iters: 7088, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.795 G_ID: 0.150 G_Rec: 0.387 D_GP: 0.022 D_real: 1.231 D_fake: 0.511 +(epoch: 265, iters: 7488, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.669 G_ID: 0.144 G_Rec: 0.284 D_GP: 0.020 D_real: 1.141 D_fake: 0.737 +(epoch: 265, iters: 7888, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.924 G_ID: 0.160 G_Rec: 0.387 D_GP: 0.046 D_real: 0.985 D_fake: 0.518 +(epoch: 265, iters: 8288, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.713 G_ID: 0.107 G_Rec: 0.330 D_GP: 0.035 D_real: 1.045 D_fake: 0.689 +(epoch: 266, iters: 80, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.962 G_ID: 0.173 G_Rec: 0.421 D_GP: 0.033 D_real: 0.747 D_fake: 0.752 +(epoch: 266, iters: 480, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.847 G_ID: 0.151 G_Rec: 0.327 D_GP: 0.045 D_real: 0.475 D_fake: 0.900 +(epoch: 266, iters: 880, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.823 G_ID: 0.163 G_Rec: 0.376 D_GP: 0.024 D_real: 1.130 D_fake: 0.754 +(epoch: 266, iters: 1280, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.696 G_ID: 0.129 G_Rec: 0.298 D_GP: 0.031 D_real: 0.932 D_fake: 0.919 +(epoch: 266, iters: 1680, time: 0.064) G_GAN: 0.943 G_GAN_Feat: 1.122 G_ID: 0.147 G_Rec: 0.440 D_GP: 0.070 D_real: 1.356 D_fake: 0.339 +(epoch: 266, iters: 2080, time: 0.064) G_GAN: -0.157 G_GAN_Feat: 0.752 G_ID: 0.151 G_Rec: 0.313 D_GP: 0.052 D_real: 0.572 D_fake: 1.157 +(epoch: 266, iters: 2480, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.851 G_ID: 0.171 G_Rec: 0.384 D_GP: 0.032 D_real: 0.933 D_fake: 0.737 +(epoch: 266, iters: 2880, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.736 G_ID: 0.134 G_Rec: 0.287 D_GP: 0.051 D_real: 1.097 D_fake: 0.780 +(epoch: 266, iters: 3280, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.771 G_ID: 0.182 G_Rec: 0.370 D_GP: 0.021 D_real: 1.093 D_fake: 0.709 +(epoch: 266, iters: 3680, time: 0.064) G_GAN: -0.063 G_GAN_Feat: 0.687 G_ID: 0.148 G_Rec: 0.287 D_GP: 0.050 D_real: 0.798 D_fake: 1.063 +(epoch: 266, iters: 4080, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.927 G_ID: 0.163 G_Rec: 0.418 D_GP: 0.034 D_real: 0.993 D_fake: 0.622 +(epoch: 266, iters: 4480, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 1.043 G_ID: 0.116 G_Rec: 0.330 D_GP: 1.340 D_real: 0.541 D_fake: 0.691 +(epoch: 266, iters: 4880, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.871 G_ID: 0.179 G_Rec: 0.382 D_GP: 0.023 D_real: 1.067 D_fake: 0.599 +(epoch: 266, iters: 5280, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.876 G_ID: 0.147 G_Rec: 0.341 D_GP: 0.057 D_real: 0.762 D_fake: 0.738 +(epoch: 266, iters: 5680, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.913 G_ID: 0.165 G_Rec: 0.389 D_GP: 0.027 D_real: 1.172 D_fake: 0.445 +(epoch: 266, iters: 6080, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.658 G_ID: 0.103 G_Rec: 0.265 D_GP: 0.022 D_real: 1.311 D_fake: 0.614 +(epoch: 266, iters: 6480, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.910 G_ID: 0.169 G_Rec: 0.411 D_GP: 0.028 D_real: 1.036 D_fake: 0.609 +(epoch: 266, iters: 6880, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.779 G_ID: 0.119 G_Rec: 0.303 D_GP: 0.052 D_real: 1.251 D_fake: 0.491 +(epoch: 266, iters: 7280, time: 0.064) G_GAN: 0.706 G_GAN_Feat: 0.852 G_ID: 0.161 G_Rec: 0.347 D_GP: 0.023 D_real: 1.561 D_fake: 0.317 +(epoch: 266, iters: 7680, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 0.681 G_ID: 0.126 G_Rec: 0.310 D_GP: 0.027 D_real: 1.349 D_fake: 0.600 +(epoch: 266, iters: 8080, time: 0.064) G_GAN: 0.609 G_GAN_Feat: 1.046 G_ID: 0.167 G_Rec: 0.400 D_GP: 0.080 D_real: 0.564 D_fake: 0.404 +(epoch: 266, iters: 8480, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.699 G_ID: 0.131 G_Rec: 0.292 D_GP: 0.026 D_real: 1.143 D_fake: 0.662 +(epoch: 267, iters: 272, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.993 G_ID: 0.160 G_Rec: 0.413 D_GP: 0.029 D_real: 1.023 D_fake: 0.594 +(epoch: 267, iters: 672, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.835 G_ID: 0.139 G_Rec: 0.296 D_GP: 0.112 D_real: 0.743 D_fake: 0.750 +(epoch: 267, iters: 1072, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.797 G_ID: 0.199 G_Rec: 0.398 D_GP: 0.019 D_real: 1.091 D_fake: 0.662 +(epoch: 267, iters: 1472, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.583 G_ID: 0.120 G_Rec: 0.316 D_GP: 0.020 D_real: 1.256 D_fake: 0.709 +(epoch: 267, iters: 1872, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.951 G_ID: 0.208 G_Rec: 0.452 D_GP: 0.055 D_real: 0.878 D_fake: 0.671 +(epoch: 267, iters: 2272, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.687 G_ID: 0.119 G_Rec: 0.294 D_GP: 0.033 D_real: 1.013 D_fake: 0.845 +(epoch: 267, iters: 2672, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.797 G_ID: 0.193 G_Rec: 0.413 D_GP: 0.020 D_real: 1.101 D_fake: 0.622 +(epoch: 267, iters: 3072, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.621 G_ID: 0.125 G_Rec: 0.298 D_GP: 0.028 D_real: 1.112 D_fake: 0.818 +(epoch: 267, iters: 3472, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.845 G_ID: 0.156 G_Rec: 0.405 D_GP: 0.030 D_real: 1.119 D_fake: 0.600 +(epoch: 267, iters: 3872, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.612 G_ID: 0.124 G_Rec: 0.286 D_GP: 0.024 D_real: 1.080 D_fake: 0.849 +(epoch: 267, iters: 4272, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.865 G_ID: 0.186 G_Rec: 0.388 D_GP: 0.139 D_real: 0.852 D_fake: 0.772 +(epoch: 267, iters: 4672, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 0.713 G_ID: 0.132 G_Rec: 0.293 D_GP: 0.041 D_real: 0.812 D_fake: 0.928 +(epoch: 267, iters: 5072, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.756 G_ID: 0.173 G_Rec: 0.335 D_GP: 0.020 D_real: 1.117 D_fake: 0.653 +(epoch: 267, iters: 5472, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.814 G_ID: 0.132 G_Rec: 0.314 D_GP: 0.093 D_real: 1.083 D_fake: 0.662 +(epoch: 267, iters: 5872, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 1.252 G_ID: 0.149 G_Rec: 0.427 D_GP: 0.091 D_real: 1.158 D_fake: 0.697 +(epoch: 267, iters: 6272, time: 0.064) G_GAN: 0.043 G_GAN_Feat: 0.691 G_ID: 0.133 G_Rec: 0.296 D_GP: 0.031 D_real: 0.830 D_fake: 0.957 +(epoch: 267, iters: 6672, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.951 G_ID: 0.153 G_Rec: 0.433 D_GP: 0.023 D_real: 1.093 D_fake: 0.716 +(epoch: 267, iters: 7072, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.732 G_ID: 0.124 G_Rec: 0.296 D_GP: 0.040 D_real: 0.987 D_fake: 0.885 +(epoch: 267, iters: 7472, time: 0.064) G_GAN: 0.572 G_GAN_Feat: 1.029 G_ID: 0.160 G_Rec: 0.432 D_GP: 0.039 D_real: 0.963 D_fake: 0.451 +(epoch: 267, iters: 7872, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.747 G_ID: 0.169 G_Rec: 0.296 D_GP: 0.024 D_real: 1.145 D_fake: 0.682 +(epoch: 267, iters: 8272, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.818 G_ID: 0.141 G_Rec: 0.403 D_GP: 0.018 D_real: 1.227 D_fake: 0.599 +(epoch: 268, iters: 64, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.704 G_ID: 0.140 G_Rec: 0.306 D_GP: 0.027 D_real: 0.863 D_fake: 0.977 +(epoch: 268, iters: 464, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.907 G_ID: 0.137 G_Rec: 0.403 D_GP: 0.033 D_real: 0.998 D_fake: 0.518 +(epoch: 268, iters: 864, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.725 G_ID: 0.108 G_Rec: 0.295 D_GP: 0.104 D_real: 1.142 D_fake: 0.540 +(epoch: 268, iters: 1264, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.990 G_ID: 0.141 G_Rec: 0.427 D_GP: 0.036 D_real: 1.046 D_fake: 0.518 +(epoch: 268, iters: 1664, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.725 G_ID: 0.141 G_Rec: 0.306 D_GP: 0.021 D_real: 1.427 D_fake: 0.506 +(epoch: 268, iters: 2064, time: 0.064) G_GAN: -0.010 G_GAN_Feat: 0.968 G_ID: 0.187 G_Rec: 0.393 D_GP: 0.051 D_real: 0.470 D_fake: 1.010 +(epoch: 268, iters: 2464, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.728 G_ID: 0.143 G_Rec: 0.329 D_GP: 0.023 D_real: 1.242 D_fake: 0.638 +(epoch: 268, iters: 2864, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.891 G_ID: 0.183 G_Rec: 0.363 D_GP: 0.020 D_real: 1.082 D_fake: 0.642 +(epoch: 268, iters: 3264, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.854 G_ID: 0.096 G_Rec: 0.330 D_GP: 0.056 D_real: 0.877 D_fake: 0.730 +(epoch: 268, iters: 3664, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.866 G_ID: 0.170 G_Rec: 0.366 D_GP: 0.047 D_real: 1.245 D_fake: 0.486 +(epoch: 268, iters: 4064, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.571 G_ID: 0.115 G_Rec: 0.293 D_GP: 0.022 D_real: 1.112 D_fake: 0.853 +(epoch: 268, iters: 4464, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.858 G_ID: 0.150 G_Rec: 0.416 D_GP: 0.034 D_real: 0.963 D_fake: 0.720 +(epoch: 268, iters: 4864, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.628 G_ID: 0.118 G_Rec: 0.278 D_GP: 0.024 D_real: 1.194 D_fake: 0.744 +(epoch: 268, iters: 5264, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.962 G_ID: 0.179 G_Rec: 0.433 D_GP: 0.157 D_real: 0.609 D_fake: 0.667 +(epoch: 268, iters: 5664, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.689 G_ID: 0.130 G_Rec: 0.290 D_GP: 0.029 D_real: 0.908 D_fake: 0.968 +(epoch: 268, iters: 6064, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.981 G_ID: 0.179 G_Rec: 0.410 D_GP: 0.033 D_real: 0.511 D_fake: 0.895 +(epoch: 268, iters: 6464, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.711 G_ID: 0.121 G_Rec: 0.281 D_GP: 0.033 D_real: 1.063 D_fake: 0.909 +(epoch: 268, iters: 6864, time: 0.064) G_GAN: 0.666 G_GAN_Feat: 0.945 G_ID: 0.186 G_Rec: 0.407 D_GP: 0.032 D_real: 1.256 D_fake: 0.359 +(epoch: 268, iters: 7264, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.596 G_ID: 0.122 G_Rec: 0.299 D_GP: 0.018 D_real: 1.254 D_fake: 0.735 +(epoch: 268, iters: 7664, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.714 G_ID: 0.153 G_Rec: 0.324 D_GP: 0.022 D_real: 1.134 D_fake: 0.697 +(epoch: 268, iters: 8064, time: 0.064) G_GAN: 0.007 G_GAN_Feat: 0.641 G_ID: 0.131 G_Rec: 0.290 D_GP: 0.022 D_real: 0.921 D_fake: 0.993 +(epoch: 268, iters: 8464, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.808 G_ID: 0.173 G_Rec: 0.388 D_GP: 0.023 D_real: 1.105 D_fake: 0.637 +(epoch: 269, iters: 256, time: 0.064) G_GAN: -0.070 G_GAN_Feat: 0.762 G_ID: 0.188 G_Rec: 0.281 D_GP: 0.200 D_real: 0.384 D_fake: 1.070 +(epoch: 269, iters: 656, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.937 G_ID: 0.167 G_Rec: 0.380 D_GP: 0.025 D_real: 0.939 D_fake: 0.688 +(epoch: 269, iters: 1056, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.815 G_ID: 0.127 G_Rec: 0.334 D_GP: 0.028 D_real: 0.901 D_fake: 0.746 +(epoch: 269, iters: 1456, time: 0.064) G_GAN: 0.139 G_GAN_Feat: 0.740 G_ID: 0.200 G_Rec: 0.391 D_GP: 0.021 D_real: 0.993 D_fake: 0.861 +(epoch: 269, iters: 1856, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.696 G_ID: 0.101 G_Rec: 0.302 D_GP: 0.027 D_real: 1.006 D_fake: 0.869 +(epoch: 269, iters: 2256, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.838 G_ID: 0.187 G_Rec: 0.393 D_GP: 0.031 D_real: 1.056 D_fake: 0.631 +(epoch: 269, iters: 2656, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.743 G_ID: 0.111 G_Rec: 0.295 D_GP: 0.034 D_real: 0.976 D_fake: 0.665 +(epoch: 269, iters: 3056, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.851 G_ID: 0.166 G_Rec: 0.422 D_GP: 0.021 D_real: 1.096 D_fake: 0.643 +(epoch: 269, iters: 3456, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.696 G_ID: 0.126 G_Rec: 0.274 D_GP: 0.035 D_real: 1.017 D_fake: 0.744 +(epoch: 269, iters: 3856, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.788 G_ID: 0.153 G_Rec: 0.355 D_GP: 0.021 D_real: 1.205 D_fake: 0.616 +(epoch: 269, iters: 4256, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.692 G_ID: 0.109 G_Rec: 0.284 D_GP: 0.020 D_real: 1.145 D_fake: 0.770 +(epoch: 269, iters: 4656, time: 0.064) G_GAN: 0.579 G_GAN_Feat: 1.175 G_ID: 0.164 G_Rec: 0.436 D_GP: 1.962 D_real: 0.407 D_fake: 0.490 +(epoch: 269, iters: 5056, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.603 G_ID: 0.123 G_Rec: 0.272 D_GP: 0.018 D_real: 1.202 D_fake: 0.750 +(epoch: 269, iters: 5456, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.841 G_ID: 0.150 G_Rec: 0.395 D_GP: 0.023 D_real: 1.251 D_fake: 0.531 +(epoch: 269, iters: 5856, time: 0.064) G_GAN: 0.040 G_GAN_Feat: 0.804 G_ID: 0.111 G_Rec: 0.315 D_GP: 0.049 D_real: 1.047 D_fake: 0.961 +(epoch: 269, iters: 6256, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.834 G_ID: 0.162 G_Rec: 0.420 D_GP: 0.043 D_real: 1.018 D_fake: 0.609 +(epoch: 269, iters: 6656, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.624 G_ID: 0.140 G_Rec: 0.260 D_GP: 0.027 D_real: 1.013 D_fake: 0.851 +(epoch: 269, iters: 7056, time: 0.064) G_GAN: 0.017 G_GAN_Feat: 0.876 G_ID: 0.163 G_Rec: 0.424 D_GP: 0.025 D_real: 0.757 D_fake: 0.983 +(epoch: 269, iters: 7456, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.740 G_ID: 0.140 G_Rec: 0.309 D_GP: 0.044 D_real: 0.883 D_fake: 0.825 +(epoch: 269, iters: 7856, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.936 G_ID: 0.205 G_Rec: 0.423 D_GP: 0.031 D_real: 0.999 D_fake: 0.646 +(epoch: 269, iters: 8256, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.674 G_ID: 0.124 G_Rec: 0.280 D_GP: 0.035 D_real: 1.054 D_fake: 0.738 +(epoch: 270, iters: 48, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.825 G_ID: 0.168 G_Rec: 0.400 D_GP: 0.022 D_real: 1.070 D_fake: 0.640 +(epoch: 270, iters: 448, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.712 G_ID: 0.109 G_Rec: 0.264 D_GP: 0.033 D_real: 0.888 D_fake: 0.845 +(epoch: 270, iters: 848, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.867 G_ID: 0.163 G_Rec: 0.352 D_GP: 0.040 D_real: 1.048 D_fake: 0.640 +(epoch: 270, iters: 1248, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.873 G_ID: 0.131 G_Rec: 0.316 D_GP: 0.065 D_real: 0.910 D_fake: 0.819 +(epoch: 270, iters: 1648, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.971 G_ID: 0.161 G_Rec: 0.402 D_GP: 0.025 D_real: 0.989 D_fake: 0.774 +(epoch: 270, iters: 2048, time: 0.064) G_GAN: -0.054 G_GAN_Feat: 0.688 G_ID: 0.162 G_Rec: 0.308 D_GP: 0.026 D_real: 0.812 D_fake: 1.054 +(epoch: 270, iters: 2448, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.887 G_ID: 0.175 G_Rec: 0.409 D_GP: 0.032 D_real: 0.965 D_fake: 0.732 +(epoch: 270, iters: 2848, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.774 G_ID: 0.122 G_Rec: 0.329 D_GP: 0.029 D_real: 0.793 D_fake: 0.855 +(epoch: 270, iters: 3248, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.883 G_ID: 0.170 G_Rec: 0.383 D_GP: 0.081 D_real: 1.177 D_fake: 0.440 +(epoch: 270, iters: 3648, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.739 G_ID: 0.137 G_Rec: 0.349 D_GP: 0.044 D_real: 0.785 D_fake: 0.882 +(epoch: 270, iters: 4048, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 1.002 G_ID: 0.150 G_Rec: 0.389 D_GP: 0.052 D_real: 0.659 D_fake: 0.522 +(epoch: 270, iters: 4448, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.683 G_ID: 0.136 G_Rec: 0.291 D_GP: 0.021 D_real: 1.157 D_fake: 0.721 +(epoch: 270, iters: 4848, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.906 G_ID: 0.174 G_Rec: 0.381 D_GP: 0.028 D_real: 1.129 D_fake: 0.566 +(epoch: 270, iters: 5248, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.726 G_ID: 0.118 G_Rec: 0.313 D_GP: 0.050 D_real: 0.946 D_fake: 0.871 +(epoch: 270, iters: 5648, time: 0.064) G_GAN: 0.555 G_GAN_Feat: 1.019 G_ID: 0.146 G_Rec: 0.439 D_GP: 0.069 D_real: 0.874 D_fake: 0.452 +(epoch: 270, iters: 6048, time: 0.064) G_GAN: 0.528 G_GAN_Feat: 0.743 G_ID: 0.138 G_Rec: 0.304 D_GP: 0.024 D_real: 1.371 D_fake: 0.476 +(epoch: 270, iters: 6448, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.828 G_ID: 0.159 G_Rec: 0.382 D_GP: 0.021 D_real: 1.247 D_fake: 0.608 +(epoch: 270, iters: 6848, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.600 G_ID: 0.124 G_Rec: 0.268 D_GP: 0.019 D_real: 1.097 D_fake: 0.860 +(epoch: 270, iters: 7248, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.866 G_ID: 0.163 G_Rec: 0.389 D_GP: 0.022 D_real: 1.147 D_fake: 0.627 +(epoch: 270, iters: 7648, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.704 G_ID: 0.140 G_Rec: 0.302 D_GP: 0.032 D_real: 0.981 D_fake: 0.856 +(epoch: 270, iters: 8048, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.907 G_ID: 0.182 G_Rec: 0.386 D_GP: 0.029 D_real: 0.888 D_fake: 0.800 +(epoch: 270, iters: 8448, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.745 G_ID: 0.157 G_Rec: 0.308 D_GP: 0.060 D_real: 0.953 D_fake: 0.705 +(epoch: 271, iters: 240, time: 0.064) G_GAN: 0.677 G_GAN_Feat: 0.908 G_ID: 0.149 G_Rec: 0.390 D_GP: 0.027 D_real: 1.356 D_fake: 0.335 +(epoch: 271, iters: 640, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.797 G_ID: 0.121 G_Rec: 0.291 D_GP: 0.027 D_real: 0.761 D_fake: 0.828 +(epoch: 271, iters: 1040, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.930 G_ID: 0.180 G_Rec: 0.394 D_GP: 0.027 D_real: 1.103 D_fake: 0.463 +(epoch: 271, iters: 1440, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.678 G_ID: 0.140 G_Rec: 0.279 D_GP: 0.021 D_real: 1.022 D_fake: 0.880 +(epoch: 271, iters: 1840, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.820 G_ID: 0.156 G_Rec: 0.413 D_GP: 0.020 D_real: 1.126 D_fake: 0.549 +(epoch: 271, iters: 2240, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.715 G_ID: 0.129 G_Rec: 0.314 D_GP: 0.044 D_real: 1.077 D_fake: 0.800 +(epoch: 271, iters: 2640, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.857 G_ID: 0.165 G_Rec: 0.403 D_GP: 0.021 D_real: 1.187 D_fake: 0.498 +(epoch: 271, iters: 3040, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.647 G_ID: 0.176 G_Rec: 0.281 D_GP: 0.022 D_real: 1.201 D_fake: 0.764 +(epoch: 271, iters: 3440, time: 0.064) G_GAN: 0.552 G_GAN_Feat: 0.915 G_ID: 0.154 G_Rec: 0.371 D_GP: 0.023 D_real: 1.229 D_fake: 0.462 +(epoch: 271, iters: 3840, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.887 G_ID: 0.129 G_Rec: 0.305 D_GP: 0.034 D_real: 0.439 D_fake: 0.837 +(epoch: 271, iters: 4240, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.914 G_ID: 0.193 G_Rec: 0.432 D_GP: 0.024 D_real: 1.213 D_fake: 0.611 +(epoch: 271, iters: 4640, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.615 G_ID: 0.144 G_Rec: 0.299 D_GP: 0.018 D_real: 1.124 D_fake: 0.838 +(epoch: 271, iters: 5040, time: 0.064) G_GAN: 0.165 G_GAN_Feat: 0.809 G_ID: 0.190 G_Rec: 0.366 D_GP: 0.030 D_real: 0.969 D_fake: 0.835 +(epoch: 271, iters: 5440, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.663 G_ID: 0.127 G_Rec: 0.297 D_GP: 0.032 D_real: 1.127 D_fake: 0.753 +(epoch: 271, iters: 5840, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.833 G_ID: 0.174 G_Rec: 0.390 D_GP: 0.027 D_real: 0.959 D_fake: 0.719 +(epoch: 271, iters: 6240, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.836 G_ID: 0.105 G_Rec: 0.322 D_GP: 0.058 D_real: 0.816 D_fake: 0.736 +(epoch: 271, iters: 6640, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.930 G_ID: 0.151 G_Rec: 0.408 D_GP: 0.024 D_real: 1.076 D_fake: 0.783 +(epoch: 271, iters: 7040, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.785 G_ID: 0.119 G_Rec: 0.325 D_GP: 0.043 D_real: 0.872 D_fake: 0.900 +(epoch: 271, iters: 7440, time: 0.064) G_GAN: 0.571 G_GAN_Feat: 0.954 G_ID: 0.170 G_Rec: 0.349 D_GP: 0.035 D_real: 1.418 D_fake: 0.439 +(epoch: 271, iters: 7840, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.725 G_ID: 0.128 G_Rec: 0.285 D_GP: 0.034 D_real: 0.977 D_fake: 0.828 +(epoch: 271, iters: 8240, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.852 G_ID: 0.155 G_Rec: 0.357 D_GP: 0.023 D_real: 1.311 D_fake: 0.554 +(epoch: 272, iters: 32, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.978 G_ID: 0.132 G_Rec: 0.341 D_GP: 0.108 D_real: 0.445 D_fake: 0.706 +(epoch: 272, iters: 432, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.915 G_ID: 0.175 G_Rec: 0.397 D_GP: 0.027 D_real: 1.022 D_fake: 0.670 +(epoch: 272, iters: 832, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.682 G_ID: 0.124 G_Rec: 0.336 D_GP: 0.028 D_real: 1.284 D_fake: 0.712 +(epoch: 272, iters: 1232, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.776 G_ID: 0.174 G_Rec: 0.397 D_GP: 0.020 D_real: 1.082 D_fake: 0.700 +(epoch: 272, iters: 1632, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.560 G_ID: 0.099 G_Rec: 0.254 D_GP: 0.021 D_real: 1.225 D_fake: 0.717 +(epoch: 272, iters: 2032, time: 0.064) G_GAN: -0.015 G_GAN_Feat: 0.963 G_ID: 0.158 G_Rec: 0.443 D_GP: 0.180 D_real: 0.612 D_fake: 1.015 +(epoch: 272, iters: 2432, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.633 G_ID: 0.141 G_Rec: 0.273 D_GP: 0.037 D_real: 1.110 D_fake: 0.739 +(epoch: 272, iters: 2832, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.974 G_ID: 0.169 G_Rec: 0.400 D_GP: 0.028 D_real: 1.195 D_fake: 0.552 +(epoch: 272, iters: 3232, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.756 G_ID: 0.115 G_Rec: 0.330 D_GP: 0.044 D_real: 0.871 D_fake: 0.952 +(epoch: 272, iters: 3632, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.862 G_ID: 0.164 G_Rec: 0.386 D_GP: 0.020 D_real: 1.258 D_fake: 0.540 +(epoch: 272, iters: 4032, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.758 G_ID: 0.121 G_Rec: 0.312 D_GP: 0.040 D_real: 0.977 D_fake: 0.755 +(epoch: 272, iters: 4432, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 0.946 G_ID: 0.160 G_Rec: 0.374 D_GP: 0.064 D_real: 0.897 D_fake: 0.462 +(epoch: 272, iters: 4832, time: 0.064) G_GAN: 0.663 G_GAN_Feat: 0.775 G_ID: 0.126 G_Rec: 0.281 D_GP: 0.112 D_real: 1.288 D_fake: 0.545 +(epoch: 272, iters: 5232, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 1.027 G_ID: 0.160 G_Rec: 0.396 D_GP: 0.186 D_real: 0.459 D_fake: 0.561 +(epoch: 272, iters: 5632, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.652 G_ID: 0.150 G_Rec: 0.307 D_GP: 0.019 D_real: 1.324 D_fake: 0.704 +(epoch: 272, iters: 6032, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.780 G_ID: 0.180 G_Rec: 0.375 D_GP: 0.020 D_real: 1.271 D_fake: 0.546 +(epoch: 272, iters: 6432, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.618 G_ID: 0.119 G_Rec: 0.297 D_GP: 0.022 D_real: 1.211 D_fake: 0.716 +(epoch: 272, iters: 6832, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.735 G_ID: 0.173 G_Rec: 0.352 D_GP: 0.023 D_real: 1.333 D_fake: 0.512 +(epoch: 272, iters: 7232, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.696 G_ID: 0.131 G_Rec: 0.328 D_GP: 0.034 D_real: 0.962 D_fake: 0.864 +(epoch: 272, iters: 7632, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.974 G_ID: 0.169 G_Rec: 0.411 D_GP: 0.069 D_real: 0.892 D_fake: 0.660 +(epoch: 272, iters: 8032, time: 0.064) G_GAN: 0.165 G_GAN_Feat: 0.622 G_ID: 0.111 G_Rec: 0.272 D_GP: 0.026 D_real: 1.077 D_fake: 0.835 +(epoch: 272, iters: 8432, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.822 G_ID: 0.166 G_Rec: 0.373 D_GP: 0.037 D_real: 1.076 D_fake: 0.619 +(epoch: 273, iters: 224, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.631 G_ID: 0.105 G_Rec: 0.329 D_GP: 0.035 D_real: 1.056 D_fake: 0.823 +(epoch: 273, iters: 624, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.886 G_ID: 0.141 G_Rec: 0.392 D_GP: 0.036 D_real: 0.927 D_fake: 0.650 +(epoch: 273, iters: 1024, time: 0.064) G_GAN: 0.109 G_GAN_Feat: 0.755 G_ID: 0.135 G_Rec: 0.358 D_GP: 0.053 D_real: 0.760 D_fake: 0.891 +(epoch: 273, iters: 1424, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.811 G_ID: 0.175 G_Rec: 0.365 D_GP: 0.029 D_real: 1.064 D_fake: 0.663 +(epoch: 273, iters: 1824, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.762 G_ID: 0.134 G_Rec: 0.341 D_GP: 0.047 D_real: 1.085 D_fake: 0.625 +(epoch: 273, iters: 2224, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 1.014 G_ID: 0.157 G_Rec: 0.453 D_GP: 0.153 D_real: 0.456 D_fake: 0.748 +(epoch: 273, iters: 2624, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.815 G_ID: 0.128 G_Rec: 0.296 D_GP: 0.028 D_real: 1.134 D_fake: 0.757 +(epoch: 273, iters: 3024, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 0.934 G_ID: 0.172 G_Rec: 0.396 D_GP: 0.060 D_real: 0.954 D_fake: 0.458 +(epoch: 273, iters: 3424, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.706 G_ID: 0.097 G_Rec: 0.277 D_GP: 0.027 D_real: 1.021 D_fake: 0.810 +(epoch: 273, iters: 3824, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.825 G_ID: 0.172 G_Rec: 0.392 D_GP: 0.022 D_real: 1.116 D_fake: 0.681 +(epoch: 273, iters: 4224, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.622 G_ID: 0.128 G_Rec: 0.289 D_GP: 0.019 D_real: 1.069 D_fake: 0.927 +(epoch: 273, iters: 4624, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.879 G_ID: 0.155 G_Rec: 0.445 D_GP: 0.037 D_real: 0.930 D_fake: 0.625 +(epoch: 273, iters: 5024, time: 0.064) G_GAN: -0.040 G_GAN_Feat: 0.789 G_ID: 0.135 G_Rec: 0.318 D_GP: 0.121 D_real: 0.510 D_fake: 1.041 +(epoch: 273, iters: 5424, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.822 G_ID: 0.176 G_Rec: 0.374 D_GP: 0.026 D_real: 1.134 D_fake: 0.555 +(epoch: 273, iters: 5824, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.636 G_ID: 0.134 G_Rec: 0.285 D_GP: 0.028 D_real: 1.090 D_fake: 0.788 +(epoch: 273, iters: 6224, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.798 G_ID: 0.168 G_Rec: 0.385 D_GP: 0.030 D_real: 1.098 D_fake: 0.669 +(epoch: 273, iters: 6624, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 1.061 G_ID: 0.135 G_Rec: 0.371 D_GP: 1.437 D_real: 0.384 D_fake: 0.766 +(epoch: 273, iters: 7024, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.859 G_ID: 0.161 G_Rec: 0.429 D_GP: 0.022 D_real: 1.153 D_fake: 0.659 +(epoch: 273, iters: 7424, time: 0.064) G_GAN: -0.042 G_GAN_Feat: 0.580 G_ID: 0.123 G_Rec: 0.302 D_GP: 0.019 D_real: 0.897 D_fake: 1.042 +(epoch: 273, iters: 7824, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.852 G_ID: 0.165 G_Rec: 0.409 D_GP: 0.036 D_real: 0.943 D_fake: 0.732 +(epoch: 273, iters: 8224, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.636 G_ID: 0.118 G_Rec: 0.272 D_GP: 0.024 D_real: 1.041 D_fake: 0.870 +(epoch: 274, iters: 16, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.906 G_ID: 0.155 G_Rec: 0.420 D_GP: 0.060 D_real: 1.038 D_fake: 0.571 +(epoch: 274, iters: 416, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.669 G_ID: 0.112 G_Rec: 0.302 D_GP: 0.022 D_real: 1.259 D_fake: 0.704 +(epoch: 274, iters: 816, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.978 G_ID: 0.161 G_Rec: 0.397 D_GP: 0.302 D_real: 0.393 D_fake: 0.721 +(epoch: 274, iters: 1216, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.773 G_ID: 0.122 G_Rec: 0.296 D_GP: 0.081 D_real: 0.699 D_fake: 0.952 +(epoch: 274, iters: 1616, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.709 G_ID: 0.158 G_Rec: 0.381 D_GP: 0.017 D_real: 1.104 D_fake: 0.647 +(epoch: 274, iters: 2016, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.557 G_ID: 0.110 G_Rec: 0.313 D_GP: 0.016 D_real: 1.154 D_fake: 0.781 +(epoch: 274, iters: 2416, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.661 G_ID: 0.168 G_Rec: 0.351 D_GP: 0.021 D_real: 1.044 D_fake: 0.749 +(epoch: 274, iters: 2816, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.527 G_ID: 0.103 G_Rec: 0.266 D_GP: 0.018 D_real: 1.102 D_fake: 0.821 +(epoch: 274, iters: 3216, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.706 G_ID: 0.182 G_Rec: 0.349 D_GP: 0.025 D_real: 1.154 D_fake: 0.648 +(epoch: 274, iters: 3616, time: 0.064) G_GAN: -0.057 G_GAN_Feat: 0.721 G_ID: 0.119 G_Rec: 0.333 D_GP: 0.042 D_real: 0.737 D_fake: 1.058 +(epoch: 274, iters: 4016, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.794 G_ID: 0.157 G_Rec: 0.404 D_GP: 0.029 D_real: 1.122 D_fake: 0.570 +(epoch: 274, iters: 4416, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.641 G_ID: 0.140 G_Rec: 0.291 D_GP: 0.038 D_real: 1.020 D_fake: 0.852 +(epoch: 274, iters: 4816, time: 0.064) G_GAN: 0.055 G_GAN_Feat: 0.832 G_ID: 0.169 G_Rec: 0.399 D_GP: 0.044 D_real: 0.713 D_fake: 0.945 +(epoch: 274, iters: 5216, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.780 G_ID: 0.130 G_Rec: 0.360 D_GP: 0.064 D_real: 0.675 D_fake: 0.913 +(epoch: 274, iters: 5616, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.779 G_ID: 0.218 G_Rec: 0.399 D_GP: 0.028 D_real: 1.010 D_fake: 0.715 +(epoch: 274, iters: 6016, time: 0.064) G_GAN: 0.050 G_GAN_Feat: 0.715 G_ID: 0.130 G_Rec: 0.289 D_GP: 0.051 D_real: 0.846 D_fake: 0.950 +(epoch: 274, iters: 6416, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.913 G_ID: 0.196 G_Rec: 0.396 D_GP: 0.050 D_real: 0.959 D_fake: 0.655 +(epoch: 274, iters: 6816, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.599 G_ID: 0.115 G_Rec: 0.267 D_GP: 0.024 D_real: 1.105 D_fake: 0.825 +(epoch: 274, iters: 7216, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.844 G_ID: 0.154 G_Rec: 0.400 D_GP: 0.021 D_real: 0.960 D_fake: 0.786 +(epoch: 274, iters: 7616, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.609 G_ID: 0.127 G_Rec: 0.278 D_GP: 0.024 D_real: 1.057 D_fake: 0.797 +(epoch: 274, iters: 8016, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.773 G_ID: 0.177 G_Rec: 0.361 D_GP: 0.029 D_real: 1.140 D_fake: 0.705 +(epoch: 274, iters: 8416, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.673 G_ID: 0.117 G_Rec: 0.306 D_GP: 0.056 D_real: 1.071 D_fake: 0.732 +(epoch: 275, iters: 208, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.720 G_ID: 0.172 G_Rec: 0.348 D_GP: 0.023 D_real: 1.172 D_fake: 0.658 +(epoch: 275, iters: 608, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.738 G_ID: 0.113 G_Rec: 0.322 D_GP: 0.037 D_real: 1.037 D_fake: 0.774 +(epoch: 275, iters: 1008, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.800 G_ID: 0.177 G_Rec: 0.359 D_GP: 0.032 D_real: 0.883 D_fake: 0.829 +(epoch: 275, iters: 1408, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.705 G_ID: 0.117 G_Rec: 0.292 D_GP: 0.082 D_real: 0.957 D_fake: 0.808 +(epoch: 275, iters: 1808, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.906 G_ID: 0.160 G_Rec: 0.384 D_GP: 0.064 D_real: 0.830 D_fake: 0.682 +(epoch: 275, iters: 2208, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.746 G_ID: 0.131 G_Rec: 0.298 D_GP: 0.051 D_real: 1.119 D_fake: 0.552 +(epoch: 275, iters: 2608, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 1.119 G_ID: 0.153 G_Rec: 0.424 D_GP: 0.226 D_real: 1.161 D_fake: 0.615 +(epoch: 275, iters: 3008, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.718 G_ID: 0.157 G_Rec: 0.303 D_GP: 0.020 D_real: 1.024 D_fake: 0.821 +(epoch: 275, iters: 3408, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.753 G_ID: 0.167 G_Rec: 0.335 D_GP: 0.035 D_real: 1.051 D_fake: 0.734 +(epoch: 275, iters: 3808, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.665 G_ID: 0.131 G_Rec: 0.278 D_GP: 0.020 D_real: 1.241 D_fake: 0.687 +(epoch: 275, iters: 4208, time: 0.064) G_GAN: 0.575 G_GAN_Feat: 0.837 G_ID: 0.144 G_Rec: 0.401 D_GP: 0.032 D_real: 1.266 D_fake: 0.439 +(epoch: 275, iters: 4608, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.730 G_ID: 0.126 G_Rec: 0.293 D_GP: 0.035 D_real: 0.967 D_fake: 0.790 +(epoch: 275, iters: 5008, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.936 G_ID: 0.157 G_Rec: 0.401 D_GP: 0.026 D_real: 1.042 D_fake: 0.499 +(epoch: 275, iters: 5408, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.674 G_ID: 0.138 G_Rec: 0.276 D_GP: 0.023 D_real: 1.068 D_fake: 0.765 +(epoch: 275, iters: 5808, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 1.014 G_ID: 0.181 G_Rec: 0.428 D_GP: 0.213 D_real: 0.539 D_fake: 0.702 +(epoch: 275, iters: 6208, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.884 G_ID: 0.108 G_Rec: 0.301 D_GP: 0.836 D_real: 0.518 D_fake: 0.742 +(epoch: 275, iters: 6608, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.767 G_ID: 0.173 G_Rec: 0.348 D_GP: 0.020 D_real: 1.097 D_fake: 0.747 +(epoch: 275, iters: 7008, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.883 G_ID: 0.110 G_Rec: 0.301 D_GP: 0.041 D_real: 0.495 D_fake: 0.883 +(epoch: 275, iters: 7408, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 1.080 G_ID: 0.144 G_Rec: 0.442 D_GP: 0.079 D_real: 0.562 D_fake: 0.458 +(epoch: 275, iters: 7808, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.873 G_ID: 0.118 G_Rec: 0.318 D_GP: 0.031 D_real: 0.811 D_fake: 0.645 +(epoch: 275, iters: 8208, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.832 G_ID: 0.166 G_Rec: 0.429 D_GP: 0.018 D_real: 1.228 D_fake: 0.558 +(epoch: 275, iters: 8608, time: 0.064) G_GAN: -0.088 G_GAN_Feat: 0.584 G_ID: 0.147 G_Rec: 0.254 D_GP: 0.021 D_real: 0.836 D_fake: 1.088 +(epoch: 276, iters: 400, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 0.962 G_ID: 0.170 G_Rec: 0.417 D_GP: 0.046 D_real: 1.069 D_fake: 0.418 +(epoch: 276, iters: 800, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.687 G_ID: 0.120 G_Rec: 0.313 D_GP: 0.036 D_real: 1.207 D_fake: 0.627 +(epoch: 276, iters: 1200, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 0.967 G_ID: 0.162 G_Rec: 0.431 D_GP: 0.025 D_real: 1.114 D_fake: 0.430 +(epoch: 276, iters: 1600, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.695 G_ID: 0.158 G_Rec: 0.309 D_GP: 0.020 D_real: 1.165 D_fake: 0.724 +(epoch: 276, iters: 2000, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.978 G_ID: 0.161 G_Rec: 0.402 D_GP: 0.062 D_real: 0.715 D_fake: 0.567 +(epoch: 276, iters: 2400, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.663 G_ID: 0.115 G_Rec: 0.296 D_GP: 0.022 D_real: 1.178 D_fake: 0.914 +(epoch: 276, iters: 2800, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.677 G_ID: 0.159 G_Rec: 0.327 D_GP: 0.020 D_real: 1.334 D_fake: 0.573 +(epoch: 276, iters: 3200, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.572 G_ID: 0.126 G_Rec: 0.277 D_GP: 0.021 D_real: 1.285 D_fake: 0.679 +(epoch: 276, iters: 3600, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.735 G_ID: 0.144 G_Rec: 0.340 D_GP: 0.022 D_real: 1.276 D_fake: 0.573 +(epoch: 276, iters: 4000, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.611 G_ID: 0.132 G_Rec: 0.290 D_GP: 0.025 D_real: 1.097 D_fake: 0.789 +(epoch: 276, iters: 4400, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.806 G_ID: 0.160 G_Rec: 0.386 D_GP: 0.023 D_real: 1.188 D_fake: 0.565 +(epoch: 276, iters: 4800, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.718 G_ID: 0.132 G_Rec: 0.317 D_GP: 0.060 D_real: 0.803 D_fake: 0.909 +(epoch: 276, iters: 5200, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.810 G_ID: 0.166 G_Rec: 0.400 D_GP: 0.024 D_real: 1.126 D_fake: 0.651 +(epoch: 276, iters: 5600, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.678 G_ID: 0.117 G_Rec: 0.314 D_GP: 0.030 D_real: 0.922 D_fake: 0.936 +(epoch: 276, iters: 6000, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.785 G_ID: 0.166 G_Rec: 0.375 D_GP: 0.028 D_real: 1.015 D_fake: 0.741 +(epoch: 276, iters: 6400, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.642 G_ID: 0.113 G_Rec: 0.272 D_GP: 0.022 D_real: 1.048 D_fake: 0.840 +(epoch: 276, iters: 6800, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.857 G_ID: 0.150 G_Rec: 0.375 D_GP: 0.028 D_real: 1.078 D_fake: 0.635 +(epoch: 276, iters: 7200, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.577 G_ID: 0.119 G_Rec: 0.261 D_GP: 0.024 D_real: 1.133 D_fake: 0.818 +(epoch: 276, iters: 7600, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.905 G_ID: 0.140 G_Rec: 0.397 D_GP: 0.036 D_real: 0.950 D_fake: 0.621 +(epoch: 276, iters: 8000, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.830 G_ID: 0.144 G_Rec: 0.339 D_GP: 0.083 D_real: 0.769 D_fake: 0.963 +(epoch: 276, iters: 8400, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.859 G_ID: 0.176 G_Rec: 0.384 D_GP: 0.021 D_real: 0.939 D_fake: 0.751 +(epoch: 277, iters: 192, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.704 G_ID: 0.116 G_Rec: 0.282 D_GP: 0.042 D_real: 1.137 D_fake: 0.686 +(epoch: 277, iters: 592, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.853 G_ID: 0.157 G_Rec: 0.376 D_GP: 0.022 D_real: 1.127 D_fake: 0.621 +(epoch: 277, iters: 992, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.742 G_ID: 0.147 G_Rec: 0.384 D_GP: 0.047 D_real: 0.957 D_fake: 0.802 +(epoch: 277, iters: 1392, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.935 G_ID: 0.161 G_Rec: 0.408 D_GP: 0.036 D_real: 0.943 D_fake: 0.634 +(epoch: 277, iters: 1792, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.684 G_ID: 0.125 G_Rec: 0.271 D_GP: 0.029 D_real: 1.092 D_fake: 0.729 +(epoch: 277, iters: 2192, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.834 G_ID: 0.185 G_Rec: 0.366 D_GP: 0.021 D_real: 1.043 D_fake: 0.742 +(epoch: 277, iters: 2592, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.902 G_ID: 0.118 G_Rec: 0.316 D_GP: 0.095 D_real: 0.582 D_fake: 0.764 +(epoch: 277, iters: 2992, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.929 G_ID: 0.188 G_Rec: 0.380 D_GP: 0.047 D_real: 1.153 D_fake: 0.557 +(epoch: 277, iters: 3392, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.631 G_ID: 0.114 G_Rec: 0.301 D_GP: 0.019 D_real: 1.244 D_fake: 0.747 +(epoch: 277, iters: 3792, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.730 G_ID: 0.148 G_Rec: 0.354 D_GP: 0.022 D_real: 1.025 D_fake: 0.781 +(epoch: 277, iters: 4192, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.719 G_ID: 0.135 G_Rec: 0.306 D_GP: 0.051 D_real: 0.914 D_fake: 0.914 +(epoch: 277, iters: 4592, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.953 G_ID: 0.167 G_Rec: 0.395 D_GP: 0.055 D_real: 0.806 D_fake: 0.661 +(epoch: 277, iters: 4992, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.800 G_ID: 0.122 G_Rec: 0.333 D_GP: 0.039 D_real: 1.110 D_fake: 0.630 +(epoch: 277, iters: 5392, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.849 G_ID: 0.180 G_Rec: 0.338 D_GP: 0.053 D_real: 0.761 D_fake: 0.898 +(epoch: 277, iters: 5792, time: 0.064) G_GAN: -0.208 G_GAN_Feat: 0.902 G_ID: 0.144 G_Rec: 0.336 D_GP: 0.130 D_real: 0.122 D_fake: 1.209 +(epoch: 277, iters: 6192, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 0.976 G_ID: 0.173 G_Rec: 0.400 D_GP: 0.038 D_real: 1.188 D_fake: 0.382 +(epoch: 277, iters: 6592, time: 0.064) G_GAN: -0.005 G_GAN_Feat: 0.747 G_ID: 0.127 G_Rec: 0.310 D_GP: 0.028 D_real: 0.881 D_fake: 1.005 +(epoch: 277, iters: 6992, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.905 G_ID: 0.166 G_Rec: 0.397 D_GP: 0.022 D_real: 0.907 D_fake: 0.778 +(epoch: 277, iters: 7392, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.722 G_ID: 0.117 G_Rec: 0.328 D_GP: 0.036 D_real: 1.295 D_fake: 0.612 +(epoch: 277, iters: 7792, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 1.067 G_ID: 0.151 G_Rec: 0.418 D_GP: 0.046 D_real: 0.470 D_fake: 0.517 +(epoch: 277, iters: 8192, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.709 G_ID: 0.116 G_Rec: 0.309 D_GP: 0.018 D_real: 1.195 D_fake: 0.746 +(epoch: 277, iters: 8592, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.935 G_ID: 0.149 G_Rec: 0.389 D_GP: 0.039 D_real: 0.759 D_fake: 0.780 +(epoch: 278, iters: 384, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.815 G_ID: 0.117 G_Rec: 0.314 D_GP: 0.107 D_real: 0.558 D_fake: 0.821 +(epoch: 278, iters: 784, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.914 G_ID: 0.187 G_Rec: 0.405 D_GP: 0.032 D_real: 1.071 D_fake: 0.780 +(epoch: 278, iters: 1184, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.626 G_ID: 0.125 G_Rec: 0.277 D_GP: 0.020 D_real: 1.053 D_fake: 0.909 +(epoch: 278, iters: 1584, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.803 G_ID: 0.161 G_Rec: 0.361 D_GP: 0.021 D_real: 1.282 D_fake: 0.541 +(epoch: 278, iters: 1984, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 1.031 G_ID: 0.121 G_Rec: 0.314 D_GP: 0.384 D_real: 0.402 D_fake: 0.753 +(epoch: 278, iters: 2384, time: 0.064) G_GAN: -0.027 G_GAN_Feat: 1.030 G_ID: 0.178 G_Rec: 0.381 D_GP: 0.096 D_real: 0.196 D_fake: 1.027 +(epoch: 278, iters: 2784, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.763 G_ID: 0.117 G_Rec: 0.291 D_GP: 0.035 D_real: 0.870 D_fake: 0.895 +(epoch: 278, iters: 3184, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 1.005 G_ID: 0.151 G_Rec: 0.419 D_GP: 0.044 D_real: 0.834 D_fake: 0.585 +(epoch: 278, iters: 3584, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.721 G_ID: 0.142 G_Rec: 0.293 D_GP: 0.025 D_real: 1.204 D_fake: 0.597 +(epoch: 278, iters: 3984, time: 0.064) G_GAN: 0.563 G_GAN_Feat: 0.875 G_ID: 0.141 G_Rec: 0.369 D_GP: 0.025 D_real: 1.214 D_fake: 0.441 +(epoch: 278, iters: 4384, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.696 G_ID: 0.151 G_Rec: 0.308 D_GP: 0.022 D_real: 1.246 D_fake: 0.615 +(epoch: 278, iters: 4784, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.742 G_ID: 0.181 G_Rec: 0.391 D_GP: 0.018 D_real: 1.171 D_fake: 0.660 +(epoch: 278, iters: 5184, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.606 G_ID: 0.107 G_Rec: 0.301 D_GP: 0.017 D_real: 1.112 D_fake: 0.889 +(epoch: 278, iters: 5584, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.708 G_ID: 0.191 G_Rec: 0.358 D_GP: 0.019 D_real: 0.903 D_fake: 0.907 +(epoch: 278, iters: 5984, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.518 G_ID: 0.115 G_Rec: 0.252 D_GP: 0.020 D_real: 1.100 D_fake: 0.872 +(epoch: 278, iters: 6384, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.723 G_ID: 0.167 G_Rec: 0.344 D_GP: 0.024 D_real: 0.964 D_fake: 0.868 +(epoch: 278, iters: 6784, time: 0.064) G_GAN: -0.101 G_GAN_Feat: 0.694 G_ID: 0.138 G_Rec: 0.324 D_GP: 0.040 D_real: 0.737 D_fake: 1.101 +(epoch: 278, iters: 7184, time: 0.064) G_GAN: 0.480 G_GAN_Feat: 0.901 G_ID: 0.153 G_Rec: 0.384 D_GP: 0.037 D_real: 1.084 D_fake: 0.534 +(epoch: 278, iters: 7584, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.622 G_ID: 0.116 G_Rec: 0.300 D_GP: 0.032 D_real: 1.219 D_fake: 0.702 +(epoch: 278, iters: 7984, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.784 G_ID: 0.154 G_Rec: 0.362 D_GP: 0.028 D_real: 1.134 D_fake: 0.691 +(epoch: 278, iters: 8384, time: 0.064) G_GAN: -0.182 G_GAN_Feat: 0.758 G_ID: 0.120 G_Rec: 0.314 D_GP: 0.048 D_real: 0.958 D_fake: 1.182 +(epoch: 279, iters: 176, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.910 G_ID: 0.177 G_Rec: 0.380 D_GP: 0.117 D_real: 0.782 D_fake: 0.725 +(epoch: 279, iters: 576, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.689 G_ID: 0.134 G_Rec: 0.299 D_GP: 0.039 D_real: 1.131 D_fake: 0.669 +(epoch: 279, iters: 976, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.907 G_ID: 0.167 G_Rec: 0.437 D_GP: 0.126 D_real: 0.660 D_fake: 0.725 +(epoch: 279, iters: 1376, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.852 G_ID: 0.124 G_Rec: 0.315 D_GP: 0.104 D_real: 0.675 D_fake: 0.754 +(epoch: 279, iters: 1776, time: 0.064) G_GAN: 0.070 G_GAN_Feat: 0.781 G_ID: 0.167 G_Rec: 0.381 D_GP: 0.025 D_real: 0.922 D_fake: 0.930 +(epoch: 279, iters: 2176, time: 0.064) G_GAN: -0.009 G_GAN_Feat: 0.930 G_ID: 0.122 G_Rec: 0.358 D_GP: 0.307 D_real: 0.308 D_fake: 1.010 +(epoch: 279, iters: 2576, time: 0.064) G_GAN: 0.715 G_GAN_Feat: 1.205 G_ID: 0.163 G_Rec: 0.436 D_GP: 0.066 D_real: 1.284 D_fake: 0.405 +(epoch: 279, iters: 2976, time: 0.064) G_GAN: 0.040 G_GAN_Feat: 0.623 G_ID: 0.128 G_Rec: 0.284 D_GP: 0.020 D_real: 0.974 D_fake: 0.962 +(epoch: 279, iters: 3376, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.929 G_ID: 0.181 G_Rec: 0.392 D_GP: 0.080 D_real: 0.496 D_fake: 0.968 +(epoch: 279, iters: 3776, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.813 G_ID: 0.118 G_Rec: 0.317 D_GP: 0.116 D_real: 0.442 D_fake: 0.874 +(epoch: 279, iters: 4176, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.849 G_ID: 0.170 G_Rec: 0.405 D_GP: 0.030 D_real: 1.098 D_fake: 0.640 +(epoch: 279, iters: 4576, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.656 G_ID: 0.140 G_Rec: 0.285 D_GP: 0.021 D_real: 1.162 D_fake: 0.719 +(epoch: 279, iters: 4976, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 0.806 G_ID: 0.156 G_Rec: 0.386 D_GP: 0.022 D_real: 1.365 D_fake: 0.399 +(epoch: 279, iters: 5376, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.626 G_ID: 0.108 G_Rec: 0.279 D_GP: 0.028 D_real: 1.149 D_fake: 0.757 +(epoch: 279, iters: 5776, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.940 G_ID: 0.193 G_Rec: 0.419 D_GP: 0.056 D_real: 0.611 D_fake: 0.772 +(epoch: 279, iters: 6176, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.792 G_ID: 0.126 G_Rec: 0.314 D_GP: 0.075 D_real: 0.773 D_fake: 0.848 +(epoch: 279, iters: 6576, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 1.053 G_ID: 0.186 G_Rec: 0.406 D_GP: 0.160 D_real: 0.700 D_fake: 0.556 +(epoch: 279, iters: 6976, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.705 G_ID: 0.124 G_Rec: 0.306 D_GP: 0.024 D_real: 1.382 D_fake: 0.615 +(epoch: 279, iters: 7376, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.935 G_ID: 0.181 G_Rec: 0.362 D_GP: 0.032 D_real: 0.795 D_fake: 0.685 +(epoch: 279, iters: 7776, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.707 G_ID: 0.110 G_Rec: 0.289 D_GP: 0.025 D_real: 1.170 D_fake: 0.633 +(epoch: 279, iters: 8176, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.877 G_ID: 0.154 G_Rec: 0.389 D_GP: 0.021 D_real: 1.427 D_fake: 0.445 +(epoch: 279, iters: 8576, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.717 G_ID: 0.135 G_Rec: 0.296 D_GP: 0.024 D_real: 1.182 D_fake: 0.770 +(epoch: 280, iters: 368, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.890 G_ID: 0.132 G_Rec: 0.394 D_GP: 0.026 D_real: 1.073 D_fake: 0.620 +(epoch: 280, iters: 768, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.737 G_ID: 0.131 G_Rec: 0.314 D_GP: 0.057 D_real: 0.818 D_fake: 0.869 +(epoch: 280, iters: 1168, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.924 G_ID: 0.202 G_Rec: 0.383 D_GP: 0.078 D_real: 1.066 D_fake: 0.540 +(epoch: 280, iters: 1568, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.666 G_ID: 0.136 G_Rec: 0.302 D_GP: 0.028 D_real: 1.303 D_fake: 0.633 +(epoch: 280, iters: 1968, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.963 G_ID: 0.176 G_Rec: 0.441 D_GP: 0.207 D_real: 0.649 D_fake: 0.677 +(epoch: 280, iters: 2368, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.787 G_ID: 0.145 G_Rec: 0.309 D_GP: 0.027 D_real: 0.871 D_fake: 0.886 +(epoch: 280, iters: 2768, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.944 G_ID: 0.172 G_Rec: 0.430 D_GP: 0.044 D_real: 0.918 D_fake: 0.682 +(epoch: 280, iters: 3168, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 1.021 G_ID: 0.132 G_Rec: 0.350 D_GP: 0.360 D_real: 0.416 D_fake: 0.722 +(epoch: 280, iters: 3568, time: 0.064) G_GAN: -0.012 G_GAN_Feat: 1.043 G_ID: 0.173 G_Rec: 0.399 D_GP: 0.042 D_real: 0.545 D_fake: 1.012 +(epoch: 280, iters: 3968, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.720 G_ID: 0.125 G_Rec: 0.272 D_GP: 0.028 D_real: 0.983 D_fake: 0.770 +(epoch: 280, iters: 4368, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.747 G_ID: 0.183 G_Rec: 0.379 D_GP: 0.019 D_real: 1.070 D_fake: 0.703 +(epoch: 280, iters: 4768, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.601 G_ID: 0.118 G_Rec: 0.269 D_GP: 0.032 D_real: 0.946 D_fake: 0.987 +(epoch: 280, iters: 5168, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.733 G_ID: 0.151 G_Rec: 0.330 D_GP: 0.023 D_real: 1.099 D_fake: 0.750 +(epoch: 280, iters: 5568, time: 0.064) G_GAN: -0.002 G_GAN_Feat: 0.633 G_ID: 0.126 G_Rec: 0.294 D_GP: 0.021 D_real: 0.962 D_fake: 1.002 +(epoch: 280, iters: 5968, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.875 G_ID: 0.173 G_Rec: 0.416 D_GP: 0.067 D_real: 0.933 D_fake: 0.695 +(epoch: 280, iters: 6368, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.771 G_ID: 0.130 G_Rec: 0.343 D_GP: 0.029 D_real: 1.005 D_fake: 0.747 +(epoch: 280, iters: 6768, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.849 G_ID: 0.173 G_Rec: 0.393 D_GP: 0.026 D_real: 1.177 D_fake: 0.617 +(epoch: 280, iters: 7168, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.581 G_ID: 0.132 G_Rec: 0.247 D_GP: 0.022 D_real: 1.126 D_fake: 0.840 +(epoch: 280, iters: 7568, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.830 G_ID: 0.168 G_Rec: 0.349 D_GP: 0.038 D_real: 0.942 D_fake: 0.819 +(epoch: 280, iters: 7968, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.743 G_ID: 0.128 G_Rec: 0.299 D_GP: 0.044 D_real: 0.975 D_fake: 0.731 +(epoch: 280, iters: 8368, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 1.089 G_ID: 0.174 G_Rec: 0.432 D_GP: 0.177 D_real: 0.297 D_fake: 0.703 +(epoch: 281, iters: 160, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.629 G_ID: 0.116 G_Rec: 0.275 D_GP: 0.022 D_real: 1.210 D_fake: 0.719 +(epoch: 281, iters: 560, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.846 G_ID: 0.175 G_Rec: 0.347 D_GP: 0.029 D_real: 1.018 D_fake: 0.645 +(epoch: 281, iters: 960, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.727 G_ID: 0.135 G_Rec: 0.274 D_GP: 0.029 D_real: 0.964 D_fake: 0.790 +(epoch: 281, iters: 1360, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.923 G_ID: 0.180 G_Rec: 0.399 D_GP: 0.028 D_real: 1.236 D_fake: 0.429 +(epoch: 281, iters: 1760, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.701 G_ID: 0.133 G_Rec: 0.282 D_GP: 0.022 D_real: 1.167 D_fake: 0.676 +(epoch: 281, iters: 2160, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.786 G_ID: 0.189 G_Rec: 0.355 D_GP: 0.021 D_real: 1.024 D_fake: 0.783 +(epoch: 281, iters: 2560, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.650 G_ID: 0.133 G_Rec: 0.310 D_GP: 0.022 D_real: 1.073 D_fake: 0.905 +(epoch: 281, iters: 2960, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.846 G_ID: 0.148 G_Rec: 0.385 D_GP: 0.058 D_real: 0.975 D_fake: 0.743 +(epoch: 281, iters: 3360, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.645 G_ID: 0.113 G_Rec: 0.274 D_GP: 0.031 D_real: 1.137 D_fake: 0.724 +(epoch: 281, iters: 3760, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.939 G_ID: 0.178 G_Rec: 0.409 D_GP: 0.076 D_real: 0.793 D_fake: 0.754 +(epoch: 281, iters: 4160, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.797 G_ID: 0.136 G_Rec: 0.283 D_GP: 0.048 D_real: 0.601 D_fake: 0.847 +(epoch: 281, iters: 4560, time: 0.064) G_GAN: 0.844 G_GAN_Feat: 0.932 G_ID: 0.148 G_Rec: 0.406 D_GP: 0.025 D_real: 1.586 D_fake: 0.203 +(epoch: 281, iters: 4960, time: 0.064) G_GAN: -0.065 G_GAN_Feat: 0.661 G_ID: 0.149 G_Rec: 0.333 D_GP: 0.019 D_real: 0.863 D_fake: 1.065 +(epoch: 281, iters: 5360, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.871 G_ID: 0.163 G_Rec: 0.376 D_GP: 0.024 D_real: 0.871 D_fake: 0.812 +(epoch: 281, iters: 5760, time: 0.064) G_GAN: -0.009 G_GAN_Feat: 0.716 G_ID: 0.149 G_Rec: 0.330 D_GP: 0.045 D_real: 0.756 D_fake: 1.009 +(epoch: 281, iters: 6160, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.844 G_ID: 0.165 G_Rec: 0.365 D_GP: 0.029 D_real: 1.031 D_fake: 0.744 +(epoch: 281, iters: 6560, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.753 G_ID: 0.117 G_Rec: 0.315 D_GP: 0.031 D_real: 0.969 D_fake: 0.782 +(epoch: 281, iters: 6960, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.916 G_ID: 0.164 G_Rec: 0.416 D_GP: 0.035 D_real: 1.073 D_fake: 0.527 +(epoch: 281, iters: 7360, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.797 G_ID: 0.114 G_Rec: 0.333 D_GP: 0.033 D_real: 0.977 D_fake: 0.662 +(epoch: 281, iters: 7760, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.864 G_ID: 0.156 G_Rec: 0.386 D_GP: 0.022 D_real: 1.196 D_fake: 0.564 +(epoch: 281, iters: 8160, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.642 G_ID: 0.134 G_Rec: 0.281 D_GP: 0.020 D_real: 1.241 D_fake: 0.664 +(epoch: 281, iters: 8560, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.877 G_ID: 0.169 G_Rec: 0.413 D_GP: 0.023 D_real: 1.132 D_fake: 0.665 +(epoch: 282, iters: 352, time: 0.064) G_GAN: 0.050 G_GAN_Feat: 0.645 G_ID: 0.129 G_Rec: 0.282 D_GP: 0.023 D_real: 0.969 D_fake: 0.950 +(epoch: 282, iters: 752, time: 0.064) G_GAN: -0.065 G_GAN_Feat: 0.836 G_ID: 0.194 G_Rec: 0.367 D_GP: 0.026 D_real: 0.768 D_fake: 1.065 +(epoch: 282, iters: 1152, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.740 G_ID: 0.122 G_Rec: 0.288 D_GP: 0.028 D_real: 1.044 D_fake: 0.656 +(epoch: 282, iters: 1552, time: 0.064) G_GAN: 0.667 G_GAN_Feat: 1.217 G_ID: 0.175 G_Rec: 0.497 D_GP: 0.052 D_real: 0.630 D_fake: 0.377 +(epoch: 282, iters: 1952, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.845 G_ID: 0.132 G_Rec: 0.297 D_GP: 0.111 D_real: 0.491 D_fake: 0.858 +(epoch: 282, iters: 2352, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.972 G_ID: 0.149 G_Rec: 0.371 D_GP: 0.021 D_real: 1.132 D_fake: 0.490 +(epoch: 282, iters: 2752, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 0.689 G_ID: 0.125 G_Rec: 0.307 D_GP: 0.028 D_real: 0.902 D_fake: 0.992 +(epoch: 282, iters: 3152, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.797 G_ID: 0.171 G_Rec: 0.371 D_GP: 0.021 D_real: 1.099 D_fake: 0.737 +(epoch: 282, iters: 3552, time: 0.064) G_GAN: -0.077 G_GAN_Feat: 0.685 G_ID: 0.135 G_Rec: 0.283 D_GP: 0.035 D_real: 0.853 D_fake: 1.077 +(epoch: 282, iters: 3952, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 1.006 G_ID: 0.165 G_Rec: 0.394 D_GP: 0.050 D_real: 0.737 D_fake: 0.665 +(epoch: 282, iters: 4352, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.635 G_ID: 0.148 G_Rec: 0.289 D_GP: 0.022 D_real: 1.152 D_fake: 0.792 +(epoch: 282, iters: 4752, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.835 G_ID: 0.163 G_Rec: 0.351 D_GP: 0.030 D_real: 1.164 D_fake: 0.649 +(epoch: 282, iters: 5152, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.740 G_ID: 0.116 G_Rec: 0.287 D_GP: 0.033 D_real: 0.920 D_fake: 0.827 +(epoch: 282, iters: 5552, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.941 G_ID: 0.194 G_Rec: 0.371 D_GP: 0.068 D_real: 1.108 D_fake: 0.584 +(epoch: 282, iters: 5952, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.774 G_ID: 0.131 G_Rec: 0.294 D_GP: 0.045 D_real: 1.189 D_fake: 0.632 +(epoch: 282, iters: 6352, time: 0.064) G_GAN: 0.569 G_GAN_Feat: 1.026 G_ID: 0.165 G_Rec: 0.397 D_GP: 0.036 D_real: 0.847 D_fake: 0.441 +(epoch: 282, iters: 6752, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.671 G_ID: 0.131 G_Rec: 0.319 D_GP: 0.021 D_real: 1.026 D_fake: 0.854 +(epoch: 282, iters: 7152, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.826 G_ID: 0.179 G_Rec: 0.397 D_GP: 0.029 D_real: 0.849 D_fake: 0.851 +(epoch: 282, iters: 7552, time: 0.064) G_GAN: -0.072 G_GAN_Feat: 0.663 G_ID: 0.114 G_Rec: 0.304 D_GP: 0.054 D_real: 0.810 D_fake: 1.072 +(epoch: 282, iters: 7952, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.838 G_ID: 0.172 G_Rec: 0.412 D_GP: 0.030 D_real: 1.019 D_fake: 0.724 +(epoch: 282, iters: 8352, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.781 G_ID: 0.120 G_Rec: 0.331 D_GP: 0.053 D_real: 0.766 D_fake: 0.888 +(epoch: 283, iters: 144, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.925 G_ID: 0.169 G_Rec: 0.399 D_GP: 0.031 D_real: 1.060 D_fake: 0.602 +(epoch: 283, iters: 544, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.815 G_ID: 0.148 G_Rec: 0.312 D_GP: 0.046 D_real: 0.758 D_fake: 0.903 +(epoch: 283, iters: 944, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 0.845 G_ID: 0.136 G_Rec: 0.382 D_GP: 0.029 D_real: 1.152 D_fake: 0.531 +(epoch: 283, iters: 1344, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.789 G_ID: 0.133 G_Rec: 0.350 D_GP: 0.063 D_real: 0.911 D_fake: 0.866 +(epoch: 283, iters: 1744, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 1.041 G_ID: 0.158 G_Rec: 0.451 D_GP: 0.040 D_real: 0.818 D_fake: 0.579 +(epoch: 283, iters: 2144, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.850 G_ID: 0.122 G_Rec: 0.291 D_GP: 0.031 D_real: 0.599 D_fake: 0.870 +(epoch: 283, iters: 2544, time: 0.064) G_GAN: 0.636 G_GAN_Feat: 0.836 G_ID: 0.187 G_Rec: 0.366 D_GP: 0.022 D_real: 1.366 D_fake: 0.406 +(epoch: 283, iters: 2944, time: 0.064) G_GAN: 0.039 G_GAN_Feat: 0.780 G_ID: 0.152 G_Rec: 0.287 D_GP: 0.026 D_real: 0.782 D_fake: 0.961 +(epoch: 283, iters: 3344, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.847 G_ID: 0.161 G_Rec: 0.373 D_GP: 0.029 D_real: 1.100 D_fake: 0.615 +(epoch: 283, iters: 3744, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.651 G_ID: 0.142 G_Rec: 0.326 D_GP: 0.019 D_real: 1.086 D_fake: 0.831 +(epoch: 283, iters: 4144, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.935 G_ID: 0.154 G_Rec: 0.375 D_GP: 0.054 D_real: 0.614 D_fake: 0.801 +(epoch: 283, iters: 4544, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.830 G_ID: 0.150 G_Rec: 0.320 D_GP: 0.039 D_real: 1.029 D_fake: 0.592 +(epoch: 283, iters: 4944, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.813 G_ID: 0.178 G_Rec: 0.385 D_GP: 0.021 D_real: 1.201 D_fake: 0.637 +(epoch: 283, iters: 5344, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.572 G_ID: 0.111 G_Rec: 0.288 D_GP: 0.018 D_real: 1.320 D_fake: 0.658 +(epoch: 283, iters: 5744, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.851 G_ID: 0.166 G_Rec: 0.378 D_GP: 0.021 D_real: 1.217 D_fake: 0.605 +(epoch: 283, iters: 6144, time: 0.064) G_GAN: 0.089 G_GAN_Feat: 0.696 G_ID: 0.110 G_Rec: 0.314 D_GP: 0.027 D_real: 0.909 D_fake: 0.911 +(epoch: 283, iters: 6544, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.878 G_ID: 0.184 G_Rec: 0.409 D_GP: 0.024 D_real: 1.097 D_fake: 0.663 +(epoch: 283, iters: 6944, time: 0.064) G_GAN: 0.071 G_GAN_Feat: 0.627 G_ID: 0.165 G_Rec: 0.306 D_GP: 0.019 D_real: 1.034 D_fake: 0.929 +(epoch: 283, iters: 7344, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.820 G_ID: 0.160 G_Rec: 0.388 D_GP: 0.024 D_real: 1.075 D_fake: 0.651 +(epoch: 283, iters: 7744, time: 0.064) G_GAN: 0.165 G_GAN_Feat: 0.682 G_ID: 0.134 G_Rec: 0.309 D_GP: 0.030 D_real: 1.040 D_fake: 0.835 +(epoch: 283, iters: 8144, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.900 G_ID: 0.160 G_Rec: 0.391 D_GP: 0.028 D_real: 1.111 D_fake: 0.578 +(epoch: 283, iters: 8544, time: 0.064) G_GAN: 0.051 G_GAN_Feat: 0.783 G_ID: 0.125 G_Rec: 0.296 D_GP: 0.036 D_real: 0.845 D_fake: 0.952 +(epoch: 284, iters: 336, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.843 G_ID: 0.148 G_Rec: 0.376 D_GP: 0.025 D_real: 1.207 D_fake: 0.500 +(epoch: 284, iters: 736, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.659 G_ID: 0.127 G_Rec: 0.272 D_GP: 0.020 D_real: 1.094 D_fake: 0.771 +(epoch: 284, iters: 1136, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.895 G_ID: 0.169 G_Rec: 0.352 D_GP: 0.028 D_real: 1.216 D_fake: 0.632 +(epoch: 284, iters: 1536, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.681 G_ID: 0.118 G_Rec: 0.281 D_GP: 0.022 D_real: 1.152 D_fake: 0.768 +(epoch: 284, iters: 1936, time: 0.064) G_GAN: 0.528 G_GAN_Feat: 0.818 G_ID: 0.153 G_Rec: 0.369 D_GP: 0.022 D_real: 1.327 D_fake: 0.484 +(epoch: 284, iters: 2336, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.585 G_ID: 0.121 G_Rec: 0.279 D_GP: 0.016 D_real: 1.087 D_fake: 0.846 +(epoch: 284, iters: 2736, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.804 G_ID: 0.148 G_Rec: 0.374 D_GP: 0.025 D_real: 1.014 D_fake: 0.711 +(epoch: 284, iters: 3136, time: 0.064) G_GAN: 0.050 G_GAN_Feat: 0.765 G_ID: 0.142 G_Rec: 0.305 D_GP: 0.038 D_real: 0.622 D_fake: 0.950 +(epoch: 284, iters: 3536, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.886 G_ID: 0.156 G_Rec: 0.389 D_GP: 0.033 D_real: 0.929 D_fake: 0.687 +(epoch: 284, iters: 3936, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.692 G_ID: 0.107 G_Rec: 0.266 D_GP: 0.024 D_real: 1.089 D_fake: 0.784 +(epoch: 284, iters: 4336, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.856 G_ID: 0.164 G_Rec: 0.367 D_GP: 0.028 D_real: 0.990 D_fake: 0.786 +(epoch: 284, iters: 4736, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.872 G_ID: 0.130 G_Rec: 0.353 D_GP: 0.154 D_real: 0.446 D_fake: 0.906 +(epoch: 284, iters: 5136, time: 0.064) G_GAN: 0.548 G_GAN_Feat: 0.975 G_ID: 0.144 G_Rec: 0.396 D_GP: 0.026 D_real: 1.228 D_fake: 0.470 +(epoch: 284, iters: 5536, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.905 G_ID: 0.176 G_Rec: 0.378 D_GP: 0.030 D_real: 0.699 D_fake: 0.806 +(epoch: 284, iters: 5936, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.921 G_ID: 0.151 G_Rec: 0.397 D_GP: 0.030 D_real: 0.956 D_fake: 0.752 +(epoch: 284, iters: 6336, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.715 G_ID: 0.104 G_Rec: 0.270 D_GP: 0.029 D_real: 1.010 D_fake: 0.775 +(epoch: 284, iters: 6736, time: 0.064) G_GAN: 0.705 G_GAN_Feat: 0.962 G_ID: 0.161 G_Rec: 0.454 D_GP: 0.032 D_real: 1.379 D_fake: 0.400 +(epoch: 284, iters: 7136, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.643 G_ID: 0.117 G_Rec: 0.254 D_GP: 0.025 D_real: 1.325 D_fake: 0.641 +(epoch: 284, iters: 7536, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.940 G_ID: 0.150 G_Rec: 0.406 D_GP: 0.049 D_real: 1.188 D_fake: 0.595 +(epoch: 284, iters: 7936, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.657 G_ID: 0.126 G_Rec: 0.287 D_GP: 0.021 D_real: 1.076 D_fake: 0.833 +(epoch: 284, iters: 8336, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.891 G_ID: 0.147 G_Rec: 0.402 D_GP: 0.044 D_real: 1.131 D_fake: 0.524 +(epoch: 285, iters: 128, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.786 G_ID: 0.138 G_Rec: 0.292 D_GP: 0.034 D_real: 1.082 D_fake: 0.591 +(epoch: 285, iters: 528, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.905 G_ID: 0.161 G_Rec: 0.352 D_GP: 0.033 D_real: 1.030 D_fake: 0.501 +(epoch: 285, iters: 928, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.856 G_ID: 0.116 G_Rec: 0.322 D_GP: 0.038 D_real: 0.746 D_fake: 0.642 +(epoch: 285, iters: 1328, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.742 G_ID: 0.159 G_Rec: 0.474 D_GP: 0.019 D_real: 1.232 D_fake: 0.590 +(epoch: 285, iters: 1728, time: 0.064) G_GAN: -0.022 G_GAN_Feat: 0.610 G_ID: 0.142 G_Rec: 0.282 D_GP: 0.020 D_real: 0.917 D_fake: 1.022 +(epoch: 285, iters: 2128, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.806 G_ID: 0.147 G_Rec: 0.361 D_GP: 0.025 D_real: 1.203 D_fake: 0.565 +(epoch: 285, iters: 2528, time: 0.064) G_GAN: -0.081 G_GAN_Feat: 0.669 G_ID: 0.127 G_Rec: 0.298 D_GP: 0.023 D_real: 0.833 D_fake: 1.081 +(epoch: 285, iters: 2928, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.834 G_ID: 0.155 G_Rec: 0.379 D_GP: 0.026 D_real: 1.107 D_fake: 0.615 +(epoch: 285, iters: 3328, time: 0.064) G_GAN: 0.028 G_GAN_Feat: 0.714 G_ID: 0.117 G_Rec: 0.297 D_GP: 0.037 D_real: 0.766 D_fake: 0.972 +(epoch: 285, iters: 3728, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 1.061 G_ID: 0.163 G_Rec: 0.439 D_GP: 0.199 D_real: 0.637 D_fake: 0.566 +(epoch: 285, iters: 4128, time: 0.064) G_GAN: 0.089 G_GAN_Feat: 0.717 G_ID: 0.106 G_Rec: 0.307 D_GP: 0.022 D_real: 0.927 D_fake: 0.911 +(epoch: 285, iters: 4528, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.910 G_ID: 0.179 G_Rec: 0.407 D_GP: 0.033 D_real: 1.094 D_fake: 0.456 +(epoch: 285, iters: 4928, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.640 G_ID: 0.124 G_Rec: 0.311 D_GP: 0.019 D_real: 1.310 D_fake: 0.696 +(epoch: 285, iters: 5328, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.814 G_ID: 0.174 G_Rec: 0.363 D_GP: 0.024 D_real: 1.216 D_fake: 0.605 +(epoch: 285, iters: 5728, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.734 G_ID: 0.135 G_Rec: 0.296 D_GP: 0.048 D_real: 0.947 D_fake: 0.788 +(epoch: 285, iters: 6128, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.919 G_ID: 0.160 G_Rec: 0.411 D_GP: 0.031 D_real: 0.907 D_fake: 0.612 +(epoch: 285, iters: 6528, time: 0.064) G_GAN: -0.023 G_GAN_Feat: 0.705 G_ID: 0.110 G_Rec: 0.288 D_GP: 0.023 D_real: 0.835 D_fake: 1.023 +(epoch: 285, iters: 6928, time: 0.064) G_GAN: 0.697 G_GAN_Feat: 0.866 G_ID: 0.156 G_Rec: 0.380 D_GP: 0.024 D_real: 1.479 D_fake: 0.318 +(epoch: 285, iters: 7328, time: 0.064) G_GAN: -0.066 G_GAN_Feat: 0.614 G_ID: 0.149 G_Rec: 0.294 D_GP: 0.025 D_real: 1.015 D_fake: 1.066 +(epoch: 285, iters: 7728, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.753 G_ID: 0.154 G_Rec: 0.397 D_GP: 0.033 D_real: 0.968 D_fake: 0.821 +(epoch: 285, iters: 8128, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.604 G_ID: 0.125 G_Rec: 0.289 D_GP: 0.028 D_real: 1.009 D_fake: 0.897 +(epoch: 285, iters: 8528, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.806 G_ID: 0.169 G_Rec: 0.382 D_GP: 0.029 D_real: 1.196 D_fake: 0.599 +(epoch: 286, iters: 320, time: 0.064) G_GAN: 0.043 G_GAN_Feat: 0.733 G_ID: 0.114 G_Rec: 0.316 D_GP: 0.288 D_real: 0.617 D_fake: 0.958 +(epoch: 286, iters: 720, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.851 G_ID: 0.153 G_Rec: 0.416 D_GP: 0.025 D_real: 0.967 D_fake: 0.670 +(epoch: 286, iters: 1120, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.631 G_ID: 0.138 G_Rec: 0.284 D_GP: 0.019 D_real: 1.163 D_fake: 0.765 +(epoch: 286, iters: 1520, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.790 G_ID: 0.168 G_Rec: 0.344 D_GP: 0.024 D_real: 1.114 D_fake: 0.628 +(epoch: 286, iters: 1920, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.755 G_ID: 0.147 G_Rec: 0.337 D_GP: 0.022 D_real: 1.340 D_fake: 0.587 +(epoch: 286, iters: 2320, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.642 G_ID: 0.146 G_Rec: 0.320 D_GP: 0.017 D_real: 1.143 D_fake: 0.666 +(epoch: 286, iters: 2720, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.573 G_ID: 0.135 G_Rec: 0.288 D_GP: 0.018 D_real: 1.109 D_fake: 0.816 +(epoch: 286, iters: 3120, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.709 G_ID: 0.153 G_Rec: 0.345 D_GP: 0.021 D_real: 1.091 D_fake: 0.650 +(epoch: 286, iters: 3520, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.649 G_ID: 0.120 G_Rec: 0.314 D_GP: 0.034 D_real: 1.160 D_fake: 0.700 +(epoch: 286, iters: 3920, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.765 G_ID: 0.158 G_Rec: 0.371 D_GP: 0.041 D_real: 1.029 D_fake: 0.765 +(epoch: 286, iters: 4320, time: 0.064) G_GAN: 0.061 G_GAN_Feat: 0.622 G_ID: 0.133 G_Rec: 0.302 D_GP: 0.029 D_real: 0.917 D_fake: 0.939 +(epoch: 286, iters: 4720, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.891 G_ID: 0.162 G_Rec: 0.386 D_GP: 0.072 D_real: 1.123 D_fake: 0.532 +(epoch: 286, iters: 5120, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.685 G_ID: 0.116 G_Rec: 0.290 D_GP: 0.054 D_real: 0.887 D_fake: 0.917 +(epoch: 286, iters: 5520, time: 0.064) G_GAN: 0.520 G_GAN_Feat: 0.881 G_ID: 0.161 G_Rec: 0.403 D_GP: 0.022 D_real: 1.201 D_fake: 0.487 +(epoch: 286, iters: 5920, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.699 G_ID: 0.149 G_Rec: 0.304 D_GP: 0.082 D_real: 0.851 D_fake: 0.871 +(epoch: 286, iters: 6320, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.888 G_ID: 0.155 G_Rec: 0.393 D_GP: 0.040 D_real: 1.004 D_fake: 0.544 +(epoch: 286, iters: 6720, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.650 G_ID: 0.116 G_Rec: 0.295 D_GP: 0.020 D_real: 1.207 D_fake: 0.741 +(epoch: 286, iters: 7120, time: 0.064) G_GAN: -0.019 G_GAN_Feat: 0.840 G_ID: 0.161 G_Rec: 0.412 D_GP: 0.036 D_real: 0.804 D_fake: 1.019 +(epoch: 286, iters: 7520, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.636 G_ID: 0.148 G_Rec: 0.271 D_GP: 0.025 D_real: 0.991 D_fake: 0.918 +(epoch: 286, iters: 7920, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.772 G_ID: 0.173 G_Rec: 0.340 D_GP: 0.022 D_real: 1.193 D_fake: 0.607 +(epoch: 286, iters: 8320, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.729 G_ID: 0.120 G_Rec: 0.290 D_GP: 0.030 D_real: 1.037 D_fake: 0.885 +(epoch: 287, iters: 112, time: 0.064) G_GAN: 0.706 G_GAN_Feat: 0.952 G_ID: 0.117 G_Rec: 0.402 D_GP: 0.032 D_real: 1.213 D_fake: 0.310 +(epoch: 287, iters: 512, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.666 G_ID: 0.115 G_Rec: 0.290 D_GP: 0.018 D_real: 1.076 D_fake: 0.878 +(epoch: 287, iters: 912, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.787 G_ID: 0.141 G_Rec: 0.360 D_GP: 0.022 D_real: 1.035 D_fake: 0.664 +(epoch: 287, iters: 1312, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.891 G_ID: 0.130 G_Rec: 0.319 D_GP: 0.028 D_real: 1.391 D_fake: 0.676 +(epoch: 287, iters: 1712, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.898 G_ID: 0.149 G_Rec: 0.387 D_GP: 0.022 D_real: 1.277 D_fake: 0.458 +(epoch: 287, iters: 2112, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.691 G_ID: 0.117 G_Rec: 0.308 D_GP: 0.024 D_real: 1.151 D_fake: 0.726 +(epoch: 287, iters: 2512, time: 0.064) G_GAN: 0.640 G_GAN_Feat: 0.993 G_ID: 0.164 G_Rec: 0.397 D_GP: 0.040 D_real: 1.085 D_fake: 0.451 +(epoch: 287, iters: 2912, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.676 G_ID: 0.118 G_Rec: 0.316 D_GP: 0.018 D_real: 1.081 D_fake: 0.833 +(epoch: 287, iters: 3312, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.838 G_ID: 0.159 G_Rec: 0.384 D_GP: 0.020 D_real: 1.274 D_fake: 0.464 +(epoch: 287, iters: 3712, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.761 G_ID: 0.138 G_Rec: 0.378 D_GP: 0.077 D_real: 0.827 D_fake: 0.989 +(epoch: 287, iters: 4112, time: 0.064) G_GAN: 0.778 G_GAN_Feat: 0.796 G_ID: 0.165 G_Rec: 0.376 D_GP: 0.021 D_real: 1.519 D_fake: 0.261 +(epoch: 287, iters: 4512, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.629 G_ID: 0.128 G_Rec: 0.269 D_GP: 0.023 D_real: 1.353 D_fake: 0.589 +(epoch: 287, iters: 4912, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.763 G_ID: 0.155 G_Rec: 0.350 D_GP: 0.023 D_real: 1.231 D_fake: 0.573 +(epoch: 287, iters: 5312, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.720 G_ID: 0.124 G_Rec: 0.302 D_GP: 0.039 D_real: 0.870 D_fake: 0.894 +(epoch: 287, iters: 5712, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 1.019 G_ID: 0.183 G_Rec: 0.437 D_GP: 0.142 D_real: 0.496 D_fake: 0.835 +(epoch: 287, iters: 6112, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.815 G_ID: 0.121 G_Rec: 0.324 D_GP: 0.099 D_real: 0.780 D_fake: 0.846 +(epoch: 287, iters: 6512, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.939 G_ID: 0.164 G_Rec: 0.404 D_GP: 0.053 D_real: 0.624 D_fake: 0.684 +(epoch: 287, iters: 6912, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.686 G_ID: 0.115 G_Rec: 0.302 D_GP: 0.016 D_real: 1.234 D_fake: 0.716 +(epoch: 287, iters: 7312, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.956 G_ID: 0.165 G_Rec: 0.446 D_GP: 0.054 D_real: 1.078 D_fake: 0.537 +(epoch: 287, iters: 7712, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.734 G_ID: 0.122 G_Rec: 0.319 D_GP: 0.044 D_real: 0.903 D_fake: 0.941 +(epoch: 287, iters: 8112, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.882 G_ID: 0.178 G_Rec: 0.436 D_GP: 0.024 D_real: 1.082 D_fake: 0.799 +(epoch: 287, iters: 8512, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 0.701 G_ID: 0.164 G_Rec: 0.274 D_GP: 0.040 D_real: 0.820 D_fake: 0.974 +(epoch: 288, iters: 304, time: 0.064) G_GAN: 0.520 G_GAN_Feat: 1.012 G_ID: 0.143 G_Rec: 0.482 D_GP: 0.054 D_real: 0.883 D_fake: 0.488 +(epoch: 288, iters: 704, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.658 G_ID: 0.114 G_Rec: 0.269 D_GP: 0.027 D_real: 1.147 D_fake: 0.744 +(epoch: 288, iters: 1104, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.947 G_ID: 0.142 G_Rec: 0.422 D_GP: 0.038 D_real: 0.610 D_fake: 0.799 +(epoch: 288, iters: 1504, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 0.598 G_ID: 0.151 G_Rec: 0.299 D_GP: 0.022 D_real: 1.030 D_fake: 0.944 +(epoch: 288, iters: 1904, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.837 G_ID: 0.158 G_Rec: 0.402 D_GP: 0.027 D_real: 1.042 D_fake: 0.691 +(epoch: 288, iters: 2304, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.703 G_ID: 0.146 G_Rec: 0.332 D_GP: 0.051 D_real: 1.002 D_fake: 0.805 +(epoch: 288, iters: 2704, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.937 G_ID: 0.149 G_Rec: 0.431 D_GP: 0.050 D_real: 1.027 D_fake: 0.543 +(epoch: 288, iters: 3104, time: 0.064) G_GAN: 0.005 G_GAN_Feat: 0.804 G_ID: 0.119 G_Rec: 0.333 D_GP: 0.198 D_real: 0.625 D_fake: 0.995 +(epoch: 288, iters: 3504, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.985 G_ID: 0.156 G_Rec: 0.444 D_GP: 0.061 D_real: 0.870 D_fake: 0.606 +(epoch: 288, iters: 3904, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.657 G_ID: 0.124 G_Rec: 0.297 D_GP: 0.031 D_real: 1.121 D_fake: 0.704 +(epoch: 288, iters: 4304, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.921 G_ID: 0.155 G_Rec: 0.377 D_GP: 0.032 D_real: 1.022 D_fake: 0.535 +(epoch: 288, iters: 4704, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.703 G_ID: 0.136 G_Rec: 0.295 D_GP: 0.039 D_real: 1.000 D_fake: 0.685 +(epoch: 288, iters: 5104, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.819 G_ID: 0.165 G_Rec: 0.371 D_GP: 0.034 D_real: 1.008 D_fake: 0.678 +(epoch: 288, iters: 5504, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.740 G_ID: 0.148 G_Rec: 0.369 D_GP: 0.035 D_real: 0.856 D_fake: 0.781 +(epoch: 288, iters: 5904, time: 0.064) G_GAN: 0.594 G_GAN_Feat: 0.876 G_ID: 0.195 G_Rec: 0.389 D_GP: 0.023 D_real: 1.315 D_fake: 0.425 +(epoch: 288, iters: 6304, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.666 G_ID: 0.130 G_Rec: 0.290 D_GP: 0.031 D_real: 1.092 D_fake: 0.785 +(epoch: 288, iters: 6704, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.848 G_ID: 0.158 G_Rec: 0.365 D_GP: 0.030 D_real: 1.071 D_fake: 0.520 +(epoch: 288, iters: 7104, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.869 G_ID: 0.134 G_Rec: 0.303 D_GP: 0.631 D_real: 0.472 D_fake: 0.707 +(epoch: 288, iters: 7504, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 1.075 G_ID: 0.181 G_Rec: 0.457 D_GP: 0.277 D_real: 0.454 D_fake: 0.642 +(epoch: 288, iters: 7904, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.791 G_ID: 0.142 G_Rec: 0.330 D_GP: 0.027 D_real: 1.010 D_fake: 0.753 +(epoch: 288, iters: 8304, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.982 G_ID: 0.157 G_Rec: 0.434 D_GP: 0.024 D_real: 0.927 D_fake: 0.694 +(epoch: 289, iters: 96, time: 0.064) G_GAN: -0.114 G_GAN_Feat: 0.674 G_ID: 0.142 G_Rec: 0.281 D_GP: 0.031 D_real: 0.832 D_fake: 1.114 +(epoch: 289, iters: 496, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 1.056 G_ID: 0.172 G_Rec: 0.433 D_GP: 0.034 D_real: 1.343 D_fake: 0.586 +(epoch: 289, iters: 896, time: 0.064) G_GAN: -0.011 G_GAN_Feat: 0.640 G_ID: 0.131 G_Rec: 0.282 D_GP: 0.032 D_real: 0.942 D_fake: 1.011 +(epoch: 289, iters: 1296, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.780 G_ID: 0.173 G_Rec: 0.364 D_GP: 0.021 D_real: 1.103 D_fake: 0.693 +(epoch: 289, iters: 1696, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.561 G_ID: 0.103 G_Rec: 0.282 D_GP: 0.021 D_real: 1.079 D_fake: 0.856 +(epoch: 289, iters: 2096, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.737 G_ID: 0.173 G_Rec: 0.372 D_GP: 0.022 D_real: 1.067 D_fake: 0.729 +(epoch: 289, iters: 2496, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.595 G_ID: 0.128 G_Rec: 0.274 D_GP: 0.021 D_real: 1.055 D_fake: 0.877 +(epoch: 289, iters: 2896, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.757 G_ID: 0.170 G_Rec: 0.398 D_GP: 0.025 D_real: 1.126 D_fake: 0.626 +(epoch: 289, iters: 3296, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.733 G_ID: 0.126 G_Rec: 0.308 D_GP: 0.035 D_real: 1.083 D_fake: 0.805 +(epoch: 289, iters: 3696, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.851 G_ID: 0.150 G_Rec: 0.392 D_GP: 0.022 D_real: 1.197 D_fake: 0.545 +(epoch: 289, iters: 4096, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.923 G_ID: 0.127 G_Rec: 0.318 D_GP: 0.084 D_real: 0.898 D_fake: 0.893 +(epoch: 289, iters: 4496, time: 0.064) G_GAN: 0.819 G_GAN_Feat: 0.880 G_ID: 0.158 G_Rec: 0.353 D_GP: 0.049 D_real: 1.361 D_fake: 0.252 +(epoch: 289, iters: 4896, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.857 G_ID: 0.128 G_Rec: 0.304 D_GP: 0.092 D_real: 0.628 D_fake: 0.704 +(epoch: 289, iters: 5296, time: 0.064) G_GAN: 0.659 G_GAN_Feat: 0.862 G_ID: 0.137 G_Rec: 0.392 D_GP: 0.022 D_real: 1.377 D_fake: 0.357 +(epoch: 289, iters: 5696, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.614 G_ID: 0.115 G_Rec: 0.264 D_GP: 0.020 D_real: 1.076 D_fake: 0.885 +(epoch: 289, iters: 6096, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.845 G_ID: 0.158 G_Rec: 0.402 D_GP: 0.032 D_real: 1.067 D_fake: 0.669 +(epoch: 289, iters: 6496, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.667 G_ID: 0.122 G_Rec: 0.276 D_GP: 0.026 D_real: 1.102 D_fake: 0.774 +(epoch: 289, iters: 6896, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.911 G_ID: 0.177 G_Rec: 0.373 D_GP: 0.036 D_real: 0.836 D_fake: 0.751 +(epoch: 289, iters: 7296, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.744 G_ID: 0.134 G_Rec: 0.280 D_GP: 0.029 D_real: 1.198 D_fake: 0.703 +(epoch: 289, iters: 7696, time: 0.064) G_GAN: 0.637 G_GAN_Feat: 0.970 G_ID: 0.198 G_Rec: 0.400 D_GP: 0.038 D_real: 1.201 D_fake: 0.424 +(epoch: 289, iters: 8096, time: 0.064) G_GAN: -0.038 G_GAN_Feat: 0.851 G_ID: 0.119 G_Rec: 0.318 D_GP: 0.038 D_real: 0.604 D_fake: 1.038 +(epoch: 289, iters: 8496, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.986 G_ID: 0.175 G_Rec: 0.383 D_GP: 0.080 D_real: 0.623 D_fake: 0.567 +(epoch: 290, iters: 288, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.648 G_ID: 0.126 G_Rec: 0.302 D_GP: 0.023 D_real: 1.065 D_fake: 0.893 +(epoch: 290, iters: 688, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.999 G_ID: 0.145 G_Rec: 0.450 D_GP: 0.046 D_real: 0.994 D_fake: 0.648 +(epoch: 290, iters: 1088, time: 0.064) G_GAN: 0.101 G_GAN_Feat: 0.634 G_ID: 0.122 G_Rec: 0.237 D_GP: 0.024 D_real: 1.106 D_fake: 0.899 +(epoch: 290, iters: 1488, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.879 G_ID: 0.143 G_Rec: 0.425 D_GP: 0.022 D_real: 0.810 D_fake: 0.918 +(epoch: 290, iters: 1888, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.684 G_ID: 0.119 G_Rec: 0.272 D_GP: 0.027 D_real: 1.121 D_fake: 0.828 +(epoch: 290, iters: 2288, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.897 G_ID: 0.167 G_Rec: 0.424 D_GP: 0.034 D_real: 1.117 D_fake: 0.546 +(epoch: 290, iters: 2688, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.636 G_ID: 0.116 G_Rec: 0.292 D_GP: 0.028 D_real: 1.224 D_fake: 0.681 +(epoch: 290, iters: 3088, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.780 G_ID: 0.156 G_Rec: 0.357 D_GP: 0.024 D_real: 1.272 D_fake: 0.495 +(epoch: 290, iters: 3488, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.777 G_ID: 0.121 G_Rec: 0.289 D_GP: 0.026 D_real: 1.354 D_fake: 0.546 +(epoch: 290, iters: 3888, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 1.113 G_ID: 0.167 G_Rec: 0.448 D_GP: 0.335 D_real: 0.462 D_fake: 0.689 +(epoch: 290, iters: 4288, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.547 G_ID: 0.122 G_Rec: 0.272 D_GP: 0.017 D_real: 1.319 D_fake: 0.659 +(epoch: 290, iters: 4688, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.836 G_ID: 0.166 G_Rec: 0.448 D_GP: 0.025 D_real: 1.093 D_fake: 0.589 +(epoch: 290, iters: 5088, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.583 G_ID: 0.121 G_Rec: 0.276 D_GP: 0.021 D_real: 1.102 D_fake: 0.864 +(epoch: 290, iters: 5488, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.795 G_ID: 0.151 G_Rec: 0.397 D_GP: 0.024 D_real: 1.025 D_fake: 0.762 +(epoch: 290, iters: 5888, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 0.685 G_ID: 0.137 G_Rec: 0.302 D_GP: 0.043 D_real: 0.836 D_fake: 0.943 +(epoch: 290, iters: 6288, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.829 G_ID: 0.153 G_Rec: 0.391 D_GP: 0.026 D_real: 1.071 D_fake: 0.648 +(epoch: 290, iters: 6688, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.673 G_ID: 0.128 G_Rec: 0.309 D_GP: 0.038 D_real: 1.065 D_fake: 0.765 +(epoch: 290, iters: 7088, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.781 G_ID: 0.146 G_Rec: 0.353 D_GP: 0.027 D_real: 1.094 D_fake: 0.714 +(epoch: 290, iters: 7488, time: 0.064) G_GAN: 0.204 G_GAN_Feat: 0.682 G_ID: 0.128 G_Rec: 0.318 D_GP: 0.133 D_real: 0.873 D_fake: 0.797 +(epoch: 290, iters: 7888, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.850 G_ID: 0.177 G_Rec: 0.363 D_GP: 0.035 D_real: 1.050 D_fake: 0.671 +(epoch: 290, iters: 8288, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.637 G_ID: 0.120 G_Rec: 0.318 D_GP: 0.024 D_real: 1.207 D_fake: 0.747 +(epoch: 291, iters: 80, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 1.008 G_ID: 0.138 G_Rec: 0.430 D_GP: 0.116 D_real: 0.556 D_fake: 0.683 +(epoch: 291, iters: 480, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.664 G_ID: 0.097 G_Rec: 0.242 D_GP: 0.026 D_real: 1.169 D_fake: 0.773 +(epoch: 291, iters: 880, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.756 G_ID: 0.147 G_Rec: 0.381 D_GP: 0.020 D_real: 1.281 D_fake: 0.541 +(epoch: 291, iters: 1280, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.665 G_ID: 0.123 G_Rec: 0.301 D_GP: 0.037 D_real: 1.010 D_fake: 0.879 +(epoch: 291, iters: 1680, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.728 G_ID: 0.168 G_Rec: 0.341 D_GP: 0.030 D_real: 0.985 D_fake: 0.818 +(epoch: 291, iters: 2080, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.857 G_ID: 0.138 G_Rec: 0.342 D_GP: 0.068 D_real: 0.698 D_fake: 0.852 +(epoch: 291, iters: 2480, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.846 G_ID: 0.151 G_Rec: 0.419 D_GP: 0.022 D_real: 1.219 D_fake: 0.500 +(epoch: 291, iters: 2880, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 0.854 G_ID: 0.124 G_Rec: 0.327 D_GP: 0.234 D_real: 0.597 D_fake: 0.892 +(epoch: 291, iters: 3280, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.878 G_ID: 0.150 G_Rec: 0.368 D_GP: 0.022 D_real: 1.126 D_fake: 0.581 +(epoch: 291, iters: 3680, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.756 G_ID: 0.122 G_Rec: 0.299 D_GP: 0.091 D_real: 0.761 D_fake: 0.736 +(epoch: 291, iters: 4080, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.867 G_ID: 0.170 G_Rec: 0.383 D_GP: 0.030 D_real: 1.002 D_fake: 0.790 +(epoch: 291, iters: 4480, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.807 G_ID: 0.107 G_Rec: 0.312 D_GP: 0.054 D_real: 0.757 D_fake: 0.785 +(epoch: 291, iters: 4880, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 1.092 G_ID: 0.162 G_Rec: 0.402 D_GP: 0.113 D_real: 0.423 D_fake: 0.660 +(epoch: 291, iters: 5280, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.596 G_ID: 0.117 G_Rec: 0.245 D_GP: 0.017 D_real: 1.262 D_fake: 0.699 +(epoch: 291, iters: 5680, time: 0.064) G_GAN: 0.509 G_GAN_Feat: 0.814 G_ID: 0.161 G_Rec: 0.402 D_GP: 0.020 D_real: 1.247 D_fake: 0.504 +(epoch: 291, iters: 6080, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.631 G_ID: 0.127 G_Rec: 0.284 D_GP: 0.023 D_real: 1.107 D_fake: 0.846 +(epoch: 291, iters: 6480, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.815 G_ID: 0.207 G_Rec: 0.343 D_GP: 0.027 D_real: 0.927 D_fake: 0.908 +(epoch: 291, iters: 6880, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.726 G_ID: 0.113 G_Rec: 0.269 D_GP: 0.038 D_real: 1.199 D_fake: 0.632 +(epoch: 291, iters: 7280, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.898 G_ID: 0.155 G_Rec: 0.404 D_GP: 0.027 D_real: 0.913 D_fake: 0.715 +(epoch: 291, iters: 7680, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.724 G_ID: 0.107 G_Rec: 0.288 D_GP: 0.122 D_real: 0.895 D_fake: 0.776 +(epoch: 291, iters: 8080, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 0.838 G_ID: 0.163 G_Rec: 0.400 D_GP: 0.019 D_real: 1.389 D_fake: 0.405 +(epoch: 291, iters: 8480, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.739 G_ID: 0.116 G_Rec: 0.304 D_GP: 0.055 D_real: 1.102 D_fake: 0.812 +(epoch: 292, iters: 272, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 1.096 G_ID: 0.182 G_Rec: 0.414 D_GP: 0.101 D_real: 0.396 D_fake: 0.448 +(epoch: 292, iters: 672, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 0.658 G_ID: 0.117 G_Rec: 0.259 D_GP: 0.021 D_real: 1.439 D_fake: 0.597 +(epoch: 292, iters: 1072, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.797 G_ID: 0.182 G_Rec: 0.378 D_GP: 0.020 D_real: 1.375 D_fake: 0.475 +(epoch: 292, iters: 1472, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.637 G_ID: 0.106 G_Rec: 0.277 D_GP: 0.019 D_real: 1.174 D_fake: 0.761 +(epoch: 292, iters: 1872, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.916 G_ID: 0.170 G_Rec: 0.386 D_GP: 0.117 D_real: 0.739 D_fake: 0.791 +(epoch: 292, iters: 2272, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.771 G_ID: 0.138 G_Rec: 0.313 D_GP: 0.047 D_real: 0.963 D_fake: 0.668 +(epoch: 292, iters: 2672, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.984 G_ID: 0.149 G_Rec: 0.429 D_GP: 0.039 D_real: 1.132 D_fake: 0.529 +(epoch: 292, iters: 3072, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.727 G_ID: 0.161 G_Rec: 0.269 D_GP: 0.024 D_real: 1.223 D_fake: 0.705 +(epoch: 292, iters: 3472, time: 0.064) G_GAN: 0.042 G_GAN_Feat: 0.833 G_ID: 0.161 G_Rec: 0.365 D_GP: 0.022 D_real: 0.943 D_fake: 0.958 +(epoch: 292, iters: 3872, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.645 G_ID: 0.116 G_Rec: 0.280 D_GP: 0.023 D_real: 1.059 D_fake: 0.854 +(epoch: 292, iters: 4272, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.665 G_ID: 0.148 G_Rec: 0.330 D_GP: 0.019 D_real: 1.281 D_fake: 0.640 +(epoch: 292, iters: 4672, time: 0.064) G_GAN: -0.000 G_GAN_Feat: 0.602 G_ID: 0.133 G_Rec: 0.294 D_GP: 0.024 D_real: 0.921 D_fake: 1.000 +(epoch: 292, iters: 5072, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.753 G_ID: 0.165 G_Rec: 0.353 D_GP: 0.027 D_real: 0.981 D_fake: 0.812 +(epoch: 292, iters: 5472, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.636 G_ID: 0.123 G_Rec: 0.296 D_GP: 0.027 D_real: 1.046 D_fake: 0.920 +(epoch: 292, iters: 5872, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.837 G_ID: 0.177 G_Rec: 0.390 D_GP: 0.023 D_real: 0.813 D_fake: 0.832 +(epoch: 292, iters: 6272, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.706 G_ID: 0.122 G_Rec: 0.358 D_GP: 0.029 D_real: 0.935 D_fake: 0.880 +(epoch: 292, iters: 6672, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.780 G_ID: 0.150 G_Rec: 0.364 D_GP: 0.022 D_real: 1.202 D_fake: 0.580 +(epoch: 292, iters: 7072, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.787 G_ID: 0.139 G_Rec: 0.308 D_GP: 0.039 D_real: 1.101 D_fake: 0.641 +(epoch: 292, iters: 7472, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.888 G_ID: 0.141 G_Rec: 0.377 D_GP: 0.042 D_real: 1.021 D_fake: 0.499 +(epoch: 292, iters: 7872, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 0.747 G_ID: 0.117 G_Rec: 0.288 D_GP: 0.041 D_real: 1.336 D_fake: 0.488 +(epoch: 292, iters: 8272, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.849 G_ID: 0.157 G_Rec: 0.400 D_GP: 0.022 D_real: 1.208 D_fake: 0.577 +(epoch: 293, iters: 64, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.659 G_ID: 0.140 G_Rec: 0.270 D_GP: 0.020 D_real: 1.233 D_fake: 0.692 +(epoch: 293, iters: 464, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.961 G_ID: 0.157 G_Rec: 0.400 D_GP: 0.051 D_real: 0.732 D_fake: 0.709 +(epoch: 293, iters: 864, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.747 G_ID: 0.147 G_Rec: 0.290 D_GP: 0.057 D_real: 1.042 D_fake: 0.603 +(epoch: 293, iters: 1264, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.894 G_ID: 0.170 G_Rec: 0.395 D_GP: 0.029 D_real: 0.961 D_fake: 0.753 +(epoch: 293, iters: 1664, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.625 G_ID: 0.119 G_Rec: 0.281 D_GP: 0.022 D_real: 1.175 D_fake: 0.753 +(epoch: 293, iters: 2064, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.846 G_ID: 0.154 G_Rec: 0.365 D_GP: 0.032 D_real: 1.163 D_fake: 0.659 +(epoch: 293, iters: 2464, time: 0.064) G_GAN: 0.021 G_GAN_Feat: 0.689 G_ID: 0.139 G_Rec: 0.297 D_GP: 0.062 D_real: 0.757 D_fake: 0.980 +(epoch: 293, iters: 2864, time: 0.064) G_GAN: 0.537 G_GAN_Feat: 0.923 G_ID: 0.174 G_Rec: 0.382 D_GP: 0.038 D_real: 1.085 D_fake: 0.484 +(epoch: 293, iters: 3264, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.629 G_ID: 0.145 G_Rec: 0.252 D_GP: 0.022 D_real: 0.992 D_fake: 0.896 +(epoch: 293, iters: 3664, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 1.114 G_ID: 0.158 G_Rec: 0.399 D_GP: 0.141 D_real: 0.351 D_fake: 0.701 +(epoch: 293, iters: 4064, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.648 G_ID: 0.158 G_Rec: 0.262 D_GP: 0.026 D_real: 1.161 D_fake: 0.738 +(epoch: 293, iters: 4464, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.895 G_ID: 0.157 G_Rec: 0.404 D_GP: 0.033 D_real: 0.913 D_fake: 0.746 +(epoch: 293, iters: 4864, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.681 G_ID: 0.122 G_Rec: 0.269 D_GP: 0.030 D_real: 0.923 D_fake: 0.914 +(epoch: 293, iters: 5264, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 0.976 G_ID: 0.177 G_Rec: 0.398 D_GP: 0.095 D_real: 0.983 D_fake: 0.432 +(epoch: 293, iters: 5664, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.870 G_ID: 0.125 G_Rec: 0.338 D_GP: 0.084 D_real: 0.491 D_fake: 0.735 +(epoch: 293, iters: 6064, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.885 G_ID: 0.171 G_Rec: 0.376 D_GP: 0.028 D_real: 0.861 D_fake: 0.844 +(epoch: 293, iters: 6464, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.831 G_ID: 0.113 G_Rec: 0.294 D_GP: 0.077 D_real: 0.616 D_fake: 0.730 +(epoch: 293, iters: 6864, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 1.158 G_ID: 0.161 G_Rec: 0.452 D_GP: 0.279 D_real: 0.482 D_fake: 0.699 +(epoch: 293, iters: 7264, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.835 G_ID: 0.129 G_Rec: 0.331 D_GP: 0.034 D_real: 0.876 D_fake: 0.999 +(epoch: 293, iters: 7664, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.865 G_ID: 0.165 G_Rec: 0.378 D_GP: 0.023 D_real: 1.005 D_fake: 0.809 +(epoch: 293, iters: 8064, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.779 G_ID: 0.126 G_Rec: 0.343 D_GP: 0.061 D_real: 0.809 D_fake: 0.895 +(epoch: 293, iters: 8464, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.903 G_ID: 0.165 G_Rec: 0.409 D_GP: 0.048 D_real: 0.775 D_fake: 0.788 +(epoch: 294, iters: 256, time: 0.064) G_GAN: -0.003 G_GAN_Feat: 0.734 G_ID: 0.134 G_Rec: 0.292 D_GP: 0.074 D_real: 0.689 D_fake: 1.003 +(epoch: 294, iters: 656, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 0.864 G_ID: 0.173 G_Rec: 0.376 D_GP: 0.026 D_real: 1.269 D_fake: 0.463 +(epoch: 294, iters: 1056, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.686 G_ID: 0.133 G_Rec: 0.284 D_GP: 0.025 D_real: 1.308 D_fake: 0.616 +(epoch: 294, iters: 1456, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.922 G_ID: 0.144 G_Rec: 0.389 D_GP: 0.029 D_real: 1.086 D_fake: 0.504 +(epoch: 294, iters: 1856, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.839 G_ID: 0.114 G_Rec: 0.287 D_GP: 0.026 D_real: 0.869 D_fake: 0.615 +(epoch: 294, iters: 2256, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.838 G_ID: 0.156 G_Rec: 0.386 D_GP: 0.023 D_real: 1.044 D_fake: 0.743 +(epoch: 294, iters: 2656, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.628 G_ID: 0.128 G_Rec: 0.268 D_GP: 0.022 D_real: 1.142 D_fake: 0.795 +(epoch: 294, iters: 3056, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.847 G_ID: 0.176 G_Rec: 0.367 D_GP: 0.022 D_real: 1.216 D_fake: 0.648 +(epoch: 294, iters: 3456, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.899 G_ID: 0.101 G_Rec: 0.315 D_GP: 0.065 D_real: 0.614 D_fake: 0.673 +(epoch: 294, iters: 3856, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.869 G_ID: 0.159 G_Rec: 0.393 D_GP: 0.022 D_real: 1.132 D_fake: 0.609 +(epoch: 294, iters: 4256, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.817 G_ID: 0.146 G_Rec: 0.292 D_GP: 0.039 D_real: 1.119 D_fake: 0.822 +(epoch: 294, iters: 4656, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.937 G_ID: 0.153 G_Rec: 0.349 D_GP: 0.027 D_real: 0.826 D_fake: 0.615 +(epoch: 294, iters: 5056, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.619 G_ID: 0.141 G_Rec: 0.282 D_GP: 0.020 D_real: 0.991 D_fake: 0.968 +(epoch: 294, iters: 5456, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.953 G_ID: 0.156 G_Rec: 0.398 D_GP: 0.101 D_real: 0.696 D_fake: 0.698 +(epoch: 294, iters: 5856, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.605 G_ID: 0.137 G_Rec: 0.285 D_GP: 0.018 D_real: 1.282 D_fake: 0.694 +(epoch: 294, iters: 6256, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.861 G_ID: 0.144 G_Rec: 0.442 D_GP: 0.029 D_real: 1.215 D_fake: 0.491 +(epoch: 294, iters: 6656, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.708 G_ID: 0.122 G_Rec: 0.298 D_GP: 0.021 D_real: 1.172 D_fake: 0.724 +(epoch: 294, iters: 7056, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.903 G_ID: 0.158 G_Rec: 0.377 D_GP: 0.051 D_real: 1.135 D_fake: 0.501 +(epoch: 294, iters: 7456, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.733 G_ID: 0.123 G_Rec: 0.279 D_GP: 0.040 D_real: 0.867 D_fake: 0.746 +(epoch: 294, iters: 7856, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.967 G_ID: 0.166 G_Rec: 0.413 D_GP: 0.054 D_real: 0.682 D_fake: 0.629 +(epoch: 294, iters: 8256, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.779 G_ID: 0.109 G_Rec: 0.288 D_GP: 0.050 D_real: 0.701 D_fake: 0.744 +(epoch: 295, iters: 48, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.832 G_ID: 0.159 G_Rec: 0.364 D_GP: 0.030 D_real: 1.209 D_fake: 0.500 +(epoch: 295, iters: 448, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.830 G_ID: 0.107 G_Rec: 0.308 D_GP: 0.039 D_real: 0.936 D_fake: 0.672 +(epoch: 295, iters: 848, time: 0.064) G_GAN: 0.616 G_GAN_Feat: 1.167 G_ID: 0.160 G_Rec: 0.428 D_GP: 0.039 D_real: 0.372 D_fake: 0.415 +(epoch: 295, iters: 1248, time: 0.064) G_GAN: -0.006 G_GAN_Feat: 0.603 G_ID: 0.121 G_Rec: 0.311 D_GP: 0.017 D_real: 0.978 D_fake: 1.006 +(epoch: 295, iters: 1648, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.689 G_ID: 0.145 G_Rec: 0.356 D_GP: 0.019 D_real: 1.196 D_fake: 0.705 +(epoch: 295, iters: 2048, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.555 G_ID: 0.106 G_Rec: 0.271 D_GP: 0.026 D_real: 1.185 D_fake: 0.781 +(epoch: 295, iters: 2448, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.748 G_ID: 0.149 G_Rec: 0.401 D_GP: 0.027 D_real: 0.947 D_fake: 0.809 +(epoch: 295, iters: 2848, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 0.566 G_ID: 0.122 G_Rec: 0.256 D_GP: 0.027 D_real: 1.021 D_fake: 0.892 +(epoch: 295, iters: 3248, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.755 G_ID: 0.165 G_Rec: 0.388 D_GP: 0.019 D_real: 1.156 D_fake: 0.676 +(epoch: 295, iters: 3648, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.674 G_ID: 0.165 G_Rec: 0.296 D_GP: 0.023 D_real: 0.948 D_fake: 0.887 +(epoch: 295, iters: 4048, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.792 G_ID: 0.166 G_Rec: 0.357 D_GP: 0.039 D_real: 1.166 D_fake: 0.581 +(epoch: 295, iters: 4448, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 0.744 G_ID: 0.105 G_Rec: 0.328 D_GP: 0.063 D_real: 0.826 D_fake: 0.904 +(epoch: 295, iters: 4848, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.838 G_ID: 0.160 G_Rec: 0.384 D_GP: 0.045 D_real: 0.969 D_fake: 0.739 +(epoch: 295, iters: 5248, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.653 G_ID: 0.113 G_Rec: 0.277 D_GP: 0.025 D_real: 1.033 D_fake: 0.896 +(epoch: 295, iters: 5648, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.823 G_ID: 0.150 G_Rec: 0.398 D_GP: 0.024 D_real: 1.215 D_fake: 0.543 +(epoch: 295, iters: 6048, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.709 G_ID: 0.179 G_Rec: 0.300 D_GP: 0.048 D_real: 0.786 D_fake: 0.898 +(epoch: 295, iters: 6448, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.894 G_ID: 0.155 G_Rec: 0.367 D_GP: 0.059 D_real: 0.724 D_fake: 0.724 +(epoch: 295, iters: 6848, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.702 G_ID: 0.122 G_Rec: 0.270 D_GP: 0.024 D_real: 1.284 D_fake: 0.532 +(epoch: 295, iters: 7248, time: 0.064) G_GAN: 0.822 G_GAN_Feat: 1.100 G_ID: 0.174 G_Rec: 0.402 D_GP: 0.064 D_real: 1.073 D_fake: 0.390 +(epoch: 295, iters: 7648, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.710 G_ID: 0.135 G_Rec: 0.299 D_GP: 0.025 D_real: 1.120 D_fake: 0.821 +(epoch: 295, iters: 8048, time: 0.064) G_GAN: 0.640 G_GAN_Feat: 0.921 G_ID: 0.163 G_Rec: 0.414 D_GP: 0.037 D_real: 1.147 D_fake: 0.405 +(epoch: 295, iters: 8448, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.611 G_ID: 0.117 G_Rec: 0.264 D_GP: 0.019 D_real: 1.106 D_fake: 0.834 +(epoch: 296, iters: 240, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.943 G_ID: 0.156 G_Rec: 0.425 D_GP: 0.065 D_real: 0.959 D_fake: 0.510 +(epoch: 296, iters: 640, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.893 G_ID: 0.135 G_Rec: 0.334 D_GP: 0.393 D_real: 0.603 D_fake: 0.903 +(epoch: 296, iters: 1040, time: 0.064) G_GAN: 0.694 G_GAN_Feat: 0.800 G_ID: 0.148 G_Rec: 0.356 D_GP: 0.022 D_real: 1.492 D_fake: 0.320 +(epoch: 296, iters: 1440, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.702 G_ID: 0.133 G_Rec: 0.322 D_GP: 0.020 D_real: 1.125 D_fake: 0.822 +(epoch: 296, iters: 1840, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.829 G_ID: 0.151 G_Rec: 0.378 D_GP: 0.019 D_real: 1.317 D_fake: 0.527 +(epoch: 296, iters: 2240, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.853 G_ID: 0.131 G_Rec: 0.337 D_GP: 0.100 D_real: 0.897 D_fake: 0.656 +(epoch: 296, iters: 2640, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.882 G_ID: 0.176 G_Rec: 0.356 D_GP: 0.037 D_real: 1.108 D_fake: 0.729 +(epoch: 296, iters: 3040, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.631 G_ID: 0.126 G_Rec: 0.282 D_GP: 0.019 D_real: 1.369 D_fake: 0.558 +(epoch: 296, iters: 3440, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.891 G_ID: 0.170 G_Rec: 0.362 D_GP: 0.030 D_real: 1.053 D_fake: 0.673 +(epoch: 296, iters: 3840, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.792 G_ID: 0.126 G_Rec: 0.293 D_GP: 0.073 D_real: 1.019 D_fake: 0.688 +(epoch: 296, iters: 4240, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.954 G_ID: 0.159 G_Rec: 0.383 D_GP: 0.046 D_real: 0.856 D_fake: 0.589 +(epoch: 296, iters: 4640, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.863 G_ID: 0.123 G_Rec: 0.312 D_GP: 0.136 D_real: 0.586 D_fake: 0.738 +(epoch: 296, iters: 5040, time: 0.064) G_GAN: 0.735 G_GAN_Feat: 1.073 G_ID: 0.172 G_Rec: 0.411 D_GP: 1.489 D_real: 0.654 D_fake: 0.317 +(epoch: 296, iters: 5440, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.540 G_ID: 0.122 G_Rec: 0.257 D_GP: 0.017 D_real: 1.075 D_fake: 0.894 +(epoch: 296, iters: 5840, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.824 G_ID: 0.157 G_Rec: 0.409 D_GP: 0.018 D_real: 1.060 D_fake: 0.650 +(epoch: 296, iters: 6240, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.606 G_ID: 0.103 G_Rec: 0.280 D_GP: 0.019 D_real: 1.214 D_fake: 0.738 +(epoch: 296, iters: 6640, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.765 G_ID: 0.178 G_Rec: 0.368 D_GP: 0.027 D_real: 1.060 D_fake: 0.804 +(epoch: 296, iters: 7040, time: 0.064) G_GAN: 0.034 G_GAN_Feat: 0.583 G_ID: 0.140 G_Rec: 0.276 D_GP: 0.026 D_real: 0.937 D_fake: 0.966 +(epoch: 296, iters: 7440, time: 0.064) G_GAN: 0.063 G_GAN_Feat: 0.780 G_ID: 0.184 G_Rec: 0.377 D_GP: 0.037 D_real: 0.791 D_fake: 0.937 +(epoch: 296, iters: 7840, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.653 G_ID: 0.129 G_Rec: 0.293 D_GP: 0.035 D_real: 0.982 D_fake: 0.909 +(epoch: 296, iters: 8240, time: 0.064) G_GAN: 0.139 G_GAN_Feat: 0.756 G_ID: 0.164 G_Rec: 0.370 D_GP: 0.023 D_real: 0.932 D_fake: 0.861 +(epoch: 297, iters: 32, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.675 G_ID: 0.128 G_Rec: 0.300 D_GP: 0.030 D_real: 1.053 D_fake: 0.865 +(epoch: 297, iters: 432, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.854 G_ID: 0.163 G_Rec: 0.399 D_GP: 0.048 D_real: 1.058 D_fake: 0.585 +(epoch: 297, iters: 832, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.940 G_ID: 0.122 G_Rec: 0.344 D_GP: 0.022 D_real: 1.280 D_fake: 0.951 +(epoch: 297, iters: 1232, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.787 G_ID: 0.177 G_Rec: 0.402 D_GP: 0.020 D_real: 0.934 D_fake: 0.821 +(epoch: 297, iters: 1632, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.598 G_ID: 0.133 G_Rec: 0.291 D_GP: 0.019 D_real: 1.106 D_fake: 0.788 +(epoch: 297, iters: 2032, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.796 G_ID: 0.143 G_Rec: 0.398 D_GP: 0.024 D_real: 1.171 D_fake: 0.609 +(epoch: 297, iters: 2432, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.631 G_ID: 0.153 G_Rec: 0.328 D_GP: 0.023 D_real: 1.040 D_fake: 0.902 +(epoch: 297, iters: 2832, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.722 G_ID: 0.162 G_Rec: 0.315 D_GP: 0.035 D_real: 1.241 D_fake: 0.584 +(epoch: 297, iters: 3232, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.706 G_ID: 0.144 G_Rec: 0.287 D_GP: 0.052 D_real: 0.745 D_fake: 1.030 +(epoch: 297, iters: 3632, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.868 G_ID: 0.139 G_Rec: 0.389 D_GP: 0.046 D_real: 0.966 D_fake: 0.681 +(epoch: 297, iters: 4032, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.621 G_ID: 0.105 G_Rec: 0.262 D_GP: 0.035 D_real: 1.236 D_fake: 0.672 +(epoch: 297, iters: 4432, time: 0.064) G_GAN: -0.221 G_GAN_Feat: 1.105 G_ID: 0.201 G_Rec: 0.466 D_GP: 1.106 D_real: 0.257 D_fake: 1.221 +(epoch: 297, iters: 4832, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.719 G_ID: 0.115 G_Rec: 0.311 D_GP: 0.027 D_real: 1.065 D_fake: 0.737 +(epoch: 297, iters: 5232, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.813 G_ID: 0.166 G_Rec: 0.347 D_GP: 0.030 D_real: 1.297 D_fake: 0.466 +(epoch: 297, iters: 5632, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.657 G_ID: 0.110 G_Rec: 0.293 D_GP: 0.026 D_real: 1.184 D_fake: 0.775 +(epoch: 297, iters: 6032, time: 0.064) G_GAN: 0.644 G_GAN_Feat: 1.034 G_ID: 0.154 G_Rec: 0.439 D_GP: 0.077 D_real: 0.854 D_fake: 0.385 +(epoch: 297, iters: 6432, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.687 G_ID: 0.128 G_Rec: 0.286 D_GP: 0.033 D_real: 1.019 D_fake: 0.868 +(epoch: 297, iters: 6832, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.946 G_ID: 0.171 G_Rec: 0.377 D_GP: 0.148 D_real: 0.583 D_fake: 0.735 +(epoch: 297, iters: 7232, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.736 G_ID: 0.131 G_Rec: 0.274 D_GP: 0.027 D_real: 1.053 D_fake: 0.735 +(epoch: 297, iters: 7632, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.895 G_ID: 0.157 G_Rec: 0.419 D_GP: 0.030 D_real: 1.020 D_fake: 0.637 +(epoch: 297, iters: 8032, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.621 G_ID: 0.116 G_Rec: 0.273 D_GP: 0.019 D_real: 1.220 D_fake: 0.711 +(epoch: 297, iters: 8432, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 0.876 G_ID: 0.187 G_Rec: 0.407 D_GP: 0.034 D_real: 1.275 D_fake: 0.373 +(epoch: 298, iters: 224, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.660 G_ID: 0.113 G_Rec: 0.290 D_GP: 0.029 D_real: 1.118 D_fake: 0.771 +(epoch: 298, iters: 624, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.936 G_ID: 0.180 G_Rec: 0.386 D_GP: 0.102 D_real: 0.748 D_fake: 0.676 +(epoch: 298, iters: 1024, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.774 G_ID: 0.103 G_Rec: 0.303 D_GP: 0.099 D_real: 0.800 D_fake: 0.723 +(epoch: 298, iters: 1424, time: 0.064) G_GAN: 0.787 G_GAN_Feat: 0.846 G_ID: 0.135 G_Rec: 0.414 D_GP: 0.026 D_real: 1.396 D_fake: 0.270 +(epoch: 298, iters: 1824, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.659 G_ID: 0.133 G_Rec: 0.293 D_GP: 0.035 D_real: 1.145 D_fake: 0.707 +(epoch: 298, iters: 2224, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.890 G_ID: 0.139 G_Rec: 0.385 D_GP: 0.028 D_real: 1.049 D_fake: 0.545 +(epoch: 298, iters: 2624, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.740 G_ID: 0.116 G_Rec: 0.276 D_GP: 0.027 D_real: 0.843 D_fake: 0.749 +(epoch: 298, iters: 3024, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.852 G_ID: 0.163 G_Rec: 0.397 D_GP: 0.035 D_real: 0.771 D_fake: 0.931 +(epoch: 298, iters: 3424, time: 0.064) G_GAN: -0.169 G_GAN_Feat: 0.733 G_ID: 0.123 G_Rec: 0.270 D_GP: 0.027 D_real: 0.654 D_fake: 1.169 +(epoch: 298, iters: 3824, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.928 G_ID: 0.160 G_Rec: 0.415 D_GP: 0.025 D_real: 1.107 D_fake: 0.507 +(epoch: 298, iters: 4224, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.782 G_ID: 0.150 G_Rec: 0.310 D_GP: 0.030 D_real: 0.961 D_fake: 0.755 +(epoch: 298, iters: 4624, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.878 G_ID: 0.147 G_Rec: 0.381 D_GP: 0.030 D_real: 1.163 D_fake: 0.521 +(epoch: 298, iters: 5024, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.736 G_ID: 0.152 G_Rec: 0.356 D_GP: 0.026 D_real: 0.972 D_fake: 0.926 +(epoch: 298, iters: 5424, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.830 G_ID: 0.192 G_Rec: 0.359 D_GP: 0.023 D_real: 0.834 D_fake: 0.864 +(epoch: 298, iters: 5824, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.756 G_ID: 0.132 G_Rec: 0.311 D_GP: 0.048 D_real: 0.996 D_fake: 0.785 +(epoch: 298, iters: 6224, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.901 G_ID: 0.158 G_Rec: 0.411 D_GP: 0.027 D_real: 1.076 D_fake: 0.590 +(epoch: 298, iters: 6624, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.704 G_ID: 0.121 G_Rec: 0.255 D_GP: 0.039 D_real: 0.935 D_fake: 0.830 +(epoch: 298, iters: 7024, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 1.070 G_ID: 0.169 G_Rec: 0.424 D_GP: 0.194 D_real: 0.442 D_fake: 0.817 +(epoch: 298, iters: 7424, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.638 G_ID: 0.132 G_Rec: 0.300 D_GP: 0.021 D_real: 1.087 D_fake: 0.860 +(epoch: 298, iters: 7824, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.918 G_ID: 0.177 G_Rec: 0.432 D_GP: 0.043 D_real: 0.733 D_fake: 0.864 +(epoch: 298, iters: 8224, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.678 G_ID: 0.123 G_Rec: 0.274 D_GP: 0.043 D_real: 0.917 D_fake: 0.901 +(epoch: 299, iters: 16, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.879 G_ID: 0.152 G_Rec: 0.349 D_GP: 0.031 D_real: 0.918 D_fake: 0.642 +(epoch: 299, iters: 416, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.653 G_ID: 0.133 G_Rec: 0.271 D_GP: 0.029 D_real: 1.036 D_fake: 0.845 +(epoch: 299, iters: 816, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.880 G_ID: 0.165 G_Rec: 0.372 D_GP: 0.025 D_real: 1.067 D_fake: 0.531 +(epoch: 299, iters: 1216, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.704 G_ID: 0.105 G_Rec: 0.267 D_GP: 0.025 D_real: 0.974 D_fake: 0.810 +(epoch: 299, iters: 1616, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 1.013 G_ID: 0.169 G_Rec: 0.374 D_GP: 0.041 D_real: 0.709 D_fake: 0.757 +(epoch: 299, iters: 2016, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.706 G_ID: 0.119 G_Rec: 0.278 D_GP: 0.023 D_real: 1.099 D_fake: 0.764 +(epoch: 299, iters: 2416, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.831 G_ID: 0.155 G_Rec: 0.376 D_GP: 0.024 D_real: 1.029 D_fake: 0.756 +(epoch: 299, iters: 2816, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.702 G_ID: 0.140 G_Rec: 0.264 D_GP: 0.026 D_real: 1.128 D_fake: 0.752 +(epoch: 299, iters: 3216, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.773 G_ID: 0.171 G_Rec: 0.362 D_GP: 0.020 D_real: 1.175 D_fake: 0.636 +(epoch: 299, iters: 3616, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.726 G_ID: 0.128 G_Rec: 0.292 D_GP: 0.030 D_real: 0.876 D_fake: 0.968 +(epoch: 299, iters: 4016, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.827 G_ID: 0.164 G_Rec: 0.365 D_GP: 0.025 D_real: 0.941 D_fake: 0.821 +(epoch: 299, iters: 4416, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.704 G_ID: 0.109 G_Rec: 0.283 D_GP: 0.038 D_real: 1.090 D_fake: 0.730 +(epoch: 299, iters: 4816, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.799 G_ID: 0.144 G_Rec: 0.363 D_GP: 0.021 D_real: 1.185 D_fake: 0.573 +(epoch: 299, iters: 5216, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.975 G_ID: 0.121 G_Rec: 0.319 D_GP: 0.041 D_real: 1.169 D_fake: 0.855 +(epoch: 299, iters: 5616, time: 0.064) G_GAN: 0.917 G_GAN_Feat: 1.094 G_ID: 0.165 G_Rec: 0.443 D_GP: 0.097 D_real: 1.350 D_fake: 0.368 +(epoch: 299, iters: 6016, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.707 G_ID: 0.113 G_Rec: 0.280 D_GP: 0.040 D_real: 1.152 D_fake: 0.581 +(epoch: 299, iters: 6416, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 1.107 G_ID: 0.188 G_Rec: 0.446 D_GP: 0.104 D_real: 0.365 D_fake: 0.632 +(epoch: 299, iters: 6816, time: 0.064) G_GAN: -0.078 G_GAN_Feat: 0.605 G_ID: 0.135 G_Rec: 0.277 D_GP: 0.019 D_real: 0.902 D_fake: 1.078 +(epoch: 299, iters: 7216, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.846 G_ID: 0.178 G_Rec: 0.400 D_GP: 0.025 D_real: 0.952 D_fake: 0.752 +(epoch: 299, iters: 7616, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.658 G_ID: 0.112 G_Rec: 0.302 D_GP: 0.034 D_real: 0.946 D_fake: 0.883 +(epoch: 299, iters: 8016, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.783 G_ID: 0.178 G_Rec: 0.371 D_GP: 0.021 D_real: 1.236 D_fake: 0.609 +(epoch: 299, iters: 8416, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.905 G_ID: 0.140 G_Rec: 0.337 D_GP: 0.115 D_real: 0.324 D_fake: 0.896 +(epoch: 300, iters: 208, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.866 G_ID: 0.166 G_Rec: 0.389 D_GP: 0.027 D_real: 1.124 D_fake: 0.555 +(epoch: 300, iters: 608, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.843 G_ID: 0.114 G_Rec: 0.327 D_GP: 0.066 D_real: 0.517 D_fake: 0.745 +(epoch: 300, iters: 1008, time: 0.064) G_GAN: -0.031 G_GAN_Feat: 0.777 G_ID: 0.203 G_Rec: 0.357 D_GP: 0.019 D_real: 0.806 D_fake: 1.031 +(epoch: 300, iters: 1408, time: 0.064) G_GAN: -0.008 G_GAN_Feat: 0.613 G_ID: 0.124 G_Rec: 0.269 D_GP: 0.018 D_real: 0.945 D_fake: 1.008 +(epoch: 300, iters: 1808, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.755 G_ID: 0.167 G_Rec: 0.371 D_GP: 0.019 D_real: 0.801 D_fake: 1.030 +(epoch: 300, iters: 2208, time: 0.064) G_GAN: -0.094 G_GAN_Feat: 0.680 G_ID: 0.116 G_Rec: 0.300 D_GP: 0.027 D_real: 0.727 D_fake: 1.094 +(epoch: 300, iters: 2608, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.863 G_ID: 0.152 G_Rec: 0.422 D_GP: 0.036 D_real: 1.144 D_fake: 0.576 +(epoch: 300, iters: 3008, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.712 G_ID: 0.151 G_Rec: 0.288 D_GP: 0.027 D_real: 0.906 D_fake: 0.850 +(epoch: 300, iters: 3408, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.877 G_ID: 0.174 G_Rec: 0.389 D_GP: 0.038 D_real: 1.000 D_fake: 0.614 +(epoch: 300, iters: 3808, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.705 G_ID: 0.112 G_Rec: 0.300 D_GP: 0.037 D_real: 1.135 D_fake: 0.703 +(epoch: 300, iters: 4208, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.881 G_ID: 0.165 G_Rec: 0.393 D_GP: 0.031 D_real: 1.135 D_fake: 0.662 +(epoch: 300, iters: 4608, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.764 G_ID: 0.123 G_Rec: 0.312 D_GP: 0.037 D_real: 1.222 D_fake: 0.491 +(epoch: 300, iters: 5008, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.971 G_ID: 0.166 G_Rec: 0.410 D_GP: 0.069 D_real: 0.706 D_fake: 0.659 +(epoch: 300, iters: 5408, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.705 G_ID: 0.124 G_Rec: 0.281 D_GP: 0.029 D_real: 1.175 D_fake: 0.661 +(epoch: 300, iters: 5808, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.956 G_ID: 0.179 G_Rec: 0.404 D_GP: 0.042 D_real: 0.744 D_fake: 0.801 +(epoch: 300, iters: 6208, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.815 G_ID: 0.131 G_Rec: 0.289 D_GP: 0.038 D_real: 1.044 D_fake: 0.547 +(epoch: 300, iters: 6608, time: 0.064) G_GAN: 0.711 G_GAN_Feat: 0.986 G_ID: 0.155 G_Rec: 0.413 D_GP: 0.039 D_real: 1.024 D_fake: 0.330 +(epoch: 300, iters: 7008, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.856 G_ID: 0.125 G_Rec: 0.291 D_GP: 0.047 D_real: 0.700 D_fake: 0.530 +(epoch: 300, iters: 7408, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.805 G_ID: 0.174 G_Rec: 0.382 D_GP: 0.022 D_real: 1.228 D_fake: 0.499 +(epoch: 300, iters: 7808, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.789 G_ID: 0.120 G_Rec: 0.311 D_GP: 0.116 D_real: 0.751 D_fake: 0.908 +(epoch: 300, iters: 8208, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.813 G_ID: 0.138 G_Rec: 0.378 D_GP: 0.023 D_real: 0.958 D_fake: 0.819 +(epoch: 300, iters: 8608, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.633 G_ID: 0.116 G_Rec: 0.282 D_GP: 0.023 D_real: 1.149 D_fake: 0.743 +(epoch: 301, iters: 400, time: 0.064) G_GAN: 0.597 G_GAN_Feat: 0.908 G_ID: 0.144 G_Rec: 0.399 D_GP: 0.027 D_real: 1.318 D_fake: 0.410 +(epoch: 301, iters: 800, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.757 G_ID: 0.117 G_Rec: 0.295 D_GP: 0.056 D_real: 0.995 D_fake: 0.868 +(epoch: 301, iters: 1200, time: 0.064) G_GAN: 0.637 G_GAN_Feat: 0.881 G_ID: 0.156 G_Rec: 0.341 D_GP: 0.026 D_real: 1.305 D_fake: 0.388 +(epoch: 301, iters: 1600, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 0.796 G_ID: 0.101 G_Rec: 0.305 D_GP: 0.025 D_real: 1.458 D_fake: 0.466 +(epoch: 301, iters: 2000, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.902 G_ID: 0.145 G_Rec: 0.378 D_GP: 0.021 D_real: 1.092 D_fake: 0.656 +(epoch: 301, iters: 2400, time: 0.064) G_GAN: 0.165 G_GAN_Feat: 0.648 G_ID: 0.114 G_Rec: 0.270 D_GP: 0.020 D_real: 1.098 D_fake: 0.835 +(epoch: 301, iters: 2800, time: 0.064) G_GAN: 0.625 G_GAN_Feat: 0.888 G_ID: 0.176 G_Rec: 0.408 D_GP: 0.032 D_real: 1.455 D_fake: 0.386 +(epoch: 301, iters: 3200, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.604 G_ID: 0.127 G_Rec: 0.280 D_GP: 0.024 D_real: 0.944 D_fake: 0.989 +(epoch: 301, iters: 3600, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.833 G_ID: 0.186 G_Rec: 0.417 D_GP: 0.032 D_real: 1.038 D_fake: 0.714 +(epoch: 301, iters: 4000, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.686 G_ID: 0.122 G_Rec: 0.294 D_GP: 0.040 D_real: 0.808 D_fake: 0.938 +(epoch: 301, iters: 4400, time: 0.064) G_GAN: 0.704 G_GAN_Feat: 0.960 G_ID: 0.144 G_Rec: 0.380 D_GP: 0.207 D_real: 1.058 D_fake: 0.321 +(epoch: 301, iters: 4800, time: 0.064) G_GAN: 0.007 G_GAN_Feat: 0.675 G_ID: 0.127 G_Rec: 0.292 D_GP: 0.020 D_real: 1.027 D_fake: 0.993 +(epoch: 301, iters: 5200, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.846 G_ID: 0.189 G_Rec: 0.370 D_GP: 0.030 D_real: 0.925 D_fake: 0.767 +(epoch: 301, iters: 5600, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.626 G_ID: 0.122 G_Rec: 0.276 D_GP: 0.018 D_real: 1.363 D_fake: 0.611 +(epoch: 301, iters: 6000, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.888 G_ID: 0.176 G_Rec: 0.372 D_GP: 0.053 D_real: 0.866 D_fake: 0.622 +(epoch: 301, iters: 6400, time: 0.064) G_GAN: 0.017 G_GAN_Feat: 0.602 G_ID: 0.120 G_Rec: 0.283 D_GP: 0.021 D_real: 1.024 D_fake: 0.983 +(epoch: 301, iters: 6800, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.841 G_ID: 0.163 G_Rec: 0.375 D_GP: 0.045 D_real: 1.013 D_fake: 0.724 +(epoch: 301, iters: 7200, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.717 G_ID: 0.110 G_Rec: 0.310 D_GP: 0.048 D_real: 0.875 D_fake: 0.910 +(epoch: 301, iters: 7600, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.986 G_ID: 0.146 G_Rec: 0.419 D_GP: 0.029 D_real: 0.848 D_fake: 0.633 +(epoch: 301, iters: 8000, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.801 G_ID: 0.123 G_Rec: 0.292 D_GP: 0.028 D_real: 0.751 D_fake: 0.834 +(epoch: 301, iters: 8400, time: 0.064) G_GAN: 0.509 G_GAN_Feat: 0.844 G_ID: 0.170 G_Rec: 0.347 D_GP: 0.041 D_real: 1.292 D_fake: 0.503 +(epoch: 302, iters: 192, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.812 G_ID: 0.124 G_Rec: 0.300 D_GP: 0.029 D_real: 0.897 D_fake: 0.748 +(epoch: 302, iters: 592, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.915 G_ID: 0.142 G_Rec: 0.361 D_GP: 0.024 D_real: 0.788 D_fake: 0.744 +(epoch: 302, iters: 992, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.716 G_ID: 0.113 G_Rec: 0.316 D_GP: 0.025 D_real: 1.279 D_fake: 0.637 +(epoch: 302, iters: 1392, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 1.158 G_ID: 0.157 G_Rec: 0.414 D_GP: 0.553 D_real: 0.309 D_fake: 0.612 +(epoch: 302, iters: 1792, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.810 G_ID: 0.113 G_Rec: 0.304 D_GP: 0.030 D_real: 0.881 D_fake: 0.678 +(epoch: 302, iters: 2192, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.836 G_ID: 0.145 G_Rec: 0.326 D_GP: 0.024 D_real: 0.951 D_fake: 0.797 +(epoch: 302, iters: 2592, time: 0.064) G_GAN: -0.098 G_GAN_Feat: 0.872 G_ID: 0.138 G_Rec: 0.315 D_GP: 0.022 D_real: 0.501 D_fake: 1.098 +(epoch: 302, iters: 2992, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 1.042 G_ID: 0.171 G_Rec: 0.412 D_GP: 0.034 D_real: 0.442 D_fake: 0.733 +(epoch: 302, iters: 3392, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.567 G_ID: 0.128 G_Rec: 0.261 D_GP: 0.016 D_real: 1.372 D_fake: 0.604 +(epoch: 302, iters: 3792, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 0.802 G_ID: 0.153 G_Rec: 0.355 D_GP: 0.022 D_real: 1.216 D_fake: 0.490 +(epoch: 302, iters: 4192, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.615 G_ID: 0.126 G_Rec: 0.285 D_GP: 0.023 D_real: 1.021 D_fake: 0.865 +(epoch: 302, iters: 4592, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 1.033 G_ID: 0.169 G_Rec: 0.415 D_GP: 0.100 D_real: 0.536 D_fake: 0.517 +(epoch: 302, iters: 4992, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.697 G_ID: 0.129 G_Rec: 0.294 D_GP: 0.030 D_real: 1.134 D_fake: 0.720 +(epoch: 302, iters: 5392, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.946 G_ID: 0.134 G_Rec: 0.409 D_GP: 0.038 D_real: 1.019 D_fake: 0.767 +(epoch: 302, iters: 5792, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 1.010 G_ID: 0.138 G_Rec: 0.318 D_GP: 0.046 D_real: 1.284 D_fake: 0.610 +(epoch: 302, iters: 6192, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.822 G_ID: 0.175 G_Rec: 0.357 D_GP: 0.027 D_real: 1.018 D_fake: 0.758 +(epoch: 302, iters: 6592, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.806 G_ID: 0.128 G_Rec: 0.321 D_GP: 0.039 D_real: 0.844 D_fake: 0.770 +(epoch: 302, iters: 6992, time: 0.064) G_GAN: 1.149 G_GAN_Feat: 1.001 G_ID: 0.162 G_Rec: 0.452 D_GP: 0.104 D_real: 1.608 D_fake: 0.274 +(epoch: 302, iters: 7392, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.625 G_ID: 0.155 G_Rec: 0.274 D_GP: 0.022 D_real: 0.962 D_fake: 0.915 +(epoch: 302, iters: 7792, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.885 G_ID: 0.172 G_Rec: 0.461 D_GP: 0.028 D_real: 0.937 D_fake: 0.723 +(epoch: 302, iters: 8192, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.660 G_ID: 0.110 G_Rec: 0.285 D_GP: 0.030 D_real: 0.966 D_fake: 0.842 +(epoch: 302, iters: 8592, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 0.985 G_ID: 0.143 G_Rec: 0.421 D_GP: 0.047 D_real: 0.864 D_fake: 0.482 +(epoch: 303, iters: 384, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.863 G_ID: 0.135 G_Rec: 0.306 D_GP: 0.048 D_real: 0.477 D_fake: 0.888 +(epoch: 303, iters: 784, time: 0.064) G_GAN: 0.722 G_GAN_Feat: 1.021 G_ID: 0.191 G_Rec: 0.410 D_GP: 0.085 D_real: 0.898 D_fake: 0.535 +(epoch: 303, iters: 1184, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.912 G_ID: 0.098 G_Rec: 0.309 D_GP: 0.253 D_real: 0.360 D_fake: 0.792 +(epoch: 303, iters: 1584, time: 0.064) G_GAN: 0.628 G_GAN_Feat: 1.083 G_ID: 0.191 G_Rec: 0.435 D_GP: 0.042 D_real: 0.924 D_fake: 0.431 +(epoch: 303, iters: 1984, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.968 G_ID: 0.127 G_Rec: 0.282 D_GP: 0.059 D_real: 0.499 D_fake: 0.606 +(epoch: 303, iters: 2384, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.988 G_ID: 0.167 G_Rec: 0.426 D_GP: 0.022 D_real: 1.358 D_fake: 0.428 +(epoch: 303, iters: 2784, time: 0.064) G_GAN: -0.022 G_GAN_Feat: 0.799 G_ID: 0.153 G_Rec: 0.297 D_GP: 0.060 D_real: 0.605 D_fake: 1.022 +(epoch: 303, iters: 3184, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.994 G_ID: 0.143 G_Rec: 0.404 D_GP: 0.163 D_real: 0.475 D_fake: 0.620 +(epoch: 303, iters: 3584, time: 0.064) G_GAN: 0.997 G_GAN_Feat: 0.787 G_ID: 0.124 G_Rec: 0.331 D_GP: 0.366 D_real: 1.473 D_fake: 0.482 +(epoch: 303, iters: 3984, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.778 G_ID: 0.140 G_Rec: 0.383 D_GP: 0.020 D_real: 1.194 D_fake: 0.680 +(epoch: 303, iters: 4384, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.659 G_ID: 0.131 G_Rec: 0.304 D_GP: 0.025 D_real: 1.090 D_fake: 0.789 +(epoch: 303, iters: 4784, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.825 G_ID: 0.166 G_Rec: 0.383 D_GP: 0.034 D_real: 1.029 D_fake: 0.698 +(epoch: 303, iters: 5184, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.688 G_ID: 0.104 G_Rec: 0.315 D_GP: 0.037 D_real: 1.027 D_fake: 0.824 +(epoch: 303, iters: 5584, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.793 G_ID: 0.148 G_Rec: 0.360 D_GP: 0.026 D_real: 1.094 D_fake: 0.752 +(epoch: 303, iters: 5984, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.649 G_ID: 0.141 G_Rec: 0.296 D_GP: 0.030 D_real: 1.006 D_fake: 0.907 +(epoch: 303, iters: 6384, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.893 G_ID: 0.173 G_Rec: 0.434 D_GP: 0.082 D_real: 0.708 D_fake: 0.826 +(epoch: 303, iters: 6784, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.697 G_ID: 0.143 G_Rec: 0.304 D_GP: 0.026 D_real: 1.050 D_fake: 0.918 +(epoch: 303, iters: 7184, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.817 G_ID: 0.150 G_Rec: 0.346 D_GP: 0.028 D_real: 1.106 D_fake: 0.678 +(epoch: 303, iters: 7584, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.726 G_ID: 0.117 G_Rec: 0.317 D_GP: 0.061 D_real: 1.022 D_fake: 0.830 +(epoch: 303, iters: 7984, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.819 G_ID: 0.146 G_Rec: 0.394 D_GP: 0.025 D_real: 1.256 D_fake: 0.522 +(epoch: 303, iters: 8384, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.777 G_ID: 0.131 G_Rec: 0.359 D_GP: 0.130 D_real: 0.780 D_fake: 0.771 +(epoch: 304, iters: 176, time: 0.064) G_GAN: 0.668 G_GAN_Feat: 0.874 G_ID: 0.171 G_Rec: 0.375 D_GP: 0.054 D_real: 1.139 D_fake: 0.358 +(epoch: 304, iters: 576, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.543 G_ID: 0.130 G_Rec: 0.292 D_GP: 0.018 D_real: 1.161 D_fake: 0.845 +(epoch: 304, iters: 976, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.717 G_ID: 0.141 G_Rec: 0.346 D_GP: 0.018 D_real: 1.265 D_fake: 0.609 +(epoch: 304, iters: 1376, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.591 G_ID: 0.122 G_Rec: 0.273 D_GP: 0.024 D_real: 1.087 D_fake: 0.801 +(epoch: 304, iters: 1776, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.774 G_ID: 0.168 G_Rec: 0.371 D_GP: 0.029 D_real: 1.114 D_fake: 0.559 +(epoch: 304, iters: 2176, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.691 G_ID: 0.118 G_Rec: 0.301 D_GP: 0.036 D_real: 1.089 D_fake: 0.751 +(epoch: 304, iters: 2576, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.924 G_ID: 0.147 G_Rec: 0.399 D_GP: 0.106 D_real: 0.723 D_fake: 0.607 +(epoch: 304, iters: 2976, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.685 G_ID: 0.100 G_Rec: 0.304 D_GP: 0.076 D_real: 1.088 D_fake: 0.697 +(epoch: 304, iters: 3376, time: 0.064) G_GAN: 0.044 G_GAN_Feat: 0.862 G_ID: 0.183 G_Rec: 0.416 D_GP: 0.030 D_real: 0.736 D_fake: 0.956 +(epoch: 304, iters: 3776, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.694 G_ID: 0.120 G_Rec: 0.295 D_GP: 0.029 D_real: 0.987 D_fake: 0.871 +(epoch: 304, iters: 4176, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.816 G_ID: 0.166 G_Rec: 0.369 D_GP: 0.030 D_real: 1.090 D_fake: 0.624 +(epoch: 304, iters: 4576, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.776 G_ID: 0.121 G_Rec: 0.289 D_GP: 0.088 D_real: 0.822 D_fake: 0.749 +(epoch: 304, iters: 4976, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 1.402 G_ID: 0.166 G_Rec: 0.506 D_GP: 1.087 D_real: 0.459 D_fake: 0.607 +(epoch: 304, iters: 5376, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.797 G_ID: 0.126 G_Rec: 0.319 D_GP: 0.049 D_real: 0.828 D_fake: 0.846 +(epoch: 304, iters: 5776, time: 0.064) G_GAN: 0.601 G_GAN_Feat: 0.972 G_ID: 0.140 G_Rec: 0.421 D_GP: 0.103 D_real: 0.883 D_fake: 0.416 +(epoch: 304, iters: 6176, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.766 G_ID: 0.117 G_Rec: 0.280 D_GP: 0.078 D_real: 0.666 D_fake: 0.923 +(epoch: 304, iters: 6576, time: 0.064) G_GAN: 0.636 G_GAN_Feat: 1.085 G_ID: 0.152 G_Rec: 0.447 D_GP: 0.065 D_real: 0.721 D_fake: 0.383 +(epoch: 304, iters: 6976, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.778 G_ID: 0.123 G_Rec: 0.286 D_GP: 0.031 D_real: 0.855 D_fake: 0.887 +(epoch: 304, iters: 7376, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.989 G_ID: 0.157 G_Rec: 0.411 D_GP: 0.028 D_real: 1.061 D_fake: 0.494 +(epoch: 304, iters: 7776, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.776 G_ID: 0.135 G_Rec: 0.282 D_GP: 0.032 D_real: 0.778 D_fake: 0.829 +(epoch: 304, iters: 8176, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.887 G_ID: 0.193 G_Rec: 0.375 D_GP: 0.026 D_real: 0.952 D_fake: 0.742 +(epoch: 304, iters: 8576, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.901 G_ID: 0.136 G_Rec: 0.307 D_GP: 0.123 D_real: 0.443 D_fake: 0.726 +(epoch: 305, iters: 368, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.782 G_ID: 0.172 G_Rec: 0.410 D_GP: 0.019 D_real: 1.223 D_fake: 0.614 +(epoch: 305, iters: 768, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.554 G_ID: 0.104 G_Rec: 0.272 D_GP: 0.021 D_real: 1.220 D_fake: 0.809 +(epoch: 305, iters: 1168, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.777 G_ID: 0.155 G_Rec: 0.363 D_GP: 0.029 D_real: 0.999 D_fake: 0.748 +(epoch: 305, iters: 1568, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.550 G_ID: 0.103 G_Rec: 0.267 D_GP: 0.018 D_real: 1.164 D_fake: 0.851 +(epoch: 305, iters: 1968, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 0.773 G_ID: 0.148 G_Rec: 0.359 D_GP: 0.020 D_real: 1.265 D_fake: 0.565 +(epoch: 305, iters: 2368, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.677 G_ID: 0.146 G_Rec: 0.308 D_GP: 0.033 D_real: 0.906 D_fake: 0.906 +(epoch: 305, iters: 2768, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.903 G_ID: 0.140 G_Rec: 0.392 D_GP: 0.037 D_real: 0.952 D_fake: 0.649 +(epoch: 305, iters: 3168, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.742 G_ID: 0.094 G_Rec: 0.309 D_GP: 0.125 D_real: 0.799 D_fake: 0.850 +(epoch: 305, iters: 3568, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.905 G_ID: 0.179 G_Rec: 0.405 D_GP: 0.024 D_real: 1.246 D_fake: 0.468 +(epoch: 305, iters: 3968, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.719 G_ID: 0.145 G_Rec: 0.304 D_GP: 0.052 D_real: 1.034 D_fake: 0.693 +(epoch: 305, iters: 4368, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 0.810 G_ID: 0.160 G_Rec: 0.383 D_GP: 0.025 D_real: 1.429 D_fake: 0.426 +(epoch: 305, iters: 4768, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.641 G_ID: 0.139 G_Rec: 0.285 D_GP: 0.046 D_real: 0.862 D_fake: 0.968 +(epoch: 305, iters: 5168, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.796 G_ID: 0.152 G_Rec: 0.382 D_GP: 0.023 D_real: 1.152 D_fake: 0.573 +(epoch: 305, iters: 5568, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.908 G_ID: 0.153 G_Rec: 0.346 D_GP: 0.398 D_real: 0.576 D_fake: 0.665 +(epoch: 305, iters: 5968, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.897 G_ID: 0.179 G_Rec: 0.414 D_GP: 0.025 D_real: 0.875 D_fake: 0.803 +(epoch: 305, iters: 6368, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.727 G_ID: 0.119 G_Rec: 0.310 D_GP: 0.022 D_real: 1.144 D_fake: 0.752 +(epoch: 305, iters: 6768, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.792 G_ID: 0.166 G_Rec: 0.351 D_GP: 0.021 D_real: 1.260 D_fake: 0.520 +(epoch: 305, iters: 7168, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.787 G_ID: 0.118 G_Rec: 0.288 D_GP: 0.060 D_real: 0.486 D_fake: 0.915 +(epoch: 305, iters: 7568, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 0.839 G_ID: 0.168 G_Rec: 0.375 D_GP: 0.023 D_real: 1.232 D_fake: 0.520 +(epoch: 305, iters: 7968, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.849 G_ID: 0.135 G_Rec: 0.325 D_GP: 0.070 D_real: 0.853 D_fake: 0.604 +(epoch: 305, iters: 8368, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.919 G_ID: 0.172 G_Rec: 0.357 D_GP: 0.027 D_real: 1.199 D_fake: 0.570 +(epoch: 306, iters: 160, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.733 G_ID: 0.109 G_Rec: 0.286 D_GP: 0.024 D_real: 0.988 D_fake: 0.840 +(epoch: 306, iters: 560, time: 0.064) G_GAN: 0.990 G_GAN_Feat: 1.099 G_ID: 0.143 G_Rec: 0.454 D_GP: 0.024 D_real: 1.433 D_fake: 0.251 +(epoch: 306, iters: 960, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 1.023 G_ID: 0.132 G_Rec: 0.323 D_GP: 0.629 D_real: 0.455 D_fake: 0.637 +(epoch: 306, iters: 1360, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.856 G_ID: 0.150 G_Rec: 0.348 D_GP: 0.021 D_real: 1.042 D_fake: 0.651 +(epoch: 306, iters: 1760, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.727 G_ID: 0.111 G_Rec: 0.285 D_GP: 0.027 D_real: 0.979 D_fake: 0.855 +(epoch: 306, iters: 2160, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.756 G_ID: 0.188 G_Rec: 0.320 D_GP: 0.025 D_real: 1.103 D_fake: 0.754 +(epoch: 306, iters: 2560, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.680 G_ID: 0.125 G_Rec: 0.288 D_GP: 0.023 D_real: 1.173 D_fake: 0.786 +(epoch: 306, iters: 2960, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 1.235 G_ID: 0.171 G_Rec: 0.429 D_GP: 0.068 D_real: 0.527 D_fake: 0.432 +(epoch: 306, iters: 3360, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.535 G_ID: 0.129 G_Rec: 0.306 D_GP: 0.018 D_real: 1.198 D_fake: 0.808 +(epoch: 306, iters: 3760, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.739 G_ID: 0.159 G_Rec: 0.388 D_GP: 0.019 D_real: 1.257 D_fake: 0.577 +(epoch: 306, iters: 4160, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.558 G_ID: 0.125 G_Rec: 0.287 D_GP: 0.032 D_real: 1.108 D_fake: 0.814 +(epoch: 306, iters: 4560, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.715 G_ID: 0.155 G_Rec: 0.378 D_GP: 0.022 D_real: 1.173 D_fake: 0.583 +(epoch: 306, iters: 4960, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.557 G_ID: 0.120 G_Rec: 0.285 D_GP: 0.024 D_real: 1.260 D_fake: 0.711 +(epoch: 306, iters: 5360, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.737 G_ID: 0.157 G_Rec: 0.373 D_GP: 0.024 D_real: 1.098 D_fake: 0.697 +(epoch: 306, iters: 5760, time: 0.064) G_GAN: 0.060 G_GAN_Feat: 0.553 G_ID: 0.134 G_Rec: 0.266 D_GP: 0.024 D_real: 0.988 D_fake: 0.940 +(epoch: 306, iters: 6160, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.808 G_ID: 0.199 G_Rec: 0.387 D_GP: 0.035 D_real: 0.996 D_fake: 0.636 +(epoch: 306, iters: 6560, time: 0.064) G_GAN: 0.033 G_GAN_Feat: 0.587 G_ID: 0.114 G_Rec: 0.260 D_GP: 0.038 D_real: 0.913 D_fake: 0.967 +(epoch: 306, iters: 6960, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.790 G_ID: 0.145 G_Rec: 0.389 D_GP: 0.046 D_real: 1.007 D_fake: 0.736 +(epoch: 306, iters: 7360, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.676 G_ID: 0.137 G_Rec: 0.304 D_GP: 0.034 D_real: 1.017 D_fake: 0.879 +(epoch: 306, iters: 7760, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.815 G_ID: 0.150 G_Rec: 0.375 D_GP: 0.030 D_real: 0.907 D_fake: 0.739 +(epoch: 306, iters: 8160, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.658 G_ID: 0.119 G_Rec: 0.311 D_GP: 0.038 D_real: 1.142 D_fake: 0.691 +(epoch: 306, iters: 8560, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.891 G_ID: 0.168 G_Rec: 0.378 D_GP: 0.037 D_real: 1.102 D_fake: 0.489 +(epoch: 307, iters: 352, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.644 G_ID: 0.121 G_Rec: 0.317 D_GP: 0.024 D_real: 1.000 D_fake: 0.827 +(epoch: 307, iters: 752, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 0.814 G_ID: 0.158 G_Rec: 0.382 D_GP: 0.024 D_real: 1.273 D_fake: 0.457 +(epoch: 307, iters: 1152, time: 0.064) G_GAN: -0.025 G_GAN_Feat: 0.784 G_ID: 0.158 G_Rec: 0.334 D_GP: 0.182 D_real: 0.506 D_fake: 1.025 +(epoch: 307, iters: 1552, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 1.010 G_ID: 0.153 G_Rec: 0.397 D_GP: 0.219 D_real: 0.335 D_fake: 0.780 +(epoch: 307, iters: 1952, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.718 G_ID: 0.115 G_Rec: 0.260 D_GP: 0.070 D_real: 0.815 D_fake: 0.722 +(epoch: 307, iters: 2352, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.888 G_ID: 0.160 G_Rec: 0.332 D_GP: 0.031 D_real: 0.985 D_fake: 0.781 +(epoch: 307, iters: 2752, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.841 G_ID: 0.123 G_Rec: 0.307 D_GP: 0.024 D_real: 0.909 D_fake: 0.833 +(epoch: 307, iters: 3152, time: 0.064) G_GAN: -0.090 G_GAN_Feat: 1.281 G_ID: 0.148 G_Rec: 0.450 D_GP: 0.051 D_real: 1.212 D_fake: 1.093 +(epoch: 307, iters: 3552, time: 0.064) G_GAN: -0.280 G_GAN_Feat: 0.760 G_ID: 0.123 G_Rec: 0.318 D_GP: 0.020 D_real: 0.644 D_fake: 1.280 +(epoch: 307, iters: 3952, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.789 G_ID: 0.156 G_Rec: 0.359 D_GP: 0.021 D_real: 0.939 D_fake: 0.904 +(epoch: 307, iters: 4352, time: 0.064) G_GAN: -0.034 G_GAN_Feat: 0.663 G_ID: 0.112 G_Rec: 0.308 D_GP: 0.023 D_real: 0.855 D_fake: 1.034 +(epoch: 307, iters: 4752, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.840 G_ID: 0.184 G_Rec: 0.342 D_GP: 0.038 D_real: 0.784 D_fake: 0.949 +(epoch: 307, iters: 5152, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.875 G_ID: 0.135 G_Rec: 0.312 D_GP: 0.201 D_real: 0.469 D_fake: 0.901 +(epoch: 307, iters: 5552, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.907 G_ID: 0.151 G_Rec: 0.430 D_GP: 0.043 D_real: 1.206 D_fake: 0.439 +(epoch: 307, iters: 5952, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.614 G_ID: 0.119 G_Rec: 0.269 D_GP: 0.021 D_real: 1.308 D_fake: 0.661 +(epoch: 307, iters: 6352, time: 0.064) G_GAN: 0.520 G_GAN_Feat: 1.062 G_ID: 0.150 G_Rec: 0.440 D_GP: 0.173 D_real: 0.592 D_fake: 0.494 +(epoch: 307, iters: 6752, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.897 G_ID: 0.118 G_Rec: 0.318 D_GP: 0.024 D_real: 0.789 D_fake: 0.609 +(epoch: 307, iters: 7152, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.965 G_ID: 0.160 G_Rec: 0.396 D_GP: 0.109 D_real: 0.674 D_fake: 0.754 +(epoch: 307, iters: 7552, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.739 G_ID: 0.121 G_Rec: 0.311 D_GP: 0.023 D_real: 1.253 D_fake: 0.636 +(epoch: 307, iters: 7952, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.959 G_ID: 0.161 G_Rec: 0.382 D_GP: 0.032 D_real: 0.937 D_fake: 0.572 +(epoch: 307, iters: 8352, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.787 G_ID: 0.134 G_Rec: 0.268 D_GP: 0.032 D_real: 0.846 D_fake: 0.675 +(epoch: 308, iters: 144, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.824 G_ID: 0.145 G_Rec: 0.374 D_GP: 0.021 D_real: 1.245 D_fake: 0.531 +(epoch: 308, iters: 544, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.644 G_ID: 0.156 G_Rec: 0.303 D_GP: 0.018 D_real: 1.139 D_fake: 0.797 +(epoch: 308, iters: 944, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.883 G_ID: 0.151 G_Rec: 0.382 D_GP: 0.026 D_real: 1.085 D_fake: 0.615 +(epoch: 308, iters: 1344, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.620 G_ID: 0.115 G_Rec: 0.290 D_GP: 0.024 D_real: 1.269 D_fake: 0.642 +(epoch: 308, iters: 1744, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.788 G_ID: 0.151 G_Rec: 0.351 D_GP: 0.031 D_real: 1.105 D_fake: 0.667 +(epoch: 308, iters: 2144, time: 0.064) G_GAN: -0.406 G_GAN_Feat: 0.635 G_ID: 0.140 G_Rec: 0.316 D_GP: 0.020 D_real: 0.609 D_fake: 1.406 +(epoch: 308, iters: 2544, time: 0.064) G_GAN: 0.065 G_GAN_Feat: 0.767 G_ID: 0.149 G_Rec: 0.355 D_GP: 0.021 D_real: 0.813 D_fake: 0.937 +(epoch: 308, iters: 2944, time: 0.064) G_GAN: -0.096 G_GAN_Feat: 0.602 G_ID: 0.122 G_Rec: 0.267 D_GP: 0.022 D_real: 0.834 D_fake: 1.099 +(epoch: 308, iters: 3344, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 0.909 G_ID: 0.169 G_Rec: 0.399 D_GP: 0.049 D_real: 0.559 D_fake: 0.990 +(epoch: 308, iters: 3744, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.669 G_ID: 0.130 G_Rec: 0.279 D_GP: 0.037 D_real: 0.834 D_fake: 0.942 +(epoch: 308, iters: 4144, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.871 G_ID: 0.135 G_Rec: 0.379 D_GP: 0.042 D_real: 0.999 D_fake: 0.489 +(epoch: 308, iters: 4544, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.715 G_ID: 0.121 G_Rec: 0.283 D_GP: 0.037 D_real: 1.242 D_fake: 0.543 +(epoch: 308, iters: 4944, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.850 G_ID: 0.169 G_Rec: 0.431 D_GP: 0.025 D_real: 1.101 D_fake: 0.711 +(epoch: 308, iters: 5344, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.618 G_ID: 0.114 G_Rec: 0.275 D_GP: 0.021 D_real: 1.133 D_fake: 0.782 +(epoch: 308, iters: 5744, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.873 G_ID: 0.157 G_Rec: 0.387 D_GP: 0.036 D_real: 0.952 D_fake: 0.710 +(epoch: 308, iters: 6144, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.611 G_ID: 0.131 G_Rec: 0.264 D_GP: 0.025 D_real: 1.046 D_fake: 0.832 +(epoch: 308, iters: 6544, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.873 G_ID: 0.163 G_Rec: 0.419 D_GP: 0.032 D_real: 1.177 D_fake: 0.511 +(epoch: 308, iters: 6944, time: 0.064) G_GAN: 0.124 G_GAN_Feat: 0.627 G_ID: 0.114 G_Rec: 0.291 D_GP: 0.023 D_real: 1.027 D_fake: 0.876 +(epoch: 308, iters: 7344, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.769 G_ID: 0.139 G_Rec: 0.358 D_GP: 0.024 D_real: 1.137 D_fake: 0.574 +(epoch: 308, iters: 7744, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.578 G_ID: 0.121 G_Rec: 0.275 D_GP: 0.023 D_real: 1.028 D_fake: 0.893 +(epoch: 308, iters: 8144, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.842 G_ID: 0.170 G_Rec: 0.369 D_GP: 0.033 D_real: 1.141 D_fake: 0.516 +(epoch: 308, iters: 8544, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.652 G_ID: 0.119 G_Rec: 0.278 D_GP: 0.052 D_real: 0.941 D_fake: 0.863 +(epoch: 309, iters: 336, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.971 G_ID: 0.161 G_Rec: 0.450 D_GP: 0.153 D_real: 0.790 D_fake: 0.522 +(epoch: 309, iters: 736, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.703 G_ID: 0.124 G_Rec: 0.283 D_GP: 0.066 D_real: 0.799 D_fake: 0.889 +(epoch: 309, iters: 1136, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 0.887 G_ID: 0.147 G_Rec: 0.392 D_GP: 0.040 D_real: 1.222 D_fake: 0.441 +(epoch: 309, iters: 1536, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.714 G_ID: 0.119 G_Rec: 0.260 D_GP: 0.032 D_real: 1.204 D_fake: 0.624 +(epoch: 309, iters: 1936, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.876 G_ID: 0.160 G_Rec: 0.384 D_GP: 0.025 D_real: 1.040 D_fake: 0.637 +(epoch: 309, iters: 2336, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.861 G_ID: 0.128 G_Rec: 0.323 D_GP: 0.169 D_real: 0.598 D_fake: 0.817 +(epoch: 309, iters: 2736, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.934 G_ID: 0.170 G_Rec: 0.404 D_GP: 0.039 D_real: 1.229 D_fake: 0.544 +(epoch: 309, iters: 3136, time: 0.064) G_GAN: 0.633 G_GAN_Feat: 0.861 G_ID: 0.126 G_Rec: 0.310 D_GP: 0.215 D_real: 0.899 D_fake: 0.557 +(epoch: 309, iters: 3536, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.935 G_ID: 0.148 G_Rec: 0.388 D_GP: 0.033 D_real: 1.126 D_fake: 0.603 +(epoch: 309, iters: 3936, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.670 G_ID: 0.124 G_Rec: 0.292 D_GP: 0.025 D_real: 1.164 D_fake: 0.652 +(epoch: 309, iters: 4336, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 1.086 G_ID: 0.158 G_Rec: 0.436 D_GP: 0.168 D_real: 0.509 D_fake: 0.552 +(epoch: 309, iters: 4736, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.767 G_ID: 0.109 G_Rec: 0.297 D_GP: 0.039 D_real: 1.044 D_fake: 0.550 +(epoch: 309, iters: 5136, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.850 G_ID: 0.146 G_Rec: 0.447 D_GP: 0.023 D_real: 1.177 D_fake: 0.643 +(epoch: 309, iters: 5536, time: 0.064) G_GAN: -0.072 G_GAN_Feat: 0.664 G_ID: 0.107 G_Rec: 0.292 D_GP: 0.019 D_real: 0.816 D_fake: 1.072 +(epoch: 309, iters: 5936, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.828 G_ID: 0.136 G_Rec: 0.364 D_GP: 0.025 D_real: 0.933 D_fake: 0.785 +(epoch: 309, iters: 6336, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.654 G_ID: 0.126 G_Rec: 0.287 D_GP: 0.025 D_real: 0.965 D_fake: 0.919 +(epoch: 309, iters: 6736, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.937 G_ID: 0.162 G_Rec: 0.386 D_GP: 0.028 D_real: 0.806 D_fake: 0.750 +(epoch: 309, iters: 7136, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.771 G_ID: 0.121 G_Rec: 0.308 D_GP: 0.031 D_real: 0.920 D_fake: 0.687 +(epoch: 309, iters: 7536, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.872 G_ID: 0.166 G_Rec: 0.358 D_GP: 0.023 D_real: 1.220 D_fake: 0.560 +(epoch: 309, iters: 7936, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.616 G_ID: 0.156 G_Rec: 0.262 D_GP: 0.023 D_real: 1.053 D_fake: 0.808 +(epoch: 309, iters: 8336, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.946 G_ID: 0.162 G_Rec: 0.359 D_GP: 0.053 D_real: 0.886 D_fake: 0.584 +(epoch: 310, iters: 128, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.757 G_ID: 0.138 G_Rec: 0.303 D_GP: 0.053 D_real: 1.131 D_fake: 0.729 +(epoch: 310, iters: 528, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 1.012 G_ID: 0.144 G_Rec: 0.418 D_GP: 0.135 D_real: 0.522 D_fake: 0.757 +(epoch: 310, iters: 928, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.716 G_ID: 0.129 G_Rec: 0.290 D_GP: 0.030 D_real: 1.193 D_fake: 0.623 +(epoch: 310, iters: 1328, time: 0.064) G_GAN: 0.797 G_GAN_Feat: 1.103 G_ID: 0.167 G_Rec: 0.428 D_GP: 0.343 D_real: 0.508 D_fake: 0.352 +(epoch: 310, iters: 1728, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.703 G_ID: 0.139 G_Rec: 0.294 D_GP: 0.028 D_real: 1.247 D_fake: 0.661 +(epoch: 310, iters: 2128, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.751 G_ID: 0.145 G_Rec: 0.313 D_GP: 0.022 D_real: 1.231 D_fake: 0.677 +(epoch: 310, iters: 2528, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.806 G_ID: 0.115 G_Rec: 0.302 D_GP: 0.047 D_real: 1.154 D_fake: 0.686 +(epoch: 310, iters: 2928, time: 0.064) G_GAN: 0.690 G_GAN_Feat: 0.983 G_ID: 0.153 G_Rec: 0.377 D_GP: 0.038 D_real: 1.078 D_fake: 0.367 +(epoch: 310, iters: 3328, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.701 G_ID: 0.102 G_Rec: 0.265 D_GP: 0.032 D_real: 1.091 D_fake: 0.662 +(epoch: 310, iters: 3728, time: 0.064) G_GAN: 0.851 G_GAN_Feat: 0.918 G_ID: 0.152 G_Rec: 0.408 D_GP: 0.068 D_real: 1.521 D_fake: 0.220 +(epoch: 310, iters: 4128, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.621 G_ID: 0.126 G_Rec: 0.267 D_GP: 0.022 D_real: 1.008 D_fake: 0.890 +(epoch: 310, iters: 4528, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.883 G_ID: 0.177 G_Rec: 0.410 D_GP: 0.025 D_real: 1.013 D_fake: 0.646 +(epoch: 310, iters: 4928, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.792 G_ID: 0.138 G_Rec: 0.292 D_GP: 0.067 D_real: 0.836 D_fake: 0.819 +(epoch: 310, iters: 5328, time: 0.064) G_GAN: 0.727 G_GAN_Feat: 0.954 G_ID: 0.158 G_Rec: 0.402 D_GP: 0.021 D_real: 1.398 D_fake: 0.289 +(epoch: 310, iters: 5728, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.703 G_ID: 0.123 G_Rec: 0.281 D_GP: 0.019 D_real: 1.095 D_fake: 0.784 +(epoch: 310, iters: 6128, time: 0.064) G_GAN: 0.579 G_GAN_Feat: 0.977 G_ID: 0.178 G_Rec: 0.406 D_GP: 0.026 D_real: 1.051 D_fake: 0.425 +(epoch: 310, iters: 6528, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.595 G_ID: 0.120 G_Rec: 0.289 D_GP: 0.020 D_real: 1.301 D_fake: 0.746 +(epoch: 310, iters: 6928, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.847 G_ID: 0.165 G_Rec: 0.491 D_GP: 0.031 D_real: 1.105 D_fake: 0.583 +(epoch: 310, iters: 7328, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.661 G_ID: 0.147 G_Rec: 0.297 D_GP: 0.029 D_real: 0.989 D_fake: 0.898 +(epoch: 310, iters: 7728, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.824 G_ID: 0.144 G_Rec: 0.391 D_GP: 0.032 D_real: 0.961 D_fake: 0.692 +(epoch: 310, iters: 8128, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.655 G_ID: 0.114 G_Rec: 0.298 D_GP: 0.023 D_real: 1.121 D_fake: 0.817 +(epoch: 310, iters: 8528, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 0.879 G_ID: 0.152 G_Rec: 0.399 D_GP: 0.039 D_real: 1.284 D_fake: 0.449 +(epoch: 311, iters: 320, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.622 G_ID: 0.112 G_Rec: 0.289 D_GP: 0.019 D_real: 1.181 D_fake: 0.754 +(epoch: 311, iters: 720, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.934 G_ID: 0.145 G_Rec: 0.375 D_GP: 0.085 D_real: 0.875 D_fake: 0.510 +(epoch: 311, iters: 1120, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.826 G_ID: 0.129 G_Rec: 0.296 D_GP: 0.061 D_real: 0.463 D_fake: 0.889 +(epoch: 311, iters: 1520, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.794 G_ID: 0.170 G_Rec: 0.386 D_GP: 0.017 D_real: 1.261 D_fake: 0.604 +(epoch: 311, iters: 1920, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.662 G_ID: 0.110 G_Rec: 0.336 D_GP: 0.021 D_real: 1.123 D_fake: 0.814 +(epoch: 311, iters: 2320, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.745 G_ID: 0.162 G_Rec: 0.362 D_GP: 0.019 D_real: 1.273 D_fake: 0.580 +(epoch: 311, iters: 2720, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.577 G_ID: 0.116 G_Rec: 0.299 D_GP: 0.024 D_real: 1.156 D_fake: 0.789 +(epoch: 311, iters: 3120, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.729 G_ID: 0.150 G_Rec: 0.337 D_GP: 0.023 D_real: 1.204 D_fake: 0.640 +(epoch: 311, iters: 3520, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.700 G_ID: 0.136 G_Rec: 0.327 D_GP: 0.104 D_real: 0.881 D_fake: 0.989 +(epoch: 311, iters: 3920, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.747 G_ID: 0.165 G_Rec: 0.346 D_GP: 0.024 D_real: 1.159 D_fake: 0.650 +(epoch: 311, iters: 4320, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.597 G_ID: 0.130 G_Rec: 0.268 D_GP: 0.023 D_real: 1.122 D_fake: 0.826 +(epoch: 311, iters: 4720, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.799 G_ID: 0.161 G_Rec: 0.370 D_GP: 0.029 D_real: 1.108 D_fake: 0.655 +(epoch: 311, iters: 5120, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.755 G_ID: 0.119 G_Rec: 0.306 D_GP: 0.242 D_real: 0.525 D_fake: 0.936 +(epoch: 311, iters: 5520, time: 0.064) G_GAN: 0.641 G_GAN_Feat: 0.854 G_ID: 0.161 G_Rec: 0.396 D_GP: 0.023 D_real: 1.384 D_fake: 0.369 +(epoch: 311, iters: 5920, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.744 G_ID: 0.097 G_Rec: 0.315 D_GP: 0.100 D_real: 0.963 D_fake: 0.695 +(epoch: 311, iters: 6320, time: 0.064) G_GAN: 0.470 G_GAN_Feat: 0.833 G_ID: 0.168 G_Rec: 0.360 D_GP: 0.025 D_real: 1.207 D_fake: 0.538 +(epoch: 311, iters: 6720, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.764 G_ID: 0.109 G_Rec: 0.332 D_GP: 0.038 D_real: 0.977 D_fake: 0.737 +(epoch: 311, iters: 7120, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.873 G_ID: 0.169 G_Rec: 0.425 D_GP: 0.051 D_real: 0.975 D_fake: 0.605 +(epoch: 311, iters: 7520, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.832 G_ID: 0.113 G_Rec: 0.298 D_GP: 0.051 D_real: 0.835 D_fake: 0.797 +(epoch: 311, iters: 7920, time: 0.064) G_GAN: 0.530 G_GAN_Feat: 0.946 G_ID: 0.156 G_Rec: 0.388 D_GP: 0.030 D_real: 1.207 D_fake: 0.475 +(epoch: 311, iters: 8320, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.568 G_ID: 0.111 G_Rec: 0.241 D_GP: 0.018 D_real: 1.157 D_fake: 0.800 +(epoch: 312, iters: 112, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.843 G_ID: 0.154 G_Rec: 0.371 D_GP: 0.037 D_real: 1.051 D_fake: 0.680 +(epoch: 312, iters: 512, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.664 G_ID: 0.103 G_Rec: 0.282 D_GP: 0.059 D_real: 1.140 D_fake: 0.781 +(epoch: 312, iters: 912, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.893 G_ID: 0.159 G_Rec: 0.391 D_GP: 0.022 D_real: 1.352 D_fake: 0.410 +(epoch: 312, iters: 1312, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.630 G_ID: 0.113 G_Rec: 0.272 D_GP: 0.017 D_real: 1.369 D_fake: 0.545 +(epoch: 312, iters: 1712, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 1.030 G_ID: 0.148 G_Rec: 0.419 D_GP: 0.151 D_real: 0.559 D_fake: 0.583 +(epoch: 312, iters: 2112, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.733 G_ID: 0.111 G_Rec: 0.307 D_GP: 0.023 D_real: 0.941 D_fake: 0.870 +(epoch: 312, iters: 2512, time: 0.064) G_GAN: 0.078 G_GAN_Feat: 0.752 G_ID: 0.170 G_Rec: 0.340 D_GP: 0.021 D_real: 0.984 D_fake: 0.922 +(epoch: 312, iters: 2912, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.685 G_ID: 0.117 G_Rec: 0.282 D_GP: 0.046 D_real: 1.098 D_fake: 0.782 +(epoch: 312, iters: 3312, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 1.071 G_ID: 0.159 G_Rec: 0.416 D_GP: 0.555 D_real: 0.662 D_fake: 0.538 +(epoch: 312, iters: 3712, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.672 G_ID: 0.100 G_Rec: 0.274 D_GP: 0.023 D_real: 1.089 D_fake: 0.783 +(epoch: 312, iters: 4112, time: 0.064) G_GAN: 0.675 G_GAN_Feat: 1.069 G_ID: 0.162 G_Rec: 0.439 D_GP: 0.181 D_real: 1.007 D_fake: 0.471 +(epoch: 312, iters: 4512, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.846 G_ID: 0.135 G_Rec: 0.336 D_GP: 0.082 D_real: 0.558 D_fake: 0.787 +(epoch: 312, iters: 4912, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.969 G_ID: 0.169 G_Rec: 0.381 D_GP: 0.063 D_real: 0.748 D_fake: 0.694 +(epoch: 312, iters: 5312, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.709 G_ID: 0.151 G_Rec: 0.292 D_GP: 0.029 D_real: 1.101 D_fake: 0.691 +(epoch: 312, iters: 5712, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.763 G_ID: 0.162 G_Rec: 0.427 D_GP: 0.020 D_real: 1.104 D_fake: 0.723 +(epoch: 312, iters: 6112, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.503 G_ID: 0.109 G_Rec: 0.299 D_GP: 0.020 D_real: 1.057 D_fake: 0.887 +(epoch: 312, iters: 6512, time: 0.064) G_GAN: -0.018 G_GAN_Feat: 0.677 G_ID: 0.152 G_Rec: 0.354 D_GP: 0.021 D_real: 0.821 D_fake: 1.018 +(epoch: 312, iters: 6912, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.505 G_ID: 0.128 G_Rec: 0.285 D_GP: 0.021 D_real: 1.135 D_fake: 0.817 +(epoch: 312, iters: 7312, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.715 G_ID: 0.148 G_Rec: 0.373 D_GP: 0.021 D_real: 1.032 D_fake: 0.711 +(epoch: 312, iters: 7712, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.545 G_ID: 0.113 G_Rec: 0.273 D_GP: 0.022 D_real: 1.037 D_fake: 0.909 +(epoch: 312, iters: 8112, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.745 G_ID: 0.163 G_Rec: 0.370 D_GP: 0.026 D_real: 0.966 D_fake: 0.760 +(epoch: 312, iters: 8512, time: 0.064) G_GAN: 0.065 G_GAN_Feat: 0.562 G_ID: 0.133 G_Rec: 0.267 D_GP: 0.028 D_real: 1.018 D_fake: 0.935 +(epoch: 313, iters: 304, time: 0.064) G_GAN: -0.055 G_GAN_Feat: 0.804 G_ID: 0.166 G_Rec: 0.400 D_GP: 0.034 D_real: 0.668 D_fake: 1.055 +(epoch: 313, iters: 704, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.571 G_ID: 0.136 G_Rec: 0.261 D_GP: 0.022 D_real: 1.079 D_fake: 0.903 +(epoch: 313, iters: 1104, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.863 G_ID: 0.182 G_Rec: 0.408 D_GP: 0.045 D_real: 0.797 D_fake: 0.848 +(epoch: 313, iters: 1504, time: 0.064) G_GAN: 0.076 G_GAN_Feat: 0.663 G_ID: 0.125 G_Rec: 0.307 D_GP: 0.034 D_real: 0.860 D_fake: 0.924 +(epoch: 313, iters: 1904, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.828 G_ID: 0.142 G_Rec: 0.398 D_GP: 0.022 D_real: 1.177 D_fake: 0.590 +(epoch: 313, iters: 2304, time: 0.064) G_GAN: 0.024 G_GAN_Feat: 0.614 G_ID: 0.134 G_Rec: 0.276 D_GP: 0.036 D_real: 0.917 D_fake: 0.976 +(epoch: 313, iters: 2704, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.737 G_ID: 0.149 G_Rec: 0.319 D_GP: 0.027 D_real: 1.089 D_fake: 0.747 +(epoch: 313, iters: 3104, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.628 G_ID: 0.118 G_Rec: 0.267 D_GP: 0.021 D_real: 1.290 D_fake: 0.702 +(epoch: 313, iters: 3504, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.852 G_ID: 0.157 G_Rec: 0.415 D_GP: 0.021 D_real: 1.215 D_fake: 0.552 +(epoch: 313, iters: 3904, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.699 G_ID: 0.111 G_Rec: 0.280 D_GP: 0.057 D_real: 0.896 D_fake: 0.780 +(epoch: 313, iters: 4304, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.934 G_ID: 0.155 G_Rec: 0.423 D_GP: 0.082 D_real: 0.957 D_fake: 0.504 +(epoch: 313, iters: 4704, time: 0.064) G_GAN: -0.016 G_GAN_Feat: 0.755 G_ID: 0.123 G_Rec: 0.312 D_GP: 0.032 D_real: 1.108 D_fake: 1.017 +(epoch: 313, iters: 5104, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.736 G_ID: 0.183 G_Rec: 0.355 D_GP: 0.018 D_real: 1.144 D_fake: 0.751 +(epoch: 313, iters: 5504, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.668 G_ID: 0.118 G_Rec: 0.303 D_GP: 0.028 D_real: 1.147 D_fake: 0.732 +(epoch: 313, iters: 5904, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.824 G_ID: 0.148 G_Rec: 0.387 D_GP: 0.028 D_real: 0.955 D_fake: 0.752 +(epoch: 313, iters: 6304, time: 0.064) G_GAN: -0.050 G_GAN_Feat: 0.706 G_ID: 0.125 G_Rec: 0.312 D_GP: 0.043 D_real: 0.718 D_fake: 1.050 +(epoch: 313, iters: 6704, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.758 G_ID: 0.147 G_Rec: 0.359 D_GP: 0.020 D_real: 1.141 D_fake: 0.694 +(epoch: 313, iters: 7104, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.616 G_ID: 0.114 G_Rec: 0.264 D_GP: 0.027 D_real: 1.091 D_fake: 0.808 +(epoch: 313, iters: 7504, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.866 G_ID: 0.168 G_Rec: 0.387 D_GP: 0.049 D_real: 0.821 D_fake: 0.702 +(epoch: 313, iters: 7904, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.696 G_ID: 0.133 G_Rec: 0.295 D_GP: 0.027 D_real: 0.957 D_fake: 0.896 +(epoch: 313, iters: 8304, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.852 G_ID: 0.160 G_Rec: 0.374 D_GP: 0.030 D_real: 1.049 D_fake: 0.585 +(epoch: 314, iters: 96, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.805 G_ID: 0.131 G_Rec: 0.331 D_GP: 0.049 D_real: 1.055 D_fake: 0.770 +(epoch: 314, iters: 496, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 1.013 G_ID: 0.156 G_Rec: 0.415 D_GP: 0.091 D_real: 0.623 D_fake: 0.697 +(epoch: 314, iters: 896, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.639 G_ID: 0.106 G_Rec: 0.259 D_GP: 0.029 D_real: 1.131 D_fake: 0.810 +(epoch: 314, iters: 1296, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.999 G_ID: 0.152 G_Rec: 0.423 D_GP: 0.039 D_real: 0.806 D_fake: 0.699 +(epoch: 314, iters: 1696, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.643 G_ID: 0.141 G_Rec: 0.296 D_GP: 0.021 D_real: 1.064 D_fake: 0.843 +(epoch: 314, iters: 2096, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.931 G_ID: 0.153 G_Rec: 0.398 D_GP: 0.081 D_real: 0.893 D_fake: 0.665 +(epoch: 314, iters: 2496, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.670 G_ID: 0.124 G_Rec: 0.289 D_GP: 0.025 D_real: 1.270 D_fake: 0.591 +(epoch: 314, iters: 2896, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.866 G_ID: 0.140 G_Rec: 0.397 D_GP: 0.028 D_real: 1.030 D_fake: 0.706 +(epoch: 314, iters: 3296, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.707 G_ID: 0.126 G_Rec: 0.303 D_GP: 0.041 D_real: 0.927 D_fake: 0.887 +(epoch: 314, iters: 3696, time: 0.064) G_GAN: 0.133 G_GAN_Feat: 0.859 G_ID: 0.177 G_Rec: 0.435 D_GP: 0.026 D_real: 0.985 D_fake: 0.868 +(epoch: 314, iters: 4096, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.631 G_ID: 0.127 G_Rec: 0.280 D_GP: 0.037 D_real: 0.885 D_fake: 1.030 +(epoch: 314, iters: 4496, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.785 G_ID: 0.168 G_Rec: 0.359 D_GP: 0.028 D_real: 0.959 D_fake: 0.765 +(epoch: 314, iters: 4896, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.688 G_ID: 0.148 G_Rec: 0.285 D_GP: 0.090 D_real: 0.813 D_fake: 0.885 +(epoch: 314, iters: 5296, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 1.000 G_ID: 0.184 G_Rec: 0.419 D_GP: 0.046 D_real: 0.509 D_fake: 0.894 +(epoch: 314, iters: 5696, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.610 G_ID: 0.110 G_Rec: 0.289 D_GP: 0.023 D_real: 1.169 D_fake: 0.813 +(epoch: 314, iters: 6096, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.901 G_ID: 0.171 G_Rec: 0.394 D_GP: 0.045 D_real: 0.865 D_fake: 0.622 +(epoch: 314, iters: 6496, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.659 G_ID: 0.124 G_Rec: 0.307 D_GP: 0.026 D_real: 1.134 D_fake: 0.663 +(epoch: 314, iters: 6896, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 1.048 G_ID: 0.163 G_Rec: 0.413 D_GP: 0.082 D_real: 0.329 D_fake: 0.637 +(epoch: 314, iters: 7296, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.822 G_ID: 0.118 G_Rec: 0.311 D_GP: 0.025 D_real: 0.949 D_fake: 0.610 +(epoch: 314, iters: 7696, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.809 G_ID: 0.159 G_Rec: 0.374 D_GP: 0.023 D_real: 1.229 D_fake: 0.604 +(epoch: 314, iters: 8096, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.823 G_ID: 0.128 G_Rec: 0.335 D_GP: 0.082 D_real: 0.668 D_fake: 0.910 +(epoch: 314, iters: 8496, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 0.969 G_ID: 0.151 G_Rec: 0.403 D_GP: 0.088 D_real: 0.736 D_fake: 0.534 +(epoch: 315, iters: 288, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.862 G_ID: 0.135 G_Rec: 0.315 D_GP: 0.063 D_real: 0.690 D_fake: 0.639 +(epoch: 315, iters: 688, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.926 G_ID: 0.158 G_Rec: 0.422 D_GP: 0.020 D_real: 0.980 D_fake: 0.788 +(epoch: 315, iters: 1088, time: 0.064) G_GAN: -0.014 G_GAN_Feat: 0.715 G_ID: 0.133 G_Rec: 0.386 D_GP: 0.021 D_real: 0.888 D_fake: 1.014 +(epoch: 315, iters: 1488, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.779 G_ID: 0.159 G_Rec: 0.374 D_GP: 0.023 D_real: 0.946 D_fake: 0.893 +(epoch: 315, iters: 1888, time: 0.064) G_GAN: -0.040 G_GAN_Feat: 0.623 G_ID: 0.121 G_Rec: 0.285 D_GP: 0.024 D_real: 0.879 D_fake: 1.040 +(epoch: 315, iters: 2288, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.816 G_ID: 0.137 G_Rec: 0.391 D_GP: 0.023 D_real: 1.120 D_fake: 0.661 +(epoch: 315, iters: 2688, time: 0.064) G_GAN: -0.143 G_GAN_Feat: 0.667 G_ID: 0.121 G_Rec: 0.285 D_GP: 0.038 D_real: 0.736 D_fake: 1.143 +(epoch: 315, iters: 3088, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.900 G_ID: 0.119 G_Rec: 0.396 D_GP: 0.034 D_real: 1.046 D_fake: 0.636 +(epoch: 315, iters: 3488, time: 0.064) G_GAN: 0.704 G_GAN_Feat: 0.861 G_ID: 0.116 G_Rec: 0.343 D_GP: 0.047 D_real: 1.123 D_fake: 0.508 +(epoch: 315, iters: 3888, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.865 G_ID: 0.173 G_Rec: 0.370 D_GP: 0.051 D_real: 0.907 D_fake: 0.800 +(epoch: 315, iters: 4288, time: 0.064) G_GAN: 0.588 G_GAN_Feat: 0.824 G_ID: 0.115 G_Rec: 0.306 D_GP: 0.045 D_real: 1.416 D_fake: 0.468 +(epoch: 315, iters: 4688, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 1.123 G_ID: 0.158 G_Rec: 0.468 D_GP: 0.215 D_real: 0.298 D_fake: 0.745 +(epoch: 315, iters: 5088, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.633 G_ID: 0.131 G_Rec: 0.271 D_GP: 0.023 D_real: 1.075 D_fake: 0.854 +(epoch: 315, iters: 5488, time: 0.064) G_GAN: 0.637 G_GAN_Feat: 1.161 G_ID: 0.162 G_Rec: 0.480 D_GP: 0.208 D_real: 0.444 D_fake: 0.434 +(epoch: 315, iters: 5888, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.712 G_ID: 0.131 G_Rec: 0.305 D_GP: 0.033 D_real: 0.880 D_fake: 0.885 +(epoch: 315, iters: 6288, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 1.027 G_ID: 0.169 G_Rec: 0.394 D_GP: 0.276 D_real: 0.638 D_fake: 0.491 +(epoch: 315, iters: 6688, time: 0.064) G_GAN: 0.594 G_GAN_Feat: 0.982 G_ID: 0.114 G_Rec: 0.289 D_GP: 0.037 D_real: 1.397 D_fake: 0.561 +(epoch: 315, iters: 7088, time: 0.064) G_GAN: 0.631 G_GAN_Feat: 0.859 G_ID: 0.144 G_Rec: 0.354 D_GP: 0.030 D_real: 1.267 D_fake: 0.383 +(epoch: 315, iters: 7488, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.900 G_ID: 0.113 G_Rec: 0.299 D_GP: 0.067 D_real: 0.379 D_fake: 0.659 +(epoch: 315, iters: 7888, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 1.046 G_ID: 0.163 G_Rec: 0.419 D_GP: 0.043 D_real: 0.788 D_fake: 0.698 +(epoch: 315, iters: 8288, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.747 G_ID: 0.118 G_Rec: 0.280 D_GP: 0.037 D_real: 0.957 D_fake: 0.782 +(epoch: 316, iters: 80, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 1.271 G_ID: 0.167 G_Rec: 0.452 D_GP: 0.528 D_real: 0.312 D_fake: 0.612 +(epoch: 316, iters: 480, time: 0.064) G_GAN: -0.151 G_GAN_Feat: 0.633 G_ID: 0.101 G_Rec: 0.319 D_GP: 0.021 D_real: 0.872 D_fake: 1.151 +(epoch: 316, iters: 880, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.858 G_ID: 0.145 G_Rec: 0.408 D_GP: 0.018 D_real: 0.895 D_fake: 0.832 +(epoch: 316, iters: 1280, time: 0.064) G_GAN: -0.111 G_GAN_Feat: 0.700 G_ID: 0.122 G_Rec: 0.327 D_GP: 0.026 D_real: 0.785 D_fake: 1.112 +(epoch: 316, iters: 1680, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.760 G_ID: 0.147 G_Rec: 0.352 D_GP: 0.024 D_real: 0.893 D_fake: 0.832 +(epoch: 316, iters: 2080, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 0.765 G_ID: 0.125 G_Rec: 0.306 D_GP: 0.082 D_real: 0.707 D_fake: 0.988 +(epoch: 316, iters: 2480, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.985 G_ID: 0.137 G_Rec: 0.415 D_GP: 0.062 D_real: 0.720 D_fake: 0.598 +(epoch: 316, iters: 2880, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.665 G_ID: 0.120 G_Rec: 0.306 D_GP: 0.029 D_real: 1.157 D_fake: 0.889 +(epoch: 316, iters: 3280, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.816 G_ID: 0.149 G_Rec: 0.381 D_GP: 0.031 D_real: 1.118 D_fake: 0.613 +(epoch: 316, iters: 3680, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.685 G_ID: 0.121 G_Rec: 0.319 D_GP: 0.041 D_real: 1.057 D_fake: 0.809 +(epoch: 316, iters: 4080, time: 0.064) G_GAN: 0.581 G_GAN_Feat: 0.785 G_ID: 0.162 G_Rec: 0.344 D_GP: 0.041 D_real: 1.327 D_fake: 0.432 +(epoch: 316, iters: 4480, time: 0.064) G_GAN: -0.057 G_GAN_Feat: 0.621 G_ID: 0.118 G_Rec: 0.271 D_GP: 0.023 D_real: 0.844 D_fake: 1.057 +(epoch: 316, iters: 4880, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.953 G_ID: 0.168 G_Rec: 0.412 D_GP: 0.030 D_real: 1.118 D_fake: 0.547 +(epoch: 316, iters: 5280, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.742 G_ID: 0.126 G_Rec: 0.290 D_GP: 0.023 D_real: 1.154 D_fake: 0.693 +(epoch: 316, iters: 5680, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 1.048 G_ID: 0.154 G_Rec: 0.446 D_GP: 0.038 D_real: 1.003 D_fake: 0.574 +(epoch: 316, iters: 6080, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.898 G_ID: 0.155 G_Rec: 0.315 D_GP: 0.122 D_real: 0.582 D_fake: 0.563 +================ Training Loss (Tue Apr 21 12:46:57 2020) ================ +(epoch: 315, iters: 7488, time: 0.075) G_GAN: 0.436 G_GAN_Feat: 0.885 G_ID: 0.143 G_Rec: 0.375 D_GP: 0.027 D_real: 1.123 D_fake: 0.566 +(epoch: 315, iters: 7888, time: 0.063) G_GAN: 0.172 G_GAN_Feat: 0.831 G_ID: 0.129 G_Rec: 0.312 D_GP: 0.032 D_real: 0.934 D_fake: 0.857 +(epoch: 315, iters: 8288, time: 0.063) G_GAN: 0.629 G_GAN_Feat: 1.244 G_ID: 0.151 G_Rec: 0.425 D_GP: 0.072 D_real: 0.481 D_fake: 0.384 +(epoch: 315, iters: 8688, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.816 G_ID: 0.118 G_Rec: 0.341 D_GP: 0.073 D_real: 0.723 D_fake: 0.699 +(epoch: 315, iters: 9088, time: 0.063) G_GAN: 0.632 G_GAN_Feat: 0.942 G_ID: 0.132 G_Rec: 0.419 D_GP: 0.027 D_real: 1.228 D_fake: 0.406 +(epoch: 315, iters: 9488, time: 0.063) G_GAN: 0.339 G_GAN_Feat: 0.769 G_ID: 0.094 G_Rec: 0.305 D_GP: 0.038 D_real: 1.035 D_fake: 0.667 +(epoch: 315, iters: 9888, time: 0.063) G_GAN: 0.933 G_GAN_Feat: 1.035 G_ID: 0.140 G_Rec: 0.405 D_GP: 0.032 D_real: 1.385 D_fake: 0.148 +(epoch: 315, iters: 10288, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.686 G_ID: 0.124 G_Rec: 0.289 D_GP: 0.023 D_real: 1.286 D_fake: 0.618 +(epoch: 315, iters: 10688, time: 0.063) G_GAN: 0.751 G_GAN_Feat: 1.101 G_ID: 0.159 G_Rec: 0.440 D_GP: 0.062 D_real: 0.495 D_fake: 0.286 +(epoch: 315, iters: 11088, time: 0.063) G_GAN: 0.415 G_GAN_Feat: 0.733 G_ID: 0.112 G_Rec: 0.283 D_GP: 0.029 D_real: 1.292 D_fake: 0.592 +(epoch: 315, iters: 11488, time: 0.063) G_GAN: 0.606 G_GAN_Feat: 1.195 G_ID: 0.163 G_Rec: 0.421 D_GP: 0.142 D_real: 0.290 D_fake: 0.508 +(epoch: 315, iters: 11888, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.732 G_ID: 0.102 G_Rec: 0.286 D_GP: 0.027 D_real: 1.314 D_fake: 0.580 +(epoch: 315, iters: 12288, time: 0.064) G_GAN: 0.795 G_GAN_Feat: 1.130 G_ID: 0.150 G_Rec: 0.454 D_GP: 0.036 D_real: 1.068 D_fake: 0.261 +(epoch: 315, iters: 12688, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.978 G_ID: 0.119 G_Rec: 0.361 D_GP: 0.087 D_real: 0.949 D_fake: 0.661 +(epoch: 315, iters: 13088, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 1.163 G_ID: 0.145 G_Rec: 0.473 D_GP: 0.084 D_real: 0.491 D_fake: 0.524 +(epoch: 315, iters: 13488, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.744 G_ID: 0.114 G_Rec: 0.312 D_GP: 0.033 D_real: 1.316 D_fake: 0.624 +(epoch: 315, iters: 13888, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.893 G_ID: 0.142 G_Rec: 0.406 D_GP: 0.045 D_real: 0.997 D_fake: 0.561 +(epoch: 315, iters: 14288, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.828 G_ID: 0.125 G_Rec: 0.318 D_GP: 0.166 D_real: 0.875 D_fake: 0.619 +(epoch: 315, iters: 14688, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.946 G_ID: 0.135 G_Rec: 0.411 D_GP: 0.028 D_real: 1.180 D_fake: 0.469 +(epoch: 315, iters: 15088, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.644 G_ID: 0.109 G_Rec: 0.272 D_GP: 0.026 D_real: 1.276 D_fake: 0.658 +(epoch: 315, iters: 15488, time: 0.063) G_GAN: 0.653 G_GAN_Feat: 0.875 G_ID: 0.142 G_Rec: 0.405 D_GP: 0.030 D_real: 1.307 D_fake: 0.364 +(epoch: 316, iters: 286, time: 0.063) G_GAN: 0.178 G_GAN_Feat: 0.858 G_ID: 0.099 G_Rec: 0.324 D_GP: 0.109 D_real: 0.509 D_fake: 0.824 +(epoch: 316, iters: 686, time: 0.063) G_GAN: 0.728 G_GAN_Feat: 1.022 G_ID: 0.135 G_Rec: 0.447 D_GP: 0.034 D_real: 1.279 D_fake: 0.301 +(epoch: 316, iters: 1086, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.753 G_ID: 0.120 G_Rec: 0.326 D_GP: 0.030 D_real: 0.947 D_fake: 0.869 +(epoch: 316, iters: 1486, time: 0.063) G_GAN: 0.516 G_GAN_Feat: 0.917 G_ID: 0.154 G_Rec: 0.416 D_GP: 0.042 D_real: 1.112 D_fake: 0.488 +(epoch: 316, iters: 1886, time: 0.063) G_GAN: 0.396 G_GAN_Feat: 0.821 G_ID: 0.095 G_Rec: 0.286 D_GP: 0.057 D_real: 0.844 D_fake: 0.605 +(epoch: 316, iters: 2286, time: 0.063) G_GAN: 0.320 G_GAN_Feat: 0.992 G_ID: 0.138 G_Rec: 0.433 D_GP: 0.033 D_real: 0.959 D_fake: 0.681 +(epoch: 316, iters: 2686, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.833 G_ID: 0.110 G_Rec: 0.334 D_GP: 0.029 D_real: 1.181 D_fake: 0.578 +(epoch: 316, iters: 3086, time: 0.063) G_GAN: 0.819 G_GAN_Feat: 1.221 G_ID: 0.150 G_Rec: 0.472 D_GP: 0.570 D_real: 0.536 D_fake: 0.269 +(epoch: 316, iters: 3486, time: 0.063) G_GAN: 0.366 G_GAN_Feat: 0.850 G_ID: 0.106 G_Rec: 0.313 D_GP: 0.028 D_real: 0.922 D_fake: 0.635 +(epoch: 316, iters: 3886, time: 0.063) G_GAN: 0.928 G_GAN_Feat: 1.102 G_ID: 0.154 G_Rec: 0.452 D_GP: 0.040 D_real: 1.486 D_fake: 0.242 +(epoch: 316, iters: 4286, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.660 G_ID: 0.120 G_Rec: 0.299 D_GP: 0.025 D_real: 1.111 D_fake: 0.799 +(epoch: 316, iters: 4686, time: 0.063) G_GAN: 0.411 G_GAN_Feat: 0.899 G_ID: 0.157 G_Rec: 0.403 D_GP: 0.034 D_real: 0.993 D_fake: 0.592 +(epoch: 316, iters: 5086, time: 0.063) G_GAN: 0.332 G_GAN_Feat: 0.691 G_ID: 0.098 G_Rec: 0.289 D_GP: 0.027 D_real: 1.151 D_fake: 0.669 +(epoch: 316, iters: 5486, time: 0.063) G_GAN: 0.598 G_GAN_Feat: 0.986 G_ID: 0.155 G_Rec: 0.445 D_GP: 0.056 D_real: 1.043 D_fake: 0.461 +(epoch: 316, iters: 5886, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.755 G_ID: 0.102 G_Rec: 0.284 D_GP: 0.044 D_real: 0.985 D_fake: 0.686 +(epoch: 316, iters: 6286, time: 0.063) G_GAN: 0.524 G_GAN_Feat: 0.996 G_ID: 0.134 G_Rec: 0.420 D_GP: 0.064 D_real: 0.750 D_fake: 0.479 +(epoch: 316, iters: 6686, time: 0.063) G_GAN: 0.219 G_GAN_Feat: 0.752 G_ID: 0.105 G_Rec: 0.302 D_GP: 0.028 D_real: 0.955 D_fake: 0.782 +(epoch: 316, iters: 7086, time: 0.063) G_GAN: 0.843 G_GAN_Feat: 0.939 G_ID: 0.124 G_Rec: 0.404 D_GP: 0.032 D_real: 1.306 D_fake: 0.231 +(epoch: 316, iters: 7486, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.809 G_ID: 0.128 G_Rec: 0.305 D_GP: 0.063 D_real: 0.570 D_fake: 0.823 +(epoch: 316, iters: 7886, time: 0.063) G_GAN: 0.548 G_GAN_Feat: 1.000 G_ID: 0.152 G_Rec: 0.410 D_GP: 0.178 D_real: 0.841 D_fake: 0.497 +(epoch: 316, iters: 8286, time: 0.063) G_GAN: 0.303 G_GAN_Feat: 0.812 G_ID: 0.126 G_Rec: 0.293 D_GP: 0.045 D_real: 0.767 D_fake: 0.698 +(epoch: 316, iters: 8686, time: 0.063) G_GAN: 0.602 G_GAN_Feat: 0.872 G_ID: 0.122 G_Rec: 0.364 D_GP: 0.030 D_real: 1.261 D_fake: 0.408 +(epoch: 317, iters: 478, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.821 G_ID: 0.115 G_Rec: 0.311 D_GP: 0.033 D_real: 0.967 D_fake: 0.674 +(epoch: 317, iters: 878, time: 0.063) G_GAN: 0.710 G_GAN_Feat: 1.150 G_ID: 0.122 G_Rec: 0.448 D_GP: 0.270 D_real: 0.414 D_fake: 0.327 +(epoch: 317, iters: 1278, time: 0.063) G_GAN: 0.463 G_GAN_Feat: 0.963 G_ID: 0.116 G_Rec: 0.306 D_GP: 0.074 D_real: 0.394 D_fake: 0.559 +(epoch: 317, iters: 1678, time: 0.063) G_GAN: 0.306 G_GAN_Feat: 0.960 G_ID: 0.137 G_Rec: 0.456 D_GP: 0.032 D_real: 1.053 D_fake: 0.695 +(epoch: 317, iters: 2078, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.768 G_ID: 0.145 G_Rec: 0.318 D_GP: 0.031 D_real: 0.970 D_fake: 0.891 +(epoch: 317, iters: 2478, time: 0.063) G_GAN: 0.473 G_GAN_Feat: 1.213 G_ID: 0.145 G_Rec: 0.467 D_GP: 0.138 D_real: 0.291 D_fake: 0.534 +(epoch: 317, iters: 2878, time: 0.063) G_GAN: 0.492 G_GAN_Feat: 0.886 G_ID: 0.112 G_Rec: 0.337 D_GP: 0.043 D_real: 1.042 D_fake: 0.563 +(epoch: 317, iters: 3278, time: 0.063) G_GAN: 0.559 G_GAN_Feat: 1.035 G_ID: 0.111 G_Rec: 0.435 D_GP: 0.028 D_real: 1.002 D_fake: 0.468 +(epoch: 317, iters: 3678, time: 0.064) G_GAN: -0.027 G_GAN_Feat: 0.831 G_ID: 0.131 G_Rec: 0.335 D_GP: 0.067 D_real: 0.549 D_fake: 1.028 +(epoch: 317, iters: 4078, time: 0.063) G_GAN: 0.615 G_GAN_Feat: 0.968 G_ID: 0.124 G_Rec: 0.427 D_GP: 0.028 D_real: 1.118 D_fake: 0.400 +(epoch: 317, iters: 4478, time: 0.063) G_GAN: 0.224 G_GAN_Feat: 0.738 G_ID: 0.106 G_Rec: 0.294 D_GP: 0.040 D_real: 0.949 D_fake: 0.776 +(epoch: 317, iters: 4878, time: 0.063) G_GAN: 0.715 G_GAN_Feat: 0.935 G_ID: 0.119 G_Rec: 0.435 D_GP: 0.026 D_real: 1.450 D_fake: 0.307 +(epoch: 317, iters: 5278, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.743 G_ID: 0.119 G_Rec: 0.302 D_GP: 0.032 D_real: 0.858 D_fake: 0.849 +(epoch: 317, iters: 5678, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 1.023 G_ID: 0.124 G_Rec: 0.407 D_GP: 0.068 D_real: 0.721 D_fake: 0.573 +(epoch: 317, iters: 6078, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.875 G_ID: 0.108 G_Rec: 0.316 D_GP: 0.064 D_real: 0.325 D_fake: 0.885 +(epoch: 317, iters: 6478, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 1.193 G_ID: 0.138 G_Rec: 0.462 D_GP: 0.093 D_real: 0.214 D_fake: 0.532 +(epoch: 317, iters: 6878, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.676 G_ID: 0.106 G_Rec: 0.297 D_GP: 0.023 D_real: 1.273 D_fake: 0.618 +(epoch: 317, iters: 7278, time: 0.063) G_GAN: 0.526 G_GAN_Feat: 0.972 G_ID: 0.129 G_Rec: 0.431 D_GP: 0.037 D_real: 0.985 D_fake: 0.479 +(epoch: 317, iters: 7678, time: 0.063) G_GAN: 0.338 G_GAN_Feat: 0.747 G_ID: 0.133 G_Rec: 0.289 D_GP: 0.037 D_real: 1.165 D_fake: 0.663 +(epoch: 317, iters: 8078, time: 0.063) G_GAN: 0.738 G_GAN_Feat: 0.902 G_ID: 0.119 G_Rec: 0.400 D_GP: 0.030 D_real: 1.337 D_fake: 0.286 +(epoch: 317, iters: 8478, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.769 G_ID: 0.132 G_Rec: 0.327 D_GP: 0.054 D_real: 0.908 D_fake: 0.918 +(epoch: 318, iters: 270, time: 0.063) G_GAN: 0.506 G_GAN_Feat: 0.874 G_ID: 0.138 G_Rec: 0.409 D_GP: 0.034 D_real: 1.092 D_fake: 0.506 +(epoch: 318, iters: 670, time: 0.063) G_GAN: 0.173 G_GAN_Feat: 0.904 G_ID: 0.121 G_Rec: 0.333 D_GP: 0.063 D_real: 0.853 D_fake: 0.845 +(epoch: 318, iters: 1070, time: 0.063) G_GAN: 0.588 G_GAN_Feat: 0.811 G_ID: 0.128 G_Rec: 0.389 D_GP: 0.031 D_real: 1.325 D_fake: 0.423 +(epoch: 318, iters: 1470, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.656 G_ID: 0.127 G_Rec: 0.297 D_GP: 0.027 D_real: 0.933 D_fake: 0.989 +(epoch: 318, iters: 1870, time: 0.063) G_GAN: 0.528 G_GAN_Feat: 0.946 G_ID: 0.121 G_Rec: 0.468 D_GP: 0.033 D_real: 1.161 D_fake: 0.485 +(epoch: 318, iters: 2270, time: 0.063) G_GAN: 0.187 G_GAN_Feat: 0.641 G_ID: 0.096 G_Rec: 0.292 D_GP: 0.028 D_real: 1.127 D_fake: 0.814 +(epoch: 318, iters: 2670, time: 0.063) G_GAN: 0.557 G_GAN_Feat: 0.844 G_ID: 0.128 G_Rec: 0.396 D_GP: 0.033 D_real: 1.299 D_fake: 0.470 +(epoch: 318, iters: 3070, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.662 G_ID: 0.121 G_Rec: 0.302 D_GP: 0.036 D_real: 0.849 D_fake: 0.968 +(epoch: 318, iters: 3470, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.871 G_ID: 0.124 G_Rec: 0.410 D_GP: 0.066 D_real: 0.811 D_fake: 0.790 +(epoch: 318, iters: 3870, time: 0.063) G_GAN: -0.087 G_GAN_Feat: 0.765 G_ID: 0.102 G_Rec: 0.324 D_GP: 0.055 D_real: 0.675 D_fake: 1.087 +(epoch: 318, iters: 4270, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 1.108 G_ID: 0.132 G_Rec: 0.491 D_GP: 0.049 D_real: 0.874 D_fake: 0.829 +(epoch: 318, iters: 4670, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.731 G_ID: 0.097 G_Rec: 0.331 D_GP: 0.043 D_real: 1.075 D_fake: 0.776 +(epoch: 318, iters: 5070, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 0.916 G_ID: 0.131 G_Rec: 0.414 D_GP: 0.046 D_real: 1.045 D_fake: 0.596 +(epoch: 318, iters: 5470, time: 0.063) G_GAN: 0.054 G_GAN_Feat: 0.740 G_ID: 0.121 G_Rec: 0.338 D_GP: 0.037 D_real: 0.907 D_fake: 0.946 +(epoch: 318, iters: 5870, time: 0.063) G_GAN: 0.628 G_GAN_Feat: 1.041 G_ID: 0.129 G_Rec: 0.479 D_GP: 0.062 D_real: 0.883 D_fake: 0.391 +(epoch: 318, iters: 6270, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.896 G_ID: 0.117 G_Rec: 0.332 D_GP: 0.160 D_real: 0.557 D_fake: 0.761 +(epoch: 318, iters: 6670, time: 0.063) G_GAN: 0.372 G_GAN_Feat: 0.878 G_ID: 0.147 G_Rec: 0.369 D_GP: 0.040 D_real: 0.925 D_fake: 0.628 +(epoch: 318, iters: 7070, time: 0.063) G_GAN: 0.367 G_GAN_Feat: 0.770 G_ID: 0.107 G_Rec: 0.320 D_GP: 0.056 D_real: 1.046 D_fake: 0.640 +(epoch: 318, iters: 7470, time: 0.063) G_GAN: 0.956 G_GAN_Feat: 0.974 G_ID: 0.126 G_Rec: 0.417 D_GP: 0.032 D_real: 1.510 D_fake: 0.164 +(epoch: 318, iters: 7870, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.789 G_ID: 0.110 G_Rec: 0.308 D_GP: 0.057 D_real: 0.782 D_fake: 0.790 +(epoch: 318, iters: 8270, time: 0.063) G_GAN: 0.369 G_GAN_Feat: 0.964 G_ID: 0.139 G_Rec: 0.426 D_GP: 0.032 D_real: 0.954 D_fake: 0.632 +(epoch: 318, iters: 8670, time: 0.063) G_GAN: -0.080 G_GAN_Feat: 0.844 G_ID: 0.119 G_Rec: 0.316 D_GP: 0.130 D_real: 0.593 D_fake: 1.082 +(epoch: 319, iters: 462, time: 0.063) G_GAN: 0.703 G_GAN_Feat: 0.989 G_ID: 0.140 G_Rec: 0.413 D_GP: 0.053 D_real: 1.029 D_fake: 0.329 +(epoch: 319, iters: 862, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.944 G_ID: 0.102 G_Rec: 0.351 D_GP: 0.480 D_real: 0.456 D_fake: 0.739 +(epoch: 319, iters: 1262, time: 0.063) G_GAN: 0.399 G_GAN_Feat: 1.009 G_ID: 0.143 G_Rec: 0.429 D_GP: 0.039 D_real: 0.882 D_fake: 0.618 +(epoch: 319, iters: 1662, time: 0.063) G_GAN: -0.030 G_GAN_Feat: 0.719 G_ID: 0.119 G_Rec: 0.276 D_GP: 0.028 D_real: 0.882 D_fake: 1.030 +(epoch: 319, iters: 2062, time: 0.063) G_GAN: 0.440 G_GAN_Feat: 1.050 G_ID: 0.152 G_Rec: 0.400 D_GP: 0.033 D_real: 0.495 D_fake: 0.561 +(epoch: 319, iters: 2462, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.965 G_ID: 0.107 G_Rec: 0.307 D_GP: 0.102 D_real: 0.273 D_fake: 0.714 +(epoch: 319, iters: 2862, time: 0.063) G_GAN: 0.485 G_GAN_Feat: 0.882 G_ID: 0.151 G_Rec: 0.475 D_GP: 0.028 D_real: 1.196 D_fake: 0.543 +(epoch: 319, iters: 3262, time: 0.063) G_GAN: -0.078 G_GAN_Feat: 0.677 G_ID: 0.108 G_Rec: 0.305 D_GP: 0.027 D_real: 0.830 D_fake: 1.078 +(epoch: 319, iters: 3662, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 0.851 G_ID: 0.117 G_Rec: 0.418 D_GP: 0.028 D_real: 0.893 D_fake: 0.718 +(epoch: 319, iters: 4062, time: 0.064) G_GAN: -0.106 G_GAN_Feat: 0.658 G_ID: 0.113 G_Rec: 0.315 D_GP: 0.028 D_real: 0.733 D_fake: 1.106 +(epoch: 319, iters: 4462, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.861 G_ID: 0.123 G_Rec: 0.410 D_GP: 0.041 D_real: 0.828 D_fake: 0.811 +(epoch: 319, iters: 4862, time: 0.063) G_GAN: -0.178 G_GAN_Feat: 0.735 G_ID: 0.132 G_Rec: 0.315 D_GP: 0.038 D_real: 0.624 D_fake: 1.178 +(epoch: 319, iters: 5262, time: 0.063) G_GAN: 0.233 G_GAN_Feat: 0.828 G_ID: 0.147 G_Rec: 0.376 D_GP: 0.033 D_real: 0.913 D_fake: 0.768 +(epoch: 319, iters: 5662, time: 0.063) G_GAN: 0.223 G_GAN_Feat: 0.757 G_ID: 0.107 G_Rec: 0.308 D_GP: 0.038 D_real: 0.955 D_fake: 0.778 +(epoch: 319, iters: 6062, time: 0.063) G_GAN: 0.430 G_GAN_Feat: 0.954 G_ID: 0.142 G_Rec: 0.418 D_GP: 0.047 D_real: 0.920 D_fake: 0.571 +(epoch: 319, iters: 6462, time: 0.063) G_GAN: 0.162 G_GAN_Feat: 0.722 G_ID: 0.120 G_Rec: 0.319 D_GP: 0.040 D_real: 1.021 D_fake: 0.838 +(epoch: 319, iters: 6862, time: 0.064) G_GAN: 0.627 G_GAN_Feat: 0.936 G_ID: 0.130 G_Rec: 0.403 D_GP: 0.039 D_real: 1.063 D_fake: 0.381 +(epoch: 319, iters: 7262, time: 0.063) G_GAN: 0.014 G_GAN_Feat: 0.815 G_ID: 0.103 G_Rec: 0.350 D_GP: 0.068 D_real: 0.587 D_fake: 0.987 +(epoch: 319, iters: 7662, time: 0.063) G_GAN: 0.418 G_GAN_Feat: 0.989 G_ID: 0.138 G_Rec: 0.419 D_GP: 0.062 D_real: 0.914 D_fake: 0.585 +(epoch: 319, iters: 8062, time: 0.063) G_GAN: 0.225 G_GAN_Feat: 0.781 G_ID: 0.103 G_Rec: 0.304 D_GP: 0.043 D_real: 0.857 D_fake: 0.776 +(epoch: 319, iters: 8462, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 1.014 G_ID: 0.141 G_Rec: 0.466 D_GP: 0.062 D_real: 0.823 D_fake: 0.622 +(epoch: 320, iters: 254, time: 0.063) G_GAN: 0.443 G_GAN_Feat: 0.874 G_ID: 0.102 G_Rec: 0.319 D_GP: 0.176 D_real: 0.753 D_fake: 0.593 +(epoch: 320, iters: 654, time: 0.063) G_GAN: 0.678 G_GAN_Feat: 0.975 G_ID: 0.129 G_Rec: 0.460 D_GP: 0.034 D_real: 1.260 D_fake: 0.335 +(epoch: 320, iters: 1054, time: 0.063) G_GAN: -0.069 G_GAN_Feat: 0.931 G_ID: 0.130 G_Rec: 0.331 D_GP: 0.040 D_real: 0.648 D_fake: 1.069 +(epoch: 320, iters: 1454, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 1.001 G_ID: 0.111 G_Rec: 0.455 D_GP: 0.035 D_real: 0.944 D_fake: 0.736 +(epoch: 320, iters: 1854, time: 0.063) G_GAN: 0.997 G_GAN_Feat: 0.909 G_ID: 0.113 G_Rec: 0.360 D_GP: 0.174 D_real: 1.446 D_fake: 0.361 +(epoch: 320, iters: 2254, time: 0.063) G_GAN: 0.581 G_GAN_Feat: 1.215 G_ID: 0.147 G_Rec: 0.484 D_GP: 0.247 D_real: 0.512 D_fake: 0.443 +(epoch: 320, iters: 2654, time: 0.063) G_GAN: 0.380 G_GAN_Feat: 0.813 G_ID: 0.108 G_Rec: 0.324 D_GP: 0.045 D_real: 0.884 D_fake: 0.623 +(epoch: 320, iters: 3054, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.918 G_ID: 0.175 G_Rec: 0.438 D_GP: 0.031 D_real: 1.162 D_fake: 0.555 +(epoch: 320, iters: 3454, time: 0.063) G_GAN: 0.216 G_GAN_Feat: 0.740 G_ID: 0.140 G_Rec: 0.343 D_GP: 0.031 D_real: 1.041 D_fake: 0.785 +(epoch: 320, iters: 3854, time: 0.063) G_GAN: 0.404 G_GAN_Feat: 0.960 G_ID: 0.152 G_Rec: 0.415 D_GP: 0.045 D_real: 0.854 D_fake: 0.601 +(epoch: 320, iters: 4254, time: 0.063) G_GAN: 0.185 G_GAN_Feat: 0.700 G_ID: 0.106 G_Rec: 0.305 D_GP: 0.028 D_real: 1.057 D_fake: 0.815 +(epoch: 320, iters: 4654, time: 0.064) G_GAN: 0.511 G_GAN_Feat: 0.986 G_ID: 0.124 G_Rec: 0.426 D_GP: 0.032 D_real: 1.062 D_fake: 0.491 +(epoch: 320, iters: 5054, time: 0.063) G_GAN: 0.322 G_GAN_Feat: 0.987 G_ID: 0.087 G_Rec: 0.325 D_GP: 1.654 D_real: 0.347 D_fake: 0.692 +(epoch: 320, iters: 5454, time: 0.063) G_GAN: 0.445 G_GAN_Feat: 0.975 G_ID: 0.111 G_Rec: 0.417 D_GP: 0.034 D_real: 0.997 D_fake: 0.557 +(epoch: 320, iters: 5854, time: 0.063) G_GAN: 0.008 G_GAN_Feat: 1.035 G_ID: 0.107 G_Rec: 0.354 D_GP: 0.436 D_real: 0.323 D_fake: 0.992 +(epoch: 320, iters: 6254, time: 0.064) G_GAN: 0.661 G_GAN_Feat: 1.234 G_ID: 0.142 G_Rec: 0.504 D_GP: 0.178 D_real: 0.386 D_fake: 0.415 +(epoch: 320, iters: 6654, time: 0.063) G_GAN: 0.478 G_GAN_Feat: 0.854 G_ID: 0.121 G_Rec: 0.340 D_GP: 0.038 D_real: 1.063 D_fake: 0.552 +(epoch: 320, iters: 7054, time: 0.063) G_GAN: 0.490 G_GAN_Feat: 0.951 G_ID: 0.134 G_Rec: 0.484 D_GP: 0.027 D_real: 1.212 D_fake: 0.523 +(epoch: 320, iters: 7454, time: 0.063) G_GAN: 0.059 G_GAN_Feat: 0.732 G_ID: 0.101 G_Rec: 0.313 D_GP: 0.031 D_real: 0.853 D_fake: 0.941 +(epoch: 320, iters: 7854, time: 0.064) G_GAN: 0.608 G_GAN_Feat: 1.072 G_ID: 0.117 G_Rec: 0.454 D_GP: 0.143 D_real: 0.673 D_fake: 0.414 +(epoch: 320, iters: 8254, time: 0.063) G_GAN: 0.337 G_GAN_Feat: 0.790 G_ID: 0.112 G_Rec: 0.335 D_GP: 0.050 D_real: 1.066 D_fake: 0.694 +(epoch: 320, iters: 8654, time: 0.063) G_GAN: 0.557 G_GAN_Feat: 1.223 G_ID: 0.135 G_Rec: 0.496 D_GP: 0.137 D_real: 0.499 D_fake: 0.469 +(epoch: 321, iters: 446, time: 0.063) G_GAN: 0.476 G_GAN_Feat: 0.743 G_ID: 0.106 G_Rec: 0.315 D_GP: 0.027 D_real: 1.314 D_fake: 0.530 +(epoch: 321, iters: 846, time: 0.064) G_GAN: -0.190 G_GAN_Feat: 1.140 G_ID: 0.146 G_Rec: 0.483 D_GP: 0.029 D_real: 0.513 D_fake: 1.191 +(epoch: 321, iters: 1246, time: 0.063) G_GAN: -0.088 G_GAN_Feat: 0.686 G_ID: 0.106 G_Rec: 0.342 D_GP: 0.024 D_real: 0.830 D_fake: 1.088 +(epoch: 321, iters: 1646, time: 0.063) G_GAN: -0.018 G_GAN_Feat: 0.813 G_ID: 0.123 G_Rec: 0.393 D_GP: 0.030 D_real: 0.764 D_fake: 1.018 +(epoch: 321, iters: 2046, time: 0.063) G_GAN: 0.077 G_GAN_Feat: 0.631 G_ID: 0.106 G_Rec: 0.291 D_GP: 0.034 D_real: 0.953 D_fake: 0.925 +(epoch: 321, iters: 2446, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.803 G_ID: 0.133 G_Rec: 0.389 D_GP: 0.032 D_real: 0.807 D_fake: 0.873 +(epoch: 321, iters: 2846, time: 0.063) G_GAN: -0.111 G_GAN_Feat: 0.738 G_ID: 0.114 G_Rec: 0.366 D_GP: 0.059 D_real: 0.682 D_fake: 1.112 +(epoch: 321, iters: 3246, time: 0.063) G_GAN: 0.573 G_GAN_Feat: 0.888 G_ID: 0.115 G_Rec: 0.441 D_GP: 0.039 D_real: 1.091 D_fake: 0.503 +(epoch: 321, iters: 3646, time: 0.063) G_GAN: 0.023 G_GAN_Feat: 0.653 G_ID: 0.112 G_Rec: 0.304 D_GP: 0.040 D_real: 0.856 D_fake: 0.977 +(epoch: 321, iters: 4046, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.944 G_ID: 0.135 G_Rec: 0.450 D_GP: 0.057 D_real: 0.986 D_fake: 0.575 +(epoch: 321, iters: 4446, time: 0.063) G_GAN: -0.041 G_GAN_Feat: 0.759 G_ID: 0.116 G_Rec: 0.321 D_GP: 0.051 D_real: 0.626 D_fake: 1.045 +(epoch: 321, iters: 4846, time: 0.063) G_GAN: 0.276 G_GAN_Feat: 0.901 G_ID: 0.121 G_Rec: 0.416 D_GP: 0.049 D_real: 0.908 D_fake: 0.725 +(epoch: 321, iters: 5246, time: 0.063) G_GAN: 0.098 G_GAN_Feat: 0.669 G_ID: 0.094 G_Rec: 0.319 D_GP: 0.037 D_real: 0.971 D_fake: 0.903 +(epoch: 321, iters: 5646, time: 0.064) G_GAN: 0.572 G_GAN_Feat: 0.960 G_ID: 0.122 G_Rec: 0.444 D_GP: 0.053 D_real: 1.109 D_fake: 0.480 +(epoch: 321, iters: 6046, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.670 G_ID: 0.112 G_Rec: 0.296 D_GP: 0.041 D_real: 1.148 D_fake: 0.768 +(epoch: 321, iters: 6446, time: 0.063) G_GAN: 0.365 G_GAN_Feat: 0.892 G_ID: 0.131 G_Rec: 0.385 D_GP: 0.047 D_real: 0.975 D_fake: 0.639 +(epoch: 321, iters: 6846, time: 0.063) G_GAN: 0.217 G_GAN_Feat: 0.688 G_ID: 0.109 G_Rec: 0.312 D_GP: 0.035 D_real: 1.165 D_fake: 0.783 +(epoch: 321, iters: 7246, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.920 G_ID: 0.154 G_Rec: 0.449 D_GP: 0.037 D_real: 0.798 D_fake: 0.714 +(epoch: 321, iters: 7646, time: 0.063) G_GAN: 0.144 G_GAN_Feat: 0.766 G_ID: 0.112 G_Rec: 0.304 D_GP: 0.051 D_real: 0.930 D_fake: 0.856 +(epoch: 321, iters: 8046, time: 0.063) G_GAN: 0.322 G_GAN_Feat: 1.001 G_ID: 0.129 G_Rec: 0.429 D_GP: 0.055 D_real: 0.854 D_fake: 0.680 +(epoch: 321, iters: 8446, time: 0.063) G_GAN: 0.262 G_GAN_Feat: 0.855 G_ID: 0.106 G_Rec: 0.314 D_GP: 0.082 D_real: 0.673 D_fake: 0.739 +(epoch: 322, iters: 238, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 1.217 G_ID: 0.152 G_Rec: 0.482 D_GP: 0.238 D_real: 0.429 D_fake: 0.616 +(epoch: 322, iters: 638, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.740 G_ID: 0.112 G_Rec: 0.315 D_GP: 0.030 D_real: 1.056 D_fake: 0.810 +(epoch: 322, iters: 1038, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 0.947 G_ID: 0.128 G_Rec: 0.443 D_GP: 0.037 D_real: 0.878 D_fake: 0.765 +(epoch: 322, iters: 1438, time: 0.063) G_GAN: 0.050 G_GAN_Feat: 0.723 G_ID: 0.105 G_Rec: 0.309 D_GP: 0.048 D_real: 0.793 D_fake: 0.950 +(epoch: 322, iters: 1838, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 1.027 G_ID: 0.137 G_Rec: 0.406 D_GP: 0.059 D_real: 0.408 D_fake: 0.847 +(epoch: 322, iters: 2238, time: 0.063) G_GAN: 0.455 G_GAN_Feat: 0.783 G_ID: 0.092 G_Rec: 0.315 D_GP: 0.039 D_real: 1.130 D_fake: 0.559 +(epoch: 322, iters: 2638, time: 0.063) G_GAN: 0.643 G_GAN_Feat: 0.969 G_ID: 0.131 G_Rec: 0.452 D_GP: 0.031 D_real: 1.250 D_fake: 0.377 +(epoch: 322, iters: 3038, time: 0.063) G_GAN: 0.232 G_GAN_Feat: 0.889 G_ID: 0.114 G_Rec: 0.354 D_GP: 0.041 D_real: 0.827 D_fake: 0.769 +(epoch: 322, iters: 3438, time: 0.064) G_GAN: 0.029 G_GAN_Feat: 1.008 G_ID: 0.151 G_Rec: 0.465 D_GP: 0.057 D_real: 0.581 D_fake: 0.973 +(epoch: 322, iters: 3838, time: 0.063) G_GAN: 0.091 G_GAN_Feat: 0.699 G_ID: 0.111 G_Rec: 0.286 D_GP: 0.030 D_real: 0.979 D_fake: 0.909 +(epoch: 322, iters: 4238, time: 0.063) G_GAN: 0.682 G_GAN_Feat: 0.900 G_ID: 0.115 G_Rec: 0.471 D_GP: 0.029 D_real: 1.326 D_fake: 0.371 +(epoch: 322, iters: 4638, time: 0.063) G_GAN: 0.193 G_GAN_Feat: 0.725 G_ID: 0.103 G_Rec: 0.305 D_GP: 0.033 D_real: 1.142 D_fake: 0.808 +(epoch: 322, iters: 5038, time: 0.064) G_GAN: 0.606 G_GAN_Feat: 0.961 G_ID: 0.140 G_Rec: 0.462 D_GP: 0.033 D_real: 1.108 D_fake: 0.441 +(epoch: 322, iters: 5438, time: 0.063) G_GAN: 0.423 G_GAN_Feat: 0.702 G_ID: 0.096 G_Rec: 0.295 D_GP: 0.029 D_real: 1.355 D_fake: 0.581 +(epoch: 322, iters: 5838, time: 0.063) G_GAN: 0.427 G_GAN_Feat: 0.937 G_ID: 0.128 G_Rec: 0.393 D_GP: 0.039 D_real: 0.889 D_fake: 0.574 +(epoch: 322, iters: 6238, time: 0.063) G_GAN: -0.181 G_GAN_Feat: 0.811 G_ID: 0.115 G_Rec: 0.365 D_GP: 0.128 D_real: 0.477 D_fake: 1.181 +(epoch: 322, iters: 6638, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.995 G_ID: 0.160 G_Rec: 0.453 D_GP: 0.060 D_real: 0.668 D_fake: 0.895 +(epoch: 322, iters: 7038, time: 0.063) G_GAN: 0.422 G_GAN_Feat: 0.812 G_ID: 0.105 G_Rec: 0.342 D_GP: 0.043 D_real: 1.037 D_fake: 0.579 +(epoch: 322, iters: 7438, time: 0.063) G_GAN: 0.825 G_GAN_Feat: 0.981 G_ID: 0.130 G_Rec: 0.441 D_GP: 0.028 D_real: 1.389 D_fake: 0.236 +(epoch: 322, iters: 7838, time: 0.063) G_GAN: 0.274 G_GAN_Feat: 0.795 G_ID: 0.098 G_Rec: 0.305 D_GP: 0.045 D_real: 1.194 D_fake: 0.727 +(epoch: 322, iters: 8238, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 1.082 G_ID: 0.144 G_Rec: 0.439 D_GP: 0.062 D_real: 0.510 D_fake: 0.682 +(epoch: 322, iters: 8638, time: 0.063) G_GAN: 0.242 G_GAN_Feat: 0.947 G_ID: 0.116 G_Rec: 0.318 D_GP: 0.112 D_real: 0.362 D_fake: 0.758 +(epoch: 323, iters: 430, time: 0.063) G_GAN: 0.023 G_GAN_Feat: 1.024 G_ID: 0.152 G_Rec: 0.431 D_GP: 0.036 D_real: 0.841 D_fake: 0.980 +(epoch: 323, iters: 830, time: 0.063) G_GAN: 0.309 G_GAN_Feat: 0.715 G_ID: 0.103 G_Rec: 0.321 D_GP: 0.032 D_real: 1.107 D_fake: 0.697 +(epoch: 323, iters: 1230, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.916 G_ID: 0.118 G_Rec: 0.440 D_GP: 0.029 D_real: 1.076 D_fake: 0.554 +(epoch: 323, iters: 1630, time: 0.063) G_GAN: 0.420 G_GAN_Feat: 0.898 G_ID: 0.112 G_Rec: 0.316 D_GP: 0.034 D_real: 0.737 D_fake: 0.591 +(epoch: 323, iters: 2030, time: 0.063) G_GAN: 0.144 G_GAN_Feat: 1.214 G_ID: 0.142 G_Rec: 0.484 D_GP: 0.337 D_real: 0.288 D_fake: 0.856 +(epoch: 323, iters: 2430, time: 0.063) G_GAN: 0.273 G_GAN_Feat: 0.684 G_ID: 0.113 G_Rec: 0.324 D_GP: 0.025 D_real: 1.173 D_fake: 0.727 +(epoch: 323, iters: 2830, time: 0.064) G_GAN: 0.554 G_GAN_Feat: 0.946 G_ID: 0.123 G_Rec: 0.475 D_GP: 0.032 D_real: 1.147 D_fake: 0.463 +(epoch: 323, iters: 3230, time: 0.063) G_GAN: 0.159 G_GAN_Feat: 0.670 G_ID: 0.095 G_Rec: 0.302 D_GP: 0.030 D_real: 0.986 D_fake: 0.841 +(epoch: 323, iters: 3630, time: 0.063) G_GAN: 0.416 G_GAN_Feat: 1.023 G_ID: 0.121 G_Rec: 0.491 D_GP: 0.079 D_real: 0.794 D_fake: 0.594 +(epoch: 323, iters: 4030, time: 0.063) G_GAN: 0.133 G_GAN_Feat: 0.771 G_ID: 0.108 G_Rec: 0.314 D_GP: 0.060 D_real: 0.713 D_fake: 0.867 +(epoch: 323, iters: 4430, time: 0.064) G_GAN: 0.812 G_GAN_Feat: 0.952 G_ID: 0.131 G_Rec: 0.413 D_GP: 0.050 D_real: 1.408 D_fake: 0.267 +(epoch: 323, iters: 4830, time: 0.063) G_GAN: 0.289 G_GAN_Feat: 0.717 G_ID: 0.094 G_Rec: 0.273 D_GP: 0.034 D_real: 1.131 D_fake: 0.711 +(epoch: 323, iters: 5230, time: 0.063) G_GAN: 0.396 G_GAN_Feat: 1.105 G_ID: 0.120 G_Rec: 0.432 D_GP: 0.057 D_real: 0.553 D_fake: 0.606 +(epoch: 323, iters: 5630, time: 0.063) G_GAN: -0.037 G_GAN_Feat: 0.738 G_ID: 0.115 G_Rec: 0.343 D_GP: 0.026 D_real: 0.888 D_fake: 1.039 +(epoch: 323, iters: 6030, time: 0.064) G_GAN: 0.601 G_GAN_Feat: 0.833 G_ID: 0.121 G_Rec: 0.384 D_GP: 0.031 D_real: 1.268 D_fake: 0.415 +(epoch: 323, iters: 6430, time: 0.063) G_GAN: 0.084 G_GAN_Feat: 0.708 G_ID: 0.110 G_Rec: 0.318 D_GP: 0.034 D_real: 0.986 D_fake: 0.916 +(epoch: 323, iters: 6830, time: 0.063) G_GAN: 0.412 G_GAN_Feat: 0.950 G_ID: 0.138 G_Rec: 0.417 D_GP: 0.054 D_real: 0.910 D_fake: 0.595 +(epoch: 323, iters: 7230, time: 0.063) G_GAN: 0.086 G_GAN_Feat: 0.686 G_ID: 0.104 G_Rec: 0.295 D_GP: 0.050 D_real: 0.891 D_fake: 0.914 +(epoch: 323, iters: 7630, time: 0.064) G_GAN: 0.532 G_GAN_Feat: 0.988 G_ID: 0.146 G_Rec: 0.419 D_GP: 0.057 D_real: 0.904 D_fake: 0.477 +(epoch: 323, iters: 8030, time: 0.063) G_GAN: 0.359 G_GAN_Feat: 0.760 G_ID: 0.099 G_Rec: 0.311 D_GP: 0.032 D_real: 1.181 D_fake: 0.642 +(epoch: 323, iters: 8430, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.950 G_ID: 0.130 G_Rec: 0.421 D_GP: 0.039 D_real: 0.905 D_fake: 0.668 +(epoch: 324, iters: 222, time: 0.063) G_GAN: 0.507 G_GAN_Feat: 0.759 G_ID: 0.104 G_Rec: 0.308 D_GP: 0.032 D_real: 1.287 D_fake: 0.496 +(epoch: 324, iters: 622, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.912 G_ID: 0.153 G_Rec: 0.417 D_GP: 0.029 D_real: 1.219 D_fake: 0.494 +(epoch: 324, iters: 1022, time: 0.063) G_GAN: -0.085 G_GAN_Feat: 0.767 G_ID: 0.106 G_Rec: 0.330 D_GP: 0.061 D_real: 0.564 D_fake: 1.085 +(epoch: 324, iters: 1422, time: 0.063) G_GAN: 0.293 G_GAN_Feat: 1.016 G_ID: 0.129 G_Rec: 0.450 D_GP: 0.056 D_real: 1.147 D_fake: 0.713 +(epoch: 324, iters: 1822, time: 0.063) G_GAN: 0.129 G_GAN_Feat: 0.705 G_ID: 0.128 G_Rec: 0.299 D_GP: 0.035 D_real: 0.938 D_fake: 0.871 +(epoch: 324, iters: 2222, time: 0.063) G_GAN: 0.484 G_GAN_Feat: 0.904 G_ID: 0.138 G_Rec: 0.419 D_GP: 0.034 D_real: 1.059 D_fake: 0.519 +(epoch: 324, iters: 2622, time: 0.063) G_GAN: 0.438 G_GAN_Feat: 0.840 G_ID: 0.119 G_Rec: 0.332 D_GP: 0.077 D_real: 0.938 D_fake: 0.566 +(epoch: 324, iters: 3022, time: 0.063) G_GAN: 0.367 G_GAN_Feat: 1.145 G_ID: 0.123 G_Rec: 0.464 D_GP: 0.910 D_real: 0.545 D_fake: 0.687 +(epoch: 324, iters: 3422, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.736 G_ID: 0.082 G_Rec: 0.315 D_GP: 0.023 D_real: 1.002 D_fake: 0.913 +(epoch: 324, iters: 3822, time: 0.063) G_GAN: 0.280 G_GAN_Feat: 0.874 G_ID: 0.121 G_Rec: 0.399 D_GP: 0.029 D_real: 0.971 D_fake: 0.721 +(epoch: 324, iters: 4222, time: 0.063) G_GAN: -0.046 G_GAN_Feat: 0.760 G_ID: 0.109 G_Rec: 0.317 D_GP: 0.062 D_real: 0.731 D_fake: 1.046 +(epoch: 324, iters: 4622, time: 0.063) G_GAN: 0.562 G_GAN_Feat: 0.928 G_ID: 0.117 G_Rec: 0.384 D_GP: 0.040 D_real: 1.144 D_fake: 0.459 +(epoch: 324, iters: 5022, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.762 G_ID: 0.128 G_Rec: 0.310 D_GP: 0.117 D_real: 0.812 D_fake: 0.861 +(epoch: 324, iters: 5422, time: 0.063) G_GAN: 0.559 G_GAN_Feat: 0.894 G_ID: 0.112 G_Rec: 0.392 D_GP: 0.035 D_real: 1.188 D_fake: 0.444 +(epoch: 324, iters: 5822, time: 0.063) G_GAN: 0.090 G_GAN_Feat: 0.760 G_ID: 0.119 G_Rec: 0.334 D_GP: 0.028 D_real: 0.989 D_fake: 0.910 +(epoch: 324, iters: 6222, time: 0.063) G_GAN: 0.475 G_GAN_Feat: 0.913 G_ID: 0.130 G_Rec: 0.448 D_GP: 0.028 D_real: 1.114 D_fake: 0.539 +(epoch: 324, iters: 6622, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.671 G_ID: 0.105 G_Rec: 0.288 D_GP: 0.031 D_real: 1.041 D_fake: 0.862 +(epoch: 324, iters: 7022, time: 0.063) G_GAN: 0.542 G_GAN_Feat: 0.921 G_ID: 0.138 G_Rec: 0.403 D_GP: 0.047 D_real: 1.120 D_fake: 0.463 +(epoch: 324, iters: 7422, time: 0.063) G_GAN: 0.528 G_GAN_Feat: 0.802 G_ID: 0.100 G_Rec: 0.334 D_GP: 0.055 D_real: 1.200 D_fake: 0.494 +(epoch: 324, iters: 7822, time: 0.063) G_GAN: 0.855 G_GAN_Feat: 1.003 G_ID: 0.145 G_Rec: 0.424 D_GP: 0.121 D_real: 1.269 D_fake: 0.303 +(epoch: 324, iters: 8222, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.750 G_ID: 0.117 G_Rec: 0.311 D_GP: 0.031 D_real: 1.209 D_fake: 0.627 +(epoch: 324, iters: 8622, time: 0.063) G_GAN: 0.396 G_GAN_Feat: 0.998 G_ID: 0.139 G_Rec: 0.440 D_GP: 0.030 D_real: 0.977 D_fake: 0.608 +(epoch: 325, iters: 414, time: 0.063) G_GAN: -0.025 G_GAN_Feat: 0.742 G_ID: 0.092 G_Rec: 0.297 D_GP: 0.033 D_real: 0.780 D_fake: 1.025 +(epoch: 325, iters: 814, time: 0.063) G_GAN: 0.696 G_GAN_Feat: 0.949 G_ID: 0.122 G_Rec: 0.435 D_GP: 0.041 D_real: 1.202 D_fake: 0.323 +(epoch: 325, iters: 1214, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.756 G_ID: 0.100 G_Rec: 0.289 D_GP: 0.032 D_real: 1.253 D_fake: 0.486 +(epoch: 325, iters: 1614, time: 0.063) G_GAN: 0.449 G_GAN_Feat: 1.050 G_ID: 0.128 G_Rec: 0.444 D_GP: 0.043 D_real: 0.862 D_fake: 0.555 +(epoch: 325, iters: 2014, time: 0.063) G_GAN: 0.178 G_GAN_Feat: 1.134 G_ID: 0.113 G_Rec: 0.350 D_GP: 0.029 D_real: 1.547 D_fake: 0.835 +(epoch: 325, iters: 2414, time: 0.063) G_GAN: 0.224 G_GAN_Feat: 0.788 G_ID: 0.141 G_Rec: 0.376 D_GP: 0.025 D_real: 1.054 D_fake: 0.779 +(epoch: 325, iters: 2814, time: 0.064) G_GAN: -0.105 G_GAN_Feat: 0.661 G_ID: 0.106 G_Rec: 0.283 D_GP: 0.023 D_real: 0.806 D_fake: 1.106 +(epoch: 325, iters: 3214, time: 0.063) G_GAN: 0.405 G_GAN_Feat: 0.924 G_ID: 0.130 G_Rec: 0.444 D_GP: 0.036 D_real: 1.056 D_fake: 0.624 +(epoch: 325, iters: 3614, time: 0.063) G_GAN: -0.100 G_GAN_Feat: 0.715 G_ID: 0.129 G_Rec: 0.337 D_GP: 0.030 D_real: 0.651 D_fake: 1.100 +(epoch: 325, iters: 4014, time: 0.063) G_GAN: 0.413 G_GAN_Feat: 0.923 G_ID: 0.123 G_Rec: 0.416 D_GP: 0.032 D_real: 1.038 D_fake: 0.589 +(epoch: 325, iters: 4414, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 0.697 G_ID: 0.115 G_Rec: 0.298 D_GP: 0.035 D_real: 0.861 D_fake: 0.904 +(epoch: 325, iters: 4814, time: 0.063) G_GAN: 0.328 G_GAN_Feat: 0.896 G_ID: 0.143 G_Rec: 0.388 D_GP: 0.050 D_real: 0.910 D_fake: 0.673 +(epoch: 325, iters: 5214, time: 0.063) G_GAN: 0.227 G_GAN_Feat: 0.854 G_ID: 0.104 G_Rec: 0.337 D_GP: 0.063 D_real: 0.694 D_fake: 0.773 +(epoch: 325, iters: 5614, time: 0.063) G_GAN: 0.540 G_GAN_Feat: 0.969 G_ID: 0.130 G_Rec: 0.413 D_GP: 0.060 D_real: 0.936 D_fake: 0.466 +(epoch: 325, iters: 6014, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.684 G_ID: 0.110 G_Rec: 0.296 D_GP: 0.028 D_real: 1.085 D_fake: 0.809 +(epoch: 325, iters: 6414, time: 0.063) G_GAN: 0.452 G_GAN_Feat: 1.129 G_ID: 0.172 G_Rec: 0.501 D_GP: 0.130 D_real: 0.462 D_fake: 0.569 +(epoch: 325, iters: 6814, time: 0.063) G_GAN: 0.252 G_GAN_Feat: 0.823 G_ID: 0.098 G_Rec: 0.307 D_GP: 0.048 D_real: 0.784 D_fake: 0.749 +(epoch: 325, iters: 7214, time: 0.063) G_GAN: 0.278 G_GAN_Feat: 0.965 G_ID: 0.136 G_Rec: 0.431 D_GP: 0.035 D_real: 0.846 D_fake: 0.724 +(epoch: 325, iters: 7614, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.807 G_ID: 0.130 G_Rec: 0.326 D_GP: 0.034 D_real: 0.883 D_fake: 0.745 +(epoch: 325, iters: 8014, time: 0.063) G_GAN: 0.987 G_GAN_Feat: 1.171 G_ID: 0.135 G_Rec: 0.466 D_GP: 0.117 D_real: 0.844 D_fake: 0.141 +(epoch: 325, iters: 8414, time: 0.063) G_GAN: 0.191 G_GAN_Feat: 0.797 G_ID: 0.114 G_Rec: 0.303 D_GP: 0.037 D_real: 1.001 D_fake: 0.818 +(epoch: 326, iters: 206, time: 0.063) G_GAN: 0.378 G_GAN_Feat: 1.044 G_ID: 0.140 G_Rec: 0.476 D_GP: 0.038 D_real: 0.851 D_fake: 0.623 +(epoch: 326, iters: 606, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.673 G_ID: 0.094 G_Rec: 0.273 D_GP: 0.031 D_real: 1.107 D_fake: 0.784 +(epoch: 326, iters: 1006, time: 0.063) G_GAN: 0.723 G_GAN_Feat: 0.964 G_ID: 0.126 G_Rec: 0.429 D_GP: 0.047 D_real: 1.243 D_fake: 0.312 +(epoch: 326, iters: 1406, time: 0.063) G_GAN: 0.349 G_GAN_Feat: 0.796 G_ID: 0.097 G_Rec: 0.300 D_GP: 0.046 D_real: 1.023 D_fake: 0.652 +(epoch: 326, iters: 1806, time: 0.063) G_GAN: 0.673 G_GAN_Feat: 1.133 G_ID: 0.132 G_Rec: 0.436 D_GP: 0.106 D_real: 0.628 D_fake: 0.344 +(epoch: 326, iters: 2206, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 1.108 G_ID: 0.108 G_Rec: 0.367 D_GP: 0.129 D_real: 1.109 D_fake: 0.858 +(epoch: 326, iters: 2606, time: 0.063) G_GAN: 0.499 G_GAN_Feat: 1.036 G_ID: 0.127 G_Rec: 0.450 D_GP: 0.039 D_real: 0.952 D_fake: 0.522 +(epoch: 326, iters: 3006, time: 0.063) G_GAN: 0.157 G_GAN_Feat: 0.775 G_ID: 0.116 G_Rec: 0.310 D_GP: 0.045 D_real: 0.948 D_fake: 0.844 +(epoch: 326, iters: 3406, time: 0.063) G_GAN: 0.756 G_GAN_Feat: 1.238 G_ID: 0.129 G_Rec: 0.464 D_GP: 0.052 D_real: 0.470 D_fake: 0.291 +(epoch: 326, iters: 3806, time: 0.064) G_GAN: 0.068 G_GAN_Feat: 0.796 G_ID: 0.122 G_Rec: 0.346 D_GP: 0.039 D_real: 0.771 D_fake: 0.932 +(epoch: 326, iters: 4206, time: 0.063) G_GAN: 0.642 G_GAN_Feat: 1.234 G_ID: 0.139 G_Rec: 0.483 D_GP: 0.076 D_real: 0.460 D_fake: 0.379 +(epoch: 326, iters: 4606, time: 0.063) G_GAN: -0.075 G_GAN_Feat: 0.911 G_ID: 0.114 G_Rec: 0.346 D_GP: 0.071 D_real: 0.270 D_fake: 1.077 +(epoch: 326, iters: 5006, time: 0.063) G_GAN: 0.778 G_GAN_Feat: 1.176 G_ID: 0.114 G_Rec: 0.455 D_GP: 0.044 D_real: 0.778 D_fake: 0.274 +(epoch: 326, iters: 5406, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.736 G_ID: 0.127 G_Rec: 0.303 D_GP: 0.045 D_real: 1.082 D_fake: 0.795 +(epoch: 326, iters: 5806, time: 0.063) G_GAN: 0.502 G_GAN_Feat: 1.104 G_ID: 0.119 G_Rec: 0.455 D_GP: 0.038 D_real: 0.982 D_fake: 0.503 +(epoch: 326, iters: 6206, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.827 G_ID: 0.111 G_Rec: 0.349 D_GP: 0.038 D_real: 1.262 D_fake: 0.604 +(epoch: 326, iters: 6606, time: 0.063) G_GAN: 0.201 G_GAN_Feat: 0.938 G_ID: 0.133 G_Rec: 0.385 D_GP: 0.036 D_real: 0.853 D_fake: 0.799 +(epoch: 326, iters: 7006, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.828 G_ID: 0.083 G_Rec: 0.314 D_GP: 0.046 D_real: 0.894 D_fake: 0.756 +(epoch: 326, iters: 7406, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 1.009 G_ID: 0.142 G_Rec: 0.453 D_GP: 0.032 D_real: 0.929 D_fake: 0.578 +(epoch: 326, iters: 7806, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 0.729 G_ID: 0.107 G_Rec: 0.335 D_GP: 0.026 D_real: 1.219 D_fake: 0.764 +(epoch: 326, iters: 8206, time: 0.063) G_GAN: 0.654 G_GAN_Feat: 0.914 G_ID: 0.132 G_Rec: 0.446 D_GP: 0.030 D_real: 1.304 D_fake: 0.376 +(epoch: 326, iters: 8606, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.721 G_ID: 0.089 G_Rec: 0.309 D_GP: 0.041 D_real: 1.120 D_fake: 0.717 +(epoch: 327, iters: 398, time: 0.063) G_GAN: 0.639 G_GAN_Feat: 0.908 G_ID: 0.115 G_Rec: 0.421 D_GP: 0.046 D_real: 1.194 D_fake: 0.403 +(epoch: 327, iters: 798, time: 0.063) G_GAN: 0.059 G_GAN_Feat: 0.650 G_ID: 0.107 G_Rec: 0.299 D_GP: 0.038 D_real: 0.928 D_fake: 0.941 +(epoch: 327, iters: 1198, time: 0.063) G_GAN: 0.042 G_GAN_Feat: 0.985 G_ID: 0.155 G_Rec: 0.463 D_GP: 0.071 D_real: 0.489 D_fake: 0.958 +(epoch: 327, iters: 1598, time: 0.064) G_GAN: 0.204 G_GAN_Feat: 0.726 G_ID: 0.127 G_Rec: 0.328 D_GP: 0.038 D_real: 0.961 D_fake: 0.796 +(epoch: 327, iters: 1998, time: 0.063) G_GAN: 0.863 G_GAN_Feat: 0.893 G_ID: 0.115 G_Rec: 0.422 D_GP: 0.027 D_real: 1.579 D_fake: 0.191 +(epoch: 327, iters: 2398, time: 0.063) G_GAN: 0.163 G_GAN_Feat: 0.693 G_ID: 0.102 G_Rec: 0.300 D_GP: 0.040 D_real: 1.025 D_fake: 0.837 +(epoch: 327, iters: 2798, time: 0.063) G_GAN: 0.493 G_GAN_Feat: 1.035 G_ID: 0.127 G_Rec: 0.469 D_GP: 0.060 D_real: 0.759 D_fake: 0.520 +(epoch: 327, iters: 3198, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.983 G_ID: 0.115 G_Rec: 0.355 D_GP: 0.368 D_real: 0.338 D_fake: 0.911 +(epoch: 327, iters: 3598, time: 0.063) G_GAN: 0.377 G_GAN_Feat: 0.980 G_ID: 0.116 G_Rec: 0.421 D_GP: 0.045 D_real: 0.873 D_fake: 0.626 +(epoch: 327, iters: 3998, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.684 G_ID: 0.099 G_Rec: 0.283 D_GP: 0.033 D_real: 1.106 D_fake: 0.786 +(epoch: 327, iters: 4398, time: 0.063) G_GAN: 0.627 G_GAN_Feat: 1.123 G_ID: 0.126 G_Rec: 0.447 D_GP: 0.079 D_real: 0.604 D_fake: 0.418 +(epoch: 327, iters: 4798, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.894 G_ID: 0.103 G_Rec: 0.340 D_GP: 0.042 D_real: 0.792 D_fake: 0.596 +(epoch: 327, iters: 5198, time: 0.063) G_GAN: 0.746 G_GAN_Feat: 1.153 G_ID: 0.151 G_Rec: 0.467 D_GP: 0.086 D_real: 0.612 D_fake: 0.314 +(epoch: 327, iters: 5598, time: 0.063) G_GAN: 0.098 G_GAN_Feat: 0.657 G_ID: 0.105 G_Rec: 0.305 D_GP: 0.032 D_real: 1.047 D_fake: 0.902 +(epoch: 327, iters: 5998, time: 0.063) G_GAN: 0.351 G_GAN_Feat: 0.857 G_ID: 0.139 G_Rec: 0.399 D_GP: 0.031 D_real: 1.009 D_fake: 0.651 +(epoch: 327, iters: 6398, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.650 G_ID: 0.112 G_Rec: 0.316 D_GP: 0.029 D_real: 1.167 D_fake: 0.788 +(epoch: 327, iters: 6798, time: 0.063) G_GAN: 0.394 G_GAN_Feat: 0.912 G_ID: 0.145 G_Rec: 0.443 D_GP: 0.059 D_real: 0.978 D_fake: 0.620 +(epoch: 327, iters: 7198, time: 0.063) G_GAN: 0.090 G_GAN_Feat: 0.700 G_ID: 0.115 G_Rec: 0.309 D_GP: 0.039 D_real: 0.892 D_fake: 0.910 +(epoch: 327, iters: 7598, time: 0.063) G_GAN: 0.386 G_GAN_Feat: 0.783 G_ID: 0.139 G_Rec: 0.356 D_GP: 0.030 D_real: 1.133 D_fake: 0.619 +(epoch: 327, iters: 7998, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.763 G_ID: 0.114 G_Rec: 0.312 D_GP: 0.085 D_real: 0.989 D_fake: 0.730 +(epoch: 327, iters: 8398, time: 0.063) G_GAN: 0.482 G_GAN_Feat: 0.838 G_ID: 0.119 G_Rec: 0.397 D_GP: 0.030 D_real: 1.196 D_fake: 0.540 +(epoch: 328, iters: 190, time: 0.063) G_GAN: -0.165 G_GAN_Feat: 0.827 G_ID: 0.114 G_Rec: 0.330 D_GP: 0.081 D_real: 0.548 D_fake: 1.165 +(epoch: 328, iters: 590, time: 0.063) G_GAN: 0.394 G_GAN_Feat: 0.889 G_ID: 0.127 G_Rec: 0.409 D_GP: 0.030 D_real: 1.072 D_fake: 0.609 +(epoch: 328, iters: 990, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.754 G_ID: 0.100 G_Rec: 0.331 D_GP: 0.031 D_real: 0.896 D_fake: 0.926 +(epoch: 328, iters: 1390, time: 0.063) G_GAN: 0.666 G_GAN_Feat: 1.010 G_ID: 0.117 G_Rec: 0.454 D_GP: 0.037 D_real: 1.062 D_fake: 0.376 +(epoch: 328, iters: 1790, time: 0.063) G_GAN: 0.025 G_GAN_Feat: 0.840 G_ID: 0.112 G_Rec: 0.333 D_GP: 0.068 D_real: 0.535 D_fake: 0.975 +(epoch: 328, iters: 2190, time: 0.063) G_GAN: 0.491 G_GAN_Feat: 0.917 G_ID: 0.123 G_Rec: 0.406 D_GP: 0.038 D_real: 1.099 D_fake: 0.519 +(epoch: 328, iters: 2590, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.890 G_ID: 0.101 G_Rec: 0.342 D_GP: 1.264 D_real: 0.495 D_fake: 0.821 +(epoch: 328, iters: 2990, time: 0.063) G_GAN: 0.127 G_GAN_Feat: 0.905 G_ID: 0.149 G_Rec: 0.406 D_GP: 0.054 D_real: 0.720 D_fake: 0.874 +(epoch: 328, iters: 3390, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.767 G_ID: 0.115 G_Rec: 0.317 D_GP: 0.043 D_real: 0.865 D_fake: 0.815 +(epoch: 328, iters: 3790, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.838 G_ID: 0.129 G_Rec: 0.398 D_GP: 0.028 D_real: 1.185 D_fake: 0.623 +(epoch: 328, iters: 4190, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.695 G_ID: 0.104 G_Rec: 0.302 D_GP: 0.033 D_real: 1.005 D_fake: 0.813 +(epoch: 328, iters: 4590, time: 0.063) G_GAN: 0.368 G_GAN_Feat: 0.980 G_ID: 0.166 G_Rec: 0.445 D_GP: 0.046 D_real: 0.950 D_fake: 0.637 +(epoch: 328, iters: 4990, time: 0.063) G_GAN: -0.170 G_GAN_Feat: 0.887 G_ID: 0.100 G_Rec: 0.330 D_GP: 0.129 D_real: 0.251 D_fake: 1.170 +(epoch: 328, iters: 5390, time: 0.063) G_GAN: 0.502 G_GAN_Feat: 0.976 G_ID: 0.129 G_Rec: 0.454 D_GP: 0.038 D_real: 1.013 D_fake: 0.501 +(epoch: 328, iters: 5790, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.836 G_ID: 0.120 G_Rec: 0.333 D_GP: 0.043 D_real: 0.770 D_fake: 0.796 +(epoch: 328, iters: 6190, time: 0.063) G_GAN: 0.801 G_GAN_Feat: 1.225 G_ID: 0.137 G_Rec: 0.487 D_GP: 0.095 D_real: 0.612 D_fake: 0.257 +(epoch: 328, iters: 6590, time: 0.063) G_GAN: 0.402 G_GAN_Feat: 0.676 G_ID: 0.100 G_Rec: 0.334 D_GP: 0.028 D_real: 1.305 D_fake: 0.600 +(epoch: 328, iters: 6990, time: 0.063) G_GAN: 0.414 G_GAN_Feat: 0.905 G_ID: 0.142 G_Rec: 0.443 D_GP: 0.034 D_real: 1.000 D_fake: 0.596 +(epoch: 328, iters: 7390, time: 0.063) G_GAN: 0.010 G_GAN_Feat: 0.796 G_ID: 0.123 G_Rec: 0.349 D_GP: 0.056 D_real: 0.668 D_fake: 0.990 +(epoch: 328, iters: 7790, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.873 G_ID: 0.130 G_Rec: 0.376 D_GP: 0.095 D_real: 1.025 D_fake: 0.617 +(epoch: 328, iters: 8190, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.678 G_ID: 0.104 G_Rec: 0.292 D_GP: 0.034 D_real: 1.240 D_fake: 0.560 +(epoch: 328, iters: 8590, time: 0.064) G_GAN: 0.535 G_GAN_Feat: 1.042 G_ID: 0.144 G_Rec: 0.460 D_GP: 0.045 D_real: 0.907 D_fake: 0.486 +(epoch: 329, iters: 382, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.741 G_ID: 0.114 G_Rec: 0.307 D_GP: 0.039 D_real: 0.762 D_fake: 0.895 +(epoch: 329, iters: 782, time: 0.064) G_GAN: 0.658 G_GAN_Feat: 0.981 G_ID: 0.128 G_Rec: 0.440 D_GP: 0.085 D_real: 1.008 D_fake: 0.360 +(epoch: 329, iters: 1182, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.944 G_ID: 0.117 G_Rec: 0.349 D_GP: 0.240 D_real: 0.373 D_fake: 0.867 +(epoch: 329, iters: 1582, time: 0.064) G_GAN: 0.635 G_GAN_Feat: 1.044 G_ID: 0.140 G_Rec: 0.448 D_GP: 0.038 D_real: 1.023 D_fake: 0.383 +(epoch: 329, iters: 1982, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.871 G_ID: 0.109 G_Rec: 0.327 D_GP: 0.057 D_real: 0.607 D_fake: 0.764 +(epoch: 329, iters: 2382, time: 0.064) G_GAN: 0.495 G_GAN_Feat: 1.211 G_ID: 0.121 G_Rec: 0.465 D_GP: 1.065 D_real: 0.505 D_fake: 0.552 +(epoch: 329, iters: 2782, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.657 G_ID: 0.095 G_Rec: 0.291 D_GP: 0.030 D_real: 0.971 D_fake: 0.887 +(epoch: 329, iters: 3182, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 1.068 G_ID: 0.123 G_Rec: 0.499 D_GP: 0.043 D_real: 0.825 D_fake: 0.621 +(epoch: 329, iters: 3582, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.771 G_ID: 0.110 G_Rec: 0.316 D_GP: 0.087 D_real: 0.868 D_fake: 0.740 +(epoch: 329, iters: 3982, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 1.036 G_ID: 0.147 G_Rec: 0.455 D_GP: 0.092 D_real: 0.547 D_fake: 0.602 +(epoch: 329, iters: 4382, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.850 G_ID: 0.099 G_Rec: 0.307 D_GP: 0.084 D_real: 0.489 D_fake: 0.908 +(epoch: 329, iters: 4782, time: 0.064) G_GAN: 0.028 G_GAN_Feat: 1.079 G_ID: 0.141 G_Rec: 0.481 D_GP: 0.034 D_real: 0.498 D_fake: 0.972 +(epoch: 329, iters: 5182, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.783 G_ID: 0.087 G_Rec: 0.304 D_GP: 0.045 D_real: 0.945 D_fake: 0.733 +(epoch: 329, iters: 5582, time: 0.064) G_GAN: 0.684 G_GAN_Feat: 1.000 G_ID: 0.150 G_Rec: 0.477 D_GP: 0.033 D_real: 1.229 D_fake: 0.335 +(epoch: 329, iters: 5982, time: 0.064) G_GAN: -0.092 G_GAN_Feat: 0.791 G_ID: 0.117 G_Rec: 0.326 D_GP: 0.063 D_real: 0.745 D_fake: 1.093 +(epoch: 329, iters: 6382, time: 0.064) G_GAN: 0.860 G_GAN_Feat: 1.086 G_ID: 0.116 G_Rec: 0.457 D_GP: 0.101 D_real: 0.891 D_fake: 0.231 +(epoch: 329, iters: 6782, time: 0.063) G_GAN: 0.421 G_GAN_Feat: 0.812 G_ID: 0.107 G_Rec: 0.331 D_GP: 0.143 D_real: 0.862 D_fake: 0.588 +(epoch: 329, iters: 7182, time: 0.063) G_GAN: 0.442 G_GAN_Feat: 0.873 G_ID: 0.132 G_Rec: 0.433 D_GP: 0.029 D_real: 1.149 D_fake: 0.559 +(epoch: 329, iters: 7582, time: 0.063) G_GAN: 0.065 G_GAN_Feat: 0.733 G_ID: 0.141 G_Rec: 0.320 D_GP: 0.034 D_real: 0.925 D_fake: 0.935 +(epoch: 329, iters: 7982, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 1.000 G_ID: 0.139 G_Rec: 0.441 D_GP: 0.036 D_real: 1.183 D_fake: 0.549 +(epoch: 329, iters: 8382, time: 0.063) G_GAN: 0.311 G_GAN_Feat: 0.716 G_ID: 0.132 G_Rec: 0.289 D_GP: 0.042 D_real: 1.194 D_fake: 0.690 +(epoch: 330, iters: 174, time: 0.063) G_GAN: 0.700 G_GAN_Feat: 0.909 G_ID: 0.119 G_Rec: 0.445 D_GP: 0.029 D_real: 1.335 D_fake: 0.348 +(epoch: 330, iters: 574, time: 0.063) G_GAN: 0.171 G_GAN_Feat: 0.773 G_ID: 0.108 G_Rec: 0.319 D_GP: 0.047 D_real: 0.782 D_fake: 0.830 +(epoch: 330, iters: 974, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 1.135 G_ID: 0.137 G_Rec: 0.447 D_GP: 0.069 D_real: 0.445 D_fake: 0.600 +(epoch: 330, iters: 1374, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.925 G_ID: 0.094 G_Rec: 0.334 D_GP: 0.491 D_real: 0.397 D_fake: 0.703 +(epoch: 330, iters: 1774, time: 0.063) G_GAN: 0.460 G_GAN_Feat: 0.912 G_ID: 0.129 G_Rec: 0.409 D_GP: 0.032 D_real: 1.188 D_fake: 0.555 +(epoch: 330, iters: 2174, time: 0.063) G_GAN: 0.149 G_GAN_Feat: 0.795 G_ID: 0.118 G_Rec: 0.308 D_GP: 0.038 D_real: 0.809 D_fake: 0.853 +(epoch: 330, iters: 2574, time: 0.064) G_GAN: 0.569 G_GAN_Feat: 1.046 G_ID: 0.153 G_Rec: 0.429 D_GP: 0.033 D_real: 0.729 D_fake: 0.434 +(epoch: 330, iters: 2974, time: 0.063) G_GAN: 0.352 G_GAN_Feat: 0.786 G_ID: 0.119 G_Rec: 0.330 D_GP: 0.034 D_real: 1.166 D_fake: 0.656 +(epoch: 330, iters: 3374, time: 0.063) G_GAN: 0.757 G_GAN_Feat: 1.016 G_ID: 0.168 G_Rec: 0.424 D_GP: 0.072 D_real: 1.066 D_fake: 0.299 +(epoch: 330, iters: 3774, time: 0.063) G_GAN: 0.312 G_GAN_Feat: 0.777 G_ID: 0.119 G_Rec: 0.295 D_GP: 0.041 D_real: 1.080 D_fake: 0.699 +(epoch: 330, iters: 4174, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.950 G_ID: 0.144 G_Rec: 0.410 D_GP: 0.031 D_real: 0.927 D_fake: 0.750 +(epoch: 330, iters: 4574, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.850 G_ID: 0.111 G_Rec: 0.315 D_GP: 0.031 D_real: 0.882 D_fake: 0.752 +(epoch: 330, iters: 4974, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 1.028 G_ID: 0.144 G_Rec: 0.447 D_GP: 0.036 D_real: 1.105 D_fake: 0.353 +(epoch: 330, iters: 5374, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.760 G_ID: 0.096 G_Rec: 0.315 D_GP: 0.033 D_real: 0.911 D_fake: 0.844 +(epoch: 330, iters: 5774, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 1.161 G_ID: 0.121 G_Rec: 0.484 D_GP: 0.115 D_real: 0.626 D_fake: 0.503 +(epoch: 330, iters: 6174, time: 0.064) G_GAN: -0.057 G_GAN_Feat: 0.792 G_ID: 0.132 G_Rec: 0.325 D_GP: 0.047 D_real: 0.626 D_fake: 1.057 +(epoch: 330, iters: 6574, time: 0.064) G_GAN: 0.770 G_GAN_Feat: 0.955 G_ID: 0.147 G_Rec: 0.414 D_GP: 0.033 D_real: 1.111 D_fake: 0.307 +(epoch: 330, iters: 6974, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.703 G_ID: 0.111 G_Rec: 0.282 D_GP: 0.051 D_real: 1.009 D_fake: 0.812 +(epoch: 330, iters: 7374, time: 0.064) G_GAN: 0.563 G_GAN_Feat: 1.023 G_ID: 0.140 G_Rec: 0.445 D_GP: 0.055 D_real: 0.960 D_fake: 0.445 +(epoch: 330, iters: 7774, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.833 G_ID: 0.098 G_Rec: 0.322 D_GP: 0.043 D_real: 0.951 D_fake: 0.721 +(epoch: 330, iters: 8174, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 1.101 G_ID: 0.131 G_Rec: 0.469 D_GP: 0.110 D_real: 0.911 D_fake: 1.004 +(epoch: 330, iters: 8574, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.650 G_ID: 0.118 G_Rec: 0.307 D_GP: 0.026 D_real: 0.956 D_fake: 1.031 +(epoch: 331, iters: 366, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.840 G_ID: 0.134 G_Rec: 0.439 D_GP: 0.029 D_real: 1.108 D_fake: 0.619 +(epoch: 331, iters: 766, time: 0.064) G_GAN: -0.016 G_GAN_Feat: 0.652 G_ID: 0.111 G_Rec: 0.292 D_GP: 0.035 D_real: 0.826 D_fake: 1.016 +(epoch: 331, iters: 1166, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.911 G_ID: 0.114 G_Rec: 0.440 D_GP: 0.034 D_real: 0.856 D_fake: 0.813 +(epoch: 331, iters: 1566, time: 0.064) G_GAN: -0.075 G_GAN_Feat: 0.660 G_ID: 0.115 G_Rec: 0.323 D_GP: 0.031 D_real: 0.776 D_fake: 1.075 +(epoch: 331, iters: 1966, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.845 G_ID: 0.129 G_Rec: 0.389 D_GP: 0.035 D_real: 0.949 D_fake: 0.662 +(epoch: 331, iters: 2366, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.677 G_ID: 0.098 G_Rec: 0.288 D_GP: 0.036 D_real: 0.978 D_fake: 0.870 +(epoch: 331, iters: 2766, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.978 G_ID: 0.114 G_Rec: 0.471 D_GP: 0.050 D_real: 0.968 D_fake: 0.522 +(epoch: 331, iters: 3166, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.696 G_ID: 0.108 G_Rec: 0.301 D_GP: 0.035 D_real: 0.977 D_fake: 0.906 +(epoch: 331, iters: 3566, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 1.085 G_ID: 0.103 G_Rec: 0.477 D_GP: 0.085 D_real: 0.524 D_fake: 0.839 +(epoch: 331, iters: 3966, time: 0.063) G_GAN: 0.072 G_GAN_Feat: 0.782 G_ID: 0.123 G_Rec: 0.333 D_GP: 0.073 D_real: 0.745 D_fake: 0.929 +(epoch: 331, iters: 4366, time: 0.063) G_GAN: 0.541 G_GAN_Feat: 0.975 G_ID: 0.135 G_Rec: 0.419 D_GP: 0.066 D_real: 1.157 D_fake: 0.507 +(epoch: 331, iters: 4766, time: 0.063) G_GAN: 0.233 G_GAN_Feat: 0.704 G_ID: 0.126 G_Rec: 0.314 D_GP: 0.029 D_real: 1.090 D_fake: 0.768 +(epoch: 331, iters: 5166, time: 0.064) G_GAN: 0.594 G_GAN_Feat: 0.916 G_ID: 0.122 G_Rec: 0.431 D_GP: 0.050 D_real: 1.023 D_fake: 0.439 +(epoch: 331, iters: 5566, time: 0.063) G_GAN: 0.147 G_GAN_Feat: 0.787 G_ID: 0.104 G_Rec: 0.346 D_GP: 0.084 D_real: 0.817 D_fake: 0.856 +(epoch: 331, iters: 5966, time: 0.063) G_GAN: 0.659 G_GAN_Feat: 0.956 G_ID: 0.126 G_Rec: 0.410 D_GP: 0.054 D_real: 1.133 D_fake: 0.412 +(epoch: 331, iters: 6366, time: 0.063) G_GAN: 0.058 G_GAN_Feat: 0.786 G_ID: 0.114 G_Rec: 0.315 D_GP: 0.043 D_real: 0.741 D_fake: 0.942 +(epoch: 331, iters: 6766, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.929 G_ID: 0.119 G_Rec: 0.446 D_GP: 0.028 D_real: 0.755 D_fake: 0.875 +(epoch: 331, iters: 7166, time: 0.063) G_GAN: 0.507 G_GAN_Feat: 0.860 G_ID: 0.112 G_Rec: 0.351 D_GP: 0.094 D_real: 0.923 D_fake: 0.509 +(epoch: 331, iters: 7566, time: 0.063) G_GAN: 0.473 G_GAN_Feat: 0.863 G_ID: 0.113 G_Rec: 0.383 D_GP: 0.029 D_real: 1.120 D_fake: 0.533 +(epoch: 331, iters: 7966, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.865 G_ID: 0.105 G_Rec: 0.376 D_GP: 0.326 D_real: 0.721 D_fake: 0.804 +(epoch: 331, iters: 8366, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.854 G_ID: 0.148 G_Rec: 0.370 D_GP: 0.032 D_real: 1.146 D_fake: 0.528 +(epoch: 332, iters: 158, time: 0.063) G_GAN: 0.222 G_GAN_Feat: 0.725 G_ID: 0.107 G_Rec: 0.298 D_GP: 0.033 D_real: 1.077 D_fake: 0.778 +(epoch: 332, iters: 558, time: 0.063) G_GAN: 0.270 G_GAN_Feat: 0.933 G_ID: 0.168 G_Rec: 0.405 D_GP: 0.033 D_real: 0.860 D_fake: 0.730 +(epoch: 332, iters: 958, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.822 G_ID: 0.119 G_Rec: 0.342 D_GP: 0.044 D_real: 0.781 D_fake: 0.876 +(epoch: 332, iters: 1358, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.929 G_ID: 0.145 G_Rec: 0.413 D_GP: 0.050 D_real: 0.781 D_fake: 0.679 +(epoch: 332, iters: 1758, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.841 G_ID: 0.090 G_Rec: 0.351 D_GP: 0.081 D_real: 0.849 D_fake: 0.818 +(epoch: 332, iters: 2158, time: 0.063) G_GAN: 0.257 G_GAN_Feat: 1.195 G_ID: 0.167 G_Rec: 0.477 D_GP: 0.169 D_real: 0.174 D_fake: 0.755 +(epoch: 332, iters: 2558, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.909 G_ID: 0.108 G_Rec: 0.418 D_GP: 0.063 D_real: 0.640 D_fake: 0.668 +(epoch: 332, iters: 2958, time: 0.064) G_GAN: 0.855 G_GAN_Feat: 0.971 G_ID: 0.128 G_Rec: 0.436 D_GP: 0.039 D_real: 1.453 D_fake: 0.215 +(epoch: 332, iters: 3358, time: 0.063) G_GAN: 0.356 G_GAN_Feat: 0.715 G_ID: 0.123 G_Rec: 0.327 D_GP: 0.030 D_real: 1.160 D_fake: 0.646 +(epoch: 332, iters: 3758, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 0.949 G_ID: 0.131 G_Rec: 0.445 D_GP: 0.040 D_real: 1.032 D_fake: 0.595 +(epoch: 332, iters: 4158, time: 0.063) G_GAN: 0.087 G_GAN_Feat: 0.676 G_ID: 0.088 G_Rec: 0.301 D_GP: 0.038 D_real: 0.947 D_fake: 0.913 +(epoch: 332, iters: 4558, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 1.157 G_ID: 0.147 G_Rec: 0.504 D_GP: 0.084 D_real: 0.497 D_fake: 0.624 +(epoch: 332, iters: 4958, time: 0.063) G_GAN: 0.254 G_GAN_Feat: 0.779 G_ID: 0.097 G_Rec: 0.321 D_GP: 0.033 D_real: 0.966 D_fake: 0.746 +(epoch: 332, iters: 5358, time: 0.063) G_GAN: 0.838 G_GAN_Feat: 1.120 G_ID: 0.134 G_Rec: 0.477 D_GP: 0.066 D_real: 0.966 D_fake: 0.236 +(epoch: 332, iters: 5758, time: 0.063) G_GAN: 0.205 G_GAN_Feat: 0.791 G_ID: 0.115 G_Rec: 0.300 D_GP: 0.069 D_real: 0.807 D_fake: 0.796 +(epoch: 332, iters: 6158, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 1.106 G_ID: 0.120 G_Rec: 0.477 D_GP: 0.135 D_real: 0.529 D_fake: 0.526 +(epoch: 332, iters: 6558, time: 0.063) G_GAN: 0.223 G_GAN_Feat: 0.963 G_ID: 0.106 G_Rec: 0.330 D_GP: 0.469 D_real: 0.253 D_fake: 0.781 +(epoch: 332, iters: 6958, time: 0.063) G_GAN: 0.268 G_GAN_Feat: 0.932 G_ID: 0.139 G_Rec: 0.442 D_GP: 0.031 D_real: 1.043 D_fake: 0.733 +(epoch: 332, iters: 7358, time: 0.063) G_GAN: 0.187 G_GAN_Feat: 0.856 G_ID: 0.122 G_Rec: 0.321 D_GP: 0.035 D_real: 0.946 D_fake: 0.815 +(epoch: 332, iters: 7758, time: 0.064) G_GAN: 0.654 G_GAN_Feat: 0.997 G_ID: 0.125 G_Rec: 0.453 D_GP: 0.029 D_real: 1.156 D_fake: 0.387 +(epoch: 332, iters: 8158, time: 0.063) G_GAN: 0.462 G_GAN_Feat: 0.751 G_ID: 0.112 G_Rec: 0.297 D_GP: 0.034 D_real: 1.270 D_fake: 0.539 +(epoch: 332, iters: 8558, time: 0.063) G_GAN: 0.849 G_GAN_Feat: 1.023 G_ID: 0.119 G_Rec: 0.490 D_GP: 0.031 D_real: 1.287 D_fake: 0.259 +(epoch: 333, iters: 350, time: 0.063) G_GAN: 0.116 G_GAN_Feat: 0.766 G_ID: 0.114 G_Rec: 0.330 D_GP: 0.030 D_real: 0.936 D_fake: 0.884 +(epoch: 333, iters: 750, time: 0.064) G_GAN: 0.696 G_GAN_Feat: 0.960 G_ID: 0.129 G_Rec: 0.415 D_GP: 0.030 D_real: 1.200 D_fake: 0.332 +(epoch: 333, iters: 1150, time: 0.063) G_GAN: 0.248 G_GAN_Feat: 0.848 G_ID: 0.115 G_Rec: 0.297 D_GP: 0.051 D_real: 0.717 D_fake: 0.752 +(epoch: 333, iters: 1550, time: 0.063) G_GAN: 0.403 G_GAN_Feat: 0.943 G_ID: 0.134 G_Rec: 0.414 D_GP: 0.043 D_real: 1.020 D_fake: 0.601 +(epoch: 333, iters: 1950, time: 0.063) G_GAN: 0.665 G_GAN_Feat: 0.716 G_ID: 0.112 G_Rec: 0.352 D_GP: 0.046 D_real: 1.502 D_fake: 0.479 +(epoch: 333, iters: 2350, time: 0.064) G_GAN: 0.606 G_GAN_Feat: 0.914 G_ID: 0.120 G_Rec: 0.447 D_GP: 0.030 D_real: 1.258 D_fake: 0.425 +(epoch: 333, iters: 2750, time: 0.063) G_GAN: 0.105 G_GAN_Feat: 0.658 G_ID: 0.108 G_Rec: 0.297 D_GP: 0.031 D_real: 0.947 D_fake: 0.895 +(epoch: 333, iters: 3150, time: 0.063) G_GAN: 0.485 G_GAN_Feat: 0.866 G_ID: 0.137 G_Rec: 0.411 D_GP: 0.041 D_real: 1.044 D_fake: 0.538 +(epoch: 333, iters: 3550, time: 0.063) G_GAN: 0.587 G_GAN_Feat: 0.690 G_ID: 0.094 G_Rec: 0.299 D_GP: 0.029 D_real: 1.515 D_fake: 0.448 +(epoch: 333, iters: 3950, time: 0.063) G_GAN: 0.266 G_GAN_Feat: 0.963 G_ID: 0.157 G_Rec: 0.436 D_GP: 0.065 D_real: 0.727 D_fake: 0.738 +(epoch: 333, iters: 4350, time: 0.063) G_GAN: 0.247 G_GAN_Feat: 0.754 G_ID: 0.104 G_Rec: 0.332 D_GP: 0.036 D_real: 0.985 D_fake: 0.755 +(epoch: 333, iters: 4750, time: 0.063) G_GAN: 0.599 G_GAN_Feat: 1.076 G_ID: 0.131 G_Rec: 0.451 D_GP: 0.239 D_real: 0.539 D_fake: 0.445 +(epoch: 333, iters: 5150, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.718 G_ID: 0.095 G_Rec: 0.311 D_GP: 0.033 D_real: 1.185 D_fake: 0.756 +(epoch: 333, iters: 5550, time: 0.063) G_GAN: 0.057 G_GAN_Feat: 1.135 G_ID: 0.122 G_Rec: 0.488 D_GP: 0.065 D_real: 0.804 D_fake: 0.946 +(epoch: 333, iters: 5950, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.763 G_ID: 0.134 G_Rec: 0.324 D_GP: 0.061 D_real: 0.818 D_fake: 0.789 +(epoch: 333, iters: 6350, time: 0.063) G_GAN: 0.624 G_GAN_Feat: 0.972 G_ID: 0.137 G_Rec: 0.431 D_GP: 0.037 D_real: 1.092 D_fake: 0.416 +(epoch: 333, iters: 6750, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.747 G_ID: 0.099 G_Rec: 0.323 D_GP: 0.035 D_real: 0.965 D_fake: 0.964 +(epoch: 333, iters: 7150, time: 0.063) G_GAN: 0.364 G_GAN_Feat: 0.953 G_ID: 0.133 G_Rec: 0.432 D_GP: 0.037 D_real: 1.023 D_fake: 0.636 +(epoch: 333, iters: 7550, time: 0.063) G_GAN: 0.171 G_GAN_Feat: 0.736 G_ID: 0.132 G_Rec: 0.292 D_GP: 0.033 D_real: 1.048 D_fake: 0.829 +(epoch: 333, iters: 7950, time: 0.063) G_GAN: 0.505 G_GAN_Feat: 1.155 G_ID: 0.133 G_Rec: 0.486 D_GP: 0.037 D_real: 0.825 D_fake: 0.504 +(epoch: 333, iters: 8350, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.760 G_ID: 0.125 G_Rec: 0.324 D_GP: 0.031 D_real: 1.148 D_fake: 0.730 +(epoch: 334, iters: 142, time: 0.063) G_GAN: 0.576 G_GAN_Feat: 1.002 G_ID: 0.120 G_Rec: 0.426 D_GP: 0.044 D_real: 1.150 D_fake: 0.461 +(epoch: 334, iters: 542, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 0.891 G_ID: 0.115 G_Rec: 0.368 D_GP: 0.089 D_real: 0.927 D_fake: 0.656 +(epoch: 334, iters: 942, time: 0.063) G_GAN: 0.347 G_GAN_Feat: 1.217 G_ID: 0.129 G_Rec: 0.505 D_GP: 0.486 D_real: 0.311 D_fake: 0.657 +(epoch: 334, iters: 1342, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.880 G_ID: 0.096 G_Rec: 0.299 D_GP: 0.034 D_real: 1.101 D_fake: 0.565 +(epoch: 334, iters: 1742, time: 0.063) G_GAN: 0.868 G_GAN_Feat: 1.241 G_ID: 0.127 G_Rec: 0.442 D_GP: 0.054 D_real: 0.620 D_fake: 0.188 +(epoch: 334, iters: 2142, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 1.024 G_ID: 0.112 G_Rec: 0.388 D_GP: 0.360 D_real: 0.089 D_fake: 0.895 +(epoch: 334, iters: 2542, time: 0.063) G_GAN: 0.586 G_GAN_Feat: 1.002 G_ID: 0.133 G_Rec: 0.409 D_GP: 0.031 D_real: 1.127 D_fake: 0.422 +(epoch: 334, iters: 2942, time: 0.064) G_GAN: 0.754 G_GAN_Feat: 0.897 G_ID: 0.105 G_Rec: 0.308 D_GP: 0.044 D_real: 1.163 D_fake: 0.366 +(epoch: 334, iters: 3342, time: 0.063) G_GAN: 0.759 G_GAN_Feat: 1.186 G_ID: 0.116 G_Rec: 0.503 D_GP: 0.040 D_real: 1.337 D_fake: 0.338 +(epoch: 334, iters: 3742, time: 0.063) G_GAN: 0.111 G_GAN_Feat: 0.654 G_ID: 0.116 G_Rec: 0.308 D_GP: 0.024 D_real: 1.099 D_fake: 0.890 +(epoch: 334, iters: 4142, time: 0.063) G_GAN: 0.369 G_GAN_Feat: 0.835 G_ID: 0.113 G_Rec: 0.434 D_GP: 0.026 D_real: 1.162 D_fake: 0.636 +(epoch: 334, iters: 4542, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.626 G_ID: 0.094 G_Rec: 0.299 D_GP: 0.027 D_real: 1.122 D_fake: 0.839 +(epoch: 334, iters: 4942, time: 0.063) G_GAN: 0.325 G_GAN_Feat: 0.800 G_ID: 0.130 G_Rec: 0.376 D_GP: 0.029 D_real: 1.115 D_fake: 0.679 +(epoch: 334, iters: 5342, time: 0.063) G_GAN: 0.041 G_GAN_Feat: 0.697 G_ID: 0.131 G_Rec: 0.314 D_GP: 0.031 D_real: 0.934 D_fake: 0.960 +(epoch: 334, iters: 5742, time: 0.063) G_GAN: 0.338 G_GAN_Feat: 0.813 G_ID: 0.123 G_Rec: 0.391 D_GP: 0.027 D_real: 1.098 D_fake: 0.668 +(epoch: 334, iters: 6142, time: 0.064) G_GAN: -0.011 G_GAN_Feat: 0.639 G_ID: 0.123 G_Rec: 0.275 D_GP: 0.034 D_real: 0.844 D_fake: 1.011 +(epoch: 334, iters: 6542, time: 0.063) G_GAN: 0.311 G_GAN_Feat: 0.860 G_ID: 0.131 G_Rec: 0.395 D_GP: 0.042 D_real: 0.920 D_fake: 0.695 +(epoch: 334, iters: 6942, time: 0.063) G_GAN: -0.163 G_GAN_Feat: 0.716 G_ID: 0.116 G_Rec: 0.307 D_GP: 0.038 D_real: 0.620 D_fake: 1.163 +(epoch: 334, iters: 7342, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.866 G_ID: 0.115 G_Rec: 0.405 D_GP: 0.034 D_real: 1.123 D_fake: 0.569 +(epoch: 334, iters: 7742, time: 0.064) G_GAN: -0.168 G_GAN_Feat: 0.731 G_ID: 0.105 G_Rec: 0.326 D_GP: 0.079 D_real: 0.681 D_fake: 1.168 +(epoch: 334, iters: 8142, time: 0.063) G_GAN: 0.485 G_GAN_Feat: 0.965 G_ID: 0.146 G_Rec: 0.435 D_GP: 0.042 D_real: 0.906 D_fake: 0.536 +(epoch: 334, iters: 8542, time: 0.063) G_GAN: 0.101 G_GAN_Feat: 0.813 G_ID: 0.118 G_Rec: 0.341 D_GP: 0.044 D_real: 0.799 D_fake: 0.900 +(epoch: 335, iters: 334, time: 0.063) G_GAN: 0.159 G_GAN_Feat: 0.916 G_ID: 0.139 G_Rec: 0.424 D_GP: 0.037 D_real: 0.708 D_fake: 0.842 +(epoch: 335, iters: 734, time: 0.064) G_GAN: -0.005 G_GAN_Feat: 0.740 G_ID: 0.096 G_Rec: 0.315 D_GP: 0.076 D_real: 0.742 D_fake: 1.006 +(epoch: 335, iters: 1134, time: 0.063) G_GAN: 0.693 G_GAN_Feat: 0.946 G_ID: 0.126 G_Rec: 0.432 D_GP: 0.033 D_real: 1.308 D_fake: 0.357 +(epoch: 335, iters: 1534, time: 0.063) G_GAN: 0.051 G_GAN_Feat: 0.682 G_ID: 0.093 G_Rec: 0.292 D_GP: 0.028 D_real: 0.918 D_fake: 0.949 +(epoch: 335, iters: 1934, time: 0.063) G_GAN: 0.721 G_GAN_Feat: 0.993 G_ID: 0.127 G_Rec: 0.433 D_GP: 0.061 D_real: 1.185 D_fake: 0.337 +(epoch: 335, iters: 2334, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.693 G_ID: 0.109 G_Rec: 0.304 D_GP: 0.030 D_real: 0.992 D_fake: 0.907 +(epoch: 335, iters: 2734, time: 0.063) G_GAN: 0.353 G_GAN_Feat: 1.129 G_ID: 0.134 G_Rec: 0.509 D_GP: 0.360 D_real: 0.502 D_fake: 0.661 +(epoch: 335, iters: 3134, time: 0.063) G_GAN: 0.390 G_GAN_Feat: 0.706 G_ID: 0.096 G_Rec: 0.315 D_GP: 0.026 D_real: 1.209 D_fake: 0.616 +(epoch: 335, iters: 3534, time: 0.063) G_GAN: 0.576 G_GAN_Feat: 0.990 G_ID: 0.126 G_Rec: 0.450 D_GP: 0.035 D_real: 1.141 D_fake: 0.441 +(epoch: 335, iters: 3934, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.714 G_ID: 0.107 G_Rec: 0.335 D_GP: 0.030 D_real: 1.204 D_fake: 0.713 +(epoch: 335, iters: 4334, time: 0.063) G_GAN: 0.506 G_GAN_Feat: 0.934 G_ID: 0.131 G_Rec: 0.437 D_GP: 0.037 D_real: 1.121 D_fake: 0.519 +(epoch: 335, iters: 4734, time: 0.063) G_GAN: -0.340 G_GAN_Feat: 0.774 G_ID: 0.120 G_Rec: 0.320 D_GP: 0.072 D_real: 0.497 D_fake: 1.340 +(epoch: 335, iters: 5134, time: 0.063) G_GAN: 0.518 G_GAN_Feat: 1.083 G_ID: 0.126 G_Rec: 0.465 D_GP: 0.161 D_real: 0.739 D_fake: 0.515 +(epoch: 335, iters: 5534, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.793 G_ID: 0.106 G_Rec: 0.305 D_GP: 0.059 D_real: 1.025 D_fake: 0.617 +(epoch: 335, iters: 5934, time: 0.063) G_GAN: 0.493 G_GAN_Feat: 1.079 G_ID: 0.130 G_Rec: 0.473 D_GP: 0.099 D_real: 0.607 D_fake: 0.526 +(epoch: 335, iters: 6334, time: 0.063) G_GAN: -0.003 G_GAN_Feat: 0.784 G_ID: 0.116 G_Rec: 0.334 D_GP: 0.032 D_real: 0.879 D_fake: 1.003 +(epoch: 335, iters: 6734, time: 0.063) G_GAN: 0.446 G_GAN_Feat: 0.946 G_ID: 0.119 G_Rec: 0.463 D_GP: 0.032 D_real: 1.041 D_fake: 0.559 +(epoch: 335, iters: 7134, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.805 G_ID: 0.111 G_Rec: 0.313 D_GP: 0.064 D_real: 0.814 D_fake: 0.750 +(epoch: 335, iters: 7534, time: 0.063) G_GAN: 0.867 G_GAN_Feat: 0.998 G_ID: 0.120 G_Rec: 0.419 D_GP: 0.044 D_real: 1.353 D_fake: 0.238 +(epoch: 335, iters: 7934, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.790 G_ID: 0.113 G_Rec: 0.339 D_GP: 0.032 D_real: 1.165 D_fake: 0.705 +(epoch: 335, iters: 8334, time: 0.063) G_GAN: 0.574 G_GAN_Feat: 0.822 G_ID: 0.108 G_Rec: 0.419 D_GP: 0.028 D_real: 1.264 D_fake: 0.438 +(epoch: 336, iters: 126, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.744 G_ID: 0.107 G_Rec: 0.286 D_GP: 0.045 D_real: 1.081 D_fake: 0.697 +(epoch: 336, iters: 526, time: 0.063) G_GAN: 0.342 G_GAN_Feat: 0.997 G_ID: 0.153 G_Rec: 0.441 D_GP: 0.034 D_real: 0.866 D_fake: 0.665 +(epoch: 336, iters: 926, time: 0.063) G_GAN: 0.184 G_GAN_Feat: 0.738 G_ID: 0.110 G_Rec: 0.297 D_GP: 0.038 D_real: 0.954 D_fake: 0.818 +(epoch: 336, iters: 1326, time: 0.064) G_GAN: 0.804 G_GAN_Feat: 1.140 G_ID: 0.125 G_Rec: 0.536 D_GP: 0.050 D_real: 0.986 D_fake: 0.300 +(epoch: 336, iters: 1726, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.884 G_ID: 0.106 G_Rec: 0.330 D_GP: 0.076 D_real: 0.739 D_fake: 0.765 +(epoch: 336, iters: 2126, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 1.021 G_ID: 0.127 G_Rec: 0.437 D_GP: 0.057 D_real: 0.524 D_fake: 0.677 +(epoch: 336, iters: 2526, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.770 G_ID: 0.112 G_Rec: 0.354 D_GP: 0.033 D_real: 0.896 D_fake: 0.908 +(epoch: 336, iters: 2926, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 1.097 G_ID: 0.138 G_Rec: 0.434 D_GP: 0.048 D_real: 0.464 D_fake: 0.667 +(epoch: 336, iters: 3326, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.747 G_ID: 0.100 G_Rec: 0.306 D_GP: 0.058 D_real: 0.911 D_fake: 0.809 +(epoch: 336, iters: 3726, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.977 G_ID: 0.147 G_Rec: 0.463 D_GP: 0.031 D_real: 0.967 D_fake: 0.614 +(epoch: 336, iters: 4126, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.743 G_ID: 0.097 G_Rec: 0.296 D_GP: 0.034 D_real: 1.263 D_fake: 0.525 +(epoch: 336, iters: 4526, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.978 G_ID: 0.142 G_Rec: 0.443 D_GP: 0.034 D_real: 1.044 D_fake: 0.631 +(epoch: 336, iters: 4926, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.807 G_ID: 0.114 G_Rec: 0.343 D_GP: 0.033 D_real: 0.876 D_fake: 0.840 +(epoch: 336, iters: 5326, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 1.205 G_ID: 0.112 G_Rec: 0.465 D_GP: 0.413 D_real: 0.271 D_fake: 0.490 +(epoch: 336, iters: 5726, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.688 G_ID: 0.099 G_Rec: 0.292 D_GP: 0.028 D_real: 0.967 D_fake: 0.959 +(epoch: 336, iters: 6126, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.955 G_ID: 0.138 G_Rec: 0.432 D_GP: 0.052 D_real: 0.951 D_fake: 0.498 +(epoch: 336, iters: 6526, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.908 G_ID: 0.113 G_Rec: 0.330 D_GP: 0.068 D_real: 0.474 D_fake: 0.674 +(epoch: 336, iters: 6926, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 0.961 G_ID: 0.120 G_Rec: 0.509 D_GP: 0.036 D_real: 1.132 D_fake: 0.495 +(epoch: 336, iters: 7326, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.609 G_ID: 0.110 G_Rec: 0.280 D_GP: 0.031 D_real: 1.100 D_fake: 0.831 +(epoch: 336, iters: 7726, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.910 G_ID: 0.136 G_Rec: 0.445 D_GP: 0.040 D_real: 0.941 D_fake: 0.749 +(epoch: 336, iters: 8126, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.766 G_ID: 0.098 G_Rec: 0.326 D_GP: 0.058 D_real: 0.950 D_fake: 0.768 +(epoch: 336, iters: 8526, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 1.005 G_ID: 0.133 G_Rec: 0.478 D_GP: 0.104 D_real: 0.418 D_fake: 0.888 +(epoch: 337, iters: 318, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.701 G_ID: 0.110 G_Rec: 0.296 D_GP: 0.030 D_real: 1.228 D_fake: 0.643 +(epoch: 337, iters: 718, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.961 G_ID: 0.128 G_Rec: 0.408 D_GP: 0.061 D_real: 0.740 D_fake: 0.619 +(epoch: 337, iters: 1118, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.847 G_ID: 0.105 G_Rec: 0.315 D_GP: 0.055 D_real: 0.623 D_fake: 0.776 +(epoch: 337, iters: 1518, time: 0.063) G_GAN: 0.728 G_GAN_Feat: 1.060 G_ID: 0.140 G_Rec: 0.440 D_GP: 0.042 D_real: 1.081 D_fake: 0.315 +(epoch: 337, iters: 1918, time: 0.063) G_GAN: 0.040 G_GAN_Feat: 0.782 G_ID: 0.118 G_Rec: 0.323 D_GP: 0.067 D_real: 0.630 D_fake: 0.960 +(epoch: 337, iters: 2318, time: 0.063) G_GAN: 0.492 G_GAN_Feat: 0.952 G_ID: 0.138 G_Rec: 0.436 D_GP: 0.034 D_real: 1.094 D_fake: 0.512 +(epoch: 337, iters: 2718, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.666 G_ID: 0.091 G_Rec: 0.290 D_GP: 0.035 D_real: 1.191 D_fake: 0.752 +(epoch: 337, iters: 3118, time: 0.063) G_GAN: 0.583 G_GAN_Feat: 1.027 G_ID: 0.127 G_Rec: 0.464 D_GP: 0.038 D_real: 1.026 D_fake: 0.438 +(epoch: 337, iters: 3518, time: 0.063) G_GAN: 0.098 G_GAN_Feat: 0.826 G_ID: 0.108 G_Rec: 0.333 D_GP: 0.089 D_real: 0.524 D_fake: 0.903 +(epoch: 337, iters: 3918, time: 0.063) G_GAN: 0.389 G_GAN_Feat: 0.921 G_ID: 0.130 G_Rec: 0.429 D_GP: 0.030 D_real: 1.079 D_fake: 0.615 +(epoch: 337, iters: 4318, time: 0.064) G_GAN: -0.207 G_GAN_Feat: 0.656 G_ID: 0.103 G_Rec: 0.320 D_GP: 0.032 D_real: 0.799 D_fake: 1.207 +(epoch: 337, iters: 4718, time: 0.063) G_GAN: 0.535 G_GAN_Feat: 0.866 G_ID: 0.129 G_Rec: 0.400 D_GP: 0.033 D_real: 1.269 D_fake: 0.483 +(epoch: 337, iters: 5118, time: 0.063) G_GAN: 0.227 G_GAN_Feat: 0.665 G_ID: 0.102 G_Rec: 0.310 D_GP: 0.028 D_real: 1.105 D_fake: 0.773 +(epoch: 337, iters: 5518, time: 0.063) G_GAN: 0.439 G_GAN_Feat: 0.963 G_ID: 0.117 G_Rec: 0.473 D_GP: 0.028 D_real: 1.169 D_fake: 0.565 +(epoch: 337, iters: 5918, time: 0.064) G_GAN: -0.001 G_GAN_Feat: 0.714 G_ID: 0.130 G_Rec: 0.287 D_GP: 0.042 D_real: 0.845 D_fake: 1.001 +(epoch: 337, iters: 6318, time: 0.063) G_GAN: 0.547 G_GAN_Feat: 1.089 G_ID: 0.142 G_Rec: 0.485 D_GP: 0.475 D_real: 0.858 D_fake: 0.543 +(epoch: 337, iters: 6718, time: 0.063) G_GAN: 0.143 G_GAN_Feat: 0.700 G_ID: 0.107 G_Rec: 0.305 D_GP: 0.034 D_real: 1.085 D_fake: 0.857 +(epoch: 337, iters: 7118, time: 0.063) G_GAN: 0.247 G_GAN_Feat: 0.899 G_ID: 0.133 G_Rec: 0.404 D_GP: 0.041 D_real: 0.856 D_fake: 0.754 +(epoch: 337, iters: 7518, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.697 G_ID: 0.110 G_Rec: 0.285 D_GP: 0.031 D_real: 1.053 D_fake: 0.866 +(epoch: 337, iters: 7918, time: 0.063) G_GAN: 0.427 G_GAN_Feat: 0.929 G_ID: 0.119 G_Rec: 0.410 D_GP: 0.035 D_real: 1.128 D_fake: 0.579 +(epoch: 337, iters: 8318, time: 0.063) G_GAN: 0.341 G_GAN_Feat: 0.755 G_ID: 0.110 G_Rec: 0.319 D_GP: 0.048 D_real: 1.018 D_fake: 0.660 +(epoch: 338, iters: 110, time: 0.063) G_GAN: 0.574 G_GAN_Feat: 1.122 G_ID: 0.140 G_Rec: 0.468 D_GP: 0.360 D_real: 0.549 D_fake: 0.451 +(epoch: 338, iters: 510, time: 0.063) G_GAN: 0.213 G_GAN_Feat: 0.934 G_ID: 0.107 G_Rec: 0.365 D_GP: 0.377 D_real: 0.391 D_fake: 0.794 +(epoch: 338, iters: 910, time: 0.063) G_GAN: 0.662 G_GAN_Feat: 1.012 G_ID: 0.153 G_Rec: 0.424 D_GP: 0.052 D_real: 0.964 D_fake: 0.364 +(epoch: 338, iters: 1310, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.988 G_ID: 0.120 G_Rec: 0.352 D_GP: 0.318 D_real: 0.380 D_fake: 0.857 +(epoch: 338, iters: 1710, time: 0.064) G_GAN: 0.869 G_GAN_Feat: 1.009 G_ID: 0.128 G_Rec: 0.476 D_GP: 0.033 D_real: 1.549 D_fake: 0.188 +(epoch: 338, iters: 2110, time: 0.063) G_GAN: 0.569 G_GAN_Feat: 0.825 G_ID: 0.102 G_Rec: 0.353 D_GP: 0.038 D_real: 1.388 D_fake: 0.444 +(epoch: 338, iters: 2510, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.111 G_ID: 0.128 G_Rec: 0.483 D_GP: 0.139 D_real: 0.715 D_fake: 0.662 +(epoch: 338, iters: 2910, time: 0.063) G_GAN: 0.158 G_GAN_Feat: 0.805 G_ID: 0.108 G_Rec: 0.336 D_GP: 0.063 D_real: 0.804 D_fake: 0.842 +(epoch: 338, iters: 3310, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 1.006 G_ID: 0.147 G_Rec: 0.419 D_GP: 0.051 D_real: 0.725 D_fake: 0.660 +(epoch: 338, iters: 3710, time: 0.063) G_GAN: 0.151 G_GAN_Feat: 0.841 G_ID: 0.104 G_Rec: 0.354 D_GP: 0.031 D_real: 0.950 D_fake: 0.849 +(epoch: 338, iters: 4110, time: 0.063) G_GAN: 0.441 G_GAN_Feat: 1.120 G_ID: 0.127 G_Rec: 0.505 D_GP: 0.125 D_real: 0.294 D_fake: 0.586 +(epoch: 338, iters: 4510, time: 0.063) G_GAN: -0.428 G_GAN_Feat: 0.842 G_ID: 0.113 G_Rec: 0.326 D_GP: 0.043 D_real: 0.431 D_fake: 1.428 +(epoch: 338, iters: 4910, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.902 G_ID: 0.145 G_Rec: 0.416 D_GP: 0.034 D_real: 0.997 D_fake: 0.675 +(epoch: 338, iters: 5310, time: 0.063) G_GAN: 0.200 G_GAN_Feat: 0.865 G_ID: 0.116 G_Rec: 0.339 D_GP: 0.078 D_real: 0.542 D_fake: 0.801 +(epoch: 338, iters: 5710, time: 0.063) G_GAN: 0.303 G_GAN_Feat: 1.165 G_ID: 0.142 G_Rec: 0.487 D_GP: 0.244 D_real: 0.513 D_fake: 0.715 +(epoch: 338, iters: 6110, time: 0.063) G_GAN: 0.200 G_GAN_Feat: 1.136 G_ID: 0.095 G_Rec: 0.384 D_GP: 1.795 D_real: 0.811 D_fake: 0.805 +(epoch: 338, iters: 6510, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.893 G_ID: 0.118 G_Rec: 0.437 D_GP: 0.028 D_real: 0.854 D_fake: 0.845 +(epoch: 338, iters: 6910, time: 0.063) G_GAN: -0.024 G_GAN_Feat: 0.812 G_ID: 0.102 G_Rec: 0.350 D_GP: 0.091 D_real: 0.736 D_fake: 1.024 +(epoch: 338, iters: 7310, time: 0.063) G_GAN: 0.208 G_GAN_Feat: 0.863 G_ID: 0.128 G_Rec: 0.411 D_GP: 0.030 D_real: 0.891 D_fake: 0.795 +(epoch: 338, iters: 7710, time: 0.063) G_GAN: -0.261 G_GAN_Feat: 0.752 G_ID: 0.101 G_Rec: 0.342 D_GP: 0.057 D_real: 0.498 D_fake: 1.261 +(epoch: 338, iters: 8110, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.941 G_ID: 0.139 G_Rec: 0.394 D_GP: 0.081 D_real: 0.613 D_fake: 0.861 +(epoch: 338, iters: 8510, time: 0.063) G_GAN: 0.009 G_GAN_Feat: 0.855 G_ID: 0.125 G_Rec: 0.340 D_GP: 0.108 D_real: 0.590 D_fake: 0.991 +(epoch: 339, iters: 302, time: 0.063) G_GAN: 0.434 G_GAN_Feat: 1.076 G_ID: 0.132 G_Rec: 0.487 D_GP: 0.164 D_real: 0.507 D_fake: 0.578 +(epoch: 339, iters: 702, time: 0.063) G_GAN: 0.326 G_GAN_Feat: 0.679 G_ID: 0.094 G_Rec: 0.323 D_GP: 0.023 D_real: 1.241 D_fake: 0.675 +(epoch: 339, iters: 1102, time: 0.064) G_GAN: 0.639 G_GAN_Feat: 1.058 G_ID: 0.137 G_Rec: 0.482 D_GP: 0.099 D_real: 0.946 D_fake: 0.370 +(epoch: 339, iters: 1502, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.805 G_ID: 0.122 G_Rec: 0.341 D_GP: 0.031 D_real: 1.074 D_fake: 0.758 +(epoch: 339, iters: 1902, time: 0.063) G_GAN: 0.421 G_GAN_Feat: 1.063 G_ID: 0.128 G_Rec: 0.469 D_GP: 0.040 D_real: 0.611 D_fake: 0.580 +(epoch: 339, iters: 2302, time: 0.063) G_GAN: 0.096 G_GAN_Feat: 0.728 G_ID: 0.117 G_Rec: 0.320 D_GP: 0.024 D_real: 0.984 D_fake: 0.905 +(epoch: 339, iters: 2702, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 1.002 G_ID: 0.120 G_Rec: 0.449 D_GP: 0.050 D_real: 0.874 D_fake: 0.665 +(epoch: 339, iters: 3102, time: 0.063) G_GAN: 0.035 G_GAN_Feat: 0.688 G_ID: 0.110 G_Rec: 0.284 D_GP: 0.031 D_real: 0.833 D_fake: 0.966 +(epoch: 339, iters: 3502, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.946 G_ID: 0.157 G_Rec: 0.407 D_GP: 0.042 D_real: 0.709 D_fake: 0.798 +(epoch: 339, iters: 3902, time: 0.063) G_GAN: 0.160 G_GAN_Feat: 0.742 G_ID: 0.097 G_Rec: 0.326 D_GP: 0.040 D_real: 1.037 D_fake: 0.840 +(epoch: 339, iters: 4302, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 1.027 G_ID: 0.130 G_Rec: 0.476 D_GP: 0.045 D_real: 0.762 D_fake: 0.595 +(epoch: 339, iters: 4702, time: 0.063) G_GAN: 0.116 G_GAN_Feat: 0.839 G_ID: 0.112 G_Rec: 0.342 D_GP: 0.102 D_real: 0.642 D_fake: 0.885 +(epoch: 339, iters: 5102, time: 0.064) G_GAN: 0.576 G_GAN_Feat: 0.967 G_ID: 0.130 G_Rec: 0.426 D_GP: 0.041 D_real: 1.199 D_fake: 0.445 +(epoch: 339, iters: 5502, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.847 G_ID: 0.101 G_Rec: 0.354 D_GP: 0.032 D_real: 0.719 D_fake: 0.785 +(epoch: 339, iters: 5902, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 1.017 G_ID: 0.122 G_Rec: 0.423 D_GP: 0.049 D_real: 0.785 D_fake: 0.465 +(epoch: 339, iters: 6302, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.709 G_ID: 0.110 G_Rec: 0.315 D_GP: 0.027 D_real: 1.284 D_fake: 0.645 +(epoch: 339, iters: 6702, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.884 G_ID: 0.134 G_Rec: 0.422 D_GP: 0.032 D_real: 0.907 D_fake: 0.748 +(epoch: 339, iters: 7102, time: 0.064) G_GAN: -0.023 G_GAN_Feat: 0.748 G_ID: 0.117 G_Rec: 0.327 D_GP: 0.035 D_real: 0.835 D_fake: 1.023 +(epoch: 339, iters: 7502, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.970 G_ID: 0.160 G_Rec: 0.433 D_GP: 0.043 D_real: 0.586 D_fake: 0.819 +(epoch: 339, iters: 7902, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.745 G_ID: 0.117 G_Rec: 0.308 D_GP: 0.082 D_real: 0.995 D_fake: 0.683 +(epoch: 339, iters: 8302, time: 0.063) G_GAN: 0.724 G_GAN_Feat: 1.103 G_ID: 0.137 G_Rec: 0.486 D_GP: 0.108 D_real: 0.704 D_fake: 0.344 +(epoch: 339, iters: 8702, time: 0.063) G_GAN: 0.311 G_GAN_Feat: 0.774 G_ID: 0.115 G_Rec: 0.303 D_GP: 0.034 D_real: 1.134 D_fake: 0.689 +(epoch: 340, iters: 494, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 1.015 G_ID: 0.127 G_Rec: 0.425 D_GP: 0.080 D_real: 0.651 D_fake: 0.703 +(epoch: 340, iters: 894, time: 0.063) G_GAN: 0.278 G_GAN_Feat: 0.783 G_ID: 0.105 G_Rec: 0.361 D_GP: 0.026 D_real: 1.100 D_fake: 0.726 +(epoch: 340, iters: 1294, time: 0.064) G_GAN: 0.620 G_GAN_Feat: 1.071 G_ID: 0.138 G_Rec: 0.456 D_GP: 0.037 D_real: 1.223 D_fake: 0.420 +(epoch: 340, iters: 1694, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.816 G_ID: 0.143 G_Rec: 0.325 D_GP: 0.062 D_real: 0.904 D_fake: 0.779 +(epoch: 340, iters: 2094, time: 0.064) G_GAN: 0.800 G_GAN_Feat: 1.232 G_ID: 0.152 G_Rec: 0.484 D_GP: 0.238 D_real: 0.404 D_fake: 0.252 +(epoch: 340, iters: 2494, time: 0.064) G_GAN: -0.021 G_GAN_Feat: 0.752 G_ID: 0.109 G_Rec: 0.335 D_GP: 0.038 D_real: 0.832 D_fake: 1.021 +(epoch: 340, iters: 2894, time: 0.063) G_GAN: 0.449 G_GAN_Feat: 0.977 G_ID: 0.139 G_Rec: 0.463 D_GP: 0.046 D_real: 0.863 D_fake: 0.562 +(epoch: 340, iters: 3294, time: 0.063) G_GAN: 0.220 G_GAN_Feat: 0.729 G_ID: 0.098 G_Rec: 0.306 D_GP: 0.033 D_real: 1.077 D_fake: 0.781 +(epoch: 340, iters: 3694, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 1.164 G_ID: 0.142 G_Rec: 0.460 D_GP: 0.864 D_real: 0.274 D_fake: 0.506 +(epoch: 340, iters: 4094, time: 0.063) G_GAN: 0.254 G_GAN_Feat: 0.700 G_ID: 0.092 G_Rec: 0.342 D_GP: 0.032 D_real: 1.202 D_fake: 0.747 +(epoch: 340, iters: 4494, time: 0.063) G_GAN: 0.605 G_GAN_Feat: 1.060 G_ID: 0.122 G_Rec: 0.429 D_GP: 0.063 D_real: 0.794 D_fake: 0.440 +(epoch: 340, iters: 4894, time: 0.063) G_GAN: 0.264 G_GAN_Feat: 0.703 G_ID: 0.116 G_Rec: 0.315 D_GP: 0.027 D_real: 1.192 D_fake: 0.827 +(epoch: 340, iters: 5294, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.851 G_ID: 0.121 G_Rec: 0.426 D_GP: 0.026 D_real: 1.058 D_fake: 0.648 +(epoch: 340, iters: 5694, time: 0.063) G_GAN: 0.220 G_GAN_Feat: 0.613 G_ID: 0.101 G_Rec: 0.309 D_GP: 0.026 D_real: 1.194 D_fake: 0.780 +(epoch: 340, iters: 6094, time: 0.063) G_GAN: 0.456 G_GAN_Feat: 0.936 G_ID: 0.116 G_Rec: 0.472 D_GP: 0.040 D_real: 1.071 D_fake: 0.554 +(epoch: 340, iters: 6494, time: 0.063) G_GAN: 0.134 G_GAN_Feat: 0.694 G_ID: 0.103 G_Rec: 0.331 D_GP: 0.046 D_real: 0.992 D_fake: 0.867 +(epoch: 340, iters: 6894, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.791 G_ID: 0.114 G_Rec: 0.375 D_GP: 0.038 D_real: 0.977 D_fake: 0.735 +(epoch: 340, iters: 7294, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.737 G_ID: 0.097 G_Rec: 0.370 D_GP: 0.061 D_real: 0.834 D_fake: 0.912 +(epoch: 340, iters: 7694, time: 0.063) G_GAN: 0.310 G_GAN_Feat: 0.911 G_ID: 0.138 G_Rec: 0.429 D_GP: 0.047 D_real: 0.880 D_fake: 0.698 +(epoch: 340, iters: 8094, time: 0.063) G_GAN: 0.001 G_GAN_Feat: 0.720 G_ID: 0.101 G_Rec: 0.327 D_GP: 0.068 D_real: 0.808 D_fake: 0.999 +(epoch: 340, iters: 8494, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.880 G_ID: 0.111 G_Rec: 0.417 D_GP: 0.037 D_real: 0.995 D_fake: 0.591 +(epoch: 341, iters: 286, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.687 G_ID: 0.099 G_Rec: 0.306 D_GP: 0.038 D_real: 1.007 D_fake: 0.884 +(epoch: 341, iters: 686, time: 0.063) G_GAN: 0.112 G_GAN_Feat: 0.931 G_ID: 0.128 G_Rec: 0.450 D_GP: 0.049 D_real: 0.752 D_fake: 0.893 +(epoch: 341, iters: 1086, time: 0.063) G_GAN: 0.079 G_GAN_Feat: 0.660 G_ID: 0.125 G_Rec: 0.280 D_GP: 0.031 D_real: 0.994 D_fake: 0.921 +(epoch: 341, iters: 1486, time: 0.064) G_GAN: 0.594 G_GAN_Feat: 0.969 G_ID: 0.132 G_Rec: 0.425 D_GP: 0.066 D_real: 1.092 D_fake: 0.446 +(epoch: 341, iters: 1886, time: 0.064) G_GAN: 0.063 G_GAN_Feat: 0.754 G_ID: 0.103 G_Rec: 0.321 D_GP: 0.042 D_real: 0.883 D_fake: 0.937 +(epoch: 341, iters: 2286, time: 0.063) G_GAN: 0.118 G_GAN_Feat: 0.906 G_ID: 0.159 G_Rec: 0.385 D_GP: 0.048 D_real: 0.694 D_fake: 0.882 +(epoch: 341, iters: 2686, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.773 G_ID: 0.099 G_Rec: 0.317 D_GP: 0.030 D_real: 0.859 D_fake: 0.964 +(epoch: 341, iters: 3086, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 1.017 G_ID: 0.115 G_Rec: 0.425 D_GP: 0.059 D_real: 0.746 D_fake: 0.669 +(epoch: 341, iters: 3486, time: 0.063) G_GAN: 0.059 G_GAN_Feat: 0.768 G_ID: 0.107 G_Rec: 0.333 D_GP: 0.038 D_real: 0.877 D_fake: 0.942 +(epoch: 341, iters: 3886, time: 0.063) G_GAN: 0.293 G_GAN_Feat: 0.893 G_ID: 0.121 G_Rec: 0.378 D_GP: 0.041 D_real: 0.878 D_fake: 0.708 +(epoch: 341, iters: 4286, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 0.795 G_ID: 0.093 G_Rec: 0.322 D_GP: 0.042 D_real: 0.851 D_fake: 0.989 +(epoch: 341, iters: 4686, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 1.001 G_ID: 0.132 G_Rec: 0.492 D_GP: 0.058 D_real: 0.987 D_fake: 0.522 +(epoch: 341, iters: 5086, time: 0.063) G_GAN: 0.151 G_GAN_Feat: 0.746 G_ID: 0.121 G_Rec: 0.310 D_GP: 0.030 D_real: 1.021 D_fake: 0.849 +(epoch: 341, iters: 5486, time: 0.063) G_GAN: 0.286 G_GAN_Feat: 0.955 G_ID: 0.126 G_Rec: 0.435 D_GP: 0.033 D_real: 0.899 D_fake: 0.716 +(epoch: 341, iters: 5886, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.905 G_ID: 0.112 G_Rec: 0.337 D_GP: 0.051 D_real: 0.483 D_fake: 0.952 +(epoch: 341, iters: 6286, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.961 G_ID: 0.132 G_Rec: 0.402 D_GP: 0.058 D_real: 0.917 D_fake: 0.506 +(epoch: 341, iters: 6686, time: 0.064) G_GAN: 0.004 G_GAN_Feat: 0.749 G_ID: 0.125 G_Rec: 0.321 D_GP: 0.026 D_real: 0.885 D_fake: 0.996 +(epoch: 341, iters: 7086, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 1.061 G_ID: 0.128 G_Rec: 0.417 D_GP: 0.033 D_real: 0.874 D_fake: 0.625 +(epoch: 341, iters: 7486, time: 0.063) G_GAN: 0.201 G_GAN_Feat: 0.861 G_ID: 0.111 G_Rec: 0.319 D_GP: 0.104 D_real: 0.550 D_fake: 0.799 +(epoch: 341, iters: 7886, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.902 G_ID: 0.146 G_Rec: 0.447 D_GP: 0.029 D_real: 1.069 D_fake: 0.665 +(epoch: 341, iters: 8286, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.730 G_ID: 0.089 G_Rec: 0.314 D_GP: 0.028 D_real: 1.144 D_fake: 0.761 +(epoch: 341, iters: 8686, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.993 G_ID: 0.146 G_Rec: 0.453 D_GP: 0.033 D_real: 0.844 D_fake: 0.681 +(epoch: 342, iters: 478, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.777 G_ID: 0.119 G_Rec: 0.310 D_GP: 0.038 D_real: 1.159 D_fake: 0.616 +(epoch: 342, iters: 878, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.988 G_ID: 0.132 G_Rec: 0.440 D_GP: 0.063 D_real: 0.970 D_fake: 0.554 +(epoch: 342, iters: 1278, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.845 G_ID: 0.112 G_Rec: 0.320 D_GP: 0.060 D_real: 0.614 D_fake: 0.880 +(epoch: 342, iters: 1678, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.965 G_ID: 0.157 G_Rec: 0.431 D_GP: 0.029 D_real: 0.774 D_fake: 0.745 +(epoch: 342, iters: 2078, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 0.789 G_ID: 0.100 G_Rec: 0.313 D_GP: 0.043 D_real: 1.070 D_fake: 0.633 +(epoch: 342, iters: 2478, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 1.074 G_ID: 0.142 G_Rec: 0.462 D_GP: 0.079 D_real: 0.513 D_fake: 0.763 +(epoch: 342, iters: 2878, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.788 G_ID: 0.110 G_Rec: 0.358 D_GP: 0.030 D_real: 1.062 D_fake: 0.792 +(epoch: 342, iters: 3278, time: 0.064) G_GAN: 0.705 G_GAN_Feat: 0.880 G_ID: 0.129 G_Rec: 0.447 D_GP: 0.031 D_real: 1.268 D_fake: 0.397 +(epoch: 342, iters: 3678, time: 0.063) G_GAN: 0.106 G_GAN_Feat: 0.689 G_ID: 0.099 G_Rec: 0.314 D_GP: 0.030 D_real: 0.979 D_fake: 0.894 +(epoch: 342, iters: 4078, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.988 G_ID: 0.130 G_Rec: 0.478 D_GP: 0.037 D_real: 1.095 D_fake: 0.534 +(epoch: 342, iters: 4478, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 0.789 G_ID: 0.117 G_Rec: 0.356 D_GP: 0.053 D_real: 0.972 D_fake: 0.688 +(epoch: 342, iters: 4878, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.960 G_ID: 0.146 G_Rec: 0.449 D_GP: 0.218 D_real: 0.874 D_fake: 0.746 +(epoch: 342, iters: 5278, time: 0.064) G_GAN: 0.002 G_GAN_Feat: 0.728 G_ID: 0.096 G_Rec: 0.289 D_GP: 0.048 D_real: 0.849 D_fake: 0.998 +(epoch: 342, iters: 5678, time: 0.063) G_GAN: 0.413 G_GAN_Feat: 1.042 G_ID: 0.134 G_Rec: 0.446 D_GP: 0.057 D_real: 0.943 D_fake: 0.604 +(epoch: 342, iters: 6078, time: 0.063) G_GAN: 0.014 G_GAN_Feat: 0.830 G_ID: 0.096 G_Rec: 0.323 D_GP: 0.033 D_real: 0.770 D_fake: 0.987 +(epoch: 342, iters: 6478, time: 0.063) G_GAN: 0.291 G_GAN_Feat: 1.018 G_ID: 0.124 G_Rec: 0.438 D_GP: 0.053 D_real: 0.724 D_fake: 0.710 +(epoch: 342, iters: 6878, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.726 G_ID: 0.112 G_Rec: 0.295 D_GP: 0.030 D_real: 1.149 D_fake: 0.664 +(epoch: 342, iters: 7278, time: 0.063) G_GAN: 0.410 G_GAN_Feat: 0.947 G_ID: 0.152 G_Rec: 0.396 D_GP: 0.035 D_real: 1.167 D_fake: 0.593 +(epoch: 342, iters: 7678, time: 0.063) G_GAN: 0.137 G_GAN_Feat: 1.002 G_ID: 0.103 G_Rec: 0.405 D_GP: 0.245 D_real: 0.440 D_fake: 0.865 +(epoch: 342, iters: 8078, time: 0.063) G_GAN: 0.448 G_GAN_Feat: 1.180 G_ID: 0.145 G_Rec: 0.448 D_GP: 0.067 D_real: 0.460 D_fake: 0.554 +(epoch: 342, iters: 8478, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.967 G_ID: 0.125 G_Rec: 0.334 D_GP: 0.080 D_real: 0.643 D_fake: 0.714 +(epoch: 343, iters: 270, time: 0.063) G_GAN: 0.520 G_GAN_Feat: 1.167 G_ID: 0.150 G_Rec: 0.447 D_GP: 0.032 D_real: 0.905 D_fake: 0.485 +(epoch: 343, iters: 670, time: 0.063) G_GAN: 0.062 G_GAN_Feat: 0.700 G_ID: 0.110 G_Rec: 0.347 D_GP: 0.027 D_real: 0.964 D_fake: 0.938 +(epoch: 343, iters: 1070, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.931 G_ID: 0.157 G_Rec: 0.435 D_GP: 0.036 D_real: 0.926 D_fake: 0.590 +(epoch: 343, iters: 1470, time: 0.064) G_GAN: -0.107 G_GAN_Feat: 0.818 G_ID: 0.118 G_Rec: 0.361 D_GP: 0.121 D_real: 0.617 D_fake: 1.107 +(epoch: 343, iters: 1870, time: 0.063) G_GAN: 0.242 G_GAN_Feat: 0.864 G_ID: 0.132 G_Rec: 0.372 D_GP: 0.043 D_real: 0.989 D_fake: 0.760 +(epoch: 343, iters: 2270, time: 0.063) G_GAN: 0.386 G_GAN_Feat: 0.692 G_ID: 0.106 G_Rec: 0.302 D_GP: 0.036 D_real: 1.285 D_fake: 0.621 +(epoch: 343, iters: 2670, time: 0.063) G_GAN: 0.560 G_GAN_Feat: 0.997 G_ID: 0.151 G_Rec: 0.486 D_GP: 0.070 D_real: 1.069 D_fake: 0.453 +(epoch: 343, iters: 3070, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.721 G_ID: 0.111 G_Rec: 0.287 D_GP: 0.042 D_real: 0.967 D_fake: 0.786 +(epoch: 343, iters: 3470, time: 0.064) G_GAN: 0.796 G_GAN_Feat: 1.043 G_ID: 0.141 G_Rec: 0.429 D_GP: 0.094 D_real: 0.997 D_fake: 0.293 +(epoch: 343, iters: 3870, time: 0.063) G_GAN: -0.083 G_GAN_Feat: 0.800 G_ID: 0.110 G_Rec: 0.312 D_GP: 0.044 D_real: 0.786 D_fake: 1.084 +(epoch: 343, iters: 4270, time: 0.064) G_GAN: 0.692 G_GAN_Feat: 1.064 G_ID: 0.125 G_Rec: 0.458 D_GP: 0.039 D_real: 0.959 D_fake: 0.343 +(epoch: 343, iters: 4670, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.943 G_ID: 0.114 G_Rec: 0.354 D_GP: 0.981 D_real: 0.401 D_fake: 0.739 +(epoch: 343, iters: 5070, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 1.181 G_ID: 0.133 G_Rec: 0.500 D_GP: 0.155 D_real: 0.439 D_fake: 0.786 +(epoch: 343, iters: 5470, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.724 G_ID: 0.115 G_Rec: 0.307 D_GP: 0.033 D_real: 1.126 D_fake: 0.738 +(epoch: 343, iters: 5870, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 1.066 G_ID: 0.127 G_Rec: 0.394 D_GP: 0.057 D_real: 0.386 D_fake: 0.723 +(epoch: 343, iters: 6270, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.857 G_ID: 0.103 G_Rec: 0.349 D_GP: 0.035 D_real: 1.306 D_fake: 0.493 +(epoch: 343, iters: 6670, time: 0.063) G_GAN: 0.105 G_GAN_Feat: 1.221 G_ID: 0.147 G_Rec: 0.490 D_GP: 0.172 D_real: 0.089 D_fake: 0.901 +(epoch: 343, iters: 7070, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.750 G_ID: 0.122 G_Rec: 0.289 D_GP: 0.043 D_real: 0.745 D_fake: 0.978 +(epoch: 343, iters: 7470, time: 0.063) G_GAN: 0.286 G_GAN_Feat: 1.123 G_ID: 0.122 G_Rec: 0.434 D_GP: 0.093 D_real: 0.751 D_fake: 0.722 +(epoch: 343, iters: 7870, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.821 G_ID: 0.099 G_Rec: 0.334 D_GP: 0.046 D_real: 1.389 D_fake: 0.468 +(epoch: 343, iters: 8270, time: 0.063) G_GAN: 0.401 G_GAN_Feat: 0.990 G_ID: 0.122 G_Rec: 0.428 D_GP: 0.029 D_real: 0.990 D_fake: 0.599 +(epoch: 343, iters: 8670, time: 0.063) G_GAN: 0.786 G_GAN_Feat: 0.828 G_ID: 0.113 G_Rec: 0.330 D_GP: 0.037 D_real: 1.386 D_fake: 0.275 +(epoch: 344, iters: 462, time: 0.063) G_GAN: 0.416 G_GAN_Feat: 0.961 G_ID: 0.136 G_Rec: 0.436 D_GP: 0.032 D_real: 1.005 D_fake: 0.587 +(epoch: 344, iters: 862, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.836 G_ID: 0.097 G_Rec: 0.329 D_GP: 0.034 D_real: 1.131 D_fake: 0.585 +(epoch: 344, iters: 1262, time: 0.063) G_GAN: 0.545 G_GAN_Feat: 0.873 G_ID: 0.132 G_Rec: 0.378 D_GP: 0.032 D_real: 1.331 D_fake: 0.459 +(epoch: 344, iters: 1662, time: 0.063) G_GAN: 0.348 G_GAN_Feat: 0.764 G_ID: 0.123 G_Rec: 0.315 D_GP: 0.045 D_real: 1.153 D_fake: 0.658 +(epoch: 344, iters: 2062, time: 0.064) G_GAN: 0.637 G_GAN_Feat: 0.885 G_ID: 0.146 G_Rec: 0.453 D_GP: 0.033 D_real: 1.288 D_fake: 0.374 +(epoch: 344, iters: 2462, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.732 G_ID: 0.113 G_Rec: 0.318 D_GP: 0.044 D_real: 0.979 D_fake: 0.832 +(epoch: 344, iters: 2862, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.980 G_ID: 0.149 G_Rec: 0.445 D_GP: 0.033 D_real: 1.053 D_fake: 0.641 +(epoch: 344, iters: 3262, time: 0.063) G_GAN: 0.201 G_GAN_Feat: 0.681 G_ID: 0.102 G_Rec: 0.305 D_GP: 0.038 D_real: 1.129 D_fake: 0.799 +(epoch: 344, iters: 3662, time: 0.063) G_GAN: 0.500 G_GAN_Feat: 0.838 G_ID: 0.130 G_Rec: 0.379 D_GP: 0.039 D_real: 1.179 D_fake: 0.510 +(epoch: 344, iters: 4062, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.687 G_ID: 0.108 G_Rec: 0.302 D_GP: 0.038 D_real: 0.934 D_fake: 0.898 +(epoch: 344, iters: 4462, time: 0.063) G_GAN: 0.623 G_GAN_Feat: 0.937 G_ID: 0.118 G_Rec: 0.413 D_GP: 0.032 D_real: 1.313 D_fake: 0.390 +(epoch: 344, iters: 4862, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.706 G_ID: 0.111 G_Rec: 0.308 D_GP: 0.028 D_real: 1.040 D_fake: 0.762 +(epoch: 344, iters: 5262, time: 0.063) G_GAN: 0.667 G_GAN_Feat: 0.998 G_ID: 0.130 G_Rec: 0.440 D_GP: 0.034 D_real: 1.393 D_fake: 0.354 +(epoch: 344, iters: 5662, time: 0.064) G_GAN: -0.011 G_GAN_Feat: 0.892 G_ID: 0.100 G_Rec: 0.326 D_GP: 0.235 D_real: 0.322 D_fake: 1.012 +(epoch: 344, iters: 6062, time: 0.063) G_GAN: 0.635 G_GAN_Feat: 1.193 G_ID: 0.127 G_Rec: 0.447 D_GP: 0.050 D_real: 0.682 D_fake: 0.446 +(epoch: 344, iters: 6462, time: 0.063) G_GAN: 0.184 G_GAN_Feat: 0.736 G_ID: 0.093 G_Rec: 0.269 D_GP: 0.036 D_real: 0.927 D_fake: 0.816 +(epoch: 344, iters: 6862, time: 0.063) G_GAN: 0.786 G_GAN_Feat: 1.003 G_ID: 0.128 G_Rec: 0.411 D_GP: 0.033 D_real: 1.091 D_fake: 0.247 +(epoch: 344, iters: 7262, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.854 G_ID: 0.126 G_Rec: 0.316 D_GP: 0.035 D_real: 0.753 D_fake: 0.901 +(epoch: 344, iters: 7662, time: 0.063) G_GAN: 0.648 G_GAN_Feat: 1.212 G_ID: 0.148 G_Rec: 0.488 D_GP: 0.044 D_real: 0.626 D_fake: 0.366 +(epoch: 344, iters: 8062, time: 0.063) G_GAN: 0.194 G_GAN_Feat: 0.735 G_ID: 0.105 G_Rec: 0.327 D_GP: 0.026 D_real: 1.125 D_fake: 0.809 +(epoch: 344, iters: 8462, time: 0.063) G_GAN: 0.723 G_GAN_Feat: 0.922 G_ID: 0.122 G_Rec: 0.434 D_GP: 0.031 D_real: 1.366 D_fake: 0.382 +(epoch: 345, iters: 254, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.998 G_ID: 0.103 G_Rec: 0.341 D_GP: 0.153 D_real: 0.435 D_fake: 0.794 +(epoch: 345, iters: 654, time: 0.063) G_GAN: 0.741 G_GAN_Feat: 0.936 G_ID: 0.115 G_Rec: 0.384 D_GP: 0.033 D_real: 1.320 D_fake: 0.281 +(epoch: 345, iters: 1054, time: 0.063) G_GAN: 0.170 G_GAN_Feat: 0.817 G_ID: 0.105 G_Rec: 0.342 D_GP: 0.036 D_real: 0.928 D_fake: 0.832 +(epoch: 345, iters: 1454, time: 0.063) G_GAN: 0.591 G_GAN_Feat: 1.013 G_ID: 0.114 G_Rec: 0.446 D_GP: 0.036 D_real: 1.055 D_fake: 0.418 +(epoch: 345, iters: 1854, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 1.051 G_ID: 0.101 G_Rec: 0.385 D_GP: 0.389 D_real: 0.475 D_fake: 0.625 +(epoch: 345, iters: 2254, time: 0.063) G_GAN: 0.382 G_GAN_Feat: 0.923 G_ID: 0.119 G_Rec: 0.429 D_GP: 0.028 D_real: 1.078 D_fake: 0.621 +(epoch: 345, iters: 2654, time: 0.063) G_GAN: 0.362 G_GAN_Feat: 0.843 G_ID: 0.097 G_Rec: 0.332 D_GP: 0.034 D_real: 1.169 D_fake: 0.655 +(epoch: 345, iters: 3054, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.946 G_ID: 0.133 G_Rec: 0.417 D_GP: 0.035 D_real: 1.050 D_fake: 0.500 +(epoch: 345, iters: 3454, time: 0.064) G_GAN: 0.571 G_GAN_Feat: 0.910 G_ID: 0.106 G_Rec: 0.351 D_GP: 0.065 D_real: 0.935 D_fake: 0.529 +(epoch: 345, iters: 3854, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 1.176 G_ID: 0.152 G_Rec: 0.470 D_GP: 0.153 D_real: 0.404 D_fake: 0.468 +(epoch: 345, iters: 4254, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.858 G_ID: 0.103 G_Rec: 0.374 D_GP: 0.031 D_real: 0.671 D_fake: 0.796 +(epoch: 345, iters: 4654, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 1.148 G_ID: 0.140 G_Rec: 0.509 D_GP: 0.205 D_real: 0.419 D_fake: 0.597 +(epoch: 345, iters: 5054, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.824 G_ID: 0.094 G_Rec: 0.318 D_GP: 0.028 D_real: 1.014 D_fake: 0.816 +(epoch: 345, iters: 5454, time: 0.064) G_GAN: 0.826 G_GAN_Feat: 0.964 G_ID: 0.118 G_Rec: 0.469 D_GP: 0.036 D_real: 1.351 D_fake: 0.336 +(epoch: 345, iters: 5854, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.661 G_ID: 0.086 G_Rec: 0.288 D_GP: 0.029 D_real: 1.027 D_fake: 0.850 +(epoch: 345, iters: 6254, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.896 G_ID: 0.125 G_Rec: 0.400 D_GP: 0.031 D_real: 0.997 D_fake: 0.725 +(epoch: 345, iters: 6654, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 0.924 G_ID: 0.097 G_Rec: 0.361 D_GP: 0.490 D_real: 0.963 D_fake: 0.472 +(epoch: 345, iters: 7054, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 1.033 G_ID: 0.124 G_Rec: 0.469 D_GP: 0.093 D_real: 0.682 D_fake: 0.689 +(epoch: 345, iters: 7454, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.764 G_ID: 0.100 G_Rec: 0.302 D_GP: 0.036 D_real: 0.936 D_fake: 0.822 +(epoch: 345, iters: 7854, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 1.005 G_ID: 0.135 G_Rec: 0.443 D_GP: 0.041 D_real: 0.768 D_fake: 0.726 +(epoch: 345, iters: 8254, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.835 G_ID: 0.101 G_Rec: 0.319 D_GP: 0.046 D_real: 0.783 D_fake: 0.917 +(epoch: 345, iters: 8654, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.982 G_ID: 0.135 G_Rec: 0.444 D_GP: 0.033 D_real: 1.192 D_fake: 0.505 +(epoch: 346, iters: 446, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 1.044 G_ID: 0.104 G_Rec: 0.356 D_GP: 0.286 D_real: 0.468 D_fake: 0.536 +(epoch: 346, iters: 846, time: 0.063) G_GAN: 0.501 G_GAN_Feat: 1.169 G_ID: 0.117 G_Rec: 0.465 D_GP: 0.037 D_real: 0.421 D_fake: 0.506 +(epoch: 346, iters: 1246, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.772 G_ID: 0.117 G_Rec: 0.342 D_GP: 0.030 D_real: 0.969 D_fake: 0.878 +(epoch: 346, iters: 1646, time: 0.063) G_GAN: 0.395 G_GAN_Feat: 0.933 G_ID: 0.122 G_Rec: 0.452 D_GP: 0.032 D_real: 1.008 D_fake: 0.614 +(epoch: 346, iters: 2046, time: 0.063) G_GAN: -0.136 G_GAN_Feat: 0.649 G_ID: 0.115 G_Rec: 0.290 D_GP: 0.030 D_real: 0.815 D_fake: 1.136 +(epoch: 346, iters: 2446, time: 0.063) G_GAN: 0.569 G_GAN_Feat: 0.990 G_ID: 0.116 G_Rec: 0.483 D_GP: 0.035 D_real: 0.963 D_fake: 0.477 +(epoch: 346, iters: 2846, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.758 G_ID: 0.104 G_Rec: 0.375 D_GP: 0.027 D_real: 1.022 D_fake: 0.779 +(epoch: 346, iters: 3246, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.909 G_ID: 0.120 G_Rec: 0.430 D_GP: 0.031 D_real: 1.138 D_fake: 0.570 +(epoch: 346, iters: 3646, time: 0.063) G_GAN: -0.001 G_GAN_Feat: 0.734 G_ID: 0.147 G_Rec: 0.338 D_GP: 0.048 D_real: 0.836 D_fake: 1.001 +(epoch: 346, iters: 4046, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 1.103 G_ID: 0.131 G_Rec: 0.480 D_GP: 0.231 D_real: 0.482 D_fake: 0.582 +(epoch: 346, iters: 4446, time: 0.064) G_GAN: 0.139 G_GAN_Feat: 0.744 G_ID: 0.120 G_Rec: 0.296 D_GP: 0.035 D_real: 1.025 D_fake: 0.870 +(epoch: 346, iters: 4846, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 1.016 G_ID: 0.107 G_Rec: 0.426 D_GP: 0.042 D_real: 0.911 D_fake: 0.588 +(epoch: 346, iters: 5246, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.886 G_ID: 0.119 G_Rec: 0.320 D_GP: 0.043 D_real: 0.555 D_fake: 0.941 +(epoch: 346, iters: 5646, time: 0.063) G_GAN: 0.287 G_GAN_Feat: 1.013 G_ID: 0.110 G_Rec: 0.406 D_GP: 0.044 D_real: 0.614 D_fake: 0.713 +(epoch: 346, iters: 6046, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.822 G_ID: 0.101 G_Rec: 0.317 D_GP: 0.049 D_real: 1.076 D_fake: 0.645 +(epoch: 346, iters: 6446, time: 0.063) G_GAN: 0.664 G_GAN_Feat: 0.996 G_ID: 0.127 G_Rec: 0.460 D_GP: 0.031 D_real: 1.309 D_fake: 0.394 +(epoch: 346, iters: 6846, time: 0.063) G_GAN: 0.270 G_GAN_Feat: 0.732 G_ID: 0.106 G_Rec: 0.292 D_GP: 0.028 D_real: 1.103 D_fake: 0.730 +(epoch: 346, iters: 7246, time: 0.063) G_GAN: 0.538 G_GAN_Feat: 1.005 G_ID: 0.136 G_Rec: 0.453 D_GP: 0.037 D_real: 1.035 D_fake: 0.483 +(epoch: 346, iters: 7646, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.793 G_ID: 0.122 G_Rec: 0.317 D_GP: 0.038 D_real: 0.967 D_fake: 0.906 +(epoch: 346, iters: 8046, time: 0.063) G_GAN: 0.738 G_GAN_Feat: 0.954 G_ID: 0.121 G_Rec: 0.445 D_GP: 0.032 D_real: 1.399 D_fake: 0.350 +(epoch: 346, iters: 8446, time: 0.063) G_GAN: 0.206 G_GAN_Feat: 0.782 G_ID: 0.116 G_Rec: 0.319 D_GP: 0.027 D_real: 1.058 D_fake: 0.794 +(epoch: 347, iters: 238, time: 0.063) G_GAN: 0.578 G_GAN_Feat: 1.049 G_ID: 0.111 G_Rec: 0.488 D_GP: 0.032 D_real: 1.070 D_fake: 0.427 +(epoch: 347, iters: 638, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.787 G_ID: 0.113 G_Rec: 0.316 D_GP: 0.031 D_real: 1.086 D_fake: 0.724 +(epoch: 347, iters: 1038, time: 0.063) G_GAN: 0.453 G_GAN_Feat: 1.089 G_ID: 0.130 G_Rec: 0.420 D_GP: 0.052 D_real: 0.690 D_fake: 0.551 +(epoch: 347, iters: 1438, time: 0.063) G_GAN: 0.682 G_GAN_Feat: 0.799 G_ID: 0.091 G_Rec: 0.345 D_GP: 0.035 D_real: 1.443 D_fake: 0.486 +(epoch: 347, iters: 1838, time: 0.063) G_GAN: 0.334 G_GAN_Feat: 0.865 G_ID: 0.136 G_Rec: 0.420 D_GP: 0.031 D_real: 1.081 D_fake: 0.667 +(epoch: 347, iters: 2238, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.696 G_ID: 0.108 G_Rec: 0.331 D_GP: 0.028 D_real: 1.086 D_fake: 0.810 +(epoch: 347, iters: 2638, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.883 G_ID: 0.126 G_Rec: 0.400 D_GP: 0.035 D_real: 1.017 D_fake: 0.596 +(epoch: 347, iters: 3038, time: 0.063) G_GAN: -0.197 G_GAN_Feat: 0.726 G_ID: 0.122 G_Rec: 0.347 D_GP: 0.039 D_real: 0.632 D_fake: 1.197 +(epoch: 347, iters: 3438, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.885 G_ID: 0.122 G_Rec: 0.418 D_GP: 0.034 D_real: 1.079 D_fake: 0.592 +(epoch: 347, iters: 3838, time: 0.064) G_GAN: -0.071 G_GAN_Feat: 0.782 G_ID: 0.111 G_Rec: 0.347 D_GP: 0.053 D_real: 0.609 D_fake: 1.071 +(epoch: 347, iters: 4238, time: 0.063) G_GAN: 0.473 G_GAN_Feat: 0.984 G_ID: 0.126 G_Rec: 0.491 D_GP: 0.040 D_real: 1.144 D_fake: 0.546 +(epoch: 347, iters: 4638, time: 0.063) G_GAN: 0.082 G_GAN_Feat: 0.667 G_ID: 0.107 G_Rec: 0.295 D_GP: 0.037 D_real: 0.958 D_fake: 0.918 +(epoch: 347, iters: 5038, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.948 G_ID: 0.130 G_Rec: 0.403 D_GP: 0.056 D_real: 1.092 D_fake: 0.568 +(epoch: 347, iters: 5438, time: 0.063) G_GAN: 0.107 G_GAN_Feat: 0.698 G_ID: 0.115 G_Rec: 0.325 D_GP: 0.035 D_real: 0.960 D_fake: 0.893 +(epoch: 347, iters: 5838, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 0.977 G_ID: 0.138 G_Rec: 0.466 D_GP: 0.082 D_real: 0.604 D_fake: 0.811 +(epoch: 347, iters: 6238, time: 0.063) G_GAN: 0.014 G_GAN_Feat: 0.834 G_ID: 0.137 G_Rec: 0.343 D_GP: 0.042 D_real: 1.090 D_fake: 0.986 +(epoch: 347, iters: 6638, time: 0.064) G_GAN: 0.623 G_GAN_Feat: 0.944 G_ID: 0.111 G_Rec: 0.435 D_GP: 0.072 D_real: 1.297 D_fake: 0.395 +(epoch: 347, iters: 7038, time: 0.063) G_GAN: 0.351 G_GAN_Feat: 0.804 G_ID: 0.101 G_Rec: 0.369 D_GP: 0.075 D_real: 1.011 D_fake: 0.657 +(epoch: 347, iters: 7438, time: 0.063) G_GAN: 0.546 G_GAN_Feat: 0.994 G_ID: 0.127 G_Rec: 0.454 D_GP: 0.036 D_real: 1.015 D_fake: 0.468 +(epoch: 347, iters: 7838, time: 0.063) G_GAN: 0.221 G_GAN_Feat: 0.813 G_ID: 0.101 G_Rec: 0.328 D_GP: 0.037 D_real: 0.941 D_fake: 0.781 +(epoch: 347, iters: 8238, time: 0.064) G_GAN: 0.628 G_GAN_Feat: 0.997 G_ID: 0.126 G_Rec: 0.450 D_GP: 0.033 D_real: 1.245 D_fake: 0.389 +(epoch: 347, iters: 8638, time: 0.063) G_GAN: 0.077 G_GAN_Feat: 0.878 G_ID: 0.114 G_Rec: 0.350 D_GP: 0.067 D_real: 0.480 D_fake: 0.923 +(epoch: 348, iters: 430, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 0.967 G_ID: 0.113 G_Rec: 0.444 D_GP: 0.039 D_real: 0.905 D_fake: 0.591 +(epoch: 348, iters: 830, time: 0.063) G_GAN: 0.387 G_GAN_Feat: 1.042 G_ID: 0.114 G_Rec: 0.362 D_GP: 0.087 D_real: 0.246 D_fake: 0.659 +(epoch: 348, iters: 1230, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 1.152 G_ID: 0.124 G_Rec: 0.474 D_GP: 0.089 D_real: 0.969 D_fake: 0.422 +(epoch: 348, iters: 1630, time: 0.063) G_GAN: 0.435 G_GAN_Feat: 0.747 G_ID: 0.101 G_Rec: 0.305 D_GP: 0.044 D_real: 1.238 D_fake: 0.575 +(epoch: 348, iters: 2030, time: 0.063) G_GAN: 0.451 G_GAN_Feat: 1.107 G_ID: 0.114 G_Rec: 0.471 D_GP: 0.046 D_real: 0.663 D_fake: 0.557 +(epoch: 348, iters: 2430, time: 0.063) G_GAN: 0.165 G_GAN_Feat: 0.773 G_ID: 0.109 G_Rec: 0.308 D_GP: 0.033 D_real: 0.949 D_fake: 0.836 +(epoch: 348, iters: 2830, time: 0.064) G_GAN: 0.652 G_GAN_Feat: 1.000 G_ID: 0.144 G_Rec: 0.423 D_GP: 0.034 D_real: 1.255 D_fake: 0.371 +(epoch: 348, iters: 3230, time: 0.063) G_GAN: 0.078 G_GAN_Feat: 0.665 G_ID: 0.120 G_Rec: 0.287 D_GP: 0.032 D_real: 1.023 D_fake: 0.922 +(epoch: 348, iters: 3630, time: 0.063) G_GAN: 0.294 G_GAN_Feat: 1.051 G_ID: 0.130 G_Rec: 0.474 D_GP: 0.053 D_real: 0.872 D_fake: 0.706 +(epoch: 348, iters: 4030, time: 0.063) G_GAN: 0.200 G_GAN_Feat: 0.742 G_ID: 0.117 G_Rec: 0.311 D_GP: 0.029 D_real: 0.897 D_fake: 0.800 +(epoch: 348, iters: 4430, time: 0.064) G_GAN: 0.682 G_GAN_Feat: 1.080 G_ID: 0.114 G_Rec: 0.477 D_GP: 0.052 D_real: 0.955 D_fake: 0.352 +(epoch: 348, iters: 4830, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.796 G_ID: 0.103 G_Rec: 0.322 D_GP: 0.051 D_real: 1.133 D_fake: 0.853 +(epoch: 348, iters: 5230, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 1.031 G_ID: 0.122 G_Rec: 0.434 D_GP: 0.034 D_real: 0.794 D_fake: 0.618 +(epoch: 348, iters: 5630, time: 0.064) G_GAN: 1.125 G_GAN_Feat: 1.159 G_ID: 0.119 G_Rec: 0.408 D_GP: 0.116 D_real: 1.515 D_fake: 0.335 +(epoch: 348, iters: 6030, time: 0.064) G_GAN: 0.015 G_GAN_Feat: 0.883 G_ID: 0.137 G_Rec: 0.438 D_GP: 0.028 D_real: 0.751 D_fake: 0.986 +(epoch: 348, iters: 6430, time: 0.064) G_GAN: -0.104 G_GAN_Feat: 0.712 G_ID: 0.109 G_Rec: 0.313 D_GP: 0.032 D_real: 0.757 D_fake: 1.104 +(epoch: 348, iters: 6830, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.948 G_ID: 0.125 G_Rec: 0.457 D_GP: 0.041 D_real: 0.940 D_fake: 0.697 +(epoch: 348, iters: 7230, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.723 G_ID: 0.112 G_Rec: 0.298 D_GP: 0.041 D_real: 0.983 D_fake: 0.879 +(epoch: 348, iters: 7630, time: 0.064) G_GAN: 1.004 G_GAN_Feat: 1.187 G_ID: 0.123 G_Rec: 0.544 D_GP: 0.085 D_real: 1.040 D_fake: 0.166 +(epoch: 348, iters: 8030, time: 0.063) G_GAN: 0.400 G_GAN_Feat: 0.778 G_ID: 0.118 G_Rec: 0.339 D_GP: 0.032 D_real: 1.206 D_fake: 0.612 +(epoch: 348, iters: 8430, time: 0.063) G_GAN: 0.656 G_GAN_Feat: 1.044 G_ID: 0.123 G_Rec: 0.434 D_GP: 0.054 D_real: 1.004 D_fake: 0.359 +(epoch: 349, iters: 222, time: 0.063) G_GAN: 0.096 G_GAN_Feat: 0.956 G_ID: 0.112 G_Rec: 0.367 D_GP: 0.050 D_real: 0.365 D_fake: 0.905 +(epoch: 349, iters: 622, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 1.153 G_ID: 0.148 G_Rec: 0.462 D_GP: 0.079 D_real: 0.372 D_fake: 0.650 +(epoch: 349, iters: 1022, time: 0.063) G_GAN: 0.572 G_GAN_Feat: 0.951 G_ID: 0.095 G_Rec: 0.328 D_GP: 0.780 D_real: 0.589 D_fake: 0.461 +(epoch: 349, iters: 1422, time: 0.063) G_GAN: 0.590 G_GAN_Feat: 1.076 G_ID: 0.127 G_Rec: 0.441 D_GP: 0.071 D_real: 0.633 D_fake: 0.432 +(epoch: 349, iters: 1822, time: 0.063) G_GAN: 0.112 G_GAN_Feat: 0.844 G_ID: 0.100 G_Rec: 0.326 D_GP: 0.060 D_real: 0.744 D_fake: 0.889 +(epoch: 349, iters: 2222, time: 0.064) G_GAN: 0.800 G_GAN_Feat: 1.086 G_ID: 0.120 G_Rec: 0.399 D_GP: 0.042 D_real: 1.006 D_fake: 0.230 +(epoch: 349, iters: 2622, time: 0.063) G_GAN: 0.568 G_GAN_Feat: 1.078 G_ID: 0.115 G_Rec: 0.385 D_GP: 0.063 D_real: 1.012 D_fake: 0.615 +(epoch: 349, iters: 3022, time: 0.063) G_GAN: 0.435 G_GAN_Feat: 1.076 G_ID: 0.136 G_Rec: 0.431 D_GP: 0.040 D_real: 0.577 D_fake: 0.568 +(epoch: 349, iters: 3422, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.975 G_ID: 0.103 G_Rec: 0.337 D_GP: 0.030 D_real: 0.785 D_fake: 0.609 +(epoch: 349, iters: 3822, time: 0.064) G_GAN: 0.916 G_GAN_Feat: 1.068 G_ID: 0.130 G_Rec: 0.415 D_GP: 0.037 D_real: 1.180 D_fake: 0.166 +(epoch: 349, iters: 4222, time: 0.063) G_GAN: 0.097 G_GAN_Feat: 1.066 G_ID: 0.117 G_Rec: 0.364 D_GP: 0.325 D_real: 0.259 D_fake: 0.908 +(epoch: 349, iters: 4622, time: 0.063) G_GAN: 0.694 G_GAN_Feat: 1.063 G_ID: 0.130 G_Rec: 0.599 D_GP: 0.037 D_real: 1.199 D_fake: 0.326 +(epoch: 349, iters: 5022, time: 0.064) G_GAN: -0.028 G_GAN_Feat: 0.792 G_ID: 0.095 G_Rec: 0.303 D_GP: 0.061 D_real: 0.716 D_fake: 1.029 +(epoch: 349, iters: 5422, time: 0.064) G_GAN: 0.589 G_GAN_Feat: 0.971 G_ID: 0.125 G_Rec: 0.477 D_GP: 0.041 D_real: 1.082 D_fake: 0.424 +(epoch: 349, iters: 5822, time: 0.063) G_GAN: 0.536 G_GAN_Feat: 0.745 G_ID: 0.102 G_Rec: 0.367 D_GP: 0.040 D_real: 1.371 D_fake: 0.470 +(epoch: 349, iters: 6222, time: 0.063) G_GAN: 0.552 G_GAN_Feat: 1.104 G_ID: 0.129 G_Rec: 0.438 D_GP: 0.042 D_real: 0.831 D_fake: 0.457 +(epoch: 349, iters: 6622, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.677 G_ID: 0.121 G_Rec: 0.327 D_GP: 0.027 D_real: 1.175 D_fake: 0.751 +(epoch: 349, iters: 7022, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.862 G_ID: 0.115 G_Rec: 0.384 D_GP: 0.044 D_real: 1.094 D_fake: 0.633 +(epoch: 349, iters: 7422, time: 0.063) G_GAN: 0.295 G_GAN_Feat: 0.788 G_ID: 0.127 G_Rec: 0.334 D_GP: 0.080 D_real: 0.967 D_fake: 0.708 +(epoch: 349, iters: 7822, time: 0.063) G_GAN: 0.467 G_GAN_Feat: 1.029 G_ID: 0.113 G_Rec: 0.450 D_GP: 0.040 D_real: 1.003 D_fake: 0.546 +(epoch: 349, iters: 8222, time: 0.063) G_GAN: 0.126 G_GAN_Feat: 0.795 G_ID: 0.090 G_Rec: 0.313 D_GP: 0.027 D_real: 1.063 D_fake: 0.874 +(epoch: 349, iters: 8622, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 1.074 G_ID: 0.146 G_Rec: 0.471 D_GP: 0.094 D_real: 0.657 D_fake: 0.624 +(epoch: 350, iters: 414, time: 0.063) G_GAN: 0.203 G_GAN_Feat: 0.933 G_ID: 0.111 G_Rec: 0.323 D_GP: 0.059 D_real: 1.159 D_fake: 0.800 +(epoch: 350, iters: 814, time: 0.063) G_GAN: 0.876 G_GAN_Feat: 1.041 G_ID: 0.139 G_Rec: 0.436 D_GP: 0.037 D_real: 1.475 D_fake: 0.194 +(epoch: 350, iters: 1214, time: 0.063) G_GAN: 0.002 G_GAN_Feat: 0.795 G_ID: 0.107 G_Rec: 0.310 D_GP: 0.025 D_real: 0.758 D_fake: 0.998 +(epoch: 350, iters: 1614, time: 0.064) G_GAN: 0.620 G_GAN_Feat: 1.113 G_ID: 0.110 G_Rec: 0.408 D_GP: 0.068 D_real: 0.417 D_fake: 0.391 +(epoch: 350, iters: 2014, time: 0.063) G_GAN: 0.178 G_GAN_Feat: 0.874 G_ID: 0.097 G_Rec: 0.332 D_GP: 0.028 D_real: 0.941 D_fake: 0.822 +(epoch: 350, iters: 2414, time: 0.063) G_GAN: 1.007 G_GAN_Feat: 1.268 G_ID: 0.122 G_Rec: 0.507 D_GP: 0.182 D_real: 0.532 D_fake: 0.257 +(epoch: 350, iters: 2814, time: 0.063) G_GAN: 0.044 G_GAN_Feat: 0.686 G_ID: 0.103 G_Rec: 0.346 D_GP: 0.025 D_real: 0.956 D_fake: 0.956 +(epoch: 350, iters: 3214, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.794 G_ID: 0.133 G_Rec: 0.382 D_GP: 0.026 D_real: 1.222 D_fake: 0.610 +(epoch: 350, iters: 3614, time: 0.063) G_GAN: 0.173 G_GAN_Feat: 0.650 G_ID: 0.107 G_Rec: 0.297 D_GP: 0.029 D_real: 1.071 D_fake: 0.828 +(epoch: 350, iters: 4014, time: 0.063) G_GAN: -0.070 G_GAN_Feat: 0.874 G_ID: 0.177 G_Rec: 0.411 D_GP: 0.031 D_real: 0.646 D_fake: 1.071 +(epoch: 350, iters: 4414, time: 0.063) G_GAN: 0.109 G_GAN_Feat: 0.678 G_ID: 0.096 G_Rec: 0.310 D_GP: 0.030 D_real: 0.964 D_fake: 0.892 +(epoch: 350, iters: 4814, time: 0.064) G_GAN: 0.078 G_GAN_Feat: 0.936 G_ID: 0.127 G_Rec: 0.463 D_GP: 0.039 D_real: 0.691 D_fake: 0.923 +(epoch: 350, iters: 5214, time: 0.063) G_GAN: 0.205 G_GAN_Feat: 0.703 G_ID: 0.101 G_Rec: 0.301 D_GP: 0.036 D_real: 1.060 D_fake: 0.802 +(epoch: 350, iters: 5614, time: 0.063) G_GAN: 0.383 G_GAN_Feat: 0.966 G_ID: 0.128 G_Rec: 0.461 D_GP: 0.049 D_real: 0.814 D_fake: 0.634 +(epoch: 350, iters: 6014, time: 0.063) G_GAN: 0.132 G_GAN_Feat: 0.753 G_ID: 0.097 G_Rec: 0.350 D_GP: 0.057 D_real: 0.796 D_fake: 0.868 +(epoch: 350, iters: 6414, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.908 G_ID: 0.125 G_Rec: 0.412 D_GP: 0.052 D_real: 0.787 D_fake: 0.679 +(epoch: 350, iters: 6814, time: 0.063) G_GAN: -0.100 G_GAN_Feat: 0.685 G_ID: 0.120 G_Rec: 0.301 D_GP: 0.034 D_real: 0.742 D_fake: 1.100 +(epoch: 350, iters: 7214, time: 0.063) G_GAN: 0.579 G_GAN_Feat: 1.001 G_ID: 0.138 G_Rec: 0.458 D_GP: 0.082 D_real: 0.844 D_fake: 0.441 +(epoch: 350, iters: 7614, time: 0.063) G_GAN: 0.053 G_GAN_Feat: 0.734 G_ID: 0.093 G_Rec: 0.343 D_GP: 0.030 D_real: 1.024 D_fake: 0.950 +(epoch: 350, iters: 8014, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.997 G_ID: 0.126 G_Rec: 0.441 D_GP: 0.055 D_real: 0.590 D_fake: 0.734 +(epoch: 350, iters: 8414, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.793 G_ID: 0.110 G_Rec: 0.356 D_GP: 0.064 D_real: 0.953 D_fake: 0.726 +(epoch: 351, iters: 206, time: 0.064) G_GAN: 0.674 G_GAN_Feat: 1.022 G_ID: 0.133 G_Rec: 0.437 D_GP: 0.090 D_real: 0.877 D_fake: 0.376 +(epoch: 351, iters: 606, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.811 G_ID: 0.101 G_Rec: 0.313 D_GP: 0.123 D_real: 0.690 D_fake: 0.814 +(epoch: 351, iters: 1006, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 1.072 G_ID: 0.134 G_Rec: 0.449 D_GP: 0.062 D_real: 0.933 D_fake: 0.493 +(epoch: 351, iters: 1406, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.703 G_ID: 0.093 G_Rec: 0.272 D_GP: 0.034 D_real: 1.402 D_fake: 0.488 +(epoch: 351, iters: 1806, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 1.152 G_ID: 0.131 G_Rec: 0.464 D_GP: 0.319 D_real: 0.411 D_fake: 0.439 +(epoch: 351, iters: 2206, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.853 G_ID: 0.110 G_Rec: 0.322 D_GP: 0.338 D_real: 0.614 D_fake: 0.644 +(epoch: 351, iters: 2606, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 1.051 G_ID: 0.120 G_Rec: 0.431 D_GP: 0.066 D_real: 0.847 D_fake: 0.509 +(epoch: 351, iters: 3006, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.923 G_ID: 0.116 G_Rec: 0.352 D_GP: 0.103 D_real: 0.514 D_fake: 0.609 +(epoch: 351, iters: 3406, time: 0.063) G_GAN: 0.568 G_GAN_Feat: 0.983 G_ID: 0.129 G_Rec: 0.456 D_GP: 0.042 D_real: 0.979 D_fake: 0.443 +(epoch: 351, iters: 3806, time: 0.063) G_GAN: 0.353 G_GAN_Feat: 0.889 G_ID: 0.106 G_Rec: 0.305 D_GP: 0.049 D_real: 0.624 D_fake: 0.649 +(epoch: 351, iters: 4206, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 1.053 G_ID: 0.137 G_Rec: 0.404 D_GP: 0.044 D_real: 0.564 D_fake: 0.763 +(epoch: 351, iters: 4606, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.749 G_ID: 0.102 G_Rec: 0.319 D_GP: 0.041 D_real: 1.449 D_fake: 0.523 +(epoch: 351, iters: 5006, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 1.006 G_ID: 0.143 G_Rec: 0.434 D_GP: 0.080 D_real: 0.634 D_fake: 0.561 +(epoch: 351, iters: 5406, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.987 G_ID: 0.114 G_Rec: 0.358 D_GP: 0.356 D_real: 0.402 D_fake: 0.651 +(epoch: 351, iters: 5806, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 0.974 G_ID: 0.115 G_Rec: 0.461 D_GP: 0.032 D_real: 0.848 D_fake: 0.807 +(epoch: 351, iters: 6206, time: 0.064) G_GAN: 0.046 G_GAN_Feat: 0.652 G_ID: 0.106 G_Rec: 0.313 D_GP: 0.032 D_real: 0.879 D_fake: 0.954 +(epoch: 351, iters: 6606, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.983 G_ID: 0.126 G_Rec: 0.466 D_GP: 0.048 D_real: 1.015 D_fake: 0.598 +(epoch: 351, iters: 7006, time: 0.063) G_GAN: 0.213 G_GAN_Feat: 0.791 G_ID: 0.100 G_Rec: 0.348 D_GP: 0.080 D_real: 0.799 D_fake: 0.787 +(epoch: 351, iters: 7406, time: 0.063) G_GAN: 0.357 G_GAN_Feat: 0.950 G_ID: 0.131 G_Rec: 0.436 D_GP: 0.048 D_real: 0.914 D_fake: 0.660 +(epoch: 351, iters: 7806, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.872 G_ID: 0.108 G_Rec: 0.363 D_GP: 0.063 D_real: 0.709 D_fake: 0.833 +(epoch: 351, iters: 8206, time: 0.063) G_GAN: 0.594 G_GAN_Feat: 1.010 G_ID: 0.120 G_Rec: 0.490 D_GP: 0.048 D_real: 1.162 D_fake: 0.436 +(epoch: 351, iters: 8606, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.739 G_ID: 0.104 G_Rec: 0.331 D_GP: 0.043 D_real: 0.863 D_fake: 0.871 +(epoch: 352, iters: 398, time: 0.063) G_GAN: 0.590 G_GAN_Feat: 0.852 G_ID: 0.113 G_Rec: 0.378 D_GP: 0.031 D_real: 1.353 D_fake: 0.442 +(epoch: 352, iters: 798, time: 0.063) G_GAN: 0.011 G_GAN_Feat: 0.805 G_ID: 0.097 G_Rec: 0.338 D_GP: 0.037 D_real: 0.797 D_fake: 0.989 +(epoch: 352, iters: 1198, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 0.992 G_ID: 0.132 G_Rec: 0.453 D_GP: 0.045 D_real: 0.720 D_fake: 0.641 +(epoch: 352, iters: 1598, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.850 G_ID: 0.105 G_Rec: 0.319 D_GP: 0.161 D_real: 0.482 D_fake: 0.783 +(epoch: 352, iters: 1998, time: 0.063) G_GAN: 0.700 G_GAN_Feat: 1.200 G_ID: 0.112 G_Rec: 0.445 D_GP: 0.089 D_real: 0.561 D_fake: 0.329 +(epoch: 352, iters: 2398, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 0.721 G_ID: 0.092 G_Rec: 0.283 D_GP: 0.031 D_real: 1.123 D_fake: 0.716 +(epoch: 352, iters: 2798, time: 0.063) G_GAN: 0.577 G_GAN_Feat: 0.984 G_ID: 0.121 G_Rec: 0.423 D_GP: 0.036 D_real: 1.187 D_fake: 0.430 +(epoch: 352, iters: 3198, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.928 G_ID: 0.105 G_Rec: 0.331 D_GP: 0.044 D_real: 1.014 D_fake: 0.489 +(epoch: 352, iters: 3598, time: 0.064) G_GAN: 0.583 G_GAN_Feat: 1.057 G_ID: 0.112 G_Rec: 0.426 D_GP: 0.040 D_real: 1.040 D_fake: 0.435 +(epoch: 352, iters: 3998, time: 0.064) G_GAN: -0.089 G_GAN_Feat: 0.886 G_ID: 0.102 G_Rec: 0.373 D_GP: 0.156 D_real: 0.558 D_fake: 1.090 +(epoch: 352, iters: 4398, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.944 G_ID: 0.120 G_Rec: 0.424 D_GP: 0.032 D_real: 0.911 D_fake: 0.733 +(epoch: 352, iters: 4798, time: 0.064) G_GAN: -0.094 G_GAN_Feat: 0.710 G_ID: 0.115 G_Rec: 0.299 D_GP: 0.032 D_real: 0.875 D_fake: 1.094 +(epoch: 352, iters: 5198, time: 0.064) G_GAN: 0.549 G_GAN_Feat: 1.031 G_ID: 0.126 G_Rec: 0.420 D_GP: 0.044 D_real: 0.980 D_fake: 0.461 +(epoch: 352, iters: 5598, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.902 G_ID: 0.119 G_Rec: 0.319 D_GP: 0.037 D_real: 0.874 D_fake: 0.629 +(epoch: 352, iters: 5998, time: 0.064) G_GAN: 0.673 G_GAN_Feat: 1.119 G_ID: 0.113 G_Rec: 0.429 D_GP: 0.064 D_real: 0.576 D_fake: 0.352 +(epoch: 352, iters: 6398, time: 0.064) G_GAN: 0.040 G_GAN_Feat: 0.681 G_ID: 0.103 G_Rec: 0.299 D_GP: 0.027 D_real: 0.967 D_fake: 0.960 +(epoch: 352, iters: 6798, time: 0.064) G_GAN: 0.661 G_GAN_Feat: 1.068 G_ID: 0.112 G_Rec: 0.454 D_GP: 0.039 D_real: 1.037 D_fake: 0.368 +(epoch: 352, iters: 7198, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.828 G_ID: 0.122 G_Rec: 0.320 D_GP: 0.036 D_real: 0.806 D_fake: 0.897 +(epoch: 352, iters: 7598, time: 0.064) G_GAN: 0.588 G_GAN_Feat: 1.122 G_ID: 0.128 G_Rec: 0.467 D_GP: 0.053 D_real: 0.980 D_fake: 0.449 +(epoch: 352, iters: 7998, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.899 G_ID: 0.129 G_Rec: 0.327 D_GP: 0.322 D_real: 0.703 D_fake: 0.838 +(epoch: 352, iters: 8398, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.954 G_ID: 0.125 G_Rec: 0.393 D_GP: 0.053 D_real: 1.097 D_fake: 0.498 +(epoch: 353, iters: 190, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.761 G_ID: 0.123 G_Rec: 0.327 D_GP: 0.028 D_real: 0.987 D_fake: 0.947 +(epoch: 353, iters: 590, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.948 G_ID: 0.121 G_Rec: 0.437 D_GP: 0.035 D_real: 1.053 D_fake: 0.565 +(epoch: 353, iters: 990, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.761 G_ID: 0.097 G_Rec: 0.307 D_GP: 0.032 D_real: 1.239 D_fake: 0.621 +(epoch: 353, iters: 1390, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.877 G_ID: 0.130 G_Rec: 0.440 D_GP: 0.032 D_real: 1.200 D_fake: 0.628 +(epoch: 353, iters: 1790, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.716 G_ID: 0.104 G_Rec: 0.295 D_GP: 0.031 D_real: 1.149 D_fake: 0.766 +(epoch: 353, iters: 2190, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.894 G_ID: 0.138 G_Rec: 0.408 D_GP: 0.035 D_real: 0.967 D_fake: 0.552 +(epoch: 353, iters: 2590, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.754 G_ID: 0.114 G_Rec: 0.372 D_GP: 0.030 D_real: 0.968 D_fake: 0.919 +(epoch: 353, iters: 2990, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 1.039 G_ID: 0.130 G_Rec: 0.439 D_GP: 0.044 D_real: 0.722 D_fake: 0.636 +(epoch: 353, iters: 3390, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.970 G_ID: 0.100 G_Rec: 0.345 D_GP: 0.185 D_real: 0.327 D_fake: 0.692 +(epoch: 353, iters: 3790, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 1.251 G_ID: 0.146 G_Rec: 0.461 D_GP: 0.065 D_real: 0.202 D_fake: 0.691 +(epoch: 353, iters: 4190, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.741 G_ID: 0.084 G_Rec: 0.341 D_GP: 0.030 D_real: 1.238 D_fake: 0.736 +(epoch: 353, iters: 4590, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.884 G_ID: 0.134 G_Rec: 0.440 D_GP: 0.031 D_real: 1.074 D_fake: 0.664 +(epoch: 353, iters: 4990, time: 0.064) G_GAN: 0.014 G_GAN_Feat: 0.656 G_ID: 0.109 G_Rec: 0.303 D_GP: 0.029 D_real: 0.903 D_fake: 0.987 +(epoch: 353, iters: 5390, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.840 G_ID: 0.119 G_Rec: 0.388 D_GP: 0.034 D_real: 0.958 D_fake: 0.663 +(epoch: 353, iters: 5790, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.565 G_ID: 0.081 G_Rec: 0.269 D_GP: 0.028 D_real: 1.081 D_fake: 0.908 +(epoch: 353, iters: 6190, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.801 G_ID: 0.132 G_Rec: 0.380 D_GP: 0.035 D_real: 1.142 D_fake: 0.596 +(epoch: 353, iters: 6590, time: 0.064) G_GAN: -0.069 G_GAN_Feat: 0.719 G_ID: 0.099 G_Rec: 0.366 D_GP: 0.045 D_real: 0.805 D_fake: 1.069 +(epoch: 353, iters: 6990, time: 0.064) G_GAN: -0.044 G_GAN_Feat: 0.840 G_ID: 0.129 G_Rec: 0.405 D_GP: 0.039 D_real: 0.591 D_fake: 1.044 +(epoch: 353, iters: 7390, time: 0.064) G_GAN: -0.122 G_GAN_Feat: 0.687 G_ID: 0.110 G_Rec: 0.321 D_GP: 0.039 D_real: 0.709 D_fake: 1.122 +(epoch: 353, iters: 7790, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.943 G_ID: 0.121 G_Rec: 0.493 D_GP: 0.042 D_real: 1.152 D_fake: 0.546 +(epoch: 353, iters: 8190, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.677 G_ID: 0.106 G_Rec: 0.314 D_GP: 0.048 D_real: 0.854 D_fake: 0.907 +(epoch: 353, iters: 8590, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.896 G_ID: 0.130 G_Rec: 0.415 D_GP: 0.060 D_real: 0.749 D_fake: 0.845 +(epoch: 354, iters: 382, time: 0.064) G_GAN: -0.078 G_GAN_Feat: 0.752 G_ID: 0.123 G_Rec: 0.339 D_GP: 0.052 D_real: 0.759 D_fake: 1.079 +(epoch: 354, iters: 782, time: 0.064) G_GAN: -0.084 G_GAN_Feat: 1.019 G_ID: 0.131 G_Rec: 0.483 D_GP: 0.052 D_real: 0.392 D_fake: 1.084 +(epoch: 354, iters: 1182, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.687 G_ID: 0.086 G_Rec: 0.314 D_GP: 0.074 D_real: 0.805 D_fake: 0.907 +(epoch: 354, iters: 1582, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.878 G_ID: 0.131 G_Rec: 0.403 D_GP: 0.040 D_real: 1.055 D_fake: 0.581 +(epoch: 354, iters: 1982, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.679 G_ID: 0.099 G_Rec: 0.286 D_GP: 0.045 D_real: 1.021 D_fake: 0.794 +(epoch: 354, iters: 2382, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.916 G_ID: 0.131 G_Rec: 0.425 D_GP: 0.064 D_real: 0.724 D_fake: 0.799 +(epoch: 354, iters: 2782, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.785 G_ID: 0.102 G_Rec: 0.343 D_GP: 0.050 D_real: 0.763 D_fake: 0.923 +(epoch: 354, iters: 3182, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.901 G_ID: 0.118 G_Rec: 0.440 D_GP: 0.042 D_real: 0.914 D_fake: 0.715 +(epoch: 354, iters: 3582, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.812 G_ID: 0.109 G_Rec: 0.333 D_GP: 0.061 D_real: 0.823 D_fake: 0.841 +(epoch: 354, iters: 3982, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 0.955 G_ID: 0.129 G_Rec: 0.426 D_GP: 0.047 D_real: 0.730 D_fake: 0.943 +(epoch: 354, iters: 4382, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.869 G_ID: 0.102 G_Rec: 0.341 D_GP: 0.089 D_real: 1.064 D_fake: 0.720 +(epoch: 354, iters: 4782, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 1.049 G_ID: 0.127 G_Rec: 0.462 D_GP: 0.427 D_real: 0.629 D_fake: 0.511 +(epoch: 354, iters: 5182, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.888 G_ID: 0.101 G_Rec: 0.351 D_GP: 0.087 D_real: 0.514 D_fake: 0.879 +(epoch: 354, iters: 5582, time: 0.064) G_GAN: 0.627 G_GAN_Feat: 0.963 G_ID: 0.115 G_Rec: 0.432 D_GP: 0.066 D_real: 1.143 D_fake: 0.395 +(epoch: 354, iters: 5982, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.824 G_ID: 0.113 G_Rec: 0.326 D_GP: 0.095 D_real: 0.520 D_fake: 0.919 +(epoch: 354, iters: 6382, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.904 G_ID: 0.113 G_Rec: 0.425 D_GP: 0.041 D_real: 1.068 D_fake: 0.601 +(epoch: 354, iters: 6782, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.758 G_ID: 0.104 G_Rec: 0.319 D_GP: 0.045 D_real: 0.910 D_fake: 0.893 +(epoch: 354, iters: 7182, time: 0.064) G_GAN: 0.733 G_GAN_Feat: 0.912 G_ID: 0.118 G_Rec: 0.433 D_GP: 0.032 D_real: 1.412 D_fake: 0.318 +(epoch: 354, iters: 7582, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.801 G_ID: 0.109 G_Rec: 0.402 D_GP: 0.041 D_real: 0.934 D_fake: 0.644 +(epoch: 354, iters: 7982, time: 0.063) G_GAN: 0.196 G_GAN_Feat: 1.052 G_ID: 0.140 G_Rec: 0.454 D_GP: 0.157 D_real: 0.269 D_fake: 0.814 +(epoch: 354, iters: 8382, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.855 G_ID: 0.107 G_Rec: 0.318 D_GP: 0.064 D_real: 0.526 D_fake: 0.862 +(epoch: 355, iters: 174, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 1.020 G_ID: 0.115 G_Rec: 0.431 D_GP: 0.040 D_real: 0.749 D_fake: 0.547 +(epoch: 355, iters: 574, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.894 G_ID: 0.115 G_Rec: 0.364 D_GP: 0.083 D_real: 0.536 D_fake: 0.882 +(epoch: 355, iters: 974, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 0.922 G_ID: 0.114 G_Rec: 0.399 D_GP: 0.035 D_real: 1.320 D_fake: 0.464 +(epoch: 355, iters: 1374, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.777 G_ID: 0.104 G_Rec: 0.349 D_GP: 0.033 D_real: 0.984 D_fake: 0.765 +(epoch: 355, iters: 1774, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.936 G_ID: 0.115 G_Rec: 0.420 D_GP: 0.037 D_real: 1.120 D_fake: 0.525 +(epoch: 355, iters: 2174, time: 0.063) G_GAN: 0.548 G_GAN_Feat: 0.835 G_ID: 0.092 G_Rec: 0.326 D_GP: 0.044 D_real: 1.094 D_fake: 0.481 +(epoch: 355, iters: 2574, time: 0.064) G_GAN: 0.627 G_GAN_Feat: 0.888 G_ID: 0.118 G_Rec: 0.417 D_GP: 0.027 D_real: 1.321 D_fake: 0.387 +(epoch: 355, iters: 2974, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.729 G_ID: 0.093 G_Rec: 0.287 D_GP: 0.038 D_real: 0.968 D_fake: 0.866 +(epoch: 355, iters: 3374, time: 0.064) G_GAN: 0.782 G_GAN_Feat: 0.875 G_ID: 0.111 G_Rec: 0.372 D_GP: 0.046 D_real: 1.342 D_fake: 0.286 +(epoch: 355, iters: 3774, time: 0.064) G_GAN: 0.702 G_GAN_Feat: 0.899 G_ID: 0.090 G_Rec: 0.304 D_GP: 0.039 D_real: 1.479 D_fake: 0.356 +(epoch: 355, iters: 4174, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 1.080 G_ID: 0.117 G_Rec: 0.487 D_GP: 0.047 D_real: 0.728 D_fake: 0.705 +(epoch: 355, iters: 4574, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.823 G_ID: 0.096 G_Rec: 0.340 D_GP: 0.049 D_real: 0.924 D_fake: 0.704 +(epoch: 355, iters: 4974, time: 0.063) G_GAN: 0.400 G_GAN_Feat: 0.919 G_ID: 0.142 G_Rec: 0.444 D_GP: 0.034 D_real: 1.013 D_fake: 0.600 +(epoch: 355, iters: 5374, time: 0.063) G_GAN: 0.327 G_GAN_Feat: 0.751 G_ID: 0.084 G_Rec: 0.359 D_GP: 0.029 D_real: 1.102 D_fake: 0.673 +(epoch: 355, iters: 5774, time: 0.064) G_GAN: 1.076 G_GAN_Feat: 1.116 G_ID: 0.107 G_Rec: 0.439 D_GP: 0.064 D_real: 1.194 D_fake: 0.163 +(epoch: 355, iters: 6174, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 0.803 G_ID: 0.120 G_Rec: 0.341 D_GP: 0.032 D_real: 1.384 D_fake: 0.446 +(epoch: 355, iters: 6574, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 1.060 G_ID: 0.130 G_Rec: 0.497 D_GP: 0.042 D_real: 0.945 D_fake: 0.525 +(epoch: 355, iters: 6974, time: 0.064) G_GAN: 0.520 G_GAN_Feat: 0.895 G_ID: 0.108 G_Rec: 0.354 D_GP: 0.061 D_real: 1.020 D_fake: 0.544 +(epoch: 355, iters: 7374, time: 0.064) G_GAN: 0.686 G_GAN_Feat: 1.369 G_ID: 0.125 G_Rec: 0.501 D_GP: 0.126 D_real: 1.111 D_fake: 0.403 +(epoch: 355, iters: 7774, time: 0.064) G_GAN: -0.080 G_GAN_Feat: 0.691 G_ID: 0.128 G_Rec: 0.331 D_GP: 0.027 D_real: 0.827 D_fake: 1.080 +(epoch: 355, iters: 8174, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 1.034 G_ID: 0.145 G_Rec: 0.486 D_GP: 0.037 D_real: 0.767 D_fake: 0.778 +(epoch: 355, iters: 8574, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.724 G_ID: 0.101 G_Rec: 0.349 D_GP: 0.064 D_real: 0.955 D_fake: 0.808 +(epoch: 356, iters: 366, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.930 G_ID: 0.141 G_Rec: 0.465 D_GP: 0.043 D_real: 0.840 D_fake: 0.670 +(epoch: 356, iters: 766, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.775 G_ID: 0.127 G_Rec: 0.317 D_GP: 0.033 D_real: 1.151 D_fake: 0.756 +(epoch: 356, iters: 1166, time: 0.064) G_GAN: 0.595 G_GAN_Feat: 1.021 G_ID: 0.124 G_Rec: 0.406 D_GP: 0.073 D_real: 0.691 D_fake: 0.421 +(epoch: 356, iters: 1566, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.910 G_ID: 0.117 G_Rec: 0.318 D_GP: 0.057 D_real: 0.500 D_fake: 0.564 +(epoch: 356, iters: 1966, time: 0.064) G_GAN: 0.751 G_GAN_Feat: 0.939 G_ID: 0.124 G_Rec: 0.432 D_GP: 0.033 D_real: 1.369 D_fake: 0.316 +(epoch: 356, iters: 2366, time: 0.064) G_GAN: 0.006 G_GAN_Feat: 0.723 G_ID: 0.099 G_Rec: 0.316 D_GP: 0.029 D_real: 0.997 D_fake: 0.994 +(epoch: 356, iters: 2766, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 0.977 G_ID: 0.130 G_Rec: 0.449 D_GP: 0.037 D_real: 0.990 D_fake: 0.546 +(epoch: 356, iters: 3166, time: 0.064) G_GAN: 0.027 G_GAN_Feat: 0.853 G_ID: 0.099 G_Rec: 0.360 D_GP: 0.077 D_real: 0.673 D_fake: 0.975 +(epoch: 356, iters: 3566, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.975 G_ID: 0.119 G_Rec: 0.433 D_GP: 0.056 D_real: 0.682 D_fake: 0.754 +(epoch: 356, iters: 3966, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.693 G_ID: 0.112 G_Rec: 0.293 D_GP: 0.039 D_real: 1.032 D_fake: 0.853 +(epoch: 356, iters: 4366, time: 0.064) G_GAN: 0.636 G_GAN_Feat: 1.074 G_ID: 0.135 G_Rec: 0.500 D_GP: 0.071 D_real: 0.858 D_fake: 0.389 +(epoch: 356, iters: 4766, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 0.973 G_ID: 0.125 G_Rec: 0.340 D_GP: 0.628 D_real: 0.482 D_fake: 0.486 +(epoch: 356, iters: 5166, time: 0.064) G_GAN: 0.874 G_GAN_Feat: 1.138 G_ID: 0.118 G_Rec: 0.448 D_GP: 0.137 D_real: 0.637 D_fake: 0.242 +(epoch: 356, iters: 5566, time: 0.064) G_GAN: -0.077 G_GAN_Feat: 0.819 G_ID: 0.117 G_Rec: 0.314 D_GP: 0.039 D_real: 0.682 D_fake: 1.077 +(epoch: 356, iters: 5966, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.986 G_ID: 0.151 G_Rec: 0.440 D_GP: 0.034 D_real: 1.162 D_fake: 0.575 +(epoch: 356, iters: 6366, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.676 G_ID: 0.135 G_Rec: 0.299 D_GP: 0.030 D_real: 0.837 D_fake: 1.030 +(epoch: 356, iters: 6766, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.931 G_ID: 0.133 G_Rec: 0.426 D_GP: 0.033 D_real: 0.930 D_fake: 0.711 +(epoch: 356, iters: 7166, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.771 G_ID: 0.091 G_Rec: 0.328 D_GP: 0.129 D_real: 0.941 D_fake: 0.742 +(epoch: 356, iters: 7566, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.997 G_ID: 0.143 G_Rec: 0.449 D_GP: 0.267 D_real: 0.515 D_fake: 0.760 +(epoch: 356, iters: 7966, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.782 G_ID: 0.095 G_Rec: 0.346 D_GP: 0.030 D_real: 0.940 D_fake: 0.793 +(epoch: 356, iters: 8366, time: 0.064) G_GAN: 0.654 G_GAN_Feat: 0.956 G_ID: 0.124 G_Rec: 0.412 D_GP: 0.039 D_real: 1.110 D_fake: 0.365 +(epoch: 357, iters: 158, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.669 G_ID: 0.104 G_Rec: 0.303 D_GP: 0.026 D_real: 1.118 D_fake: 0.874 +(epoch: 357, iters: 558, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.891 G_ID: 0.138 G_Rec: 0.440 D_GP: 0.036 D_real: 0.957 D_fake: 0.760 +(epoch: 357, iters: 958, time: 0.064) G_GAN: -0.155 G_GAN_Feat: 0.710 G_ID: 0.111 G_Rec: 0.310 D_GP: 0.049 D_real: 0.617 D_fake: 1.155 +(epoch: 357, iters: 1358, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.967 G_ID: 0.131 G_Rec: 0.449 D_GP: 0.053 D_real: 0.527 D_fake: 0.922 +(epoch: 357, iters: 1758, time: 0.064) G_GAN: -0.001 G_GAN_Feat: 0.799 G_ID: 0.107 G_Rec: 0.331 D_GP: 0.046 D_real: 0.721 D_fake: 1.001 +(epoch: 357, iters: 2158, time: 0.064) G_GAN: 0.797 G_GAN_Feat: 0.924 G_ID: 0.111 G_Rec: 0.426 D_GP: 0.035 D_real: 1.349 D_fake: 0.349 +(epoch: 357, iters: 2558, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.819 G_ID: 0.098 G_Rec: 0.332 D_GP: 0.067 D_real: 1.027 D_fake: 0.741 +(epoch: 357, iters: 2958, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 1.086 G_ID: 0.113 G_Rec: 0.450 D_GP: 0.178 D_real: 0.455 D_fake: 0.681 +(epoch: 357, iters: 3358, time: 0.064) G_GAN: -0.191 G_GAN_Feat: 0.898 G_ID: 0.112 G_Rec: 0.335 D_GP: 0.292 D_real: 0.262 D_fake: 1.191 +(epoch: 357, iters: 3758, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.995 G_ID: 0.142 G_Rec: 0.471 D_GP: 0.033 D_real: 0.798 D_fake: 0.725 +(epoch: 357, iters: 4158, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.804 G_ID: 0.095 G_Rec: 0.372 D_GP: 0.083 D_real: 0.661 D_fake: 0.915 +(epoch: 357, iters: 4558, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.984 G_ID: 0.134 G_Rec: 0.388 D_GP: 0.044 D_real: 0.735 D_fake: 0.889 +(epoch: 357, iters: 4958, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.973 G_ID: 0.100 G_Rec: 0.359 D_GP: 0.098 D_real: 0.627 D_fake: 0.560 +(epoch: 357, iters: 5358, time: 0.064) G_GAN: 0.657 G_GAN_Feat: 0.933 G_ID: 0.121 G_Rec: 0.380 D_GP: 0.036 D_real: 1.303 D_fake: 0.371 +(epoch: 357, iters: 5758, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.819 G_ID: 0.131 G_Rec: 0.328 D_GP: 0.051 D_real: 1.174 D_fake: 0.632 +(epoch: 357, iters: 6158, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.950 G_ID: 0.114 G_Rec: 0.456 D_GP: 0.034 D_real: 1.010 D_fake: 0.555 +(epoch: 357, iters: 6558, time: 0.063) G_GAN: 0.419 G_GAN_Feat: 0.753 G_ID: 0.093 G_Rec: 0.311 D_GP: 0.033 D_real: 1.235 D_fake: 0.589 +(epoch: 357, iters: 6958, time: 0.063) G_GAN: 0.365 G_GAN_Feat: 0.952 G_ID: 0.103 G_Rec: 0.430 D_GP: 0.035 D_real: 1.049 D_fake: 0.636 +(epoch: 357, iters: 7358, time: 0.063) G_GAN: 0.137 G_GAN_Feat: 0.842 G_ID: 0.089 G_Rec: 0.303 D_GP: 0.028 D_real: 0.846 D_fake: 0.863 +(epoch: 357, iters: 7758, time: 0.064) G_GAN: 0.694 G_GAN_Feat: 0.974 G_ID: 0.114 G_Rec: 0.474 D_GP: 0.030 D_real: 1.328 D_fake: 0.344 +(epoch: 357, iters: 8158, time: 0.063) G_GAN: 0.130 G_GAN_Feat: 0.815 G_ID: 0.093 G_Rec: 0.347 D_GP: 0.049 D_real: 0.842 D_fake: 0.872 +(epoch: 357, iters: 8558, time: 0.063) G_GAN: 0.442 G_GAN_Feat: 1.233 G_ID: 0.125 G_Rec: 0.512 D_GP: 0.220 D_real: 0.406 D_fake: 0.570 +(epoch: 358, iters: 350, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.826 G_ID: 0.094 G_Rec: 0.304 D_GP: 0.045 D_real: 0.951 D_fake: 0.638 +(epoch: 358, iters: 750, time: 0.064) G_GAN: 0.651 G_GAN_Feat: 1.036 G_ID: 0.137 G_Rec: 0.399 D_GP: 0.045 D_real: 0.954 D_fake: 0.377 +(epoch: 358, iters: 1150, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.837 G_ID: 0.091 G_Rec: 0.332 D_GP: 0.040 D_real: 0.849 D_fake: 0.901 +(epoch: 358, iters: 1550, time: 0.063) G_GAN: 0.453 G_GAN_Feat: 0.894 G_ID: 0.148 G_Rec: 0.437 D_GP: 0.030 D_real: 1.066 D_fake: 0.551 +(epoch: 358, iters: 1950, time: 0.063) G_GAN: 0.186 G_GAN_Feat: 0.902 G_ID: 0.109 G_Rec: 0.321 D_GP: 0.099 D_real: 0.385 D_fake: 0.830 +(epoch: 358, iters: 2350, time: 0.064) G_GAN: -0.043 G_GAN_Feat: 1.263 G_ID: 0.139 G_Rec: 0.490 D_GP: 0.124 D_real: 0.712 D_fake: 1.044 +(epoch: 358, iters: 2750, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 0.720 G_ID: 0.140 G_Rec: 0.307 D_GP: 0.030 D_real: 0.956 D_fake: 0.875 +(epoch: 358, iters: 3150, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.933 G_ID: 0.149 G_Rec: 0.440 D_GP: 0.030 D_real: 1.101 D_fake: 0.620 +(epoch: 358, iters: 3550, time: 0.063) G_GAN: 0.285 G_GAN_Feat: 0.835 G_ID: 0.103 G_Rec: 0.340 D_GP: 0.052 D_real: 0.777 D_fake: 0.723 +(epoch: 358, iters: 3950, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.890 G_ID: 0.124 G_Rec: 0.384 D_GP: 0.039 D_real: 1.159 D_fake: 0.542 +(epoch: 358, iters: 4350, time: 0.063) G_GAN: 0.437 G_GAN_Feat: 0.636 G_ID: 0.091 G_Rec: 0.270 D_GP: 0.029 D_real: 1.364 D_fake: 0.564 +(epoch: 358, iters: 4750, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.946 G_ID: 0.131 G_Rec: 0.423 D_GP: 0.035 D_real: 0.938 D_fake: 0.533 +(epoch: 358, iters: 5150, time: 0.063) G_GAN: 0.074 G_GAN_Feat: 0.768 G_ID: 0.092 G_Rec: 0.319 D_GP: 0.050 D_real: 0.695 D_fake: 0.926 +(epoch: 358, iters: 5550, time: 0.064) G_GAN: 0.600 G_GAN_Feat: 1.044 G_ID: 0.124 G_Rec: 0.473 D_GP: 0.064 D_real: 1.018 D_fake: 0.460 +(epoch: 358, iters: 5950, time: 0.063) G_GAN: 0.438 G_GAN_Feat: 0.775 G_ID: 0.102 G_Rec: 0.340 D_GP: 0.055 D_real: 1.218 D_fake: 0.568 +(epoch: 358, iters: 6350, time: 0.063) G_GAN: 0.585 G_GAN_Feat: 1.227 G_ID: 0.122 G_Rec: 0.446 D_GP: 0.178 D_real: 0.519 D_fake: 0.437 +(epoch: 358, iters: 6750, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.869 G_ID: 0.114 G_Rec: 0.330 D_GP: 0.043 D_real: 0.894 D_fake: 0.668 +(epoch: 358, iters: 7150, time: 0.064) G_GAN: 0.841 G_GAN_Feat: 1.128 G_ID: 0.130 G_Rec: 0.405 D_GP: 0.412 D_real: 0.743 D_fake: 0.202 +(epoch: 358, iters: 7550, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 0.800 G_ID: 0.102 G_Rec: 0.332 D_GP: 0.034 D_real: 1.208 D_fake: 0.700 +(epoch: 358, iters: 7950, time: 0.063) G_GAN: 0.435 G_GAN_Feat: 1.150 G_ID: 0.131 G_Rec: 0.455 D_GP: 0.165 D_real: 0.302 D_fake: 0.575 +(epoch: 358, iters: 8350, time: 0.063) G_GAN: 0.039 G_GAN_Feat: 0.663 G_ID: 0.102 G_Rec: 0.321 D_GP: 0.028 D_real: 0.952 D_fake: 0.961 +(epoch: 359, iters: 142, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.947 G_ID: 0.116 G_Rec: 0.436 D_GP: 0.035 D_real: 1.080 D_fake: 0.654 +(epoch: 359, iters: 542, time: 0.063) G_GAN: 0.088 G_GAN_Feat: 0.643 G_ID: 0.092 G_Rec: 0.314 D_GP: 0.028 D_real: 0.993 D_fake: 0.912 +(epoch: 359, iters: 942, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.850 G_ID: 0.123 G_Rec: 0.386 D_GP: 0.032 D_real: 1.042 D_fake: 0.686 +(epoch: 359, iters: 1342, time: 0.063) G_GAN: 0.090 G_GAN_Feat: 0.645 G_ID: 0.101 G_Rec: 0.298 D_GP: 0.029 D_real: 1.045 D_fake: 0.910 +(epoch: 359, iters: 1742, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.874 G_ID: 0.138 G_Rec: 0.402 D_GP: 0.044 D_real: 0.930 D_fake: 0.676 +(epoch: 359, iters: 2142, time: 0.063) G_GAN: 0.308 G_GAN_Feat: 0.705 G_ID: 0.098 G_Rec: 0.286 D_GP: 0.040 D_real: 1.116 D_fake: 0.694 +(epoch: 359, iters: 2542, time: 0.063) G_GAN: 0.518 G_GAN_Feat: 1.013 G_ID: 0.107 G_Rec: 0.474 D_GP: 0.064 D_real: 1.022 D_fake: 0.530 +(epoch: 359, iters: 2942, time: 0.063) G_GAN: 0.290 G_GAN_Feat: 0.721 G_ID: 0.122 G_Rec: 0.325 D_GP: 0.030 D_real: 1.142 D_fake: 0.710 +(epoch: 359, iters: 3342, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 1.005 G_ID: 0.109 G_Rec: 0.478 D_GP: 0.042 D_real: 1.121 D_fake: 0.492 +(epoch: 359, iters: 3742, time: 0.063) G_GAN: 0.160 G_GAN_Feat: 0.654 G_ID: 0.097 G_Rec: 0.294 D_GP: 0.029 D_real: 1.006 D_fake: 0.840 +(epoch: 359, iters: 4142, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.920 G_ID: 0.136 G_Rec: 0.434 D_GP: 0.044 D_real: 0.860 D_fake: 0.764 +(epoch: 359, iters: 4542, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 0.728 G_ID: 0.104 G_Rec: 0.299 D_GP: 0.034 D_real: 1.081 D_fake: 0.694 +(epoch: 359, iters: 4942, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 1.026 G_ID: 0.128 G_Rec: 0.464 D_GP: 0.060 D_real: 1.031 D_fake: 0.407 +(epoch: 359, iters: 5342, time: 0.063) G_GAN: 0.188 G_GAN_Feat: 0.752 G_ID: 0.101 G_Rec: 0.308 D_GP: 0.037 D_real: 0.986 D_fake: 0.812 +(epoch: 359, iters: 5742, time: 0.063) G_GAN: 0.011 G_GAN_Feat: 1.137 G_ID: 0.123 G_Rec: 0.469 D_GP: 0.041 D_real: 0.471 D_fake: 0.989 +(epoch: 359, iters: 6142, time: 0.063) G_GAN: 0.192 G_GAN_Feat: 0.876 G_ID: 0.108 G_Rec: 0.340 D_GP: 0.151 D_real: 0.493 D_fake: 0.808 +(epoch: 359, iters: 6542, time: 0.064) G_GAN: 0.712 G_GAN_Feat: 0.903 G_ID: 0.133 G_Rec: 0.448 D_GP: 0.032 D_real: 1.365 D_fake: 0.303 +(epoch: 359, iters: 6942, time: 0.063) G_GAN: 0.232 G_GAN_Feat: 0.751 G_ID: 0.121 G_Rec: 0.300 D_GP: 0.038 D_real: 1.101 D_fake: 0.769 +(epoch: 359, iters: 7342, time: 0.063) G_GAN: 0.180 G_GAN_Feat: 0.987 G_ID: 0.140 G_Rec: 0.454 D_GP: 0.029 D_real: 0.816 D_fake: 0.821 +(epoch: 359, iters: 7742, time: 0.063) G_GAN: -0.106 G_GAN_Feat: 0.741 G_ID: 0.107 G_Rec: 0.326 D_GP: 0.029 D_real: 0.783 D_fake: 1.106 +(epoch: 359, iters: 8142, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.936 G_ID: 0.104 G_Rec: 0.432 D_GP: 0.030 D_real: 0.931 D_fake: 0.591 +(epoch: 359, iters: 8542, time: 0.063) G_GAN: 0.102 G_GAN_Feat: 0.791 G_ID: 0.090 G_Rec: 0.335 D_GP: 0.058 D_real: 0.792 D_fake: 0.899 +(epoch: 360, iters: 334, time: 0.063) G_GAN: 0.321 G_GAN_Feat: 0.936 G_ID: 0.134 G_Rec: 0.420 D_GP: 0.036 D_real: 0.981 D_fake: 0.679 +(epoch: 360, iters: 734, time: 0.063) G_GAN: 0.270 G_GAN_Feat: 0.736 G_ID: 0.091 G_Rec: 0.314 D_GP: 0.035 D_real: 1.148 D_fake: 0.731 +(epoch: 360, iters: 1134, time: 0.064) G_GAN: 0.738 G_GAN_Feat: 0.923 G_ID: 0.122 G_Rec: 0.511 D_GP: 0.029 D_real: 1.408 D_fake: 0.299 +(epoch: 360, iters: 1534, time: 0.063) G_GAN: 0.306 G_GAN_Feat: 0.800 G_ID: 0.104 G_Rec: 0.296 D_GP: 0.060 D_real: 0.792 D_fake: 0.739 +(epoch: 360, iters: 1934, time: 0.063) G_GAN: 0.393 G_GAN_Feat: 1.121 G_ID: 0.137 G_Rec: 0.440 D_GP: 0.094 D_real: 0.257 D_fake: 0.616 +(epoch: 360, iters: 2334, time: 0.063) G_GAN: 0.285 G_GAN_Feat: 0.762 G_ID: 0.109 G_Rec: 0.304 D_GP: 0.032 D_real: 1.089 D_fake: 0.715 +(epoch: 360, iters: 2734, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 1.007 G_ID: 0.121 G_Rec: 0.464 D_GP: 0.034 D_real: 1.033 D_fake: 0.442 +(epoch: 360, iters: 3134, time: 0.063) G_GAN: 0.043 G_GAN_Feat: 0.675 G_ID: 0.108 G_Rec: 0.253 D_GP: 0.035 D_real: 1.017 D_fake: 0.957 +(epoch: 360, iters: 3534, time: 0.063) G_GAN: 0.385 G_GAN_Feat: 0.889 G_ID: 0.133 G_Rec: 0.417 D_GP: 0.029 D_real: 1.045 D_fake: 0.626 +(epoch: 360, iters: 3934, time: 0.063) G_GAN: 0.408 G_GAN_Feat: 0.967 G_ID: 0.118 G_Rec: 0.356 D_GP: 0.101 D_real: 0.394 D_fake: 0.647 +(epoch: 360, iters: 4334, time: 0.064) G_GAN: 0.549 G_GAN_Feat: 1.021 G_ID: 0.129 G_Rec: 0.441 D_GP: 0.041 D_real: 0.993 D_fake: 0.466 +(epoch: 360, iters: 4734, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.722 G_ID: 0.101 G_Rec: 0.310 D_GP: 0.029 D_real: 1.012 D_fake: 0.763 +(epoch: 360, iters: 5134, time: 0.063) G_GAN: 0.571 G_GAN_Feat: 0.920 G_ID: 0.119 G_Rec: 0.441 D_GP: 0.029 D_real: 1.321 D_fake: 0.456 +(epoch: 360, iters: 5534, time: 0.063) G_GAN: -0.041 G_GAN_Feat: 0.743 G_ID: 0.121 G_Rec: 0.333 D_GP: 0.036 D_real: 0.824 D_fake: 1.041 +(epoch: 360, iters: 5934, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.876 G_ID: 0.114 G_Rec: 0.413 D_GP: 0.033 D_real: 1.052 D_fake: 0.596 +(epoch: 360, iters: 6334, time: 0.063) G_GAN: 0.144 G_GAN_Feat: 0.718 G_ID: 0.114 G_Rec: 0.330 D_GP: 0.042 D_real: 0.910 D_fake: 0.856 +(epoch: 360, iters: 6734, time: 0.063) G_GAN: 0.576 G_GAN_Feat: 0.943 G_ID: 0.134 G_Rec: 0.422 D_GP: 0.033 D_real: 1.098 D_fake: 0.462 +(epoch: 360, iters: 7134, time: 0.063) G_GAN: 0.161 G_GAN_Feat: 0.725 G_ID: 0.096 G_Rec: 0.328 D_GP: 0.029 D_real: 1.029 D_fake: 0.839 +(epoch: 360, iters: 7534, time: 0.064) G_GAN: 0.809 G_GAN_Feat: 0.981 G_ID: 0.120 G_Rec: 0.455 D_GP: 0.035 D_real: 1.350 D_fake: 0.247 +(epoch: 360, iters: 7934, time: 0.063) G_GAN: 0.299 G_GAN_Feat: 0.752 G_ID: 0.104 G_Rec: 0.314 D_GP: 0.028 D_real: 1.078 D_fake: 0.701 +(epoch: 360, iters: 8334, time: 0.063) G_GAN: 0.601 G_GAN_Feat: 1.238 G_ID: 0.151 G_Rec: 0.509 D_GP: 0.079 D_real: 0.232 D_fake: 0.438 +(epoch: 361, iters: 126, time: 0.063) G_GAN: 0.532 G_GAN_Feat: 0.971 G_ID: 0.099 G_Rec: 0.339 D_GP: 0.913 D_real: 0.581 D_fake: 0.588 +(epoch: 361, iters: 526, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 0.998 G_ID: 0.152 G_Rec: 0.470 D_GP: 0.035 D_real: 0.896 D_fake: 0.695 +(epoch: 361, iters: 926, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.950 G_ID: 0.102 G_Rec: 0.332 D_GP: 0.169 D_real: 0.470 D_fake: 0.674 +(epoch: 361, iters: 1326, time: 0.063) G_GAN: 0.647 G_GAN_Feat: 1.090 G_ID: 0.123 G_Rec: 0.420 D_GP: 0.092 D_real: 0.496 D_fake: 0.372 +(epoch: 361, iters: 1726, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.656 G_ID: 0.098 G_Rec: 0.272 D_GP: 0.035 D_real: 1.198 D_fake: 0.744 +(epoch: 361, iters: 2126, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 0.855 G_ID: 0.128 G_Rec: 0.409 D_GP: 0.033 D_real: 0.990 D_fake: 0.669 +(epoch: 361, iters: 2526, time: 0.063) G_GAN: 0.199 G_GAN_Feat: 0.760 G_ID: 0.107 G_Rec: 0.311 D_GP: 0.033 D_real: 0.930 D_fake: 0.801 +(epoch: 361, iters: 2926, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 1.049 G_ID: 0.128 G_Rec: 0.452 D_GP: 0.073 D_real: 0.831 D_fake: 0.575 +(epoch: 361, iters: 3326, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.724 G_ID: 0.101 G_Rec: 0.298 D_GP: 0.032 D_real: 0.891 D_fake: 0.850 +(epoch: 361, iters: 3726, time: 0.063) G_GAN: 0.141 G_GAN_Feat: 0.917 G_ID: 0.137 G_Rec: 0.445 D_GP: 0.036 D_real: 0.765 D_fake: 0.861 +(epoch: 361, iters: 4126, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.791 G_ID: 0.111 G_Rec: 0.295 D_GP: 0.037 D_real: 0.745 D_fake: 0.989 +(epoch: 361, iters: 4526, time: 0.064) G_GAN: 0.799 G_GAN_Feat: 1.035 G_ID: 0.132 G_Rec: 0.482 D_GP: 0.038 D_real: 1.266 D_fake: 0.268 +(epoch: 361, iters: 4926, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.731 G_ID: 0.102 G_Rec: 0.312 D_GP: 0.035 D_real: 1.114 D_fake: 0.711 +(epoch: 361, iters: 5326, time: 0.063) G_GAN: 0.563 G_GAN_Feat: 0.977 G_ID: 0.124 G_Rec: 0.425 D_GP: 0.073 D_real: 0.789 D_fake: 0.459 +(epoch: 361, iters: 5726, time: 0.063) G_GAN: 0.338 G_GAN_Feat: 0.805 G_ID: 0.103 G_Rec: 0.326 D_GP: 0.038 D_real: 1.253 D_fake: 0.667 +(epoch: 361, iters: 6126, time: 0.063) G_GAN: 0.703 G_GAN_Feat: 0.860 G_ID: 0.115 G_Rec: 0.417 D_GP: 0.027 D_real: 1.415 D_fake: 0.352 +(epoch: 361, iters: 6526, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.717 G_ID: 0.109 G_Rec: 0.317 D_GP: 0.036 D_real: 0.918 D_fake: 0.918 +(epoch: 361, iters: 6926, time: 0.063) G_GAN: 0.579 G_GAN_Feat: 0.863 G_ID: 0.124 G_Rec: 0.403 D_GP: 0.027 D_real: 1.267 D_fake: 0.454 +(epoch: 361, iters: 7326, time: 0.063) G_GAN: 0.263 G_GAN_Feat: 0.743 G_ID: 0.088 G_Rec: 0.301 D_GP: 0.034 D_real: 1.117 D_fake: 0.738 +(epoch: 361, iters: 7726, time: 0.063) G_GAN: 0.402 G_GAN_Feat: 1.013 G_ID: 0.127 G_Rec: 0.442 D_GP: 0.045 D_real: 0.817 D_fake: 0.599 +(epoch: 361, iters: 8126, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.785 G_ID: 0.094 G_Rec: 0.336 D_GP: 0.054 D_real: 0.984 D_fake: 0.606 +(epoch: 361, iters: 8526, time: 0.063) G_GAN: 0.437 G_GAN_Feat: 0.877 G_ID: 0.121 G_Rec: 0.413 D_GP: 0.030 D_real: 1.180 D_fake: 0.567 +(epoch: 362, iters: 318, time: 0.063) G_GAN: 0.106 G_GAN_Feat: 0.818 G_ID: 0.112 G_Rec: 0.341 D_GP: 0.087 D_real: 0.752 D_fake: 0.895 +(epoch: 362, iters: 718, time: 0.063) G_GAN: 0.281 G_GAN_Feat: 0.934 G_ID: 0.120 G_Rec: 0.398 D_GP: 0.037 D_real: 0.765 D_fake: 0.721 +(epoch: 362, iters: 1118, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.768 G_ID: 0.101 G_Rec: 0.438 D_GP: 0.029 D_real: 0.843 D_fake: 0.882 +(epoch: 362, iters: 1518, time: 0.063) G_GAN: 0.700 G_GAN_Feat: 1.002 G_ID: 0.119 G_Rec: 0.428 D_GP: 0.036 D_real: 1.297 D_fake: 0.313 +(epoch: 362, iters: 1918, time: 0.063) G_GAN: 0.436 G_GAN_Feat: 0.817 G_ID: 0.096 G_Rec: 0.309 D_GP: 0.037 D_real: 1.201 D_fake: 0.568 +(epoch: 362, iters: 2318, time: 0.063) G_GAN: 0.418 G_GAN_Feat: 1.088 G_ID: 0.120 G_Rec: 0.465 D_GP: 0.037 D_real: 0.942 D_fake: 0.585 +(epoch: 362, iters: 2718, time: 0.064) G_GAN: -0.110 G_GAN_Feat: 0.872 G_ID: 0.116 G_Rec: 0.375 D_GP: 0.036 D_real: 0.666 D_fake: 1.110 +(epoch: 362, iters: 3118, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 1.029 G_ID: 0.138 G_Rec: 0.438 D_GP: 0.088 D_real: 0.735 D_fake: 0.481 +(epoch: 362, iters: 3518, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.923 G_ID: 0.123 G_Rec: 0.320 D_GP: 0.067 D_real: 0.220 D_fake: 0.870 +(epoch: 362, iters: 3918, time: 0.063) G_GAN: 0.240 G_GAN_Feat: 0.837 G_ID: 0.132 G_Rec: 0.469 D_GP: 0.031 D_real: 1.006 D_fake: 0.761 +(epoch: 362, iters: 4318, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.602 G_ID: 0.099 G_Rec: 0.313 D_GP: 0.029 D_real: 0.983 D_fake: 0.915 +(epoch: 362, iters: 4718, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.792 G_ID: 0.128 G_Rec: 0.405 D_GP: 0.031 D_real: 1.200 D_fake: 0.574 +(epoch: 362, iters: 5118, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.655 G_ID: 0.103 G_Rec: 0.315 D_GP: 0.038 D_real: 1.137 D_fake: 0.740 +(epoch: 362, iters: 5518, time: 0.063) G_GAN: 0.388 G_GAN_Feat: 0.875 G_ID: 0.120 G_Rec: 0.460 D_GP: 0.042 D_real: 1.037 D_fake: 0.616 +(epoch: 362, iters: 5918, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.599 G_ID: 0.105 G_Rec: 0.285 D_GP: 0.033 D_real: 1.144 D_fake: 0.784 +(epoch: 362, iters: 6318, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.930 G_ID: 0.124 G_Rec: 0.497 D_GP: 0.062 D_real: 0.930 D_fake: 0.628 +(epoch: 362, iters: 6718, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.693 G_ID: 0.104 G_Rec: 0.325 D_GP: 0.040 D_real: 0.873 D_fake: 0.964 +(epoch: 362, iters: 7118, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.792 G_ID: 0.125 G_Rec: 0.408 D_GP: 0.039 D_real: 0.936 D_fake: 0.771 +(epoch: 362, iters: 7518, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.611 G_ID: 0.093 G_Rec: 0.288 D_GP: 0.029 D_real: 1.002 D_fake: 0.903 +(epoch: 362, iters: 7918, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.936 G_ID: 0.126 G_Rec: 0.468 D_GP: 0.068 D_real: 0.694 D_fake: 0.795 +(epoch: 362, iters: 8318, time: 0.063) G_GAN: 0.037 G_GAN_Feat: 0.656 G_ID: 0.132 G_Rec: 0.285 D_GP: 0.042 D_real: 0.886 D_fake: 0.963 +(epoch: 363, iters: 110, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 0.924 G_ID: 0.141 G_Rec: 0.419 D_GP: 0.043 D_real: 1.128 D_fake: 0.436 +(epoch: 363, iters: 510, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.691 G_ID: 0.099 G_Rec: 0.304 D_GP: 0.042 D_real: 1.124 D_fake: 0.698 +(epoch: 363, iters: 910, time: 0.064) G_GAN: 0.550 G_GAN_Feat: 0.924 G_ID: 0.115 G_Rec: 0.434 D_GP: 0.036 D_real: 1.082 D_fake: 0.475 +(epoch: 363, iters: 1310, time: 0.063) G_GAN: 0.210 G_GAN_Feat: 0.736 G_ID: 0.094 G_Rec: 0.302 D_GP: 0.052 D_real: 0.890 D_fake: 0.791 +(epoch: 363, iters: 1710, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.905 G_ID: 0.126 G_Rec: 0.407 D_GP: 0.073 D_real: 1.059 D_fake: 0.467 +(epoch: 363, iters: 2110, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 0.782 G_ID: 0.130 G_Rec: 0.321 D_GP: 0.064 D_real: 0.658 D_fake: 0.992 +(epoch: 363, iters: 2510, time: 0.063) G_GAN: 0.545 G_GAN_Feat: 0.967 G_ID: 0.136 G_Rec: 0.466 D_GP: 0.064 D_real: 0.945 D_fake: 0.480 +(epoch: 363, iters: 2910, time: 0.064) G_GAN: -0.059 G_GAN_Feat: 0.778 G_ID: 0.118 G_Rec: 0.353 D_GP: 0.037 D_real: 0.941 D_fake: 1.060 +(epoch: 363, iters: 3310, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.916 G_ID: 0.132 G_Rec: 0.446 D_GP: 0.086 D_real: 0.875 D_fake: 0.632 +(epoch: 363, iters: 3710, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.801 G_ID: 0.101 G_Rec: 0.316 D_GP: 0.096 D_real: 0.797 D_fake: 0.719 +(epoch: 363, iters: 4110, time: 0.063) G_GAN: 0.582 G_GAN_Feat: 0.959 G_ID: 0.137 G_Rec: 0.391 D_GP: 0.046 D_real: 1.212 D_fake: 0.434 +(epoch: 363, iters: 4510, time: 0.063) G_GAN: 0.351 G_GAN_Feat: 0.841 G_ID: 0.099 G_Rec: 0.362 D_GP: 0.211 D_real: 0.564 D_fake: 0.657 +(epoch: 363, iters: 4910, time: 0.064) G_GAN: 0.564 G_GAN_Feat: 0.929 G_ID: 0.113 G_Rec: 0.449 D_GP: 0.034 D_real: 1.226 D_fake: 0.450 +(epoch: 363, iters: 5310, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.786 G_ID: 0.104 G_Rec: 0.315 D_GP: 0.047 D_real: 1.017 D_fake: 0.545 +(epoch: 363, iters: 5710, time: 0.063) G_GAN: 0.403 G_GAN_Feat: 0.882 G_ID: 0.119 G_Rec: 0.391 D_GP: 0.032 D_real: 1.205 D_fake: 0.598 +(epoch: 363, iters: 6110, time: 0.063) G_GAN: 0.239 G_GAN_Feat: 0.797 G_ID: 0.127 G_Rec: 0.318 D_GP: 0.049 D_real: 0.994 D_fake: 0.762 +(epoch: 363, iters: 6510, time: 0.064) G_GAN: 0.653 G_GAN_Feat: 1.099 G_ID: 0.147 G_Rec: 0.451 D_GP: 0.059 D_real: 0.671 D_fake: 0.362 +(epoch: 363, iters: 6910, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.858 G_ID: 0.109 G_Rec: 0.320 D_GP: 0.128 D_real: 0.603 D_fake: 0.811 +(epoch: 363, iters: 7310, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 1.085 G_ID: 0.140 G_Rec: 0.469 D_GP: 0.260 D_real: 0.494 D_fake: 0.890 +(epoch: 363, iters: 7710, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.747 G_ID: 0.103 G_Rec: 0.322 D_GP: 0.046 D_real: 1.019 D_fake: 0.740 +(epoch: 363, iters: 8110, time: 0.064) G_GAN: 0.006 G_GAN_Feat: 0.997 G_ID: 0.147 G_Rec: 0.397 D_GP: 0.067 D_real: 0.446 D_fake: 0.994 +(epoch: 363, iters: 8510, time: 0.064) G_GAN: -0.213 G_GAN_Feat: 0.849 G_ID: 0.096 G_Rec: 0.335 D_GP: 0.181 D_real: 0.239 D_fake: 1.214 +(epoch: 364, iters: 302, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.884 G_ID: 0.119 G_Rec: 0.390 D_GP: 0.034 D_real: 1.105 D_fake: 0.489 +(epoch: 364, iters: 702, time: 0.063) G_GAN: 0.446 G_GAN_Feat: 1.005 G_ID: 0.113 G_Rec: 0.364 D_GP: 0.467 D_real: 0.555 D_fake: 0.563 +(epoch: 364, iters: 1102, time: 0.064) G_GAN: 0.769 G_GAN_Feat: 1.082 G_ID: 0.150 G_Rec: 0.503 D_GP: 0.055 D_real: 1.026 D_fake: 0.267 +(epoch: 364, iters: 1502, time: 0.064) G_GAN: -0.060 G_GAN_Feat: 0.964 G_ID: 0.112 G_Rec: 0.360 D_GP: 0.330 D_real: 0.188 D_fake: 1.063 +(epoch: 364, iters: 1902, time: 0.063) G_GAN: 0.926 G_GAN_Feat: 1.001 G_ID: 0.134 G_Rec: 0.458 D_GP: 0.032 D_real: 1.391 D_fake: 0.169 +(epoch: 364, iters: 2302, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.925 G_ID: 0.104 G_Rec: 0.358 D_GP: 0.056 D_real: 0.491 D_fake: 0.836 +(epoch: 364, iters: 2702, time: 0.064) G_GAN: 0.751 G_GAN_Feat: 0.992 G_ID: 0.121 G_Rec: 0.414 D_GP: 0.031 D_real: 1.239 D_fake: 0.316 +(epoch: 364, iters: 3102, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.643 G_ID: 0.121 G_Rec: 0.303 D_GP: 0.027 D_real: 0.997 D_fake: 0.898 +(epoch: 364, iters: 3502, time: 0.063) G_GAN: 0.166 G_GAN_Feat: 0.851 G_ID: 0.143 G_Rec: 0.439 D_GP: 0.031 D_real: 0.891 D_fake: 0.834 +(epoch: 364, iters: 3902, time: 0.063) G_GAN: -0.066 G_GAN_Feat: 0.688 G_ID: 0.115 G_Rec: 0.311 D_GP: 0.043 D_real: 0.748 D_fake: 1.066 +(epoch: 364, iters: 4302, time: 0.063) G_GAN: 0.118 G_GAN_Feat: 0.900 G_ID: 0.132 G_Rec: 0.407 D_GP: 0.045 D_real: 0.693 D_fake: 0.882 +(epoch: 364, iters: 4702, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.740 G_ID: 0.109 G_Rec: 0.339 D_GP: 0.139 D_real: 0.736 D_fake: 0.914 +(epoch: 364, iters: 5102, time: 0.063) G_GAN: 0.531 G_GAN_Feat: 0.994 G_ID: 0.122 G_Rec: 0.470 D_GP: 0.196 D_real: 0.872 D_fake: 0.506 +(epoch: 364, iters: 5502, time: 0.063) G_GAN: 0.160 G_GAN_Feat: 0.619 G_ID: 0.101 G_Rec: 0.295 D_GP: 0.029 D_real: 1.091 D_fake: 0.840 +(epoch: 364, iters: 5902, time: 0.063) G_GAN: 0.755 G_GAN_Feat: 0.999 G_ID: 0.138 G_Rec: 0.445 D_GP: 0.054 D_real: 1.217 D_fake: 0.363 +(epoch: 364, iters: 6302, time: 0.064) G_GAN: -0.082 G_GAN_Feat: 0.737 G_ID: 0.099 G_Rec: 0.355 D_GP: 0.070 D_real: 0.669 D_fake: 1.082 +(epoch: 364, iters: 6702, time: 0.063) G_GAN: 0.343 G_GAN_Feat: 1.039 G_ID: 0.111 G_Rec: 0.485 D_GP: 0.052 D_real: 0.756 D_fake: 0.662 +(epoch: 364, iters: 7102, time: 0.063) G_GAN: -0.126 G_GAN_Feat: 0.705 G_ID: 0.100 G_Rec: 0.292 D_GP: 0.049 D_real: 0.673 D_fake: 1.126 +(epoch: 364, iters: 7502, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 1.019 G_ID: 0.115 G_Rec: 0.474 D_GP: 0.054 D_real: 0.766 D_fake: 0.719 +(epoch: 364, iters: 7902, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.714 G_ID: 0.105 G_Rec: 0.284 D_GP: 0.032 D_real: 1.019 D_fake: 0.840 +(epoch: 364, iters: 8302, time: 0.063) G_GAN: 0.438 G_GAN_Feat: 0.848 G_ID: 0.118 G_Rec: 0.367 D_GP: 0.037 D_real: 1.118 D_fake: 0.566 +(epoch: 364, iters: 8702, time: 0.064) G_GAN: 0.067 G_GAN_Feat: 0.773 G_ID: 0.128 G_Rec: 0.359 D_GP: 0.029 D_real: 0.966 D_fake: 0.934 +(epoch: 365, iters: 494, time: 0.063) G_GAN: 0.509 G_GAN_Feat: 1.055 G_ID: 0.112 G_Rec: 0.458 D_GP: 0.054 D_real: 0.800 D_fake: 0.497 +(epoch: 365, iters: 894, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.757 G_ID: 0.105 G_Rec: 0.299 D_GP: 0.032 D_real: 1.001 D_fake: 0.737 +(epoch: 365, iters: 1294, time: 0.063) G_GAN: 0.588 G_GAN_Feat: 0.956 G_ID: 0.105 G_Rec: 0.400 D_GP: 0.031 D_real: 1.112 D_fake: 0.445 +(epoch: 365, iters: 1694, time: 0.063) G_GAN: 0.259 G_GAN_Feat: 0.840 G_ID: 0.090 G_Rec: 0.322 D_GP: 0.112 D_real: 0.752 D_fake: 0.747 +(epoch: 365, iters: 2094, time: 0.063) G_GAN: 0.500 G_GAN_Feat: 1.062 G_ID: 0.115 G_Rec: 0.421 D_GP: 0.041 D_real: 0.798 D_fake: 0.505 +(epoch: 365, iters: 2494, time: 0.064) G_GAN: 0.885 G_GAN_Feat: 1.002 G_ID: 0.095 G_Rec: 0.333 D_GP: 0.066 D_real: 0.980 D_fake: 0.205 +(epoch: 365, iters: 2894, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 1.064 G_ID: 0.120 G_Rec: 0.419 D_GP: 0.030 D_real: 0.811 D_fake: 0.409 +(epoch: 365, iters: 3294, time: 0.063) G_GAN: 0.386 G_GAN_Feat: 0.930 G_ID: 0.101 G_Rec: 0.313 D_GP: 0.042 D_real: 0.556 D_fake: 0.666 +(epoch: 365, iters: 3694, time: 0.063) G_GAN: 0.775 G_GAN_Feat: 1.091 G_ID: 0.124 G_Rec: 0.497 D_GP: 0.112 D_real: 0.631 D_fake: 0.295 +(epoch: 365, iters: 4094, time: 0.064) G_GAN: 0.022 G_GAN_Feat: 0.940 G_ID: 0.100 G_Rec: 0.327 D_GP: 0.050 D_real: 0.232 D_fake: 0.978 +(epoch: 365, iters: 4494, time: 0.063) G_GAN: 0.545 G_GAN_Feat: 1.259 G_ID: 0.124 G_Rec: 0.486 D_GP: 0.040 D_real: 1.136 D_fake: 0.535 +(epoch: 365, iters: 4894, time: 0.063) G_GAN: 0.322 G_GAN_Feat: 0.714 G_ID: 0.098 G_Rec: 0.317 D_GP: 0.026 D_real: 1.278 D_fake: 0.683 +(epoch: 365, iters: 5294, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 0.942 G_ID: 0.124 G_Rec: 0.426 D_GP: 0.034 D_real: 1.034 D_fake: 0.578 +(epoch: 365, iters: 5694, time: 0.063) G_GAN: 0.676 G_GAN_Feat: 0.782 G_ID: 0.101 G_Rec: 0.324 D_GP: 0.090 D_real: 1.270 D_fake: 0.343 +(epoch: 365, iters: 6094, time: 0.063) G_GAN: 0.489 G_GAN_Feat: 0.826 G_ID: 0.131 G_Rec: 0.392 D_GP: 0.030 D_real: 1.214 D_fake: 0.518 +(epoch: 365, iters: 6494, time: 0.063) G_GAN: 0.225 G_GAN_Feat: 0.771 G_ID: 0.109 G_Rec: 0.316 D_GP: 0.037 D_real: 0.979 D_fake: 0.776 +(epoch: 365, iters: 6894, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 1.249 G_ID: 0.118 G_Rec: 0.518 D_GP: 0.050 D_real: 1.182 D_fake: 0.544 +(epoch: 365, iters: 7294, time: 0.063) G_GAN: 0.288 G_GAN_Feat: 0.749 G_ID: 0.126 G_Rec: 0.325 D_GP: 0.030 D_real: 1.152 D_fake: 0.718 +(epoch: 365, iters: 7694, time: 0.063) G_GAN: 0.605 G_GAN_Feat: 0.875 G_ID: 0.131 G_Rec: 0.408 D_GP: 0.041 D_real: 1.180 D_fake: 0.469 +(epoch: 365, iters: 8094, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.737 G_ID: 0.095 G_Rec: 0.327 D_GP: 0.036 D_real: 0.904 D_fake: 0.783 +(epoch: 365, iters: 8494, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.894 G_ID: 0.135 G_Rec: 0.434 D_GP: 0.030 D_real: 1.183 D_fake: 0.550 +(epoch: 366, iters: 286, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.859 G_ID: 0.111 G_Rec: 0.346 D_GP: 0.034 D_real: 0.859 D_fake: 0.765 +(epoch: 366, iters: 686, time: 0.064) G_GAN: 0.733 G_GAN_Feat: 1.119 G_ID: 0.145 G_Rec: 0.493 D_GP: 0.047 D_real: 0.870 D_fake: 0.319 +(epoch: 366, iters: 1086, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 1.082 G_ID: 0.126 G_Rec: 0.347 D_GP: 0.073 D_real: 0.088 D_fake: 0.964 +(epoch: 366, iters: 1486, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.867 G_ID: 0.115 G_Rec: 0.419 D_GP: 0.027 D_real: 1.125 D_fake: 0.555 +(epoch: 366, iters: 1886, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.734 G_ID: 0.087 G_Rec: 0.303 D_GP: 0.030 D_real: 0.909 D_fake: 0.978 +(epoch: 366, iters: 2286, time: 0.063) G_GAN: 0.221 G_GAN_Feat: 0.883 G_ID: 0.130 G_Rec: 0.402 D_GP: 0.037 D_real: 0.930 D_fake: 0.780 +(epoch: 366, iters: 2686, time: 0.063) G_GAN: 0.140 G_GAN_Feat: 0.735 G_ID: 0.095 G_Rec: 0.284 D_GP: 0.047 D_real: 0.787 D_fake: 0.860 +(epoch: 366, iters: 3086, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 1.020 G_ID: 0.123 G_Rec: 0.430 D_GP: 0.036 D_real: 0.925 D_fake: 0.425 +(epoch: 366, iters: 3486, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.908 G_ID: 0.098 G_Rec: 0.334 D_GP: 0.086 D_real: 0.517 D_fake: 0.728 +(epoch: 366, iters: 3886, time: 0.063) G_GAN: 0.567 G_GAN_Feat: 1.193 G_ID: 0.137 G_Rec: 0.446 D_GP: 0.131 D_real: 0.259 D_fake: 0.488 +(epoch: 366, iters: 4286, time: 0.063) G_GAN: 0.363 G_GAN_Feat: 0.856 G_ID: 0.116 G_Rec: 0.312 D_GP: 0.052 D_real: 0.966 D_fake: 0.643 +(epoch: 366, iters: 4686, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.979 G_ID: 0.135 G_Rec: 0.476 D_GP: 0.037 D_real: 0.964 D_fake: 0.603 +(epoch: 366, iters: 5086, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 0.731 G_ID: 0.096 G_Rec: 0.327 D_GP: 0.030 D_real: 1.018 D_fake: 0.816 +(epoch: 366, iters: 5486, time: 0.063) G_GAN: 0.574 G_GAN_Feat: 1.018 G_ID: 0.128 G_Rec: 0.485 D_GP: 0.096 D_real: 0.896 D_fake: 0.474 +(epoch: 366, iters: 5886, time: 0.063) G_GAN: 0.194 G_GAN_Feat: 0.736 G_ID: 0.101 G_Rec: 0.301 D_GP: 0.036 D_real: 1.037 D_fake: 0.807 +(epoch: 366, iters: 6286, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.866 G_ID: 0.137 G_Rec: 0.380 D_GP: 0.036 D_real: 1.001 D_fake: 0.719 +(epoch: 366, iters: 6686, time: 0.063) G_GAN: 0.028 G_GAN_Feat: 0.957 G_ID: 0.115 G_Rec: 0.360 D_GP: 0.432 D_real: 0.184 D_fake: 0.979 +(epoch: 366, iters: 7086, time: 0.063) G_GAN: 0.662 G_GAN_Feat: 1.098 G_ID: 0.126 G_Rec: 0.477 D_GP: 0.049 D_real: 0.907 D_fake: 0.377 +(epoch: 366, iters: 7486, time: 0.063) G_GAN: 0.226 G_GAN_Feat: 0.915 G_ID: 0.097 G_Rec: 0.342 D_GP: 0.072 D_real: 0.502 D_fake: 0.775 +(epoch: 366, iters: 7886, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 1.164 G_ID: 0.122 G_Rec: 0.437 D_GP: 0.089 D_real: 0.491 D_fake: 0.647 +(epoch: 366, iters: 8286, time: 0.063) G_GAN: 0.336 G_GAN_Feat: 0.846 G_ID: 0.106 G_Rec: 0.307 D_GP: 0.062 D_real: 0.725 D_fake: 0.664 +(epoch: 366, iters: 8686, time: 0.063) G_GAN: 0.511 G_GAN_Feat: 1.022 G_ID: 0.141 G_Rec: 0.462 D_GP: 0.038 D_real: 1.033 D_fake: 0.502 +(epoch: 367, iters: 478, time: 0.063) G_GAN: 0.251 G_GAN_Feat: 0.778 G_ID: 0.098 G_Rec: 0.320 D_GP: 0.043 D_real: 1.115 D_fake: 0.749 +(epoch: 367, iters: 878, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.926 G_ID: 0.136 G_Rec: 0.453 D_GP: 0.037 D_real: 0.964 D_fake: 0.683 +(epoch: 367, iters: 1278, time: 0.063) G_GAN: 0.274 G_GAN_Feat: 0.811 G_ID: 0.105 G_Rec: 0.313 D_GP: 0.039 D_real: 0.893 D_fake: 0.727 +(epoch: 367, iters: 1678, time: 0.063) G_GAN: 0.975 G_GAN_Feat: 1.169 G_ID: 0.105 G_Rec: 0.464 D_GP: 0.324 D_real: 0.414 D_fake: 0.193 +(epoch: 367, iters: 2078, time: 0.063) G_GAN: 0.457 G_GAN_Feat: 0.946 G_ID: 0.104 G_Rec: 0.342 D_GP: 0.063 D_real: 0.967 D_fake: 0.615 +(epoch: 367, iters: 2478, time: 0.064) G_GAN: 0.701 G_GAN_Feat: 1.117 G_ID: 0.125 G_Rec: 0.460 D_GP: 0.101 D_real: 0.711 D_fake: 0.335 +(epoch: 367, iters: 2878, time: 0.063) G_GAN: -0.062 G_GAN_Feat: 0.864 G_ID: 0.115 G_Rec: 0.342 D_GP: 0.029 D_real: 0.781 D_fake: 1.062 +(epoch: 367, iters: 3278, time: 0.063) G_GAN: 0.897 G_GAN_Feat: 1.111 G_ID: 0.105 G_Rec: 0.464 D_GP: 0.033 D_real: 1.112 D_fake: 0.171 +(epoch: 367, iters: 3678, time: 0.063) G_GAN: -0.066 G_GAN_Feat: 0.731 G_ID: 0.120 G_Rec: 0.311 D_GP: 0.028 D_real: 0.872 D_fake: 1.066 +(epoch: 367, iters: 4078, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 1.128 G_ID: 0.121 G_Rec: 0.470 D_GP: 0.069 D_real: 0.731 D_fake: 0.603 +(epoch: 367, iters: 4478, time: 0.064) G_GAN: -0.045 G_GAN_Feat: 0.846 G_ID: 0.103 G_Rec: 0.332 D_GP: 0.118 D_real: 0.501 D_fake: 1.045 +(epoch: 367, iters: 4878, time: 0.064) G_GAN: 0.936 G_GAN_Feat: 1.109 G_ID: 0.125 G_Rec: 0.480 D_GP: 0.105 D_real: 1.054 D_fake: 0.214 +(epoch: 367, iters: 5278, time: 0.063) G_GAN: 0.287 G_GAN_Feat: 0.756 G_ID: 0.102 G_Rec: 0.315 D_GP: 0.036 D_real: 1.115 D_fake: 0.714 +(epoch: 367, iters: 5678, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 0.937 G_ID: 0.120 G_Rec: 0.397 D_GP: 0.042 D_real: 1.211 D_fake: 0.431 +(epoch: 367, iters: 6078, time: 0.063) G_GAN: 0.159 G_GAN_Feat: 0.890 G_ID: 0.107 G_Rec: 0.304 D_GP: 0.033 D_real: 0.558 D_fake: 0.841 +(epoch: 367, iters: 6478, time: 0.063) G_GAN: 0.785 G_GAN_Feat: 1.078 G_ID: 0.144 G_Rec: 0.484 D_GP: 0.032 D_real: 0.998 D_fake: 0.346 +(epoch: 367, iters: 6878, time: 0.063) G_GAN: 0.381 G_GAN_Feat: 1.062 G_ID: 0.094 G_Rec: 0.369 D_GP: 0.032 D_real: 1.135 D_fake: 0.687 +(epoch: 367, iters: 7278, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.871 G_ID: 0.136 G_Rec: 0.442 D_GP: 0.027 D_real: 1.085 D_fake: 0.681 +(epoch: 367, iters: 7678, time: 0.063) G_GAN: 0.111 G_GAN_Feat: 0.620 G_ID: 0.081 G_Rec: 0.308 D_GP: 0.026 D_real: 1.092 D_fake: 0.890 +(epoch: 367, iters: 8078, time: 0.063) G_GAN: 0.398 G_GAN_Feat: 0.808 G_ID: 0.113 G_Rec: 0.416 D_GP: 0.028 D_real: 1.203 D_fake: 0.612 +(epoch: 367, iters: 8478, time: 0.063) G_GAN: 0.049 G_GAN_Feat: 0.614 G_ID: 0.090 G_Rec: 0.293 D_GP: 0.033 D_real: 0.979 D_fake: 0.951 +(epoch: 368, iters: 270, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.826 G_ID: 0.121 G_Rec: 0.467 D_GP: 0.038 D_real: 0.992 D_fake: 0.719 +(epoch: 368, iters: 670, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.623 G_ID: 0.103 G_Rec: 0.291 D_GP: 0.037 D_real: 0.927 D_fake: 0.901 +(epoch: 368, iters: 1070, time: 0.063) G_GAN: 0.089 G_GAN_Feat: 0.759 G_ID: 0.110 G_Rec: 0.366 D_GP: 0.038 D_real: 0.797 D_fake: 0.911 +(epoch: 368, iters: 1470, time: 0.063) G_GAN: -0.104 G_GAN_Feat: 0.648 G_ID: 0.105 G_Rec: 0.331 D_GP: 0.035 D_real: 0.796 D_fake: 1.104 +(epoch: 368, iters: 1870, time: 0.064) G_GAN: 0.029 G_GAN_Feat: 0.853 G_ID: 0.127 G_Rec: 0.412 D_GP: 0.046 D_real: 0.694 D_fake: 0.971 +(epoch: 368, iters: 2270, time: 0.063) G_GAN: -0.177 G_GAN_Feat: 0.678 G_ID: 0.111 G_Rec: 0.320 D_GP: 0.050 D_real: 0.673 D_fake: 1.177 +(epoch: 368, iters: 2670, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.817 G_ID: 0.111 G_Rec: 0.405 D_GP: 0.041 D_real: 1.053 D_fake: 0.646 +(epoch: 368, iters: 3070, time: 0.063) G_GAN: 0.005 G_GAN_Feat: 0.697 G_ID: 0.096 G_Rec: 0.319 D_GP: 0.039 D_real: 0.903 D_fake: 0.995 +(epoch: 368, iters: 3470, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.857 G_ID: 0.134 G_Rec: 0.381 D_GP: 0.044 D_real: 1.030 D_fake: 0.697 +(epoch: 368, iters: 3870, time: 0.063) G_GAN: 0.322 G_GAN_Feat: 0.704 G_ID: 0.105 G_Rec: 0.309 D_GP: 0.036 D_real: 1.160 D_fake: 0.679 +(epoch: 368, iters: 4270, time: 0.063) G_GAN: 0.112 G_GAN_Feat: 0.833 G_ID: 0.124 G_Rec: 0.373 D_GP: 0.043 D_real: 0.771 D_fake: 0.889 +(epoch: 368, iters: 4670, time: 0.063) G_GAN: 0.196 G_GAN_Feat: 0.783 G_ID: 0.101 G_Rec: 0.301 D_GP: 0.107 D_real: 0.769 D_fake: 0.804 +(epoch: 368, iters: 5070, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 0.901 G_ID: 0.108 G_Rec: 0.378 D_GP: 0.047 D_real: 1.204 D_fake: 0.388 +(epoch: 368, iters: 5470, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.659 G_ID: 0.094 G_Rec: 0.303 D_GP: 0.031 D_real: 1.007 D_fake: 0.913 +(epoch: 368, iters: 5870, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.890 G_ID: 0.119 G_Rec: 0.432 D_GP: 0.034 D_real: 0.996 D_fake: 0.662 +(epoch: 368, iters: 6270, time: 0.064) G_GAN: -0.100 G_GAN_Feat: 0.721 G_ID: 0.145 G_Rec: 0.321 D_GP: 0.047 D_real: 0.722 D_fake: 1.100 +(epoch: 368, iters: 6670, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 1.023 G_ID: 0.145 G_Rec: 0.473 D_GP: 0.103 D_real: 0.430 D_fake: 0.886 +(epoch: 368, iters: 7070, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.702 G_ID: 0.104 G_Rec: 0.302 D_GP: 0.038 D_real: 0.869 D_fake: 0.871 +(epoch: 368, iters: 7470, time: 0.064) G_GAN: 0.678 G_GAN_Feat: 0.968 G_ID: 0.126 G_Rec: 0.474 D_GP: 0.035 D_real: 1.217 D_fake: 0.351 +(epoch: 368, iters: 7870, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.785 G_ID: 0.121 G_Rec: 0.354 D_GP: 0.050 D_real: 0.817 D_fake: 0.888 +(epoch: 368, iters: 8270, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.883 G_ID: 0.129 G_Rec: 0.379 D_GP: 0.041 D_real: 1.061 D_fake: 0.624 +(epoch: 368, iters: 8670, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.771 G_ID: 0.104 G_Rec: 0.354 D_GP: 0.033 D_real: 1.103 D_fake: 0.695 +(epoch: 369, iters: 462, time: 0.064) G_GAN: 0.568 G_GAN_Feat: 0.923 G_ID: 0.131 G_Rec: 0.388 D_GP: 0.048 D_real: 1.243 D_fake: 0.439 +(epoch: 369, iters: 862, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.668 G_ID: 0.103 G_Rec: 0.292 D_GP: 0.029 D_real: 1.245 D_fake: 0.692 +(epoch: 369, iters: 1262, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.958 G_ID: 0.122 G_Rec: 0.434 D_GP: 0.042 D_real: 1.006 D_fake: 0.595 +(epoch: 369, iters: 1662, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.788 G_ID: 0.117 G_Rec: 0.311 D_GP: 0.063 D_real: 0.659 D_fake: 0.910 +(epoch: 369, iters: 2062, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 1.080 G_ID: 0.138 G_Rec: 0.490 D_GP: 0.051 D_real: 0.743 D_fake: 0.551 +(epoch: 369, iters: 2462, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.839 G_ID: 0.111 G_Rec: 0.332 D_GP: 0.042 D_real: 1.314 D_fake: 0.531 +(epoch: 369, iters: 2862, time: 0.064) G_GAN: 0.874 G_GAN_Feat: 0.868 G_ID: 0.104 G_Rec: 0.433 D_GP: 0.028 D_real: 1.570 D_fake: 0.324 +(epoch: 369, iters: 3262, time: 0.064) G_GAN: -0.026 G_GAN_Feat: 0.637 G_ID: 0.119 G_Rec: 0.292 D_GP: 0.029 D_real: 0.899 D_fake: 1.026 +(epoch: 369, iters: 3662, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.845 G_ID: 0.120 G_Rec: 0.406 D_GP: 0.036 D_real: 0.959 D_fake: 0.634 +(epoch: 369, iters: 4062, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.626 G_ID: 0.113 G_Rec: 0.278 D_GP: 0.035 D_real: 1.015 D_fake: 0.871 +(epoch: 369, iters: 4462, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.919 G_ID: 0.156 G_Rec: 0.440 D_GP: 0.037 D_real: 0.745 D_fake: 0.814 +(epoch: 369, iters: 4862, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.724 G_ID: 0.099 G_Rec: 0.300 D_GP: 0.047 D_real: 0.871 D_fake: 0.881 +(epoch: 369, iters: 5262, time: 0.064) G_GAN: 0.564 G_GAN_Feat: 0.938 G_ID: 0.131 G_Rec: 0.427 D_GP: 0.074 D_real: 1.047 D_fake: 0.463 +(epoch: 369, iters: 5662, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.805 G_ID: 0.092 G_Rec: 0.331 D_GP: 0.045 D_real: 0.951 D_fake: 0.742 +(epoch: 369, iters: 6062, time: 0.064) G_GAN: 0.617 G_GAN_Feat: 0.992 G_ID: 0.128 G_Rec: 0.442 D_GP: 0.041 D_real: 1.215 D_fake: 0.391 +(epoch: 369, iters: 6462, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.725 G_ID: 0.092 G_Rec: 0.287 D_GP: 0.032 D_real: 1.147 D_fake: 0.742 +(epoch: 369, iters: 6862, time: 0.064) G_GAN: 0.649 G_GAN_Feat: 1.054 G_ID: 0.111 G_Rec: 0.451 D_GP: 0.053 D_real: 0.942 D_fake: 0.371 +(epoch: 369, iters: 7262, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.871 G_ID: 0.100 G_Rec: 0.337 D_GP: 0.070 D_real: 0.704 D_fake: 0.694 +(epoch: 369, iters: 7662, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.960 G_ID: 0.131 G_Rec: 0.452 D_GP: 0.052 D_real: 1.012 D_fake: 0.555 +(epoch: 369, iters: 8062, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.717 G_ID: 0.116 G_Rec: 0.317 D_GP: 0.036 D_real: 1.043 D_fake: 0.773 +(epoch: 369, iters: 8462, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 1.100 G_ID: 0.150 G_Rec: 0.489 D_GP: 0.061 D_real: 0.652 D_fake: 0.657 +(epoch: 370, iters: 254, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.808 G_ID: 0.095 G_Rec: 0.303 D_GP: 0.034 D_real: 1.031 D_fake: 0.615 +(epoch: 370, iters: 654, time: 0.064) G_GAN: 1.037 G_GAN_Feat: 1.029 G_ID: 0.112 G_Rec: 0.478 D_GP: 0.102 D_real: 1.488 D_fake: 0.246 +(epoch: 370, iters: 1054, time: 0.064) G_GAN: 0.005 G_GAN_Feat: 0.754 G_ID: 0.112 G_Rec: 0.322 D_GP: 0.034 D_real: 0.766 D_fake: 0.995 +(epoch: 370, iters: 1454, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 1.027 G_ID: 0.142 G_Rec: 0.487 D_GP: 0.068 D_real: 0.460 D_fake: 0.945 +(epoch: 370, iters: 1854, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.681 G_ID: 0.088 G_Rec: 0.290 D_GP: 0.033 D_real: 1.019 D_fake: 0.895 +(epoch: 370, iters: 2254, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.941 G_ID: 0.124 G_Rec: 0.424 D_GP: 0.031 D_real: 1.013 D_fake: 0.668 +(epoch: 370, iters: 2654, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.769 G_ID: 0.091 G_Rec: 0.311 D_GP: 0.037 D_real: 0.960 D_fake: 0.751 +(epoch: 370, iters: 3054, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 1.105 G_ID: 0.135 G_Rec: 0.496 D_GP: 0.095 D_real: 0.658 D_fake: 0.536 +(epoch: 370, iters: 3454, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.817 G_ID: 0.105 G_Rec: 0.313 D_GP: 0.050 D_real: 0.792 D_fake: 0.866 +(epoch: 370, iters: 3854, time: 0.064) G_GAN: 0.806 G_GAN_Feat: 1.058 G_ID: 0.119 G_Rec: 0.459 D_GP: 0.067 D_real: 1.067 D_fake: 0.290 +(epoch: 370, iters: 4254, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.838 G_ID: 0.098 G_Rec: 0.311 D_GP: 0.041 D_real: 0.946 D_fake: 0.671 +(epoch: 370, iters: 4654, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 1.115 G_ID: 0.161 G_Rec: 0.454 D_GP: 0.138 D_real: 0.322 D_fake: 0.552 +(epoch: 370, iters: 5054, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.832 G_ID: 0.101 G_Rec: 0.321 D_GP: 0.053 D_real: 0.942 D_fake: 0.772 +(epoch: 370, iters: 5454, time: 0.064) G_GAN: 1.058 G_GAN_Feat: 1.194 G_ID: 0.128 G_Rec: 0.543 D_GP: 0.156 D_real: 0.601 D_fake: 0.117 +(epoch: 370, iters: 5854, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.802 G_ID: 0.087 G_Rec: 0.315 D_GP: 0.069 D_real: 0.930 D_fake: 0.839 +(epoch: 370, iters: 6254, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 1.024 G_ID: 0.124 G_Rec: 0.462 D_GP: 0.034 D_real: 0.878 D_fake: 0.624 +(epoch: 370, iters: 6654, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.836 G_ID: 0.100 G_Rec: 0.335 D_GP: 0.037 D_real: 0.878 D_fake: 0.722 +(epoch: 370, iters: 7054, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 1.035 G_ID: 0.119 G_Rec: 0.474 D_GP: 0.032 D_real: 0.988 D_fake: 0.575 +(epoch: 370, iters: 7454, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.772 G_ID: 0.123 G_Rec: 0.291 D_GP: 0.032 D_real: 1.138 D_fake: 0.647 +(epoch: 370, iters: 7854, time: 0.064) G_GAN: 0.029 G_GAN_Feat: 1.009 G_ID: 0.118 G_Rec: 0.458 D_GP: 0.047 D_real: 0.539 D_fake: 0.971 +(epoch: 370, iters: 8254, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.895 G_ID: 0.117 G_Rec: 0.348 D_GP: 0.136 D_real: 0.632 D_fake: 0.666 +(epoch: 370, iters: 8654, time: 0.064) G_GAN: 0.714 G_GAN_Feat: 1.005 G_ID: 0.126 G_Rec: 0.453 D_GP: 0.036 D_real: 1.389 D_fake: 0.315 +(epoch: 371, iters: 446, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.814 G_ID: 0.102 G_Rec: 0.316 D_GP: 0.040 D_real: 0.931 D_fake: 0.786 +(epoch: 371, iters: 846, time: 0.064) G_GAN: 0.809 G_GAN_Feat: 1.174 G_ID: 0.122 G_Rec: 0.483 D_GP: 0.061 D_real: 0.634 D_fake: 0.237 +(epoch: 371, iters: 1246, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.960 G_ID: 0.098 G_Rec: 0.377 D_GP: 0.103 D_real: 0.679 D_fake: 0.770 +(epoch: 371, iters: 1646, time: 0.064) G_GAN: 0.855 G_GAN_Feat: 1.034 G_ID: 0.133 G_Rec: 0.466 D_GP: 0.042 D_real: 1.432 D_fake: 0.207 +(epoch: 371, iters: 2046, time: 0.064) G_GAN: -0.022 G_GAN_Feat: 0.752 G_ID: 0.109 G_Rec: 0.363 D_GP: 0.031 D_real: 0.790 D_fake: 1.022 +(epoch: 371, iters: 2446, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 1.056 G_ID: 0.114 G_Rec: 0.432 D_GP: 0.054 D_real: 0.614 D_fake: 0.633 +(epoch: 371, iters: 2846, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.725 G_ID: 0.102 G_Rec: 0.315 D_GP: 0.030 D_real: 0.954 D_fake: 0.916 +(epoch: 371, iters: 3246, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 0.952 G_ID: 0.127 G_Rec: 0.417 D_GP: 0.046 D_real: 1.104 D_fake: 0.476 +(epoch: 371, iters: 3646, time: 0.064) G_GAN: -0.083 G_GAN_Feat: 0.861 G_ID: 0.111 G_Rec: 0.324 D_GP: 0.134 D_real: 0.353 D_fake: 1.091 +(epoch: 371, iters: 4046, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.953 G_ID: 0.126 G_Rec: 0.449 D_GP: 0.055 D_real: 1.046 D_fake: 0.566 +(epoch: 371, iters: 4446, time: 0.064) G_GAN: 0.029 G_GAN_Feat: 0.836 G_ID: 0.106 G_Rec: 0.347 D_GP: 0.102 D_real: 0.622 D_fake: 0.972 +(epoch: 371, iters: 4846, time: 0.064) G_GAN: -0.088 G_GAN_Feat: 1.010 G_ID: 0.129 G_Rec: 0.433 D_GP: 1.688 D_real: 0.349 D_fake: 1.090 +(epoch: 371, iters: 5246, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.629 G_ID: 0.088 G_Rec: 0.290 D_GP: 0.026 D_real: 1.169 D_fake: 0.773 +(epoch: 371, iters: 5646, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.897 G_ID: 0.111 G_Rec: 0.454 D_GP: 0.036 D_real: 1.101 D_fake: 0.556 +(epoch: 371, iters: 6046, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.681 G_ID: 0.098 G_Rec: 0.279 D_GP: 0.037 D_real: 0.896 D_fake: 0.912 +(epoch: 371, iters: 6446, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.937 G_ID: 0.144 G_Rec: 0.439 D_GP: 0.050 D_real: 0.975 D_fake: 0.644 +(epoch: 371, iters: 6846, time: 0.064) G_GAN: -0.067 G_GAN_Feat: 0.694 G_ID: 0.096 G_Rec: 0.305 D_GP: 0.038 D_real: 0.757 D_fake: 1.067 +(epoch: 371, iters: 7246, time: 0.064) G_GAN: 0.551 G_GAN_Feat: 0.986 G_ID: 0.124 G_Rec: 0.453 D_GP: 0.052 D_real: 0.939 D_fake: 0.471 +(epoch: 371, iters: 7646, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.776 G_ID: 0.153 G_Rec: 0.360 D_GP: 0.070 D_real: 1.038 D_fake: 0.665 +(epoch: 371, iters: 8046, time: 0.064) G_GAN: 0.683 G_GAN_Feat: 1.005 G_ID: 0.134 G_Rec: 0.481 D_GP: 0.045 D_real: 1.072 D_fake: 0.351 +(epoch: 371, iters: 8446, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 1.036 G_ID: 0.116 G_Rec: 0.381 D_GP: 0.080 D_real: 0.819 D_fake: 0.520 +(epoch: 372, iters: 238, time: 0.064) G_GAN: 0.614 G_GAN_Feat: 0.998 G_ID: 0.118 G_Rec: 0.431 D_GP: 0.047 D_real: 0.910 D_fake: 0.407 +(epoch: 372, iters: 638, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.866 G_ID: 0.110 G_Rec: 0.336 D_GP: 0.072 D_real: 0.753 D_fake: 0.612 +(epoch: 372, iters: 1038, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 1.147 G_ID: 0.113 G_Rec: 0.451 D_GP: 0.100 D_real: 0.310 D_fake: 0.693 +(epoch: 372, iters: 1438, time: 0.064) G_GAN: 0.652 G_GAN_Feat: 0.761 G_ID: 0.114 G_Rec: 0.310 D_GP: 0.077 D_real: 1.337 D_fake: 0.546 +(epoch: 372, iters: 1838, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.891 G_ID: 0.118 G_Rec: 0.398 D_GP: 0.030 D_real: 1.038 D_fake: 0.666 +(epoch: 372, iters: 2238, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.708 G_ID: 0.097 G_Rec: 0.309 D_GP: 0.033 D_real: 1.121 D_fake: 0.802 +(epoch: 372, iters: 2638, time: 0.064) G_GAN: 0.101 G_GAN_Feat: 1.085 G_ID: 0.143 G_Rec: 0.461 D_GP: 0.109 D_real: 0.353 D_fake: 0.907 +(epoch: 372, iters: 3038, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.983 G_ID: 0.104 G_Rec: 0.342 D_GP: 0.039 D_real: 1.480 D_fake: 0.773 +(epoch: 372, iters: 3438, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 0.929 G_ID: 0.132 G_Rec: 0.432 D_GP: 0.032 D_real: 1.298 D_fake: 0.389 +(epoch: 372, iters: 3838, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.772 G_ID: 0.110 G_Rec: 0.295 D_GP: 0.036 D_real: 1.337 D_fake: 0.513 +(epoch: 372, iters: 4238, time: 0.064) G_GAN: 0.709 G_GAN_Feat: 1.208 G_ID: 0.119 G_Rec: 0.502 D_GP: 0.648 D_real: 0.494 D_fake: 0.361 +(epoch: 372, iters: 4638, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.722 G_ID: 0.102 G_Rec: 0.309 D_GP: 0.028 D_real: 1.052 D_fake: 0.858 +(epoch: 372, iters: 5038, time: 0.064) G_GAN: 0.636 G_GAN_Feat: 0.847 G_ID: 0.129 G_Rec: 0.404 D_GP: 0.031 D_real: 1.377 D_fake: 0.371 +(epoch: 372, iters: 5438, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.629 G_ID: 0.092 G_Rec: 0.285 D_GP: 0.028 D_real: 1.058 D_fake: 0.915 +(epoch: 372, iters: 5838, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.929 G_ID: 0.140 G_Rec: 0.426 D_GP: 0.038 D_real: 0.878 D_fake: 0.727 +(epoch: 372, iters: 6238, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.710 G_ID: 0.088 G_Rec: 0.295 D_GP: 0.083 D_real: 0.892 D_fake: 0.787 +(epoch: 372, iters: 6638, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.876 G_ID: 0.140 G_Rec: 0.440 D_GP: 0.033 D_real: 1.105 D_fake: 0.665 +(epoch: 372, iters: 7038, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.760 G_ID: 0.125 G_Rec: 0.328 D_GP: 0.038 D_real: 0.951 D_fake: 0.817 +(epoch: 372, iters: 7438, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 0.845 G_ID: 0.119 G_Rec: 0.372 D_GP: 0.032 D_real: 1.287 D_fake: 0.414 +(epoch: 372, iters: 7838, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.805 G_ID: 0.102 G_Rec: 0.357 D_GP: 0.051 D_real: 1.086 D_fake: 0.642 +(epoch: 372, iters: 8238, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 1.218 G_ID: 0.121 G_Rec: 0.444 D_GP: 0.053 D_real: 1.501 D_fake: 0.799 +(epoch: 372, iters: 8638, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.721 G_ID: 0.100 G_Rec: 0.309 D_GP: 0.035 D_real: 1.013 D_fake: 0.840 +(epoch: 373, iters: 430, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 1.126 G_ID: 0.128 G_Rec: 0.490 D_GP: 0.144 D_real: 0.415 D_fake: 0.713 +(epoch: 373, iters: 830, time: 0.064) G_GAN: 0.520 G_GAN_Feat: 0.723 G_ID: 0.101 G_Rec: 0.305 D_GP: 0.035 D_real: 1.402 D_fake: 0.488 +(epoch: 373, iters: 1230, time: 0.064) G_GAN: 0.732 G_GAN_Feat: 1.019 G_ID: 0.113 G_Rec: 0.409 D_GP: 0.061 D_real: 1.046 D_fake: 0.286 +(epoch: 373, iters: 1630, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.915 G_ID: 0.110 G_Rec: 0.336 D_GP: 0.117 D_real: 0.319 D_fake: 0.805 +(epoch: 373, iters: 2030, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 1.053 G_ID: 0.138 G_Rec: 0.477 D_GP: 0.062 D_real: 0.581 D_fake: 0.667 +(epoch: 373, iters: 2430, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.752 G_ID: 0.103 G_Rec: 0.307 D_GP: 0.029 D_real: 1.061 D_fake: 0.801 +(epoch: 373, iters: 2830, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 1.005 G_ID: 0.123 G_Rec: 0.503 D_GP: 0.039 D_real: 0.821 D_fake: 0.835 +(epoch: 373, iters: 3230, time: 0.064) G_GAN: 0.204 G_GAN_Feat: 0.686 G_ID: 0.099 G_Rec: 0.300 D_GP: 0.032 D_real: 1.114 D_fake: 0.796 +(epoch: 373, iters: 3630, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.902 G_ID: 0.113 G_Rec: 0.382 D_GP: 0.035 D_real: 0.956 D_fake: 0.703 +(epoch: 373, iters: 4030, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.814 G_ID: 0.105 G_Rec: 0.323 D_GP: 0.062 D_real: 0.898 D_fake: 0.874 +(epoch: 373, iters: 4430, time: 0.064) G_GAN: 0.444 G_GAN_Feat: 1.233 G_ID: 0.123 G_Rec: 0.471 D_GP: 0.054 D_real: 0.320 D_fake: 0.560 +(epoch: 373, iters: 4830, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.649 G_ID: 0.107 G_Rec: 0.312 D_GP: 0.029 D_real: 1.108 D_fake: 0.850 +(epoch: 373, iters: 5230, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.874 G_ID: 0.137 G_Rec: 0.404 D_GP: 0.032 D_real: 0.981 D_fake: 0.738 +(epoch: 373, iters: 5630, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.609 G_ID: 0.100 G_Rec: 0.276 D_GP: 0.029 D_real: 1.197 D_fake: 0.704 +(epoch: 373, iters: 6030, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.918 G_ID: 0.120 G_Rec: 0.446 D_GP: 0.035 D_real: 1.105 D_fake: 0.571 +(epoch: 373, iters: 6430, time: 0.064) G_GAN: -0.168 G_GAN_Feat: 0.629 G_ID: 0.102 G_Rec: 0.272 D_GP: 0.033 D_real: 0.731 D_fake: 1.168 +(epoch: 373, iters: 6830, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.814 G_ID: 0.106 G_Rec: 0.379 D_GP: 0.033 D_real: 1.166 D_fake: 0.576 +(epoch: 373, iters: 7230, time: 0.064) G_GAN: -0.168 G_GAN_Feat: 0.755 G_ID: 0.131 G_Rec: 0.329 D_GP: 0.065 D_real: 0.546 D_fake: 1.168 +(epoch: 373, iters: 7630, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.901 G_ID: 0.118 G_Rec: 0.406 D_GP: 0.048 D_real: 0.821 D_fake: 0.701 +(epoch: 373, iters: 8030, time: 0.064) G_GAN: 0.133 G_GAN_Feat: 0.771 G_ID: 0.107 G_Rec: 0.306 D_GP: 0.032 D_real: 0.885 D_fake: 0.867 +(epoch: 373, iters: 8430, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.984 G_ID: 0.123 G_Rec: 0.439 D_GP: 0.062 D_real: 1.066 D_fake: 0.491 +(epoch: 374, iters: 222, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.740 G_ID: 0.111 G_Rec: 0.320 D_GP: 0.043 D_real: 1.077 D_fake: 0.714 +(epoch: 374, iters: 622, time: 0.064) G_GAN: 0.744 G_GAN_Feat: 1.133 G_ID: 0.117 G_Rec: 0.498 D_GP: 0.737 D_real: 0.619 D_fake: 0.330 +(epoch: 374, iters: 1022, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.687 G_ID: 0.108 G_Rec: 0.291 D_GP: 0.030 D_real: 1.099 D_fake: 0.771 +(epoch: 374, iters: 1422, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 1.022 G_ID: 0.131 G_Rec: 0.402 D_GP: 0.084 D_real: 0.360 D_fake: 0.782 +(epoch: 374, iters: 1822, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.789 G_ID: 0.101 G_Rec: 0.302 D_GP: 0.044 D_real: 0.885 D_fake: 0.786 +(epoch: 374, iters: 2222, time: 0.064) G_GAN: 0.620 G_GAN_Feat: 0.970 G_ID: 0.124 G_Rec: 0.402 D_GP: 0.053 D_real: 1.145 D_fake: 0.412 +(epoch: 374, iters: 2622, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.860 G_ID: 0.103 G_Rec: 0.351 D_GP: 0.040 D_real: 0.906 D_fake: 0.826 +(epoch: 374, iters: 3022, time: 0.063) G_GAN: 0.945 G_GAN_Feat: 1.029 G_ID: 0.117 G_Rec: 0.449 D_GP: 0.048 D_real: 1.238 D_fake: 0.163 +(epoch: 374, iters: 3422, time: 0.063) G_GAN: -0.120 G_GAN_Feat: 0.982 G_ID: 0.096 G_Rec: 0.351 D_GP: 0.244 D_real: 0.289 D_fake: 1.120 +(epoch: 374, iters: 3822, time: 0.063) G_GAN: 0.394 G_GAN_Feat: 0.964 G_ID: 0.136 G_Rec: 0.417 D_GP: 0.034 D_real: 0.888 D_fake: 0.620 +(epoch: 374, iters: 4222, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.796 G_ID: 0.107 G_Rec: 0.331 D_GP: 0.030 D_real: 1.087 D_fake: 0.643 +(epoch: 374, iters: 4622, time: 0.063) G_GAN: 0.762 G_GAN_Feat: 1.119 G_ID: 0.118 G_Rec: 0.426 D_GP: 0.078 D_real: 0.706 D_fake: 0.384 +(epoch: 374, iters: 5022, time: 0.063) G_GAN: -0.127 G_GAN_Feat: 0.835 G_ID: 0.096 G_Rec: 0.288 D_GP: 0.037 D_real: 0.579 D_fake: 1.127 +(epoch: 374, iters: 5422, time: 0.063) G_GAN: 0.432 G_GAN_Feat: 1.009 G_ID: 0.124 G_Rec: 0.458 D_GP: 0.037 D_real: 1.002 D_fake: 0.571 +(epoch: 374, iters: 5822, time: 0.064) G_GAN: -0.164 G_GAN_Feat: 0.827 G_ID: 0.105 G_Rec: 0.333 D_GP: 0.068 D_real: 0.406 D_fake: 1.164 +(epoch: 374, iters: 6222, time: 0.063) G_GAN: 0.672 G_GAN_Feat: 1.031 G_ID: 0.137 G_Rec: 0.454 D_GP: 0.051 D_real: 0.930 D_fake: 0.346 +(epoch: 374, iters: 6622, time: 0.063) G_GAN: 0.926 G_GAN_Feat: 1.004 G_ID: 0.110 G_Rec: 0.358 D_GP: 0.071 D_real: 1.320 D_fake: 0.332 +(epoch: 374, iters: 7022, time: 0.063) G_GAN: 0.550 G_GAN_Feat: 0.982 G_ID: 0.123 G_Rec: 0.463 D_GP: 0.030 D_real: 1.274 D_fake: 0.472 +(epoch: 374, iters: 7422, time: 0.063) G_GAN: -0.049 G_GAN_Feat: 0.708 G_ID: 0.110 G_Rec: 0.305 D_GP: 0.028 D_real: 0.876 D_fake: 1.049 +(epoch: 374, iters: 7822, time: 0.063) G_GAN: 0.423 G_GAN_Feat: 0.901 G_ID: 0.125 G_Rec: 0.455 D_GP: 0.040 D_real: 1.187 D_fake: 0.593 +(epoch: 374, iters: 8222, time: 0.063) G_GAN: 0.021 G_GAN_Feat: 0.693 G_ID: 0.096 G_Rec: 0.305 D_GP: 0.035 D_real: 0.837 D_fake: 0.979 +(epoch: 374, iters: 8622, time: 0.064) G_GAN: -0.011 G_GAN_Feat: 0.821 G_ID: 0.132 G_Rec: 0.401 D_GP: 0.027 D_real: 0.896 D_fake: 1.011 +(epoch: 375, iters: 414, time: 0.063) G_GAN: 0.324 G_GAN_Feat: 0.692 G_ID: 0.093 G_Rec: 0.307 D_GP: 0.026 D_real: 1.257 D_fake: 0.678 +(epoch: 375, iters: 814, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 0.871 G_ID: 0.122 G_Rec: 0.392 D_GP: 0.036 D_real: 1.105 D_fake: 0.596 +(epoch: 375, iters: 1214, time: 0.063) G_GAN: 0.249 G_GAN_Feat: 0.723 G_ID: 0.094 G_Rec: 0.304 D_GP: 0.041 D_real: 1.083 D_fake: 0.751 +(epoch: 375, iters: 1614, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.942 G_ID: 0.112 G_Rec: 0.459 D_GP: 0.051 D_real: 1.066 D_fake: 0.567 +(epoch: 375, iters: 2014, time: 0.063) G_GAN: -0.091 G_GAN_Feat: 0.792 G_ID: 0.109 G_Rec: 0.331 D_GP: 0.046 D_real: 0.641 D_fake: 1.091 +(epoch: 375, iters: 2414, time: 0.063) G_GAN: 0.191 G_GAN_Feat: 1.021 G_ID: 0.124 G_Rec: 0.498 D_GP: 0.050 D_real: 0.795 D_fake: 0.810 +(epoch: 375, iters: 2814, time: 0.063) G_GAN: 0.139 G_GAN_Feat: 0.816 G_ID: 0.107 G_Rec: 0.336 D_GP: 0.076 D_real: 0.667 D_fake: 0.861 +(epoch: 375, iters: 3214, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 0.995 G_ID: 0.130 G_Rec: 0.424 D_GP: 0.056 D_real: 0.948 D_fake: 0.518 +(epoch: 375, iters: 3614, time: 0.063) G_GAN: 0.475 G_GAN_Feat: 0.909 G_ID: 0.109 G_Rec: 0.340 D_GP: 0.180 D_real: 0.776 D_fake: 0.570 +(epoch: 375, iters: 4014, time: 0.063) G_GAN: 0.971 G_GAN_Feat: 1.253 G_ID: 0.115 G_Rec: 0.500 D_GP: 0.036 D_real: 1.680 D_fake: 0.171 +(epoch: 375, iters: 4414, time: 0.063) G_GAN: 0.145 G_GAN_Feat: 0.807 G_ID: 0.111 G_Rec: 0.348 D_GP: 0.056 D_real: 0.945 D_fake: 0.856 +(epoch: 375, iters: 4814, time: 0.064) G_GAN: 0.697 G_GAN_Feat: 0.999 G_ID: 0.105 G_Rec: 0.434 D_GP: 0.080 D_real: 1.050 D_fake: 0.343 +(epoch: 375, iters: 5214, time: 0.063) G_GAN: 0.282 G_GAN_Feat: 0.814 G_ID: 0.102 G_Rec: 0.308 D_GP: 0.057 D_real: 0.991 D_fake: 0.752 +(epoch: 375, iters: 5614, time: 0.063) G_GAN: 0.684 G_GAN_Feat: 1.123 G_ID: 0.131 G_Rec: 0.425 D_GP: 0.052 D_real: 0.537 D_fake: 0.348 +(epoch: 375, iters: 6014, time: 0.063) G_GAN: 0.226 G_GAN_Feat: 0.936 G_ID: 0.100 G_Rec: 0.326 D_GP: 0.167 D_real: 0.544 D_fake: 0.774 +(epoch: 375, iters: 6414, time: 0.064) G_GAN: 0.788 G_GAN_Feat: 1.075 G_ID: 0.128 G_Rec: 0.433 D_GP: 0.056 D_real: 0.720 D_fake: 0.253 +(epoch: 375, iters: 6814, time: 0.063) G_GAN: 0.492 G_GAN_Feat: 0.934 G_ID: 0.102 G_Rec: 0.360 D_GP: 0.055 D_real: 0.893 D_fake: 0.527 +(epoch: 375, iters: 7214, time: 0.063) G_GAN: 0.422 G_GAN_Feat: 0.842 G_ID: 0.124 G_Rec: 0.404 D_GP: 0.034 D_real: 1.109 D_fake: 0.582 +(epoch: 375, iters: 7614, time: 0.063) G_GAN: 0.142 G_GAN_Feat: 0.662 G_ID: 0.101 G_Rec: 0.285 D_GP: 0.032 D_real: 1.007 D_fake: 0.858 +(epoch: 375, iters: 8014, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 1.007 G_ID: 0.127 G_Rec: 0.471 D_GP: 0.048 D_real: 0.739 D_fake: 0.679 +(epoch: 375, iters: 8414, time: 0.063) G_GAN: 0.096 G_GAN_Feat: 0.854 G_ID: 0.111 G_Rec: 0.384 D_GP: 0.086 D_real: 0.783 D_fake: 0.905 +(epoch: 376, iters: 206, time: 0.063) G_GAN: 0.524 G_GAN_Feat: 0.903 G_ID: 0.120 G_Rec: 0.429 D_GP: 0.031 D_real: 1.184 D_fake: 0.482 +(epoch: 376, iters: 606, time: 0.063) G_GAN: 0.147 G_GAN_Feat: 0.882 G_ID: 0.107 G_Rec: 0.360 D_GP: 0.347 D_real: 0.499 D_fake: 0.868 +(epoch: 376, iters: 1006, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.964 G_ID: 0.125 G_Rec: 0.435 D_GP: 0.058 D_real: 0.949 D_fake: 0.581 +(epoch: 376, iters: 1406, time: 0.063) G_GAN: 0.299 G_GAN_Feat: 0.852 G_ID: 0.102 G_Rec: 0.361 D_GP: 0.097 D_real: 0.882 D_fake: 0.707 +(epoch: 376, iters: 1806, time: 0.063) G_GAN: 0.505 G_GAN_Feat: 1.016 G_ID: 0.176 G_Rec: 0.493 D_GP: 0.051 D_real: 1.059 D_fake: 0.498 +(epoch: 376, iters: 2206, time: 0.063) G_GAN: 0.355 G_GAN_Feat: 0.734 G_ID: 0.095 G_Rec: 0.314 D_GP: 0.033 D_real: 1.184 D_fake: 0.649 +(epoch: 376, iters: 2606, time: 0.064) G_GAN: 0.640 G_GAN_Feat: 1.011 G_ID: 0.121 G_Rec: 0.437 D_GP: 0.039 D_real: 1.019 D_fake: 0.393 +(epoch: 376, iters: 3006, time: 0.064) G_GAN: 0.644 G_GAN_Feat: 0.768 G_ID: 0.097 G_Rec: 0.332 D_GP: 0.036 D_real: 1.551 D_fake: 0.370 +(epoch: 376, iters: 3406, time: 0.064) G_GAN: 0.758 G_GAN_Feat: 0.934 G_ID: 0.108 G_Rec: 0.391 D_GP: 0.039 D_real: 1.409 D_fake: 0.277 +(epoch: 376, iters: 3806, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.741 G_ID: 0.116 G_Rec: 0.272 D_GP: 0.038 D_real: 1.223 D_fake: 0.607 +(epoch: 376, iters: 4206, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 1.187 G_ID: 0.134 G_Rec: 0.424 D_GP: 0.035 D_real: 1.157 D_fake: 0.710 +(epoch: 376, iters: 4606, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.617 G_ID: 0.108 G_Rec: 0.271 D_GP: 0.028 D_real: 1.108 D_fake: 0.913 +(epoch: 376, iters: 5006, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.827 G_ID: 0.114 G_Rec: 0.462 D_GP: 0.030 D_real: 1.082 D_fake: 0.651 +(epoch: 376, iters: 5406, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.678 G_ID: 0.097 G_Rec: 0.314 D_GP: 0.038 D_real: 1.061 D_fake: 0.820 +(epoch: 376, iters: 5806, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.969 G_ID: 0.121 G_Rec: 0.482 D_GP: 0.055 D_real: 0.796 D_fake: 0.715 +(epoch: 376, iters: 6206, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.816 G_ID: 0.108 G_Rec: 0.360 D_GP: 0.114 D_real: 0.726 D_fake: 0.885 +(epoch: 376, iters: 6606, time: 0.064) G_GAN: 0.562 G_GAN_Feat: 0.985 G_ID: 0.120 G_Rec: 0.416 D_GP: 0.034 D_real: 1.028 D_fake: 0.453 +(epoch: 376, iters: 7006, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.686 G_ID: 0.086 G_Rec: 0.296 D_GP: 0.035 D_real: 1.220 D_fake: 0.674 +(epoch: 376, iters: 7406, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.991 G_ID: 0.120 G_Rec: 0.453 D_GP: 0.063 D_real: 0.844 D_fake: 0.534 +(epoch: 376, iters: 7806, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.782 G_ID: 0.103 G_Rec: 0.313 D_GP: 0.043 D_real: 0.961 D_fake: 0.781 +(epoch: 376, iters: 8206, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.937 G_ID: 0.128 G_Rec: 0.410 D_GP: 0.035 D_real: 1.150 D_fake: 0.498 +(epoch: 376, iters: 8606, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.761 G_ID: 0.087 G_Rec: 0.315 D_GP: 0.033 D_real: 1.050 D_fake: 0.833 +(epoch: 377, iters: 398, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 1.013 G_ID: 0.119 G_Rec: 0.452 D_GP: 0.036 D_real: 1.035 D_fake: 0.431 +(epoch: 377, iters: 798, time: 0.063) G_GAN: -0.068 G_GAN_Feat: 0.941 G_ID: 0.113 G_Rec: 0.337 D_GP: 0.578 D_real: 0.206 D_fake: 1.079 +(epoch: 377, iters: 1198, time: 0.063) G_GAN: 0.450 G_GAN_Feat: 1.001 G_ID: 0.109 G_Rec: 0.428 D_GP: 0.049 D_real: 0.853 D_fake: 0.561 +(epoch: 377, iters: 1598, time: 0.063) G_GAN: 0.109 G_GAN_Feat: 0.707 G_ID: 0.119 G_Rec: 0.361 D_GP: 0.029 D_real: 0.924 D_fake: 0.891 +(epoch: 377, iters: 1998, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 1.086 G_ID: 0.120 G_Rec: 0.450 D_GP: 0.042 D_real: 0.861 D_fake: 0.493 +(epoch: 377, iters: 2398, time: 0.063) G_GAN: 0.332 G_GAN_Feat: 1.021 G_ID: 0.093 G_Rec: 0.349 D_GP: 0.107 D_real: 0.479 D_fake: 0.677 +(epoch: 377, iters: 2798, time: 0.063) G_GAN: 0.529 G_GAN_Feat: 0.967 G_ID: 0.133 G_Rec: 0.441 D_GP: 0.034 D_real: 1.093 D_fake: 0.488 +(epoch: 377, iters: 3198, time: 0.063) G_GAN: 0.109 G_GAN_Feat: 0.697 G_ID: 0.122 G_Rec: 0.313 D_GP: 0.038 D_real: 0.996 D_fake: 0.891 +(epoch: 377, iters: 3598, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.931 G_ID: 0.111 G_Rec: 0.429 D_GP: 0.033 D_real: 0.976 D_fake: 0.665 +(epoch: 377, iters: 3998, time: 0.063) G_GAN: 0.143 G_GAN_Feat: 0.666 G_ID: 0.102 G_Rec: 0.288 D_GP: 0.042 D_real: 0.971 D_fake: 0.857 +(epoch: 377, iters: 4398, time: 0.063) G_GAN: 0.231 G_GAN_Feat: 0.977 G_ID: 0.142 G_Rec: 0.457 D_GP: 0.040 D_real: 1.041 D_fake: 0.776 +(epoch: 377, iters: 4798, time: 0.063) G_GAN: 0.159 G_GAN_Feat: 0.723 G_ID: 0.090 G_Rec: 0.305 D_GP: 0.031 D_real: 1.039 D_fake: 0.841 +(epoch: 377, iters: 5198, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.953 G_ID: 0.130 G_Rec: 0.425 D_GP: 0.063 D_real: 0.880 D_fake: 0.592 +(epoch: 377, iters: 5598, time: 0.063) G_GAN: 0.118 G_GAN_Feat: 0.852 G_ID: 0.107 G_Rec: 0.325 D_GP: 0.059 D_real: 0.560 D_fake: 0.882 +(epoch: 377, iters: 5998, time: 0.063) G_GAN: 0.316 G_GAN_Feat: 1.014 G_ID: 0.109 G_Rec: 0.480 D_GP: 0.047 D_real: 0.888 D_fake: 0.685 +(epoch: 377, iters: 6398, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 0.857 G_ID: 0.103 G_Rec: 0.330 D_GP: 0.037 D_real: 1.006 D_fake: 0.791 +(epoch: 377, iters: 6798, time: 0.064) G_GAN: 0.996 G_GAN_Feat: 1.008 G_ID: 0.120 G_Rec: 0.434 D_GP: 0.035 D_real: 1.554 D_fake: 0.115 +(epoch: 377, iters: 7198, time: 0.063) G_GAN: 0.458 G_GAN_Feat: 0.798 G_ID: 0.096 G_Rec: 0.319 D_GP: 0.055 D_real: 1.445 D_fake: 0.544 +(epoch: 377, iters: 7598, time: 0.063) G_GAN: 0.283 G_GAN_Feat: 0.995 G_ID: 0.148 G_Rec: 0.442 D_GP: 0.033 D_real: 0.901 D_fake: 0.719 +(epoch: 377, iters: 7998, time: 0.063) G_GAN: -0.124 G_GAN_Feat: 0.867 G_ID: 0.107 G_Rec: 0.321 D_GP: 0.048 D_real: 0.538 D_fake: 1.124 +(epoch: 377, iters: 8398, time: 0.064) G_GAN: 0.679 G_GAN_Feat: 1.026 G_ID: 0.119 G_Rec: 0.440 D_GP: 0.052 D_real: 1.110 D_fake: 0.354 +(epoch: 378, iters: 190, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 0.804 G_ID: 0.114 G_Rec: 0.310 D_GP: 0.041 D_real: 1.022 D_fake: 0.731 +(epoch: 378, iters: 590, time: 0.063) G_GAN: 0.509 G_GAN_Feat: 0.922 G_ID: 0.115 G_Rec: 0.456 D_GP: 0.026 D_real: 1.230 D_fake: 0.497 +(epoch: 378, iters: 990, time: 0.063) G_GAN: 0.104 G_GAN_Feat: 0.793 G_ID: 0.107 G_Rec: 0.335 D_GP: 0.042 D_real: 0.755 D_fake: 0.896 +(epoch: 378, iters: 1390, time: 0.064) G_GAN: 0.678 G_GAN_Feat: 1.002 G_ID: 0.126 G_Rec: 0.440 D_GP: 0.036 D_real: 0.979 D_fake: 0.346 +(epoch: 378, iters: 1790, time: 0.063) G_GAN: 0.418 G_GAN_Feat: 1.007 G_ID: 0.114 G_Rec: 0.356 D_GP: 0.416 D_real: 0.314 D_fake: 0.712 +(epoch: 378, iters: 2190, time: 0.063) G_GAN: 0.416 G_GAN_Feat: 0.858 G_ID: 0.130 G_Rec: 0.392 D_GP: 0.036 D_real: 1.179 D_fake: 0.589 +(epoch: 378, iters: 2590, time: 0.063) G_GAN: 0.108 G_GAN_Feat: 0.692 G_ID: 0.102 G_Rec: 0.293 D_GP: 0.028 D_real: 1.064 D_fake: 0.893 +(epoch: 378, iters: 2990, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 1.003 G_ID: 0.135 G_Rec: 0.453 D_GP: 0.040 D_real: 0.892 D_fake: 0.764 +(epoch: 378, iters: 3390, time: 0.063) G_GAN: 0.133 G_GAN_Feat: 0.701 G_ID: 0.105 G_Rec: 0.296 D_GP: 0.040 D_real: 1.023 D_fake: 0.870 +(epoch: 378, iters: 3790, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 1.040 G_ID: 0.135 G_Rec: 0.462 D_GP: 0.071 D_real: 0.438 D_fake: 0.875 +(epoch: 378, iters: 4190, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.756 G_ID: 0.110 G_Rec: 0.333 D_GP: 0.034 D_real: 0.988 D_fake: 0.847 +(epoch: 378, iters: 4590, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.985 G_ID: 0.122 G_Rec: 0.484 D_GP: 0.042 D_real: 1.262 D_fake: 0.509 +(epoch: 378, iters: 4990, time: 0.063) G_GAN: 0.042 G_GAN_Feat: 0.886 G_ID: 0.110 G_Rec: 0.335 D_GP: 0.052 D_real: 0.405 D_fake: 0.958 +(epoch: 378, iters: 5390, time: 0.063) G_GAN: 0.713 G_GAN_Feat: 0.999 G_ID: 0.124 G_Rec: 0.424 D_GP: 0.037 D_real: 1.180 D_fake: 0.309 +(epoch: 378, iters: 5790, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.836 G_ID: 0.100 G_Rec: 0.341 D_GP: 0.034 D_real: 1.046 D_fake: 0.769 +(epoch: 378, iters: 6190, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.900 G_ID: 0.129 G_Rec: 0.423 D_GP: 0.028 D_real: 1.110 D_fake: 0.669 +(epoch: 378, iters: 6590, time: 0.063) G_GAN: 0.119 G_GAN_Feat: 0.726 G_ID: 0.100 G_Rec: 0.293 D_GP: 0.031 D_real: 0.944 D_fake: 0.881 +(epoch: 378, iters: 6990, time: 0.063) G_GAN: 0.388 G_GAN_Feat: 1.031 G_ID: 0.140 G_Rec: 0.496 D_GP: 0.095 D_real: 0.838 D_fake: 0.624 +(epoch: 378, iters: 7390, time: 0.063) G_GAN: 0.069 G_GAN_Feat: 0.747 G_ID: 0.098 G_Rec: 0.308 D_GP: 0.040 D_real: 0.933 D_fake: 0.932 +(epoch: 378, iters: 7790, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 0.926 G_ID: 0.121 G_Rec: 0.415 D_GP: 0.038 D_real: 1.042 D_fake: 0.527 +(epoch: 378, iters: 8190, time: 0.063) G_GAN: -0.035 G_GAN_Feat: 0.767 G_ID: 0.095 G_Rec: 0.326 D_GP: 0.047 D_real: 0.728 D_fake: 1.035 +(epoch: 378, iters: 8590, time: 0.063) G_GAN: 0.530 G_GAN_Feat: 1.029 G_ID: 0.121 G_Rec: 0.456 D_GP: 0.045 D_real: 0.833 D_fake: 0.489 +(epoch: 379, iters: 382, time: 0.063) G_GAN: 0.220 G_GAN_Feat: 0.673 G_ID: 0.105 G_Rec: 0.269 D_GP: 0.034 D_real: 1.092 D_fake: 0.780 +(epoch: 379, iters: 782, time: 0.064) G_GAN: 0.665 G_GAN_Feat: 0.956 G_ID: 0.112 G_Rec: 0.494 D_GP: 0.037 D_real: 1.285 D_fake: 0.352 +(epoch: 379, iters: 1182, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.869 G_ID: 0.079 G_Rec: 0.308 D_GP: 0.124 D_real: 0.730 D_fake: 0.526 +(epoch: 379, iters: 1582, time: 0.063) G_GAN: 0.491 G_GAN_Feat: 1.218 G_ID: 0.138 G_Rec: 0.557 D_GP: 0.043 D_real: 0.987 D_fake: 0.541 +(epoch: 379, iters: 1982, time: 0.063) G_GAN: -0.050 G_GAN_Feat: 0.709 G_ID: 0.110 G_Rec: 0.305 D_GP: 0.030 D_real: 0.824 D_fake: 1.050 +(epoch: 379, iters: 2382, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 1.035 G_ID: 0.140 G_Rec: 0.467 D_GP: 0.120 D_real: 0.530 D_fake: 0.548 +(epoch: 379, iters: 2782, time: 0.063) G_GAN: 0.432 G_GAN_Feat: 0.827 G_ID: 0.089 G_Rec: 0.315 D_GP: 0.059 D_real: 0.933 D_fake: 0.573 +(epoch: 379, iters: 3182, time: 0.063) G_GAN: 0.682 G_GAN_Feat: 0.995 G_ID: 0.132 G_Rec: 0.414 D_GP: 0.047 D_real: 1.255 D_fake: 0.343 +(epoch: 379, iters: 3582, time: 0.063) G_GAN: 0.102 G_GAN_Feat: 0.805 G_ID: 0.109 G_Rec: 0.329 D_GP: 0.062 D_real: 0.748 D_fake: 0.898 +(epoch: 379, iters: 3982, time: 0.063) G_GAN: 1.118 G_GAN_Feat: 0.951 G_ID: 0.158 G_Rec: 0.421 D_GP: 0.035 D_real: 1.666 D_fake: 0.106 +(epoch: 379, iters: 4382, time: 0.063) G_GAN: 0.336 G_GAN_Feat: 0.734 G_ID: 0.100 G_Rec: 0.292 D_GP: 0.035 D_real: 1.114 D_fake: 0.667 +(epoch: 379, iters: 4782, time: 0.063) G_GAN: 0.678 G_GAN_Feat: 1.050 G_ID: 0.124 G_Rec: 0.495 D_GP: 0.036 D_real: 1.211 D_fake: 0.367 +(epoch: 379, iters: 5182, time: 0.064) G_GAN: 0.071 G_GAN_Feat: 0.879 G_ID: 0.106 G_Rec: 0.342 D_GP: 0.116 D_real: 0.667 D_fake: 0.929 +(epoch: 379, iters: 5582, time: 0.063) G_GAN: 0.606 G_GAN_Feat: 1.127 G_ID: 0.114 G_Rec: 0.473 D_GP: 0.079 D_real: 0.714 D_fake: 0.409 +(epoch: 379, iters: 5982, time: 0.063) G_GAN: -0.109 G_GAN_Feat: 0.785 G_ID: 0.128 G_Rec: 0.347 D_GP: 0.035 D_real: 0.797 D_fake: 1.109 +(epoch: 379, iters: 6382, time: 0.063) G_GAN: 0.673 G_GAN_Feat: 0.990 G_ID: 0.106 G_Rec: 0.440 D_GP: 0.033 D_real: 1.273 D_fake: 0.359 +(epoch: 379, iters: 6782, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.791 G_ID: 0.109 G_Rec: 0.340 D_GP: 0.037 D_real: 0.899 D_fake: 0.767 +(epoch: 379, iters: 7182, time: 0.063) G_GAN: 0.559 G_GAN_Feat: 0.873 G_ID: 0.128 G_Rec: 0.395 D_GP: 0.029 D_real: 1.240 D_fake: 0.456 +(epoch: 379, iters: 7582, time: 0.063) G_GAN: 0.158 G_GAN_Feat: 0.806 G_ID: 0.125 G_Rec: 0.340 D_GP: 0.036 D_real: 0.762 D_fake: 0.843 +(epoch: 379, iters: 7982, time: 0.063) G_GAN: 0.330 G_GAN_Feat: 0.906 G_ID: 0.123 G_Rec: 0.414 D_GP: 0.034 D_real: 1.036 D_fake: 0.673 +(epoch: 379, iters: 8382, time: 0.064) G_GAN: -0.235 G_GAN_Feat: 0.697 G_ID: 0.100 G_Rec: 0.290 D_GP: 0.033 D_real: 0.648 D_fake: 1.235 +(epoch: 380, iters: 174, time: 0.063) G_GAN: 0.348 G_GAN_Feat: 1.080 G_ID: 0.118 G_Rec: 0.491 D_GP: 0.050 D_real: 0.647 D_fake: 0.668 +(epoch: 380, iters: 574, time: 0.063) G_GAN: 0.077 G_GAN_Feat: 0.714 G_ID: 0.098 G_Rec: 0.299 D_GP: 0.043 D_real: 0.873 D_fake: 0.923 +(epoch: 380, iters: 974, time: 0.063) G_GAN: 0.488 G_GAN_Feat: 1.011 G_ID: 0.109 G_Rec: 0.450 D_GP: 0.037 D_real: 1.028 D_fake: 0.517 +(epoch: 380, iters: 1374, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.790 G_ID: 0.098 G_Rec: 0.307 D_GP: 0.034 D_real: 1.179 D_fake: 0.662 +(epoch: 380, iters: 1774, time: 0.063) G_GAN: 0.900 G_GAN_Feat: 1.237 G_ID: 0.123 G_Rec: 0.463 D_GP: 0.072 D_real: 1.352 D_fake: 0.254 +(epoch: 380, iters: 2174, time: 0.063) G_GAN: 0.166 G_GAN_Feat: 0.740 G_ID: 0.113 G_Rec: 0.299 D_GP: 0.029 D_real: 1.000 D_fake: 0.835 +(epoch: 380, iters: 2574, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 1.083 G_ID: 0.128 G_Rec: 0.431 D_GP: 0.049 D_real: 0.467 D_fake: 0.811 +(epoch: 380, iters: 2974, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.804 G_ID: 0.098 G_Rec: 0.312 D_GP: 0.034 D_real: 0.809 D_fake: 0.893 +(epoch: 380, iters: 3374, time: 0.063) G_GAN: 0.732 G_GAN_Feat: 1.098 G_ID: 0.116 G_Rec: 0.452 D_GP: 0.054 D_real: 1.093 D_fake: 0.308 +(epoch: 380, iters: 3774, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 0.907 G_ID: 0.094 G_Rec: 0.311 D_GP: 0.057 D_real: 0.625 D_fake: 0.695 +(epoch: 380, iters: 4174, time: 0.063) G_GAN: 0.562 G_GAN_Feat: 0.845 G_ID: 0.112 G_Rec: 0.401 D_GP: 0.032 D_real: 1.270 D_fake: 0.459 +(epoch: 380, iters: 4574, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.729 G_ID: 0.092 G_Rec: 0.325 D_GP: 0.038 D_real: 1.182 D_fake: 0.669 +(epoch: 380, iters: 4974, time: 0.063) G_GAN: 0.533 G_GAN_Feat: 0.948 G_ID: 0.127 G_Rec: 0.440 D_GP: 0.039 D_real: 1.158 D_fake: 0.497 +(epoch: 380, iters: 5374, time: 0.063) G_GAN: 0.262 G_GAN_Feat: 0.797 G_ID: 0.105 G_Rec: 0.336 D_GP: 0.082 D_real: 0.832 D_fake: 0.743 +(epoch: 380, iters: 5774, time: 0.063) G_GAN: 0.558 G_GAN_Feat: 0.912 G_ID: 0.116 G_Rec: 0.417 D_GP: 0.033 D_real: 1.073 D_fake: 0.450 +(epoch: 380, iters: 6174, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.848 G_ID: 0.117 G_Rec: 0.323 D_GP: 0.046 D_real: 0.883 D_fake: 0.858 +(epoch: 380, iters: 6574, time: 0.063) G_GAN: 0.431 G_GAN_Feat: 1.079 G_ID: 0.109 G_Rec: 0.453 D_GP: 0.044 D_real: 0.637 D_fake: 0.572 +(epoch: 380, iters: 6974, time: 0.063) G_GAN: -0.222 G_GAN_Feat: 0.888 G_ID: 0.114 G_Rec: 0.352 D_GP: 0.051 D_real: 0.412 D_fake: 1.222 +(epoch: 380, iters: 7374, time: 0.063) G_GAN: 0.697 G_GAN_Feat: 1.016 G_ID: 0.120 G_Rec: 0.476 D_GP: 0.035 D_real: 1.323 D_fake: 0.325 +(epoch: 380, iters: 7774, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.734 G_ID: 0.099 G_Rec: 0.318 D_GP: 0.041 D_real: 1.026 D_fake: 0.819 +(epoch: 380, iters: 8174, time: 0.063) G_GAN: 0.499 G_GAN_Feat: 1.039 G_ID: 0.123 G_Rec: 0.477 D_GP: 0.062 D_real: 0.778 D_fake: 0.511 +(epoch: 380, iters: 8574, time: 0.063) G_GAN: -0.057 G_GAN_Feat: 0.987 G_ID: 0.123 G_Rec: 0.349 D_GP: 0.219 D_real: 0.392 D_fake: 1.060 +(epoch: 381, iters: 366, time: 0.063) G_GAN: 0.834 G_GAN_Feat: 1.283 G_ID: 0.130 G_Rec: 0.520 D_GP: 0.636 D_real: 0.404 D_fake: 0.315 +(epoch: 381, iters: 766, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.849 G_ID: 0.127 G_Rec: 0.300 D_GP: 0.037 D_real: 0.891 D_fake: 0.764 +(epoch: 381, iters: 1166, time: 0.063) G_GAN: 0.305 G_GAN_Feat: 0.927 G_ID: 0.120 G_Rec: 0.481 D_GP: 0.037 D_real: 1.025 D_fake: 0.699 +(epoch: 381, iters: 1566, time: 0.063) G_GAN: 0.275 G_GAN_Feat: 0.756 G_ID: 0.103 G_Rec: 0.331 D_GP: 0.036 D_real: 1.124 D_fake: 0.725 +(epoch: 381, iters: 1966, time: 0.063) G_GAN: 0.739 G_GAN_Feat: 0.943 G_ID: 0.122 G_Rec: 0.440 D_GP: 0.044 D_real: 1.320 D_fake: 0.386 +(epoch: 381, iters: 2366, time: 0.064) G_GAN: -0.055 G_GAN_Feat: 0.799 G_ID: 0.109 G_Rec: 0.354 D_GP: 0.097 D_real: 0.610 D_fake: 1.056 +(epoch: 381, iters: 2766, time: 0.063) G_GAN: 0.211 G_GAN_Feat: 0.932 G_ID: 0.121 G_Rec: 0.438 D_GP: 0.056 D_real: 0.735 D_fake: 0.790 +(epoch: 381, iters: 3166, time: 0.063) G_GAN: 0.306 G_GAN_Feat: 0.739 G_ID: 0.106 G_Rec: 0.318 D_GP: 0.037 D_real: 1.220 D_fake: 0.696 +(epoch: 381, iters: 3566, time: 0.063) G_GAN: 0.494 G_GAN_Feat: 1.105 G_ID: 0.117 G_Rec: 0.486 D_GP: 0.093 D_real: 0.469 D_fake: 0.507 +(epoch: 381, iters: 3966, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.909 G_ID: 0.107 G_Rec: 0.328 D_GP: 0.293 D_real: 0.483 D_fake: 0.838 +(epoch: 381, iters: 4366, time: 0.063) G_GAN: 0.532 G_GAN_Feat: 1.052 G_ID: 0.130 G_Rec: 0.412 D_GP: 0.042 D_real: 0.846 D_fake: 0.477 +(epoch: 381, iters: 4766, time: 0.063) G_GAN: 0.082 G_GAN_Feat: 0.791 G_ID: 0.121 G_Rec: 0.329 D_GP: 0.034 D_real: 0.911 D_fake: 0.919 +(epoch: 381, iters: 5166, time: 0.063) G_GAN: 0.472 G_GAN_Feat: 1.171 G_ID: 0.138 G_Rec: 0.478 D_GP: 0.153 D_real: 0.294 D_fake: 0.536 +(epoch: 381, iters: 5566, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.737 G_ID: 0.094 G_Rec: 0.312 D_GP: 0.036 D_real: 1.039 D_fake: 0.757 +(epoch: 381, iters: 5966, time: 0.063) G_GAN: 0.540 G_GAN_Feat: 1.054 G_ID: 0.129 G_Rec: 0.432 D_GP: 0.132 D_real: 0.610 D_fake: 0.471 +(epoch: 381, iters: 6366, time: 0.063) G_GAN: -0.027 G_GAN_Feat: 0.871 G_ID: 0.103 G_Rec: 0.319 D_GP: 0.036 D_real: 0.352 D_fake: 1.027 +(epoch: 381, iters: 6766, time: 0.063) G_GAN: 0.611 G_GAN_Feat: 1.153 G_ID: 0.126 G_Rec: 0.501 D_GP: 0.375 D_real: 0.829 D_fake: 0.518 +(epoch: 381, iters: 7166, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.836 G_ID: 0.119 G_Rec: 0.318 D_GP: 0.035 D_real: 0.982 D_fake: 0.637 +(epoch: 381, iters: 7566, time: 0.063) G_GAN: 0.196 G_GAN_Feat: 0.985 G_ID: 0.128 G_Rec: 0.457 D_GP: 0.033 D_real: 0.862 D_fake: 0.804 +(epoch: 381, iters: 7966, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 0.958 G_ID: 0.095 G_Rec: 0.354 D_GP: 0.125 D_real: 0.595 D_fake: 0.691 +(epoch: 381, iters: 8366, time: 0.063) G_GAN: 0.580 G_GAN_Feat: 0.867 G_ID: 0.112 G_Rec: 0.436 D_GP: 0.029 D_real: 1.406 D_fake: 0.429 +(epoch: 382, iters: 158, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.707 G_ID: 0.093 G_Rec: 0.321 D_GP: 0.033 D_real: 1.192 D_fake: 0.763 +(epoch: 382, iters: 558, time: 0.063) G_GAN: 0.439 G_GAN_Feat: 0.937 G_ID: 0.122 G_Rec: 0.447 D_GP: 0.031 D_real: 1.038 D_fake: 0.563 +(epoch: 382, iters: 958, time: 0.063) G_GAN: -0.036 G_GAN_Feat: 0.679 G_ID: 0.090 G_Rec: 0.303 D_GP: 0.031 D_real: 0.880 D_fake: 1.036 +(epoch: 382, iters: 1358, time: 0.063) G_GAN: 0.281 G_GAN_Feat: 0.899 G_ID: 0.144 G_Rec: 0.402 D_GP: 0.035 D_real: 0.967 D_fake: 0.719 +(epoch: 382, iters: 1758, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.821 G_ID: 0.112 G_Rec: 0.349 D_GP: 0.029 D_real: 1.019 D_fake: 0.858 +(epoch: 382, iters: 2158, time: 0.063) G_GAN: 0.208 G_GAN_Feat: 0.903 G_ID: 0.129 G_Rec: 0.423 D_GP: 0.047 D_real: 0.767 D_fake: 0.794 +(epoch: 382, iters: 2558, time: 0.063) G_GAN: 0.063 G_GAN_Feat: 0.738 G_ID: 0.110 G_Rec: 0.306 D_GP: 0.033 D_real: 0.894 D_fake: 0.937 +(epoch: 382, iters: 2958, time: 0.063) G_GAN: 0.350 G_GAN_Feat: 1.111 G_ID: 0.133 G_Rec: 0.444 D_GP: 0.104 D_real: 0.349 D_fake: 0.655 +(epoch: 382, iters: 3358, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.693 G_ID: 0.130 G_Rec: 0.299 D_GP: 0.032 D_real: 0.960 D_fake: 0.910 +(epoch: 382, iters: 3758, time: 0.063) G_GAN: 0.471 G_GAN_Feat: 1.006 G_ID: 0.112 G_Rec: 0.425 D_GP: 0.033 D_real: 1.011 D_fake: 0.530 +(epoch: 382, iters: 4158, time: 0.063) G_GAN: 0.465 G_GAN_Feat: 0.686 G_ID: 0.094 G_Rec: 0.282 D_GP: 0.035 D_real: 1.435 D_fake: 0.543 +(epoch: 382, iters: 4558, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.945 G_ID: 0.108 G_Rec: 0.411 D_GP: 0.030 D_real: 1.102 D_fake: 0.536 +(epoch: 382, iters: 4958, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.886 G_ID: 0.126 G_Rec: 0.360 D_GP: 0.211 D_real: 0.679 D_fake: 0.747 +(epoch: 382, iters: 5358, time: 0.063) G_GAN: 0.276 G_GAN_Feat: 0.998 G_ID: 0.123 G_Rec: 0.412 D_GP: 0.103 D_real: 0.624 D_fake: 0.724 +(epoch: 382, iters: 5758, time: 0.063) G_GAN: 0.279 G_GAN_Feat: 0.759 G_ID: 0.102 G_Rec: 0.328 D_GP: 0.033 D_real: 1.151 D_fake: 0.722 +(epoch: 382, iters: 6158, time: 0.063) G_GAN: 0.677 G_GAN_Feat: 1.040 G_ID: 0.114 G_Rec: 0.444 D_GP: 0.108 D_real: 1.105 D_fake: 0.374 +(epoch: 382, iters: 6558, time: 0.064) G_GAN: 0.671 G_GAN_Feat: 0.819 G_ID: 0.101 G_Rec: 0.323 D_GP: 0.037 D_real: 1.556 D_fake: 0.409 +(epoch: 382, iters: 6958, time: 0.063) G_GAN: 0.099 G_GAN_Feat: 1.060 G_ID: 0.121 G_Rec: 0.435 D_GP: 0.114 D_real: 0.348 D_fake: 0.901 +(epoch: 382, iters: 7358, time: 0.063) G_GAN: 0.510 G_GAN_Feat: 0.928 G_ID: 0.095 G_Rec: 0.346 D_GP: 0.086 D_real: 0.633 D_fake: 0.497 +(epoch: 382, iters: 7758, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 0.993 G_ID: 0.131 G_Rec: 0.450 D_GP: 0.036 D_real: 0.900 D_fake: 0.730 +(epoch: 382, iters: 8158, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.729 G_ID: 0.106 G_Rec: 0.281 D_GP: 0.034 D_real: 1.220 D_fake: 0.544 +(epoch: 382, iters: 8558, time: 0.063) G_GAN: 0.811 G_GAN_Feat: 1.118 G_ID: 0.151 G_Rec: 0.459 D_GP: 0.160 D_real: 0.918 D_fake: 0.397 +(epoch: 383, iters: 350, time: 0.063) G_GAN: -0.408 G_GAN_Feat: 0.774 G_ID: 0.118 G_Rec: 0.354 D_GP: 0.031 D_real: 0.676 D_fake: 1.408 +(epoch: 383, iters: 750, time: 0.063) G_GAN: 0.278 G_GAN_Feat: 0.903 G_ID: 0.151 G_Rec: 0.418 D_GP: 0.034 D_real: 0.895 D_fake: 0.724 +(epoch: 383, iters: 1150, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.771 G_ID: 0.093 G_Rec: 0.339 D_GP: 0.035 D_real: 0.832 D_fake: 0.922 +(epoch: 383, iters: 1550, time: 0.063) G_GAN: 0.210 G_GAN_Feat: 0.997 G_ID: 0.127 G_Rec: 0.409 D_GP: 0.051 D_real: 0.752 D_fake: 0.794 +(epoch: 383, iters: 1950, time: 0.063) G_GAN: 0.321 G_GAN_Feat: 0.944 G_ID: 0.104 G_Rec: 0.317 D_GP: 0.396 D_real: 0.503 D_fake: 0.679 +(epoch: 383, iters: 2350, time: 0.063) G_GAN: 0.672 G_GAN_Feat: 0.886 G_ID: 0.105 G_Rec: 0.372 D_GP: 0.029 D_real: 1.361 D_fake: 0.352 +(epoch: 383, iters: 2750, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.858 G_ID: 0.091 G_Rec: 0.355 D_GP: 0.032 D_real: 0.881 D_fake: 0.721 +(epoch: 383, iters: 3150, time: 0.063) G_GAN: 0.469 G_GAN_Feat: 0.974 G_ID: 0.140 G_Rec: 0.455 D_GP: 0.041 D_real: 1.018 D_fake: 0.539 +(epoch: 383, iters: 3550, time: 0.063) G_GAN: 0.077 G_GAN_Feat: 0.757 G_ID: 0.106 G_Rec: 0.301 D_GP: 0.042 D_real: 0.791 D_fake: 0.923 +(epoch: 383, iters: 3950, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 1.034 G_ID: 0.133 G_Rec: 0.466 D_GP: 0.052 D_real: 0.830 D_fake: 0.598 +(epoch: 383, iters: 4350, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.896 G_ID: 0.109 G_Rec: 0.298 D_GP: 0.067 D_real: 0.588 D_fake: 0.683 +(epoch: 383, iters: 4750, time: 0.063) G_GAN: 0.298 G_GAN_Feat: 1.149 G_ID: 0.129 G_Rec: 0.422 D_GP: 0.042 D_real: 0.473 D_fake: 0.704 +(epoch: 383, iters: 5150, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 0.794 G_ID: 0.109 G_Rec: 0.315 D_GP: 0.033 D_real: 1.096 D_fake: 0.687 +(epoch: 383, iters: 5550, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 0.903 G_ID: 0.138 G_Rec: 0.423 D_GP: 0.030 D_real: 1.050 D_fake: 0.695 +(epoch: 383, iters: 5950, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.833 G_ID: 0.088 G_Rec: 0.355 D_GP: 0.040 D_real: 0.871 D_fake: 0.875 +(epoch: 383, iters: 6350, time: 0.063) G_GAN: 0.212 G_GAN_Feat: 1.000 G_ID: 0.133 G_Rec: 0.471 D_GP: 0.037 D_real: 0.728 D_fake: 0.788 +(epoch: 383, iters: 6750, time: 0.063) G_GAN: 0.143 G_GAN_Feat: 0.722 G_ID: 0.096 G_Rec: 0.297 D_GP: 0.031 D_real: 1.047 D_fake: 0.857 +(epoch: 383, iters: 7150, time: 0.063) G_GAN: 0.351 G_GAN_Feat: 1.014 G_ID: 0.120 G_Rec: 0.435 D_GP: 0.040 D_real: 0.851 D_fake: 0.652 +(epoch: 383, iters: 7550, time: 0.064) G_GAN: -0.022 G_GAN_Feat: 0.791 G_ID: 0.103 G_Rec: 0.312 D_GP: 0.035 D_real: 0.904 D_fake: 1.022 +(epoch: 383, iters: 7950, time: 0.063) G_GAN: 0.423 G_GAN_Feat: 1.052 G_ID: 0.126 G_Rec: 0.426 D_GP: 0.152 D_real: 0.500 D_fake: 0.582 +(epoch: 383, iters: 8350, time: 0.064) G_GAN: 0.555 G_GAN_Feat: 0.932 G_ID: 0.116 G_Rec: 0.359 D_GP: 0.179 D_real: 0.663 D_fake: 0.507 +(epoch: 384, iters: 142, time: 0.064) G_GAN: 0.610 G_GAN_Feat: 0.938 G_ID: 0.131 G_Rec: 0.421 D_GP: 0.030 D_real: 1.207 D_fake: 0.443 +(epoch: 384, iters: 542, time: 0.064) G_GAN: 0.594 G_GAN_Feat: 1.078 G_ID: 0.100 G_Rec: 0.374 D_GP: 0.038 D_real: 1.363 D_fake: 0.510 +(epoch: 384, iters: 942, time: 0.064) G_GAN: 0.763 G_GAN_Feat: 1.143 G_ID: 0.122 G_Rec: 0.455 D_GP: 0.082 D_real: 0.677 D_fake: 0.275 +(epoch: 384, iters: 1342, time: 0.064) G_GAN: -0.047 G_GAN_Feat: 0.845 G_ID: 0.119 G_Rec: 0.349 D_GP: 0.036 D_real: 0.728 D_fake: 1.047 +(epoch: 384, iters: 1742, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.950 G_ID: 0.121 G_Rec: 0.420 D_GP: 0.044 D_real: 0.939 D_fake: 0.590 +(epoch: 384, iters: 2142, time: 0.063) G_GAN: 0.195 G_GAN_Feat: 0.758 G_ID: 0.101 G_Rec: 0.291 D_GP: 0.045 D_real: 0.941 D_fake: 0.807 +(epoch: 384, iters: 2542, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 1.161 G_ID: 0.130 G_Rec: 0.464 D_GP: 0.325 D_real: 0.152 D_fake: 0.642 +(epoch: 384, iters: 2942, time: 0.063) G_GAN: 0.583 G_GAN_Feat: 1.005 G_ID: 0.113 G_Rec: 0.365 D_GP: 0.286 D_real: 0.439 D_fake: 0.560 +(epoch: 384, iters: 3342, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 1.238 G_ID: 0.119 G_Rec: 0.465 D_GP: 0.045 D_real: 0.323 D_fake: 0.705 +(epoch: 384, iters: 3742, time: 0.063) G_GAN: 0.094 G_GAN_Feat: 0.891 G_ID: 0.109 G_Rec: 0.349 D_GP: 0.039 D_real: 0.695 D_fake: 0.907 +(epoch: 384, iters: 4142, time: 0.063) G_GAN: 0.508 G_GAN_Feat: 1.031 G_ID: 0.129 G_Rec: 0.491 D_GP: 0.031 D_real: 1.070 D_fake: 0.494 +(epoch: 384, iters: 4542, time: 0.063) G_GAN: 0.715 G_GAN_Feat: 1.099 G_ID: 0.114 G_Rec: 0.406 D_GP: 0.136 D_real: 0.492 D_fake: 0.441 +(epoch: 384, iters: 4942, time: 0.064) G_GAN: 0.842 G_GAN_Feat: 1.189 G_ID: 0.120 G_Rec: 0.469 D_GP: 0.047 D_real: 1.175 D_fake: 0.232 +(epoch: 384, iters: 5342, time: 0.063) G_GAN: 0.300 G_GAN_Feat: 1.004 G_ID: 0.111 G_Rec: 0.365 D_GP: 0.094 D_real: 0.881 D_fake: 0.707 +(epoch: 384, iters: 5742, time: 0.063) G_GAN: 0.393 G_GAN_Feat: 1.075 G_ID: 0.137 G_Rec: 0.479 D_GP: 0.050 D_real: 0.727 D_fake: 0.608 +(epoch: 384, iters: 6142, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 1.001 G_ID: 0.096 G_Rec: 0.408 D_GP: 0.270 D_real: 0.387 D_fake: 0.786 +(epoch: 384, iters: 6542, time: 0.064) G_GAN: 0.865 G_GAN_Feat: 1.090 G_ID: 0.131 G_Rec: 0.464 D_GP: 0.038 D_real: 0.927 D_fake: 0.181 +(epoch: 384, iters: 6942, time: 0.063) G_GAN: 0.149 G_GAN_Feat: 0.701 G_ID: 0.106 G_Rec: 0.314 D_GP: 0.024 D_real: 1.086 D_fake: 0.851 +(epoch: 384, iters: 7342, time: 0.063) G_GAN: 0.177 G_GAN_Feat: 1.059 G_ID: 0.126 G_Rec: 0.467 D_GP: 0.040 D_real: 0.658 D_fake: 0.823 +(epoch: 384, iters: 7742, time: 0.063) G_GAN: 0.203 G_GAN_Feat: 0.704 G_ID: 0.124 G_Rec: 0.309 D_GP: 0.031 D_real: 1.053 D_fake: 0.798 +(epoch: 384, iters: 8142, time: 0.064) G_GAN: 0.786 G_GAN_Feat: 1.094 G_ID: 0.114 G_Rec: 0.488 D_GP: 0.078 D_real: 1.361 D_fake: 0.268 +(epoch: 384, iters: 8542, time: 0.063) G_GAN: 0.058 G_GAN_Feat: 0.718 G_ID: 0.121 G_Rec: 0.338 D_GP: 0.033 D_real: 0.832 D_fake: 0.942 +(epoch: 385, iters: 334, time: 0.063) G_GAN: 0.068 G_GAN_Feat: 0.882 G_ID: 0.135 G_Rec: 0.401 D_GP: 0.038 D_real: 0.681 D_fake: 0.932 +(epoch: 385, iters: 734, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.742 G_ID: 0.101 G_Rec: 0.323 D_GP: 0.035 D_real: 0.848 D_fake: 0.979 +(epoch: 385, iters: 1134, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.975 G_ID: 0.129 G_Rec: 0.463 D_GP: 0.030 D_real: 1.176 D_fake: 0.542 +(epoch: 385, iters: 1534, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 0.764 G_ID: 0.099 G_Rec: 0.348 D_GP: 0.031 D_real: 0.992 D_fake: 0.808 +(epoch: 385, iters: 1934, time: 0.063) G_GAN: 0.512 G_GAN_Feat: 0.860 G_ID: 0.122 G_Rec: 0.404 D_GP: 0.037 D_real: 1.220 D_fake: 0.521 +(epoch: 385, iters: 2334, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 0.713 G_ID: 0.121 G_Rec: 0.316 D_GP: 0.039 D_real: 1.010 D_fake: 0.875 +(epoch: 385, iters: 2734, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 1.003 G_ID: 0.144 G_Rec: 0.458 D_GP: 0.060 D_real: 0.421 D_fake: 0.944 +(epoch: 385, iters: 3134, time: 0.063) G_GAN: -0.093 G_GAN_Feat: 0.903 G_ID: 0.128 G_Rec: 0.342 D_GP: 0.294 D_real: 0.302 D_fake: 1.093 +(epoch: 385, iters: 3534, time: 0.063) G_GAN: 0.422 G_GAN_Feat: 1.075 G_ID: 0.118 G_Rec: 0.473 D_GP: 0.111 D_real: 0.505 D_fake: 0.591 +(epoch: 385, iters: 3934, time: 0.063) G_GAN: 0.761 G_GAN_Feat: 0.754 G_ID: 0.092 G_Rec: 0.313 D_GP: 0.034 D_real: 1.682 D_fake: 0.274 +(epoch: 385, iters: 4334, time: 0.064) G_GAN: 0.827 G_GAN_Feat: 0.991 G_ID: 0.124 G_Rec: 0.407 D_GP: 0.045 D_real: 1.404 D_fake: 0.219 +(epoch: 385, iters: 4734, time: 0.063) G_GAN: 0.421 G_GAN_Feat: 0.935 G_ID: 0.098 G_Rec: 0.312 D_GP: 0.044 D_real: 1.214 D_fake: 0.629 +(epoch: 385, iters: 5134, time: 0.063) G_GAN: 0.460 G_GAN_Feat: 0.983 G_ID: 0.157 G_Rec: 0.413 D_GP: 0.043 D_real: 1.117 D_fake: 0.541 +(epoch: 385, iters: 5534, time: 0.063) G_GAN: 0.448 G_GAN_Feat: 0.798 G_ID: 0.101 G_Rec: 0.312 D_GP: 0.032 D_real: 1.372 D_fake: 0.557 +(epoch: 385, iters: 5934, time: 0.064) G_GAN: 0.711 G_GAN_Feat: 1.036 G_ID: 0.124 G_Rec: 0.459 D_GP: 0.055 D_real: 1.056 D_fake: 0.319 +(epoch: 385, iters: 6334, time: 0.063) G_GAN: 0.328 G_GAN_Feat: 0.771 G_ID: 0.099 G_Rec: 0.352 D_GP: 0.059 D_real: 1.122 D_fake: 0.697 +(epoch: 385, iters: 6734, time: 0.063) G_GAN: 0.552 G_GAN_Feat: 0.911 G_ID: 0.132 G_Rec: 0.441 D_GP: 0.040 D_real: 1.096 D_fake: 0.480 +(epoch: 385, iters: 7134, time: 0.063) G_GAN: 0.053 G_GAN_Feat: 0.687 G_ID: 0.104 G_Rec: 0.328 D_GP: 0.032 D_real: 0.915 D_fake: 0.947 +(epoch: 385, iters: 7534, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.882 G_ID: 0.123 G_Rec: 0.425 D_GP: 0.031 D_real: 1.022 D_fake: 0.672 +(epoch: 385, iters: 7934, time: 0.063) G_GAN: 0.195 G_GAN_Feat: 0.723 G_ID: 0.124 G_Rec: 0.301 D_GP: 0.042 D_real: 0.877 D_fake: 0.805 +(epoch: 385, iters: 8334, time: 0.063) G_GAN: 0.519 G_GAN_Feat: 0.927 G_ID: 0.124 G_Rec: 0.435 D_GP: 0.029 D_real: 1.099 D_fake: 0.500 +(epoch: 386, iters: 126, time: 0.063) G_GAN: 0.039 G_GAN_Feat: 0.844 G_ID: 0.106 G_Rec: 0.337 D_GP: 0.043 D_real: 0.698 D_fake: 0.961 +(epoch: 386, iters: 526, time: 0.064) G_GAN: 0.810 G_GAN_Feat: 0.900 G_ID: 0.106 G_Rec: 0.410 D_GP: 0.033 D_real: 1.514 D_fake: 0.233 +(epoch: 386, iters: 926, time: 0.063) G_GAN: 0.454 G_GAN_Feat: 0.740 G_ID: 0.095 G_Rec: 0.294 D_GP: 0.030 D_real: 1.297 D_fake: 0.550 +(epoch: 386, iters: 1326, time: 0.063) G_GAN: 0.686 G_GAN_Feat: 1.033 G_ID: 0.120 G_Rec: 0.412 D_GP: 0.076 D_real: 0.909 D_fake: 0.345 +(epoch: 386, iters: 1726, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.744 G_ID: 0.107 G_Rec: 0.340 D_GP: 0.037 D_real: 1.011 D_fake: 0.860 +(epoch: 386, iters: 2126, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.888 G_ID: 0.140 G_Rec: 0.432 D_GP: 0.033 D_real: 0.847 D_fake: 0.875 +(epoch: 386, iters: 2526, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.642 G_ID: 0.095 G_Rec: 0.259 D_GP: 0.027 D_real: 1.010 D_fake: 0.923 +(epoch: 386, iters: 2926, time: 0.064) G_GAN: 0.535 G_GAN_Feat: 0.994 G_ID: 0.127 G_Rec: 0.468 D_GP: 0.032 D_real: 1.127 D_fake: 0.475 +(epoch: 386, iters: 3326, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.852 G_ID: 0.118 G_Rec: 0.332 D_GP: 0.061 D_real: 0.565 D_fake: 0.959 +(epoch: 386, iters: 3726, time: 0.064) G_GAN: 0.618 G_GAN_Feat: 0.924 G_ID: 0.131 G_Rec: 0.392 D_GP: 0.041 D_real: 1.098 D_fake: 0.391 +(epoch: 386, iters: 4126, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.794 G_ID: 0.096 G_Rec: 0.294 D_GP: 0.035 D_real: 1.002 D_fake: 0.666 +(epoch: 386, iters: 4526, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.934 G_ID: 0.133 G_Rec: 0.454 D_GP: 0.036 D_real: 1.240 D_fake: 0.492 +(epoch: 386, iters: 4926, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.805 G_ID: 0.101 G_Rec: 0.366 D_GP: 0.039 D_real: 1.032 D_fake: 0.792 +(epoch: 386, iters: 5326, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.916 G_ID: 0.113 G_Rec: 0.441 D_GP: 0.034 D_real: 0.960 D_fake: 0.628 +(epoch: 386, iters: 5726, time: 0.064) G_GAN: 0.028 G_GAN_Feat: 0.723 G_ID: 0.107 G_Rec: 0.319 D_GP: 0.035 D_real: 0.877 D_fake: 0.972 +(epoch: 386, iters: 6126, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.919 G_ID: 0.120 G_Rec: 0.418 D_GP: 0.053 D_real: 0.913 D_fake: 0.696 +(epoch: 386, iters: 6526, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 0.915 G_ID: 0.115 G_Rec: 0.365 D_GP: 0.045 D_real: 0.589 D_fake: 0.944 +(epoch: 386, iters: 6926, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.943 G_ID: 0.126 G_Rec: 0.402 D_GP: 0.036 D_real: 0.903 D_fake: 0.657 +(epoch: 386, iters: 7326, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.724 G_ID: 0.105 G_Rec: 0.287 D_GP: 0.033 D_real: 1.294 D_fake: 0.560 +(epoch: 386, iters: 7726, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 1.071 G_ID: 0.118 G_Rec: 0.434 D_GP: 0.092 D_real: 0.590 D_fake: 0.442 +(epoch: 386, iters: 8126, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.935 G_ID: 0.097 G_Rec: 0.346 D_GP: 0.078 D_real: 0.771 D_fake: 0.722 +(epoch: 386, iters: 8526, time: 0.064) G_GAN: 0.659 G_GAN_Feat: 1.151 G_ID: 0.135 G_Rec: 0.517 D_GP: 0.093 D_real: 1.027 D_fake: 0.544 +(epoch: 387, iters: 318, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.900 G_ID: 0.106 G_Rec: 0.339 D_GP: 0.142 D_real: 0.651 D_fake: 0.626 +(epoch: 387, iters: 718, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 1.173 G_ID: 0.163 G_Rec: 0.487 D_GP: 0.046 D_real: 0.545 D_fake: 0.745 +(epoch: 387, iters: 1118, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.771 G_ID: 0.107 G_Rec: 0.315 D_GP: 0.037 D_real: 1.160 D_fake: 0.723 +(epoch: 387, iters: 1518, time: 0.064) G_GAN: 0.717 G_GAN_Feat: 1.098 G_ID: 0.142 G_Rec: 0.439 D_GP: 0.043 D_real: 0.950 D_fake: 0.331 +(epoch: 387, iters: 1918, time: 0.064) G_GAN: -0.143 G_GAN_Feat: 0.997 G_ID: 0.110 G_Rec: 0.322 D_GP: 0.040 D_real: 0.462 D_fake: 1.143 +(epoch: 387, iters: 2318, time: 0.064) G_GAN: 0.827 G_GAN_Feat: 1.036 G_ID: 0.116 G_Rec: 0.462 D_GP: 0.054 D_real: 1.362 D_fake: 0.298 +(epoch: 387, iters: 2718, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.855 G_ID: 0.109 G_Rec: 0.371 D_GP: 0.043 D_real: 0.939 D_fake: 0.857 +(epoch: 387, iters: 3118, time: 0.064) G_GAN: 0.714 G_GAN_Feat: 0.975 G_ID: 0.122 G_Rec: 0.404 D_GP: 0.048 D_real: 1.273 D_fake: 0.330 +(epoch: 387, iters: 3518, time: 0.063) G_GAN: 0.742 G_GAN_Feat: 0.980 G_ID: 0.115 G_Rec: 0.335 D_GP: 0.100 D_real: 0.525 D_fake: 0.506 +(epoch: 387, iters: 3918, time: 0.063) G_GAN: 1.195 G_GAN_Feat: 1.289 G_ID: 0.116 G_Rec: 0.489 D_GP: 0.251 D_real: 0.618 D_fake: 0.130 +(epoch: 387, iters: 4318, time: 0.063) G_GAN: 0.363 G_GAN_Feat: 0.888 G_ID: 0.107 G_Rec: 0.310 D_GP: 0.040 D_real: 0.801 D_fake: 0.680 +(epoch: 387, iters: 4718, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.952 G_ID: 0.125 G_Rec: 0.467 D_GP: 0.028 D_real: 1.109 D_fake: 0.557 +(epoch: 387, iters: 5118, time: 0.063) G_GAN: 0.095 G_GAN_Feat: 0.660 G_ID: 0.131 G_Rec: 0.306 D_GP: 0.029 D_real: 1.019 D_fake: 0.905 +(epoch: 387, iters: 5518, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 0.837 G_ID: 0.132 G_Rec: 0.381 D_GP: 0.033 D_real: 1.077 D_fake: 0.581 +(epoch: 387, iters: 5918, time: 0.063) G_GAN: -0.076 G_GAN_Feat: 0.703 G_ID: 0.085 G_Rec: 0.308 D_GP: 0.031 D_real: 0.800 D_fake: 1.076 +(epoch: 387, iters: 6318, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.969 G_ID: 0.118 G_Rec: 0.489 D_GP: 0.069 D_real: 1.032 D_fake: 0.557 +(epoch: 387, iters: 6718, time: 0.063) G_GAN: -0.000 G_GAN_Feat: 0.713 G_ID: 0.169 G_Rec: 0.314 D_GP: 0.036 D_real: 0.768 D_fake: 1.000 +(epoch: 387, iters: 7118, time: 0.063) G_GAN: 0.388 G_GAN_Feat: 1.100 G_ID: 0.119 G_Rec: 0.498 D_GP: 0.127 D_real: 0.493 D_fake: 0.635 +(epoch: 387, iters: 7518, time: 0.063) G_GAN: 0.078 G_GAN_Feat: 0.717 G_ID: 0.126 G_Rec: 0.291 D_GP: 0.042 D_real: 0.743 D_fake: 0.922 +(epoch: 387, iters: 7918, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.975 G_ID: 0.127 G_Rec: 0.425 D_GP: 0.052 D_real: 0.771 D_fake: 0.606 +(epoch: 387, iters: 8318, time: 0.063) G_GAN: -0.091 G_GAN_Feat: 0.861 G_ID: 0.101 G_Rec: 0.354 D_GP: 0.036 D_real: 1.012 D_fake: 1.108 +(epoch: 388, iters: 110, time: 0.063) G_GAN: 0.626 G_GAN_Feat: 0.885 G_ID: 0.114 G_Rec: 0.426 D_GP: 0.030 D_real: 1.340 D_fake: 0.402 +(epoch: 388, iters: 510, time: 0.063) G_GAN: 0.215 G_GAN_Feat: 0.813 G_ID: 0.106 G_Rec: 0.301 D_GP: 0.096 D_real: 0.787 D_fake: 0.786 +(epoch: 388, iters: 910, time: 0.064) G_GAN: 0.829 G_GAN_Feat: 1.056 G_ID: 0.110 G_Rec: 0.476 D_GP: 0.040 D_real: 1.196 D_fake: 0.223 +(epoch: 388, iters: 1310, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.802 G_ID: 0.113 G_Rec: 0.337 D_GP: 0.098 D_real: 1.095 D_fake: 0.603 +(epoch: 388, iters: 1710, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 1.053 G_ID: 0.125 G_Rec: 0.417 D_GP: 0.047 D_real: 0.802 D_fake: 0.415 +(epoch: 388, iters: 2110, time: 0.063) G_GAN: 0.577 G_GAN_Feat: 0.833 G_ID: 0.153 G_Rec: 0.307 D_GP: 0.045 D_real: 1.149 D_fake: 0.429 +(epoch: 388, iters: 2510, time: 0.064) G_GAN: -0.090 G_GAN_Feat: 0.868 G_ID: 0.121 G_Rec: 0.389 D_GP: 0.032 D_real: 0.843 D_fake: 1.090 +(epoch: 388, iters: 2910, time: 0.063) G_GAN: 0.432 G_GAN_Feat: 0.851 G_ID: 0.104 G_Rec: 0.291 D_GP: 0.091 D_real: 0.836 D_fake: 0.569 +(epoch: 388, iters: 3310, time: 0.063) G_GAN: 0.609 G_GAN_Feat: 1.048 G_ID: 0.143 G_Rec: 0.455 D_GP: 0.040 D_real: 1.187 D_fake: 0.401 +(epoch: 388, iters: 3710, time: 0.063) G_GAN: 0.225 G_GAN_Feat: 0.757 G_ID: 0.111 G_Rec: 0.317 D_GP: 0.034 D_real: 1.087 D_fake: 0.776 +(epoch: 388, iters: 4110, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.984 G_ID: 0.142 G_Rec: 0.460 D_GP: 0.038 D_real: 0.786 D_fake: 0.830 +(epoch: 388, iters: 4510, time: 0.063) G_GAN: 0.171 G_GAN_Feat: 0.755 G_ID: 0.122 G_Rec: 0.308 D_GP: 0.031 D_real: 1.091 D_fake: 0.829 +(epoch: 388, iters: 4910, time: 0.063) G_GAN: 0.327 G_GAN_Feat: 1.299 G_ID: 0.138 G_Rec: 0.499 D_GP: 0.331 D_real: 0.862 D_fake: 0.688 +(epoch: 388, iters: 5310, time: 0.063) G_GAN: 0.132 G_GAN_Feat: 0.752 G_ID: 0.112 G_Rec: 0.317 D_GP: 0.030 D_real: 0.940 D_fake: 0.868 +(epoch: 388, iters: 5710, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 0.916 G_ID: 0.110 G_Rec: 0.421 D_GP: 0.038 D_real: 1.076 D_fake: 0.476 +(epoch: 388, iters: 6110, time: 0.063) G_GAN: 0.375 G_GAN_Feat: 0.797 G_ID: 0.107 G_Rec: 0.321 D_GP: 0.046 D_real: 0.986 D_fake: 0.633 +(epoch: 388, iters: 6510, time: 0.064) G_GAN: 0.823 G_GAN_Feat: 0.982 G_ID: 0.126 G_Rec: 0.390 D_GP: 0.063 D_real: 1.372 D_fake: 0.264 +(epoch: 388, iters: 6910, time: 0.064) G_GAN: -0.004 G_GAN_Feat: 0.936 G_ID: 0.101 G_Rec: 0.365 D_GP: 0.053 D_real: 0.953 D_fake: 1.004 +(epoch: 388, iters: 7310, time: 0.063) G_GAN: 0.623 G_GAN_Feat: 0.958 G_ID: 0.143 G_Rec: 0.417 D_GP: 0.047 D_real: 1.262 D_fake: 0.415 +(epoch: 388, iters: 7710, time: 0.063) G_GAN: 0.322 G_GAN_Feat: 0.749 G_ID: 0.110 G_Rec: 0.281 D_GP: 0.034 D_real: 0.967 D_fake: 0.680 +(epoch: 388, iters: 8110, time: 0.063) G_GAN: 0.520 G_GAN_Feat: 1.062 G_ID: 0.119 G_Rec: 0.418 D_GP: 0.089 D_real: 0.859 D_fake: 0.526 +(epoch: 388, iters: 8510, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.842 G_ID: 0.096 G_Rec: 0.310 D_GP: 0.062 D_real: 1.039 D_fake: 0.545 +(epoch: 389, iters: 302, time: 0.063) G_GAN: 0.570 G_GAN_Feat: 1.065 G_ID: 0.118 G_Rec: 0.475 D_GP: 0.035 D_real: 1.066 D_fake: 0.439 +(epoch: 389, iters: 702, time: 0.063) G_GAN: 0.260 G_GAN_Feat: 0.894 G_ID: 0.080 G_Rec: 0.324 D_GP: 1.041 D_real: 0.477 D_fake: 0.744 +(epoch: 389, iters: 1102, time: 0.063) G_GAN: 0.577 G_GAN_Feat: 1.008 G_ID: 0.107 G_Rec: 0.467 D_GP: 0.031 D_real: 1.118 D_fake: 0.471 +(epoch: 389, iters: 1502, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.739 G_ID: 0.092 G_Rec: 0.297 D_GP: 0.031 D_real: 1.361 D_fake: 0.444 +(epoch: 389, iters: 1902, time: 0.063) G_GAN: 0.276 G_GAN_Feat: 1.119 G_ID: 0.136 G_Rec: 0.478 D_GP: 0.031 D_real: 0.711 D_fake: 0.725 +(epoch: 389, iters: 2302, time: 0.063) G_GAN: 0.747 G_GAN_Feat: 1.047 G_ID: 0.112 G_Rec: 0.401 D_GP: 0.152 D_real: 0.524 D_fake: 0.551 +(epoch: 389, iters: 2702, time: 0.063) G_GAN: 0.541 G_GAN_Feat: 0.960 G_ID: 0.122 G_Rec: 0.434 D_GP: 0.030 D_real: 1.275 D_fake: 0.463 +(epoch: 389, iters: 3102, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.979 G_ID: 0.096 G_Rec: 0.349 D_GP: 0.044 D_real: 0.545 D_fake: 0.863 +(epoch: 389, iters: 3502, time: 0.063) G_GAN: 0.895 G_GAN_Feat: 1.142 G_ID: 0.133 G_Rec: 0.458 D_GP: 0.210 D_real: 0.843 D_fake: 0.207 +(epoch: 389, iters: 3902, time: 0.063) G_GAN: 0.102 G_GAN_Feat: 0.793 G_ID: 0.108 G_Rec: 0.339 D_GP: 0.033 D_real: 0.947 D_fake: 0.898 +(epoch: 389, iters: 4302, time: 0.063) G_GAN: 0.779 G_GAN_Feat: 0.995 G_ID: 0.115 G_Rec: 0.442 D_GP: 0.036 D_real: 1.339 D_fake: 0.250 +(epoch: 389, iters: 4702, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.887 G_ID: 0.088 G_Rec: 0.323 D_GP: 0.046 D_real: 0.820 D_fake: 0.540 +(epoch: 389, iters: 5102, time: 0.063) G_GAN: 0.443 G_GAN_Feat: 1.160 G_ID: 0.132 G_Rec: 0.465 D_GP: 0.043 D_real: 1.019 D_fake: 0.563 +(epoch: 389, iters: 5502, time: 0.063) G_GAN: -0.071 G_GAN_Feat: 0.880 G_ID: 0.115 G_Rec: 0.328 D_GP: 0.039 D_real: 0.465 D_fake: 1.072 +(epoch: 389, iters: 5902, time: 0.063) G_GAN: 0.477 G_GAN_Feat: 1.022 G_ID: 0.114 G_Rec: 0.461 D_GP: 0.047 D_real: 0.988 D_fake: 0.531 +(epoch: 389, iters: 6302, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.806 G_ID: 0.095 G_Rec: 0.318 D_GP: 0.044 D_real: 0.992 D_fake: 0.793 +(epoch: 389, iters: 6702, time: 0.063) G_GAN: 0.635 G_GAN_Feat: 0.942 G_ID: 0.121 G_Rec: 0.434 D_GP: 0.030 D_real: 1.401 D_fake: 0.387 +(epoch: 389, iters: 7102, time: 0.063) G_GAN: 0.207 G_GAN_Feat: 0.857 G_ID: 0.088 G_Rec: 0.298 D_GP: 0.030 D_real: 0.741 D_fake: 0.794 +(epoch: 389, iters: 7502, time: 0.063) G_GAN: 0.394 G_GAN_Feat: 1.119 G_ID: 0.136 G_Rec: 0.466 D_GP: 0.047 D_real: 0.597 D_fake: 0.610 +(epoch: 389, iters: 7902, time: 0.064) G_GAN: -0.389 G_GAN_Feat: 0.762 G_ID: 0.097 G_Rec: 0.343 D_GP: 0.036 D_real: 0.634 D_fake: 1.389 +(epoch: 389, iters: 8302, time: 0.063) G_GAN: 0.487 G_GAN_Feat: 0.962 G_ID: 0.132 G_Rec: 0.420 D_GP: 0.033 D_real: 1.037 D_fake: 0.525 +(epoch: 389, iters: 8702, time: 0.063) G_GAN: 0.264 G_GAN_Feat: 0.757 G_ID: 0.122 G_Rec: 0.322 D_GP: 0.033 D_real: 1.114 D_fake: 0.736 +(epoch: 390, iters: 494, time: 0.063) G_GAN: 0.454 G_GAN_Feat: 0.989 G_ID: 0.124 G_Rec: 0.422 D_GP: 0.042 D_real: 0.942 D_fake: 0.562 +(epoch: 390, iters: 894, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.776 G_ID: 0.105 G_Rec: 0.313 D_GP: 0.041 D_real: 1.364 D_fake: 0.715 +(epoch: 390, iters: 1294, time: 0.063) G_GAN: 0.560 G_GAN_Feat: 0.886 G_ID: 0.113 G_Rec: 0.407 D_GP: 0.037 D_real: 1.276 D_fake: 0.443 +(epoch: 390, iters: 1694, time: 0.063) G_GAN: 0.313 G_GAN_Feat: 0.729 G_ID: 0.097 G_Rec: 0.304 D_GP: 0.032 D_real: 1.216 D_fake: 0.688 +(epoch: 390, iters: 2094, time: 0.063) G_GAN: 0.529 G_GAN_Feat: 0.991 G_ID: 0.116 G_Rec: 0.423 D_GP: 0.038 D_real: 1.180 D_fake: 0.492 +(epoch: 390, iters: 2494, time: 0.064) G_GAN: -0.001 G_GAN_Feat: 0.920 G_ID: 0.119 G_Rec: 0.332 D_GP: 0.050 D_real: 0.349 D_fake: 1.001 +(epoch: 390, iters: 2894, time: 0.063) G_GAN: 0.561 G_GAN_Feat: 0.954 G_ID: 0.115 G_Rec: 0.440 D_GP: 0.030 D_real: 1.256 D_fake: 0.455 +(epoch: 390, iters: 3294, time: 0.063) G_GAN: -0.004 G_GAN_Feat: 0.741 G_ID: 0.104 G_Rec: 0.347 D_GP: 0.051 D_real: 0.781 D_fake: 1.004 +(epoch: 390, iters: 3694, time: 0.063) G_GAN: 0.614 G_GAN_Feat: 1.079 G_ID: 0.143 G_Rec: 0.464 D_GP: 0.156 D_real: 0.806 D_fake: 0.398 +(epoch: 390, iters: 4094, time: 0.064) G_GAN: -0.120 G_GAN_Feat: 0.943 G_ID: 0.135 G_Rec: 0.332 D_GP: 0.063 D_real: 0.188 D_fake: 1.120 +(epoch: 390, iters: 4494, time: 0.063) G_GAN: 0.461 G_GAN_Feat: 1.048 G_ID: 0.135 G_Rec: 0.387 D_GP: 0.046 D_real: 0.813 D_fake: 0.540 +(epoch: 390, iters: 4894, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.999 G_ID: 0.113 G_Rec: 0.362 D_GP: 0.065 D_real: 0.383 D_fake: 0.730 +(epoch: 390, iters: 5294, time: 0.063) G_GAN: 0.293 G_GAN_Feat: 1.224 G_ID: 0.136 G_Rec: 0.469 D_GP: 0.049 D_real: 0.461 D_fake: 0.709 +(epoch: 390, iters: 5694, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.872 G_ID: 0.114 G_Rec: 0.346 D_GP: 0.151 D_real: 0.535 D_fake: 0.889 +(epoch: 390, iters: 6094, time: 0.063) G_GAN: 0.630 G_GAN_Feat: 1.109 G_ID: 0.138 G_Rec: 0.401 D_GP: 0.047 D_real: 0.760 D_fake: 0.414 +(epoch: 390, iters: 6494, time: 0.063) G_GAN: 0.428 G_GAN_Feat: 0.863 G_ID: 0.099 G_Rec: 0.300 D_GP: 0.042 D_real: 0.790 D_fake: 0.592 +(epoch: 390, iters: 6894, time: 0.063) G_GAN: 0.386 G_GAN_Feat: 0.965 G_ID: 0.130 G_Rec: 0.427 D_GP: 0.033 D_real: 1.018 D_fake: 0.615 +(epoch: 390, iters: 7294, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.800 G_ID: 0.094 G_Rec: 0.305 D_GP: 0.039 D_real: 1.158 D_fake: 0.589 +(epoch: 390, iters: 7694, time: 0.063) G_GAN: 0.726 G_GAN_Feat: 0.972 G_ID: 0.143 G_Rec: 0.434 D_GP: 0.038 D_real: 1.293 D_fake: 0.296 +(epoch: 390, iters: 8094, time: 0.063) G_GAN: 0.488 G_GAN_Feat: 1.095 G_ID: 0.104 G_Rec: 0.381 D_GP: 0.058 D_real: 1.474 D_fake: 0.548 +(epoch: 390, iters: 8494, time: 0.063) G_GAN: 0.383 G_GAN_Feat: 0.988 G_ID: 0.127 G_Rec: 0.471 D_GP: 0.031 D_real: 1.039 D_fake: 0.652 +(epoch: 391, iters: 286, time: 0.064) G_GAN: -0.046 G_GAN_Feat: 0.711 G_ID: 0.103 G_Rec: 0.281 D_GP: 0.034 D_real: 0.930 D_fake: 1.046 +(epoch: 391, iters: 686, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 1.056 G_ID: 0.121 G_Rec: 0.448 D_GP: 0.038 D_real: 0.784 D_fake: 0.578 +(epoch: 391, iters: 1086, time: 0.063) G_GAN: 0.347 G_GAN_Feat: 0.907 G_ID: 0.104 G_Rec: 0.313 D_GP: 0.037 D_real: 0.582 D_fake: 0.657 +(epoch: 391, iters: 1486, time: 0.064) G_GAN: 0.820 G_GAN_Feat: 1.011 G_ID: 0.124 G_Rec: 0.425 D_GP: 0.070 D_real: 1.651 D_fake: 0.238 +(epoch: 391, iters: 1886, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.692 G_ID: 0.092 G_Rec: 0.309 D_GP: 0.028 D_real: 1.100 D_fake: 0.820 +(epoch: 391, iters: 2286, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.875 G_ID: 0.122 G_Rec: 0.410 D_GP: 0.033 D_real: 0.998 D_fake: 0.552 +(epoch: 391, iters: 2686, time: 0.064) G_GAN: -0.302 G_GAN_Feat: 0.735 G_ID: 0.092 G_Rec: 0.306 D_GP: 0.035 D_real: 0.602 D_fake: 1.302 +(epoch: 391, iters: 3086, time: 0.064) G_GAN: 0.774 G_GAN_Feat: 1.079 G_ID: 0.116 G_Rec: 0.479 D_GP: 0.112 D_real: 1.048 D_fake: 0.369 +(epoch: 391, iters: 3486, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.808 G_ID: 0.099 G_Rec: 0.326 D_GP: 0.047 D_real: 0.754 D_fake: 0.906 +(epoch: 391, iters: 3886, time: 0.063) G_GAN: 0.433 G_GAN_Feat: 1.054 G_ID: 0.118 G_Rec: 0.451 D_GP: 0.053 D_real: 0.892 D_fake: 0.575 +(epoch: 391, iters: 4286, time: 0.063) G_GAN: 0.156 G_GAN_Feat: 0.895 G_ID: 0.129 G_Rec: 0.372 D_GP: 0.051 D_real: 0.766 D_fake: 0.845 +(epoch: 391, iters: 4686, time: 0.063) G_GAN: 0.663 G_GAN_Feat: 1.049 G_ID: 0.125 G_Rec: 0.469 D_GP: 0.052 D_real: 0.930 D_fake: 0.363 +(epoch: 391, iters: 5086, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 0.876 G_ID: 0.083 G_Rec: 0.332 D_GP: 0.050 D_real: 1.201 D_fake: 0.553 +(epoch: 391, iters: 5486, time: 0.063) G_GAN: 0.746 G_GAN_Feat: 1.114 G_ID: 0.121 G_Rec: 0.475 D_GP: 0.053 D_real: 0.878 D_fake: 0.340 +(epoch: 391, iters: 5886, time: 0.063) G_GAN: 0.160 G_GAN_Feat: 0.972 G_ID: 0.092 G_Rec: 0.315 D_GP: 0.112 D_real: 0.285 D_fake: 0.840 +(epoch: 391, iters: 6286, time: 0.063) G_GAN: 0.653 G_GAN_Feat: 1.321 G_ID: 0.115 G_Rec: 0.475 D_GP: 0.172 D_real: 0.254 D_fake: 0.382 +(epoch: 391, iters: 6686, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.935 G_ID: 0.099 G_Rec: 0.322 D_GP: 0.045 D_real: 0.490 D_fake: 0.626 +(epoch: 391, iters: 7086, time: 0.063) G_GAN: 0.364 G_GAN_Feat: 1.025 G_ID: 0.133 G_Rec: 0.432 D_GP: 0.045 D_real: 0.711 D_fake: 0.637 +(epoch: 391, iters: 7486, time: 0.063) G_GAN: 0.224 G_GAN_Feat: 0.940 G_ID: 0.103 G_Rec: 0.301 D_GP: 0.079 D_real: 0.324 D_fake: 0.781 +(epoch: 391, iters: 7886, time: 0.063) G_GAN: 0.474 G_GAN_Feat: 1.043 G_ID: 0.147 G_Rec: 0.490 D_GP: 0.033 D_real: 1.014 D_fake: 0.530 +(epoch: 391, iters: 8286, time: 0.064) G_GAN: -0.029 G_GAN_Feat: 0.753 G_ID: 0.109 G_Rec: 0.324 D_GP: 0.030 D_real: 0.851 D_fake: 1.029 +(epoch: 391, iters: 8686, time: 0.063) G_GAN: 0.147 G_GAN_Feat: 1.043 G_ID: 0.109 G_Rec: 0.448 D_GP: 0.084 D_real: 0.460 D_fake: 0.855 +(epoch: 392, iters: 478, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.951 G_ID: 0.123 G_Rec: 0.324 D_GP: 0.211 D_real: 0.372 D_fake: 0.774 +(epoch: 392, iters: 878, time: 0.063) G_GAN: 0.851 G_GAN_Feat: 1.320 G_ID: 0.121 G_Rec: 0.544 D_GP: 0.060 D_real: 0.427 D_fake: 0.212 +(epoch: 392, iters: 1278, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.879 G_ID: 0.091 G_Rec: 0.288 D_GP: 0.077 D_real: 0.680 D_fake: 0.580 +(epoch: 392, iters: 1678, time: 0.063) G_GAN: 0.485 G_GAN_Feat: 1.001 G_ID: 0.124 G_Rec: 0.506 D_GP: 0.035 D_real: 1.173 D_fake: 0.523 +(epoch: 392, iters: 2078, time: 0.063) G_GAN: 0.093 G_GAN_Feat: 0.717 G_ID: 0.097 G_Rec: 0.302 D_GP: 0.040 D_real: 0.995 D_fake: 0.908 +(epoch: 392, iters: 2478, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 1.116 G_ID: 0.129 G_Rec: 0.436 D_GP: 0.227 D_real: 0.388 D_fake: 0.584 +(epoch: 392, iters: 2878, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.852 G_ID: 0.108 G_Rec: 0.399 D_GP: 0.069 D_real: 0.851 D_fake: 0.619 +(epoch: 392, iters: 3278, time: 0.063) G_GAN: 0.342 G_GAN_Feat: 0.932 G_ID: 0.106 G_Rec: 0.441 D_GP: 0.032 D_real: 1.056 D_fake: 0.664 +(epoch: 392, iters: 3678, time: 0.063) G_GAN: 0.218 G_GAN_Feat: 0.825 G_ID: 0.106 G_Rec: 0.323 D_GP: 0.056 D_real: 0.845 D_fake: 0.782 +(epoch: 392, iters: 4078, time: 0.063) G_GAN: 0.466 G_GAN_Feat: 1.183 G_ID: 0.134 G_Rec: 0.473 D_GP: 0.042 D_real: 1.184 D_fake: 0.591 +(epoch: 392, iters: 4478, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.676 G_ID: 0.099 G_Rec: 0.304 D_GP: 0.025 D_real: 1.125 D_fake: 0.794 +(epoch: 392, iters: 4878, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 0.916 G_ID: 0.110 G_Rec: 0.475 D_GP: 0.036 D_real: 0.841 D_fake: 0.730 +(epoch: 392, iters: 5278, time: 0.063) G_GAN: -0.082 G_GAN_Feat: 0.677 G_ID: 0.093 G_Rec: 0.309 D_GP: 0.033 D_real: 0.793 D_fake: 1.082 +(epoch: 392, iters: 5678, time: 0.063) G_GAN: 0.108 G_GAN_Feat: 0.953 G_ID: 0.118 G_Rec: 0.461 D_GP: 0.090 D_real: 0.672 D_fake: 0.894 +(epoch: 392, iters: 6078, time: 0.064) G_GAN: 0.061 G_GAN_Feat: 0.658 G_ID: 0.121 G_Rec: 0.298 D_GP: 0.031 D_real: 0.975 D_fake: 0.939 +(epoch: 392, iters: 6478, time: 0.063) G_GAN: 0.324 G_GAN_Feat: 0.862 G_ID: 0.104 G_Rec: 0.399 D_GP: 0.033 D_real: 0.991 D_fake: 0.677 +(epoch: 392, iters: 6878, time: 0.063) G_GAN: -0.030 G_GAN_Feat: 0.850 G_ID: 0.126 G_Rec: 0.356 D_GP: 0.143 D_real: 0.549 D_fake: 1.030 +(epoch: 392, iters: 7278, time: 0.063) G_GAN: 0.469 G_GAN_Feat: 1.013 G_ID: 0.113 G_Rec: 0.424 D_GP: 0.081 D_real: 0.721 D_fake: 0.551 +(epoch: 392, iters: 7678, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.717 G_ID: 0.093 G_Rec: 0.307 D_GP: 0.042 D_real: 1.076 D_fake: 0.776 +(epoch: 392, iters: 8078, time: 0.063) G_GAN: 0.283 G_GAN_Feat: 0.921 G_ID: 0.136 G_Rec: 0.408 D_GP: 0.039 D_real: 0.964 D_fake: 0.725 +(epoch: 392, iters: 8478, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.859 G_ID: 0.117 G_Rec: 0.326 D_GP: 0.082 D_real: 0.849 D_fake: 0.692 +(epoch: 393, iters: 270, time: 0.063) G_GAN: 0.475 G_GAN_Feat: 0.919 G_ID: 0.122 G_Rec: 0.448 D_GP: 0.032 D_real: 1.162 D_fake: 0.562 +(epoch: 393, iters: 670, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.652 G_ID: 0.111 G_Rec: 0.292 D_GP: 0.029 D_real: 1.013 D_fake: 0.908 +(epoch: 393, iters: 1070, time: 0.063) G_GAN: 0.227 G_GAN_Feat: 0.857 G_ID: 0.103 G_Rec: 0.411 D_GP: 0.039 D_real: 0.877 D_fake: 0.774 +(epoch: 393, iters: 1470, time: 0.063) G_GAN: 0.014 G_GAN_Feat: 0.768 G_ID: 0.096 G_Rec: 0.336 D_GP: 0.044 D_real: 0.810 D_fake: 0.986 +(epoch: 393, iters: 1870, time: 0.063) G_GAN: 0.801 G_GAN_Feat: 1.007 G_ID: 0.107 G_Rec: 0.416 D_GP: 0.045 D_real: 1.190 D_fake: 0.318 +(epoch: 393, iters: 2270, time: 0.063) G_GAN: 0.265 G_GAN_Feat: 0.850 G_ID: 0.115 G_Rec: 0.342 D_GP: 0.095 D_real: 0.947 D_fake: 0.757 +(epoch: 393, iters: 2670, time: 0.063) G_GAN: 0.606 G_GAN_Feat: 1.033 G_ID: 0.114 G_Rec: 0.435 D_GP: 0.042 D_real: 1.239 D_fake: 0.401 +(epoch: 393, iters: 3070, time: 0.063) G_GAN: 0.368 G_GAN_Feat: 0.896 G_ID: 0.098 G_Rec: 0.321 D_GP: 0.205 D_real: 0.605 D_fake: 0.642 +(epoch: 393, iters: 3470, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 1.272 G_ID: 0.137 G_Rec: 0.470 D_GP: 0.116 D_real: 0.304 D_fake: 0.690 +(epoch: 393, iters: 3870, time: 0.063) G_GAN: 0.436 G_GAN_Feat: 0.881 G_ID: 0.102 G_Rec: 0.322 D_GP: 0.049 D_real: 0.909 D_fake: 0.568 +(epoch: 393, iters: 4270, time: 0.063) G_GAN: 0.791 G_GAN_Feat: 1.346 G_ID: 0.139 G_Rec: 0.506 D_GP: 0.073 D_real: 0.442 D_fake: 0.261 +(epoch: 393, iters: 4670, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.683 G_ID: 0.090 G_Rec: 0.365 D_GP: 0.026 D_real: 1.157 D_fake: 0.702 +(epoch: 393, iters: 5070, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.828 G_ID: 0.111 G_Rec: 0.395 D_GP: 0.033 D_real: 1.198 D_fake: 0.582 +(epoch: 393, iters: 5470, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.619 G_ID: 0.109 G_Rec: 0.284 D_GP: 0.029 D_real: 1.104 D_fake: 0.817 +(epoch: 393, iters: 5870, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.868 G_ID: 0.121 G_Rec: 0.444 D_GP: 0.030 D_real: 1.101 D_fake: 0.589 +(epoch: 393, iters: 6270, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.703 G_ID: 0.084 G_Rec: 0.325 D_GP: 0.046 D_real: 1.000 D_fake: 0.866 +(epoch: 393, iters: 6670, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.859 G_ID: 0.110 G_Rec: 0.406 D_GP: 0.034 D_real: 1.208 D_fake: 0.514 +(epoch: 393, iters: 7070, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.652 G_ID: 0.099 G_Rec: 0.310 D_GP: 0.032 D_real: 1.298 D_fake: 0.688 +(epoch: 393, iters: 7470, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 0.867 G_ID: 0.126 G_Rec: 0.429 D_GP: 0.041 D_real: 1.012 D_fake: 0.554 +(epoch: 393, iters: 7870, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.638 G_ID: 0.098 G_Rec: 0.289 D_GP: 0.043 D_real: 1.122 D_fake: 0.756 +(epoch: 393, iters: 8270, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.888 G_ID: 0.135 G_Rec: 0.448 D_GP: 0.037 D_real: 1.133 D_fake: 0.536 +(epoch: 393, iters: 8670, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.780 G_ID: 0.134 G_Rec: 0.366 D_GP: 0.072 D_real: 0.669 D_fake: 1.030 +(epoch: 394, iters: 462, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.892 G_ID: 0.120 G_Rec: 0.431 D_GP: 0.039 D_real: 1.097 D_fake: 0.586 +(epoch: 394, iters: 862, time: 0.064) G_GAN: -0.153 G_GAN_Feat: 0.675 G_ID: 0.103 G_Rec: 0.309 D_GP: 0.040 D_real: 0.729 D_fake: 1.153 +(epoch: 394, iters: 1262, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.934 G_ID: 0.112 G_Rec: 0.446 D_GP: 0.049 D_real: 0.943 D_fake: 0.553 +(epoch: 394, iters: 1662, time: 0.064) G_GAN: 0.015 G_GAN_Feat: 0.685 G_ID: 0.096 G_Rec: 0.321 D_GP: 0.037 D_real: 0.879 D_fake: 0.987 +(epoch: 394, iters: 2062, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.899 G_ID: 0.119 G_Rec: 0.422 D_GP: 0.060 D_real: 0.895 D_fake: 0.666 +(epoch: 394, iters: 2462, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.729 G_ID: 0.106 G_Rec: 0.296 D_GP: 0.082 D_real: 0.750 D_fake: 0.843 +(epoch: 394, iters: 2862, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 0.880 G_ID: 0.103 G_Rec: 0.409 D_GP: 0.038 D_real: 1.254 D_fake: 0.429 +(epoch: 394, iters: 3262, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.740 G_ID: 0.117 G_Rec: 0.325 D_GP: 0.043 D_real: 0.845 D_fake: 0.798 +(epoch: 394, iters: 3662, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.940 G_ID: 0.126 G_Rec: 0.409 D_GP: 0.128 D_real: 0.796 D_fake: 0.509 +(epoch: 394, iters: 4062, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.727 G_ID: 0.120 G_Rec: 0.333 D_GP: 0.045 D_real: 0.953 D_fake: 0.805 +(epoch: 394, iters: 4462, time: 0.064) G_GAN: 0.570 G_GAN_Feat: 0.970 G_ID: 0.109 G_Rec: 0.499 D_GP: 0.069 D_real: 1.076 D_fake: 0.444 +(epoch: 394, iters: 4862, time: 0.063) G_GAN: 0.380 G_GAN_Feat: 0.707 G_ID: 0.112 G_Rec: 0.285 D_GP: 0.045 D_real: 1.170 D_fake: 0.623 +(epoch: 394, iters: 5262, time: 0.063) G_GAN: 0.275 G_GAN_Feat: 0.930 G_ID: 0.130 G_Rec: 0.439 D_GP: 0.054 D_real: 0.841 D_fake: 0.725 +(epoch: 394, iters: 5662, time: 0.063) G_GAN: 0.246 G_GAN_Feat: 0.741 G_ID: 0.103 G_Rec: 0.303 D_GP: 0.051 D_real: 1.085 D_fake: 0.754 +(epoch: 394, iters: 6062, time: 0.064) G_GAN: 0.755 G_GAN_Feat: 1.090 G_ID: 0.119 G_Rec: 0.488 D_GP: 0.137 D_real: 0.802 D_fake: 0.334 +(epoch: 394, iters: 6462, time: 0.063) G_GAN: -0.105 G_GAN_Feat: 0.731 G_ID: 0.107 G_Rec: 0.348 D_GP: 0.041 D_real: 0.852 D_fake: 1.105 +(epoch: 394, iters: 6862, time: 0.063) G_GAN: 0.452 G_GAN_Feat: 0.871 G_ID: 0.116 G_Rec: 0.456 D_GP: 0.027 D_real: 1.262 D_fake: 0.562 +(epoch: 394, iters: 7262, time: 0.063) G_GAN: 0.064 G_GAN_Feat: 0.635 G_ID: 0.083 G_Rec: 0.278 D_GP: 0.030 D_real: 1.014 D_fake: 0.936 +(epoch: 394, iters: 7662, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.861 G_ID: 0.107 G_Rec: 0.423 D_GP: 0.033 D_real: 1.003 D_fake: 0.720 +(epoch: 394, iters: 8062, time: 0.063) G_GAN: 0.094 G_GAN_Feat: 0.648 G_ID: 0.108 G_Rec: 0.283 D_GP: 0.038 D_real: 0.983 D_fake: 0.907 +(epoch: 394, iters: 8462, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.925 G_ID: 0.130 G_Rec: 0.438 D_GP: 0.052 D_real: 0.854 D_fake: 0.789 +(epoch: 395, iters: 254, time: 0.063) G_GAN: 0.112 G_GAN_Feat: 0.635 G_ID: 0.101 G_Rec: 0.291 D_GP: 0.044 D_real: 0.979 D_fake: 0.888 +(epoch: 395, iters: 654, time: 0.064) G_GAN: 0.043 G_GAN_Feat: 0.887 G_ID: 0.142 G_Rec: 0.418 D_GP: 0.046 D_real: 0.668 D_fake: 0.957 +(epoch: 395, iters: 1054, time: 0.063) G_GAN: 0.186 G_GAN_Feat: 0.717 G_ID: 0.083 G_Rec: 0.309 D_GP: 0.114 D_real: 0.865 D_fake: 0.815 +(epoch: 395, iters: 1454, time: 0.063) G_GAN: 0.800 G_GAN_Feat: 0.999 G_ID: 0.117 G_Rec: 0.470 D_GP: 0.060 D_real: 1.170 D_fake: 0.273 +(epoch: 395, iters: 1854, time: 0.063) G_GAN: 0.346 G_GAN_Feat: 0.699 G_ID: 0.117 G_Rec: 0.281 D_GP: 0.042 D_real: 1.103 D_fake: 0.654 +(epoch: 395, iters: 2254, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 1.001 G_ID: 0.128 G_Rec: 0.476 D_GP: 0.040 D_real: 0.866 D_fake: 0.559 +(epoch: 395, iters: 2654, time: 0.063) G_GAN: 0.204 G_GAN_Feat: 0.723 G_ID: 0.100 G_Rec: 0.325 D_GP: 0.033 D_real: 0.986 D_fake: 0.796 +(epoch: 395, iters: 3054, time: 0.063) G_GAN: 0.551 G_GAN_Feat: 0.946 G_ID: 0.115 G_Rec: 0.446 D_GP: 0.042 D_real: 1.329 D_fake: 0.456 +(epoch: 395, iters: 3454, time: 0.063) G_GAN: 0.204 G_GAN_Feat: 0.669 G_ID: 0.103 G_Rec: 0.290 D_GP: 0.031 D_real: 1.090 D_fake: 0.796 +(epoch: 395, iters: 3854, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.896 G_ID: 0.135 G_Rec: 0.400 D_GP: 0.035 D_real: 0.898 D_fake: 0.625 +(epoch: 395, iters: 4254, time: 0.063) G_GAN: 0.453 G_GAN_Feat: 0.869 G_ID: 0.109 G_Rec: 0.329 D_GP: 0.070 D_real: 0.853 D_fake: 0.552 +(epoch: 395, iters: 4654, time: 0.063) G_GAN: 0.340 G_GAN_Feat: 0.993 G_ID: 0.121 G_Rec: 0.427 D_GP: 0.035 D_real: 0.857 D_fake: 0.664 +(epoch: 395, iters: 5054, time: 0.063) G_GAN: 0.559 G_GAN_Feat: 0.747 G_ID: 0.098 G_Rec: 0.290 D_GP: 0.034 D_real: 1.281 D_fake: 0.445 +(epoch: 395, iters: 5454, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 1.023 G_ID: 0.131 G_Rec: 0.481 D_GP: 0.041 D_real: 0.650 D_fake: 0.855 +(epoch: 395, iters: 5854, time: 0.063) G_GAN: 0.361 G_GAN_Feat: 0.714 G_ID: 0.105 G_Rec: 0.307 D_GP: 0.046 D_real: 1.127 D_fake: 0.643 +(epoch: 395, iters: 6254, time: 0.063) G_GAN: 0.288 G_GAN_Feat: 1.067 G_ID: 0.126 G_Rec: 0.494 D_GP: 0.115 D_real: 0.441 D_fake: 0.714 +(epoch: 395, iters: 6654, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 0.754 G_ID: 0.088 G_Rec: 0.292 D_GP: 0.049 D_real: 0.857 D_fake: 0.811 +(epoch: 395, iters: 7054, time: 0.064) G_GAN: 0.911 G_GAN_Feat: 0.999 G_ID: 0.134 G_Rec: 0.448 D_GP: 0.151 D_real: 1.397 D_fake: 0.206 +(epoch: 395, iters: 7454, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.927 G_ID: 0.106 G_Rec: 0.338 D_GP: 0.184 D_real: 0.399 D_fake: 0.817 +(epoch: 395, iters: 7854, time: 0.063) G_GAN: 0.492 G_GAN_Feat: 1.107 G_ID: 0.128 G_Rec: 0.441 D_GP: 0.163 D_real: 0.533 D_fake: 0.514 +(epoch: 395, iters: 8254, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.936 G_ID: 0.113 G_Rec: 0.336 D_GP: 0.123 D_real: 0.477 D_fake: 0.814 +(epoch: 395, iters: 8654, time: 0.064) G_GAN: 0.779 G_GAN_Feat: 1.140 G_ID: 0.129 G_Rec: 0.466 D_GP: 0.459 D_real: 0.863 D_fake: 0.400 +(epoch: 396, iters: 446, time: 0.063) G_GAN: 0.327 G_GAN_Feat: 0.922 G_ID: 0.113 G_Rec: 0.348 D_GP: 0.072 D_real: 0.465 D_fake: 0.749 +(epoch: 396, iters: 846, time: 0.063) G_GAN: 0.266 G_GAN_Feat: 1.186 G_ID: 0.126 G_Rec: 0.457 D_GP: 0.364 D_real: 0.209 D_fake: 0.746 +(epoch: 396, iters: 1246, time: 0.063) G_GAN: 0.160 G_GAN_Feat: 0.859 G_ID: 0.106 G_Rec: 0.382 D_GP: 0.050 D_real: 0.964 D_fake: 0.851 +(epoch: 396, iters: 1646, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.991 G_ID: 0.112 G_Rec: 0.444 D_GP: 0.035 D_real: 0.924 D_fake: 0.539 +(epoch: 396, iters: 2046, time: 0.063) G_GAN: 0.291 G_GAN_Feat: 0.714 G_ID: 0.086 G_Rec: 0.269 D_GP: 0.036 D_real: 1.198 D_fake: 0.711 +(epoch: 396, iters: 2446, time: 0.063) G_GAN: 0.686 G_GAN_Feat: 0.929 G_ID: 0.129 G_Rec: 0.418 D_GP: 0.035 D_real: 1.287 D_fake: 0.368 +(epoch: 396, iters: 2846, time: 0.063) G_GAN: 0.249 G_GAN_Feat: 0.716 G_ID: 0.096 G_Rec: 0.288 D_GP: 0.029 D_real: 1.135 D_fake: 0.752 +(epoch: 396, iters: 3246, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.999 G_ID: 0.122 G_Rec: 0.386 D_GP: 0.055 D_real: 0.776 D_fake: 0.620 +(epoch: 396, iters: 3646, time: 0.063) G_GAN: 0.288 G_GAN_Feat: 0.744 G_ID: 0.110 G_Rec: 0.290 D_GP: 0.040 D_real: 1.132 D_fake: 0.712 +(epoch: 396, iters: 4046, time: 0.063) G_GAN: 0.768 G_GAN_Feat: 1.230 G_ID: 0.120 G_Rec: 0.440 D_GP: 0.116 D_real: 0.589 D_fake: 0.298 +(epoch: 396, iters: 4446, time: 0.063) G_GAN: 0.517 G_GAN_Feat: 0.772 G_ID: 0.095 G_Rec: 0.311 D_GP: 0.033 D_real: 1.348 D_fake: 0.488 +(epoch: 396, iters: 4846, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 0.958 G_ID: 0.106 G_Rec: 0.463 D_GP: 0.040 D_real: 1.338 D_fake: 0.405 +(epoch: 396, iters: 5246, time: 0.063) G_GAN: 0.499 G_GAN_Feat: 0.777 G_ID: 0.102 G_Rec: 0.302 D_GP: 0.044 D_real: 1.269 D_fake: 0.522 +(epoch: 396, iters: 5646, time: 0.063) G_GAN: 0.647 G_GAN_Feat: 1.050 G_ID: 0.122 G_Rec: 0.467 D_GP: 0.044 D_real: 1.031 D_fake: 0.368 +(epoch: 396, iters: 6046, time: 0.063) G_GAN: 0.481 G_GAN_Feat: 1.134 G_ID: 0.096 G_Rec: 0.347 D_GP: 0.625 D_real: 1.176 D_fake: 0.585 +(epoch: 396, iters: 6446, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.908 G_ID: 0.114 G_Rec: 0.447 D_GP: 0.027 D_real: 0.849 D_fake: 0.918 +(epoch: 396, iters: 6846, time: 0.063) G_GAN: -0.146 G_GAN_Feat: 0.684 G_ID: 0.090 G_Rec: 0.309 D_GP: 0.028 D_real: 0.775 D_fake: 1.146 +(epoch: 396, iters: 7246, time: 0.063) G_GAN: 0.062 G_GAN_Feat: 0.980 G_ID: 0.116 G_Rec: 0.474 D_GP: 0.037 D_real: 0.670 D_fake: 0.939 +(epoch: 396, iters: 7646, time: 0.063) G_GAN: -0.055 G_GAN_Feat: 0.762 G_ID: 0.092 G_Rec: 0.312 D_GP: 0.039 D_real: 0.791 D_fake: 1.055 +(epoch: 396, iters: 8046, time: 0.064) G_GAN: -0.025 G_GAN_Feat: 1.147 G_ID: 0.115 G_Rec: 0.465 D_GP: 0.401 D_real: 0.233 D_fake: 1.026 +(epoch: 396, iters: 8446, time: 0.063) G_GAN: 0.034 G_GAN_Feat: 0.757 G_ID: 0.101 G_Rec: 0.300 D_GP: 0.056 D_real: 0.822 D_fake: 0.966 +(epoch: 397, iters: 238, time: 0.063) G_GAN: 0.258 G_GAN_Feat: 1.038 G_ID: 0.128 G_Rec: 0.427 D_GP: 0.096 D_real: 0.515 D_fake: 0.742 +(epoch: 397, iters: 638, time: 0.063) G_GAN: 0.152 G_GAN_Feat: 0.839 G_ID: 0.100 G_Rec: 0.347 D_GP: 0.035 D_real: 0.865 D_fake: 0.848 +(epoch: 397, iters: 1038, time: 0.064) G_GAN: 0.664 G_GAN_Feat: 1.157 G_ID: 0.117 G_Rec: 0.441 D_GP: 0.051 D_real: 0.793 D_fake: 0.393 +(epoch: 397, iters: 1438, time: 0.063) G_GAN: 0.247 G_GAN_Feat: 0.812 G_ID: 0.094 G_Rec: 0.334 D_GP: 0.063 D_real: 0.957 D_fake: 0.754 +(epoch: 397, iters: 1838, time: 0.063) G_GAN: 0.679 G_GAN_Feat: 0.887 G_ID: 0.130 G_Rec: 0.447 D_GP: 0.039 D_real: 1.282 D_fake: 0.357 +(epoch: 397, iters: 2238, time: 0.063) G_GAN: 0.049 G_GAN_Feat: 0.795 G_ID: 0.096 G_Rec: 0.327 D_GP: 0.054 D_real: 0.840 D_fake: 0.951 +(epoch: 397, iters: 2638, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 1.017 G_ID: 0.134 G_Rec: 0.455 D_GP: 0.057 D_real: 0.869 D_fake: 0.564 +(epoch: 397, iters: 3038, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.933 G_ID: 0.117 G_Rec: 0.355 D_GP: 0.101 D_real: 0.678 D_fake: 0.764 +(epoch: 397, iters: 3438, time: 0.063) G_GAN: 0.580 G_GAN_Feat: 1.003 G_ID: 0.139 G_Rec: 0.458 D_GP: 0.047 D_real: 1.116 D_fake: 0.434 +(epoch: 397, iters: 3838, time: 0.063) G_GAN: 0.399 G_GAN_Feat: 0.749 G_ID: 0.093 G_Rec: 0.282 D_GP: 0.037 D_real: 1.222 D_fake: 0.604 +(epoch: 397, iters: 4238, time: 0.064) G_GAN: 0.920 G_GAN_Feat: 0.983 G_ID: 0.130 G_Rec: 0.446 D_GP: 0.053 D_real: 1.527 D_fake: 0.224 +(epoch: 397, iters: 4638, time: 0.063) G_GAN: 0.371 G_GAN_Feat: 0.661 G_ID: 0.084 G_Rec: 0.279 D_GP: 0.029 D_real: 1.244 D_fake: 0.633 +(epoch: 397, iters: 5038, time: 0.063) G_GAN: 0.137 G_GAN_Feat: 0.994 G_ID: 0.121 G_Rec: 0.446 D_GP: 0.043 D_real: 0.507 D_fake: 0.863 +(epoch: 397, iters: 5438, time: 0.063) G_GAN: 0.168 G_GAN_Feat: 0.895 G_ID: 0.095 G_Rec: 0.328 D_GP: 0.095 D_real: 0.396 D_fake: 0.837 +(epoch: 397, iters: 5838, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 0.951 G_ID: 0.115 G_Rec: 0.418 D_GP: 0.031 D_real: 1.277 D_fake: 0.357 +(epoch: 397, iters: 6238, time: 0.063) G_GAN: 0.360 G_GAN_Feat: 0.877 G_ID: 0.093 G_Rec: 0.331 D_GP: 0.074 D_real: 0.613 D_fake: 0.691 +(epoch: 397, iters: 6638, time: 0.063) G_GAN: 0.375 G_GAN_Feat: 0.921 G_ID: 0.118 G_Rec: 0.427 D_GP: 0.034 D_real: 1.017 D_fake: 0.627 +(epoch: 397, iters: 7038, time: 0.063) G_GAN: 0.519 G_GAN_Feat: 0.729 G_ID: 0.101 G_Rec: 0.355 D_GP: 0.030 D_real: 1.350 D_fake: 0.496 +(epoch: 397, iters: 7438, time: 0.063) G_GAN: 0.733 G_GAN_Feat: 1.051 G_ID: 0.118 G_Rec: 0.448 D_GP: 0.068 D_real: 1.002 D_fake: 0.305 +(epoch: 397, iters: 7838, time: 0.063) G_GAN: 0.233 G_GAN_Feat: 0.801 G_ID: 0.115 G_Rec: 0.321 D_GP: 0.041 D_real: 0.803 D_fake: 0.771 +(epoch: 397, iters: 8238, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.973 G_ID: 0.139 G_Rec: 0.462 D_GP: 0.065 D_real: 0.908 D_fake: 0.767 +(epoch: 397, iters: 8638, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.740 G_ID: 0.093 G_Rec: 0.316 D_GP: 0.057 D_real: 1.092 D_fake: 0.850 +(epoch: 398, iters: 430, time: 0.063) G_GAN: 0.269 G_GAN_Feat: 0.981 G_ID: 0.108 G_Rec: 0.437 D_GP: 0.063 D_real: 0.891 D_fake: 0.732 +(epoch: 398, iters: 830, time: 0.063) G_GAN: 0.181 G_GAN_Feat: 0.699 G_ID: 0.093 G_Rec: 0.295 D_GP: 0.042 D_real: 1.148 D_fake: 0.821 +(epoch: 398, iters: 1230, time: 0.063) G_GAN: 0.347 G_GAN_Feat: 0.984 G_ID: 0.121 G_Rec: 0.459 D_GP: 0.067 D_real: 0.886 D_fake: 0.660 +(epoch: 398, iters: 1630, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.816 G_ID: 0.104 G_Rec: 0.349 D_GP: 0.069 D_real: 0.700 D_fake: 0.987 +(epoch: 398, iters: 2030, time: 0.063) G_GAN: 0.285 G_GAN_Feat: 0.923 G_ID: 0.125 G_Rec: 0.427 D_GP: 0.054 D_real: 0.858 D_fake: 0.725 +(epoch: 398, iters: 2430, time: 0.063) G_GAN: 0.157 G_GAN_Feat: 0.736 G_ID: 0.092 G_Rec: 0.330 D_GP: 0.048 D_real: 0.984 D_fake: 0.844 +(epoch: 398, iters: 2830, time: 0.063) G_GAN: 0.218 G_GAN_Feat: 0.933 G_ID: 0.123 G_Rec: 0.417 D_GP: 0.102 D_real: 0.787 D_fake: 0.783 +(epoch: 398, iters: 3230, time: 0.064) G_GAN: 0.071 G_GAN_Feat: 0.703 G_ID: 0.106 G_Rec: 0.304 D_GP: 0.039 D_real: 0.881 D_fake: 0.929 +(epoch: 398, iters: 3630, time: 0.063) G_GAN: 0.533 G_GAN_Feat: 0.993 G_ID: 0.124 G_Rec: 0.466 D_GP: 0.050 D_real: 1.028 D_fake: 0.486 +(epoch: 398, iters: 4030, time: 0.063) G_GAN: 0.147 G_GAN_Feat: 0.774 G_ID: 0.119 G_Rec: 0.337 D_GP: 0.043 D_real: 0.986 D_fake: 0.854 +(epoch: 398, iters: 4430, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 1.007 G_ID: 0.102 G_Rec: 0.428 D_GP: 0.048 D_real: 1.181 D_fake: 0.608 +(epoch: 398, iters: 4830, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.772 G_ID: 0.106 G_Rec: 0.305 D_GP: 0.052 D_real: 0.863 D_fake: 0.879 +(epoch: 398, iters: 5230, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 0.949 G_ID: 0.125 G_Rec: 0.420 D_GP: 0.051 D_real: 0.804 D_fake: 0.794 +(epoch: 398, iters: 5630, time: 0.063) G_GAN: 0.212 G_GAN_Feat: 0.736 G_ID: 0.117 G_Rec: 0.296 D_GP: 0.036 D_real: 1.050 D_fake: 0.790 +(epoch: 398, iters: 6030, time: 0.063) G_GAN: 0.381 G_GAN_Feat: 0.915 G_ID: 0.101 G_Rec: 0.438 D_GP: 0.038 D_real: 0.969 D_fake: 0.620 +(epoch: 398, iters: 6430, time: 0.064) G_GAN: 0.014 G_GAN_Feat: 0.805 G_ID: 0.110 G_Rec: 0.336 D_GP: 0.075 D_real: 0.789 D_fake: 0.986 +(epoch: 398, iters: 6830, time: 0.063) G_GAN: 0.463 G_GAN_Feat: 0.970 G_ID: 0.108 G_Rec: 0.443 D_GP: 0.041 D_real: 0.900 D_fake: 0.548 +(epoch: 398, iters: 7230, time: 0.063) G_GAN: 0.070 G_GAN_Feat: 0.793 G_ID: 0.094 G_Rec: 0.321 D_GP: 0.052 D_real: 0.658 D_fake: 0.930 +(epoch: 398, iters: 7630, time: 0.063) G_GAN: 0.663 G_GAN_Feat: 1.020 G_ID: 0.105 G_Rec: 0.431 D_GP: 0.036 D_real: 1.196 D_fake: 0.399 +(epoch: 398, iters: 8030, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.803 G_ID: 0.099 G_Rec: 0.310 D_GP: 0.049 D_real: 0.801 D_fake: 0.845 +(epoch: 398, iters: 8430, time: 0.063) G_GAN: 0.538 G_GAN_Feat: 1.030 G_ID: 0.115 G_Rec: 0.437 D_GP: 0.330 D_real: 0.752 D_fake: 0.475 +(epoch: 399, iters: 222, time: 0.063) G_GAN: 0.085 G_GAN_Feat: 0.759 G_ID: 0.097 G_Rec: 0.306 D_GP: 0.050 D_real: 0.793 D_fake: 0.915 +(epoch: 399, iters: 622, time: 0.063) G_GAN: 0.490 G_GAN_Feat: 0.933 G_ID: 0.129 G_Rec: 0.412 D_GP: 0.037 D_real: 1.099 D_fake: 0.513 +(epoch: 399, iters: 1022, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.867 G_ID: 0.105 G_Rec: 0.334 D_GP: 0.121 D_real: 0.831 D_fake: 0.632 +(epoch: 399, iters: 1422, time: 0.063) G_GAN: 0.406 G_GAN_Feat: 1.030 G_ID: 0.119 G_Rec: 0.432 D_GP: 0.043 D_real: 0.869 D_fake: 0.597 +(epoch: 399, iters: 1822, time: 0.063) G_GAN: -0.107 G_GAN_Feat: 0.755 G_ID: 0.101 G_Rec: 0.294 D_GP: 0.035 D_real: 0.694 D_fake: 1.107 +(epoch: 399, iters: 2222, time: 0.063) G_GAN: 0.379 G_GAN_Feat: 0.967 G_ID: 0.129 G_Rec: 0.457 D_GP: 0.035 D_real: 1.036 D_fake: 0.622 +(epoch: 399, iters: 2622, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.793 G_ID: 0.095 G_Rec: 0.302 D_GP: 0.034 D_real: 1.299 D_fake: 0.653 +(epoch: 399, iters: 3022, time: 0.063) G_GAN: 0.218 G_GAN_Feat: 0.920 G_ID: 0.123 G_Rec: 0.399 D_GP: 0.042 D_real: 0.828 D_fake: 0.784 +(epoch: 399, iters: 3422, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.803 G_ID: 0.091 G_Rec: 0.307 D_GP: 0.051 D_real: 0.973 D_fake: 0.704 +(epoch: 399, iters: 3822, time: 0.063) G_GAN: 0.898 G_GAN_Feat: 1.011 G_ID: 0.123 G_Rec: 0.436 D_GP: 0.045 D_real: 1.359 D_fake: 0.213 +(epoch: 399, iters: 4222, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.802 G_ID: 0.102 G_Rec: 0.318 D_GP: 0.039 D_real: 1.048 D_fake: 0.683 +(epoch: 399, iters: 4622, time: 0.063) G_GAN: 0.558 G_GAN_Feat: 1.073 G_ID: 0.123 G_Rec: 0.452 D_GP: 0.117 D_real: 0.694 D_fake: 0.451 +(epoch: 399, iters: 5022, time: 0.063) G_GAN: 0.602 G_GAN_Feat: 0.828 G_ID: 0.092 G_Rec: 0.272 D_GP: 0.038 D_real: 1.167 D_fake: 0.412 +(epoch: 399, iters: 5422, time: 0.063) G_GAN: 0.619 G_GAN_Feat: 0.975 G_ID: 0.138 G_Rec: 0.433 D_GP: 0.033 D_real: 1.271 D_fake: 0.390 +(epoch: 399, iters: 5822, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.896 G_ID: 0.103 G_Rec: 0.335 D_GP: 0.063 D_real: 0.795 D_fake: 0.748 +(epoch: 399, iters: 6222, time: 0.063) G_GAN: 0.607 G_GAN_Feat: 1.048 G_ID: 0.114 G_Rec: 0.435 D_GP: 0.040 D_real: 1.089 D_fake: 0.408 +(epoch: 399, iters: 6622, time: 0.063) G_GAN: 0.261 G_GAN_Feat: 1.030 G_ID: 0.108 G_Rec: 0.365 D_GP: 0.031 D_real: 1.354 D_fake: 0.775 +(epoch: 399, iters: 7022, time: 0.063) G_GAN: 0.156 G_GAN_Feat: 0.807 G_ID: 0.136 G_Rec: 0.420 D_GP: 0.026 D_real: 0.921 D_fake: 0.844 +(epoch: 399, iters: 7422, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.672 G_ID: 0.100 G_Rec: 0.362 D_GP: 0.025 D_real: 0.986 D_fake: 0.917 +(epoch: 399, iters: 7822, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.843 G_ID: 0.122 G_Rec: 0.430 D_GP: 0.030 D_real: 0.895 D_fake: 0.763 +(epoch: 399, iters: 8222, time: 0.063) G_GAN: -0.026 G_GAN_Feat: 0.635 G_ID: 0.102 G_Rec: 0.319 D_GP: 0.035 D_real: 0.844 D_fake: 1.026 +(epoch: 399, iters: 8622, time: 0.063) G_GAN: -0.030 G_GAN_Feat: 0.879 G_ID: 0.156 G_Rec: 0.442 D_GP: 0.055 D_real: 0.561 D_fake: 1.031 +(epoch: 400, iters: 414, time: 0.064) G_GAN: -0.273 G_GAN_Feat: 0.859 G_ID: 0.118 G_Rec: 0.355 D_GP: 0.091 D_real: 0.374 D_fake: 1.273 +(epoch: 400, iters: 814, time: 0.063) G_GAN: 0.541 G_GAN_Feat: 0.848 G_ID: 0.106 G_Rec: 0.404 D_GP: 0.036 D_real: 1.260 D_fake: 0.514 +(epoch: 400, iters: 1214, time: 0.063) G_GAN: 0.054 G_GAN_Feat: 0.699 G_ID: 0.104 G_Rec: 0.296 D_GP: 0.037 D_real: 0.966 D_fake: 0.946 +(epoch: 400, iters: 1614, time: 0.063) G_GAN: 0.619 G_GAN_Feat: 1.016 G_ID: 0.120 G_Rec: 0.440 D_GP: 0.042 D_real: 1.027 D_fake: 0.417 +(epoch: 400, iters: 2014, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.592 G_ID: 0.092 G_Rec: 0.273 D_GP: 0.027 D_real: 1.076 D_fake: 0.869 +(epoch: 400, iters: 2414, time: 0.063) G_GAN: 0.347 G_GAN_Feat: 0.873 G_ID: 0.105 G_Rec: 0.445 D_GP: 0.036 D_real: 1.002 D_fake: 0.668 +(epoch: 400, iters: 2814, time: 0.063) G_GAN: -0.021 G_GAN_Feat: 0.617 G_ID: 0.091 G_Rec: 0.305 D_GP: 0.032 D_real: 0.796 D_fake: 1.021 +(epoch: 400, iters: 3214, time: 0.063) G_GAN: 0.339 G_GAN_Feat: 0.872 G_ID: 0.127 G_Rec: 0.411 D_GP: 0.077 D_real: 0.914 D_fake: 0.687 +(epoch: 400, iters: 3614, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.662 G_ID: 0.093 G_Rec: 0.351 D_GP: 0.035 D_real: 0.986 D_fake: 0.901 +(epoch: 400, iters: 4014, time: 0.063) G_GAN: 0.268 G_GAN_Feat: 1.007 G_ID: 0.118 G_Rec: 0.459 D_GP: 0.107 D_real: 0.775 D_fake: 0.742 +(epoch: 400, iters: 4414, time: 0.063) G_GAN: -0.157 G_GAN_Feat: 0.657 G_ID: 0.104 G_Rec: 0.320 D_GP: 0.029 D_real: 0.694 D_fake: 1.157 +(epoch: 400, iters: 4814, time: 0.063) G_GAN: 0.149 G_GAN_Feat: 1.035 G_ID: 0.137 G_Rec: 0.466 D_GP: 0.056 D_real: 0.547 D_fake: 0.854 +(epoch: 400, iters: 5214, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.641 G_ID: 0.093 G_Rec: 0.262 D_GP: 0.033 D_real: 1.224 D_fake: 0.641 +(epoch: 400, iters: 5614, time: 0.063) G_GAN: 0.336 G_GAN_Feat: 0.999 G_ID: 0.132 G_Rec: 0.429 D_GP: 0.155 D_real: 0.761 D_fake: 0.672 +(epoch: 400, iters: 6014, time: 0.063) G_GAN: 0.041 G_GAN_Feat: 0.836 G_ID: 0.117 G_Rec: 0.343 D_GP: 0.096 D_real: 0.682 D_fake: 0.959 +(epoch: 400, iters: 6414, time: 0.063) G_GAN: 0.282 G_GAN_Feat: 1.012 G_ID: 0.121 G_Rec: 0.426 D_GP: 0.075 D_real: 0.553 D_fake: 0.719 +(epoch: 400, iters: 6814, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.775 G_ID: 0.086 G_Rec: 0.291 D_GP: 0.040 D_real: 0.743 D_fake: 0.942 +(epoch: 400, iters: 7214, time: 0.063) G_GAN: 0.519 G_GAN_Feat: 0.868 G_ID: 0.134 G_Rec: 0.438 D_GP: 0.029 D_real: 1.245 D_fake: 0.509 +(epoch: 400, iters: 7614, time: 0.063) G_GAN: 0.317 G_GAN_Feat: 0.679 G_ID: 0.097 G_Rec: 0.310 D_GP: 0.030 D_real: 1.207 D_fake: 0.683 +(epoch: 400, iters: 8014, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 0.842 G_ID: 0.121 G_Rec: 0.381 D_GP: 0.035 D_real: 1.034 D_fake: 0.581 +(epoch: 400, iters: 8414, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.687 G_ID: 0.122 G_Rec: 0.285 D_GP: 0.037 D_real: 1.101 D_fake: 0.755 +(epoch: 401, iters: 206, time: 0.064) G_GAN: 0.659 G_GAN_Feat: 0.965 G_ID: 0.101 G_Rec: 0.435 D_GP: 0.032 D_real: 1.337 D_fake: 0.349 +(epoch: 401, iters: 606, time: 0.063) G_GAN: 0.376 G_GAN_Feat: 0.890 G_ID: 0.108 G_Rec: 0.363 D_GP: 0.115 D_real: 0.576 D_fake: 0.650 +(epoch: 401, iters: 1006, time: 0.063) G_GAN: 0.886 G_GAN_Feat: 1.085 G_ID: 0.130 G_Rec: 0.426 D_GP: 0.146 D_real: 0.897 D_fake: 0.349 +(epoch: 401, iters: 1406, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.744 G_ID: 0.114 G_Rec: 0.308 D_GP: 0.028 D_real: 1.186 D_fake: 0.694 +(epoch: 401, iters: 1806, time: 0.063) G_GAN: 0.896 G_GAN_Feat: 1.130 G_ID: 0.112 G_Rec: 0.478 D_GP: 0.129 D_real: 0.765 D_fake: 0.211 +(epoch: 401, iters: 2206, time: 0.063) G_GAN: 0.226 G_GAN_Feat: 1.001 G_ID: 0.097 G_Rec: 0.338 D_GP: 0.809 D_real: 0.295 D_fake: 0.779 +(epoch: 401, iters: 2606, time: 0.063) G_GAN: 0.119 G_GAN_Feat: 0.951 G_ID: 0.134 G_Rec: 0.439 D_GP: 0.060 D_real: 0.643 D_fake: 0.881 +(epoch: 401, iters: 3006, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.765 G_ID: 0.102 G_Rec: 0.324 D_GP: 0.032 D_real: 1.131 D_fake: 0.761 +(epoch: 401, iters: 3406, time: 0.063) G_GAN: 0.476 G_GAN_Feat: 0.955 G_ID: 0.117 G_Rec: 0.412 D_GP: 0.040 D_real: 1.047 D_fake: 0.531 +(epoch: 401, iters: 3806, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 0.725 G_ID: 0.101 G_Rec: 0.297 D_GP: 0.036 D_real: 1.106 D_fake: 0.730 +(epoch: 401, iters: 4206, time: 0.063) G_GAN: 0.541 G_GAN_Feat: 0.917 G_ID: 0.127 G_Rec: 0.450 D_GP: 0.033 D_real: 1.237 D_fake: 0.470 +(epoch: 401, iters: 4606, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.811 G_ID: 0.098 G_Rec: 0.318 D_GP: 0.037 D_real: 0.871 D_fake: 0.802 +(epoch: 401, iters: 5006, time: 0.063) G_GAN: 0.710 G_GAN_Feat: 0.976 G_ID: 0.123 G_Rec: 0.385 D_GP: 0.046 D_real: 1.122 D_fake: 0.308 +(epoch: 401, iters: 5406, time: 0.063) G_GAN: 0.168 G_GAN_Feat: 0.722 G_ID: 0.116 G_Rec: 0.301 D_GP: 0.038 D_real: 1.045 D_fake: 0.836 +(epoch: 401, iters: 5806, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 1.036 G_ID: 0.149 G_Rec: 0.472 D_GP: 0.285 D_real: 0.616 D_fake: 0.702 +(epoch: 401, iters: 6206, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.738 G_ID: 0.099 G_Rec: 0.327 D_GP: 0.038 D_real: 1.207 D_fake: 0.596 +(epoch: 401, iters: 6606, time: 0.063) G_GAN: 0.672 G_GAN_Feat: 1.126 G_ID: 0.122 G_Rec: 0.483 D_GP: 0.154 D_real: 0.500 D_fake: 0.373 +(epoch: 401, iters: 7006, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 0.795 G_ID: 0.108 G_Rec: 0.344 D_GP: 0.038 D_real: 1.101 D_fake: 0.716 +(epoch: 401, iters: 7406, time: 0.063) G_GAN: 0.673 G_GAN_Feat: 0.899 G_ID: 0.116 G_Rec: 0.390 D_GP: 0.041 D_real: 1.323 D_fake: 0.365 +(epoch: 401, iters: 7806, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.879 G_ID: 0.088 G_Rec: 0.352 D_GP: 0.054 D_real: 0.818 D_fake: 0.638 +(epoch: 401, iters: 8206, time: 0.063) G_GAN: 0.626 G_GAN_Feat: 1.027 G_ID: 0.121 G_Rec: 0.434 D_GP: 0.032 D_real: 1.125 D_fake: 0.425 +(epoch: 401, iters: 8606, time: 0.063) G_GAN: -0.029 G_GAN_Feat: 0.964 G_ID: 0.110 G_Rec: 0.351 D_GP: 0.214 D_real: 0.426 D_fake: 1.029 +(epoch: 402, iters: 398, time: 0.063) G_GAN: 0.343 G_GAN_Feat: 0.909 G_ID: 0.130 G_Rec: 0.429 D_GP: 0.030 D_real: 1.018 D_fake: 0.660 +(epoch: 402, iters: 798, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.684 G_ID: 0.105 G_Rec: 0.311 D_GP: 0.027 D_real: 1.031 D_fake: 0.910 +(epoch: 402, iters: 1198, time: 0.063) G_GAN: 0.425 G_GAN_Feat: 0.966 G_ID: 0.119 G_Rec: 0.457 D_GP: 0.045 D_real: 0.892 D_fake: 0.610 +(epoch: 402, iters: 1598, time: 0.063) G_GAN: 0.100 G_GAN_Feat: 0.681 G_ID: 0.091 G_Rec: 0.303 D_GP: 0.037 D_real: 1.020 D_fake: 0.900 +(epoch: 402, iters: 1998, time: 0.063) G_GAN: 0.323 G_GAN_Feat: 0.868 G_ID: 0.110 G_Rec: 0.435 D_GP: 0.041 D_real: 1.002 D_fake: 0.692 +(epoch: 402, iters: 2398, time: 0.064) G_GAN: 0.047 G_GAN_Feat: 0.731 G_ID: 0.098 G_Rec: 0.306 D_GP: 0.041 D_real: 0.830 D_fake: 0.953 +(epoch: 402, iters: 2798, time: 0.063) G_GAN: 0.556 G_GAN_Feat: 0.961 G_ID: 0.108 G_Rec: 0.476 D_GP: 0.036 D_real: 1.260 D_fake: 0.484 +(epoch: 402, iters: 3198, time: 0.063) G_GAN: 0.004 G_GAN_Feat: 0.928 G_ID: 0.118 G_Rec: 0.387 D_GP: 0.556 D_real: 0.441 D_fake: 0.997 +(epoch: 402, iters: 3598, time: 0.063) G_GAN: 0.412 G_GAN_Feat: 1.053 G_ID: 0.104 G_Rec: 0.500 D_GP: 0.040 D_real: 0.857 D_fake: 0.592 +(epoch: 402, iters: 3998, time: 0.063) G_GAN: 0.405 G_GAN_Feat: 0.700 G_ID: 0.091 G_Rec: 0.316 D_GP: 0.049 D_real: 1.268 D_fake: 0.604 +(epoch: 402, iters: 4398, time: 0.063) G_GAN: 0.469 G_GAN_Feat: 0.978 G_ID: 0.114 G_Rec: 0.417 D_GP: 0.113 D_real: 0.904 D_fake: 0.538 +(epoch: 402, iters: 4798, time: 0.063) G_GAN: 0.224 G_GAN_Feat: 0.775 G_ID: 0.103 G_Rec: 0.317 D_GP: 0.088 D_real: 0.867 D_fake: 0.777 +(epoch: 402, iters: 5198, time: 0.064) G_GAN: 0.730 G_GAN_Feat: 0.993 G_ID: 0.135 G_Rec: 0.469 D_GP: 0.041 D_real: 1.173 D_fake: 0.303 +(epoch: 402, iters: 5598, time: 0.063) G_GAN: 0.145 G_GAN_Feat: 0.745 G_ID: 0.122 G_Rec: 0.313 D_GP: 0.036 D_real: 0.932 D_fake: 0.855 +(epoch: 402, iters: 5998, time: 0.063) G_GAN: 0.696 G_GAN_Feat: 1.016 G_ID: 0.118 G_Rec: 0.440 D_GP: 0.545 D_real: 0.854 D_fake: 0.346 +(epoch: 402, iters: 6398, time: 0.063) G_GAN: 0.220 G_GAN_Feat: 0.739 G_ID: 0.111 G_Rec: 0.299 D_GP: 0.038 D_real: 1.044 D_fake: 0.780 +(epoch: 402, iters: 6798, time: 0.064) G_GAN: 0.557 G_GAN_Feat: 1.016 G_ID: 0.127 G_Rec: 0.466 D_GP: 0.047 D_real: 1.023 D_fake: 0.450 +(epoch: 402, iters: 7198, time: 0.063) G_GAN: 0.416 G_GAN_Feat: 0.855 G_ID: 0.097 G_Rec: 0.314 D_GP: 0.080 D_real: 0.793 D_fake: 0.591 +(epoch: 402, iters: 7598, time: 0.063) G_GAN: 0.257 G_GAN_Feat: 1.197 G_ID: 0.124 G_Rec: 0.510 D_GP: 0.255 D_real: 0.240 D_fake: 0.749 +(epoch: 402, iters: 7998, time: 0.063) G_GAN: 0.298 G_GAN_Feat: 0.858 G_ID: 0.119 G_Rec: 0.356 D_GP: 0.049 D_real: 0.969 D_fake: 0.712 +(epoch: 402, iters: 8398, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 1.039 G_ID: 0.113 G_Rec: 0.420 D_GP: 0.050 D_real: 0.581 D_fake: 0.580 +(epoch: 403, iters: 190, time: 0.063) G_GAN: 0.673 G_GAN_Feat: 1.016 G_ID: 0.110 G_Rec: 0.318 D_GP: 0.484 D_real: 0.566 D_fake: 0.386 +(epoch: 403, iters: 590, time: 0.063) G_GAN: 0.966 G_GAN_Feat: 1.084 G_ID: 0.120 G_Rec: 0.471 D_GP: 0.039 D_real: 1.289 D_fake: 0.181 +(epoch: 403, iters: 990, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.978 G_ID: 0.114 G_Rec: 0.355 D_GP: 0.394 D_real: 0.284 D_fake: 0.833 +(epoch: 403, iters: 1390, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.890 G_ID: 0.124 G_Rec: 0.413 D_GP: 0.029 D_real: 1.097 D_fake: 0.677 +(epoch: 403, iters: 1790, time: 0.063) G_GAN: -0.012 G_GAN_Feat: 0.878 G_ID: 0.110 G_Rec: 0.331 D_GP: 0.129 D_real: 0.382 D_fake: 1.012 +(epoch: 403, iters: 2190, time: 0.063) G_GAN: 0.717 G_GAN_Feat: 0.987 G_ID: 0.110 G_Rec: 0.395 D_GP: 0.032 D_real: 1.191 D_fake: 0.305 +(epoch: 403, iters: 2590, time: 0.063) G_GAN: 0.192 G_GAN_Feat: 0.807 G_ID: 0.120 G_Rec: 0.332 D_GP: 0.066 D_real: 0.864 D_fake: 0.808 +(epoch: 403, iters: 2990, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 0.936 G_ID: 0.139 G_Rec: 0.412 D_GP: 0.034 D_real: 1.316 D_fake: 0.423 +(epoch: 403, iters: 3390, time: 0.063) G_GAN: 0.147 G_GAN_Feat: 0.854 G_ID: 0.090 G_Rec: 0.319 D_GP: 0.032 D_real: 0.913 D_fake: 0.853 +(epoch: 403, iters: 3790, time: 0.063) G_GAN: 0.417 G_GAN_Feat: 0.914 G_ID: 0.127 G_Rec: 0.453 D_GP: 0.036 D_real: 1.209 D_fake: 0.586 +(epoch: 403, iters: 4190, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 0.693 G_ID: 0.104 G_Rec: 0.295 D_GP: 0.043 D_real: 0.814 D_fake: 0.934 +(epoch: 403, iters: 4590, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.997 G_ID: 0.115 G_Rec: 0.443 D_GP: 0.057 D_real: 0.892 D_fake: 0.511 +(epoch: 403, iters: 4990, time: 0.063) G_GAN: 0.289 G_GAN_Feat: 0.838 G_ID: 0.104 G_Rec: 0.323 D_GP: 0.034 D_real: 0.873 D_fake: 0.712 +(epoch: 403, iters: 5390, time: 0.063) G_GAN: 0.451 G_GAN_Feat: 0.986 G_ID: 0.106 G_Rec: 0.464 D_GP: 0.038 D_real: 1.075 D_fake: 0.552 +(epoch: 403, iters: 5790, time: 0.063) G_GAN: 0.285 G_GAN_Feat: 0.781 G_ID: 0.096 G_Rec: 0.324 D_GP: 0.110 D_real: 0.864 D_fake: 0.715 +(epoch: 403, iters: 6190, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 1.015 G_ID: 0.139 G_Rec: 0.448 D_GP: 0.067 D_real: 0.864 D_fake: 0.639 +(epoch: 403, iters: 6590, time: 0.063) G_GAN: 0.309 G_GAN_Feat: 0.798 G_ID: 0.095 G_Rec: 0.299 D_GP: 0.083 D_real: 0.856 D_fake: 0.693 +(epoch: 403, iters: 6990, time: 0.063) G_GAN: 0.700 G_GAN_Feat: 1.163 G_ID: 0.126 G_Rec: 0.425 D_GP: 0.740 D_real: 0.589 D_fake: 0.325 +(epoch: 403, iters: 7390, time: 0.063) G_GAN: 0.180 G_GAN_Feat: 0.836 G_ID: 0.096 G_Rec: 0.301 D_GP: 0.049 D_real: 0.689 D_fake: 0.821 +(epoch: 403, iters: 7790, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.981 G_ID: 0.118 G_Rec: 0.431 D_GP: 0.039 D_real: 1.048 D_fake: 0.472 +(epoch: 403, iters: 8190, time: 0.063) G_GAN: 0.230 G_GAN_Feat: 0.822 G_ID: 0.096 G_Rec: 0.320 D_GP: 0.039 D_real: 0.981 D_fake: 0.770 +(epoch: 403, iters: 8590, time: 0.063) G_GAN: 0.654 G_GAN_Feat: 1.029 G_ID: 0.108 G_Rec: 0.440 D_GP: 0.035 D_real: 1.161 D_fake: 0.383 +(epoch: 404, iters: 382, time: 0.063) G_GAN: 0.260 G_GAN_Feat: 0.879 G_ID: 0.111 G_Rec: 0.336 D_GP: 0.096 D_real: 0.367 D_fake: 0.744 +(epoch: 404, iters: 782, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 1.254 G_ID: 0.126 G_Rec: 0.533 D_GP: 1.268 D_real: 0.302 D_fake: 0.489 +(epoch: 404, iters: 1182, time: 0.063) G_GAN: -0.237 G_GAN_Feat: 0.670 G_ID: 0.115 G_Rec: 0.296 D_GP: 0.025 D_real: 0.681 D_fake: 1.237 +(epoch: 404, iters: 1582, time: 0.063) G_GAN: 0.061 G_GAN_Feat: 0.958 G_ID: 0.115 G_Rec: 0.479 D_GP: 0.024 D_real: 0.840 D_fake: 0.940 +(epoch: 404, iters: 1982, time: 0.063) G_GAN: -0.116 G_GAN_Feat: 0.698 G_ID: 0.094 G_Rec: 0.327 D_GP: 0.031 D_real: 0.756 D_fake: 1.117 +(epoch: 404, iters: 2382, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 0.819 G_ID: 0.111 G_Rec: 0.382 D_GP: 0.035 D_real: 0.706 D_fake: 0.946 +(epoch: 404, iters: 2782, time: 0.063) G_GAN: -0.041 G_GAN_Feat: 0.669 G_ID: 0.089 G_Rec: 0.308 D_GP: 0.034 D_real: 0.838 D_fake: 1.041 +(epoch: 404, iters: 3182, time: 0.063) G_GAN: -0.019 G_GAN_Feat: 0.943 G_ID: 0.118 G_Rec: 0.418 D_GP: 0.055 D_real: 0.532 D_fake: 1.019 +(epoch: 404, iters: 3582, time: 0.063) G_GAN: 0.201 G_GAN_Feat: 0.742 G_ID: 0.096 G_Rec: 0.314 D_GP: 0.057 D_real: 0.930 D_fake: 0.804 +(epoch: 404, iters: 3982, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 0.965 G_ID: 0.114 G_Rec: 0.365 D_GP: 0.043 D_real: 0.979 D_fake: 0.475 +(epoch: 404, iters: 4382, time: 0.063) G_GAN: 0.168 G_GAN_Feat: 0.722 G_ID: 0.091 G_Rec: 0.276 D_GP: 0.041 D_real: 0.907 D_fake: 0.832 +(epoch: 404, iters: 4782, time: 0.063) G_GAN: 0.668 G_GAN_Feat: 0.991 G_ID: 0.103 G_Rec: 0.421 D_GP: 0.036 D_real: 1.075 D_fake: 0.356 +(epoch: 404, iters: 5182, time: 0.063) G_GAN: 0.660 G_GAN_Feat: 0.853 G_ID: 0.095 G_Rec: 0.341 D_GP: 0.039 D_real: 1.451 D_fake: 0.355 +(epoch: 404, iters: 5582, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 1.081 G_ID: 0.119 G_Rec: 0.443 D_GP: 0.045 D_real: 0.760 D_fake: 0.842 +(epoch: 404, iters: 5982, time: 0.063) G_GAN: 0.229 G_GAN_Feat: 0.853 G_ID: 0.107 G_Rec: 0.303 D_GP: 0.044 D_real: 0.528 D_fake: 0.771 +(epoch: 404, iters: 6382, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 0.928 G_ID: 0.120 G_Rec: 0.445 D_GP: 0.031 D_real: 1.058 D_fake: 0.671 +(epoch: 404, iters: 6782, time: 0.063) G_GAN: 0.216 G_GAN_Feat: 0.712 G_ID: 0.116 G_Rec: 0.301 D_GP: 0.034 D_real: 1.054 D_fake: 0.786 +(epoch: 404, iters: 7182, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 1.107 G_ID: 0.114 G_Rec: 0.518 D_GP: 0.127 D_real: 0.702 D_fake: 0.555 +(epoch: 404, iters: 7582, time: 0.063) G_GAN: 0.299 G_GAN_Feat: 0.768 G_ID: 0.103 G_Rec: 0.335 D_GP: 0.037 D_real: 0.998 D_fake: 0.704 +(epoch: 404, iters: 7982, time: 0.063) G_GAN: 0.429 G_GAN_Feat: 1.163 G_ID: 0.111 G_Rec: 0.505 D_GP: 0.433 D_real: 1.237 D_fake: 0.809 +(epoch: 404, iters: 8382, time: 0.063) G_GAN: -0.106 G_GAN_Feat: 0.821 G_ID: 0.109 G_Rec: 0.347 D_GP: 0.032 D_real: 0.647 D_fake: 1.106 +(epoch: 405, iters: 174, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 1.016 G_ID: 0.133 G_Rec: 0.422 D_GP: 0.037 D_real: 0.772 D_fake: 0.727 +(epoch: 405, iters: 574, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 0.791 G_ID: 0.115 G_Rec: 0.298 D_GP: 0.065 D_real: 0.915 D_fake: 0.717 +(epoch: 405, iters: 974, time: 0.063) G_GAN: 0.379 G_GAN_Feat: 0.911 G_ID: 0.121 G_Rec: 0.432 D_GP: 0.032 D_real: 1.046 D_fake: 0.623 +(epoch: 405, iters: 1374, time: 0.063) G_GAN: 0.063 G_GAN_Feat: 0.824 G_ID: 0.112 G_Rec: 0.338 D_GP: 0.077 D_real: 0.713 D_fake: 0.937 +(epoch: 405, iters: 1774, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 1.061 G_ID: 0.110 G_Rec: 0.424 D_GP: 0.050 D_real: 0.735 D_fake: 0.479 +(epoch: 405, iters: 2174, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 0.966 G_ID: 0.095 G_Rec: 0.367 D_GP: 0.642 D_real: 0.271 D_fake: 0.804 +(epoch: 405, iters: 2574, time: 0.063) G_GAN: 0.399 G_GAN_Feat: 0.969 G_ID: 0.112 G_Rec: 0.423 D_GP: 0.041 D_real: 1.064 D_fake: 0.601 +(epoch: 405, iters: 2974, time: 0.063) G_GAN: 0.169 G_GAN_Feat: 1.022 G_ID: 0.114 G_Rec: 0.359 D_GP: 3.856 D_real: 0.381 D_fake: 0.835 +(epoch: 405, iters: 3374, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.859 G_ID: 0.110 G_Rec: 0.434 D_GP: 0.030 D_real: 1.175 D_fake: 0.581 +(epoch: 405, iters: 3774, time: 0.063) G_GAN: 0.059 G_GAN_Feat: 0.730 G_ID: 0.091 G_Rec: 0.354 D_GP: 0.052 D_real: 0.824 D_fake: 0.941 +(epoch: 405, iters: 4174, time: 0.063) G_GAN: 0.282 G_GAN_Feat: 0.919 G_ID: 0.113 G_Rec: 0.447 D_GP: 0.034 D_real: 0.945 D_fake: 0.719 +(epoch: 405, iters: 4574, time: 0.063) G_GAN: 0.042 G_GAN_Feat: 0.723 G_ID: 0.092 G_Rec: 0.311 D_GP: 0.051 D_real: 0.813 D_fake: 0.958 +(epoch: 405, iters: 4974, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.895 G_ID: 0.118 G_Rec: 0.391 D_GP: 0.063 D_real: 0.983 D_fake: 0.589 +(epoch: 405, iters: 5374, time: 0.063) G_GAN: -0.120 G_GAN_Feat: 0.748 G_ID: 0.122 G_Rec: 0.331 D_GP: 0.063 D_real: 0.551 D_fake: 1.120 +(epoch: 405, iters: 5774, time: 0.063) G_GAN: 0.506 G_GAN_Feat: 0.837 G_ID: 0.138 G_Rec: 0.375 D_GP: 0.028 D_real: 1.275 D_fake: 0.505 +(epoch: 405, iters: 6174, time: 0.063) G_GAN: -0.044 G_GAN_Feat: 0.754 G_ID: 0.111 G_Rec: 0.334 D_GP: 0.049 D_real: 0.625 D_fake: 1.044 +(epoch: 405, iters: 6574, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.956 G_ID: 0.113 G_Rec: 0.460 D_GP: 0.029 D_real: 1.049 D_fake: 0.578 +(epoch: 405, iters: 6974, time: 0.063) G_GAN: -0.126 G_GAN_Feat: 0.842 G_ID: 0.098 G_Rec: 0.359 D_GP: 0.255 D_real: 0.419 D_fake: 1.127 +(epoch: 405, iters: 7374, time: 0.063) G_GAN: 0.559 G_GAN_Feat: 0.956 G_ID: 0.122 G_Rec: 0.520 D_GP: 0.029 D_real: 1.104 D_fake: 0.445 +(epoch: 405, iters: 7774, time: 0.063) G_GAN: 0.437 G_GAN_Feat: 0.713 G_ID: 0.093 G_Rec: 0.292 D_GP: 0.040 D_real: 1.203 D_fake: 0.571 +(epoch: 405, iters: 8174, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 1.115 G_ID: 0.132 G_Rec: 0.491 D_GP: 0.115 D_real: 0.488 D_fake: 0.519 +(epoch: 405, iters: 8574, time: 0.063) G_GAN: 0.278 G_GAN_Feat: 0.712 G_ID: 0.102 G_Rec: 0.289 D_GP: 0.035 D_real: 1.048 D_fake: 0.722 +(epoch: 406, iters: 366, time: 0.063) G_GAN: 0.518 G_GAN_Feat: 1.114 G_ID: 0.122 G_Rec: 0.471 D_GP: 0.219 D_real: 0.439 D_fake: 0.510 +(epoch: 406, iters: 766, time: 0.063) G_GAN: 0.320 G_GAN_Feat: 0.854 G_ID: 0.112 G_Rec: 0.361 D_GP: 0.205 D_real: 0.985 D_fake: 0.817 +(epoch: 406, iters: 1166, time: 0.064) G_GAN: 0.009 G_GAN_Feat: 0.925 G_ID: 0.142 G_Rec: 0.480 D_GP: 0.033 D_real: 0.681 D_fake: 0.991 +(epoch: 406, iters: 1566, time: 0.063) G_GAN: 0.070 G_GAN_Feat: 0.784 G_ID: 0.099 G_Rec: 0.311 D_GP: 0.054 D_real: 0.882 D_fake: 0.930 +(epoch: 406, iters: 1966, time: 0.063) G_GAN: 0.452 G_GAN_Feat: 1.114 G_ID: 0.127 G_Rec: 0.507 D_GP: 0.080 D_real: 0.363 D_fake: 0.553 +(epoch: 406, iters: 2366, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 1.011 G_ID: 0.097 G_Rec: 0.365 D_GP: 0.888 D_real: 0.374 D_fake: 0.767 +(epoch: 406, iters: 2766, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.961 G_ID: 0.151 G_Rec: 0.417 D_GP: 0.038 D_real: 1.036 D_fake: 0.544 +(epoch: 406, iters: 3166, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 0.816 G_ID: 0.094 G_Rec: 0.325 D_GP: 0.046 D_real: 1.074 D_fake: 0.688 +(epoch: 406, iters: 3566, time: 0.063) G_GAN: 0.793 G_GAN_Feat: 1.001 G_ID: 0.113 G_Rec: 0.469 D_GP: 0.041 D_real: 1.345 D_fake: 0.276 +(epoch: 406, iters: 3966, time: 0.063) G_GAN: 0.233 G_GAN_Feat: 0.862 G_ID: 0.101 G_Rec: 0.310 D_GP: 0.065 D_real: 0.650 D_fake: 0.772 +(epoch: 406, iters: 4366, time: 0.064) G_GAN: 0.700 G_GAN_Feat: 1.133 G_ID: 0.112 G_Rec: 0.445 D_GP: 0.035 D_real: 1.184 D_fake: 0.349 +(epoch: 406, iters: 4766, time: 0.063) G_GAN: 0.157 G_GAN_Feat: 0.999 G_ID: 0.092 G_Rec: 0.337 D_GP: 0.189 D_real: 0.511 D_fake: 0.857 +(epoch: 406, iters: 5166, time: 0.063) G_GAN: 0.414 G_GAN_Feat: 1.284 G_ID: 0.150 G_Rec: 0.451 D_GP: 0.153 D_real: 0.139 D_fake: 0.616 +(epoch: 406, iters: 5566, time: 0.063) G_GAN: 0.049 G_GAN_Feat: 0.975 G_ID: 0.141 G_Rec: 0.357 D_GP: 0.552 D_real: 0.383 D_fake: 0.954 +(epoch: 406, iters: 5966, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.919 G_ID: 0.131 G_Rec: 0.426 D_GP: 0.031 D_real: 1.048 D_fake: 0.592 +(epoch: 406, iters: 6366, time: 0.063) G_GAN: 0.220 G_GAN_Feat: 0.841 G_ID: 0.112 G_Rec: 0.351 D_GP: 0.033 D_real: 1.014 D_fake: 0.781 +(epoch: 406, iters: 6766, time: 0.063) G_GAN: 0.220 G_GAN_Feat: 1.107 G_ID: 0.123 G_Rec: 0.464 D_GP: 0.100 D_real: 0.260 D_fake: 0.785 +(epoch: 406, iters: 7166, time: 0.063) G_GAN: 0.162 G_GAN_Feat: 0.720 G_ID: 0.123 G_Rec: 0.309 D_GP: 0.031 D_real: 1.111 D_fake: 0.867 +(epoch: 406, iters: 7566, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.841 G_ID: 0.140 G_Rec: 0.415 D_GP: 0.026 D_real: 0.957 D_fake: 0.798 +(epoch: 406, iters: 7966, time: 0.063) G_GAN: 0.167 G_GAN_Feat: 0.778 G_ID: 0.118 G_Rec: 0.340 D_GP: 0.031 D_real: 0.987 D_fake: 0.834 +(epoch: 406, iters: 8366, time: 0.063) G_GAN: 0.391 G_GAN_Feat: 0.937 G_ID: 0.127 G_Rec: 0.455 D_GP: 0.031 D_real: 1.052 D_fake: 0.623 +(epoch: 407, iters: 158, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.704 G_ID: 0.091 G_Rec: 0.314 D_GP: 0.032 D_real: 0.925 D_fake: 0.978 +(epoch: 407, iters: 558, time: 0.063) G_GAN: 0.108 G_GAN_Feat: 0.899 G_ID: 0.134 G_Rec: 0.416 D_GP: 0.055 D_real: 0.709 D_fake: 0.894 +(epoch: 407, iters: 958, time: 0.064) G_GAN: -0.131 G_GAN_Feat: 0.720 G_ID: 0.098 G_Rec: 0.379 D_GP: 0.040 D_real: 0.647 D_fake: 1.131 +(epoch: 407, iters: 1358, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 1.076 G_ID: 0.110 G_Rec: 0.486 D_GP: 0.049 D_real: 0.895 D_fake: 0.529 +(epoch: 407, iters: 1758, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.812 G_ID: 0.101 G_Rec: 0.316 D_GP: 0.121 D_real: 0.638 D_fake: 0.964 +(epoch: 407, iters: 2158, time: 0.063) G_GAN: 0.347 G_GAN_Feat: 1.045 G_ID: 0.118 G_Rec: 0.449 D_GP: 0.046 D_real: 0.864 D_fake: 0.655 +(epoch: 407, iters: 2558, time: 0.063) G_GAN: 0.401 G_GAN_Feat: 0.832 G_ID: 0.095 G_Rec: 0.305 D_GP: 0.039 D_real: 1.033 D_fake: 0.600 +(epoch: 407, iters: 2958, time: 0.063) G_GAN: 0.661 G_GAN_Feat: 1.216 G_ID: 0.115 G_Rec: 0.454 D_GP: 0.067 D_real: 0.678 D_fake: 0.373 +(epoch: 407, iters: 3358, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 0.723 G_ID: 0.082 G_Rec: 0.269 D_GP: 0.034 D_real: 1.668 D_fake: 0.495 +(epoch: 407, iters: 3758, time: 0.063) G_GAN: 0.827 G_GAN_Feat: 1.122 G_ID: 0.115 G_Rec: 0.435 D_GP: 0.046 D_real: 0.592 D_fake: 0.226 +(epoch: 407, iters: 4158, time: 0.063) G_GAN: -0.015 G_GAN_Feat: 0.690 G_ID: 0.105 G_Rec: 0.339 D_GP: 0.045 D_real: 0.925 D_fake: 1.015 +(epoch: 407, iters: 4558, time: 0.063) G_GAN: 0.074 G_GAN_Feat: 0.769 G_ID: 0.111 G_Rec: 0.403 D_GP: 0.035 D_real: 0.894 D_fake: 0.927 +(epoch: 407, iters: 4958, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.591 G_ID: 0.097 G_Rec: 0.292 D_GP: 0.035 D_real: 1.077 D_fake: 0.839 +(epoch: 407, iters: 5358, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 0.801 G_ID: 0.140 G_Rec: 0.407 D_GP: 0.038 D_real: 0.828 D_fake: 0.813 +(epoch: 407, iters: 5758, time: 0.063) G_GAN: 0.036 G_GAN_Feat: 0.625 G_ID: 0.093 G_Rec: 0.317 D_GP: 0.038 D_real: 0.998 D_fake: 0.964 +(epoch: 407, iters: 6158, time: 0.063) G_GAN: 0.340 G_GAN_Feat: 0.804 G_ID: 0.118 G_Rec: 0.397 D_GP: 0.038 D_real: 0.976 D_fake: 0.669 +(epoch: 407, iters: 6558, time: 0.064) G_GAN: -0.125 G_GAN_Feat: 0.682 G_ID: 0.097 G_Rec: 0.347 D_GP: 0.049 D_real: 0.750 D_fake: 1.125 +(epoch: 407, iters: 6958, time: 0.063) G_GAN: 0.210 G_GAN_Feat: 0.882 G_ID: 0.128 G_Rec: 0.466 D_GP: 0.047 D_real: 0.742 D_fake: 0.795 +(epoch: 407, iters: 7358, time: 0.063) G_GAN: -0.012 G_GAN_Feat: 0.711 G_ID: 0.097 G_Rec: 0.344 D_GP: 0.050 D_real: 0.809 D_fake: 1.012 +(epoch: 407, iters: 7758, time: 0.063) G_GAN: 0.079 G_GAN_Feat: 0.787 G_ID: 0.118 G_Rec: 0.377 D_GP: 0.037 D_real: 0.736 D_fake: 0.921 +(epoch: 407, iters: 8158, time: 0.064) G_GAN: -0.139 G_GAN_Feat: 0.577 G_ID: 0.105 G_Rec: 0.272 D_GP: 0.037 D_real: 0.848 D_fake: 1.139 +(epoch: 407, iters: 8558, time: 0.063) G_GAN: 0.155 G_GAN_Feat: 0.850 G_ID: 0.116 G_Rec: 0.410 D_GP: 0.059 D_real: 0.850 D_fake: 0.847 +(epoch: 408, iters: 350, time: 0.063) G_GAN: -0.262 G_GAN_Feat: 0.665 G_ID: 0.099 G_Rec: 0.313 D_GP: 0.043 D_real: 0.597 D_fake: 1.262 +(epoch: 408, iters: 750, time: 0.063) G_GAN: -0.075 G_GAN_Feat: 0.848 G_ID: 0.137 G_Rec: 0.415 D_GP: 0.044 D_real: 0.579 D_fake: 1.075 +(epoch: 408, iters: 1150, time: 0.064) G_GAN: -0.060 G_GAN_Feat: 0.675 G_ID: 0.107 G_Rec: 0.331 D_GP: 0.058 D_real: 0.714 D_fake: 1.063 +(epoch: 408, iters: 1550, time: 0.063) G_GAN: 0.243 G_GAN_Feat: 0.934 G_ID: 0.113 G_Rec: 0.472 D_GP: 0.058 D_real: 0.837 D_fake: 0.760 +(epoch: 408, iters: 1950, time: 0.063) G_GAN: 0.062 G_GAN_Feat: 0.600 G_ID: 0.094 G_Rec: 0.307 D_GP: 0.037 D_real: 0.901 D_fake: 0.939 +(epoch: 408, iters: 2350, time: 0.063) G_GAN: 0.369 G_GAN_Feat: 0.792 G_ID: 0.115 G_Rec: 0.385 D_GP: 0.036 D_real: 0.982 D_fake: 0.645 +(epoch: 408, iters: 2750, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.621 G_ID: 0.106 G_Rec: 0.281 D_GP: 0.034 D_real: 1.064 D_fake: 0.815 +(epoch: 408, iters: 3150, time: 0.063) G_GAN: 0.315 G_GAN_Feat: 0.888 G_ID: 0.119 G_Rec: 0.442 D_GP: 0.040 D_real: 0.941 D_fake: 0.696 +(epoch: 408, iters: 3550, time: 0.063) G_GAN: 0.052 G_GAN_Feat: 0.659 G_ID: 0.098 G_Rec: 0.302 D_GP: 0.033 D_real: 0.897 D_fake: 0.949 +(epoch: 408, iters: 3950, time: 0.063) G_GAN: 0.267 G_GAN_Feat: 0.819 G_ID: 0.105 G_Rec: 0.376 D_GP: 0.039 D_real: 1.016 D_fake: 0.734 +(epoch: 408, iters: 4350, time: 0.064) G_GAN: -0.021 G_GAN_Feat: 0.747 G_ID: 0.107 G_Rec: 0.326 D_GP: 0.053 D_real: 0.673 D_fake: 1.021 +(epoch: 408, iters: 4750, time: 0.063) G_GAN: 0.383 G_GAN_Feat: 0.955 G_ID: 0.125 G_Rec: 0.420 D_GP: 0.079 D_real: 0.867 D_fake: 0.623 +(epoch: 408, iters: 5150, time: 0.063) G_GAN: 0.126 G_GAN_Feat: 0.723 G_ID: 0.111 G_Rec: 0.328 D_GP: 0.039 D_real: 0.959 D_fake: 0.874 +(epoch: 408, iters: 5550, time: 0.063) G_GAN: 0.444 G_GAN_Feat: 0.932 G_ID: 0.123 G_Rec: 0.418 D_GP: 0.061 D_real: 0.959 D_fake: 0.568 +(epoch: 408, iters: 5950, time: 0.064) G_GAN: -0.063 G_GAN_Feat: 0.692 G_ID: 0.111 G_Rec: 0.337 D_GP: 0.036 D_real: 0.814 D_fake: 1.063 +(epoch: 408, iters: 6350, time: 0.063) G_GAN: 0.505 G_GAN_Feat: 1.121 G_ID: 0.108 G_Rec: 0.507 D_GP: 0.142 D_real: 0.570 D_fake: 0.523 +(epoch: 408, iters: 6750, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 0.704 G_ID: 0.109 G_Rec: 0.300 D_GP: 0.043 D_real: 1.149 D_fake: 0.666 +(epoch: 408, iters: 7150, time: 0.063) G_GAN: 0.112 G_GAN_Feat: 1.008 G_ID: 0.127 G_Rec: 0.488 D_GP: 0.052 D_real: 0.536 D_fake: 0.888 +(epoch: 408, iters: 7550, time: 0.064) G_GAN: 0.055 G_GAN_Feat: 0.691 G_ID: 0.102 G_Rec: 0.301 D_GP: 0.035 D_real: 0.944 D_fake: 0.946 +(epoch: 408, iters: 7950, time: 0.063) G_GAN: 0.384 G_GAN_Feat: 1.007 G_ID: 0.113 G_Rec: 0.436 D_GP: 0.057 D_real: 0.757 D_fake: 0.622 +(epoch: 408, iters: 8350, time: 0.063) G_GAN: 0.227 G_GAN_Feat: 0.747 G_ID: 0.104 G_Rec: 0.328 D_GP: 0.039 D_real: 1.041 D_fake: 0.774 +(epoch: 409, iters: 142, time: 0.063) G_GAN: 0.317 G_GAN_Feat: 0.911 G_ID: 0.142 G_Rec: 0.403 D_GP: 0.045 D_real: 0.796 D_fake: 0.683 +(epoch: 409, iters: 542, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.748 G_ID: 0.114 G_Rec: 0.330 D_GP: 0.072 D_real: 0.869 D_fake: 0.741 +(epoch: 409, iters: 942, time: 0.063) G_GAN: 0.229 G_GAN_Feat: 0.981 G_ID: 0.120 G_Rec: 0.449 D_GP: 0.041 D_real: 0.845 D_fake: 0.773 +(epoch: 409, iters: 1342, time: 0.063) G_GAN: -0.051 G_GAN_Feat: 0.819 G_ID: 0.122 G_Rec: 0.320 D_GP: 0.082 D_real: 0.321 D_fake: 1.051 +(epoch: 409, iters: 1742, time: 0.063) G_GAN: 0.478 G_GAN_Feat: 1.079 G_ID: 0.111 G_Rec: 0.491 D_GP: 0.083 D_real: 0.568 D_fake: 0.531 +(epoch: 409, iters: 2142, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.638 G_ID: 0.100 G_Rec: 0.291 D_GP: 0.027 D_real: 1.080 D_fake: 0.826 +(epoch: 409, iters: 2542, time: 0.063) G_GAN: 0.597 G_GAN_Feat: 0.953 G_ID: 0.114 G_Rec: 0.447 D_GP: 0.030 D_real: 1.158 D_fake: 0.421 +(epoch: 409, iters: 2942, time: 0.063) G_GAN: 0.233 G_GAN_Feat: 0.711 G_ID: 0.092 G_Rec: 0.332 D_GP: 0.036 D_real: 1.113 D_fake: 0.768 +(epoch: 409, iters: 3342, time: 0.063) G_GAN: 0.394 G_GAN_Feat: 1.051 G_ID: 0.123 G_Rec: 0.481 D_GP: 0.113 D_real: 0.662 D_fake: 0.615 +(epoch: 409, iters: 3742, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.668 G_ID: 0.097 G_Rec: 0.336 D_GP: 0.031 D_real: 1.306 D_fake: 0.638 +(epoch: 409, iters: 4142, time: 0.063) G_GAN: 0.344 G_GAN_Feat: 0.995 G_ID: 0.119 G_Rec: 0.449 D_GP: 0.054 D_real: 0.705 D_fake: 0.657 +(epoch: 409, iters: 4542, time: 0.063) G_GAN: 0.080 G_GAN_Feat: 0.760 G_ID: 0.108 G_Rec: 0.334 D_GP: 0.039 D_real: 0.830 D_fake: 0.920 +(epoch: 409, iters: 4942, time: 0.063) G_GAN: 0.752 G_GAN_Feat: 0.959 G_ID: 0.122 G_Rec: 0.452 D_GP: 0.038 D_real: 1.221 D_fake: 0.288 +(epoch: 409, iters: 5342, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.730 G_ID: 0.111 G_Rec: 0.352 D_GP: 0.028 D_real: 1.187 D_fake: 0.661 +(epoch: 409, iters: 5742, time: 0.063) G_GAN: 0.426 G_GAN_Feat: 0.880 G_ID: 0.122 G_Rec: 0.405 D_GP: 0.037 D_real: 1.111 D_fake: 0.577 +(epoch: 409, iters: 6142, time: 0.063) G_GAN: 0.528 G_GAN_Feat: 0.724 G_ID: 0.091 G_Rec: 0.297 D_GP: 0.031 D_real: 1.371 D_fake: 0.473 +(epoch: 409, iters: 6542, time: 0.063) G_GAN: 0.531 G_GAN_Feat: 0.948 G_ID: 0.125 G_Rec: 0.446 D_GP: 0.033 D_real: 1.142 D_fake: 0.473 +(epoch: 409, iters: 6942, time: 0.064) G_GAN: -0.000 G_GAN_Feat: 0.672 G_ID: 0.099 G_Rec: 0.267 D_GP: 0.031 D_real: 0.951 D_fake: 1.000 +(epoch: 409, iters: 7342, time: 0.063) G_GAN: 0.388 G_GAN_Feat: 0.916 G_ID: 0.127 G_Rec: 0.461 D_GP: 0.034 D_real: 1.159 D_fake: 0.614 +(epoch: 409, iters: 7742, time: 0.063) G_GAN: 0.312 G_GAN_Feat: 0.743 G_ID: 0.116 G_Rec: 0.323 D_GP: 0.036 D_real: 1.089 D_fake: 0.689 +(epoch: 409, iters: 8142, time: 0.063) G_GAN: 0.443 G_GAN_Feat: 0.913 G_ID: 0.113 G_Rec: 0.412 D_GP: 0.038 D_real: 0.973 D_fake: 0.567 +(epoch: 409, iters: 8542, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.826 G_ID: 0.102 G_Rec: 0.327 D_GP: 0.094 D_real: 0.729 D_fake: 0.752 +(epoch: 410, iters: 334, time: 0.063) G_GAN: 0.660 G_GAN_Feat: 1.062 G_ID: 0.119 G_Rec: 0.460 D_GP: 0.050 D_real: 0.799 D_fake: 0.363 +(epoch: 410, iters: 734, time: 0.063) G_GAN: 0.089 G_GAN_Feat: 0.859 G_ID: 0.089 G_Rec: 0.339 D_GP: 0.089 D_real: 0.314 D_fake: 0.911 +(epoch: 410, iters: 1134, time: 0.063) G_GAN: 0.655 G_GAN_Feat: 1.004 G_ID: 0.121 G_Rec: 0.456 D_GP: 0.032 D_real: 1.113 D_fake: 0.363 +(epoch: 410, iters: 1534, time: 0.064) G_GAN: 0.002 G_GAN_Feat: 0.756 G_ID: 0.103 G_Rec: 0.306 D_GP: 0.028 D_real: 0.938 D_fake: 0.998 +(epoch: 410, iters: 1934, time: 0.063) G_GAN: 0.223 G_GAN_Feat: 1.045 G_ID: 0.109 G_Rec: 0.471 D_GP: 0.078 D_real: 0.697 D_fake: 0.779 +(epoch: 410, iters: 2334, time: 0.063) G_GAN: 0.208 G_GAN_Feat: 0.702 G_ID: 0.096 G_Rec: 0.280 D_GP: 0.038 D_real: 1.034 D_fake: 0.793 +(epoch: 410, iters: 2734, time: 0.063) G_GAN: 0.343 G_GAN_Feat: 1.007 G_ID: 0.138 G_Rec: 0.433 D_GP: 0.044 D_real: 0.740 D_fake: 0.657 +(epoch: 410, iters: 3134, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.774 G_ID: 0.093 G_Rec: 0.321 D_GP: 0.033 D_real: 1.050 D_fake: 0.725 +(epoch: 410, iters: 3534, time: 0.063) G_GAN: 0.458 G_GAN_Feat: 0.873 G_ID: 0.121 G_Rec: 0.374 D_GP: 0.031 D_real: 1.148 D_fake: 0.551 +(epoch: 410, iters: 3934, time: 0.063) G_GAN: 0.045 G_GAN_Feat: 0.878 G_ID: 0.116 G_Rec: 0.361 D_GP: 0.123 D_real: 0.600 D_fake: 0.955 +(epoch: 410, iters: 4334, time: 0.063) G_GAN: 0.319 G_GAN_Feat: 0.919 G_ID: 0.119 G_Rec: 0.472 D_GP: 0.030 D_real: 0.974 D_fake: 0.684 +(epoch: 410, iters: 4734, time: 0.064) G_GAN: 0.054 G_GAN_Feat: 0.819 G_ID: 0.111 G_Rec: 0.311 D_GP: 0.031 D_real: 0.719 D_fake: 0.946 +(epoch: 410, iters: 5134, time: 0.063) G_GAN: -0.186 G_GAN_Feat: 0.991 G_ID: 0.133 G_Rec: 0.428 D_GP: 0.166 D_real: 0.168 D_fake: 1.187 +(epoch: 410, iters: 5534, time: 0.063) G_GAN: 0.602 G_GAN_Feat: 0.739 G_ID: 0.089 G_Rec: 0.305 D_GP: 0.035 D_real: 1.351 D_fake: 0.405 +(epoch: 410, iters: 5934, time: 0.063) G_GAN: 0.377 G_GAN_Feat: 0.939 G_ID: 0.117 G_Rec: 0.430 D_GP: 0.032 D_real: 1.083 D_fake: 0.624 +(epoch: 410, iters: 6334, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.766 G_ID: 0.107 G_Rec: 0.335 D_GP: 0.032 D_real: 0.950 D_fake: 0.863 +(epoch: 410, iters: 6734, time: 0.063) G_GAN: 0.564 G_GAN_Feat: 0.966 G_ID: 0.123 G_Rec: 0.479 D_GP: 0.030 D_real: 1.167 D_fake: 0.440 +(epoch: 410, iters: 7134, time: 0.063) G_GAN: 0.490 G_GAN_Feat: 0.841 G_ID: 0.100 G_Rec: 0.347 D_GP: 0.053 D_real: 1.020 D_fake: 0.530 +(epoch: 410, iters: 7534, time: 0.063) G_GAN: 0.884 G_GAN_Feat: 0.980 G_ID: 0.110 G_Rec: 0.425 D_GP: 0.050 D_real: 1.330 D_fake: 0.165 +(epoch: 410, iters: 7934, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.786 G_ID: 0.125 G_Rec: 0.307 D_GP: 0.034 D_real: 0.893 D_fake: 0.895 +(epoch: 410, iters: 8334, time: 0.063) G_GAN: 0.883 G_GAN_Feat: 1.061 G_ID: 0.122 G_Rec: 0.444 D_GP: 0.062 D_real: 1.184 D_fake: 0.285 +(epoch: 411, iters: 126, time: 0.063) G_GAN: 0.219 G_GAN_Feat: 0.720 G_ID: 0.102 G_Rec: 0.335 D_GP: 0.029 D_real: 1.052 D_fake: 0.781 +(epoch: 411, iters: 526, time: 0.063) G_GAN: 0.244 G_GAN_Feat: 0.960 G_ID: 0.124 G_Rec: 0.409 D_GP: 0.050 D_real: 0.746 D_fake: 0.756 +(epoch: 411, iters: 926, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.950 G_ID: 0.119 G_Rec: 0.335 D_GP: 0.549 D_real: 0.266 D_fake: 0.923 +(epoch: 411, iters: 1326, time: 0.063) G_GAN: 0.676 G_GAN_Feat: 1.017 G_ID: 0.126 G_Rec: 0.431 D_GP: 0.035 D_real: 1.182 D_fake: 0.343 +(epoch: 411, iters: 1726, time: 0.063) G_GAN: 0.516 G_GAN_Feat: 0.868 G_ID: 0.100 G_Rec: 0.337 D_GP: 0.052 D_real: 0.934 D_fake: 0.492 +(epoch: 411, iters: 2126, time: 0.063) G_GAN: 0.577 G_GAN_Feat: 0.977 G_ID: 0.140 G_Rec: 0.400 D_GP: 0.029 D_real: 1.085 D_fake: 0.453 +(epoch: 411, iters: 2526, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.882 G_ID: 0.099 G_Rec: 0.357 D_GP: 0.141 D_real: 0.775 D_fake: 0.719 +(epoch: 411, iters: 2926, time: 0.063) G_GAN: 0.953 G_GAN_Feat: 0.979 G_ID: 0.122 G_Rec: 0.442 D_GP: 0.034 D_real: 1.602 D_fake: 0.213 +(epoch: 411, iters: 3326, time: 0.063) G_GAN: 0.126 G_GAN_Feat: 0.808 G_ID: 0.101 G_Rec: 0.284 D_GP: 0.047 D_real: 0.862 D_fake: 0.874 +(epoch: 411, iters: 3726, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 1.035 G_ID: 0.122 G_Rec: 0.456 D_GP: 0.073 D_real: 0.788 D_fake: 0.687 +(epoch: 411, iters: 4126, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.726 G_ID: 0.103 G_Rec: 0.303 D_GP: 0.034 D_real: 1.001 D_fake: 0.742 +(epoch: 411, iters: 4526, time: 0.063) G_GAN: 0.704 G_GAN_Feat: 1.181 G_ID: 0.120 G_Rec: 0.476 D_GP: 0.988 D_real: 0.790 D_fake: 0.342 +(epoch: 411, iters: 4926, time: 0.063) G_GAN: 0.309 G_GAN_Feat: 0.798 G_ID: 0.096 G_Rec: 0.309 D_GP: 0.059 D_real: 1.044 D_fake: 0.691 +(epoch: 411, iters: 5326, time: 0.063) G_GAN: 0.730 G_GAN_Feat: 0.916 G_ID: 0.119 G_Rec: 0.411 D_GP: 0.060 D_real: 1.339 D_fake: 0.389 +(epoch: 411, iters: 5726, time: 0.063) G_GAN: 0.072 G_GAN_Feat: 0.833 G_ID: 0.112 G_Rec: 0.348 D_GP: 0.045 D_real: 0.792 D_fake: 0.929 +(epoch: 411, iters: 6126, time: 0.063) G_GAN: 0.423 G_GAN_Feat: 0.929 G_ID: 0.113 G_Rec: 0.425 D_GP: 0.030 D_real: 1.054 D_fake: 0.579 +(epoch: 411, iters: 6526, time: 0.063) G_GAN: 0.583 G_GAN_Feat: 0.791 G_ID: 0.086 G_Rec: 0.315 D_GP: 0.032 D_real: 1.330 D_fake: 0.436 +(epoch: 411, iters: 6926, time: 0.064) G_GAN: 0.628 G_GAN_Feat: 1.150 G_ID: 0.115 G_Rec: 0.454 D_GP: 0.049 D_real: 0.920 D_fake: 0.391 +(epoch: 411, iters: 7326, time: 0.063) G_GAN: 0.583 G_GAN_Feat: 1.013 G_ID: 0.104 G_Rec: 0.344 D_GP: 0.059 D_real: 0.516 D_fake: 0.433 +(epoch: 411, iters: 7726, time: 0.063) G_GAN: 0.871 G_GAN_Feat: 1.070 G_ID: 0.115 G_Rec: 0.492 D_GP: 0.040 D_real: 1.262 D_fake: 0.186 +(epoch: 411, iters: 8126, time: 0.063) G_GAN: 0.713 G_GAN_Feat: 0.851 G_ID: 0.099 G_Rec: 0.329 D_GP: 0.060 D_real: 1.275 D_fake: 0.475 +(epoch: 411, iters: 8526, time: 0.064) G_GAN: 0.016 G_GAN_Feat: 0.992 G_ID: 0.134 G_Rec: 0.439 D_GP: 0.044 D_real: 0.649 D_fake: 0.984 +(epoch: 412, iters: 318, time: 0.063) G_GAN: 0.521 G_GAN_Feat: 0.756 G_ID: 0.094 G_Rec: 0.285 D_GP: 0.038 D_real: 1.254 D_fake: 0.486 +(epoch: 412, iters: 718, time: 0.063) G_GAN: 0.288 G_GAN_Feat: 1.164 G_ID: 0.129 G_Rec: 0.439 D_GP: 0.048 D_real: 0.369 D_fake: 0.713 +(epoch: 412, iters: 1118, time: 0.063) G_GAN: 0.376 G_GAN_Feat: 0.930 G_ID: 0.106 G_Rec: 0.329 D_GP: 0.270 D_real: 0.511 D_fake: 0.627 +(epoch: 412, iters: 1518, time: 0.064) G_GAN: 0.801 G_GAN_Feat: 0.990 G_ID: 0.107 G_Rec: 0.451 D_GP: 0.032 D_real: 1.392 D_fake: 0.233 +(epoch: 412, iters: 1918, time: 0.063) G_GAN: 0.402 G_GAN_Feat: 0.721 G_ID: 0.089 G_Rec: 0.287 D_GP: 0.028 D_real: 1.310 D_fake: 0.603 +(epoch: 412, iters: 2318, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 0.835 G_ID: 0.119 G_Rec: 0.376 D_GP: 0.026 D_real: 1.118 D_fake: 0.687 +(epoch: 412, iters: 2718, time: 0.063) G_GAN: 0.215 G_GAN_Feat: 0.779 G_ID: 0.097 G_Rec: 0.319 D_GP: 0.028 D_real: 1.114 D_fake: 0.787 +(epoch: 412, iters: 3118, time: 0.064) G_GAN: 0.793 G_GAN_Feat: 1.228 G_ID: 0.121 G_Rec: 0.473 D_GP: 0.138 D_real: 0.674 D_fake: 0.387 +(epoch: 412, iters: 3518, time: 0.063) G_GAN: 0.186 G_GAN_Feat: 0.765 G_ID: 0.090 G_Rec: 0.292 D_GP: 0.035 D_real: 1.077 D_fake: 0.814 +(epoch: 412, iters: 3918, time: 0.063) G_GAN: 0.136 G_GAN_Feat: 0.943 G_ID: 0.123 G_Rec: 0.394 D_GP: 0.037 D_real: 0.800 D_fake: 0.865 +(epoch: 412, iters: 4318, time: 0.063) G_GAN: 0.082 G_GAN_Feat: 0.785 G_ID: 0.111 G_Rec: 0.305 D_GP: 0.038 D_real: 0.867 D_fake: 0.918 +(epoch: 412, iters: 4718, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 1.002 G_ID: 0.112 G_Rec: 0.470 D_GP: 0.067 D_real: 0.909 D_fake: 0.546 +(epoch: 412, iters: 5118, time: 0.063) G_GAN: 0.291 G_GAN_Feat: 0.800 G_ID: 0.077 G_Rec: 0.296 D_GP: 0.080 D_real: 0.936 D_fake: 0.711 +(epoch: 412, iters: 5518, time: 0.063) G_GAN: 0.509 G_GAN_Feat: 1.028 G_ID: 0.118 G_Rec: 0.422 D_GP: 0.057 D_real: 0.934 D_fake: 0.499 +(epoch: 412, iters: 5918, time: 0.063) G_GAN: 0.263 G_GAN_Feat: 0.835 G_ID: 0.093 G_Rec: 0.310 D_GP: 0.074 D_real: 0.691 D_fake: 0.737 +(epoch: 412, iters: 6318, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 1.043 G_ID: 0.136 G_Rec: 0.504 D_GP: 0.031 D_real: 0.984 D_fake: 0.518 +(epoch: 412, iters: 6718, time: 0.063) G_GAN: 0.358 G_GAN_Feat: 0.841 G_ID: 0.097 G_Rec: 0.323 D_GP: 0.071 D_real: 0.847 D_fake: 0.645 +(epoch: 412, iters: 7118, time: 0.063) G_GAN: 0.593 G_GAN_Feat: 1.006 G_ID: 0.116 G_Rec: 0.422 D_GP: 0.044 D_real: 0.990 D_fake: 0.417 +(epoch: 412, iters: 7518, time: 0.063) G_GAN: 0.260 G_GAN_Feat: 0.673 G_ID: 0.091 G_Rec: 0.295 D_GP: 0.028 D_real: 1.173 D_fake: 0.742 +(epoch: 412, iters: 7918, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.930 G_ID: 0.128 G_Rec: 0.423 D_GP: 0.039 D_real: 0.956 D_fake: 0.689 +(epoch: 412, iters: 8318, time: 0.063) G_GAN: -0.061 G_GAN_Feat: 0.720 G_ID: 0.107 G_Rec: 0.313 D_GP: 0.036 D_real: 0.770 D_fake: 1.061 +(epoch: 413, iters: 110, time: 0.063) G_GAN: 0.760 G_GAN_Feat: 0.921 G_ID: 0.100 G_Rec: 0.441 D_GP: 0.033 D_real: 1.294 D_fake: 0.353 +(epoch: 413, iters: 510, time: 0.063) G_GAN: 0.127 G_GAN_Feat: 0.741 G_ID: 0.089 G_Rec: 0.298 D_GP: 0.035 D_real: 1.099 D_fake: 0.873 +(epoch: 413, iters: 910, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.988 G_ID: 0.121 G_Rec: 0.439 D_GP: 0.113 D_real: 0.776 D_fake: 0.577 +(epoch: 413, iters: 1310, time: 0.063) G_GAN: 0.216 G_GAN_Feat: 0.763 G_ID: 0.105 G_Rec: 0.303 D_GP: 0.035 D_real: 1.060 D_fake: 0.786 +(epoch: 413, iters: 1710, time: 0.063) G_GAN: 0.591 G_GAN_Feat: 0.984 G_ID: 0.117 G_Rec: 0.460 D_GP: 0.030 D_real: 1.197 D_fake: 0.419 +(epoch: 413, iters: 2110, time: 0.063) G_GAN: 0.389 G_GAN_Feat: 0.701 G_ID: 0.099 G_Rec: 0.306 D_GP: 0.026 D_real: 1.297 D_fake: 0.612 +(epoch: 413, iters: 2510, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.917 G_ID: 0.112 G_Rec: 0.421 D_GP: 0.033 D_real: 0.888 D_fake: 0.723 +(epoch: 413, iters: 2910, time: 0.063) G_GAN: 0.459 G_GAN_Feat: 0.815 G_ID: 0.105 G_Rec: 0.319 D_GP: 0.055 D_real: 0.987 D_fake: 0.561 +(epoch: 413, iters: 3310, time: 0.063) G_GAN: 0.905 G_GAN_Feat: 1.078 G_ID: 0.103 G_Rec: 0.456 D_GP: 0.052 D_real: 0.999 D_fake: 0.170 +(epoch: 413, iters: 3710, time: 0.063) G_GAN: 0.545 G_GAN_Feat: 0.879 G_ID: 0.104 G_Rec: 0.339 D_GP: 0.059 D_real: 1.197 D_fake: 0.574 +(epoch: 413, iters: 4110, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 1.059 G_ID: 0.124 G_Rec: 0.438 D_GP: 0.040 D_real: 0.625 D_fake: 0.554 +(epoch: 413, iters: 4510, time: 0.063) G_GAN: 0.027 G_GAN_Feat: 0.771 G_ID: 0.121 G_Rec: 0.313 D_GP: 0.034 D_real: 0.843 D_fake: 0.973 +(epoch: 413, iters: 4910, time: 0.063) G_GAN: 0.698 G_GAN_Feat: 1.031 G_ID: 0.111 G_Rec: 0.436 D_GP: 0.068 D_real: 0.776 D_fake: 0.335 +(epoch: 413, iters: 5310, time: 0.063) G_GAN: -0.034 G_GAN_Feat: 0.890 G_ID: 0.103 G_Rec: 0.343 D_GP: 0.035 D_real: 0.696 D_fake: 1.034 +(epoch: 413, iters: 5710, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.998 G_ID: 0.132 G_Rec: 0.448 D_GP: 0.038 D_real: 0.729 D_fake: 0.716 +(epoch: 413, iters: 6110, time: 0.063) G_GAN: 0.369 G_GAN_Feat: 0.935 G_ID: 0.107 G_Rec: 0.314 D_GP: 0.089 D_real: 0.591 D_fake: 0.632 +(epoch: 413, iters: 6510, time: 0.063) G_GAN: 0.397 G_GAN_Feat: 0.837 G_ID: 0.102 G_Rec: 0.418 D_GP: 0.033 D_real: 1.171 D_fake: 0.604 +(epoch: 413, iters: 6910, time: 0.063) G_GAN: 0.168 G_GAN_Feat: 0.693 G_ID: 0.103 G_Rec: 0.313 D_GP: 0.029 D_real: 1.082 D_fake: 0.832 +(epoch: 413, iters: 7310, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 1.013 G_ID: 0.115 G_Rec: 0.486 D_GP: 0.032 D_real: 0.890 D_fake: 0.571 +(epoch: 413, iters: 7710, time: 0.063) G_GAN: -0.017 G_GAN_Feat: 0.677 G_ID: 0.102 G_Rec: 0.317 D_GP: 0.033 D_real: 0.868 D_fake: 1.017 +(epoch: 413, iters: 8110, time: 0.063) G_GAN: 0.217 G_GAN_Feat: 0.888 G_ID: 0.126 G_Rec: 0.416 D_GP: 0.037 D_real: 0.894 D_fake: 0.789 +(epoch: 413, iters: 8510, time: 0.063) G_GAN: 0.073 G_GAN_Feat: 0.745 G_ID: 0.090 G_Rec: 0.309 D_GP: 0.039 D_real: 0.858 D_fake: 0.927 +(epoch: 414, iters: 302, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.982 G_ID: 0.145 G_Rec: 0.430 D_GP: 0.065 D_real: 0.835 D_fake: 0.564 +(epoch: 414, iters: 702, time: 0.063) G_GAN: 0.006 G_GAN_Feat: 0.746 G_ID: 0.106 G_Rec: 0.333 D_GP: 0.056 D_real: 0.618 D_fake: 0.994 +(epoch: 414, iters: 1102, time: 0.063) G_GAN: 0.445 G_GAN_Feat: 0.948 G_ID: 0.124 G_Rec: 0.455 D_GP: 0.040 D_real: 1.024 D_fake: 0.557 +(epoch: 414, iters: 1502, time: 0.063) G_GAN: -0.012 G_GAN_Feat: 0.836 G_ID: 0.124 G_Rec: 0.346 D_GP: 0.040 D_real: 0.551 D_fake: 1.012 +(epoch: 414, iters: 1902, time: 0.064) G_GAN: 0.576 G_GAN_Feat: 1.049 G_ID: 0.106 G_Rec: 0.421 D_GP: 0.066 D_real: 0.728 D_fake: 0.431 +(epoch: 414, iters: 2302, time: 0.063) G_GAN: 0.505 G_GAN_Feat: 0.803 G_ID: 0.139 G_Rec: 0.336 D_GP: 0.034 D_real: 1.241 D_fake: 0.513 +(epoch: 414, iters: 2702, time: 0.063) G_GAN: 0.412 G_GAN_Feat: 0.950 G_ID: 0.113 G_Rec: 0.440 D_GP: 0.034 D_real: 0.955 D_fake: 0.596 +(epoch: 414, iters: 3102, time: 0.063) G_GAN: 0.339 G_GAN_Feat: 0.835 G_ID: 0.105 G_Rec: 0.326 D_GP: 0.032 D_real: 1.328 D_fake: 0.667 +(epoch: 414, iters: 3502, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.859 G_ID: 0.117 G_Rec: 0.434 D_GP: 0.028 D_real: 1.172 D_fake: 0.566 +(epoch: 414, iters: 3902, time: 0.063) G_GAN: 0.215 G_GAN_Feat: 0.716 G_ID: 0.092 G_Rec: 0.332 D_GP: 0.040 D_real: 1.043 D_fake: 0.785 +(epoch: 414, iters: 4302, time: 0.063) G_GAN: 0.554 G_GAN_Feat: 0.913 G_ID: 0.132 G_Rec: 0.418 D_GP: 0.047 D_real: 1.152 D_fake: 0.456 +(epoch: 414, iters: 4702, time: 0.063) G_GAN: 0.156 G_GAN_Feat: 0.737 G_ID: 0.090 G_Rec: 0.309 D_GP: 0.044 D_real: 0.932 D_fake: 0.844 +(epoch: 414, iters: 5102, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.924 G_ID: 0.151 G_Rec: 0.409 D_GP: 0.036 D_real: 0.767 D_fake: 0.915 +(epoch: 414, iters: 5502, time: 0.063) G_GAN: 0.084 G_GAN_Feat: 0.805 G_ID: 0.113 G_Rec: 0.352 D_GP: 0.065 D_real: 0.771 D_fake: 0.917 +(epoch: 414, iters: 5902, time: 0.063) G_GAN: 0.699 G_GAN_Feat: 1.030 G_ID: 0.123 G_Rec: 0.416 D_GP: 0.162 D_real: 0.967 D_fake: 0.354 +(epoch: 414, iters: 6302, time: 0.063) G_GAN: 0.168 G_GAN_Feat: 0.778 G_ID: 0.099 G_Rec: 0.316 D_GP: 0.057 D_real: 0.788 D_fake: 0.832 +(epoch: 414, iters: 6702, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.966 G_ID: 0.114 G_Rec: 0.427 D_GP: 0.032 D_real: 1.099 D_fake: 0.471 +(epoch: 414, iters: 7102, time: 0.063) G_GAN: 0.617 G_GAN_Feat: 0.818 G_ID: 0.095 G_Rec: 0.323 D_GP: 0.047 D_real: 1.413 D_fake: 0.505 +(epoch: 414, iters: 7502, time: 0.063) G_GAN: 0.629 G_GAN_Feat: 0.989 G_ID: 0.117 G_Rec: 0.432 D_GP: 0.056 D_real: 1.287 D_fake: 0.387 +(epoch: 414, iters: 7902, time: 0.063) G_GAN: 0.103 G_GAN_Feat: 0.841 G_ID: 0.106 G_Rec: 0.335 D_GP: 0.052 D_real: 0.604 D_fake: 0.897 +(epoch: 414, iters: 8302, time: 0.064) G_GAN: 0.685 G_GAN_Feat: 1.036 G_ID: 0.127 G_Rec: 0.447 D_GP: 0.036 D_real: 1.007 D_fake: 0.337 +(epoch: 414, iters: 8702, time: 0.063) G_GAN: 0.148 G_GAN_Feat: 0.859 G_ID: 0.092 G_Rec: 0.340 D_GP: 0.038 D_real: 0.745 D_fake: 0.857 +(epoch: 415, iters: 494, time: 0.063) G_GAN: 0.652 G_GAN_Feat: 1.149 G_ID: 0.149 G_Rec: 0.477 D_GP: 0.045 D_real: 1.011 D_fake: 0.390 +(epoch: 415, iters: 894, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.651 G_ID: 0.100 G_Rec: 0.316 D_GP: 0.030 D_real: 1.127 D_fake: 0.787 +(epoch: 415, iters: 1294, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.895 G_ID: 0.120 G_Rec: 0.479 D_GP: 0.031 D_real: 1.231 D_fake: 0.562 +(epoch: 415, iters: 1694, time: 0.063) G_GAN: 0.077 G_GAN_Feat: 0.692 G_ID: 0.092 G_Rec: 0.316 D_GP: 0.028 D_real: 1.041 D_fake: 0.924 +(epoch: 415, iters: 2094, time: 0.063) G_GAN: 0.485 G_GAN_Feat: 0.872 G_ID: 0.101 G_Rec: 0.437 D_GP: 0.033 D_real: 1.140 D_fake: 0.528 +(epoch: 415, iters: 2494, time: 0.063) G_GAN: -0.188 G_GAN_Feat: 0.651 G_ID: 0.089 G_Rec: 0.300 D_GP: 0.037 D_real: 0.769 D_fake: 1.188 +(epoch: 415, iters: 2894, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.896 G_ID: 0.106 G_Rec: 0.450 D_GP: 0.037 D_real: 0.839 D_fake: 0.773 +(epoch: 415, iters: 3294, time: 0.063) G_GAN: -0.046 G_GAN_Feat: 0.643 G_ID: 0.100 G_Rec: 0.292 D_GP: 0.044 D_real: 0.793 D_fake: 1.046 +(epoch: 415, iters: 3694, time: 0.063) G_GAN: 0.464 G_GAN_Feat: 0.906 G_ID: 0.114 G_Rec: 0.452 D_GP: 0.041 D_real: 1.050 D_fake: 0.588 +(epoch: 415, iters: 4094, time: 0.063) G_GAN: -0.055 G_GAN_Feat: 0.742 G_ID: 0.101 G_Rec: 0.357 D_GP: 0.064 D_real: 0.668 D_fake: 1.055 +(epoch: 415, iters: 4494, time: 0.064) G_GAN: 0.917 G_GAN_Feat: 0.910 G_ID: 0.120 G_Rec: 0.448 D_GP: 0.041 D_real: 1.518 D_fake: 0.410 +(epoch: 415, iters: 4894, time: 0.063) G_GAN: -0.131 G_GAN_Feat: 0.710 G_ID: 0.093 G_Rec: 0.324 D_GP: 0.048 D_real: 0.621 D_fake: 1.131 +(epoch: 415, iters: 5294, time: 0.063) G_GAN: 0.199 G_GAN_Feat: 0.910 G_ID: 0.110 G_Rec: 0.469 D_GP: 0.036 D_real: 0.802 D_fake: 0.801 +(epoch: 415, iters: 5694, time: 0.063) G_GAN: -0.059 G_GAN_Feat: 0.736 G_ID: 0.101 G_Rec: 0.320 D_GP: 0.054 D_real: 0.745 D_fake: 1.060 +(epoch: 415, iters: 6094, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.887 G_ID: 0.117 G_Rec: 0.407 D_GP: 0.048 D_real: 0.731 D_fake: 0.775 +(epoch: 415, iters: 6494, time: 0.063) G_GAN: 0.241 G_GAN_Feat: 0.711 G_ID: 0.101 G_Rec: 0.298 D_GP: 0.037 D_real: 1.056 D_fake: 0.760 +(epoch: 415, iters: 6894, time: 0.063) G_GAN: 0.417 G_GAN_Feat: 0.965 G_ID: 0.119 G_Rec: 0.431 D_GP: 0.056 D_real: 0.869 D_fake: 0.596 +(epoch: 415, iters: 7294, time: 0.063) G_GAN: -0.007 G_GAN_Feat: 0.838 G_ID: 0.100 G_Rec: 0.337 D_GP: 0.797 D_real: 0.414 D_fake: 1.008 +(epoch: 415, iters: 7694, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 1.013 G_ID: 0.117 G_Rec: 0.481 D_GP: 0.081 D_real: 0.942 D_fake: 0.500 +(epoch: 415, iters: 8094, time: 0.063) G_GAN: 0.041 G_GAN_Feat: 0.730 G_ID: 0.105 G_Rec: 0.303 D_GP: 0.048 D_real: 0.781 D_fake: 0.959 +(epoch: 415, iters: 8494, time: 0.063) G_GAN: 0.305 G_GAN_Feat: 0.946 G_ID: 0.130 G_Rec: 0.461 D_GP: 0.050 D_real: 0.885 D_fake: 0.695 +(epoch: 416, iters: 286, time: 0.063) G_GAN: 0.517 G_GAN_Feat: 0.833 G_ID: 0.101 G_Rec: 0.352 D_GP: 0.056 D_real: 1.210 D_fake: 0.496 +(epoch: 416, iters: 686, time: 0.064) G_GAN: 0.725 G_GAN_Feat: 0.912 G_ID: 0.111 G_Rec: 0.449 D_GP: 0.033 D_real: 1.306 D_fake: 0.294 +(epoch: 416, iters: 1086, time: 0.063) G_GAN: 0.153 G_GAN_Feat: 0.864 G_ID: 0.097 G_Rec: 0.358 D_GP: 0.140 D_real: 0.351 D_fake: 0.852 +(epoch: 416, iters: 1486, time: 0.063) G_GAN: 0.852 G_GAN_Feat: 0.936 G_ID: 0.099 G_Rec: 0.401 D_GP: 0.036 D_real: 1.466 D_fake: 0.202 +(epoch: 416, iters: 1886, time: 0.063) G_GAN: 0.008 G_GAN_Feat: 0.603 G_ID: 0.100 G_Rec: 0.306 D_GP: 0.028 D_real: 0.990 D_fake: 0.992 +(epoch: 416, iters: 2286, time: 0.063) G_GAN: 0.275 G_GAN_Feat: 0.792 G_ID: 0.130 G_Rec: 0.406 D_GP: 0.026 D_real: 1.134 D_fake: 0.727 +(epoch: 416, iters: 2686, time: 0.063) G_GAN: 0.062 G_GAN_Feat: 0.656 G_ID: 0.089 G_Rec: 0.300 D_GP: 0.029 D_real: 0.924 D_fake: 0.938 +(epoch: 416, iters: 3086, time: 0.063) G_GAN: 0.228 G_GAN_Feat: 0.917 G_ID: 0.121 G_Rec: 0.432 D_GP: 0.057 D_real: 0.926 D_fake: 0.779 +(epoch: 416, iters: 3486, time: 0.064) G_GAN: -0.010 G_GAN_Feat: 0.791 G_ID: 0.093 G_Rec: 0.327 D_GP: 0.092 D_real: 0.562 D_fake: 1.010 +(epoch: 416, iters: 3886, time: 0.063) G_GAN: 0.653 G_GAN_Feat: 0.989 G_ID: 0.115 G_Rec: 0.481 D_GP: 0.038 D_real: 1.109 D_fake: 0.381 +(epoch: 416, iters: 4286, time: 0.063) G_GAN: 0.080 G_GAN_Feat: 0.725 G_ID: 0.094 G_Rec: 0.302 D_GP: 0.051 D_real: 0.879 D_fake: 0.920 +(epoch: 416, iters: 4686, time: 0.063) G_GAN: 0.567 G_GAN_Feat: 0.840 G_ID: 0.111 G_Rec: 0.421 D_GP: 0.036 D_real: 1.199 D_fake: 0.438 +(epoch: 416, iters: 5086, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.719 G_ID: 0.094 G_Rec: 0.306 D_GP: 0.044 D_real: 1.262 D_fake: 0.501 +(epoch: 416, iters: 5486, time: 0.063) G_GAN: 0.429 G_GAN_Feat: 0.967 G_ID: 0.117 G_Rec: 0.464 D_GP: 0.048 D_real: 0.954 D_fake: 0.575 +(epoch: 416, iters: 5886, time: 0.063) G_GAN: -0.007 G_GAN_Feat: 0.799 G_ID: 0.102 G_Rec: 0.358 D_GP: 0.036 D_real: 0.751 D_fake: 1.007 +(epoch: 416, iters: 6286, time: 0.063) G_GAN: 0.484 G_GAN_Feat: 0.900 G_ID: 0.137 G_Rec: 0.411 D_GP: 0.043 D_real: 1.019 D_fake: 0.521 +(epoch: 416, iters: 6686, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.788 G_ID: 0.089 G_Rec: 0.321 D_GP: 0.045 D_real: 0.953 D_fake: 0.692 +(epoch: 416, iters: 7086, time: 0.063) G_GAN: 0.537 G_GAN_Feat: 1.008 G_ID: 0.128 G_Rec: 0.418 D_GP: 0.071 D_real: 0.856 D_fake: 0.474 +(epoch: 416, iters: 7486, time: 0.063) G_GAN: 0.425 G_GAN_Feat: 0.851 G_ID: 0.091 G_Rec: 0.313 D_GP: 0.050 D_real: 0.874 D_fake: 0.576 +(epoch: 416, iters: 7886, time: 0.063) G_GAN: 0.410 G_GAN_Feat: 1.245 G_ID: 0.130 G_Rec: 0.468 D_GP: 0.065 D_real: 0.885 D_fake: 0.613 +(epoch: 416, iters: 8286, time: 0.064) G_GAN: -0.103 G_GAN_Feat: 0.689 G_ID: 0.097 G_Rec: 0.318 D_GP: 0.028 D_real: 0.861 D_fake: 1.103 +(epoch: 416, iters: 8686, time: 0.063) G_GAN: 0.139 G_GAN_Feat: 0.872 G_ID: 0.112 G_Rec: 0.425 D_GP: 0.029 D_real: 0.788 D_fake: 0.863 +(epoch: 417, iters: 478, time: 0.063) G_GAN: -0.050 G_GAN_Feat: 0.666 G_ID: 0.122 G_Rec: 0.289 D_GP: 0.036 D_real: 0.826 D_fake: 1.050 +(epoch: 417, iters: 878, time: 0.063) G_GAN: 0.395 G_GAN_Feat: 0.916 G_ID: 0.110 G_Rec: 0.451 D_GP: 0.043 D_real: 0.902 D_fake: 0.628 +(epoch: 417, iters: 1278, time: 0.064) G_GAN: -0.024 G_GAN_Feat: 0.668 G_ID: 0.097 G_Rec: 0.308 D_GP: 0.039 D_real: 0.854 D_fake: 1.024 +(epoch: 417, iters: 1678, time: 0.063) G_GAN: 0.534 G_GAN_Feat: 0.942 G_ID: 0.114 G_Rec: 0.478 D_GP: 0.035 D_real: 1.074 D_fake: 0.507 +(epoch: 417, iters: 2078, time: 0.063) G_GAN: -0.031 G_GAN_Feat: 0.671 G_ID: 0.109 G_Rec: 0.325 D_GP: 0.029 D_real: 0.864 D_fake: 1.031 +(epoch: 417, iters: 2478, time: 0.063) G_GAN: 0.150 G_GAN_Feat: 0.942 G_ID: 0.127 G_Rec: 0.477 D_GP: 0.042 D_real: 0.674 D_fake: 0.852 +(epoch: 417, iters: 2878, time: 0.064) G_GAN: 0.002 G_GAN_Feat: 0.649 G_ID: 0.112 G_Rec: 0.282 D_GP: 0.031 D_real: 0.964 D_fake: 0.999 +(epoch: 417, iters: 3278, time: 0.063) G_GAN: 0.501 G_GAN_Feat: 0.949 G_ID: 0.154 G_Rec: 0.452 D_GP: 0.046 D_real: 0.993 D_fake: 0.523 +(epoch: 417, iters: 3678, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 0.776 G_ID: 0.095 G_Rec: 0.334 D_GP: 0.051 D_real: 1.045 D_fake: 0.732 +(epoch: 417, iters: 4078, time: 0.063) G_GAN: 0.543 G_GAN_Feat: 0.860 G_ID: 0.113 G_Rec: 0.385 D_GP: 0.032 D_real: 1.276 D_fake: 0.470 +(epoch: 417, iters: 4478, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.762 G_ID: 0.087 G_Rec: 0.305 D_GP: 0.045 D_real: 0.932 D_fake: 0.775 +(epoch: 417, iters: 4878, time: 0.063) G_GAN: 0.496 G_GAN_Feat: 0.930 G_ID: 0.116 G_Rec: 0.440 D_GP: 0.035 D_real: 1.145 D_fake: 0.512 +(epoch: 417, iters: 5278, time: 0.063) G_GAN: 0.216 G_GAN_Feat: 0.733 G_ID: 0.093 G_Rec: 0.304 D_GP: 0.040 D_real: 1.056 D_fake: 0.786 +(epoch: 417, iters: 5678, time: 0.063) G_GAN: 0.486 G_GAN_Feat: 0.925 G_ID: 0.113 G_Rec: 0.448 D_GP: 0.063 D_real: 0.930 D_fake: 0.530 +(epoch: 417, iters: 6078, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.789 G_ID: 0.088 G_Rec: 0.307 D_GP: 0.064 D_real: 0.879 D_fake: 0.637 +(epoch: 417, iters: 6478, time: 0.063) G_GAN: 0.549 G_GAN_Feat: 0.875 G_ID: 0.125 G_Rec: 0.391 D_GP: 0.031 D_real: 1.153 D_fake: 0.467 +(epoch: 417, iters: 6878, time: 0.063) G_GAN: -0.040 G_GAN_Feat: 0.815 G_ID: 0.113 G_Rec: 0.340 D_GP: 0.098 D_real: 0.464 D_fake: 1.040 +(epoch: 417, iters: 7278, time: 0.063) G_GAN: 0.528 G_GAN_Feat: 1.117 G_ID: 0.108 G_Rec: 0.492 D_GP: 0.110 D_real: 0.744 D_fake: 0.487 +(epoch: 417, iters: 7678, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.960 G_ID: 0.115 G_Rec: 0.356 D_GP: 0.054 D_real: 0.811 D_fake: 0.870 +(epoch: 417, iters: 8078, time: 0.063) G_GAN: 0.703 G_GAN_Feat: 0.977 G_ID: 0.110 G_Rec: 0.430 D_GP: 0.033 D_real: 1.298 D_fake: 0.333 +(epoch: 417, iters: 8478, time: 0.063) G_GAN: -0.024 G_GAN_Feat: 0.938 G_ID: 0.102 G_Rec: 0.322 D_GP: 3.707 D_real: 0.330 D_fake: 1.025 +(epoch: 418, iters: 270, time: 0.063) G_GAN: 0.360 G_GAN_Feat: 0.882 G_ID: 0.122 G_Rec: 0.441 D_GP: 0.029 D_real: 1.077 D_fake: 0.640 +(epoch: 418, iters: 670, time: 0.064) G_GAN: -0.062 G_GAN_Feat: 0.643 G_ID: 0.140 G_Rec: 0.328 D_GP: 0.028 D_real: 0.808 D_fake: 1.062 +(epoch: 418, iters: 1070, time: 0.063) G_GAN: 0.241 G_GAN_Feat: 0.837 G_ID: 0.116 G_Rec: 0.409 D_GP: 0.035 D_real: 0.984 D_fake: 0.760 +(epoch: 418, iters: 1470, time: 0.063) G_GAN: -0.007 G_GAN_Feat: 0.603 G_ID: 0.104 G_Rec: 0.285 D_GP: 0.034 D_real: 0.902 D_fake: 1.007 +(epoch: 418, iters: 1870, time: 0.063) G_GAN: 0.316 G_GAN_Feat: 0.810 G_ID: 0.126 G_Rec: 0.416 D_GP: 0.038 D_real: 1.006 D_fake: 0.698 +(epoch: 418, iters: 2270, time: 0.064) G_GAN: -0.061 G_GAN_Feat: 0.698 G_ID: 0.101 G_Rec: 0.323 D_GP: 0.054 D_real: 0.768 D_fake: 1.061 +(epoch: 418, iters: 2670, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.876 G_ID: 0.121 G_Rec: 0.433 D_GP: 0.046 D_real: 0.971 D_fake: 0.763 +(epoch: 418, iters: 3070, time: 0.063) G_GAN: -0.143 G_GAN_Feat: 0.669 G_ID: 0.078 G_Rec: 0.312 D_GP: 0.049 D_real: 0.664 D_fake: 1.143 +(epoch: 418, iters: 3470, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.928 G_ID: 0.120 G_Rec: 0.462 D_GP: 0.044 D_real: 0.926 D_fake: 0.713 +(epoch: 418, iters: 3870, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.676 G_ID: 0.110 G_Rec: 0.290 D_GP: 0.037 D_real: 1.156 D_fake: 0.762 +(epoch: 418, iters: 4270, time: 0.063) G_GAN: 0.485 G_GAN_Feat: 0.961 G_ID: 0.107 G_Rec: 0.457 D_GP: 0.042 D_real: 1.220 D_fake: 0.540 +(epoch: 418, iters: 4670, time: 0.063) G_GAN: 0.526 G_GAN_Feat: 0.783 G_ID: 0.119 G_Rec: 0.343 D_GP: 0.033 D_real: 1.416 D_fake: 0.481 +(epoch: 418, iters: 5070, time: 0.063) G_GAN: 0.316 G_GAN_Feat: 0.952 G_ID: 0.120 G_Rec: 0.439 D_GP: 0.082 D_real: 0.763 D_fake: 0.691 +(epoch: 418, iters: 5470, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.669 G_ID: 0.096 G_Rec: 0.298 D_GP: 0.040 D_real: 1.127 D_fake: 0.761 +(epoch: 418, iters: 5870, time: 0.063) G_GAN: 0.342 G_GAN_Feat: 1.010 G_ID: 0.126 G_Rec: 0.472 D_GP: 0.052 D_real: 0.840 D_fake: 0.659 +(epoch: 418, iters: 6270, time: 0.063) G_GAN: 0.094 G_GAN_Feat: 0.656 G_ID: 0.099 G_Rec: 0.302 D_GP: 0.028 D_real: 1.014 D_fake: 0.909 +(epoch: 418, iters: 6670, time: 0.063) G_GAN: 0.448 G_GAN_Feat: 1.090 G_ID: 0.124 G_Rec: 0.464 D_GP: 0.085 D_real: 0.463 D_fake: 0.557 +(epoch: 418, iters: 7070, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.743 G_ID: 0.091 G_Rec: 0.311 D_GP: 0.065 D_real: 1.264 D_fake: 0.587 +(epoch: 418, iters: 7470, time: 0.063) G_GAN: 0.487 G_GAN_Feat: 0.970 G_ID: 0.118 G_Rec: 0.455 D_GP: 0.091 D_real: 1.075 D_fake: 0.523 +(epoch: 418, iters: 7870, time: 0.063) G_GAN: 0.056 G_GAN_Feat: 0.660 G_ID: 0.110 G_Rec: 0.374 D_GP: 0.035 D_real: 0.955 D_fake: 0.944 +(epoch: 418, iters: 8270, time: 0.063) G_GAN: 0.142 G_GAN_Feat: 0.943 G_ID: 0.111 G_Rec: 0.450 D_GP: 0.050 D_real: 0.777 D_fake: 0.858 +(epoch: 418, iters: 8670, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.760 G_ID: 0.107 G_Rec: 0.318 D_GP: 0.034 D_real: 1.026 D_fake: 0.738 +(epoch: 419, iters: 462, time: 0.063) G_GAN: 0.383 G_GAN_Feat: 0.891 G_ID: 0.124 G_Rec: 0.419 D_GP: 0.031 D_real: 0.951 D_fake: 0.618 +(epoch: 419, iters: 862, time: 0.063) G_GAN: 0.431 G_GAN_Feat: 0.656 G_ID: 0.100 G_Rec: 0.270 D_GP: 0.025 D_real: 1.312 D_fake: 0.571 +(epoch: 419, iters: 1262, time: 0.063) G_GAN: 0.541 G_GAN_Feat: 0.906 G_ID: 0.104 G_Rec: 0.408 D_GP: 0.034 D_real: 1.124 D_fake: 0.467 +(epoch: 419, iters: 1662, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.739 G_ID: 0.105 G_Rec: 0.281 D_GP: 0.051 D_real: 1.160 D_fake: 0.728 +(epoch: 419, iters: 2062, time: 0.063) G_GAN: 0.835 G_GAN_Feat: 0.984 G_ID: 0.114 G_Rec: 0.470 D_GP: 0.037 D_real: 1.401 D_fake: 0.250 +(epoch: 419, iters: 2462, time: 0.063) G_GAN: 0.089 G_GAN_Feat: 0.718 G_ID: 0.096 G_Rec: 0.328 D_GP: 0.036 D_real: 0.977 D_fake: 0.911 +(epoch: 419, iters: 2862, time: 0.063) G_GAN: 0.512 G_GAN_Feat: 0.891 G_ID: 0.101 G_Rec: 0.391 D_GP: 0.038 D_real: 1.121 D_fake: 0.507 +(epoch: 419, iters: 3262, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.746 G_ID: 0.096 G_Rec: 0.320 D_GP: 0.042 D_real: 1.021 D_fake: 0.805 +(epoch: 419, iters: 3662, time: 0.063) G_GAN: 0.500 G_GAN_Feat: 0.911 G_ID: 0.112 G_Rec: 0.422 D_GP: 0.031 D_real: 1.218 D_fake: 0.512 +(epoch: 419, iters: 4062, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.710 G_ID: 0.122 G_Rec: 0.290 D_GP: 0.032 D_real: 1.060 D_fake: 0.784 +(epoch: 419, iters: 4462, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.914 G_ID: 0.126 G_Rec: 0.392 D_GP: 0.037 D_real: 1.051 D_fake: 0.529 +(epoch: 419, iters: 4862, time: 0.064) G_GAN: -0.112 G_GAN_Feat: 0.785 G_ID: 0.120 G_Rec: 0.325 D_GP: 0.042 D_real: 0.600 D_fake: 1.112 +(epoch: 419, iters: 5262, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.892 G_ID: 0.119 G_Rec: 0.376 D_GP: 0.030 D_real: 0.707 D_fake: 0.866 +(epoch: 419, iters: 5662, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.732 G_ID: 0.103 G_Rec: 0.294 D_GP: 0.053 D_real: 1.033 D_fake: 0.675 +(epoch: 419, iters: 6062, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 1.059 G_ID: 0.131 G_Rec: 0.459 D_GP: 0.062 D_real: 0.647 D_fake: 0.569 +(epoch: 419, iters: 6462, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.812 G_ID: 0.105 G_Rec: 0.322 D_GP: 0.046 D_real: 0.995 D_fake: 0.756 +(epoch: 419, iters: 6862, time: 0.064) G_GAN: 0.800 G_GAN_Feat: 1.035 G_ID: 0.132 G_Rec: 0.437 D_GP: 0.064 D_real: 0.818 D_fake: 0.255 +(epoch: 419, iters: 7262, time: 0.064) G_GAN: -0.239 G_GAN_Feat: 0.832 G_ID: 0.100 G_Rec: 0.321 D_GP: 0.048 D_real: 0.546 D_fake: 1.240 +(epoch: 419, iters: 7662, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 1.029 G_ID: 0.127 G_Rec: 0.433 D_GP: 0.034 D_real: 0.831 D_fake: 0.572 +(epoch: 419, iters: 8062, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.716 G_ID: 0.099 G_Rec: 0.316 D_GP: 0.039 D_real: 1.180 D_fake: 0.775 +(epoch: 419, iters: 8462, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.951 G_ID: 0.114 G_Rec: 0.431 D_GP: 0.029 D_real: 0.974 D_fake: 0.708 +(epoch: 420, iters: 254, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.723 G_ID: 0.106 G_Rec: 0.274 D_GP: 0.036 D_real: 1.177 D_fake: 0.654 +(epoch: 420, iters: 654, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.924 G_ID: 0.124 G_Rec: 0.430 D_GP: 0.034 D_real: 1.078 D_fake: 0.509 +(epoch: 420, iters: 1054, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.793 G_ID: 0.096 G_Rec: 0.313 D_GP: 0.057 D_real: 0.953 D_fake: 0.651 +(epoch: 420, iters: 1454, time: 0.063) G_GAN: 0.533 G_GAN_Feat: 0.929 G_ID: 0.117 G_Rec: 0.486 D_GP: 0.027 D_real: 1.179 D_fake: 0.473 +(epoch: 420, iters: 1854, time: 0.063) G_GAN: 0.153 G_GAN_Feat: 0.796 G_ID: 0.105 G_Rec: 0.314 D_GP: 0.097 D_real: 0.840 D_fake: 0.848 +(epoch: 420, iters: 2254, time: 0.063) G_GAN: 0.617 G_GAN_Feat: 0.916 G_ID: 0.112 G_Rec: 0.423 D_GP: 0.034 D_real: 1.198 D_fake: 0.395 +(epoch: 420, iters: 2654, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.801 G_ID: 0.087 G_Rec: 0.310 D_GP: 0.051 D_real: 1.149 D_fake: 0.572 +(epoch: 420, iters: 3054, time: 0.063) G_GAN: 0.931 G_GAN_Feat: 1.168 G_ID: 0.113 G_Rec: 0.467 D_GP: 0.057 D_real: 0.877 D_fake: 0.237 +(epoch: 420, iters: 3454, time: 0.063) G_GAN: 0.242 G_GAN_Feat: 0.901 G_ID: 0.110 G_Rec: 0.322 D_GP: 0.077 D_real: 0.493 D_fake: 0.761 +(epoch: 420, iters: 3854, time: 0.063) G_GAN: 0.454 G_GAN_Feat: 1.039 G_ID: 0.125 G_Rec: 0.499 D_GP: 0.030 D_real: 1.089 D_fake: 0.558 +(epoch: 420, iters: 4254, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.752 G_ID: 0.095 G_Rec: 0.283 D_GP: 0.037 D_real: 1.136 D_fake: 0.637 +(epoch: 420, iters: 4654, time: 0.063) G_GAN: 1.416 G_GAN_Feat: 1.097 G_ID: 0.132 G_Rec: 0.460 D_GP: 0.121 D_real: 1.590 D_fake: 0.148 +(epoch: 420, iters: 5054, time: 0.063) G_GAN: 0.045 G_GAN_Feat: 0.731 G_ID: 0.104 G_Rec: 0.308 D_GP: 0.030 D_real: 0.996 D_fake: 0.955 +(epoch: 420, iters: 5454, time: 0.063) G_GAN: 0.241 G_GAN_Feat: 0.929 G_ID: 0.116 G_Rec: 0.418 D_GP: 0.046 D_real: 0.877 D_fake: 0.762 +(epoch: 420, iters: 5854, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.882 G_ID: 0.102 G_Rec: 0.336 D_GP: 0.083 D_real: 0.611 D_fake: 0.728 +(epoch: 420, iters: 6254, time: 0.063) G_GAN: 0.442 G_GAN_Feat: 0.936 G_ID: 0.138 G_Rec: 0.457 D_GP: 0.035 D_real: 1.051 D_fake: 0.561 +(epoch: 420, iters: 6654, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.701 G_ID: 0.086 G_Rec: 0.307 D_GP: 0.047 D_real: 1.140 D_fake: 0.763 +(epoch: 420, iters: 7054, time: 0.063) G_GAN: 0.643 G_GAN_Feat: 0.952 G_ID: 0.137 G_Rec: 0.463 D_GP: 0.038 D_real: 1.136 D_fake: 0.383 +(epoch: 420, iters: 7454, time: 0.063) G_GAN: 0.407 G_GAN_Feat: 0.643 G_ID: 0.090 G_Rec: 0.271 D_GP: 0.043 D_real: 1.285 D_fake: 0.594 +(epoch: 420, iters: 7854, time: 0.063) G_GAN: 0.480 G_GAN_Feat: 0.925 G_ID: 0.126 G_Rec: 0.391 D_GP: 0.043 D_real: 1.072 D_fake: 0.529 +(epoch: 420, iters: 8254, time: 0.063) G_GAN: 0.503 G_GAN_Feat: 0.933 G_ID: 0.098 G_Rec: 0.353 D_GP: 0.282 D_real: 0.645 D_fake: 0.526 +(epoch: 420, iters: 8654, time: 0.064) G_GAN: 0.643 G_GAN_Feat: 1.010 G_ID: 0.116 G_Rec: 0.439 D_GP: 0.037 D_real: 1.175 D_fake: 0.371 +(epoch: 421, iters: 446, time: 0.063) G_GAN: 0.376 G_GAN_Feat: 0.771 G_ID: 0.122 G_Rec: 0.327 D_GP: 0.037 D_real: 1.211 D_fake: 0.640 +(epoch: 421, iters: 846, time: 0.063) G_GAN: 0.577 G_GAN_Feat: 1.018 G_ID: 0.120 G_Rec: 0.499 D_GP: 0.135 D_real: 0.932 D_fake: 0.431 +(epoch: 421, iters: 1246, time: 0.063) G_GAN: 0.283 G_GAN_Feat: 0.789 G_ID: 0.098 G_Rec: 0.318 D_GP: 0.051 D_real: 0.818 D_fake: 0.718 +(epoch: 421, iters: 1646, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 1.042 G_ID: 0.131 G_Rec: 0.459 D_GP: 0.065 D_real: 0.620 D_fake: 0.563 +(epoch: 421, iters: 2046, time: 0.063) G_GAN: 0.112 G_GAN_Feat: 0.854 G_ID: 0.130 G_Rec: 0.334 D_GP: 0.044 D_real: 0.822 D_fake: 0.889 +(epoch: 421, iters: 2446, time: 0.063) G_GAN: 0.676 G_GAN_Feat: 1.279 G_ID: 0.128 G_Rec: 0.467 D_GP: 0.047 D_real: 0.531 D_fake: 0.345 +(epoch: 421, iters: 2846, time: 0.063) G_GAN: 0.250 G_GAN_Feat: 0.842 G_ID: 0.119 G_Rec: 0.339 D_GP: 0.032 D_real: 1.092 D_fake: 0.751 +(epoch: 421, iters: 3246, time: 0.064) G_GAN: 0.535 G_GAN_Feat: 0.991 G_ID: 0.122 G_Rec: 0.458 D_GP: 0.034 D_real: 1.111 D_fake: 0.491 +(epoch: 421, iters: 3646, time: 0.063) G_GAN: -0.159 G_GAN_Feat: 0.712 G_ID: 0.095 G_Rec: 0.301 D_GP: 0.040 D_real: 0.669 D_fake: 1.159 +(epoch: 421, iters: 4046, time: 0.063) G_GAN: 0.416 G_GAN_Feat: 0.971 G_ID: 0.136 G_Rec: 0.484 D_GP: 0.033 D_real: 1.027 D_fake: 0.590 +(epoch: 421, iters: 4446, time: 0.063) G_GAN: 0.431 G_GAN_Feat: 0.990 G_ID: 0.098 G_Rec: 0.338 D_GP: 0.057 D_real: 0.657 D_fake: 0.574 +(epoch: 421, iters: 4846, time: 0.064) G_GAN: 1.212 G_GAN_Feat: 1.140 G_ID: 0.105 G_Rec: 0.446 D_GP: 0.179 D_real: 1.433 D_fake: 0.236 +(epoch: 421, iters: 5246, time: 0.063) G_GAN: 0.358 G_GAN_Feat: 1.009 G_ID: 0.097 G_Rec: 0.334 D_GP: 0.644 D_real: 0.382 D_fake: 0.708 +(epoch: 421, iters: 5646, time: 0.063) G_GAN: 0.298 G_GAN_Feat: 1.038 G_ID: 0.133 G_Rec: 0.468 D_GP: 0.072 D_real: 0.572 D_fake: 0.706 +(epoch: 421, iters: 6046, time: 0.063) G_GAN: 0.347 G_GAN_Feat: 0.963 G_ID: 0.097 G_Rec: 0.349 D_GP: 0.078 D_real: 0.379 D_fake: 0.665 +(epoch: 421, iters: 6446, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 1.223 G_ID: 0.165 G_Rec: 0.464 D_GP: 0.069 D_real: 0.773 D_fake: 0.711 +(epoch: 421, iters: 6846, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.975 G_ID: 0.099 G_Rec: 0.364 D_GP: 0.072 D_real: 0.331 D_fake: 0.812 +(epoch: 421, iters: 7246, time: 0.063) G_GAN: 0.490 G_GAN_Feat: 1.242 G_ID: 0.153 G_Rec: 0.485 D_GP: 0.060 D_real: 0.315 D_fake: 0.515 +(epoch: 421, iters: 7646, time: 0.063) G_GAN: 0.534 G_GAN_Feat: 0.817 G_ID: 0.087 G_Rec: 0.348 D_GP: 0.037 D_real: 1.109 D_fake: 0.480 +(epoch: 421, iters: 8046, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.802 G_ID: 0.120 G_Rec: 0.422 D_GP: 0.026 D_real: 1.051 D_fake: 0.759 +(epoch: 421, iters: 8446, time: 0.063) G_GAN: 0.155 G_GAN_Feat: 0.579 G_ID: 0.093 G_Rec: 0.284 D_GP: 0.026 D_real: 1.107 D_fake: 0.845 +(epoch: 422, iters: 238, time: 0.063) G_GAN: 0.459 G_GAN_Feat: 0.847 G_ID: 0.120 G_Rec: 0.413 D_GP: 0.030 D_real: 1.177 D_fake: 0.547 +(epoch: 422, iters: 638, time: 0.063) G_GAN: 0.253 G_GAN_Feat: 0.612 G_ID: 0.095 G_Rec: 0.344 D_GP: 0.030 D_real: 1.178 D_fake: 0.748 +(epoch: 422, iters: 1038, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.880 G_ID: 0.113 G_Rec: 0.422 D_GP: 0.047 D_real: 1.069 D_fake: 0.566 +(epoch: 422, iters: 1438, time: 0.063) G_GAN: 0.181 G_GAN_Feat: 0.617 G_ID: 0.096 G_Rec: 0.287 D_GP: 0.035 D_real: 1.117 D_fake: 0.820 +(epoch: 422, iters: 1838, time: 0.063) G_GAN: 0.516 G_GAN_Feat: 0.887 G_ID: 0.101 G_Rec: 0.411 D_GP: 0.039 D_real: 1.112 D_fake: 0.517 +(epoch: 422, iters: 2238, time: 0.063) G_GAN: -0.082 G_GAN_Feat: 0.618 G_ID: 0.095 G_Rec: 0.293 D_GP: 0.030 D_real: 0.766 D_fake: 1.082 +(epoch: 422, iters: 2638, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.882 G_ID: 0.112 G_Rec: 0.432 D_GP: 0.037 D_real: 1.122 D_fake: 0.599 +(epoch: 422, iters: 3038, time: 0.063) G_GAN: -0.025 G_GAN_Feat: 0.657 G_ID: 0.114 G_Rec: 0.302 D_GP: 0.035 D_real: 0.796 D_fake: 1.025 +(epoch: 422, iters: 3438, time: 0.063) G_GAN: 0.311 G_GAN_Feat: 0.906 G_ID: 0.107 G_Rec: 0.426 D_GP: 0.036 D_real: 0.897 D_fake: 0.696 +(epoch: 422, iters: 3838, time: 0.063) G_GAN: 0.108 G_GAN_Feat: 0.708 G_ID: 0.101 G_Rec: 0.341 D_GP: 0.038 D_real: 0.949 D_fake: 0.894 +(epoch: 422, iters: 4238, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.882 G_ID: 0.127 G_Rec: 0.446 D_GP: 0.034 D_real: 0.903 D_fake: 0.784 +(epoch: 422, iters: 4638, time: 0.063) G_GAN: -0.196 G_GAN_Feat: 0.767 G_ID: 0.119 G_Rec: 0.364 D_GP: 0.067 D_real: 0.471 D_fake: 1.196 +(epoch: 422, iters: 5038, time: 0.063) G_GAN: 0.376 G_GAN_Feat: 0.966 G_ID: 0.126 G_Rec: 0.490 D_GP: 0.039 D_real: 0.916 D_fake: 0.633 +(epoch: 422, iters: 5438, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 0.697 G_ID: 0.116 G_Rec: 0.307 D_GP: 0.041 D_real: 0.892 D_fake: 0.875 +(epoch: 422, iters: 5838, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.907 G_ID: 0.115 G_Rec: 0.456 D_GP: 0.036 D_real: 0.861 D_fake: 0.773 +(epoch: 422, iters: 6238, time: 0.063) G_GAN: 0.227 G_GAN_Feat: 0.691 G_ID: 0.100 G_Rec: 0.302 D_GP: 0.031 D_real: 1.094 D_fake: 0.774 +(epoch: 422, iters: 6638, time: 0.063) G_GAN: 0.458 G_GAN_Feat: 0.966 G_ID: 0.112 G_Rec: 0.439 D_GP: 0.055 D_real: 1.055 D_fake: 0.544 +(epoch: 422, iters: 7038, time: 0.063) G_GAN: 0.202 G_GAN_Feat: 0.831 G_ID: 0.102 G_Rec: 0.333 D_GP: 0.126 D_real: 0.808 D_fake: 0.800 +(epoch: 422, iters: 7438, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.916 G_ID: 0.128 G_Rec: 0.422 D_GP: 0.031 D_real: 1.007 D_fake: 0.650 +(epoch: 422, iters: 7838, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.604 G_ID: 0.096 G_Rec: 0.291 D_GP: 0.027 D_real: 1.219 D_fake: 0.766 +(epoch: 422, iters: 8238, time: 0.063) G_GAN: 0.485 G_GAN_Feat: 0.915 G_ID: 0.118 G_Rec: 0.425 D_GP: 0.032 D_real: 1.266 D_fake: 0.520 +(epoch: 422, iters: 8638, time: 0.063) G_GAN: 0.208 G_GAN_Feat: 0.704 G_ID: 0.102 G_Rec: 0.320 D_GP: 0.035 D_real: 1.066 D_fake: 0.792 +(epoch: 423, iters: 430, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 0.925 G_ID: 0.109 G_Rec: 0.424 D_GP: 0.050 D_real: 1.197 D_fake: 0.395 +(epoch: 423, iters: 830, time: 0.063) G_GAN: -0.040 G_GAN_Feat: 0.766 G_ID: 0.108 G_Rec: 0.358 D_GP: 0.062 D_real: 0.821 D_fake: 1.040 +(epoch: 423, iters: 1230, time: 0.063) G_GAN: 0.518 G_GAN_Feat: 0.987 G_ID: 0.112 G_Rec: 0.484 D_GP: 0.068 D_real: 0.872 D_fake: 0.509 +(epoch: 423, iters: 1630, time: 0.063) G_GAN: 0.139 G_GAN_Feat: 0.756 G_ID: 0.099 G_Rec: 0.295 D_GP: 0.051 D_real: 0.779 D_fake: 0.861 +(epoch: 423, iters: 2030, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.910 G_ID: 0.136 G_Rec: 0.413 D_GP: 0.058 D_real: 0.891 D_fake: 0.585 +(epoch: 423, iters: 2430, time: 0.063) G_GAN: 0.436 G_GAN_Feat: 0.783 G_ID: 0.108 G_Rec: 0.310 D_GP: 0.067 D_real: 0.959 D_fake: 0.575 +(epoch: 423, iters: 2830, time: 0.063) G_GAN: 0.418 G_GAN_Feat: 0.910 G_ID: 0.105 G_Rec: 0.394 D_GP: 0.036 D_real: 1.052 D_fake: 0.583 +(epoch: 423, iters: 3230, time: 0.063) G_GAN: 0.246 G_GAN_Feat: 0.730 G_ID: 0.083 G_Rec: 0.304 D_GP: 0.038 D_real: 1.088 D_fake: 0.754 +(epoch: 423, iters: 3630, time: 0.064) G_GAN: 0.671 G_GAN_Feat: 0.981 G_ID: 0.108 G_Rec: 0.429 D_GP: 0.126 D_real: 0.951 D_fake: 0.370 +(epoch: 423, iters: 4030, time: 0.063) G_GAN: 0.148 G_GAN_Feat: 0.776 G_ID: 0.108 G_Rec: 0.307 D_GP: 0.054 D_real: 0.771 D_fake: 0.852 +(epoch: 423, iters: 4430, time: 0.063) G_GAN: 0.315 G_GAN_Feat: 1.010 G_ID: 0.113 G_Rec: 0.494 D_GP: 0.043 D_real: 0.852 D_fake: 0.700 +(epoch: 423, iters: 4830, time: 0.063) G_GAN: 0.199 G_GAN_Feat: 0.780 G_ID: 0.086 G_Rec: 0.315 D_GP: 0.067 D_real: 0.911 D_fake: 0.803 +(epoch: 423, iters: 5230, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.978 G_ID: 0.123 G_Rec: 0.443 D_GP: 0.046 D_real: 0.670 D_fake: 0.829 +(epoch: 423, iters: 5630, time: 0.064) G_GAN: -0.109 G_GAN_Feat: 0.899 G_ID: 0.092 G_Rec: 0.373 D_GP: 0.155 D_real: 0.205 D_fake: 1.111 +(epoch: 423, iters: 6030, time: 0.063) G_GAN: 0.274 G_GAN_Feat: 0.974 G_ID: 0.135 G_Rec: 0.424 D_GP: 0.040 D_real: 0.683 D_fake: 0.731 +(epoch: 423, iters: 6430, time: 0.063) G_GAN: 0.108 G_GAN_Feat: 0.861 G_ID: 0.101 G_Rec: 0.357 D_GP: 0.094 D_real: 0.569 D_fake: 0.892 +(epoch: 423, iters: 6830, time: 0.064) G_GAN: 0.629 G_GAN_Feat: 0.995 G_ID: 0.116 G_Rec: 0.490 D_GP: 0.052 D_real: 0.995 D_fake: 0.402 +(epoch: 423, iters: 7230, time: 0.063) G_GAN: 0.221 G_GAN_Feat: 0.752 G_ID: 0.097 G_Rec: 0.340 D_GP: 0.034 D_real: 1.074 D_fake: 0.779 +(epoch: 423, iters: 7630, time: 0.063) G_GAN: 0.546 G_GAN_Feat: 0.962 G_ID: 0.114 G_Rec: 0.430 D_GP: 0.043 D_real: 1.177 D_fake: 0.500 +(epoch: 423, iters: 8030, time: 0.063) G_GAN: 0.442 G_GAN_Feat: 0.835 G_ID: 0.091 G_Rec: 0.315 D_GP: 0.054 D_real: 1.045 D_fake: 0.562 +(epoch: 423, iters: 8430, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.997 G_ID: 0.104 G_Rec: 0.529 D_GP: 0.036 D_real: 0.774 D_fake: 0.622 +(epoch: 424, iters: 222, time: 0.063) G_GAN: 0.245 G_GAN_Feat: 0.826 G_ID: 0.111 G_Rec: 0.297 D_GP: 0.050 D_real: 0.698 D_fake: 0.761 +(epoch: 424, iters: 622, time: 0.063) G_GAN: 0.446 G_GAN_Feat: 0.869 G_ID: 0.114 G_Rec: 0.415 D_GP: 0.026 D_real: 1.185 D_fake: 0.557 +(epoch: 424, iters: 1022, time: 0.063) G_GAN: 0.259 G_GAN_Feat: 0.692 G_ID: 0.103 G_Rec: 0.305 D_GP: 0.032 D_real: 1.113 D_fake: 0.742 +(epoch: 424, iters: 1422, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 1.008 G_ID: 0.111 G_Rec: 0.450 D_GP: 0.050 D_real: 0.725 D_fake: 0.684 +(epoch: 424, iters: 1822, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.781 G_ID: 0.093 G_Rec: 0.301 D_GP: 0.045 D_real: 0.999 D_fake: 0.532 +(epoch: 424, iters: 2222, time: 0.063) G_GAN: 0.962 G_GAN_Feat: 1.097 G_ID: 0.120 G_Rec: 0.462 D_GP: 0.139 D_real: 0.944 D_fake: 0.201 +(epoch: 424, iters: 2622, time: 0.063) G_GAN: 0.223 G_GAN_Feat: 0.715 G_ID: 0.093 G_Rec: 0.304 D_GP: 0.036 D_real: 1.119 D_fake: 0.780 +(epoch: 424, iters: 3022, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.880 G_ID: 0.133 G_Rec: 0.382 D_GP: 0.029 D_real: 1.202 D_fake: 0.524 +(epoch: 424, iters: 3422, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.907 G_ID: 0.105 G_Rec: 0.342 D_GP: 0.136 D_real: 0.499 D_fake: 0.588 +(epoch: 424, iters: 3822, time: 0.063) G_GAN: 0.869 G_GAN_Feat: 1.011 G_ID: 0.131 G_Rec: 0.454 D_GP: 0.045 D_real: 1.296 D_fake: 0.209 +(epoch: 424, iters: 4222, time: 0.063) G_GAN: 0.123 G_GAN_Feat: 0.700 G_ID: 0.089 G_Rec: 0.309 D_GP: 0.025 D_real: 1.073 D_fake: 0.877 +(epoch: 424, iters: 4622, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.960 G_ID: 0.103 G_Rec: 0.437 D_GP: 0.031 D_real: 0.849 D_fake: 0.765 +(epoch: 424, iters: 5022, time: 0.063) G_GAN: -0.190 G_GAN_Feat: 0.747 G_ID: 0.108 G_Rec: 0.297 D_GP: 0.033 D_real: 0.817 D_fake: 1.191 +(epoch: 424, iters: 5422, time: 0.063) G_GAN: 0.808 G_GAN_Feat: 1.071 G_ID: 0.105 G_Rec: 0.485 D_GP: 0.034 D_real: 0.946 D_fake: 0.274 +(epoch: 424, iters: 5822, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.788 G_ID: 0.101 G_Rec: 0.313 D_GP: 0.040 D_real: 0.954 D_fake: 0.811 +(epoch: 424, iters: 6222, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 1.124 G_ID: 0.131 G_Rec: 0.444 D_GP: 0.042 D_real: 0.984 D_fake: 0.761 +(epoch: 424, iters: 6622, time: 0.063) G_GAN: -0.164 G_GAN_Feat: 0.752 G_ID: 0.121 G_Rec: 0.325 D_GP: 0.031 D_real: 0.752 D_fake: 1.164 +(epoch: 424, iters: 7022, time: 0.063) G_GAN: 0.260 G_GAN_Feat: 0.974 G_ID: 0.113 G_Rec: 0.457 D_GP: 0.035 D_real: 0.852 D_fake: 0.742 +(epoch: 424, iters: 7422, time: 0.063) G_GAN: 0.350 G_GAN_Feat: 0.750 G_ID: 0.090 G_Rec: 0.313 D_GP: 0.035 D_real: 1.176 D_fake: 0.652 +(epoch: 424, iters: 7822, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.935 G_ID: 0.110 G_Rec: 0.419 D_GP: 0.027 D_real: 0.901 D_fake: 0.784 +(epoch: 424, iters: 8222, time: 0.063) G_GAN: -0.044 G_GAN_Feat: 0.764 G_ID: 0.100 G_Rec: 0.322 D_GP: 0.034 D_real: 0.742 D_fake: 1.045 +(epoch: 424, iters: 8622, time: 0.063) G_GAN: 0.254 G_GAN_Feat: 1.003 G_ID: 0.111 G_Rec: 0.425 D_GP: 0.147 D_real: 0.652 D_fake: 0.747 +(epoch: 425, iters: 414, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.683 G_ID: 0.123 G_Rec: 0.278 D_GP: 0.031 D_real: 1.165 D_fake: 0.786 +(epoch: 425, iters: 814, time: 0.064) G_GAN: 0.849 G_GAN_Feat: 1.068 G_ID: 0.107 G_Rec: 0.485 D_GP: 0.111 D_real: 1.138 D_fake: 0.230 +(epoch: 425, iters: 1214, time: 0.063) G_GAN: 0.504 G_GAN_Feat: 0.728 G_ID: 0.096 G_Rec: 0.310 D_GP: 0.031 D_real: 1.430 D_fake: 0.504 +(epoch: 425, iters: 1614, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.852 G_ID: 0.134 G_Rec: 0.354 D_GP: 0.038 D_real: 0.920 D_fake: 0.786 +(epoch: 425, iters: 2014, time: 0.063) G_GAN: 0.030 G_GAN_Feat: 0.739 G_ID: 0.128 G_Rec: 0.299 D_GP: 0.043 D_real: 0.943 D_fake: 0.970 +(epoch: 425, iters: 2414, time: 0.064) G_GAN: 0.696 G_GAN_Feat: 0.861 G_ID: 0.132 G_Rec: 0.419 D_GP: 0.034 D_real: 1.407 D_fake: 0.330 +(epoch: 425, iters: 2814, time: 0.063) G_GAN: 0.143 G_GAN_Feat: 0.644 G_ID: 0.105 G_Rec: 0.287 D_GP: 0.030 D_real: 1.033 D_fake: 0.857 +(epoch: 425, iters: 3214, time: 0.063) G_GAN: 0.397 G_GAN_Feat: 0.875 G_ID: 0.120 G_Rec: 0.434 D_GP: 0.037 D_real: 1.033 D_fake: 0.615 +(epoch: 425, iters: 3614, time: 0.063) G_GAN: 0.035 G_GAN_Feat: 0.696 G_ID: 0.107 G_Rec: 0.318 D_GP: 0.044 D_real: 0.885 D_fake: 0.965 +(epoch: 425, iters: 4014, time: 0.063) G_GAN: 0.194 G_GAN_Feat: 0.910 G_ID: 0.111 G_Rec: 0.406 D_GP: 0.057 D_real: 0.761 D_fake: 0.807 +(epoch: 425, iters: 4414, time: 0.063) G_GAN: 0.018 G_GAN_Feat: 0.732 G_ID: 0.093 G_Rec: 0.321 D_GP: 0.056 D_real: 0.805 D_fake: 0.982 +(epoch: 425, iters: 4814, time: 0.063) G_GAN: 0.223 G_GAN_Feat: 0.843 G_ID: 0.129 G_Rec: 0.387 D_GP: 0.032 D_real: 0.990 D_fake: 0.777 +(epoch: 425, iters: 5214, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.819 G_ID: 0.092 G_Rec: 0.360 D_GP: 0.084 D_real: 0.753 D_fake: 0.834 +(epoch: 425, iters: 5614, time: 0.063) G_GAN: 0.247 G_GAN_Feat: 0.939 G_ID: 0.113 G_Rec: 0.449 D_GP: 0.038 D_real: 0.906 D_fake: 0.754 +(epoch: 425, iters: 6014, time: 0.063) G_GAN: 0.247 G_GAN_Feat: 0.686 G_ID: 0.099 G_Rec: 0.283 D_GP: 0.052 D_real: 1.038 D_fake: 0.753 +(epoch: 425, iters: 6414, time: 0.063) G_GAN: 0.917 G_GAN_Feat: 0.970 G_ID: 0.102 G_Rec: 0.435 D_GP: 0.038 D_real: 1.505 D_fake: 0.173 +(epoch: 425, iters: 6814, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.766 G_ID: 0.110 G_Rec: 0.304 D_GP: 0.033 D_real: 0.848 D_fake: 0.865 +(epoch: 425, iters: 7214, time: 0.063) G_GAN: 0.529 G_GAN_Feat: 0.988 G_ID: 0.115 G_Rec: 0.441 D_GP: 0.033 D_real: 1.092 D_fake: 0.474 +(epoch: 425, iters: 7614, time: 0.063) G_GAN: 0.290 G_GAN_Feat: 0.806 G_ID: 0.112 G_Rec: 0.312 D_GP: 0.046 D_real: 0.861 D_fake: 0.731 +(epoch: 425, iters: 8014, time: 0.063) G_GAN: 0.491 G_GAN_Feat: 0.907 G_ID: 0.145 G_Rec: 0.474 D_GP: 0.034 D_real: 1.101 D_fake: 0.515 +(epoch: 425, iters: 8414, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.690 G_ID: 0.113 G_Rec: 0.333 D_GP: 0.031 D_real: 1.146 D_fake: 0.731 +(epoch: 426, iters: 206, time: 0.063) G_GAN: 0.746 G_GAN_Feat: 0.903 G_ID: 0.114 G_Rec: 0.446 D_GP: 0.035 D_real: 1.331 D_fake: 0.345 +(epoch: 426, iters: 606, time: 0.063) G_GAN: 0.028 G_GAN_Feat: 0.817 G_ID: 0.095 G_Rec: 0.351 D_GP: 0.061 D_real: 0.648 D_fake: 0.972 +(epoch: 426, iters: 1006, time: 0.063) G_GAN: 0.474 G_GAN_Feat: 0.867 G_ID: 0.122 G_Rec: 0.392 D_GP: 0.036 D_real: 1.146 D_fake: 0.545 +(epoch: 426, iters: 1406, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.803 G_ID: 0.104 G_Rec: 0.373 D_GP: 0.042 D_real: 0.956 D_fake: 0.818 +(epoch: 426, iters: 1806, time: 0.063) G_GAN: 0.716 G_GAN_Feat: 1.045 G_ID: 0.106 G_Rec: 0.486 D_GP: 0.033 D_real: 1.416 D_fake: 0.305 +(epoch: 426, iters: 2206, time: 0.063) G_GAN: 0.216 G_GAN_Feat: 0.706 G_ID: 0.088 G_Rec: 0.267 D_GP: 0.039 D_real: 1.048 D_fake: 0.784 +(epoch: 426, iters: 2606, time: 0.063) G_GAN: 0.434 G_GAN_Feat: 0.916 G_ID: 0.100 G_Rec: 0.440 D_GP: 0.027 D_real: 1.137 D_fake: 0.572 +(epoch: 426, iters: 3006, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.774 G_ID: 0.090 G_Rec: 0.298 D_GP: 0.032 D_real: 1.020 D_fake: 0.752 +(epoch: 426, iters: 3406, time: 0.063) G_GAN: 0.445 G_GAN_Feat: 0.951 G_ID: 0.137 G_Rec: 0.397 D_GP: 0.039 D_real: 1.055 D_fake: 0.574 +(epoch: 426, iters: 3806, time: 0.063) G_GAN: -0.328 G_GAN_Feat: 1.137 G_ID: 0.119 G_Rec: 0.375 D_GP: 0.220 D_real: 0.927 D_fake: 1.328 +(epoch: 426, iters: 4206, time: 0.063) G_GAN: 0.348 G_GAN_Feat: 0.880 G_ID: 0.126 G_Rec: 0.462 D_GP: 0.024 D_real: 1.076 D_fake: 0.658 +(epoch: 426, iters: 4606, time: 0.064) G_GAN: -0.098 G_GAN_Feat: 0.696 G_ID: 0.092 G_Rec: 0.311 D_GP: 0.037 D_real: 0.810 D_fake: 1.098 +(epoch: 426, iters: 5006, time: 0.063) G_GAN: 0.276 G_GAN_Feat: 0.955 G_ID: 0.129 G_Rec: 0.436 D_GP: 0.063 D_real: 0.751 D_fake: 0.726 +(epoch: 426, iters: 5406, time: 0.063) G_GAN: 0.359 G_GAN_Feat: 0.823 G_ID: 0.103 G_Rec: 0.343 D_GP: 0.044 D_real: 0.845 D_fake: 0.645 +(epoch: 426, iters: 5806, time: 0.063) G_GAN: 0.337 G_GAN_Feat: 0.897 G_ID: 0.114 G_Rec: 0.414 D_GP: 0.035 D_real: 0.999 D_fake: 0.668 +(epoch: 426, iters: 6206, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.717 G_ID: 0.092 G_Rec: 0.314 D_GP: 0.033 D_real: 0.986 D_fake: 0.874 +(epoch: 426, iters: 6606, time: 0.063) G_GAN: 0.324 G_GAN_Feat: 1.064 G_ID: 0.125 G_Rec: 0.438 D_GP: 0.063 D_real: 0.718 D_fake: 0.677 +(epoch: 426, iters: 7006, time: 0.063) G_GAN: 0.179 G_GAN_Feat: 0.767 G_ID: 0.090 G_Rec: 0.319 D_GP: 0.060 D_real: 0.827 D_fake: 0.821 +(epoch: 426, iters: 7406, time: 0.063) G_GAN: 0.669 G_GAN_Feat: 1.028 G_ID: 0.111 G_Rec: 0.441 D_GP: 0.049 D_real: 0.932 D_fake: 0.347 +(epoch: 426, iters: 7806, time: 0.064) G_GAN: -0.125 G_GAN_Feat: 0.771 G_ID: 0.090 G_Rec: 0.330 D_GP: 0.068 D_real: 0.626 D_fake: 1.125 +(epoch: 426, iters: 8206, time: 0.063) G_GAN: 0.573 G_GAN_Feat: 1.013 G_ID: 0.106 G_Rec: 0.491 D_GP: 0.030 D_real: 0.967 D_fake: 0.461 +(epoch: 426, iters: 8606, time: 0.063) G_GAN: 0.355 G_GAN_Feat: 0.839 G_ID: 0.108 G_Rec: 0.310 D_GP: 0.067 D_real: 0.961 D_fake: 0.647 +(epoch: 427, iters: 398, time: 0.063) G_GAN: 0.832 G_GAN_Feat: 1.136 G_ID: 0.117 G_Rec: 0.452 D_GP: 0.113 D_real: 0.678 D_fake: 0.328 +(epoch: 427, iters: 798, time: 0.064) G_GAN: -0.039 G_GAN_Feat: 0.724 G_ID: 0.119 G_Rec: 0.315 D_GP: 0.034 D_real: 0.835 D_fake: 1.040 +(epoch: 427, iters: 1198, time: 0.064) G_GAN: 0.630 G_GAN_Feat: 0.965 G_ID: 0.117 G_Rec: 0.454 D_GP: 0.036 D_real: 1.197 D_fake: 0.387 +(epoch: 427, iters: 1598, time: 0.063) G_GAN: -0.024 G_GAN_Feat: 0.826 G_ID: 0.089 G_Rec: 0.344 D_GP: 0.076 D_real: 0.561 D_fake: 1.025 +(epoch: 427, iters: 1998, time: 0.063) G_GAN: 1.528 G_GAN_Feat: 1.058 G_ID: 0.116 G_Rec: 0.505 D_GP: 0.165 D_real: 1.850 D_fake: 0.025 +(epoch: 427, iters: 2398, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.718 G_ID: 0.099 G_Rec: 0.309 D_GP: 0.040 D_real: 0.904 D_fake: 0.881 +(epoch: 427, iters: 2798, time: 0.063) G_GAN: 0.768 G_GAN_Feat: 0.973 G_ID: 0.124 G_Rec: 0.433 D_GP: 0.134 D_real: 1.051 D_fake: 0.351 +(epoch: 427, iters: 3198, time: 0.063) G_GAN: 0.173 G_GAN_Feat: 0.837 G_ID: 0.100 G_Rec: 0.338 D_GP: 0.047 D_real: 0.698 D_fake: 0.828 +(epoch: 427, iters: 3598, time: 0.063) G_GAN: 0.965 G_GAN_Feat: 1.248 G_ID: 0.135 G_Rec: 0.495 D_GP: 0.084 D_real: 0.494 D_fake: 0.150 +(epoch: 427, iters: 3998, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.954 G_ID: 0.099 G_Rec: 0.346 D_GP: 0.055 D_real: 0.729 D_fake: 0.605 +(epoch: 427, iters: 4398, time: 0.063) G_GAN: 0.846 G_GAN_Feat: 0.996 G_ID: 0.110 G_Rec: 0.381 D_GP: 0.038 D_real: 1.394 D_fake: 0.211 +(epoch: 427, iters: 4798, time: 0.063) G_GAN: 0.285 G_GAN_Feat: 0.765 G_ID: 0.116 G_Rec: 0.339 D_GP: 0.033 D_real: 1.064 D_fake: 0.716 +(epoch: 427, iters: 5198, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 0.924 G_ID: 0.136 G_Rec: 0.463 D_GP: 0.031 D_real: 0.901 D_fake: 0.791 +(epoch: 427, iters: 5598, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.859 G_ID: 0.091 G_Rec: 0.344 D_GP: 0.352 D_real: 0.583 D_fake: 0.897 +(epoch: 427, iters: 5998, time: 0.063) G_GAN: 0.187 G_GAN_Feat: 0.964 G_ID: 0.124 G_Rec: 0.421 D_GP: 0.038 D_real: 0.852 D_fake: 0.815 +(epoch: 427, iters: 6398, time: 0.063) G_GAN: 0.252 G_GAN_Feat: 0.693 G_ID: 0.116 G_Rec: 0.295 D_GP: 0.027 D_real: 1.105 D_fake: 0.748 +(epoch: 427, iters: 6798, time: 0.063) G_GAN: 0.672 G_GAN_Feat: 0.931 G_ID: 0.119 G_Rec: 0.434 D_GP: 0.032 D_real: 1.382 D_fake: 0.343 +(epoch: 427, iters: 7198, time: 0.064) G_GAN: 0.078 G_GAN_Feat: 0.805 G_ID: 0.114 G_Rec: 0.356 D_GP: 0.107 D_real: 0.756 D_fake: 0.924 +(epoch: 427, iters: 7598, time: 0.063) G_GAN: 0.397 G_GAN_Feat: 1.204 G_ID: 0.115 G_Rec: 0.467 D_GP: 0.090 D_real: 0.229 D_fake: 0.606 +(epoch: 427, iters: 7998, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.740 G_ID: 0.091 G_Rec: 0.306 D_GP: 0.029 D_real: 1.053 D_fake: 0.877 +(epoch: 427, iters: 8398, time: 0.063) G_GAN: 0.544 G_GAN_Feat: 0.955 G_ID: 0.122 G_Rec: 0.417 D_GP: 0.042 D_real: 1.097 D_fake: 0.490 +(epoch: 428, iters: 190, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.761 G_ID: 0.097 G_Rec: 0.299 D_GP: 0.052 D_real: 1.226 D_fake: 0.656 +(epoch: 428, iters: 590, time: 0.063) G_GAN: 0.763 G_GAN_Feat: 1.187 G_ID: 0.134 G_Rec: 0.524 D_GP: 0.151 D_real: 0.419 D_fake: 0.306 +(epoch: 428, iters: 990, time: 0.063) G_GAN: 0.535 G_GAN_Feat: 0.816 G_ID: 0.109 G_Rec: 0.315 D_GP: 0.060 D_real: 1.268 D_fake: 0.509 +(epoch: 428, iters: 1390, time: 0.063) G_GAN: 0.498 G_GAN_Feat: 1.129 G_ID: 0.146 G_Rec: 0.450 D_GP: 0.091 D_real: 0.368 D_fake: 0.504 +(epoch: 428, iters: 1790, time: 0.064) G_GAN: 0.865 G_GAN_Feat: 0.956 G_ID: 0.109 G_Rec: 0.338 D_GP: 0.050 D_real: 0.883 D_fake: 0.204 +(epoch: 428, iters: 2190, time: 0.063) G_GAN: 0.955 G_GAN_Feat: 1.040 G_ID: 0.103 G_Rec: 0.433 D_GP: 0.078 D_real: 1.454 D_fake: 0.224 +(epoch: 428, iters: 2590, time: 0.063) G_GAN: 0.107 G_GAN_Feat: 0.649 G_ID: 0.094 G_Rec: 0.263 D_GP: 0.026 D_real: 1.068 D_fake: 0.893 +(epoch: 428, iters: 2990, time: 0.063) G_GAN: 0.514 G_GAN_Feat: 1.101 G_ID: 0.125 G_Rec: 0.488 D_GP: 0.090 D_real: 0.563 D_fake: 0.500 +(epoch: 428, iters: 3390, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.897 G_ID: 0.093 G_Rec: 0.300 D_GP: 0.143 D_real: 0.531 D_fake: 0.561 +(epoch: 428, iters: 3790, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 1.053 G_ID: 0.118 G_Rec: 0.433 D_GP: 0.052 D_real: 0.966 D_fake: 0.374 +(epoch: 428, iters: 4190, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.817 G_ID: 0.090 G_Rec: 0.318 D_GP: 0.049 D_real: 0.826 D_fake: 0.674 +(epoch: 428, iters: 4590, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 1.190 G_ID: 0.144 G_Rec: 0.439 D_GP: 0.044 D_real: 1.077 D_fake: 0.837 +(epoch: 428, iters: 4990, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.610 G_ID: 0.097 G_Rec: 0.297 D_GP: 0.022 D_real: 1.201 D_fake: 0.760 +(epoch: 428, iters: 5390, time: 0.063) G_GAN: 0.533 G_GAN_Feat: 0.913 G_ID: 0.105 G_Rec: 0.429 D_GP: 0.031 D_real: 1.190 D_fake: 0.491 +(epoch: 428, iters: 5790, time: 0.063) G_GAN: 0.191 G_GAN_Feat: 0.693 G_ID: 0.092 G_Rec: 0.304 D_GP: 0.037 D_real: 1.011 D_fake: 0.809 +(epoch: 428, iters: 6190, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 0.887 G_ID: 0.123 G_Rec: 0.450 D_GP: 0.039 D_real: 0.760 D_fake: 0.765 +(epoch: 428, iters: 6590, time: 0.064) G_GAN: 0.035 G_GAN_Feat: 0.763 G_ID: 0.106 G_Rec: 0.327 D_GP: 0.061 D_real: 0.926 D_fake: 0.966 +(epoch: 428, iters: 6990, time: 0.063) G_GAN: 0.642 G_GAN_Feat: 0.908 G_ID: 0.112 G_Rec: 0.438 D_GP: 0.040 D_real: 1.195 D_fake: 0.402 +(epoch: 428, iters: 7390, time: 0.063) G_GAN: 0.058 G_GAN_Feat: 0.812 G_ID: 0.086 G_Rec: 0.346 D_GP: 0.146 D_real: 0.801 D_fake: 0.942 +(epoch: 428, iters: 7790, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 0.963 G_ID: 0.114 G_Rec: 0.406 D_GP: 0.044 D_real: 0.891 D_fake: 0.595 +(epoch: 428, iters: 8190, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.786 G_ID: 0.094 G_Rec: 0.320 D_GP: 0.068 D_real: 0.978 D_fake: 0.666 +(epoch: 428, iters: 8590, time: 0.063) G_GAN: 0.313 G_GAN_Feat: 1.035 G_ID: 0.144 G_Rec: 0.417 D_GP: 0.060 D_real: 0.576 D_fake: 0.688 +(epoch: 429, iters: 382, time: 0.063) G_GAN: 0.439 G_GAN_Feat: 0.815 G_ID: 0.099 G_Rec: 0.334 D_GP: 0.061 D_real: 0.999 D_fake: 0.563 +(epoch: 429, iters: 782, time: 0.063) G_GAN: 0.384 G_GAN_Feat: 0.975 G_ID: 0.124 G_Rec: 0.411 D_GP: 0.043 D_real: 0.904 D_fake: 0.618 +(epoch: 429, iters: 1182, time: 0.064) G_GAN: -0.109 G_GAN_Feat: 0.890 G_ID: 0.100 G_Rec: 0.349 D_GP: 0.141 D_real: 0.510 D_fake: 1.109 +(epoch: 429, iters: 1582, time: 0.063) G_GAN: 0.679 G_GAN_Feat: 1.033 G_ID: 0.098 G_Rec: 0.417 D_GP: 0.050 D_real: 1.031 D_fake: 0.338 +(epoch: 429, iters: 1982, time: 0.063) G_GAN: 0.089 G_GAN_Feat: 0.731 G_ID: 0.096 G_Rec: 0.300 D_GP: 0.041 D_real: 0.952 D_fake: 0.911 +(epoch: 429, iters: 2382, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 1.041 G_ID: 0.116 G_Rec: 0.454 D_GP: 0.092 D_real: 0.644 D_fake: 0.640 +(epoch: 429, iters: 2782, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.755 G_ID: 0.093 G_Rec: 0.297 D_GP: 0.046 D_real: 1.242 D_fake: 0.691 +(epoch: 429, iters: 3182, time: 0.064) G_GAN: 1.030 G_GAN_Feat: 1.183 G_ID: 0.110 G_Rec: 0.479 D_GP: 0.110 D_real: 0.924 D_fake: 0.184 +(epoch: 429, iters: 3582, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.811 G_ID: 0.100 G_Rec: 0.332 D_GP: 0.032 D_real: 1.019 D_fake: 0.834 +(epoch: 429, iters: 3982, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 1.111 G_ID: 0.125 G_Rec: 0.464 D_GP: 0.074 D_real: 0.684 D_fake: 0.644 +(epoch: 429, iters: 4382, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.730 G_ID: 0.092 G_Rec: 0.302 D_GP: 0.032 D_real: 1.438 D_fake: 0.520 +(epoch: 429, iters: 4782, time: 0.064) G_GAN: 0.617 G_GAN_Feat: 1.035 G_ID: 0.108 G_Rec: 0.475 D_GP: 0.057 D_real: 1.071 D_fake: 0.395 +(epoch: 429, iters: 5182, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.754 G_ID: 0.091 G_Rec: 0.310 D_GP: 0.051 D_real: 0.995 D_fake: 0.629 +(epoch: 429, iters: 5582, time: 0.064) G_GAN: 0.701 G_GAN_Feat: 1.055 G_ID: 0.111 G_Rec: 0.410 D_GP: 0.108 D_real: 0.908 D_fake: 0.418 +(epoch: 429, iters: 5982, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.887 G_ID: 0.111 G_Rec: 0.314 D_GP: 0.039 D_real: 0.859 D_fake: 0.836 +(epoch: 429, iters: 6382, time: 0.064) G_GAN: 0.688 G_GAN_Feat: 1.203 G_ID: 0.116 G_Rec: 0.487 D_GP: 0.063 D_real: 0.342 D_fake: 0.336 +(epoch: 429, iters: 6782, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.901 G_ID: 0.108 G_Rec: 0.309 D_GP: 0.037 D_real: 0.597 D_fake: 0.904 +(epoch: 429, iters: 7182, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.797 G_ID: 0.112 G_Rec: 0.398 D_GP: 0.027 D_real: 1.157 D_fake: 0.611 +(epoch: 429, iters: 7582, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.664 G_ID: 0.115 G_Rec: 0.347 D_GP: 0.026 D_real: 1.120 D_fake: 0.796 +(epoch: 429, iters: 7982, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.899 G_ID: 0.121 G_Rec: 0.429 D_GP: 0.030 D_real: 1.035 D_fake: 0.663 +(epoch: 429, iters: 8382, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.641 G_ID: 0.094 G_Rec: 0.296 D_GP: 0.028 D_real: 1.071 D_fake: 0.863 +(epoch: 430, iters: 174, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.855 G_ID: 0.128 G_Rec: 0.408 D_GP: 0.032 D_real: 0.930 D_fake: 0.692 +(epoch: 430, iters: 574, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.709 G_ID: 0.120 G_Rec: 0.343 D_GP: 0.035 D_real: 0.857 D_fake: 0.897 +(epoch: 430, iters: 974, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.863 G_ID: 0.124 G_Rec: 0.426 D_GP: 0.038 D_real: 0.944 D_fake: 0.673 +(epoch: 430, iters: 1374, time: 0.064) G_GAN: -0.065 G_GAN_Feat: 0.762 G_ID: 0.116 G_Rec: 0.357 D_GP: 0.072 D_real: 0.683 D_fake: 1.065 +(epoch: 430, iters: 1774, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.918 G_ID: 0.110 G_Rec: 0.416 D_GP: 0.054 D_real: 0.786 D_fake: 0.769 +(epoch: 430, iters: 2174, time: 0.063) G_GAN: 0.160 G_GAN_Feat: 0.729 G_ID: 0.095 G_Rec: 0.314 D_GP: 0.042 D_real: 0.932 D_fake: 0.841 +(epoch: 430, iters: 2574, time: 0.063) G_GAN: 0.215 G_GAN_Feat: 0.874 G_ID: 0.124 G_Rec: 0.403 D_GP: 0.040 D_real: 0.808 D_fake: 0.785 +(epoch: 430, iters: 2974, time: 0.063) G_GAN: 0.228 G_GAN_Feat: 0.825 G_ID: 0.123 G_Rec: 0.374 D_GP: 0.064 D_real: 0.933 D_fake: 0.780 +(epoch: 430, iters: 3374, time: 0.064) G_GAN: 0.582 G_GAN_Feat: 0.940 G_ID: 0.125 G_Rec: 0.442 D_GP: 0.033 D_real: 1.160 D_fake: 0.425 +(epoch: 430, iters: 3774, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.734 G_ID: 0.094 G_Rec: 0.300 D_GP: 0.048 D_real: 1.053 D_fake: 0.701 +(epoch: 430, iters: 4174, time: 0.064) G_GAN: 0.701 G_GAN_Feat: 1.006 G_ID: 0.116 G_Rec: 0.446 D_GP: 0.049 D_real: 1.091 D_fake: 0.328 +(epoch: 430, iters: 4574, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.745 G_ID: 0.092 G_Rec: 0.304 D_GP: 0.046 D_real: 0.928 D_fake: 0.897 +(epoch: 430, iters: 4974, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 0.989 G_ID: 0.104 G_Rec: 0.468 D_GP: 0.032 D_real: 1.195 D_fake: 0.427 +(epoch: 430, iters: 5374, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.769 G_ID: 0.106 G_Rec: 0.311 D_GP: 0.035 D_real: 1.108 D_fake: 0.683 +(epoch: 430, iters: 5774, time: 0.064) G_GAN: 0.663 G_GAN_Feat: 1.013 G_ID: 0.118 G_Rec: 0.450 D_GP: 0.039 D_real: 1.114 D_fake: 0.349 +(epoch: 430, iters: 6174, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.654 G_ID: 0.102 G_Rec: 0.302 D_GP: 0.033 D_real: 1.209 D_fake: 0.718 +(epoch: 430, iters: 6574, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.906 G_ID: 0.129 G_Rec: 0.443 D_GP: 0.048 D_real: 0.925 D_fake: 0.676 +(epoch: 430, iters: 6974, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.715 G_ID: 0.088 G_Rec: 0.301 D_GP: 0.037 D_real: 1.183 D_fake: 0.669 +(epoch: 430, iters: 7374, time: 0.064) G_GAN: 0.487 G_GAN_Feat: 0.978 G_ID: 0.149 G_Rec: 0.449 D_GP: 0.063 D_real: 0.984 D_fake: 0.525 +(epoch: 430, iters: 7774, time: 0.064) G_GAN: -0.058 G_GAN_Feat: 0.732 G_ID: 0.119 G_Rec: 0.291 D_GP: 0.046 D_real: 0.743 D_fake: 1.058 +(epoch: 430, iters: 8174, time: 0.064) G_GAN: 0.579 G_GAN_Feat: 0.944 G_ID: 0.110 G_Rec: 0.432 D_GP: 0.029 D_real: 1.274 D_fake: 0.427 +(epoch: 430, iters: 8574, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.729 G_ID: 0.106 G_Rec: 0.319 D_GP: 0.034 D_real: 1.220 D_fake: 0.737 +(epoch: 431, iters: 366, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.983 G_ID: 0.112 G_Rec: 0.459 D_GP: 0.043 D_real: 0.979 D_fake: 0.542 +(epoch: 431, iters: 766, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.723 G_ID: 0.106 G_Rec: 0.299 D_GP: 0.044 D_real: 0.905 D_fake: 0.886 +(epoch: 431, iters: 1166, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.938 G_ID: 0.115 G_Rec: 0.427 D_GP: 0.042 D_real: 0.951 D_fake: 0.577 +(epoch: 431, iters: 1566, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.709 G_ID: 0.093 G_Rec: 0.282 D_GP: 0.035 D_real: 1.155 D_fake: 0.756 +(epoch: 431, iters: 1966, time: 0.063) G_GAN: 0.336 G_GAN_Feat: 1.187 G_ID: 0.115 G_Rec: 0.508 D_GP: 0.118 D_real: 0.372 D_fake: 0.678 +(epoch: 431, iters: 2366, time: 0.063) G_GAN: 0.290 G_GAN_Feat: 0.789 G_ID: 0.094 G_Rec: 0.301 D_GP: 0.040 D_real: 0.850 D_fake: 0.711 +(epoch: 431, iters: 2766, time: 0.064) G_GAN: 0.704 G_GAN_Feat: 1.024 G_ID: 0.122 G_Rec: 0.447 D_GP: 0.035 D_real: 1.315 D_fake: 0.312 +(epoch: 431, iters: 3166, time: 0.063) G_GAN: 0.444 G_GAN_Feat: 1.087 G_ID: 0.112 G_Rec: 0.364 D_GP: 0.141 D_real: 0.416 D_fake: 0.563 +(epoch: 431, iters: 3566, time: 0.063) G_GAN: 0.630 G_GAN_Feat: 0.856 G_ID: 0.113 G_Rec: 0.399 D_GP: 0.037 D_real: 1.315 D_fake: 0.427 +(epoch: 431, iters: 3966, time: 0.063) G_GAN: 0.029 G_GAN_Feat: 0.631 G_ID: 0.100 G_Rec: 0.301 D_GP: 0.026 D_real: 0.988 D_fake: 0.971 +(epoch: 431, iters: 4366, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.886 G_ID: 0.120 G_Rec: 0.426 D_GP: 0.030 D_real: 0.829 D_fake: 0.782 +(epoch: 431, iters: 4766, time: 0.063) G_GAN: 0.024 G_GAN_Feat: 0.708 G_ID: 0.079 G_Rec: 0.326 D_GP: 0.031 D_real: 0.934 D_fake: 0.976 +(epoch: 431, iters: 5166, time: 0.063) G_GAN: 0.489 G_GAN_Feat: 1.008 G_ID: 0.112 G_Rec: 0.487 D_GP: 0.056 D_real: 1.053 D_fake: 0.559 +(epoch: 431, iters: 5566, time: 0.063) G_GAN: -0.026 G_GAN_Feat: 0.664 G_ID: 0.104 G_Rec: 0.310 D_GP: 0.029 D_real: 0.949 D_fake: 1.027 +(epoch: 431, iters: 5966, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 1.018 G_ID: 0.118 G_Rec: 0.466 D_GP: 0.049 D_real: 0.548 D_fake: 0.880 +(epoch: 431, iters: 6366, time: 0.063) G_GAN: -0.160 G_GAN_Feat: 0.732 G_ID: 0.109 G_Rec: 0.323 D_GP: 0.065 D_real: 0.672 D_fake: 1.161 +(epoch: 431, iters: 6766, time: 0.063) G_GAN: 0.249 G_GAN_Feat: 1.001 G_ID: 0.110 G_Rec: 0.463 D_GP: 0.043 D_real: 0.666 D_fake: 0.754 +(epoch: 431, iters: 7166, time: 0.063) G_GAN: 0.063 G_GAN_Feat: 0.794 G_ID: 0.115 G_Rec: 0.321 D_GP: 0.039 D_real: 1.049 D_fake: 0.937 +(epoch: 431, iters: 7566, time: 0.064) G_GAN: 0.637 G_GAN_Feat: 0.936 G_ID: 0.123 G_Rec: 0.442 D_GP: 0.045 D_real: 1.095 D_fake: 0.407 +(epoch: 431, iters: 7966, time: 0.063) G_GAN: 0.097 G_GAN_Feat: 0.675 G_ID: 0.098 G_Rec: 0.311 D_GP: 0.044 D_real: 0.978 D_fake: 0.904 +(epoch: 431, iters: 8366, time: 0.063) G_GAN: 0.365 G_GAN_Feat: 0.995 G_ID: 0.111 G_Rec: 0.459 D_GP: 0.049 D_real: 0.908 D_fake: 0.639 +(epoch: 432, iters: 158, time: 0.063) G_GAN: 0.126 G_GAN_Feat: 0.829 G_ID: 0.106 G_Rec: 0.332 D_GP: 0.044 D_real: 1.008 D_fake: 0.877 +(epoch: 432, iters: 558, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.953 G_ID: 0.117 G_Rec: 0.435 D_GP: 0.038 D_real: 0.876 D_fake: 0.644 +(epoch: 432, iters: 958, time: 0.063) G_GAN: -0.059 G_GAN_Feat: 0.771 G_ID: 0.095 G_Rec: 0.322 D_GP: 0.048 D_real: 0.695 D_fake: 1.059 +(epoch: 432, iters: 1358, time: 0.063) G_GAN: 0.301 G_GAN_Feat: 0.988 G_ID: 0.153 G_Rec: 0.458 D_GP: 0.036 D_real: 0.833 D_fake: 0.700 +(epoch: 432, iters: 1758, time: 0.063) G_GAN: 0.457 G_GAN_Feat: 0.892 G_ID: 0.093 G_Rec: 0.330 D_GP: 0.086 D_real: 1.296 D_fake: 0.554 +(epoch: 432, iters: 2158, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 1.000 G_ID: 0.124 G_Rec: 0.438 D_GP: 0.049 D_real: 0.816 D_fake: 0.654 +(epoch: 432, iters: 2558, time: 0.063) G_GAN: 0.175 G_GAN_Feat: 0.983 G_ID: 0.107 G_Rec: 0.344 D_GP: 0.423 D_real: 0.265 D_fake: 0.835 +(epoch: 432, iters: 2958, time: 0.063) G_GAN: 0.573 G_GAN_Feat: 1.078 G_ID: 0.125 G_Rec: 0.458 D_GP: 0.052 D_real: 0.807 D_fake: 0.438 +(epoch: 432, iters: 3358, time: 0.063) G_GAN: 0.141 G_GAN_Feat: 0.870 G_ID: 0.094 G_Rec: 0.327 D_GP: 0.166 D_real: 0.335 D_fake: 0.860 +(epoch: 432, iters: 3758, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.974 G_ID: 0.104 G_Rec: 0.439 D_GP: 0.037 D_real: 1.158 D_fake: 0.554 +(epoch: 432, iters: 4158, time: 0.063) G_GAN: -0.037 G_GAN_Feat: 0.736 G_ID: 0.095 G_Rec: 0.333 D_GP: 0.064 D_real: 0.693 D_fake: 1.038 +(epoch: 432, iters: 4558, time: 0.063) G_GAN: 0.379 G_GAN_Feat: 1.041 G_ID: 0.116 G_Rec: 0.478 D_GP: 0.043 D_real: 1.084 D_fake: 0.627 +(epoch: 432, iters: 4958, time: 0.063) G_GAN: 0.154 G_GAN_Feat: 0.792 G_ID: 0.107 G_Rec: 0.325 D_GP: 0.034 D_real: 0.869 D_fake: 0.846 +(epoch: 432, iters: 5358, time: 0.064) G_GAN: 0.700 G_GAN_Feat: 1.027 G_ID: 0.132 G_Rec: 0.445 D_GP: 0.059 D_real: 1.139 D_fake: 0.439 +(epoch: 432, iters: 5758, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.873 G_ID: 0.122 G_Rec: 0.347 D_GP: 0.045 D_real: 0.901 D_fake: 0.787 +(epoch: 432, iters: 6158, time: 0.063) G_GAN: 0.515 G_GAN_Feat: 1.031 G_ID: 0.115 G_Rec: 0.437 D_GP: 0.055 D_real: 0.803 D_fake: 0.496 +(epoch: 432, iters: 6558, time: 0.063) G_GAN: 0.425 G_GAN_Feat: 0.889 G_ID: 0.116 G_Rec: 0.300 D_GP: 0.048 D_real: 0.663 D_fake: 0.584 +(epoch: 432, iters: 6958, time: 0.064) G_GAN: 0.558 G_GAN_Feat: 1.172 G_ID: 0.117 G_Rec: 0.455 D_GP: 0.051 D_real: 0.372 D_fake: 0.449 +(epoch: 432, iters: 7358, time: 0.063) G_GAN: 0.149 G_GAN_Feat: 0.861 G_ID: 0.125 G_Rec: 0.335 D_GP: 0.043 D_real: 0.925 D_fake: 0.851 +(epoch: 432, iters: 7758, time: 0.063) G_GAN: 0.714 G_GAN_Feat: 1.009 G_ID: 0.105 G_Rec: 0.433 D_GP: 0.038 D_real: 1.143 D_fake: 0.343 +(epoch: 432, iters: 8158, time: 0.063) G_GAN: -0.154 G_GAN_Feat: 0.777 G_ID: 0.099 G_Rec: 0.282 D_GP: 0.030 D_real: 0.689 D_fake: 1.154 +(epoch: 432, iters: 8558, time: 0.064) G_GAN: 0.532 G_GAN_Feat: 1.028 G_ID: 0.119 G_Rec: 0.398 D_GP: 0.038 D_real: 0.971 D_fake: 0.470 +(epoch: 433, iters: 350, time: 0.063) G_GAN: 0.295 G_GAN_Feat: 0.753 G_ID: 0.123 G_Rec: 0.320 D_GP: 0.031 D_real: 1.233 D_fake: 0.706 +(epoch: 433, iters: 750, time: 0.063) G_GAN: 0.369 G_GAN_Feat: 0.935 G_ID: 0.118 G_Rec: 0.399 D_GP: 0.028 D_real: 1.035 D_fake: 0.636 +(epoch: 433, iters: 1150, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.759 G_ID: 0.094 G_Rec: 0.314 D_GP: 0.029 D_real: 1.058 D_fake: 0.876 +(epoch: 433, iters: 1550, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.930 G_ID: 0.107 G_Rec: 0.413 D_GP: 0.037 D_real: 0.951 D_fake: 0.724 +(epoch: 433, iters: 1950, time: 0.063) G_GAN: 0.062 G_GAN_Feat: 0.788 G_ID: 0.090 G_Rec: 0.295 D_GP: 0.031 D_real: 0.918 D_fake: 0.938 +(epoch: 433, iters: 2350, time: 0.063) G_GAN: 0.850 G_GAN_Feat: 1.063 G_ID: 0.124 G_Rec: 0.412 D_GP: 0.072 D_real: 0.975 D_fake: 0.243 +(epoch: 433, iters: 2750, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.904 G_ID: 0.105 G_Rec: 0.324 D_GP: 0.046 D_real: 0.540 D_fake: 0.869 +(epoch: 433, iters: 3150, time: 0.064) G_GAN: 0.814 G_GAN_Feat: 1.386 G_ID: 0.128 G_Rec: 0.517 D_GP: 0.047 D_real: 1.185 D_fake: 0.283 +(epoch: 433, iters: 3550, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 0.754 G_ID: 0.107 G_Rec: 0.307 D_GP: 0.034 D_real: 1.044 D_fake: 0.813 +(epoch: 433, iters: 3950, time: 0.063) G_GAN: 0.921 G_GAN_Feat: 0.891 G_ID: 0.115 G_Rec: 0.446 D_GP: 0.034 D_real: 1.563 D_fake: 0.161 +(epoch: 433, iters: 4350, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.617 G_ID: 0.101 G_Rec: 0.273 D_GP: 0.027 D_real: 1.115 D_fake: 0.850 +(epoch: 433, iters: 4750, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.897 G_ID: 0.122 G_Rec: 0.492 D_GP: 0.041 D_real: 0.855 D_fake: 0.739 +(epoch: 433, iters: 5150, time: 0.063) G_GAN: 0.089 G_GAN_Feat: 0.628 G_ID: 0.092 G_Rec: 0.296 D_GP: 0.032 D_real: 0.993 D_fake: 0.911 +(epoch: 433, iters: 5550, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 0.849 G_ID: 0.111 G_Rec: 0.404 D_GP: 0.038 D_real: 1.104 D_fake: 0.585 +(epoch: 433, iters: 5950, time: 0.064) G_GAN: -0.160 G_GAN_Feat: 0.635 G_ID: 0.106 G_Rec: 0.272 D_GP: 0.037 D_real: 0.740 D_fake: 1.160 +(epoch: 433, iters: 6350, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.934 G_ID: 0.118 G_Rec: 0.450 D_GP: 0.041 D_real: 1.089 D_fake: 0.529 +(epoch: 433, iters: 6750, time: 0.063) G_GAN: 0.060 G_GAN_Feat: 0.655 G_ID: 0.101 G_Rec: 0.331 D_GP: 0.034 D_real: 0.924 D_fake: 0.940 +(epoch: 433, iters: 7150, time: 0.063) G_GAN: 0.561 G_GAN_Feat: 0.967 G_ID: 0.114 G_Rec: 0.440 D_GP: 0.063 D_real: 0.890 D_fake: 0.459 +(epoch: 433, iters: 7550, time: 0.063) G_GAN: 0.252 G_GAN_Feat: 0.770 G_ID: 0.107 G_Rec: 0.313 D_GP: 0.162 D_real: 0.708 D_fake: 0.757 +(epoch: 433, iters: 7950, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.975 G_ID: 0.123 G_Rec: 0.416 D_GP: 0.046 D_real: 0.748 D_fake: 0.772 +(epoch: 433, iters: 8350, time: 0.063) G_GAN: 0.149 G_GAN_Feat: 0.794 G_ID: 0.102 G_Rec: 0.333 D_GP: 0.044 D_real: 1.012 D_fake: 0.854 +(epoch: 434, iters: 142, time: 0.063) G_GAN: 0.396 G_GAN_Feat: 0.971 G_ID: 0.131 G_Rec: 0.436 D_GP: 0.030 D_real: 1.037 D_fake: 0.610 +(epoch: 434, iters: 542, time: 0.063) G_GAN: 0.264 G_GAN_Feat: 0.782 G_ID: 0.099 G_Rec: 0.309 D_GP: 0.040 D_real: 1.000 D_fake: 0.737 +(epoch: 434, iters: 942, time: 0.064) G_GAN: 0.856 G_GAN_Feat: 0.977 G_ID: 0.106 G_Rec: 0.401 D_GP: 0.050 D_real: 1.326 D_fake: 0.184 +(epoch: 434, iters: 1342, time: 0.063) G_GAN: 0.493 G_GAN_Feat: 0.798 G_ID: 0.085 G_Rec: 0.269 D_GP: 0.039 D_real: 0.919 D_fake: 0.513 +(epoch: 434, iters: 1742, time: 0.063) G_GAN: 0.482 G_GAN_Feat: 1.127 G_ID: 0.124 G_Rec: 0.443 D_GP: 0.042 D_real: 0.246 D_fake: 0.526 +(epoch: 434, iters: 2142, time: 0.063) G_GAN: 0.546 G_GAN_Feat: 0.823 G_ID: 0.110 G_Rec: 0.315 D_GP: 0.043 D_real: 1.387 D_fake: 0.493 +(epoch: 434, iters: 2542, time: 0.064) G_GAN: 0.710 G_GAN_Feat: 1.067 G_ID: 0.120 G_Rec: 0.448 D_GP: 0.048 D_real: 1.075 D_fake: 0.317 +(epoch: 434, iters: 2942, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.797 G_ID: 0.107 G_Rec: 0.316 D_GP: 0.031 D_real: 1.072 D_fake: 0.632 +(epoch: 434, iters: 3342, time: 0.063) G_GAN: 0.560 G_GAN_Feat: 0.952 G_ID: 0.125 G_Rec: 0.418 D_GP: 0.039 D_real: 1.220 D_fake: 0.490 +(epoch: 434, iters: 3742, time: 0.063) G_GAN: -0.041 G_GAN_Feat: 0.772 G_ID: 0.093 G_Rec: 0.311 D_GP: 0.038 D_real: 0.881 D_fake: 1.041 +(epoch: 434, iters: 4142, time: 0.064) G_GAN: 0.706 G_GAN_Feat: 1.127 G_ID: 0.104 G_Rec: 0.459 D_GP: 0.031 D_real: 1.344 D_fake: 0.347 +(epoch: 434, iters: 4542, time: 0.063) G_GAN: 0.023 G_GAN_Feat: 0.774 G_ID: 0.110 G_Rec: 0.324 D_GP: 0.031 D_real: 0.925 D_fake: 0.978 +(epoch: 434, iters: 4942, time: 0.063) G_GAN: 0.318 G_GAN_Feat: 0.935 G_ID: 0.122 G_Rec: 0.413 D_GP: 0.038 D_real: 0.914 D_fake: 0.684 +(epoch: 434, iters: 5342, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 0.786 G_ID: 0.108 G_Rec: 0.321 D_GP: 0.036 D_real: 1.025 D_fake: 0.729 +(epoch: 434, iters: 5742, time: 0.063) G_GAN: 0.715 G_GAN_Feat: 1.093 G_ID: 0.117 G_Rec: 0.467 D_GP: 0.044 D_real: 1.114 D_fake: 0.339 +(epoch: 434, iters: 6142, time: 0.063) G_GAN: 0.120 G_GAN_Feat: 0.740 G_ID: 0.087 G_Rec: 0.313 D_GP: 0.036 D_real: 0.924 D_fake: 0.880 +(epoch: 434, iters: 6542, time: 0.063) G_GAN: 0.589 G_GAN_Feat: 1.213 G_ID: 0.126 G_Rec: 0.507 D_GP: 0.281 D_real: 0.396 D_fake: 0.436 +(epoch: 434, iters: 6942, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.827 G_ID: 0.107 G_Rec: 0.321 D_GP: 0.040 D_real: 1.162 D_fake: 0.669 +(epoch: 434, iters: 7342, time: 0.063) G_GAN: 0.857 G_GAN_Feat: 1.037 G_ID: 0.115 G_Rec: 0.425 D_GP: 0.054 D_real: 1.160 D_fake: 0.197 +(epoch: 434, iters: 7742, time: 0.063) G_GAN: 0.656 G_GAN_Feat: 0.959 G_ID: 0.094 G_Rec: 0.338 D_GP: 0.093 D_real: 0.511 D_fake: 0.558 +(epoch: 434, iters: 8142, time: 0.063) G_GAN: 0.565 G_GAN_Feat: 1.003 G_ID: 0.114 G_Rec: 0.440 D_GP: 0.034 D_real: 1.090 D_fake: 0.462 +(epoch: 434, iters: 8542, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.862 G_ID: 0.108 G_Rec: 0.300 D_GP: 0.042 D_real: 0.603 D_fake: 0.778 +(epoch: 435, iters: 334, time: 0.063) G_GAN: 0.535 G_GAN_Feat: 1.015 G_ID: 0.114 G_Rec: 0.425 D_GP: 0.069 D_real: 0.719 D_fake: 0.485 +(epoch: 435, iters: 734, time: 0.063) G_GAN: 0.480 G_GAN_Feat: 0.957 G_ID: 0.108 G_Rec: 0.331 D_GP: 0.037 D_real: 0.981 D_fake: 0.527 +(epoch: 435, iters: 1134, time: 0.063) G_GAN: 0.606 G_GAN_Feat: 0.961 G_ID: 0.127 G_Rec: 0.416 D_GP: 0.040 D_real: 1.243 D_fake: 0.427 +(epoch: 435, iters: 1534, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 0.761 G_ID: 0.112 G_Rec: 0.349 D_GP: 0.035 D_real: 0.878 D_fake: 0.974 +(epoch: 435, iters: 1934, time: 0.063) G_GAN: 0.188 G_GAN_Feat: 0.964 G_ID: 0.111 G_Rec: 0.445 D_GP: 0.040 D_real: 0.841 D_fake: 0.812 +(epoch: 435, iters: 2334, time: 0.063) G_GAN: 0.173 G_GAN_Feat: 0.755 G_ID: 0.092 G_Rec: 0.320 D_GP: 0.034 D_real: 0.984 D_fake: 0.827 +(epoch: 435, iters: 2734, time: 0.063) G_GAN: 0.573 G_GAN_Feat: 0.968 G_ID: 0.110 G_Rec: 0.505 D_GP: 0.033 D_real: 1.073 D_fake: 0.457 +(epoch: 435, iters: 3134, time: 0.064) G_GAN: -0.100 G_GAN_Feat: 0.697 G_ID: 0.108 G_Rec: 0.310 D_GP: 0.027 D_real: 0.868 D_fake: 1.100 +(epoch: 435, iters: 3534, time: 0.063) G_GAN: 0.448 G_GAN_Feat: 1.001 G_ID: 0.116 G_Rec: 0.509 D_GP: 0.035 D_real: 1.011 D_fake: 0.554 +(epoch: 435, iters: 3934, time: 0.063) G_GAN: 0.410 G_GAN_Feat: 0.896 G_ID: 0.092 G_Rec: 0.351 D_GP: 0.197 D_real: 0.739 D_fake: 0.592 +(epoch: 435, iters: 4334, time: 0.063) G_GAN: 0.658 G_GAN_Feat: 0.910 G_ID: 0.107 G_Rec: 0.385 D_GP: 0.043 D_real: 1.278 D_fake: 0.360 +(epoch: 435, iters: 4734, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.826 G_ID: 0.096 G_Rec: 0.333 D_GP: 0.039 D_real: 1.444 D_fake: 0.560 +(epoch: 435, iters: 5134, time: 0.063) G_GAN: 0.548 G_GAN_Feat: 1.077 G_ID: 0.106 G_Rec: 0.449 D_GP: 0.040 D_real: 0.765 D_fake: 0.465 +(epoch: 435, iters: 5534, time: 0.063) G_GAN: 0.241 G_GAN_Feat: 0.723 G_ID: 0.083 G_Rec: 0.302 D_GP: 0.034 D_real: 1.140 D_fake: 0.760 +(epoch: 435, iters: 5934, time: 0.063) G_GAN: 0.504 G_GAN_Feat: 0.899 G_ID: 0.121 G_Rec: 0.405 D_GP: 0.037 D_real: 1.064 D_fake: 0.512 +(epoch: 435, iters: 6334, time: 0.064) G_GAN: 0.043 G_GAN_Feat: 0.746 G_ID: 0.097 G_Rec: 0.317 D_GP: 0.040 D_real: 0.832 D_fake: 0.957 +(epoch: 435, iters: 6734, time: 0.063) G_GAN: 0.581 G_GAN_Feat: 1.014 G_ID: 0.115 G_Rec: 0.442 D_GP: 0.124 D_real: 0.903 D_fake: 0.460 +(epoch: 435, iters: 7134, time: 0.063) G_GAN: 0.436 G_GAN_Feat: 0.849 G_ID: 0.097 G_Rec: 0.339 D_GP: 0.042 D_real: 0.996 D_fake: 0.576 +(epoch: 435, iters: 7534, time: 0.063) G_GAN: 0.320 G_GAN_Feat: 0.941 G_ID: 0.124 G_Rec: 0.427 D_GP: 0.036 D_real: 0.958 D_fake: 0.680 +(epoch: 435, iters: 7934, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.825 G_ID: 0.101 G_Rec: 0.303 D_GP: 0.066 D_real: 0.634 D_fake: 0.753 +(epoch: 435, iters: 8334, time: 0.063) G_GAN: 0.159 G_GAN_Feat: 0.903 G_ID: 0.135 G_Rec: 0.507 D_GP: 0.032 D_real: 0.994 D_fake: 0.842 +(epoch: 436, iters: 126, time: 0.063) G_GAN: 0.085 G_GAN_Feat: 0.645 G_ID: 0.096 G_Rec: 0.307 D_GP: 0.025 D_real: 1.012 D_fake: 0.917 +(epoch: 436, iters: 526, time: 0.063) G_GAN: 0.388 G_GAN_Feat: 0.830 G_ID: 0.118 G_Rec: 0.423 D_GP: 0.031 D_real: 1.165 D_fake: 0.619 +(epoch: 436, iters: 926, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.718 G_ID: 0.100 G_Rec: 0.320 D_GP: 0.039 D_real: 0.881 D_fake: 0.879 +(epoch: 436, iters: 1326, time: 0.063) G_GAN: 0.225 G_GAN_Feat: 0.986 G_ID: 0.120 G_Rec: 0.454 D_GP: 0.073 D_real: 0.667 D_fake: 0.784 +(epoch: 436, iters: 1726, time: 0.063) G_GAN: -0.241 G_GAN_Feat: 0.707 G_ID: 0.101 G_Rec: 0.307 D_GP: 0.038 D_real: 0.706 D_fake: 1.241 +(epoch: 436, iters: 2126, time: 0.063) G_GAN: 0.487 G_GAN_Feat: 0.929 G_ID: 0.106 G_Rec: 0.422 D_GP: 0.049 D_real: 1.062 D_fake: 0.535 +(epoch: 436, iters: 2526, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.647 G_ID: 0.090 G_Rec: 0.289 D_GP: 0.037 D_real: 1.150 D_fake: 0.781 +(epoch: 436, iters: 2926, time: 0.063) G_GAN: 0.249 G_GAN_Feat: 0.926 G_ID: 0.118 G_Rec: 0.453 D_GP: 0.033 D_real: 0.925 D_fake: 0.755 +(epoch: 436, iters: 3326, time: 0.063) G_GAN: -0.070 G_GAN_Feat: 0.826 G_ID: 0.111 G_Rec: 0.355 D_GP: 0.110 D_real: 0.385 D_fake: 1.070 +(epoch: 436, iters: 3726, time: 0.063) G_GAN: 0.460 G_GAN_Feat: 0.925 G_ID: 0.111 G_Rec: 0.443 D_GP: 0.034 D_real: 1.107 D_fake: 0.545 +(epoch: 436, iters: 4126, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.690 G_ID: 0.091 G_Rec: 0.322 D_GP: 0.029 D_real: 1.058 D_fake: 0.862 +(epoch: 436, iters: 4526, time: 0.063) G_GAN: 0.352 G_GAN_Feat: 0.974 G_ID: 0.117 G_Rec: 0.476 D_GP: 0.038 D_real: 1.013 D_fake: 0.648 +(epoch: 436, iters: 4926, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 0.744 G_ID: 0.102 G_Rec: 0.378 D_GP: 0.039 D_real: 1.125 D_fake: 0.696 +(epoch: 436, iters: 5326, time: 0.063) G_GAN: 0.712 G_GAN_Feat: 0.929 G_ID: 0.102 G_Rec: 0.428 D_GP: 0.038 D_real: 1.351 D_fake: 0.316 +(epoch: 436, iters: 5726, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.788 G_ID: 0.115 G_Rec: 0.310 D_GP: 0.061 D_real: 1.031 D_fake: 0.594 +(epoch: 436, iters: 6126, time: 0.063) G_GAN: 0.570 G_GAN_Feat: 0.983 G_ID: 0.113 G_Rec: 0.421 D_GP: 0.043 D_real: 1.022 D_fake: 0.441 +(epoch: 436, iters: 6526, time: 0.063) G_GAN: 0.299 G_GAN_Feat: 0.709 G_ID: 0.089 G_Rec: 0.279 D_GP: 0.035 D_real: 1.136 D_fake: 0.701 +(epoch: 436, iters: 6926, time: 0.063) G_GAN: 0.890 G_GAN_Feat: 0.996 G_ID: 0.126 G_Rec: 0.410 D_GP: 0.061 D_real: 1.257 D_fake: 0.184 +(epoch: 436, iters: 7326, time: 0.064) G_GAN: 0.019 G_GAN_Feat: 0.862 G_ID: 0.093 G_Rec: 0.343 D_GP: 0.038 D_real: 0.656 D_fake: 0.981 +(epoch: 436, iters: 7726, time: 0.063) G_GAN: 0.533 G_GAN_Feat: 1.205 G_ID: 0.117 G_Rec: 0.537 D_GP: 0.438 D_real: 0.445 D_fake: 0.482 +(epoch: 436, iters: 8126, time: 0.063) G_GAN: 0.456 G_GAN_Feat: 0.919 G_ID: 0.092 G_Rec: 0.369 D_GP: 0.126 D_real: 0.656 D_fake: 0.555 +(epoch: 436, iters: 8526, time: 0.063) G_GAN: 1.024 G_GAN_Feat: 0.951 G_ID: 0.114 G_Rec: 0.440 D_GP: 0.059 D_real: 1.508 D_fake: 0.107 +(epoch: 437, iters: 318, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.960 G_ID: 0.097 G_Rec: 0.347 D_GP: 0.331 D_real: 0.378 D_fake: 0.775 +(epoch: 437, iters: 718, time: 0.063) G_GAN: 0.675 G_GAN_Feat: 1.061 G_ID: 0.117 G_Rec: 0.402 D_GP: 0.037 D_real: 0.872 D_fake: 0.335 +(epoch: 437, iters: 1118, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.918 G_ID: 0.111 G_Rec: 0.334 D_GP: 0.058 D_real: 0.281 D_fake: 0.879 +(epoch: 437, iters: 1518, time: 0.063) G_GAN: 0.913 G_GAN_Feat: 1.362 G_ID: 0.117 G_Rec: 0.511 D_GP: 0.077 D_real: 1.543 D_fake: 0.255 +(epoch: 437, iters: 1918, time: 0.064) G_GAN: -0.207 G_GAN_Feat: 0.609 G_ID: 0.095 G_Rec: 0.312 D_GP: 0.026 D_real: 0.823 D_fake: 1.207 +(epoch: 437, iters: 2318, time: 0.063) G_GAN: 0.515 G_GAN_Feat: 0.893 G_ID: 0.111 G_Rec: 0.451 D_GP: 0.026 D_real: 1.140 D_fake: 0.526 +(epoch: 437, iters: 2718, time: 0.063) G_GAN: 0.043 G_GAN_Feat: 0.664 G_ID: 0.102 G_Rec: 0.328 D_GP: 0.033 D_real: 0.954 D_fake: 0.957 +(epoch: 437, iters: 3118, time: 0.063) G_GAN: 0.078 G_GAN_Feat: 0.890 G_ID: 0.106 G_Rec: 0.439 D_GP: 0.048 D_real: 0.757 D_fake: 0.923 +(epoch: 437, iters: 3518, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.619 G_ID: 0.092 G_Rec: 0.276 D_GP: 0.031 D_real: 0.989 D_fake: 0.990 +(epoch: 437, iters: 3918, time: 0.063) G_GAN: 0.320 G_GAN_Feat: 0.874 G_ID: 0.113 G_Rec: 0.418 D_GP: 0.035 D_real: 1.012 D_fake: 0.691 +(epoch: 437, iters: 4318, time: 0.063) G_GAN: 0.185 G_GAN_Feat: 0.697 G_ID: 0.093 G_Rec: 0.302 D_GP: 0.033 D_real: 1.074 D_fake: 0.817 +(epoch: 437, iters: 4718, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 0.882 G_ID: 0.124 G_Rec: 0.376 D_GP: 0.041 D_real: 1.007 D_fake: 0.635 +(epoch: 437, iters: 5118, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.805 G_ID: 0.118 G_Rec: 0.334 D_GP: 0.037 D_real: 0.942 D_fake: 0.851 +(epoch: 437, iters: 5518, time: 0.063) G_GAN: 0.488 G_GAN_Feat: 0.849 G_ID: 0.118 G_Rec: 0.406 D_GP: 0.030 D_real: 1.282 D_fake: 0.515 +(epoch: 437, iters: 5918, time: 0.063) G_GAN: 0.309 G_GAN_Feat: 0.647 G_ID: 0.087 G_Rec: 0.298 D_GP: 0.029 D_real: 1.234 D_fake: 0.692 +(epoch: 437, iters: 6318, time: 0.063) G_GAN: 0.384 G_GAN_Feat: 0.879 G_ID: 0.117 G_Rec: 0.405 D_GP: 0.040 D_real: 1.017 D_fake: 0.622 +(epoch: 437, iters: 6718, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.620 G_ID: 0.093 G_Rec: 0.264 D_GP: 0.032 D_real: 1.029 D_fake: 0.896 +(epoch: 437, iters: 7118, time: 0.063) G_GAN: 0.067 G_GAN_Feat: 0.987 G_ID: 0.116 G_Rec: 0.438 D_GP: 0.083 D_real: 0.571 D_fake: 0.934 +(epoch: 437, iters: 7518, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.741 G_ID: 0.115 G_Rec: 0.314 D_GP: 0.045 D_real: 0.955 D_fake: 0.818 +(epoch: 437, iters: 7918, time: 0.063) G_GAN: 0.639 G_GAN_Feat: 0.950 G_ID: 0.125 G_Rec: 0.445 D_GP: 0.032 D_real: 1.199 D_fake: 0.394 +(epoch: 437, iters: 8318, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.797 G_ID: 0.144 G_Rec: 0.314 D_GP: 0.037 D_real: 0.850 D_fake: 0.791 +(epoch: 438, iters: 110, time: 0.063) G_GAN: 0.306 G_GAN_Feat: 0.929 G_ID: 0.119 G_Rec: 0.420 D_GP: 0.037 D_real: 1.014 D_fake: 0.694 +(epoch: 438, iters: 510, time: 0.063) G_GAN: 0.132 G_GAN_Feat: 0.660 G_ID: 0.097 G_Rec: 0.287 D_GP: 0.029 D_real: 1.031 D_fake: 0.868 +(epoch: 438, iters: 910, time: 0.063) G_GAN: 0.457 G_GAN_Feat: 0.853 G_ID: 0.135 G_Rec: 0.394 D_GP: 0.042 D_real: 1.140 D_fake: 0.557 +(epoch: 438, iters: 1310, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.778 G_ID: 0.083 G_Rec: 0.332 D_GP: 0.059 D_real: 1.072 D_fake: 0.756 +(epoch: 438, iters: 1710, time: 0.063) G_GAN: 0.200 G_GAN_Feat: 1.002 G_ID: 0.100 G_Rec: 0.424 D_GP: 0.099 D_real: 0.523 D_fake: 0.803 +(epoch: 438, iters: 2110, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 0.764 G_ID: 0.101 G_Rec: 0.316 D_GP: 0.031 D_real: 1.075 D_fake: 0.716 +(epoch: 438, iters: 2510, time: 0.063) G_GAN: 0.508 G_GAN_Feat: 0.976 G_ID: 0.112 G_Rec: 0.415 D_GP: 0.034 D_real: 1.106 D_fake: 0.497 +(epoch: 438, iters: 2910, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.846 G_ID: 0.102 G_Rec: 0.334 D_GP: 0.044 D_real: 0.776 D_fake: 0.795 +(epoch: 438, iters: 3310, time: 0.063) G_GAN: 0.524 G_GAN_Feat: 1.100 G_ID: 0.107 G_Rec: 0.420 D_GP: 0.127 D_real: 0.373 D_fake: 0.487 +(epoch: 438, iters: 3710, time: 0.063) G_GAN: 0.332 G_GAN_Feat: 0.827 G_ID: 0.094 G_Rec: 0.301 D_GP: 0.032 D_real: 0.844 D_fake: 0.676 +(epoch: 438, iters: 4110, time: 0.063) G_GAN: 0.495 G_GAN_Feat: 1.074 G_ID: 0.107 G_Rec: 0.399 D_GP: 0.061 D_real: 0.577 D_fake: 0.516 +(epoch: 438, iters: 4510, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.746 G_ID: 0.089 G_Rec: 0.324 D_GP: 0.031 D_real: 0.968 D_fake: 0.900 +(epoch: 438, iters: 4910, time: 0.063) G_GAN: 0.494 G_GAN_Feat: 1.052 G_ID: 0.115 G_Rec: 0.486 D_GP: 0.044 D_real: 0.939 D_fake: 0.546 +(epoch: 438, iters: 5310, time: 0.063) G_GAN: 0.140 G_GAN_Feat: 0.855 G_ID: 0.098 G_Rec: 0.343 D_GP: 0.163 D_real: 0.651 D_fake: 0.860 +(epoch: 438, iters: 5710, time: 0.063) G_GAN: 0.595 G_GAN_Feat: 1.098 G_ID: 0.126 G_Rec: 0.490 D_GP: 0.085 D_real: 0.756 D_fake: 0.423 +(epoch: 438, iters: 6110, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.933 G_ID: 0.098 G_Rec: 0.338 D_GP: 0.191 D_real: 0.423 D_fake: 0.932 +(epoch: 438, iters: 6510, time: 0.063) G_GAN: 0.268 G_GAN_Feat: 0.861 G_ID: 0.107 G_Rec: 0.419 D_GP: 0.031 D_real: 1.021 D_fake: 0.733 +(epoch: 438, iters: 6910, time: 0.063) G_GAN: -0.022 G_GAN_Feat: 0.658 G_ID: 0.110 G_Rec: 0.300 D_GP: 0.029 D_real: 0.877 D_fake: 1.022 +(epoch: 438, iters: 7310, time: 0.063) G_GAN: 0.106 G_GAN_Feat: 0.856 G_ID: 0.124 G_Rec: 0.429 D_GP: 0.035 D_real: 0.925 D_fake: 0.897 +(epoch: 438, iters: 7710, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.709 G_ID: 0.098 G_Rec: 0.359 D_GP: 0.053 D_real: 0.930 D_fake: 0.950 +(epoch: 438, iters: 8110, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.920 G_ID: 0.130 G_Rec: 0.474 D_GP: 0.043 D_real: 0.728 D_fake: 0.818 +(epoch: 438, iters: 8510, time: 0.063) G_GAN: -0.089 G_GAN_Feat: 0.627 G_ID: 0.120 G_Rec: 0.309 D_GP: 0.030 D_real: 0.789 D_fake: 1.089 +(epoch: 439, iters: 302, time: 0.063) G_GAN: 0.365 G_GAN_Feat: 0.870 G_ID: 0.118 G_Rec: 0.423 D_GP: 0.042 D_real: 1.014 D_fake: 0.648 +(epoch: 439, iters: 702, time: 0.064) G_GAN: -0.253 G_GAN_Feat: 0.731 G_ID: 0.096 G_Rec: 0.336 D_GP: 0.046 D_real: 0.570 D_fake: 1.253 +(epoch: 439, iters: 1102, time: 0.063) G_GAN: 0.374 G_GAN_Feat: 0.919 G_ID: 0.132 G_Rec: 0.454 D_GP: 0.052 D_real: 0.924 D_fake: 0.640 +(epoch: 439, iters: 1502, time: 0.063) G_GAN: 0.011 G_GAN_Feat: 0.680 G_ID: 0.083 G_Rec: 0.308 D_GP: 0.048 D_real: 0.865 D_fake: 0.989 +(epoch: 439, iters: 1902, time: 0.063) G_GAN: 0.419 G_GAN_Feat: 0.873 G_ID: 0.126 G_Rec: 0.421 D_GP: 0.032 D_real: 0.981 D_fake: 0.593 +(epoch: 439, iters: 2302, time: 0.063) G_GAN: 0.080 G_GAN_Feat: 0.687 G_ID: 0.114 G_Rec: 0.298 D_GP: 0.038 D_real: 0.903 D_fake: 0.920 +(epoch: 439, iters: 2702, time: 0.063) G_GAN: 0.376 G_GAN_Feat: 0.948 G_ID: 0.137 G_Rec: 0.437 D_GP: 0.050 D_real: 0.969 D_fake: 0.633 +(epoch: 439, iters: 3102, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 0.647 G_ID: 0.089 G_Rec: 0.275 D_GP: 0.035 D_real: 1.233 D_fake: 0.687 +(epoch: 439, iters: 3502, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.959 G_ID: 0.125 G_Rec: 0.449 D_GP: 0.059 D_real: 0.833 D_fake: 0.661 +(epoch: 439, iters: 3902, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 0.737 G_ID: 0.090 G_Rec: 0.301 D_GP: 0.063 D_real: 0.994 D_fake: 0.640 +(epoch: 439, iters: 4302, time: 0.063) G_GAN: 0.400 G_GAN_Feat: 0.911 G_ID: 0.123 G_Rec: 0.410 D_GP: 0.037 D_real: 1.001 D_fake: 0.605 +(epoch: 439, iters: 4702, time: 0.063) G_GAN: 0.074 G_GAN_Feat: 0.691 G_ID: 0.094 G_Rec: 0.314 D_GP: 0.025 D_real: 0.999 D_fake: 0.926 +(epoch: 439, iters: 5102, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.976 G_ID: 0.108 G_Rec: 0.446 D_GP: 0.052 D_real: 0.905 D_fake: 0.675 +(epoch: 439, iters: 5502, time: 0.063) G_GAN: 0.121 G_GAN_Feat: 0.667 G_ID: 0.099 G_Rec: 0.281 D_GP: 0.036 D_real: 1.013 D_fake: 0.879 +(epoch: 439, iters: 5902, time: 0.063) G_GAN: 0.575 G_GAN_Feat: 0.925 G_ID: 0.113 G_Rec: 0.402 D_GP: 0.061 D_real: 1.159 D_fake: 0.465 +(epoch: 439, iters: 6302, time: 0.063) G_GAN: 0.133 G_GAN_Feat: 0.766 G_ID: 0.094 G_Rec: 0.297 D_GP: 0.044 D_real: 0.897 D_fake: 0.867 +(epoch: 439, iters: 6702, time: 0.064) G_GAN: 0.602 G_GAN_Feat: 1.025 G_ID: 0.134 G_Rec: 0.469 D_GP: 0.038 D_real: 1.087 D_fake: 0.422 +(epoch: 439, iters: 7102, time: 0.063) G_GAN: -0.103 G_GAN_Feat: 0.795 G_ID: 0.097 G_Rec: 0.321 D_GP: 0.049 D_real: 0.567 D_fake: 1.103 +(epoch: 439, iters: 7502, time: 0.063) G_GAN: 0.327 G_GAN_Feat: 1.029 G_ID: 0.111 G_Rec: 0.463 D_GP: 0.100 D_real: 0.797 D_fake: 0.674 +(epoch: 439, iters: 7902, time: 0.063) G_GAN: 0.163 G_GAN_Feat: 0.696 G_ID: 0.092 G_Rec: 0.267 D_GP: 0.051 D_real: 0.950 D_fake: 0.837 +(epoch: 439, iters: 8302, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 1.052 G_ID: 0.125 G_Rec: 0.460 D_GP: 0.051 D_real: 0.908 D_fake: 0.441 +(epoch: 439, iters: 8702, time: 0.063) G_GAN: -0.022 G_GAN_Feat: 0.828 G_ID: 0.103 G_Rec: 0.342 D_GP: 0.039 D_real: 0.675 D_fake: 1.022 +(epoch: 440, iters: 494, time: 0.063) G_GAN: 0.201 G_GAN_Feat: 1.013 G_ID: 0.138 G_Rec: 0.460 D_GP: 0.086 D_real: 0.597 D_fake: 0.801 +(epoch: 440, iters: 894, time: 0.063) G_GAN: 0.149 G_GAN_Feat: 0.757 G_ID: 0.093 G_Rec: 0.328 D_GP: 0.041 D_real: 0.920 D_fake: 0.851 +(epoch: 440, iters: 1294, time: 0.064) G_GAN: 0.396 G_GAN_Feat: 1.011 G_ID: 0.114 G_Rec: 0.448 D_GP: 0.041 D_real: 0.734 D_fake: 0.604 +(epoch: 440, iters: 1694, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 0.779 G_ID: 0.108 G_Rec: 0.332 D_GP: 0.032 D_real: 1.048 D_fake: 0.811 +(epoch: 440, iters: 2094, time: 0.063) G_GAN: 0.289 G_GAN_Feat: 1.100 G_ID: 0.120 G_Rec: 0.480 D_GP: 0.347 D_real: 0.258 D_fake: 0.716 +(epoch: 440, iters: 2494, time: 0.063) G_GAN: 0.099 G_GAN_Feat: 0.882 G_ID: 0.103 G_Rec: 0.347 D_GP: 0.073 D_real: 0.295 D_fake: 0.901 +(epoch: 440, iters: 2894, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 0.984 G_ID: 0.104 G_Rec: 0.447 D_GP: 0.038 D_real: 1.062 D_fake: 0.506 +(epoch: 440, iters: 3294, time: 0.063) G_GAN: 0.367 G_GAN_Feat: 0.860 G_ID: 0.093 G_Rec: 0.364 D_GP: 0.036 D_real: 1.078 D_fake: 0.635 +(epoch: 440, iters: 3694, time: 0.063) G_GAN: 0.767 G_GAN_Feat: 1.168 G_ID: 0.123 G_Rec: 0.461 D_GP: 0.132 D_real: 0.501 D_fake: 0.284 +(epoch: 440, iters: 4094, time: 0.063) G_GAN: 0.289 G_GAN_Feat: 0.749 G_ID: 0.097 G_Rec: 0.311 D_GP: 0.033 D_real: 1.287 D_fake: 0.716 +(epoch: 440, iters: 4494, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 0.874 G_ID: 0.135 G_Rec: 0.417 D_GP: 0.028 D_real: 1.158 D_fake: 0.562 +(epoch: 440, iters: 4894, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.726 G_ID: 0.091 G_Rec: 0.317 D_GP: 0.048 D_real: 0.992 D_fake: 0.810 +(epoch: 440, iters: 5294, time: 0.063) G_GAN: 0.326 G_GAN_Feat: 0.939 G_ID: 0.122 G_Rec: 0.452 D_GP: 0.054 D_real: 0.902 D_fake: 0.677 +(epoch: 440, iters: 5694, time: 0.063) G_GAN: 0.146 G_GAN_Feat: 0.703 G_ID: 0.091 G_Rec: 0.323 D_GP: 0.031 D_real: 0.960 D_fake: 0.855 +(epoch: 440, iters: 6094, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.981 G_ID: 0.109 G_Rec: 0.473 D_GP: 0.028 D_real: 1.081 D_fake: 0.484 +(epoch: 440, iters: 6494, time: 0.063) G_GAN: -0.093 G_GAN_Feat: 0.730 G_ID: 0.104 G_Rec: 0.330 D_GP: 0.064 D_real: 0.667 D_fake: 1.093 +(epoch: 440, iters: 6894, time: 0.063) G_GAN: 0.470 G_GAN_Feat: 1.038 G_ID: 0.131 G_Rec: 0.448 D_GP: 0.096 D_real: 0.605 D_fake: 0.545 +(epoch: 440, iters: 7294, time: 0.063) G_GAN: 0.203 G_GAN_Feat: 0.779 G_ID: 0.095 G_Rec: 0.299 D_GP: 0.040 D_real: 0.914 D_fake: 0.797 +(epoch: 440, iters: 7694, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.907 G_ID: 0.112 G_Rec: 0.413 D_GP: 0.034 D_real: 1.054 D_fake: 0.592 +(epoch: 440, iters: 8094, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 0.714 G_ID: 0.090 G_Rec: 0.273 D_GP: 0.032 D_real: 1.004 D_fake: 0.875 +(epoch: 440, iters: 8494, time: 0.063) G_GAN: 0.477 G_GAN_Feat: 0.932 G_ID: 0.164 G_Rec: 0.418 D_GP: 0.035 D_real: 1.011 D_fake: 0.524 +(epoch: 441, iters: 286, time: 0.063) G_GAN: 0.486 G_GAN_Feat: 0.802 G_ID: 0.102 G_Rec: 0.335 D_GP: 0.046 D_real: 1.235 D_fake: 0.523 +(epoch: 441, iters: 686, time: 0.064) G_GAN: 0.856 G_GAN_Feat: 1.025 G_ID: 0.133 G_Rec: 0.467 D_GP: 0.034 D_real: 1.165 D_fake: 0.227 +(epoch: 441, iters: 1086, time: 0.063) G_GAN: 0.232 G_GAN_Feat: 0.836 G_ID: 0.123 G_Rec: 0.326 D_GP: 0.063 D_real: 0.820 D_fake: 0.779 +(epoch: 441, iters: 1486, time: 0.063) G_GAN: 0.412 G_GAN_Feat: 0.947 G_ID: 0.124 G_Rec: 0.424 D_GP: 0.030 D_real: 1.010 D_fake: 0.626 +(epoch: 441, iters: 1886, time: 0.063) G_GAN: 0.036 G_GAN_Feat: 0.775 G_ID: 0.087 G_Rec: 0.314 D_GP: 0.034 D_real: 0.811 D_fake: 0.968 +(epoch: 441, iters: 2286, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.996 G_ID: 0.125 G_Rec: 0.439 D_GP: 0.078 D_real: 0.839 D_fake: 0.614 +(epoch: 441, iters: 2686, time: 0.063) G_GAN: -0.070 G_GAN_Feat: 0.710 G_ID: 0.104 G_Rec: 0.280 D_GP: 0.028 D_real: 0.831 D_fake: 1.070 +(epoch: 441, iters: 3086, time: 0.063) G_GAN: 0.618 G_GAN_Feat: 1.185 G_ID: 0.128 G_Rec: 0.455 D_GP: 0.060 D_real: 0.390 D_fake: 0.427 +(epoch: 441, iters: 3486, time: 0.063) G_GAN: 0.542 G_GAN_Feat: 0.791 G_ID: 0.101 G_Rec: 0.304 D_GP: 0.063 D_real: 1.164 D_fake: 0.470 +(epoch: 441, iters: 3886, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 1.193 G_ID: 0.121 G_Rec: 0.445 D_GP: 0.147 D_real: 0.259 D_fake: 0.651 +(epoch: 441, iters: 4286, time: 0.063) G_GAN: 0.513 G_GAN_Feat: 0.783 G_ID: 0.106 G_Rec: 0.300 D_GP: 0.041 D_real: 1.226 D_fake: 0.494 +(epoch: 441, iters: 4686, time: 0.063) G_GAN: 0.529 G_GAN_Feat: 0.914 G_ID: 0.105 G_Rec: 0.442 D_GP: 0.029 D_real: 1.225 D_fake: 0.520 +(epoch: 441, iters: 5086, time: 0.063) G_GAN: -0.083 G_GAN_Feat: 0.699 G_ID: 0.102 G_Rec: 0.295 D_GP: 0.030 D_real: 0.819 D_fake: 1.083 +(epoch: 441, iters: 5486, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 0.905 G_ID: 0.119 G_Rec: 0.443 D_GP: 0.038 D_real: 0.719 D_fake: 0.945 +(epoch: 441, iters: 5886, time: 0.063) G_GAN: -0.166 G_GAN_Feat: 0.712 G_ID: 0.108 G_Rec: 0.314 D_GP: 0.036 D_real: 0.707 D_fake: 1.166 +(epoch: 441, iters: 6286, time: 0.063) G_GAN: -0.010 G_GAN_Feat: 0.992 G_ID: 0.134 G_Rec: 0.464 D_GP: 0.109 D_real: 0.509 D_fake: 1.010 +(epoch: 441, iters: 6686, time: 0.063) G_GAN: -0.204 G_GAN_Feat: 0.697 G_ID: 0.096 G_Rec: 0.315 D_GP: 0.043 D_real: 0.666 D_fake: 1.204 +(epoch: 441, iters: 7086, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 1.031 G_ID: 0.115 G_Rec: 0.475 D_GP: 0.069 D_real: 0.796 D_fake: 0.688 +(epoch: 441, iters: 7486, time: 0.063) G_GAN: -0.298 G_GAN_Feat: 0.911 G_ID: 0.114 G_Rec: 0.371 D_GP: 0.127 D_real: 0.204 D_fake: 1.298 +(epoch: 441, iters: 7886, time: 0.063) G_GAN: 0.466 G_GAN_Feat: 0.986 G_ID: 0.121 G_Rec: 0.480 D_GP: 0.073 D_real: 0.839 D_fake: 0.574 +(epoch: 441, iters: 8286, time: 0.063) G_GAN: 0.230 G_GAN_Feat: 0.698 G_ID: 0.100 G_Rec: 0.276 D_GP: 0.042 D_real: 1.094 D_fake: 0.772 +(epoch: 441, iters: 8686, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.994 G_ID: 0.118 G_Rec: 0.492 D_GP: 0.038 D_real: 1.025 D_fake: 0.579 +(epoch: 442, iters: 478, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 0.813 G_ID: 0.110 G_Rec: 0.320 D_GP: 0.111 D_real: 0.778 D_fake: 0.735 +(epoch: 442, iters: 878, time: 0.063) G_GAN: 0.926 G_GAN_Feat: 0.994 G_ID: 0.114 G_Rec: 0.452 D_GP: 0.047 D_real: 1.296 D_fake: 0.203 +(epoch: 442, iters: 1278, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.718 G_ID: 0.116 G_Rec: 0.366 D_GP: 0.028 D_real: 0.975 D_fake: 0.978 +(epoch: 442, iters: 1678, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.888 G_ID: 0.118 G_Rec: 0.430 D_GP: 0.026 D_real: 1.124 D_fake: 0.660 +(epoch: 442, iters: 2078, time: 0.063) G_GAN: 0.111 G_GAN_Feat: 0.676 G_ID: 0.106 G_Rec: 0.324 D_GP: 0.030 D_real: 1.039 D_fake: 0.890 +(epoch: 442, iters: 2478, time: 0.063) G_GAN: 0.161 G_GAN_Feat: 0.838 G_ID: 0.106 G_Rec: 0.384 D_GP: 0.036 D_real: 0.958 D_fake: 0.839 +(epoch: 442, iters: 2878, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 0.637 G_ID: 0.095 G_Rec: 0.260 D_GP: 0.032 D_real: 1.133 D_fake: 0.764 +(epoch: 442, iters: 3278, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.880 G_ID: 0.143 G_Rec: 0.392 D_GP: 0.032 D_real: 0.883 D_fake: 0.840 +(epoch: 442, iters: 3678, time: 0.063) G_GAN: -0.039 G_GAN_Feat: 0.803 G_ID: 0.097 G_Rec: 0.347 D_GP: 0.073 D_real: 0.627 D_fake: 1.039 +(epoch: 442, iters: 4078, time: 0.063) G_GAN: 0.537 G_GAN_Feat: 0.981 G_ID: 0.114 G_Rec: 0.430 D_GP: 0.044 D_real: 1.037 D_fake: 0.470 +(epoch: 442, iters: 4478, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 0.735 G_ID: 0.079 G_Rec: 0.298 D_GP: 0.033 D_real: 1.169 D_fake: 0.665 +(epoch: 442, iters: 4878, time: 0.064) G_GAN: 0.628 G_GAN_Feat: 1.020 G_ID: 0.101 G_Rec: 0.420 D_GP: 0.094 D_real: 0.998 D_fake: 0.396 +(epoch: 442, iters: 5278, time: 0.063) G_GAN: 0.062 G_GAN_Feat: 0.736 G_ID: 0.109 G_Rec: 0.294 D_GP: 0.031 D_real: 0.874 D_fake: 0.938 +(epoch: 442, iters: 5678, time: 0.063) G_GAN: 0.503 G_GAN_Feat: 1.020 G_ID: 0.128 G_Rec: 0.431 D_GP: 0.053 D_real: 0.955 D_fake: 0.512 +(epoch: 442, iters: 6078, time: 0.063) G_GAN: 0.208 G_GAN_Feat: 0.759 G_ID: 0.086 G_Rec: 0.303 D_GP: 0.047 D_real: 0.900 D_fake: 0.792 +(epoch: 442, iters: 6478, time: 0.064) G_GAN: 0.646 G_GAN_Feat: 0.893 G_ID: 0.122 G_Rec: 0.366 D_GP: 0.032 D_real: 1.351 D_fake: 0.370 +(epoch: 442, iters: 6878, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 0.970 G_ID: 0.121 G_Rec: 0.353 D_GP: 0.050 D_real: 1.071 D_fake: 0.657 +(epoch: 442, iters: 7278, time: 0.063) G_GAN: 0.676 G_GAN_Feat: 1.024 G_ID: 0.104 G_Rec: 0.455 D_GP: 0.039 D_real: 1.304 D_fake: 0.455 +(epoch: 442, iters: 7678, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.804 G_ID: 0.094 G_Rec: 0.317 D_GP: 0.043 D_real: 1.062 D_fake: 0.762 +(epoch: 442, iters: 8078, time: 0.064) G_GAN: 0.735 G_GAN_Feat: 1.119 G_ID: 0.130 G_Rec: 0.508 D_GP: 0.046 D_real: 0.930 D_fake: 0.403 +(epoch: 442, iters: 8478, time: 0.063) G_GAN: 0.104 G_GAN_Feat: 0.774 G_ID: 0.091 G_Rec: 0.296 D_GP: 0.028 D_real: 0.984 D_fake: 0.896 +(epoch: 443, iters: 270, time: 0.063) G_GAN: 0.572 G_GAN_Feat: 1.013 G_ID: 0.120 G_Rec: 0.452 D_GP: 0.036 D_real: 1.103 D_fake: 0.433 +(epoch: 443, iters: 670, time: 0.063) G_GAN: 0.204 G_GAN_Feat: 1.287 G_ID: 0.130 G_Rec: 0.430 D_GP: 0.212 D_real: 0.909 D_fake: 0.833 +(epoch: 443, iters: 1070, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.883 G_ID: 0.140 G_Rec: 0.457 D_GP: 0.028 D_real: 0.964 D_fake: 0.843 +(epoch: 443, iters: 1470, time: 0.063) G_GAN: -0.003 G_GAN_Feat: 0.691 G_ID: 0.107 G_Rec: 0.304 D_GP: 0.029 D_real: 0.837 D_fake: 1.003 +(epoch: 443, iters: 1870, time: 0.063) G_GAN: 0.197 G_GAN_Feat: 0.978 G_ID: 0.115 G_Rec: 0.488 D_GP: 0.038 D_real: 0.740 D_fake: 0.810 +(epoch: 443, iters: 2270, time: 0.063) G_GAN: 0.021 G_GAN_Feat: 0.721 G_ID: 0.103 G_Rec: 0.287 D_GP: 0.037 D_real: 0.836 D_fake: 0.979 +(epoch: 443, iters: 2670, time: 0.064) G_GAN: 0.509 G_GAN_Feat: 0.969 G_ID: 0.124 G_Rec: 0.402 D_GP: 0.041 D_real: 1.108 D_fake: 0.533 +(epoch: 443, iters: 3070, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.850 G_ID: 0.091 G_Rec: 0.299 D_GP: 0.037 D_real: 0.791 D_fake: 0.817 +(epoch: 443, iters: 3470, time: 0.063) G_GAN: 0.689 G_GAN_Feat: 0.967 G_ID: 0.099 G_Rec: 0.413 D_GP: 0.038 D_real: 1.382 D_fake: 0.331 +(epoch: 443, iters: 3870, time: 0.063) G_GAN: 0.182 G_GAN_Feat: 0.845 G_ID: 0.111 G_Rec: 0.305 D_GP: 0.039 D_real: 0.832 D_fake: 0.819 +(epoch: 443, iters: 4270, time: 0.064) G_GAN: 0.892 G_GAN_Feat: 1.015 G_ID: 0.114 G_Rec: 0.432 D_GP: 0.043 D_real: 1.315 D_fake: 0.179 +(epoch: 443, iters: 4670, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.616 G_ID: 0.094 G_Rec: 0.306 D_GP: 0.026 D_real: 1.130 D_fake: 0.763 +(epoch: 443, iters: 5070, time: 0.063) G_GAN: -0.049 G_GAN_Feat: 0.902 G_ID: 0.121 G_Rec: 0.447 D_GP: 0.050 D_real: 0.605 D_fake: 1.049 +(epoch: 443, iters: 5470, time: 0.063) G_GAN: -0.037 G_GAN_Feat: 0.687 G_ID: 0.113 G_Rec: 0.326 D_GP: 0.045 D_real: 0.834 D_fake: 1.037 +(epoch: 443, iters: 5870, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.924 G_ID: 0.139 G_Rec: 0.484 D_GP: 0.043 D_real: 0.834 D_fake: 0.706 +(epoch: 443, iters: 6270, time: 0.063) G_GAN: 0.159 G_GAN_Feat: 0.698 G_ID: 0.096 G_Rec: 0.304 D_GP: 0.068 D_real: 0.953 D_fake: 0.843 +(epoch: 443, iters: 6670, time: 0.063) G_GAN: 0.435 G_GAN_Feat: 1.017 G_ID: 0.133 G_Rec: 0.477 D_GP: 0.153 D_real: 0.558 D_fake: 0.580 +(epoch: 443, iters: 7070, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 0.759 G_ID: 0.089 G_Rec: 0.321 D_GP: 0.046 D_real: 1.280 D_fake: 0.669 +(epoch: 443, iters: 7470, time: 0.063) G_GAN: 0.476 G_GAN_Feat: 0.892 G_ID: 0.129 G_Rec: 0.380 D_GP: 0.041 D_real: 1.070 D_fake: 0.536 +(epoch: 443, iters: 7870, time: 0.063) G_GAN: 0.444 G_GAN_Feat: 0.751 G_ID: 0.100 G_Rec: 0.320 D_GP: 0.048 D_real: 1.253 D_fake: 0.571 +(epoch: 443, iters: 8270, time: 0.063) G_GAN: 0.129 G_GAN_Feat: 0.941 G_ID: 0.123 G_Rec: 0.433 D_GP: 0.040 D_real: 0.771 D_fake: 0.871 +(epoch: 443, iters: 8670, time: 0.064) G_GAN: -0.131 G_GAN_Feat: 0.714 G_ID: 0.113 G_Rec: 0.297 D_GP: 0.045 D_real: 0.809 D_fake: 1.131 +(epoch: 444, iters: 462, time: 0.063) G_GAN: 0.182 G_GAN_Feat: 0.929 G_ID: 0.131 G_Rec: 0.434 D_GP: 0.030 D_real: 0.882 D_fake: 0.818 +(epoch: 444, iters: 862, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.913 G_ID: 0.105 G_Rec: 0.351 D_GP: 0.211 D_real: 0.403 D_fake: 0.731 +(epoch: 444, iters: 1262, time: 0.063) G_GAN: 0.215 G_GAN_Feat: 1.019 G_ID: 0.146 G_Rec: 0.442 D_GP: 0.044 D_real: 0.497 D_fake: 0.788 +(epoch: 444, iters: 1662, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 1.010 G_ID: 0.112 G_Rec: 0.361 D_GP: 0.205 D_real: 0.282 D_fake: 0.771 +(epoch: 444, iters: 2062, time: 0.063) G_GAN: 0.571 G_GAN_Feat: 0.932 G_ID: 0.119 G_Rec: 0.378 D_GP: 0.033 D_real: 1.087 D_fake: 0.469 +(epoch: 444, iters: 2462, time: 0.063) G_GAN: 0.047 G_GAN_Feat: 0.899 G_ID: 0.105 G_Rec: 0.328 D_GP: 0.107 D_real: 0.378 D_fake: 0.960 +(epoch: 444, iters: 2862, time: 0.063) G_GAN: 0.388 G_GAN_Feat: 1.069 G_ID: 0.137 G_Rec: 0.389 D_GP: 0.044 D_real: 0.844 D_fake: 0.614 +(epoch: 444, iters: 3262, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.780 G_ID: 0.096 G_Rec: 0.318 D_GP: 0.036 D_real: 0.867 D_fake: 0.865 +(epoch: 444, iters: 3662, time: 0.064) G_GAN: 0.589 G_GAN_Feat: 1.128 G_ID: 0.117 G_Rec: 0.497 D_GP: 0.094 D_real: 0.765 D_fake: 0.423 +(epoch: 444, iters: 4062, time: 0.064) G_GAN: -0.105 G_GAN_Feat: 0.879 G_ID: 0.095 G_Rec: 0.339 D_GP: 0.422 D_real: 0.351 D_fake: 1.105 +(epoch: 444, iters: 4462, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 1.028 G_ID: 0.133 G_Rec: 0.412 D_GP: 0.036 D_real: 1.095 D_fake: 0.755 +(epoch: 444, iters: 4862, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.772 G_ID: 0.095 G_Rec: 0.291 D_GP: 0.029 D_real: 1.238 D_fake: 0.542 +(epoch: 444, iters: 5262, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.903 G_ID: 0.104 G_Rec: 0.381 D_GP: 0.035 D_real: 0.980 D_fake: 0.635 +(epoch: 444, iters: 5662, time: 0.063) G_GAN: 0.263 G_GAN_Feat: 0.828 G_ID: 0.095 G_Rec: 0.293 D_GP: 0.033 D_real: 0.815 D_fake: 0.738 +(epoch: 444, iters: 6062, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.961 G_ID: 0.114 G_Rec: 0.459 D_GP: 0.034 D_real: 0.949 D_fake: 0.678 +(epoch: 444, iters: 6462, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.758 G_ID: 0.095 G_Rec: 0.337 D_GP: 0.032 D_real: 1.246 D_fake: 0.665 +(epoch: 444, iters: 6862, time: 0.064) G_GAN: 0.075 G_GAN_Feat: 0.891 G_ID: 0.117 G_Rec: 0.420 D_GP: 0.033 D_real: 0.702 D_fake: 0.925 +(epoch: 444, iters: 7262, time: 0.064) G_GAN: -0.182 G_GAN_Feat: 0.697 G_ID: 0.123 G_Rec: 0.315 D_GP: 0.033 D_real: 0.702 D_fake: 1.182 +(epoch: 444, iters: 7662, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.857 G_ID: 0.115 G_Rec: 0.397 D_GP: 0.034 D_real: 0.929 D_fake: 0.839 +(epoch: 444, iters: 8062, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.758 G_ID: 0.100 G_Rec: 0.346 D_GP: 0.050 D_real: 0.865 D_fake: 0.903 +(epoch: 444, iters: 8462, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.816 G_ID: 0.125 G_Rec: 0.374 D_GP: 0.034 D_real: 1.152 D_fake: 0.602 +(epoch: 445, iters: 254, time: 0.064) G_GAN: -0.104 G_GAN_Feat: 0.699 G_ID: 0.100 G_Rec: 0.314 D_GP: 0.031 D_real: 0.790 D_fake: 1.104 +(epoch: 445, iters: 654, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 1.007 G_ID: 0.119 G_Rec: 0.458 D_GP: 0.054 D_real: 0.712 D_fake: 0.799 +(epoch: 445, iters: 1054, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.752 G_ID: 0.089 G_Rec: 0.300 D_GP: 0.042 D_real: 1.175 D_fake: 0.606 +(epoch: 445, iters: 1454, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 0.935 G_ID: 0.114 G_Rec: 0.424 D_GP: 0.039 D_real: 1.195 D_fake: 0.379 +(epoch: 445, iters: 1854, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.744 G_ID: 0.104 G_Rec: 0.338 D_GP: 0.034 D_real: 1.378 D_fake: 0.517 +(epoch: 445, iters: 2254, time: 0.063) G_GAN: 0.552 G_GAN_Feat: 0.845 G_ID: 0.103 G_Rec: 0.460 D_GP: 0.028 D_real: 1.274 D_fake: 0.466 +(epoch: 445, iters: 2654, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.618 G_ID: 0.087 G_Rec: 0.285 D_GP: 0.030 D_real: 1.124 D_fake: 0.747 +(epoch: 445, iters: 3054, time: 0.064) G_GAN: 0.027 G_GAN_Feat: 0.930 G_ID: 0.121 G_Rec: 0.449 D_GP: 0.043 D_real: 0.703 D_fake: 0.973 +(epoch: 445, iters: 3454, time: 0.063) G_GAN: -0.039 G_GAN_Feat: 0.744 G_ID: 0.106 G_Rec: 0.309 D_GP: 0.043 D_real: 0.784 D_fake: 1.039 +(epoch: 445, iters: 3854, time: 0.063) G_GAN: 0.545 G_GAN_Feat: 0.957 G_ID: 0.110 G_Rec: 0.433 D_GP: 0.052 D_real: 1.070 D_fake: 0.473 +(epoch: 445, iters: 4254, time: 0.064) G_GAN: -0.029 G_GAN_Feat: 0.816 G_ID: 0.110 G_Rec: 0.338 D_GP: 0.047 D_real: 0.793 D_fake: 1.031 +(epoch: 445, iters: 4654, time: 0.063) G_GAN: 0.570 G_GAN_Feat: 1.041 G_ID: 0.115 G_Rec: 0.410 D_GP: 0.172 D_real: 0.685 D_fake: 0.440 +(epoch: 445, iters: 5054, time: 0.063) G_GAN: 0.110 G_GAN_Feat: 0.760 G_ID: 0.120 G_Rec: 0.300 D_GP: 0.037 D_real: 0.854 D_fake: 0.890 +(epoch: 445, iters: 5454, time: 0.063) G_GAN: 0.251 G_GAN_Feat: 1.000 G_ID: 0.107 G_Rec: 0.413 D_GP: 0.038 D_real: 0.799 D_fake: 0.749 +(epoch: 445, iters: 5854, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.858 G_ID: 0.094 G_Rec: 0.327 D_GP: 0.038 D_real: 1.058 D_fake: 0.630 +(epoch: 445, iters: 6254, time: 0.063) G_GAN: 0.483 G_GAN_Feat: 0.967 G_ID: 0.128 G_Rec: 0.416 D_GP: 0.036 D_real: 1.037 D_fake: 0.523 +(epoch: 445, iters: 6654, time: 0.063) G_GAN: 0.350 G_GAN_Feat: 1.045 G_ID: 0.126 G_Rec: 0.354 D_GP: 0.211 D_real: 0.277 D_fake: 0.725 +(epoch: 445, iters: 7054, time: 0.063) G_GAN: 0.526 G_GAN_Feat: 1.251 G_ID: 0.124 G_Rec: 0.502 D_GP: 0.359 D_real: 0.528 D_fake: 0.499 +(epoch: 445, iters: 7454, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.947 G_ID: 0.101 G_Rec: 0.401 D_GP: 0.045 D_real: 1.064 D_fake: 0.699 +(epoch: 445, iters: 7854, time: 0.063) G_GAN: 0.266 G_GAN_Feat: 0.860 G_ID: 0.104 G_Rec: 0.439 D_GP: 0.027 D_real: 0.987 D_fake: 0.738 +(epoch: 445, iters: 8254, time: 0.063) G_GAN: 0.121 G_GAN_Feat: 0.667 G_ID: 0.082 G_Rec: 0.298 D_GP: 0.029 D_real: 1.063 D_fake: 0.879 +(epoch: 445, iters: 8654, time: 0.063) G_GAN: 0.166 G_GAN_Feat: 0.874 G_ID: 0.126 G_Rec: 0.411 D_GP: 0.038 D_real: 0.785 D_fake: 0.834 +(epoch: 446, iters: 446, time: 0.064) G_GAN: -0.054 G_GAN_Feat: 0.684 G_ID: 0.084 G_Rec: 0.349 D_GP: 0.055 D_real: 0.846 D_fake: 1.054 +(epoch: 446, iters: 846, time: 0.063) G_GAN: 0.390 G_GAN_Feat: 0.909 G_ID: 0.114 G_Rec: 0.439 D_GP: 0.033 D_real: 0.940 D_fake: 0.621 +(epoch: 446, iters: 1246, time: 0.063) G_GAN: 0.207 G_GAN_Feat: 0.735 G_ID: 0.100 G_Rec: 0.320 D_GP: 0.039 D_real: 1.041 D_fake: 0.794 +(epoch: 446, iters: 1646, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 0.994 G_ID: 0.112 G_Rec: 0.427 D_GP: 0.037 D_real: 0.852 D_fake: 0.693 +(epoch: 446, iters: 2046, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.705 G_ID: 0.102 G_Rec: 0.299 D_GP: 0.040 D_real: 1.180 D_fake: 0.691 +(epoch: 446, iters: 2446, time: 0.063) G_GAN: 0.486 G_GAN_Feat: 0.985 G_ID: 0.130 G_Rec: 0.439 D_GP: 0.037 D_real: 1.112 D_fake: 0.553 +(epoch: 446, iters: 2846, time: 0.063) G_GAN: 0.337 G_GAN_Feat: 0.749 G_ID: 0.098 G_Rec: 0.336 D_GP: 0.033 D_real: 1.214 D_fake: 0.666 +(epoch: 446, iters: 3246, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 0.843 G_ID: 0.109 G_Rec: 0.430 D_GP: 0.029 D_real: 1.067 D_fake: 0.666 +(epoch: 446, iters: 3646, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.735 G_ID: 0.099 G_Rec: 0.324 D_GP: 0.033 D_real: 1.176 D_fake: 0.635 +(epoch: 446, iters: 4046, time: 0.063) G_GAN: 0.318 G_GAN_Feat: 0.924 G_ID: 0.129 G_Rec: 0.395 D_GP: 0.061 D_real: 0.957 D_fake: 0.685 +(epoch: 446, iters: 4446, time: 0.063) G_GAN: 0.371 G_GAN_Feat: 0.838 G_ID: 0.105 G_Rec: 0.319 D_GP: 0.084 D_real: 0.656 D_fake: 0.643 +(epoch: 446, iters: 4846, time: 0.063) G_GAN: 0.593 G_GAN_Feat: 1.064 G_ID: 0.106 G_Rec: 0.439 D_GP: 0.061 D_real: 0.848 D_fake: 0.419 +(epoch: 446, iters: 5246, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.718 G_ID: 0.095 G_Rec: 0.283 D_GP: 0.028 D_real: 1.153 D_fake: 0.717 +(epoch: 446, iters: 5646, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 1.025 G_ID: 0.123 G_Rec: 0.412 D_GP: 0.044 D_real: 0.914 D_fake: 0.547 +(epoch: 446, iters: 6046, time: 0.064) G_GAN: 0.643 G_GAN_Feat: 0.789 G_ID: 0.107 G_Rec: 0.321 D_GP: 0.040 D_real: 1.457 D_fake: 0.385 +(epoch: 446, iters: 6446, time: 0.063) G_GAN: 0.482 G_GAN_Feat: 1.030 G_ID: 0.130 G_Rec: 0.408 D_GP: 0.056 D_real: 0.687 D_fake: 0.537 +(epoch: 446, iters: 6846, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.872 G_ID: 0.110 G_Rec: 0.302 D_GP: 0.039 D_real: 0.525 D_fake: 0.747 +(epoch: 446, iters: 7246, time: 0.063) G_GAN: 0.258 G_GAN_Feat: 0.917 G_ID: 0.118 G_Rec: 0.443 D_GP: 0.034 D_real: 1.034 D_fake: 0.753 +(epoch: 446, iters: 7646, time: 0.063) G_GAN: 0.103 G_GAN_Feat: 0.674 G_ID: 0.087 G_Rec: 0.301 D_GP: 0.040 D_real: 1.018 D_fake: 0.897 +(epoch: 446, iters: 8046, time: 0.063) G_GAN: 0.222 G_GAN_Feat: 1.004 G_ID: 0.117 G_Rec: 0.502 D_GP: 0.076 D_real: 0.801 D_fake: 0.783 +(epoch: 446, iters: 8446, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.660 G_ID: 0.093 G_Rec: 0.296 D_GP: 0.039 D_real: 0.869 D_fake: 0.989 +(epoch: 447, iters: 238, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.907 G_ID: 0.106 G_Rec: 0.454 D_GP: 0.034 D_real: 0.918 D_fake: 0.661 +(epoch: 447, iters: 638, time: 0.063) G_GAN: 0.145 G_GAN_Feat: 0.700 G_ID: 0.091 G_Rec: 0.315 D_GP: 0.033 D_real: 1.045 D_fake: 0.858 +(epoch: 447, iters: 1038, time: 0.063) G_GAN: 0.569 G_GAN_Feat: 0.886 G_ID: 0.119 G_Rec: 0.400 D_GP: 0.032 D_real: 1.147 D_fake: 0.480 +(epoch: 447, iters: 1438, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.723 G_ID: 0.091 G_Rec: 0.310 D_GP: 0.054 D_real: 0.870 D_fake: 0.905 +(epoch: 447, iters: 1838, time: 0.063) G_GAN: 0.288 G_GAN_Feat: 0.897 G_ID: 0.128 G_Rec: 0.401 D_GP: 0.036 D_real: 0.882 D_fake: 0.713 +(epoch: 447, iters: 2238, time: 0.063) G_GAN: 0.316 G_GAN_Feat: 0.772 G_ID: 0.105 G_Rec: 0.336 D_GP: 0.039 D_real: 1.020 D_fake: 0.690 +(epoch: 447, iters: 2638, time: 0.063) G_GAN: 0.382 G_GAN_Feat: 1.035 G_ID: 0.110 G_Rec: 0.478 D_GP: 0.067 D_real: 0.851 D_fake: 0.626 +(epoch: 447, iters: 3038, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.768 G_ID: 0.101 G_Rec: 0.333 D_GP: 0.043 D_real: 1.119 D_fake: 0.783 +(epoch: 447, iters: 3438, time: 0.063) G_GAN: 0.507 G_GAN_Feat: 1.064 G_ID: 0.116 G_Rec: 0.455 D_GP: 0.131 D_real: 0.866 D_fake: 0.503 +(epoch: 447, iters: 3838, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.753 G_ID: 0.100 G_Rec: 0.329 D_GP: 0.054 D_real: 0.989 D_fake: 0.757 +(epoch: 447, iters: 4238, time: 0.063) G_GAN: 0.411 G_GAN_Feat: 0.953 G_ID: 0.138 G_Rec: 0.429 D_GP: 0.039 D_real: 0.934 D_fake: 0.593 +(epoch: 447, iters: 4638, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.686 G_ID: 0.107 G_Rec: 0.292 D_GP: 0.032 D_real: 1.129 D_fake: 0.759 +(epoch: 447, iters: 5038, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.948 G_ID: 0.113 G_Rec: 0.402 D_GP: 0.051 D_real: 0.770 D_fake: 0.664 +(epoch: 447, iters: 5438, time: 0.063) G_GAN: 0.244 G_GAN_Feat: 0.685 G_ID: 0.102 G_Rec: 0.326 D_GP: 0.032 D_real: 1.147 D_fake: 0.756 +(epoch: 447, iters: 5838, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.983 G_ID: 0.116 G_Rec: 0.477 D_GP: 0.037 D_real: 0.687 D_fake: 0.762 +(epoch: 447, iters: 6238, time: 0.064) G_GAN: -0.070 G_GAN_Feat: 0.797 G_ID: 0.115 G_Rec: 0.314 D_GP: 0.044 D_real: 0.622 D_fake: 1.070 +(epoch: 447, iters: 6638, time: 0.064) G_GAN: 0.688 G_GAN_Feat: 0.983 G_ID: 0.121 G_Rec: 0.443 D_GP: 0.040 D_real: 1.216 D_fake: 0.381 +(epoch: 447, iters: 7038, time: 0.063) G_GAN: 0.177 G_GAN_Feat: 0.805 G_ID: 0.106 G_Rec: 0.344 D_GP: 0.041 D_real: 0.834 D_fake: 0.824 +(epoch: 447, iters: 7438, time: 0.063) G_GAN: 0.363 G_GAN_Feat: 1.012 G_ID: 0.137 G_Rec: 0.423 D_GP: 0.050 D_real: 0.842 D_fake: 0.638 +(epoch: 447, iters: 7838, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.695 G_ID: 0.097 G_Rec: 0.309 D_GP: 0.036 D_real: 1.084 D_fake: 0.749 +(epoch: 447, iters: 8238, time: 0.063) G_GAN: 0.520 G_GAN_Feat: 1.038 G_ID: 0.112 G_Rec: 0.443 D_GP: 0.040 D_real: 0.914 D_fake: 0.485 +(epoch: 447, iters: 8638, time: 0.063) G_GAN: 0.306 G_GAN_Feat: 0.766 G_ID: 0.107 G_Rec: 0.313 D_GP: 0.039 D_real: 1.013 D_fake: 0.695 +(epoch: 448, iters: 430, time: 0.063) G_GAN: 0.564 G_GAN_Feat: 0.913 G_ID: 0.130 G_Rec: 0.411 D_GP: 0.037 D_real: 1.244 D_fake: 0.453 +(epoch: 448, iters: 830, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.882 G_ID: 0.098 G_Rec: 0.346 D_GP: 0.137 D_real: 0.365 D_fake: 0.921 +(epoch: 448, iters: 1230, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.994 G_ID: 0.114 G_Rec: 0.433 D_GP: 0.035 D_real: 0.927 D_fake: 0.521 +(epoch: 448, iters: 1630, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.836 G_ID: 0.121 G_Rec: 0.351 D_GP: 0.054 D_real: 0.541 D_fake: 0.878 +(epoch: 448, iters: 2030, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 1.002 G_ID: 0.116 G_Rec: 0.420 D_GP: 0.117 D_real: 0.525 D_fake: 0.649 +(epoch: 448, iters: 2430, time: 0.064) G_GAN: -0.086 G_GAN_Feat: 0.836 G_ID: 0.083 G_Rec: 0.319 D_GP: 0.247 D_real: 0.370 D_fake: 1.091 +(epoch: 448, iters: 2830, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 1.047 G_ID: 0.097 G_Rec: 0.456 D_GP: 0.040 D_real: 1.098 D_fake: 0.394 +(epoch: 448, iters: 3230, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.865 G_ID: 0.090 G_Rec: 0.332 D_GP: 0.187 D_real: 0.507 D_fake: 0.866 +(epoch: 448, iters: 3630, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 1.024 G_ID: 0.128 G_Rec: 0.434 D_GP: 0.052 D_real: 0.790 D_fake: 0.552 +(epoch: 448, iters: 4030, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.848 G_ID: 0.112 G_Rec: 0.305 D_GP: 0.079 D_real: 0.350 D_fake: 0.994 +(epoch: 448, iters: 4430, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.994 G_ID: 0.121 G_Rec: 0.455 D_GP: 0.036 D_real: 1.001 D_fake: 0.643 +(epoch: 448, iters: 4830, time: 0.064) G_GAN: -0.046 G_GAN_Feat: 0.799 G_ID: 0.092 G_Rec: 0.333 D_GP: 0.061 D_real: 0.792 D_fake: 1.046 +(epoch: 448, iters: 5230, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 1.029 G_ID: 0.133 G_Rec: 0.434 D_GP: 0.180 D_real: 0.273 D_fake: 0.800 +(epoch: 448, iters: 5630, time: 0.063) G_GAN: 0.113 G_GAN_Feat: 0.747 G_ID: 0.090 G_Rec: 0.296 D_GP: 0.033 D_real: 0.792 D_fake: 0.888 +(epoch: 448, iters: 6030, time: 0.063) G_GAN: 0.386 G_GAN_Feat: 1.100 G_ID: 0.131 G_Rec: 0.485 D_GP: 0.043 D_real: 0.940 D_fake: 0.619 +(epoch: 448, iters: 6430, time: 0.063) G_GAN: 0.388 G_GAN_Feat: 0.757 G_ID: 0.096 G_Rec: 0.315 D_GP: 0.030 D_real: 1.162 D_fake: 0.614 +(epoch: 448, iters: 6830, time: 0.064) G_GAN: 0.956 G_GAN_Feat: 1.229 G_ID: 0.121 G_Rec: 0.461 D_GP: 0.071 D_real: 0.469 D_fake: 0.193 +(epoch: 448, iters: 7230, time: 0.063) G_GAN: 0.158 G_GAN_Feat: 0.747 G_ID: 0.090 G_Rec: 0.329 D_GP: 0.029 D_real: 1.080 D_fake: 0.842 +(epoch: 448, iters: 7630, time: 0.063) G_GAN: 0.050 G_GAN_Feat: 0.968 G_ID: 0.115 G_Rec: 0.429 D_GP: 0.036 D_real: 0.755 D_fake: 0.950 +(epoch: 448, iters: 8030, time: 0.063) G_GAN: -0.038 G_GAN_Feat: 0.858 G_ID: 0.110 G_Rec: 0.316 D_GP: 0.045 D_real: 0.657 D_fake: 1.038 +(epoch: 448, iters: 8430, time: 0.064) G_GAN: 0.674 G_GAN_Feat: 0.941 G_ID: 0.135 G_Rec: 0.354 D_GP: 0.041 D_real: 1.109 D_fake: 0.343 +(epoch: 449, iters: 222, time: 0.064) G_GAN: 0.652 G_GAN_Feat: 0.769 G_ID: 0.097 G_Rec: 0.328 D_GP: 0.045 D_real: 1.480 D_fake: 0.379 +(epoch: 449, iters: 622, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.947 G_ID: 0.118 G_Rec: 0.439 D_GP: 0.033 D_real: 1.008 D_fake: 0.499 +(epoch: 449, iters: 1022, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.730 G_ID: 0.116 G_Rec: 0.295 D_GP: 0.033 D_real: 1.142 D_fake: 0.699 +(epoch: 449, iters: 1422, time: 0.064) G_GAN: 0.771 G_GAN_Feat: 1.042 G_ID: 0.119 G_Rec: 0.461 D_GP: 0.048 D_real: 1.159 D_fake: 0.274 +(epoch: 449, iters: 1822, time: 0.063) G_GAN: 0.289 G_GAN_Feat: 0.806 G_ID: 0.105 G_Rec: 0.322 D_GP: 0.067 D_real: 0.844 D_fake: 0.716 +(epoch: 449, iters: 2222, time: 0.063) G_GAN: 0.686 G_GAN_Feat: 1.162 G_ID: 0.123 G_Rec: 0.435 D_GP: 0.748 D_real: 0.494 D_fake: 0.343 +(epoch: 449, iters: 2622, time: 0.063) G_GAN: 0.168 G_GAN_Feat: 0.943 G_ID: 0.096 G_Rec: 0.339 D_GP: 0.766 D_real: 0.371 D_fake: 0.838 +(epoch: 449, iters: 3022, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 1.038 G_ID: 0.145 G_Rec: 0.445 D_GP: 0.035 D_real: 0.957 D_fake: 0.427 +(epoch: 449, iters: 3422, time: 0.063) G_GAN: 0.429 G_GAN_Feat: 0.733 G_ID: 0.106 G_Rec: 0.268 D_GP: 0.035 D_real: 1.241 D_fake: 0.571 +(epoch: 449, iters: 3822, time: 0.063) G_GAN: 0.192 G_GAN_Feat: 0.930 G_ID: 0.131 G_Rec: 0.483 D_GP: 0.031 D_real: 0.835 D_fake: 0.810 +(epoch: 449, iters: 4222, time: 0.063) G_GAN: -0.063 G_GAN_Feat: 0.752 G_ID: 0.090 G_Rec: 0.297 D_GP: 0.055 D_real: 0.632 D_fake: 1.063 +(epoch: 449, iters: 4622, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.955 G_ID: 0.138 G_Rec: 0.417 D_GP: 0.038 D_real: 0.948 D_fake: 0.651 +(epoch: 449, iters: 5022, time: 0.063) G_GAN: 0.054 G_GAN_Feat: 0.879 G_ID: 0.107 G_Rec: 0.336 D_GP: 0.130 D_real: 0.340 D_fake: 0.947 +(epoch: 449, iters: 5422, time: 0.063) G_GAN: 0.565 G_GAN_Feat: 1.059 G_ID: 0.110 G_Rec: 0.487 D_GP: 0.044 D_real: 0.784 D_fake: 0.445 +(epoch: 449, iters: 5822, time: 0.063) G_GAN: -0.012 G_GAN_Feat: 0.763 G_ID: 0.111 G_Rec: 0.287 D_GP: 0.034 D_real: 0.789 D_fake: 1.014 +(epoch: 449, iters: 6222, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 0.947 G_ID: 0.123 G_Rec: 0.449 D_GP: 0.042 D_real: 1.353 D_fake: 0.392 +(epoch: 449, iters: 6622, time: 0.063) G_GAN: 0.179 G_GAN_Feat: 0.682 G_ID: 0.099 G_Rec: 0.275 D_GP: 0.028 D_real: 1.141 D_fake: 0.821 +(epoch: 449, iters: 7022, time: 0.063) G_GAN: 0.325 G_GAN_Feat: 0.852 G_ID: 0.117 G_Rec: 0.359 D_GP: 0.033 D_real: 1.079 D_fake: 0.676 +(epoch: 449, iters: 7422, time: 0.063) G_GAN: 0.218 G_GAN_Feat: 0.738 G_ID: 0.093 G_Rec: 0.304 D_GP: 0.031 D_real: 1.124 D_fake: 0.783 +(epoch: 449, iters: 7822, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 1.022 G_ID: 0.131 G_Rec: 0.465 D_GP: 0.067 D_real: 0.752 D_fake: 0.658 +(epoch: 449, iters: 8222, time: 0.063) G_GAN: 0.242 G_GAN_Feat: 0.866 G_ID: 0.113 G_Rec: 0.377 D_GP: 0.223 D_real: 0.752 D_fake: 0.765 +(epoch: 449, iters: 8622, time: 0.063) G_GAN: 0.498 G_GAN_Feat: 0.936 G_ID: 0.130 G_Rec: 0.425 D_GP: 0.033 D_real: 1.152 D_fake: 0.507 +(epoch: 450, iters: 414, time: 0.063) G_GAN: -0.158 G_GAN_Feat: 0.748 G_ID: 0.116 G_Rec: 0.315 D_GP: 0.038 D_real: 0.656 D_fake: 1.158 +(epoch: 450, iters: 814, time: 0.064) G_GAN: 0.616 G_GAN_Feat: 0.930 G_ID: 0.129 G_Rec: 0.445 D_GP: 0.033 D_real: 1.230 D_fake: 0.417 +(epoch: 450, iters: 1214, time: 0.063) G_GAN: 0.158 G_GAN_Feat: 0.851 G_ID: 0.134 G_Rec: 0.330 D_GP: 0.053 D_real: 0.673 D_fake: 0.842 +(epoch: 450, iters: 1614, time: 0.063) G_GAN: 0.694 G_GAN_Feat: 1.133 G_ID: 0.103 G_Rec: 0.454 D_GP: 0.079 D_real: 0.648 D_fake: 0.329 +(epoch: 450, iters: 2014, time: 0.063) G_GAN: 0.423 G_GAN_Feat: 0.873 G_ID: 0.098 G_Rec: 0.328 D_GP: 0.036 D_real: 0.937 D_fake: 0.581 +(epoch: 450, iters: 2414, time: 0.064) G_GAN: 0.716 G_GAN_Feat: 0.886 G_ID: 0.110 G_Rec: 0.394 D_GP: 0.080 D_real: 1.279 D_fake: 0.397 +(epoch: 450, iters: 2814, time: 0.063) G_GAN: 0.068 G_GAN_Feat: 0.742 G_ID: 0.091 G_Rec: 0.306 D_GP: 0.032 D_real: 0.938 D_fake: 0.932 +(epoch: 450, iters: 3214, time: 0.063) G_GAN: 0.392 G_GAN_Feat: 0.967 G_ID: 0.156 G_Rec: 0.421 D_GP: 0.037 D_real: 0.964 D_fake: 0.610 +(epoch: 450, iters: 3614, time: 0.063) G_GAN: 0.256 G_GAN_Feat: 0.720 G_ID: 0.103 G_Rec: 0.288 D_GP: 0.033 D_real: 1.177 D_fake: 0.745 +(epoch: 450, iters: 4014, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.978 G_ID: 0.111 G_Rec: 0.449 D_GP: 0.036 D_real: 1.233 D_fake: 0.460 +(epoch: 450, iters: 4414, time: 0.063) G_GAN: 0.430 G_GAN_Feat: 0.716 G_ID: 0.079 G_Rec: 0.272 D_GP: 0.025 D_real: 1.304 D_fake: 0.571 +(epoch: 450, iters: 4814, time: 0.063) G_GAN: 0.833 G_GAN_Feat: 1.063 G_ID: 0.128 G_Rec: 0.403 D_GP: 0.048 D_real: 1.121 D_fake: 0.203 +(epoch: 450, iters: 5214, time: 0.063) G_GAN: 0.312 G_GAN_Feat: 0.900 G_ID: 0.091 G_Rec: 0.327 D_GP: 0.040 D_real: 0.600 D_fake: 0.688 +(epoch: 450, iters: 5614, time: 0.064) G_GAN: 0.812 G_GAN_Feat: 0.911 G_ID: 0.109 G_Rec: 0.419 D_GP: 0.027 D_real: 1.513 D_fake: 0.279 +(epoch: 450, iters: 6014, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.788 G_ID: 0.087 G_Rec: 0.324 D_GP: 0.039 D_real: 1.079 D_fake: 0.651 +(epoch: 450, iters: 6414, time: 0.063) G_GAN: 0.649 G_GAN_Feat: 1.042 G_ID: 0.109 G_Rec: 0.460 D_GP: 0.044 D_real: 1.218 D_fake: 0.378 +(epoch: 450, iters: 6814, time: 0.063) G_GAN: 0.420 G_GAN_Feat: 0.689 G_ID: 0.092 G_Rec: 0.281 D_GP: 0.027 D_real: 1.272 D_fake: 0.581 +(epoch: 450, iters: 7214, time: 0.064) G_GAN: 0.712 G_GAN_Feat: 0.958 G_ID: 0.127 G_Rec: 0.388 D_GP: 0.069 D_real: 1.002 D_fake: 0.306 +(epoch: 450, iters: 7614, time: 0.063) G_GAN: 0.281 G_GAN_Feat: 0.785 G_ID: 0.096 G_Rec: 0.300 D_GP: 0.030 D_real: 0.946 D_fake: 0.719 +(epoch: 450, iters: 8014, time: 0.063) G_GAN: 0.675 G_GAN_Feat: 1.119 G_ID: 0.123 G_Rec: 0.420 D_GP: 0.059 D_real: 0.604 D_fake: 0.356 +(epoch: 450, iters: 8414, time: 0.063) G_GAN: 0.547 G_GAN_Feat: 0.956 G_ID: 0.101 G_Rec: 0.337 D_GP: 0.091 D_real: 0.629 D_fake: 0.522 +(epoch: 451, iters: 206, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.867 G_ID: 0.131 G_Rec: 0.411 D_GP: 0.031 D_real: 1.075 D_fake: 0.647 +(epoch: 451, iters: 606, time: 0.063) G_GAN: 0.222 G_GAN_Feat: 0.659 G_ID: 0.096 G_Rec: 0.280 D_GP: 0.026 D_real: 1.208 D_fake: 0.778 +(epoch: 451, iters: 1006, time: 0.063) G_GAN: 0.358 G_GAN_Feat: 0.981 G_ID: 0.113 G_Rec: 0.443 D_GP: 0.037 D_real: 0.979 D_fake: 0.649 +(epoch: 451, iters: 1406, time: 0.063) G_GAN: -0.209 G_GAN_Feat: 0.712 G_ID: 0.118 G_Rec: 0.359 D_GP: 0.032 D_real: 0.731 D_fake: 1.209 +(epoch: 451, iters: 1806, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.884 G_ID: 0.122 G_Rec: 0.396 D_GP: 0.043 D_real: 0.769 D_fake: 0.871 +(epoch: 451, iters: 2206, time: 0.063) G_GAN: -0.086 G_GAN_Feat: 0.755 G_ID: 0.121 G_Rec: 0.321 D_GP: 0.042 D_real: 0.565 D_fake: 1.086 +(epoch: 451, iters: 2606, time: 0.063) G_GAN: 0.088 G_GAN_Feat: 0.970 G_ID: 0.117 G_Rec: 0.430 D_GP: 0.053 D_real: 0.581 D_fake: 0.912 +(epoch: 451, iters: 3006, time: 0.063) G_GAN: 0.248 G_GAN_Feat: 0.791 G_ID: 0.082 G_Rec: 0.300 D_GP: 0.080 D_real: 0.794 D_fake: 0.757 +(epoch: 451, iters: 3406, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.997 G_ID: 0.113 G_Rec: 0.452 D_GP: 0.035 D_real: 0.984 D_fake: 0.637 +(epoch: 451, iters: 3806, time: 0.063) G_GAN: 0.264 G_GAN_Feat: 0.723 G_ID: 0.098 G_Rec: 0.317 D_GP: 0.038 D_real: 1.151 D_fake: 0.736 +(epoch: 451, iters: 4206, time: 0.063) G_GAN: 0.463 G_GAN_Feat: 0.956 G_ID: 0.117 G_Rec: 0.417 D_GP: 0.054 D_real: 0.938 D_fake: 0.546 +(epoch: 451, iters: 4606, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.763 G_ID: 0.089 G_Rec: 0.308 D_GP: 0.064 D_real: 0.858 D_fake: 0.747 +(epoch: 451, iters: 5006, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 1.044 G_ID: 0.112 G_Rec: 0.455 D_GP: 0.097 D_real: 0.599 D_fake: 0.508 +(epoch: 451, iters: 5406, time: 0.063) G_GAN: 0.298 G_GAN_Feat: 0.988 G_ID: 0.113 G_Rec: 0.388 D_GP: 0.149 D_real: 0.320 D_fake: 0.723 +(epoch: 451, iters: 5806, time: 0.063) G_GAN: 0.432 G_GAN_Feat: 1.177 G_ID: 0.115 G_Rec: 0.471 D_GP: 0.080 D_real: 0.422 D_fake: 0.568 +(epoch: 451, iters: 6206, time: 0.063) G_GAN: 0.072 G_GAN_Feat: 0.942 G_ID: 0.126 G_Rec: 0.347 D_GP: 0.141 D_real: 0.234 D_fake: 0.932 +(epoch: 451, iters: 6606, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 1.001 G_ID: 0.147 G_Rec: 0.453 D_GP: 0.052 D_real: 0.673 D_fake: 0.724 +(epoch: 451, iters: 7006, time: 0.063) G_GAN: 0.078 G_GAN_Feat: 0.776 G_ID: 0.107 G_Rec: 0.287 D_GP: 0.065 D_real: 0.748 D_fake: 0.924 +(epoch: 451, iters: 7406, time: 0.063) G_GAN: 0.609 G_GAN_Feat: 1.211 G_ID: 0.109 G_Rec: 0.473 D_GP: 0.049 D_real: 0.603 D_fake: 0.407 +(epoch: 451, iters: 7806, time: 0.063) G_GAN: 0.249 G_GAN_Feat: 0.723 G_ID: 0.090 G_Rec: 0.291 D_GP: 0.029 D_real: 1.132 D_fake: 0.751 +(epoch: 451, iters: 8206, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 1.003 G_ID: 0.129 G_Rec: 0.440 D_GP: 0.047 D_real: 0.866 D_fake: 0.631 +(epoch: 451, iters: 8606, time: 0.063) G_GAN: 0.166 G_GAN_Feat: 0.902 G_ID: 0.093 G_Rec: 0.358 D_GP: 0.186 D_real: 0.555 D_fake: 0.841 +(epoch: 452, iters: 398, time: 0.063) G_GAN: 0.602 G_GAN_Feat: 1.172 G_ID: 0.126 G_Rec: 0.459 D_GP: 0.073 D_real: 0.292 D_fake: 0.405 +(epoch: 452, iters: 798, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.907 G_ID: 0.092 G_Rec: 0.324 D_GP: 0.067 D_real: 0.709 D_fake: 0.525 +(epoch: 452, iters: 1198, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 1.089 G_ID: 0.129 G_Rec: 0.465 D_GP: 0.040 D_real: 0.608 D_fake: 0.552 +(epoch: 452, iters: 1598, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.735 G_ID: 0.090 G_Rec: 0.290 D_GP: 0.027 D_real: 1.056 D_fake: 0.810 +(epoch: 452, iters: 1998, time: 0.064) G_GAN: 0.679 G_GAN_Feat: 1.083 G_ID: 0.120 G_Rec: 0.455 D_GP: 0.048 D_real: 0.846 D_fake: 0.354 +(epoch: 452, iters: 2398, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 1.104 G_ID: 0.095 G_Rec: 0.352 D_GP: 0.224 D_real: 0.415 D_fake: 0.645 +(epoch: 452, iters: 2798, time: 0.064) G_GAN: 1.045 G_GAN_Feat: 1.166 G_ID: 0.110 G_Rec: 0.442 D_GP: 0.091 D_real: 0.887 D_fake: 0.111 +(epoch: 452, iters: 3198, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.839 G_ID: 0.087 G_Rec: 0.293 D_GP: 0.044 D_real: 0.645 D_fake: 0.908 +(epoch: 452, iters: 3598, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 1.020 G_ID: 0.119 G_Rec: 0.408 D_GP: 0.049 D_real: 0.658 D_fake: 0.446 +(epoch: 452, iters: 3998, time: 0.063) G_GAN: 0.492 G_GAN_Feat: 0.776 G_ID: 0.092 G_Rec: 0.283 D_GP: 0.041 D_real: 1.099 D_fake: 0.524 +(epoch: 452, iters: 4398, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.926 G_ID: 0.113 G_Rec: 0.431 D_GP: 0.029 D_real: 0.896 D_fake: 0.766 +(epoch: 452, iters: 4798, time: 0.063) G_GAN: 0.169 G_GAN_Feat: 0.741 G_ID: 0.088 G_Rec: 0.302 D_GP: 0.099 D_real: 0.970 D_fake: 0.832 +(epoch: 452, iters: 5198, time: 0.063) G_GAN: 0.505 G_GAN_Feat: 1.003 G_ID: 0.129 G_Rec: 0.408 D_GP: 0.039 D_real: 0.927 D_fake: 0.503 +(epoch: 452, iters: 5598, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.738 G_ID: 0.089 G_Rec: 0.310 D_GP: 0.033 D_real: 1.339 D_fake: 0.643 +(epoch: 452, iters: 5998, time: 0.064) G_GAN: 0.640 G_GAN_Feat: 1.038 G_ID: 0.116 G_Rec: 0.415 D_GP: 0.064 D_real: 0.529 D_fake: 0.375 +(epoch: 452, iters: 6398, time: 0.063) G_GAN: 0.081 G_GAN_Feat: 0.708 G_ID: 0.096 G_Rec: 0.326 D_GP: 0.034 D_real: 0.868 D_fake: 0.919 +(epoch: 452, iters: 6798, time: 0.063) G_GAN: 0.439 G_GAN_Feat: 1.017 G_ID: 0.125 G_Rec: 0.432 D_GP: 0.085 D_real: 0.741 D_fake: 0.584 +(epoch: 452, iters: 7198, time: 0.063) G_GAN: -0.148 G_GAN_Feat: 0.798 G_ID: 0.095 G_Rec: 0.326 D_GP: 0.037 D_real: 0.724 D_fake: 1.148 +(epoch: 452, iters: 7598, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.943 G_ID: 0.135 G_Rec: 0.414 D_GP: 0.046 D_real: 0.796 D_fake: 0.809 +(epoch: 452, iters: 7998, time: 0.063) G_GAN: -0.047 G_GAN_Feat: 0.732 G_ID: 0.081 G_Rec: 0.291 D_GP: 0.055 D_real: 0.840 D_fake: 1.047 +(epoch: 452, iters: 8398, time: 0.063) G_GAN: 0.517 G_GAN_Feat: 1.036 G_ID: 0.116 G_Rec: 0.458 D_GP: 0.045 D_real: 0.936 D_fake: 0.498 +(epoch: 453, iters: 190, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.772 G_ID: 0.096 G_Rec: 0.301 D_GP: 0.038 D_real: 1.136 D_fake: 0.705 +(epoch: 453, iters: 590, time: 0.063) G_GAN: 0.379 G_GAN_Feat: 0.931 G_ID: 0.116 G_Rec: 0.424 D_GP: 0.033 D_real: 1.096 D_fake: 0.621 +(epoch: 453, iters: 990, time: 0.063) G_GAN: 0.298 G_GAN_Feat: 0.858 G_ID: 0.090 G_Rec: 0.335 D_GP: 0.059 D_real: 0.833 D_fake: 0.702 +(epoch: 453, iters: 1390, time: 0.063) G_GAN: 0.452 G_GAN_Feat: 1.078 G_ID: 0.098 G_Rec: 0.470 D_GP: 0.051 D_real: 0.769 D_fake: 0.551 +(epoch: 453, iters: 1790, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.894 G_ID: 0.107 G_Rec: 0.334 D_GP: 0.037 D_real: 0.750 D_fake: 0.717 +(epoch: 453, iters: 2190, time: 0.063) G_GAN: 0.861 G_GAN_Feat: 1.103 G_ID: 0.122 G_Rec: 0.478 D_GP: 0.076 D_real: 0.714 D_fake: 0.233 +(epoch: 453, iters: 2590, time: 0.063) G_GAN: 0.131 G_GAN_Feat: 0.720 G_ID: 0.084 G_Rec: 0.306 D_GP: 0.032 D_real: 1.057 D_fake: 0.869 +(epoch: 453, iters: 2990, time: 0.063) G_GAN: 0.432 G_GAN_Feat: 0.929 G_ID: 0.124 G_Rec: 0.449 D_GP: 0.041 D_real: 1.066 D_fake: 0.587 +(epoch: 453, iters: 3390, time: 0.064) G_GAN: -0.082 G_GAN_Feat: 0.625 G_ID: 0.100 G_Rec: 0.276 D_GP: 0.032 D_real: 0.764 D_fake: 1.082 +(epoch: 453, iters: 3790, time: 0.063) G_GAN: 0.197 G_GAN_Feat: 0.922 G_ID: 0.110 G_Rec: 0.412 D_GP: 0.034 D_real: 0.823 D_fake: 0.804 +(epoch: 453, iters: 4190, time: 0.063) G_GAN: 0.038 G_GAN_Feat: 0.755 G_ID: 0.103 G_Rec: 0.318 D_GP: 0.052 D_real: 0.818 D_fake: 0.962 +(epoch: 453, iters: 4590, time: 0.063) G_GAN: 0.228 G_GAN_Feat: 1.014 G_ID: 0.115 G_Rec: 0.478 D_GP: 0.043 D_real: 0.722 D_fake: 0.774 +(epoch: 453, iters: 4990, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.714 G_ID: 0.080 G_Rec: 0.292 D_GP: 0.037 D_real: 1.091 D_fake: 0.724 +(epoch: 453, iters: 5390, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.907 G_ID: 0.121 G_Rec: 0.407 D_GP: 0.028 D_real: 1.087 D_fake: 0.763 +(epoch: 453, iters: 5790, time: 0.063) G_GAN: 0.182 G_GAN_Feat: 0.728 G_ID: 0.107 G_Rec: 0.281 D_GP: 0.033 D_real: 1.101 D_fake: 0.818 +(epoch: 453, iters: 6190, time: 0.063) G_GAN: 0.377 G_GAN_Feat: 0.995 G_ID: 0.125 G_Rec: 0.427 D_GP: 0.072 D_real: 0.793 D_fake: 0.625 +(epoch: 453, iters: 6590, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.859 G_ID: 0.104 G_Rec: 0.330 D_GP: 0.064 D_real: 0.850 D_fake: 0.735 +(epoch: 453, iters: 6990, time: 0.063) G_GAN: 0.563 G_GAN_Feat: 0.970 G_ID: 0.120 G_Rec: 0.445 D_GP: 0.032 D_real: 1.151 D_fake: 0.448 +(epoch: 453, iters: 7390, time: 0.063) G_GAN: 0.157 G_GAN_Feat: 0.774 G_ID: 0.106 G_Rec: 0.317 D_GP: 0.033 D_real: 0.907 D_fake: 0.843 +(epoch: 453, iters: 7790, time: 0.063) G_GAN: 0.657 G_GAN_Feat: 1.036 G_ID: 0.121 G_Rec: 0.465 D_GP: 0.037 D_real: 1.157 D_fake: 0.355 +(epoch: 453, iters: 8190, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.875 G_ID: 0.116 G_Rec: 0.350 D_GP: 0.075 D_real: 0.639 D_fake: 0.752 +(epoch: 453, iters: 8590, time: 0.063) G_GAN: 0.750 G_GAN_Feat: 1.182 G_ID: 0.122 G_Rec: 0.476 D_GP: 0.073 D_real: 0.651 D_fake: 0.274 +(epoch: 454, iters: 382, time: 0.063) G_GAN: -0.017 G_GAN_Feat: 0.883 G_ID: 0.110 G_Rec: 0.339 D_GP: 0.039 D_real: 0.375 D_fake: 1.017 +(epoch: 454, iters: 782, time: 0.063) G_GAN: 0.324 G_GAN_Feat: 0.948 G_ID: 0.124 G_Rec: 0.389 D_GP: 0.037 D_real: 0.900 D_fake: 0.677 +(epoch: 454, iters: 1182, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.737 G_ID: 0.078 G_Rec: 0.326 D_GP: 0.029 D_real: 1.211 D_fake: 0.651 +(epoch: 454, iters: 1582, time: 0.063) G_GAN: 0.629 G_GAN_Feat: 0.850 G_ID: 0.110 G_Rec: 0.383 D_GP: 0.042 D_real: 1.296 D_fake: 0.403 +(epoch: 454, iters: 1982, time: 0.063) G_GAN: 0.090 G_GAN_Feat: 0.751 G_ID: 0.090 G_Rec: 0.330 D_GP: 0.036 D_real: 0.881 D_fake: 0.910 +(epoch: 454, iters: 2382, time: 0.063) G_GAN: 0.545 G_GAN_Feat: 1.022 G_ID: 0.110 G_Rec: 0.410 D_GP: 0.035 D_real: 1.092 D_fake: 0.464 +(epoch: 454, iters: 2782, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.697 G_ID: 0.079 G_Rec: 0.293 D_GP: 0.032 D_real: 0.953 D_fake: 0.894 +(epoch: 454, iters: 3182, time: 0.063) G_GAN: 0.353 G_GAN_Feat: 1.042 G_ID: 0.111 G_Rec: 0.464 D_GP: 0.055 D_real: 0.930 D_fake: 0.649 +(epoch: 454, iters: 3582, time: 0.063) G_GAN: 0.527 G_GAN_Feat: 0.751 G_ID: 0.095 G_Rec: 0.330 D_GP: 0.037 D_real: 1.406 D_fake: 0.477 +(epoch: 454, iters: 3982, time: 0.063) G_GAN: 0.523 G_GAN_Feat: 0.894 G_ID: 0.132 G_Rec: 0.396 D_GP: 0.034 D_real: 1.143 D_fake: 0.487 +(epoch: 454, iters: 4382, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.690 G_ID: 0.112 G_Rec: 0.341 D_GP: 0.031 D_real: 1.279 D_fake: 0.642 +(epoch: 454, iters: 4782, time: 0.063) G_GAN: 0.362 G_GAN_Feat: 1.059 G_ID: 0.115 G_Rec: 0.472 D_GP: 0.090 D_real: 0.698 D_fake: 0.647 +(epoch: 454, iters: 5182, time: 0.063) G_GAN: 0.344 G_GAN_Feat: 0.886 G_ID: 0.102 G_Rec: 0.299 D_GP: 0.072 D_real: 0.590 D_fake: 0.658 +(epoch: 454, iters: 5582, time: 0.063) G_GAN: 0.421 G_GAN_Feat: 1.151 G_ID: 0.125 G_Rec: 0.471 D_GP: 0.672 D_real: 0.344 D_fake: 0.590 +(epoch: 454, iters: 5982, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.925 G_ID: 0.085 G_Rec: 0.321 D_GP: 0.132 D_real: 0.324 D_fake: 0.679 +(epoch: 454, iters: 6382, time: 0.063) G_GAN: 0.697 G_GAN_Feat: 1.006 G_ID: 0.119 G_Rec: 0.424 D_GP: 0.035 D_real: 1.197 D_fake: 0.316 +(epoch: 454, iters: 6782, time: 0.063) G_GAN: 0.118 G_GAN_Feat: 0.817 G_ID: 0.094 G_Rec: 0.330 D_GP: 0.044 D_real: 0.796 D_fake: 0.882 +(epoch: 454, iters: 7182, time: 0.063) G_GAN: 0.426 G_GAN_Feat: 0.936 G_ID: 0.126 G_Rec: 0.407 D_GP: 0.036 D_real: 1.076 D_fake: 0.582 +(epoch: 454, iters: 7582, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.821 G_ID: 0.100 G_Rec: 0.333 D_GP: 0.075 D_real: 0.720 D_fake: 0.847 +(epoch: 454, iters: 7982, time: 0.063) G_GAN: 0.548 G_GAN_Feat: 1.156 G_ID: 0.119 G_Rec: 0.480 D_GP: 0.067 D_real: 0.442 D_fake: 0.466 +(epoch: 454, iters: 8382, time: 0.063) G_GAN: 0.097 G_GAN_Feat: 0.708 G_ID: 0.095 G_Rec: 0.294 D_GP: 0.027 D_real: 0.977 D_fake: 0.903 +(epoch: 455, iters: 174, time: 0.063) G_GAN: 0.863 G_GAN_Feat: 1.181 G_ID: 0.123 G_Rec: 0.457 D_GP: 0.091 D_real: 0.645 D_fake: 0.376 +(epoch: 455, iters: 574, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.784 G_ID: 0.110 G_Rec: 0.319 D_GP: 0.035 D_real: 0.965 D_fake: 0.855 +(epoch: 455, iters: 974, time: 0.063) G_GAN: 0.609 G_GAN_Feat: 1.105 G_ID: 0.120 G_Rec: 0.447 D_GP: 0.051 D_real: 0.950 D_fake: 0.409 +(epoch: 455, iters: 1374, time: 0.063) G_GAN: 0.118 G_GAN_Feat: 0.754 G_ID: 0.093 G_Rec: 0.320 D_GP: 0.036 D_real: 0.996 D_fake: 0.882 +(epoch: 455, iters: 1774, time: 0.063) G_GAN: 0.689 G_GAN_Feat: 1.051 G_ID: 0.103 G_Rec: 0.489 D_GP: 0.036 D_real: 1.127 D_fake: 0.371 +(epoch: 455, iters: 2174, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.833 G_ID: 0.088 G_Rec: 0.353 D_GP: 0.096 D_real: 0.808 D_fake: 0.690 +(epoch: 455, iters: 2574, time: 0.063) G_GAN: 0.431 G_GAN_Feat: 1.051 G_ID: 0.136 G_Rec: 0.475 D_GP: 0.050 D_real: 0.759 D_fake: 0.582 +(epoch: 455, iters: 2974, time: 0.063) G_GAN: 0.246 G_GAN_Feat: 1.074 G_ID: 0.095 G_Rec: 0.340 D_GP: 0.213 D_real: 0.301 D_fake: 0.756 +(epoch: 455, iters: 3374, time: 0.063) G_GAN: 1.050 G_GAN_Feat: 1.150 G_ID: 0.128 G_Rec: 0.406 D_GP: 0.095 D_real: 0.853 D_fake: 0.234 +(epoch: 455, iters: 3774, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.869 G_ID: 0.101 G_Rec: 0.327 D_GP: 0.050 D_real: 0.801 D_fake: 0.621 +(epoch: 455, iters: 4174, time: 0.063) G_GAN: 0.664 G_GAN_Feat: 1.026 G_ID: 0.123 G_Rec: 0.479 D_GP: 0.035 D_real: 1.279 D_fake: 0.400 +(epoch: 455, iters: 4574, time: 0.063) G_GAN: -0.041 G_GAN_Feat: 0.749 G_ID: 0.106 G_Rec: 0.321 D_GP: 0.039 D_real: 0.749 D_fake: 1.041 +(epoch: 455, iters: 4974, time: 0.063) G_GAN: 0.718 G_GAN_Feat: 0.996 G_ID: 0.106 G_Rec: 0.422 D_GP: 0.054 D_real: 1.200 D_fake: 0.310 +(epoch: 455, iters: 5374, time: 0.064) G_GAN: 0.572 G_GAN_Feat: 0.809 G_ID: 0.099 G_Rec: 0.303 D_GP: 0.087 D_real: 1.164 D_fake: 0.437 +(epoch: 455, iters: 5774, time: 0.063) G_GAN: 0.816 G_GAN_Feat: 1.038 G_ID: 0.125 G_Rec: 0.427 D_GP: 0.043 D_real: 1.259 D_fake: 0.313 +(epoch: 455, iters: 6174, time: 0.063) G_GAN: 0.465 G_GAN_Feat: 0.772 G_ID: 0.102 G_Rec: 0.297 D_GP: 0.036 D_real: 1.273 D_fake: 0.544 +(epoch: 455, iters: 6574, time: 0.063) G_GAN: 0.454 G_GAN_Feat: 1.150 G_ID: 0.127 G_Rec: 0.468 D_GP: 0.066 D_real: 0.543 D_fake: 0.559 +(epoch: 455, iters: 6974, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.778 G_ID: 0.100 G_Rec: 0.345 D_GP: 0.034 D_real: 1.052 D_fake: 0.764 +(epoch: 455, iters: 7374, time: 0.063) G_GAN: 0.141 G_GAN_Feat: 0.897 G_ID: 0.134 G_Rec: 0.440 D_GP: 0.041 D_real: 0.834 D_fake: 0.860 +(epoch: 455, iters: 7774, time: 0.063) G_GAN: 0.167 G_GAN_Feat: 0.723 G_ID: 0.092 G_Rec: 0.317 D_GP: 0.037 D_real: 1.072 D_fake: 0.834 +(epoch: 455, iters: 8174, time: 0.063) G_GAN: 0.369 G_GAN_Feat: 0.922 G_ID: 0.104 G_Rec: 0.433 D_GP: 0.069 D_real: 1.028 D_fake: 0.636 +(epoch: 455, iters: 8574, time: 0.064) G_GAN: -0.033 G_GAN_Feat: 0.754 G_ID: 0.118 G_Rec: 0.329 D_GP: 0.042 D_real: 0.747 D_fake: 1.033 +(epoch: 456, iters: 366, time: 0.063) G_GAN: 0.385 G_GAN_Feat: 0.931 G_ID: 0.116 G_Rec: 0.423 D_GP: 0.045 D_real: 0.900 D_fake: 0.621 +(epoch: 456, iters: 766, time: 0.063) G_GAN: 0.242 G_GAN_Feat: 0.645 G_ID: 0.107 G_Rec: 0.299 D_GP: 0.028 D_real: 1.126 D_fake: 0.759 +(epoch: 456, iters: 1166, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.893 G_ID: 0.122 G_Rec: 0.402 D_GP: 0.048 D_real: 1.078 D_fake: 0.528 +(epoch: 456, iters: 1566, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.769 G_ID: 0.091 G_Rec: 0.316 D_GP: 0.060 D_real: 1.007 D_fake: 0.764 +(epoch: 456, iters: 1966, time: 0.063) G_GAN: 0.512 G_GAN_Feat: 0.925 G_ID: 0.115 G_Rec: 0.402 D_GP: 0.028 D_real: 1.152 D_fake: 0.492 +(epoch: 456, iters: 2366, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.930 G_ID: 0.087 G_Rec: 0.347 D_GP: 0.162 D_real: 0.510 D_fake: 0.660 +(epoch: 456, iters: 2766, time: 0.064) G_GAN: 0.141 G_GAN_Feat: 1.048 G_ID: 0.135 G_Rec: 0.461 D_GP: 0.036 D_real: 0.868 D_fake: 0.861 +(epoch: 456, iters: 3166, time: 0.064) G_GAN: -0.067 G_GAN_Feat: 0.737 G_ID: 0.124 G_Rec: 0.342 D_GP: 0.029 D_real: 0.840 D_fake: 1.067 +(epoch: 456, iters: 3566, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.890 G_ID: 0.122 G_Rec: 0.419 D_GP: 0.033 D_real: 0.838 D_fake: 0.835 +(epoch: 456, iters: 3966, time: 0.063) G_GAN: -0.153 G_GAN_Feat: 0.728 G_ID: 0.102 G_Rec: 0.327 D_GP: 0.035 D_real: 0.704 D_fake: 1.153 +(epoch: 456, iters: 4366, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.937 G_ID: 0.123 G_Rec: 0.426 D_GP: 0.040 D_real: 0.889 D_fake: 0.708 +(epoch: 456, iters: 4766, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.641 G_ID: 0.097 G_Rec: 0.269 D_GP: 0.035 D_real: 1.053 D_fake: 0.872 +(epoch: 456, iters: 5166, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.991 G_ID: 0.113 G_Rec: 0.459 D_GP: 0.060 D_real: 0.566 D_fake: 0.808 +(epoch: 456, iters: 5566, time: 0.064) G_GAN: -0.027 G_GAN_Feat: 0.670 G_ID: 0.107 G_Rec: 0.287 D_GP: 0.041 D_real: 0.813 D_fake: 1.030 +(epoch: 456, iters: 5966, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 1.002 G_ID: 0.118 G_Rec: 0.459 D_GP: 0.052 D_real: 0.750 D_fake: 0.648 +(epoch: 456, iters: 6366, time: 0.064) G_GAN: -0.178 G_GAN_Feat: 0.789 G_ID: 0.105 G_Rec: 0.347 D_GP: 0.070 D_real: 0.464 D_fake: 1.178 +(epoch: 456, iters: 6766, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 1.039 G_ID: 0.120 G_Rec: 0.415 D_GP: 0.063 D_real: 0.743 D_fake: 0.549 +(epoch: 456, iters: 7166, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.827 G_ID: 0.085 G_Rec: 0.331 D_GP: 0.047 D_real: 0.866 D_fake: 0.758 +(epoch: 456, iters: 7566, time: 0.064) G_GAN: 0.609 G_GAN_Feat: 1.169 G_ID: 0.138 G_Rec: 0.479 D_GP: 0.073 D_real: 0.324 D_fake: 0.442 +(epoch: 456, iters: 7966, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.762 G_ID: 0.088 G_Rec: 0.304 D_GP: 0.036 D_real: 1.181 D_fake: 0.725 +(epoch: 456, iters: 8366, time: 0.064) G_GAN: 0.854 G_GAN_Feat: 1.106 G_ID: 0.115 G_Rec: 0.503 D_GP: 0.055 D_real: 1.121 D_fake: 0.221 +(epoch: 457, iters: 158, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 0.947 G_ID: 0.099 G_Rec: 0.333 D_GP: 0.046 D_real: 0.573 D_fake: 0.520 +(epoch: 457, iters: 558, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.904 G_ID: 0.104 G_Rec: 0.449 D_GP: 0.030 D_real: 1.006 D_fake: 0.751 +(epoch: 457, iters: 958, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.680 G_ID: 0.098 G_Rec: 0.300 D_GP: 0.029 D_real: 0.943 D_fake: 0.923 +(epoch: 457, iters: 1358, time: 0.063) G_GAN: 0.477 G_GAN_Feat: 0.965 G_ID: 0.122 G_Rec: 0.417 D_GP: 0.039 D_real: 1.033 D_fake: 0.526 +(epoch: 457, iters: 1758, time: 0.063) G_GAN: 0.095 G_GAN_Feat: 0.656 G_ID: 0.093 G_Rec: 0.267 D_GP: 0.038 D_real: 0.967 D_fake: 0.905 +(epoch: 457, iters: 2158, time: 0.063) G_GAN: 0.476 G_GAN_Feat: 0.987 G_ID: 0.112 G_Rec: 0.444 D_GP: 0.037 D_real: 1.024 D_fake: 0.526 +(epoch: 457, iters: 2558, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.775 G_ID: 0.099 G_Rec: 0.279 D_GP: 0.066 D_real: 0.730 D_fake: 0.889 +(epoch: 457, iters: 2958, time: 0.063) G_GAN: 0.647 G_GAN_Feat: 1.095 G_ID: 0.113 G_Rec: 0.470 D_GP: 0.048 D_real: 0.950 D_fake: 0.482 +(epoch: 457, iters: 3358, time: 0.063) G_GAN: 0.339 G_GAN_Feat: 0.671 G_ID: 0.124 G_Rec: 0.335 D_GP: 0.044 D_real: 1.275 D_fake: 0.663 +(epoch: 457, iters: 3758, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.809 G_ID: 0.112 G_Rec: 0.391 D_GP: 0.029 D_real: 1.123 D_fake: 0.531 +(epoch: 457, iters: 4158, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.714 G_ID: 0.107 G_Rec: 0.319 D_GP: 0.040 D_real: 1.097 D_fake: 0.781 +(epoch: 457, iters: 4558, time: 0.063) G_GAN: 0.359 G_GAN_Feat: 0.822 G_ID: 0.117 G_Rec: 0.398 D_GP: 0.029 D_real: 1.077 D_fake: 0.653 +(epoch: 457, iters: 4958, time: 0.063) G_GAN: -0.047 G_GAN_Feat: 0.658 G_ID: 0.096 G_Rec: 0.318 D_GP: 0.034 D_real: 0.871 D_fake: 1.047 +(epoch: 457, iters: 5358, time: 0.063) G_GAN: 0.081 G_GAN_Feat: 0.856 G_ID: 0.114 G_Rec: 0.426 D_GP: 0.033 D_real: 0.738 D_fake: 0.919 +(epoch: 457, iters: 5758, time: 0.063) G_GAN: 0.073 G_GAN_Feat: 0.686 G_ID: 0.104 G_Rec: 0.331 D_GP: 0.040 D_real: 0.937 D_fake: 0.927 +(epoch: 457, iters: 6158, time: 0.063) G_GAN: 0.311 G_GAN_Feat: 0.846 G_ID: 0.100 G_Rec: 0.413 D_GP: 0.037 D_real: 1.017 D_fake: 0.704 +(epoch: 457, iters: 6558, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.650 G_ID: 0.090 G_Rec: 0.294 D_GP: 0.046 D_real: 0.926 D_fake: 0.979 +(epoch: 457, iters: 6958, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.869 G_ID: 0.110 G_Rec: 0.409 D_GP: 0.037 D_real: 0.820 D_fake: 0.844 +(epoch: 457, iters: 7358, time: 0.063) G_GAN: 0.157 G_GAN_Feat: 0.674 G_ID: 0.091 G_Rec: 0.340 D_GP: 0.035 D_real: 0.927 D_fake: 0.843 +(epoch: 457, iters: 7758, time: 0.063) G_GAN: 0.348 G_GAN_Feat: 0.911 G_ID: 0.110 G_Rec: 0.476 D_GP: 0.036 D_real: 0.974 D_fake: 0.658 +(epoch: 457, iters: 8158, time: 0.063) G_GAN: 0.068 G_GAN_Feat: 0.639 G_ID: 0.102 G_Rec: 0.275 D_GP: 0.036 D_real: 0.946 D_fake: 0.932 +(epoch: 457, iters: 8558, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 1.028 G_ID: 0.113 G_Rec: 0.471 D_GP: 0.098 D_real: 0.804 D_fake: 0.568 +(epoch: 458, iters: 350, time: 0.063) G_GAN: 0.230 G_GAN_Feat: 0.670 G_ID: 0.084 G_Rec: 0.287 D_GP: 0.042 D_real: 1.092 D_fake: 0.774 +(epoch: 458, iters: 750, time: 0.063) G_GAN: 0.168 G_GAN_Feat: 0.983 G_ID: 0.120 G_Rec: 0.467 D_GP: 0.060 D_real: 0.685 D_fake: 0.835 +(epoch: 458, iters: 1150, time: 0.063) G_GAN: 0.274 G_GAN_Feat: 0.748 G_ID: 0.107 G_Rec: 0.307 D_GP: 0.084 D_real: 0.882 D_fake: 0.729 +(epoch: 458, iters: 1550, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.882 G_ID: 0.145 G_Rec: 0.418 D_GP: 0.044 D_real: 0.884 D_fake: 0.706 +(epoch: 458, iters: 1950, time: 0.063) G_GAN: 0.148 G_GAN_Feat: 0.734 G_ID: 0.084 G_Rec: 0.309 D_GP: 0.040 D_real: 0.940 D_fake: 0.852 +(epoch: 458, iters: 2350, time: 0.063) G_GAN: 0.276 G_GAN_Feat: 0.915 G_ID: 0.127 G_Rec: 0.417 D_GP: 0.087 D_real: 0.818 D_fake: 0.724 +(epoch: 458, iters: 2750, time: 0.063) G_GAN: 0.389 G_GAN_Feat: 0.765 G_ID: 0.099 G_Rec: 0.322 D_GP: 0.039 D_real: 1.165 D_fake: 0.626 +(epoch: 458, iters: 3150, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.945 G_ID: 0.139 G_Rec: 0.403 D_GP: 0.042 D_real: 0.988 D_fake: 0.551 +(epoch: 458, iters: 3550, time: 0.063) G_GAN: 0.121 G_GAN_Feat: 0.781 G_ID: 0.104 G_Rec: 0.332 D_GP: 0.051 D_real: 0.799 D_fake: 0.879 +(epoch: 458, iters: 3950, time: 0.063) G_GAN: 0.793 G_GAN_Feat: 0.967 G_ID: 0.111 G_Rec: 0.405 D_GP: 0.047 D_real: 1.485 D_fake: 0.252 +(epoch: 458, iters: 4350, time: 0.063) G_GAN: 0.019 G_GAN_Feat: 0.848 G_ID: 0.080 G_Rec: 0.346 D_GP: 0.092 D_real: 0.393 D_fake: 0.981 +(epoch: 458, iters: 4750, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.970 G_ID: 0.116 G_Rec: 0.432 D_GP: 0.044 D_real: 0.788 D_fake: 0.671 +(epoch: 458, iters: 5150, time: 0.063) G_GAN: 0.421 G_GAN_Feat: 0.765 G_ID: 0.103 G_Rec: 0.300 D_GP: 0.032 D_real: 1.137 D_fake: 0.583 +(epoch: 458, iters: 5550, time: 0.063) G_GAN: 0.478 G_GAN_Feat: 0.969 G_ID: 0.128 G_Rec: 0.423 D_GP: 0.037 D_real: 1.105 D_fake: 0.527 +(epoch: 458, iters: 5950, time: 0.063) G_GAN: 0.031 G_GAN_Feat: 0.880 G_ID: 0.109 G_Rec: 0.335 D_GP: 0.084 D_real: 0.419 D_fake: 0.969 +(epoch: 458, iters: 6350, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.937 G_ID: 0.096 G_Rec: 0.407 D_GP: 0.052 D_real: 0.967 D_fake: 0.550 +(epoch: 458, iters: 6750, time: 0.063) G_GAN: -0.005 G_GAN_Feat: 0.782 G_ID: 0.104 G_Rec: 0.324 D_GP: 0.042 D_real: 0.570 D_fake: 1.005 +(epoch: 458, iters: 7150, time: 0.063) G_GAN: 0.398 G_GAN_Feat: 0.889 G_ID: 0.113 G_Rec: 0.429 D_GP: 0.035 D_real: 1.120 D_fake: 0.604 +(epoch: 458, iters: 7550, time: 0.063) G_GAN: 0.258 G_GAN_Feat: 0.688 G_ID: 0.090 G_Rec: 0.325 D_GP: 0.055 D_real: 1.068 D_fake: 0.743 +(epoch: 458, iters: 7950, time: 0.064) G_GAN: 0.634 G_GAN_Feat: 1.037 G_ID: 0.114 G_Rec: 0.429 D_GP: 0.158 D_real: 0.790 D_fake: 0.406 +(epoch: 458, iters: 8350, time: 0.063) G_GAN: 0.177 G_GAN_Feat: 0.756 G_ID: 0.102 G_Rec: 0.308 D_GP: 0.053 D_real: 0.938 D_fake: 0.827 +(epoch: 459, iters: 142, time: 0.063) G_GAN: 0.084 G_GAN_Feat: 0.985 G_ID: 0.112 G_Rec: 0.461 D_GP: 0.036 D_real: 0.612 D_fake: 0.916 +(epoch: 459, iters: 542, time: 0.063) G_GAN: -0.042 G_GAN_Feat: 0.692 G_ID: 0.099 G_Rec: 0.289 D_GP: 0.054 D_real: 0.763 D_fake: 1.044 +(epoch: 459, iters: 942, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 1.067 G_ID: 0.145 G_Rec: 0.457 D_GP: 0.054 D_real: 0.789 D_fake: 0.562 +(epoch: 459, iters: 1342, time: 0.063) G_GAN: -0.025 G_GAN_Feat: 0.892 G_ID: 0.121 G_Rec: 0.332 D_GP: 0.685 D_real: 0.179 D_fake: 1.031 +(epoch: 459, iters: 1742, time: 0.063) G_GAN: 0.356 G_GAN_Feat: 1.064 G_ID: 0.116 G_Rec: 0.447 D_GP: 0.082 D_real: 0.566 D_fake: 0.645 +(epoch: 459, iters: 2142, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.766 G_ID: 0.083 G_Rec: 0.297 D_GP: 0.033 D_real: 1.021 D_fake: 0.766 +(epoch: 459, iters: 2542, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.899 G_ID: 0.121 G_Rec: 0.394 D_GP: 0.045 D_real: 1.255 D_fake: 0.533 +(epoch: 459, iters: 2942, time: 0.063) G_GAN: 0.272 G_GAN_Feat: 0.719 G_ID: 0.104 G_Rec: 0.307 D_GP: 0.032 D_real: 1.065 D_fake: 0.731 +(epoch: 459, iters: 3342, time: 0.063) G_GAN: 0.585 G_GAN_Feat: 0.982 G_ID: 0.108 G_Rec: 0.395 D_GP: 0.042 D_real: 1.080 D_fake: 0.424 +(epoch: 459, iters: 3742, time: 0.063) G_GAN: 0.128 G_GAN_Feat: 0.821 G_ID: 0.125 G_Rec: 0.278 D_GP: 0.082 D_real: 0.591 D_fake: 0.874 +(epoch: 459, iters: 4142, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 1.077 G_ID: 0.108 G_Rec: 0.452 D_GP: 0.131 D_real: 0.584 D_fake: 0.808 +(epoch: 459, iters: 4542, time: 0.063) G_GAN: 0.244 G_GAN_Feat: 0.753 G_ID: 0.111 G_Rec: 0.300 D_GP: 0.035 D_real: 0.976 D_fake: 0.757 +(epoch: 459, iters: 4942, time: 0.063) G_GAN: 0.505 G_GAN_Feat: 0.990 G_ID: 0.115 G_Rec: 0.421 D_GP: 0.097 D_real: 0.747 D_fake: 0.511 +(epoch: 459, iters: 5342, time: 0.063) G_GAN: -0.010 G_GAN_Feat: 0.857 G_ID: 0.114 G_Rec: 0.308 D_GP: 0.051 D_real: 0.528 D_fake: 1.010 +(epoch: 459, iters: 5742, time: 0.064) G_GAN: 0.601 G_GAN_Feat: 1.082 G_ID: 0.128 G_Rec: 0.402 D_GP: 0.041 D_real: 0.629 D_fake: 0.408 +(epoch: 459, iters: 6142, time: 0.063) G_GAN: 0.597 G_GAN_Feat: 0.867 G_ID: 0.102 G_Rec: 0.331 D_GP: 0.041 D_real: 1.389 D_fake: 0.427 +(epoch: 459, iters: 6542, time: 0.063) G_GAN: 0.226 G_GAN_Feat: 0.995 G_ID: 0.119 G_Rec: 0.430 D_GP: 0.182 D_real: 0.505 D_fake: 0.775 +(epoch: 459, iters: 6942, time: 0.063) G_GAN: 0.099 G_GAN_Feat: 0.759 G_ID: 0.091 G_Rec: 0.299 D_GP: 0.033 D_real: 0.924 D_fake: 0.901 +(epoch: 459, iters: 7342, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 1.064 G_ID: 0.122 G_Rec: 0.480 D_GP: 0.039 D_real: 0.941 D_fake: 0.536 +(epoch: 459, iters: 7742, time: 0.063) G_GAN: 0.620 G_GAN_Feat: 0.914 G_ID: 0.094 G_Rec: 0.312 D_GP: 0.057 D_real: 0.685 D_fake: 0.402 +(epoch: 459, iters: 8142, time: 0.063) G_GAN: 0.346 G_GAN_Feat: 0.931 G_ID: 0.126 G_Rec: 0.443 D_GP: 0.033 D_real: 0.985 D_fake: 0.654 +(epoch: 459, iters: 8542, time: 0.063) G_GAN: 0.134 G_GAN_Feat: 0.795 G_ID: 0.098 G_Rec: 0.336 D_GP: 0.078 D_real: 0.803 D_fake: 0.867 +(epoch: 460, iters: 334, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.947 G_ID: 0.108 G_Rec: 0.434 D_GP: 0.033 D_real: 1.018 D_fake: 0.592 +(epoch: 460, iters: 734, time: 0.063) G_GAN: 0.200 G_GAN_Feat: 0.787 G_ID: 0.112 G_Rec: 0.303 D_GP: 0.048 D_real: 0.959 D_fake: 0.802 +(epoch: 460, iters: 1134, time: 0.063) G_GAN: 0.459 G_GAN_Feat: 1.056 G_ID: 0.122 G_Rec: 0.449 D_GP: 0.033 D_real: 0.979 D_fake: 0.544 +(epoch: 460, iters: 1534, time: 0.063) G_GAN: 0.069 G_GAN_Feat: 0.795 G_ID: 0.095 G_Rec: 0.333 D_GP: 0.045 D_real: 0.849 D_fake: 0.931 +(epoch: 460, iters: 1934, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.998 G_ID: 0.122 G_Rec: 0.424 D_GP: 0.037 D_real: 0.957 D_fake: 0.505 +(epoch: 460, iters: 2334, time: 0.063) G_GAN: 0.367 G_GAN_Feat: 0.874 G_ID: 0.099 G_Rec: 0.376 D_GP: 0.034 D_real: 0.953 D_fake: 0.633 +(epoch: 460, iters: 2734, time: 0.063) G_GAN: 0.706 G_GAN_Feat: 1.097 G_ID: 0.132 G_Rec: 0.437 D_GP: 0.128 D_real: 0.548 D_fake: 0.329 +(epoch: 460, iters: 3134, time: 0.063) G_GAN: 0.053 G_GAN_Feat: 0.748 G_ID: 0.109 G_Rec: 0.358 D_GP: 0.032 D_real: 0.958 D_fake: 0.947 +(epoch: 460, iters: 3534, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.884 G_ID: 0.122 G_Rec: 0.411 D_GP: 0.033 D_real: 1.043 D_fake: 0.754 +(epoch: 460, iters: 3934, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.683 G_ID: 0.115 G_Rec: 0.279 D_GP: 0.035 D_real: 0.883 D_fake: 0.978 +(epoch: 460, iters: 4334, time: 0.063) G_GAN: 0.286 G_GAN_Feat: 0.889 G_ID: 0.135 G_Rec: 0.397 D_GP: 0.036 D_real: 0.932 D_fake: 0.717 +(epoch: 460, iters: 4734, time: 0.063) G_GAN: -0.009 G_GAN_Feat: 0.784 G_ID: 0.100 G_Rec: 0.338 D_GP: 0.057 D_real: 0.760 D_fake: 1.009 +(epoch: 460, iters: 5134, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.950 G_ID: 0.117 G_Rec: 0.423 D_GP: 0.034 D_real: 1.086 D_fake: 0.579 +(epoch: 460, iters: 5534, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.739 G_ID: 0.095 G_Rec: 0.286 D_GP: 0.036 D_real: 1.142 D_fake: 0.702 +(epoch: 460, iters: 5934, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.988 G_ID: 0.112 G_Rec: 0.454 D_GP: 0.033 D_real: 0.945 D_fake: 0.693 +(epoch: 460, iters: 6334, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.849 G_ID: 0.098 G_Rec: 0.307 D_GP: 0.050 D_real: 0.632 D_fake: 0.721 +(epoch: 460, iters: 6734, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 1.172 G_ID: 0.123 G_Rec: 0.479 D_GP: 0.072 D_real: 0.183 D_fake: 0.810 +(epoch: 460, iters: 7134, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.741 G_ID: 0.091 G_Rec: 0.310 D_GP: 0.033 D_real: 1.206 D_fake: 0.754 +(epoch: 460, iters: 7534, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.993 G_ID: 0.128 G_Rec: 0.430 D_GP: 0.039 D_real: 0.755 D_fake: 0.708 +(epoch: 460, iters: 7934, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.868 G_ID: 0.097 G_Rec: 0.303 D_GP: 0.068 D_real: 0.775 D_fake: 0.583 +(epoch: 460, iters: 8334, time: 0.064) G_GAN: 0.745 G_GAN_Feat: 1.277 G_ID: 0.109 G_Rec: 0.506 D_GP: 0.106 D_real: 0.942 D_fake: 0.375 +(epoch: 461, iters: 126, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.967 G_ID: 0.101 G_Rec: 0.366 D_GP: 0.227 D_real: 0.323 D_fake: 0.696 +(epoch: 461, iters: 526, time: 0.063) G_GAN: 0.256 G_GAN_Feat: 1.001 G_ID: 0.149 G_Rec: 0.449 D_GP: 0.038 D_real: 0.911 D_fake: 0.745 +(epoch: 461, iters: 926, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.836 G_ID: 0.099 G_Rec: 0.326 D_GP: 0.042 D_real: 0.797 D_fake: 0.792 +(epoch: 461, iters: 1326, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.948 G_ID: 0.115 G_Rec: 0.388 D_GP: 0.035 D_real: 1.173 D_fake: 0.546 +(epoch: 461, iters: 1726, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.812 G_ID: 0.127 G_Rec: 0.357 D_GP: 0.039 D_real: 1.075 D_fake: 0.827 +(epoch: 461, iters: 2126, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.871 G_ID: 0.104 G_Rec: 0.419 D_GP: 0.030 D_real: 1.061 D_fake: 0.645 +(epoch: 461, iters: 2526, time: 0.063) G_GAN: 0.067 G_GAN_Feat: 0.656 G_ID: 0.110 G_Rec: 0.294 D_GP: 0.033 D_real: 1.012 D_fake: 0.934 +(epoch: 461, iters: 2926, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.882 G_ID: 0.115 G_Rec: 0.410 D_GP: 0.045 D_real: 0.996 D_fake: 0.672 +(epoch: 461, iters: 3326, time: 0.064) G_GAN: 0.109 G_GAN_Feat: 0.728 G_ID: 0.081 G_Rec: 0.321 D_GP: 0.035 D_real: 0.928 D_fake: 0.892 +(epoch: 461, iters: 3726, time: 0.063) G_GAN: 0.317 G_GAN_Feat: 0.959 G_ID: 0.106 G_Rec: 0.434 D_GP: 0.038 D_real: 0.980 D_fake: 0.689 +(epoch: 461, iters: 4126, time: 0.064) G_GAN: -0.106 G_GAN_Feat: 0.697 G_ID: 0.109 G_Rec: 0.297 D_GP: 0.041 D_real: 0.785 D_fake: 1.106 +(epoch: 461, iters: 4526, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.873 G_ID: 0.121 G_Rec: 0.404 D_GP: 0.040 D_real: 0.831 D_fake: 0.790 +(epoch: 461, iters: 4926, time: 0.063) G_GAN: 0.216 G_GAN_Feat: 0.761 G_ID: 0.111 G_Rec: 0.331 D_GP: 0.036 D_real: 0.980 D_fake: 0.785 +(epoch: 461, iters: 5326, time: 0.064) G_GAN: 0.633 G_GAN_Feat: 0.916 G_ID: 0.102 G_Rec: 0.403 D_GP: 0.043 D_real: 1.341 D_fake: 0.405 +(epoch: 461, iters: 5726, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 0.679 G_ID: 0.101 G_Rec: 0.293 D_GP: 0.030 D_real: 1.464 D_fake: 0.462 +(epoch: 461, iters: 6126, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 1.004 G_ID: 0.118 G_Rec: 0.455 D_GP: 0.052 D_real: 0.813 D_fake: 0.662 +(epoch: 461, iters: 6526, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.791 G_ID: 0.082 G_Rec: 0.378 D_GP: 0.044 D_real: 0.954 D_fake: 0.953 +(epoch: 461, iters: 6926, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.033 G_ID: 0.113 G_Rec: 0.453 D_GP: 0.045 D_real: 0.798 D_fake: 0.652 +(epoch: 461, iters: 7326, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.743 G_ID: 0.094 G_Rec: 0.296 D_GP: 0.044 D_real: 0.931 D_fake: 0.851 +(epoch: 461, iters: 7726, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 1.151 G_ID: 0.112 G_Rec: 0.496 D_GP: 0.209 D_real: 0.413 D_fake: 0.565 +(epoch: 461, iters: 8126, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.858 G_ID: 0.097 G_Rec: 0.305 D_GP: 0.040 D_real: 1.133 D_fake: 0.541 +(epoch: 461, iters: 8526, time: 0.064) G_GAN: 0.543 G_GAN_Feat: 1.046 G_ID: 0.114 G_Rec: 0.458 D_GP: 0.065 D_real: 0.949 D_fake: 0.523 +(epoch: 462, iters: 318, time: 0.064) G_GAN: 0.018 G_GAN_Feat: 0.733 G_ID: 0.102 G_Rec: 0.330 D_GP: 0.037 D_real: 0.960 D_fake: 0.982 +(epoch: 462, iters: 718, time: 0.064) G_GAN: 0.684 G_GAN_Feat: 0.891 G_ID: 0.104 G_Rec: 0.420 D_GP: 0.030 D_real: 1.405 D_fake: 0.349 +(epoch: 462, iters: 1118, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.798 G_ID: 0.109 G_Rec: 0.311 D_GP: 0.055 D_real: 0.697 D_fake: 0.923 +(epoch: 462, iters: 1518, time: 0.064) G_GAN: 0.859 G_GAN_Feat: 0.964 G_ID: 0.106 G_Rec: 0.447 D_GP: 0.039 D_real: 1.615 D_fake: 0.239 +(epoch: 462, iters: 1918, time: 0.064) G_GAN: -0.037 G_GAN_Feat: 0.780 G_ID: 0.107 G_Rec: 0.330 D_GP: 0.031 D_real: 0.821 D_fake: 1.037 +(epoch: 462, iters: 2318, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.871 G_ID: 0.116 G_Rec: 0.387 D_GP: 0.029 D_real: 0.749 D_fake: 0.914 +(epoch: 462, iters: 2718, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.641 G_ID: 0.104 G_Rec: 0.280 D_GP: 0.033 D_real: 1.030 D_fake: 0.877 +(epoch: 462, iters: 3118, time: 0.064) G_GAN: 0.133 G_GAN_Feat: 0.920 G_ID: 0.126 G_Rec: 0.399 D_GP: 0.050 D_real: 0.685 D_fake: 0.867 +(epoch: 462, iters: 3518, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.650 G_ID: 0.096 G_Rec: 0.293 D_GP: 0.032 D_real: 1.146 D_fake: 0.745 +(epoch: 462, iters: 3918, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 1.026 G_ID: 0.124 G_Rec: 0.467 D_GP: 0.072 D_real: 0.726 D_fake: 0.594 +(epoch: 462, iters: 4318, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.897 G_ID: 0.108 G_Rec: 0.368 D_GP: 0.071 D_real: 0.727 D_fake: 0.817 +(epoch: 462, iters: 4718, time: 0.064) G_GAN: 0.607 G_GAN_Feat: 0.920 G_ID: 0.114 G_Rec: 0.390 D_GP: 0.037 D_real: 1.238 D_fake: 0.415 +(epoch: 462, iters: 5118, time: 0.064) G_GAN: 0.005 G_GAN_Feat: 0.821 G_ID: 0.099 G_Rec: 0.323 D_GP: 0.043 D_real: 0.719 D_fake: 0.995 +(epoch: 462, iters: 5518, time: 0.064) G_GAN: 0.523 G_GAN_Feat: 1.201 G_ID: 0.128 G_Rec: 0.477 D_GP: 0.188 D_real: 0.300 D_fake: 0.491 +(epoch: 462, iters: 5918, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 1.074 G_ID: 0.096 G_Rec: 0.339 D_GP: 1.703 D_real: 0.660 D_fake: 0.996 +(epoch: 462, iters: 6318, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 1.032 G_ID: 0.109 G_Rec: 0.476 D_GP: 0.034 D_real: 1.135 D_fake: 0.451 +(epoch: 462, iters: 6718, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.700 G_ID: 0.101 G_Rec: 0.305 D_GP: 0.040 D_real: 1.139 D_fake: 0.794 +(epoch: 462, iters: 7118, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 1.024 G_ID: 0.127 G_Rec: 0.389 D_GP: 0.050 D_real: 1.141 D_fake: 0.559 +(epoch: 462, iters: 7518, time: 0.064) G_GAN: 0.570 G_GAN_Feat: 0.687 G_ID: 0.101 G_Rec: 0.281 D_GP: 0.031 D_real: 1.424 D_fake: 0.440 +(epoch: 462, iters: 7918, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 1.115 G_ID: 0.127 G_Rec: 0.415 D_GP: 0.047 D_real: 0.976 D_fake: 0.647 +(epoch: 462, iters: 8318, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.910 G_ID: 0.103 G_Rec: 0.326 D_GP: 0.059 D_real: 0.395 D_fake: 0.695 +(epoch: 463, iters: 110, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 1.282 G_ID: 0.116 G_Rec: 0.497 D_GP: 0.174 D_real: 0.351 D_fake: 0.513 +(epoch: 463, iters: 510, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.946 G_ID: 0.098 G_Rec: 0.336 D_GP: 0.092 D_real: 0.584 D_fake: 0.638 +(epoch: 463, iters: 910, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.964 G_ID: 0.112 G_Rec: 0.459 D_GP: 0.030 D_real: 1.104 D_fake: 0.572 +(epoch: 463, iters: 1310, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.671 G_ID: 0.095 G_Rec: 0.291 D_GP: 0.029 D_real: 1.191 D_fake: 0.758 +(epoch: 463, iters: 1710, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.855 G_ID: 0.123 G_Rec: 0.418 D_GP: 0.037 D_real: 0.796 D_fake: 0.909 +(epoch: 463, iters: 2110, time: 0.064) G_GAN: -0.052 G_GAN_Feat: 0.719 G_ID: 0.095 G_Rec: 0.344 D_GP: 0.036 D_real: 0.802 D_fake: 1.052 +(epoch: 463, iters: 2510, time: 0.063) G_GAN: 0.283 G_GAN_Feat: 0.883 G_ID: 0.115 G_Rec: 0.405 D_GP: 0.034 D_real: 1.023 D_fake: 0.730 +(epoch: 463, iters: 2910, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.670 G_ID: 0.093 G_Rec: 0.308 D_GP: 0.035 D_real: 1.105 D_fake: 0.768 +(epoch: 463, iters: 3310, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.918 G_ID: 0.113 G_Rec: 0.428 D_GP: 0.033 D_real: 0.677 D_fake: 0.886 +(epoch: 463, iters: 3710, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.699 G_ID: 0.088 G_Rec: 0.311 D_GP: 0.050 D_real: 0.926 D_fake: 0.890 +(epoch: 463, iters: 4110, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 1.008 G_ID: 0.112 G_Rec: 0.465 D_GP: 0.057 D_real: 0.596 D_fake: 0.693 +(epoch: 463, iters: 4510, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.763 G_ID: 0.100 G_Rec: 0.285 D_GP: 0.044 D_real: 1.197 D_fake: 0.765 +(epoch: 463, iters: 4910, time: 0.064) G_GAN: 0.677 G_GAN_Feat: 0.970 G_ID: 0.128 G_Rec: 0.452 D_GP: 0.046 D_real: 1.199 D_fake: 0.342 +(epoch: 463, iters: 5310, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.753 G_ID: 0.089 G_Rec: 0.305 D_GP: 0.045 D_real: 1.012 D_fake: 0.670 +(epoch: 463, iters: 5710, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 0.911 G_ID: 0.118 G_Rec: 0.423 D_GP: 0.031 D_real: 1.213 D_fake: 0.564 +(epoch: 463, iters: 6110, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.722 G_ID: 0.092 G_Rec: 0.295 D_GP: 0.032 D_real: 1.158 D_fake: 0.714 +(epoch: 463, iters: 6510, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.894 G_ID: 0.107 G_Rec: 0.400 D_GP: 0.032 D_real: 1.054 D_fake: 0.575 +(epoch: 463, iters: 6910, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.860 G_ID: 0.111 G_Rec: 0.317 D_GP: 0.152 D_real: 0.574 D_fake: 0.750 +(epoch: 463, iters: 7310, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 1.070 G_ID: 0.130 G_Rec: 0.530 D_GP: 0.704 D_real: 0.307 D_fake: 0.700 +(epoch: 463, iters: 7710, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.818 G_ID: 0.096 G_Rec: 0.321 D_GP: 0.049 D_real: 1.063 D_fake: 0.578 +(epoch: 463, iters: 8110, time: 0.063) G_GAN: 0.463 G_GAN_Feat: 0.946 G_ID: 0.110 G_Rec: 0.469 D_GP: 0.035 D_real: 1.056 D_fake: 0.554 +(epoch: 463, iters: 8510, time: 0.064) G_GAN: 0.045 G_GAN_Feat: 0.585 G_ID: 0.098 G_Rec: 0.279 D_GP: 0.027 D_real: 0.997 D_fake: 0.955 +(epoch: 464, iters: 302, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.968 G_ID: 0.113 G_Rec: 0.465 D_GP: 0.036 D_real: 0.712 D_fake: 0.786 +(epoch: 464, iters: 702, time: 0.064) G_GAN: -0.066 G_GAN_Feat: 0.719 G_ID: 0.104 G_Rec: 0.325 D_GP: 0.050 D_real: 0.769 D_fake: 1.067 +(epoch: 464, iters: 1102, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.910 G_ID: 0.111 G_Rec: 0.439 D_GP: 0.065 D_real: 1.015 D_fake: 0.569 +(epoch: 464, iters: 1502, time: 0.064) G_GAN: -0.025 G_GAN_Feat: 0.697 G_ID: 0.117 G_Rec: 0.294 D_GP: 0.037 D_real: 0.847 D_fake: 1.025 +(epoch: 464, iters: 1902, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.911 G_ID: 0.113 G_Rec: 0.428 D_GP: 0.036 D_real: 0.980 D_fake: 0.629 +(epoch: 464, iters: 2302, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.656 G_ID: 0.091 G_Rec: 0.277 D_GP: 0.030 D_real: 0.997 D_fake: 0.875 +(epoch: 464, iters: 2702, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 1.050 G_ID: 0.117 G_Rec: 0.495 D_GP: 0.079 D_real: 0.628 D_fake: 0.670 +(epoch: 464, iters: 3102, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.699 G_ID: 0.090 G_Rec: 0.307 D_GP: 0.038 D_real: 1.065 D_fake: 0.829 +(epoch: 464, iters: 3502, time: 0.064) G_GAN: 0.675 G_GAN_Feat: 1.026 G_ID: 0.120 G_Rec: 0.399 D_GP: 0.042 D_real: 1.078 D_fake: 0.380 +(epoch: 464, iters: 3902, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.763 G_ID: 0.097 G_Rec: 0.360 D_GP: 0.036 D_real: 1.061 D_fake: 0.754 +(epoch: 464, iters: 4302, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.990 G_ID: 0.115 G_Rec: 0.442 D_GP: 0.056 D_real: 0.813 D_fake: 0.452 +(epoch: 464, iters: 4702, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.795 G_ID: 0.103 G_Rec: 0.329 D_GP: 0.046 D_real: 1.120 D_fake: 0.597 +(epoch: 464, iters: 5102, time: 0.064) G_GAN: 0.819 G_GAN_Feat: 1.049 G_ID: 0.104 G_Rec: 0.471 D_GP: 0.061 D_real: 1.081 D_fake: 0.274 +(epoch: 464, iters: 5502, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.798 G_ID: 0.099 G_Rec: 0.332 D_GP: 0.032 D_real: 0.825 D_fake: 0.914 +(epoch: 464, iters: 5902, time: 0.064) G_GAN: -0.233 G_GAN_Feat: 1.176 G_ID: 0.115 G_Rec: 0.527 D_GP: 0.066 D_real: 0.815 D_fake: 1.234 +(epoch: 464, iters: 6302, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.687 G_ID: 0.096 G_Rec: 0.303 D_GP: 0.024 D_real: 1.057 D_fake: 0.909 +(epoch: 464, iters: 6702, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.953 G_ID: 0.113 G_Rec: 0.423 D_GP: 0.030 D_real: 0.796 D_fake: 0.838 +(epoch: 464, iters: 7102, time: 0.064) G_GAN: -0.184 G_GAN_Feat: 0.746 G_ID: 0.094 G_Rec: 0.340 D_GP: 0.044 D_real: 0.651 D_fake: 1.184 +(epoch: 464, iters: 7502, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 1.011 G_ID: 0.119 G_Rec: 0.475 D_GP: 0.048 D_real: 0.742 D_fake: 0.788 +(epoch: 464, iters: 7902, time: 0.064) G_GAN: -0.217 G_GAN_Feat: 0.773 G_ID: 0.096 G_Rec: 0.323 D_GP: 0.062 D_real: 0.553 D_fake: 1.217 +(epoch: 464, iters: 8302, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.930 G_ID: 0.116 G_Rec: 0.380 D_GP: 0.054 D_real: 1.021 D_fake: 0.598 +(epoch: 464, iters: 8702, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.879 G_ID: 0.091 G_Rec: 0.321 D_GP: 0.533 D_real: 0.395 D_fake: 0.847 +(epoch: 465, iters: 494, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 1.027 G_ID: 0.124 G_Rec: 0.475 D_GP: 0.036 D_real: 1.316 D_fake: 0.486 +(epoch: 465, iters: 894, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.679 G_ID: 0.081 G_Rec: 0.272 D_GP: 0.028 D_real: 1.153 D_fake: 0.752 +(epoch: 465, iters: 1294, time: 0.064) G_GAN: 0.590 G_GAN_Feat: 0.950 G_ID: 0.119 G_Rec: 0.441 D_GP: 0.040 D_real: 1.252 D_fake: 0.417 +(epoch: 465, iters: 1694, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.958 G_ID: 0.088 G_Rec: 0.318 D_GP: 0.120 D_real: 0.620 D_fake: 0.518 +(epoch: 465, iters: 2094, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.933 G_ID: 0.126 G_Rec: 0.451 D_GP: 0.041 D_real: 1.009 D_fake: 0.747 +(epoch: 465, iters: 2494, time: 0.064) G_GAN: 0.027 G_GAN_Feat: 0.731 G_ID: 0.106 G_Rec: 0.317 D_GP: 0.035 D_real: 0.906 D_fake: 0.973 +(epoch: 465, iters: 2894, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.848 G_ID: 0.098 G_Rec: 0.424 D_GP: 0.027 D_real: 1.119 D_fake: 0.607 +(epoch: 465, iters: 3294, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.657 G_ID: 0.104 G_Rec: 0.303 D_GP: 0.035 D_real: 1.013 D_fake: 0.872 +(epoch: 465, iters: 3694, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.893 G_ID: 0.107 G_Rec: 0.451 D_GP: 0.032 D_real: 1.003 D_fake: 0.675 +(epoch: 465, iters: 4094, time: 0.064) G_GAN: -0.059 G_GAN_Feat: 0.668 G_ID: 0.095 G_Rec: 0.324 D_GP: 0.030 D_real: 0.847 D_fake: 1.059 +(epoch: 465, iters: 4494, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.913 G_ID: 0.108 G_Rec: 0.446 D_GP: 0.039 D_real: 1.077 D_fake: 0.602 +(epoch: 465, iters: 4894, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.728 G_ID: 0.090 G_Rec: 0.325 D_GP: 0.044 D_real: 0.948 D_fake: 0.887 +(epoch: 465, iters: 5294, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.939 G_ID: 0.108 G_Rec: 0.455 D_GP: 0.035 D_real: 0.926 D_fake: 0.679 +(epoch: 465, iters: 5694, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.707 G_ID: 0.095 G_Rec: 0.301 D_GP: 0.041 D_real: 0.949 D_fake: 0.896 +(epoch: 465, iters: 6094, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 1.010 G_ID: 0.119 G_Rec: 0.448 D_GP: 0.057 D_real: 0.789 D_fake: 0.791 +(epoch: 465, iters: 6494, time: 0.064) G_GAN: -0.049 G_GAN_Feat: 0.721 G_ID: 0.103 G_Rec: 0.277 D_GP: 0.043 D_real: 0.714 D_fake: 1.049 +(epoch: 465, iters: 6894, time: 0.063) G_GAN: 0.194 G_GAN_Feat: 0.879 G_ID: 0.129 G_Rec: 0.364 D_GP: 0.040 D_real: 0.973 D_fake: 0.809 +(epoch: 465, iters: 7294, time: 0.064) G_GAN: 0.054 G_GAN_Feat: 0.791 G_ID: 0.104 G_Rec: 0.323 D_GP: 0.062 D_real: 0.615 D_fake: 0.946 +(epoch: 465, iters: 7694, time: 0.064) G_GAN: 0.762 G_GAN_Feat: 1.002 G_ID: 0.117 G_Rec: 0.428 D_GP: 0.043 D_real: 1.193 D_fake: 0.301 +(epoch: 465, iters: 8094, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.845 G_ID: 0.103 G_Rec: 0.340 D_GP: 0.063 D_real: 0.945 D_fake: 0.603 +(epoch: 465, iters: 8494, time: 0.064) G_GAN: 0.658 G_GAN_Feat: 1.031 G_ID: 0.110 G_Rec: 0.467 D_GP: 0.049 D_real: 1.049 D_fake: 0.373 +(epoch: 466, iters: 286, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.846 G_ID: 0.103 G_Rec: 0.332 D_GP: 0.033 D_real: 0.916 D_fake: 0.719 +(epoch: 466, iters: 686, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 1.186 G_ID: 0.097 G_Rec: 0.467 D_GP: 0.136 D_real: 0.450 D_fake: 0.561 +(epoch: 466, iters: 1086, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.972 G_ID: 0.105 G_Rec: 0.356 D_GP: 0.075 D_real: 0.494 D_fake: 0.648 +(epoch: 466, iters: 1486, time: 0.064) G_GAN: 0.954 G_GAN_Feat: 1.159 G_ID: 0.130 G_Rec: 0.465 D_GP: 0.124 D_real: 0.724 D_fake: 0.204 +(epoch: 466, iters: 1886, time: 0.063) G_GAN: -0.078 G_GAN_Feat: 0.836 G_ID: 0.106 G_Rec: 0.359 D_GP: 0.037 D_real: 0.605 D_fake: 1.078 +(epoch: 466, iters: 2286, time: 0.064) G_GAN: 0.768 G_GAN_Feat: 1.033 G_ID: 0.120 G_Rec: 0.451 D_GP: 0.042 D_real: 1.474 D_fake: 0.288 +(epoch: 466, iters: 2686, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.748 G_ID: 0.099 G_Rec: 0.276 D_GP: 0.029 D_real: 0.998 D_fake: 0.834 +(epoch: 466, iters: 3086, time: 0.063) G_GAN: 0.986 G_GAN_Feat: 1.179 G_ID: 0.125 G_Rec: 0.449 D_GP: 0.056 D_real: 1.087 D_fake: 0.195 +(epoch: 466, iters: 3486, time: 0.063) G_GAN: 0.270 G_GAN_Feat: 0.735 G_ID: 0.083 G_Rec: 0.329 D_GP: 0.031 D_real: 1.142 D_fake: 0.731 +(epoch: 466, iters: 3886, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.933 G_ID: 0.113 G_Rec: 0.475 D_GP: 0.028 D_real: 1.063 D_fake: 0.611 +(epoch: 466, iters: 4286, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.648 G_ID: 0.107 G_Rec: 0.289 D_GP: 0.028 D_real: 1.025 D_fake: 0.860 +(epoch: 466, iters: 4686, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.922 G_ID: 0.104 G_Rec: 0.441 D_GP: 0.042 D_real: 0.845 D_fake: 0.680 +(epoch: 466, iters: 5086, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.686 G_ID: 0.081 G_Rec: 0.315 D_GP: 0.059 D_real: 0.903 D_fake: 0.878 +(epoch: 466, iters: 5486, time: 0.063) G_GAN: 0.549 G_GAN_Feat: 0.920 G_ID: 0.117 G_Rec: 0.464 D_GP: 0.038 D_real: 1.165 D_fake: 0.487 +(epoch: 466, iters: 5886, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.697 G_ID: 0.088 G_Rec: 0.294 D_GP: 0.039 D_real: 0.978 D_fake: 0.841 +(epoch: 466, iters: 6286, time: 0.063) G_GAN: 0.422 G_GAN_Feat: 0.953 G_ID: 0.110 G_Rec: 0.414 D_GP: 0.036 D_real: 1.113 D_fake: 0.581 +(epoch: 466, iters: 6686, time: 0.063) G_GAN: 0.218 G_GAN_Feat: 0.770 G_ID: 0.084 G_Rec: 0.318 D_GP: 0.034 D_real: 1.007 D_fake: 0.783 +(epoch: 466, iters: 7086, time: 0.063) G_GAN: 0.472 G_GAN_Feat: 0.926 G_ID: 0.110 G_Rec: 0.414 D_GP: 0.036 D_real: 1.159 D_fake: 0.529 +(epoch: 466, iters: 7486, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.813 G_ID: 0.095 G_Rec: 0.309 D_GP: 0.039 D_real: 1.155 D_fake: 0.863 +(epoch: 466, iters: 7886, time: 0.063) G_GAN: 0.597 G_GAN_Feat: 1.018 G_ID: 0.110 G_Rec: 0.433 D_GP: 0.051 D_real: 1.090 D_fake: 0.423 +(epoch: 466, iters: 8286, time: 0.063) G_GAN: 0.369 G_GAN_Feat: 0.756 G_ID: 0.091 G_Rec: 0.295 D_GP: 0.030 D_real: 1.263 D_fake: 0.656 +(epoch: 466, iters: 8686, time: 0.064) G_GAN: 0.636 G_GAN_Feat: 1.090 G_ID: 0.127 G_Rec: 0.446 D_GP: 0.047 D_real: 0.926 D_fake: 0.373 +(epoch: 467, iters: 478, time: 0.063) G_GAN: 0.696 G_GAN_Feat: 0.719 G_ID: 0.084 G_Rec: 0.258 D_GP: 0.031 D_real: 1.548 D_fake: 0.316 +(epoch: 467, iters: 878, time: 0.063) G_GAN: 0.449 G_GAN_Feat: 0.965 G_ID: 0.112 G_Rec: 0.465 D_GP: 0.030 D_real: 1.055 D_fake: 0.557 +(epoch: 467, iters: 1278, time: 0.063) G_GAN: 0.267 G_GAN_Feat: 0.808 G_ID: 0.113 G_Rec: 0.320 D_GP: 0.034 D_real: 0.969 D_fake: 0.733 +(epoch: 467, iters: 1678, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 1.066 G_ID: 0.110 G_Rec: 0.472 D_GP: 0.032 D_real: 1.127 D_fake: 0.460 +(epoch: 467, iters: 2078, time: 0.063) G_GAN: 0.309 G_GAN_Feat: 0.740 G_ID: 0.087 G_Rec: 0.287 D_GP: 0.035 D_real: 1.276 D_fake: 0.691 +(epoch: 467, iters: 2478, time: 0.064) G_GAN: 0.608 G_GAN_Feat: 0.952 G_ID: 0.127 G_Rec: 0.412 D_GP: 0.034 D_real: 1.346 D_fake: 0.415 +(epoch: 467, iters: 2878, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.965 G_ID: 0.119 G_Rec: 0.340 D_GP: 0.149 D_real: 0.378 D_fake: 0.864 +(epoch: 467, iters: 3278, time: 0.064) G_GAN: 0.820 G_GAN_Feat: 0.962 G_ID: 0.122 G_Rec: 0.427 D_GP: 0.034 D_real: 1.334 D_fake: 0.272 +(epoch: 467, iters: 3678, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 1.008 G_ID: 0.108 G_Rec: 0.349 D_GP: 0.056 D_real: 0.330 D_fake: 0.719 +(epoch: 467, iters: 4078, time: 0.064) G_GAN: 0.589 G_GAN_Feat: 1.235 G_ID: 0.112 G_Rec: 0.426 D_GP: 0.035 D_real: 0.378 D_fake: 0.425 +(epoch: 467, iters: 4478, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.914 G_ID: 0.105 G_Rec: 0.361 D_GP: 0.076 D_real: 0.767 D_fake: 0.790 +(epoch: 467, iters: 4878, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 0.953 G_ID: 0.106 G_Rec: 0.437 D_GP: 0.030 D_real: 1.169 D_fake: 0.463 +(epoch: 467, iters: 5278, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.589 G_ID: 0.093 G_Rec: 0.265 D_GP: 0.024 D_real: 1.313 D_fake: 0.624 +(epoch: 467, iters: 5678, time: 0.063) G_GAN: 0.441 G_GAN_Feat: 0.872 G_ID: 0.114 G_Rec: 0.396 D_GP: 0.030 D_real: 1.129 D_fake: 0.566 +(epoch: 467, iters: 6078, time: 0.063) G_GAN: 0.272 G_GAN_Feat: 0.639 G_ID: 0.100 G_Rec: 0.290 D_GP: 0.027 D_real: 1.209 D_fake: 0.731 +(epoch: 467, iters: 6478, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.926 G_ID: 0.107 G_Rec: 0.442 D_GP: 0.040 D_real: 0.769 D_fake: 0.778 +(epoch: 467, iters: 6878, time: 0.064) G_GAN: 0.018 G_GAN_Feat: 0.687 G_ID: 0.086 G_Rec: 0.275 D_GP: 0.037 D_real: 0.900 D_fake: 0.982 +(epoch: 467, iters: 7278, time: 0.064) G_GAN: 0.678 G_GAN_Feat: 0.975 G_ID: 0.112 G_Rec: 0.453 D_GP: 0.035 D_real: 1.317 D_fake: 0.360 +(epoch: 467, iters: 7678, time: 0.064) G_GAN: -0.001 G_GAN_Feat: 0.796 G_ID: 0.157 G_Rec: 0.318 D_GP: 0.034 D_real: 0.829 D_fake: 1.001 +(epoch: 467, iters: 8078, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.930 G_ID: 0.115 G_Rec: 0.421 D_GP: 0.061 D_real: 0.845 D_fake: 0.649 +(epoch: 467, iters: 8478, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.781 G_ID: 0.082 G_Rec: 0.294 D_GP: 0.072 D_real: 0.888 D_fake: 0.565 +(epoch: 468, iters: 270, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 0.993 G_ID: 0.107 G_Rec: 0.485 D_GP: 0.032 D_real: 1.116 D_fake: 0.551 +(epoch: 468, iters: 670, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.918 G_ID: 0.103 G_Rec: 0.334 D_GP: 0.095 D_real: 0.377 D_fake: 0.808 +(epoch: 468, iters: 1070, time: 0.064) G_GAN: 0.980 G_GAN_Feat: 1.116 G_ID: 0.111 G_Rec: 0.498 D_GP: 0.096 D_real: 0.955 D_fake: 0.154 +(epoch: 468, iters: 1470, time: 0.064) G_GAN: -0.035 G_GAN_Feat: 0.903 G_ID: 0.114 G_Rec: 0.369 D_GP: 0.137 D_real: 0.414 D_fake: 1.035 +(epoch: 468, iters: 1870, time: 0.064) G_GAN: 0.687 G_GAN_Feat: 1.186 G_ID: 0.108 G_Rec: 0.451 D_GP: 0.103 D_real: 1.061 D_fake: 0.382 +(epoch: 468, iters: 2270, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.732 G_ID: 0.100 G_Rec: 0.283 D_GP: 0.034 D_real: 1.210 D_fake: 0.717 +(epoch: 468, iters: 2670, time: 0.064) G_GAN: 0.873 G_GAN_Feat: 1.053 G_ID: 0.111 G_Rec: 0.494 D_GP: 0.035 D_real: 1.337 D_fake: 0.215 +(epoch: 468, iters: 3070, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.840 G_ID: 0.102 G_Rec: 0.315 D_GP: 0.111 D_real: 0.995 D_fake: 0.723 +(epoch: 468, iters: 3470, time: 0.063) G_GAN: 0.398 G_GAN_Feat: 1.071 G_ID: 0.124 G_Rec: 0.456 D_GP: 0.039 D_real: 0.818 D_fake: 0.603 +(epoch: 468, iters: 3870, time: 0.064) G_GAN: -0.048 G_GAN_Feat: 0.727 G_ID: 0.098 G_Rec: 0.315 D_GP: 0.034 D_real: 0.784 D_fake: 1.048 +(epoch: 468, iters: 4270, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.937 G_ID: 0.118 G_Rec: 0.434 D_GP: 0.036 D_real: 0.971 D_fake: 0.719 +(epoch: 468, iters: 4670, time: 0.063) G_GAN: 0.175 G_GAN_Feat: 0.715 G_ID: 0.090 G_Rec: 0.294 D_GP: 0.031 D_real: 1.027 D_fake: 0.825 +(epoch: 468, iters: 5070, time: 0.063) G_GAN: 0.499 G_GAN_Feat: 1.058 G_ID: 0.116 G_Rec: 0.469 D_GP: 0.049 D_real: 0.843 D_fake: 0.508 +(epoch: 468, iters: 5470, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.740 G_ID: 0.107 G_Rec: 0.287 D_GP: 0.057 D_real: 0.840 D_fake: 0.827 +(epoch: 468, iters: 5870, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.838 G_ID: 0.115 G_Rec: 0.434 D_GP: 0.026 D_real: 1.266 D_fake: 0.514 +(epoch: 468, iters: 6270, time: 0.063) G_GAN: 0.289 G_GAN_Feat: 0.651 G_ID: 0.093 G_Rec: 0.299 D_GP: 0.027 D_real: 1.181 D_fake: 0.715 +(epoch: 468, iters: 6670, time: 0.064) G_GAN: 0.646 G_GAN_Feat: 0.922 G_ID: 0.110 G_Rec: 0.476 D_GP: 0.032 D_real: 1.282 D_fake: 0.405 +(epoch: 468, iters: 7070, time: 0.063) G_GAN: 0.191 G_GAN_Feat: 0.720 G_ID: 0.101 G_Rec: 0.359 D_GP: 0.045 D_real: 1.023 D_fake: 0.811 +(epoch: 468, iters: 7470, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.949 G_ID: 0.101 G_Rec: 0.446 D_GP: 0.039 D_real: 1.074 D_fake: 0.609 +(epoch: 468, iters: 7870, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.745 G_ID: 0.080 G_Rec: 0.354 D_GP: 0.039 D_real: 0.917 D_fake: 0.864 +(epoch: 468, iters: 8270, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.941 G_ID: 0.137 G_Rec: 0.417 D_GP: 0.036 D_real: 1.034 D_fake: 0.566 +(epoch: 468, iters: 8670, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.829 G_ID: 0.097 G_Rec: 0.324 D_GP: 0.085 D_real: 0.845 D_fake: 0.777 +(epoch: 469, iters: 462, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 0.969 G_ID: 0.116 G_Rec: 0.484 D_GP: 0.031 D_real: 1.136 D_fake: 0.447 +(epoch: 469, iters: 862, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.785 G_ID: 0.132 G_Rec: 0.308 D_GP: 0.049 D_real: 0.839 D_fake: 0.844 +(epoch: 469, iters: 1262, time: 0.064) G_GAN: 0.756 G_GAN_Feat: 1.061 G_ID: 0.116 G_Rec: 0.423 D_GP: 0.048 D_real: 1.180 D_fake: 0.360 +(epoch: 469, iters: 1662, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 0.656 G_ID: 0.125 G_Rec: 0.338 D_GP: 0.028 D_real: 0.969 D_fake: 0.934 +(epoch: 469, iters: 2062, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.786 G_ID: 0.104 G_Rec: 0.373 D_GP: 0.031 D_real: 1.027 D_fake: 0.726 +(epoch: 469, iters: 2462, time: 0.064) G_GAN: -0.207 G_GAN_Feat: 0.616 G_ID: 0.093 G_Rec: 0.294 D_GP: 0.030 D_real: 0.755 D_fake: 1.207 +(epoch: 469, iters: 2862, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.830 G_ID: 0.118 G_Rec: 0.443 D_GP: 0.029 D_real: 0.944 D_fake: 0.705 +(epoch: 469, iters: 3262, time: 0.064) G_GAN: -0.113 G_GAN_Feat: 0.640 G_ID: 0.097 G_Rec: 0.302 D_GP: 0.029 D_real: 0.750 D_fake: 1.113 +(epoch: 469, iters: 3662, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.868 G_ID: 0.112 G_Rec: 0.431 D_GP: 0.039 D_real: 0.962 D_fake: 0.698 +(epoch: 469, iters: 4062, time: 0.064) G_GAN: 0.051 G_GAN_Feat: 0.630 G_ID: 0.097 G_Rec: 0.285 D_GP: 0.040 D_real: 0.864 D_fake: 0.949 +(epoch: 469, iters: 4462, time: 0.063) G_GAN: 0.351 G_GAN_Feat: 0.838 G_ID: 0.113 G_Rec: 0.387 D_GP: 0.036 D_real: 1.030 D_fake: 0.659 +(epoch: 469, iters: 4862, time: 0.064) G_GAN: -0.032 G_GAN_Feat: 0.689 G_ID: 0.118 G_Rec: 0.309 D_GP: 0.047 D_real: 0.757 D_fake: 1.032 +(epoch: 469, iters: 5262, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.974 G_ID: 0.118 G_Rec: 0.455 D_GP: 0.050 D_real: 0.687 D_fake: 0.840 +(epoch: 469, iters: 5662, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.684 G_ID: 0.101 G_Rec: 0.322 D_GP: 0.037 D_real: 0.970 D_fake: 0.826 +(epoch: 469, iters: 6062, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.954 G_ID: 0.125 G_Rec: 0.466 D_GP: 0.064 D_real: 0.788 D_fake: 0.841 +(epoch: 469, iters: 6462, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.799 G_ID: 0.099 G_Rec: 0.349 D_GP: 0.056 D_real: 0.848 D_fake: 0.778 +(epoch: 469, iters: 6862, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.888 G_ID: 0.130 G_Rec: 0.423 D_GP: 0.035 D_real: 0.931 D_fake: 0.673 +(epoch: 469, iters: 7262, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.709 G_ID: 0.096 G_Rec: 0.317 D_GP: 0.034 D_real: 1.105 D_fake: 0.780 +(epoch: 469, iters: 7662, time: 0.064) G_GAN: 0.562 G_GAN_Feat: 0.946 G_ID: 0.105 G_Rec: 0.426 D_GP: 0.037 D_real: 1.113 D_fake: 0.448 +(epoch: 469, iters: 8062, time: 0.064) G_GAN: -0.186 G_GAN_Feat: 0.749 G_ID: 0.096 G_Rec: 0.334 D_GP: 0.056 D_real: 0.795 D_fake: 1.186 +(epoch: 469, iters: 8462, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.953 G_ID: 0.112 G_Rec: 0.443 D_GP: 0.077 D_real: 0.840 D_fake: 0.710 +(epoch: 470, iters: 254, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.706 G_ID: 0.084 G_Rec: 0.290 D_GP: 0.034 D_real: 1.144 D_fake: 0.821 +(epoch: 470, iters: 654, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 1.077 G_ID: 0.135 G_Rec: 0.487 D_GP: 0.185 D_real: 0.306 D_fake: 0.666 +(epoch: 470, iters: 1054, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.731 G_ID: 0.108 G_Rec: 0.359 D_GP: 0.028 D_real: 1.080 D_fake: 0.832 +(epoch: 470, iters: 1454, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.827 G_ID: 0.127 G_Rec: 0.389 D_GP: 0.033 D_real: 0.892 D_fake: 0.765 +(epoch: 470, iters: 1854, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.706 G_ID: 0.106 G_Rec: 0.298 D_GP: 0.036 D_real: 0.861 D_fake: 0.926 +(epoch: 470, iters: 2254, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.962 G_ID: 0.117 G_Rec: 0.445 D_GP: 0.041 D_real: 0.798 D_fake: 0.893 +(epoch: 470, iters: 2654, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 0.750 G_ID: 0.093 G_Rec: 0.330 D_GP: 0.109 D_real: 0.850 D_fake: 0.892 +(epoch: 470, iters: 3054, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.905 G_ID: 0.142 G_Rec: 0.399 D_GP: 0.051 D_real: 0.837 D_fake: 0.698 +(epoch: 470, iters: 3454, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.679 G_ID: 0.090 G_Rec: 0.281 D_GP: 0.030 D_real: 1.079 D_fake: 0.832 +(epoch: 470, iters: 3854, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.883 G_ID: 0.119 G_Rec: 0.405 D_GP: 0.038 D_real: 1.088 D_fake: 0.526 +(epoch: 470, iters: 4254, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.951 G_ID: 0.121 G_Rec: 0.337 D_GP: 0.483 D_real: 0.217 D_fake: 0.990 +(epoch: 470, iters: 4654, time: 0.064) G_GAN: 0.684 G_GAN_Feat: 1.056 G_ID: 0.127 G_Rec: 0.463 D_GP: 0.093 D_real: 0.454 D_fake: 0.416 +(epoch: 470, iters: 5054, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.628 G_ID: 0.106 G_Rec: 0.323 D_GP: 0.025 D_real: 1.168 D_fake: 0.768 +(epoch: 470, iters: 5454, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.825 G_ID: 0.128 G_Rec: 0.418 D_GP: 0.028 D_real: 1.213 D_fake: 0.546 +(epoch: 470, iters: 5854, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.607 G_ID: 0.093 G_Rec: 0.291 D_GP: 0.024 D_real: 1.102 D_fake: 0.845 +(epoch: 470, iters: 6254, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.797 G_ID: 0.125 G_Rec: 0.390 D_GP: 0.025 D_real: 1.041 D_fake: 0.736 +(epoch: 470, iters: 6654, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.633 G_ID: 0.101 G_Rec: 0.293 D_GP: 0.029 D_real: 1.050 D_fake: 0.914 +(epoch: 470, iters: 7054, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.882 G_ID: 0.098 G_Rec: 0.431 D_GP: 0.035 D_real: 1.153 D_fake: 0.491 +(epoch: 470, iters: 7454, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.639 G_ID: 0.087 G_Rec: 0.292 D_GP: 0.039 D_real: 1.008 D_fake: 0.866 +(epoch: 470, iters: 7854, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.850 G_ID: 0.127 G_Rec: 0.406 D_GP: 0.043 D_real: 0.858 D_fake: 0.815 +(epoch: 470, iters: 8254, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.641 G_ID: 0.108 G_Rec: 0.290 D_GP: 0.035 D_real: 0.954 D_fake: 0.909 +(epoch: 470, iters: 8654, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.888 G_ID: 0.118 G_Rec: 0.439 D_GP: 0.039 D_real: 0.792 D_fake: 0.791 +(epoch: 471, iters: 446, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.674 G_ID: 0.097 G_Rec: 0.293 D_GP: 0.036 D_real: 1.029 D_fake: 0.809 +(epoch: 471, iters: 846, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.890 G_ID: 0.117 G_Rec: 0.425 D_GP: 0.038 D_real: 0.973 D_fake: 0.639 +(epoch: 471, iters: 1246, time: 0.064) G_GAN: 0.047 G_GAN_Feat: 0.695 G_ID: 0.082 G_Rec: 0.296 D_GP: 0.082 D_real: 0.851 D_fake: 0.953 +(epoch: 471, iters: 1646, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 0.880 G_ID: 0.125 G_Rec: 0.387 D_GP: 0.043 D_real: 0.857 D_fake: 0.807 +(epoch: 471, iters: 2046, time: 0.064) G_GAN: -0.042 G_GAN_Feat: 0.850 G_ID: 0.101 G_Rec: 0.337 D_GP: 0.070 D_real: 0.844 D_fake: 1.043 +(epoch: 471, iters: 2446, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.881 G_ID: 0.138 G_Rec: 0.415 D_GP: 0.032 D_real: 0.828 D_fake: 0.864 +(epoch: 471, iters: 2846, time: 0.064) G_GAN: 0.002 G_GAN_Feat: 0.679 G_ID: 0.101 G_Rec: 0.284 D_GP: 0.044 D_real: 0.818 D_fake: 0.998 +(epoch: 471, iters: 3246, time: 0.063) G_GAN: 0.486 G_GAN_Feat: 0.881 G_ID: 0.109 G_Rec: 0.389 D_GP: 0.037 D_real: 1.185 D_fake: 0.533 +(epoch: 471, iters: 3646, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.654 G_ID: 0.090 G_Rec: 0.274 D_GP: 0.031 D_real: 1.106 D_fake: 0.797 +(epoch: 471, iters: 4046, time: 0.064) G_GAN: 0.608 G_GAN_Feat: 0.870 G_ID: 0.103 G_Rec: 0.389 D_GP: 0.032 D_real: 1.370 D_fake: 0.404 +(epoch: 471, iters: 4446, time: 0.063) G_GAN: -0.100 G_GAN_Feat: 0.846 G_ID: 0.096 G_Rec: 0.362 D_GP: 0.193 D_real: 0.666 D_fake: 1.102 +(epoch: 471, iters: 4846, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.910 G_ID: 0.120 G_Rec: 0.451 D_GP: 0.031 D_real: 1.006 D_fake: 0.621 +(epoch: 471, iters: 5246, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.641 G_ID: 0.078 G_Rec: 0.274 D_GP: 0.030 D_real: 1.097 D_fake: 0.807 +(epoch: 471, iters: 5646, time: 0.064) G_GAN: 0.691 G_GAN_Feat: 0.931 G_ID: 0.110 G_Rec: 0.442 D_GP: 0.032 D_real: 1.248 D_fake: 0.345 +(epoch: 471, iters: 6046, time: 0.063) G_GAN: 0.224 G_GAN_Feat: 0.678 G_ID: 0.094 G_Rec: 0.290 D_GP: 0.028 D_real: 1.074 D_fake: 0.776 +(epoch: 471, iters: 6446, time: 0.063) G_GAN: 0.530 G_GAN_Feat: 0.897 G_ID: 0.106 G_Rec: 0.408 D_GP: 0.031 D_real: 1.215 D_fake: 0.476 +(epoch: 471, iters: 6846, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.781 G_ID: 0.090 G_Rec: 0.315 D_GP: 0.046 D_real: 1.059 D_fake: 0.719 +(epoch: 471, iters: 7246, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.924 G_ID: 0.154 G_Rec: 0.430 D_GP: 0.033 D_real: 0.782 D_fake: 0.898 +(epoch: 471, iters: 7646, time: 0.063) G_GAN: 0.281 G_GAN_Feat: 0.702 G_ID: 0.115 G_Rec: 0.301 D_GP: 0.034 D_real: 1.153 D_fake: 0.725 +(epoch: 471, iters: 8046, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.921 G_ID: 0.116 G_Rec: 0.441 D_GP: 0.037 D_real: 1.176 D_fake: 0.499 +(epoch: 471, iters: 8446, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.664 G_ID: 0.095 G_Rec: 0.294 D_GP: 0.035 D_real: 1.006 D_fake: 0.839 +(epoch: 472, iters: 238, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 1.018 G_ID: 0.125 G_Rec: 0.448 D_GP: 0.070 D_real: 0.821 D_fake: 0.724 +(epoch: 472, iters: 638, time: 0.063) G_GAN: 0.085 G_GAN_Feat: 0.650 G_ID: 0.102 G_Rec: 0.268 D_GP: 0.037 D_real: 1.019 D_fake: 0.915 +(epoch: 472, iters: 1038, time: 0.063) G_GAN: 0.523 G_GAN_Feat: 0.970 G_ID: 0.138 G_Rec: 0.409 D_GP: 0.037 D_real: 1.050 D_fake: 0.487 +(epoch: 472, iters: 1438, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.788 G_ID: 0.109 G_Rec: 0.334 D_GP: 0.044 D_real: 0.972 D_fake: 0.742 +(epoch: 472, iters: 1838, time: 0.063) G_GAN: 0.354 G_GAN_Feat: 1.044 G_ID: 0.136 G_Rec: 0.422 D_GP: 0.151 D_real: 0.395 D_fake: 0.649 +(epoch: 472, iters: 2238, time: 0.063) G_GAN: 0.050 G_GAN_Feat: 0.755 G_ID: 0.109 G_Rec: 0.330 D_GP: 0.032 D_real: 0.973 D_fake: 0.951 +(epoch: 472, iters: 2638, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.996 G_ID: 0.134 G_Rec: 0.494 D_GP: 0.048 D_real: 0.701 D_fake: 0.737 +(epoch: 472, iters: 3038, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.779 G_ID: 0.098 G_Rec: 0.329 D_GP: 0.058 D_real: 0.786 D_fake: 0.960 +(epoch: 472, iters: 3438, time: 0.063) G_GAN: 0.734 G_GAN_Feat: 1.099 G_ID: 0.112 G_Rec: 0.464 D_GP: 0.039 D_real: 1.296 D_fake: 0.306 +(epoch: 472, iters: 3838, time: 0.063) G_GAN: -0.092 G_GAN_Feat: 0.850 G_ID: 0.091 G_Rec: 0.347 D_GP: 0.063 D_real: 0.405 D_fake: 1.092 +(epoch: 472, iters: 4238, time: 0.063) G_GAN: 0.788 G_GAN_Feat: 0.992 G_ID: 0.115 G_Rec: 0.433 D_GP: 0.057 D_real: 1.053 D_fake: 0.268 +(epoch: 472, iters: 4638, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.783 G_ID: 0.097 G_Rec: 0.337 D_GP: 0.044 D_real: 0.774 D_fake: 0.918 +(epoch: 472, iters: 5038, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.969 G_ID: 0.135 G_Rec: 0.450 D_GP: 0.041 D_real: 0.893 D_fake: 0.640 +(epoch: 472, iters: 5438, time: 0.063) G_GAN: 0.405 G_GAN_Feat: 0.804 G_ID: 0.114 G_Rec: 0.314 D_GP: 0.036 D_real: 1.112 D_fake: 0.596 +(epoch: 472, iters: 5838, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 1.121 G_ID: 0.110 G_Rec: 0.424 D_GP: 0.041 D_real: 0.642 D_fake: 0.406 +(epoch: 472, iters: 6238, time: 0.064) G_GAN: 0.018 G_GAN_Feat: 0.699 G_ID: 0.106 G_Rec: 0.334 D_GP: 0.030 D_real: 0.934 D_fake: 0.984 +(epoch: 472, iters: 6638, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.937 G_ID: 0.119 G_Rec: 0.417 D_GP: 0.046 D_real: 0.677 D_fake: 0.878 +(epoch: 472, iters: 7038, time: 0.063) G_GAN: -0.155 G_GAN_Feat: 0.699 G_ID: 0.100 G_Rec: 0.289 D_GP: 0.033 D_real: 0.838 D_fake: 1.155 +(epoch: 472, iters: 7438, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.914 G_ID: 0.109 G_Rec: 0.387 D_GP: 0.034 D_real: 1.107 D_fake: 0.692 +(epoch: 472, iters: 7838, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.756 G_ID: 0.107 G_Rec: 0.294 D_GP: 0.044 D_real: 1.012 D_fake: 0.839 +(epoch: 472, iters: 8238, time: 0.063) G_GAN: 0.682 G_GAN_Feat: 1.063 G_ID: 0.115 G_Rec: 0.476 D_GP: 0.062 D_real: 0.994 D_fake: 0.348 +(epoch: 472, iters: 8638, time: 0.063) G_GAN: 0.484 G_GAN_Feat: 0.855 G_ID: 0.110 G_Rec: 0.310 D_GP: 0.122 D_real: 0.763 D_fake: 0.530 +(epoch: 473, iters: 430, time: 0.063) G_GAN: 0.739 G_GAN_Feat: 1.186 G_ID: 0.104 G_Rec: 0.479 D_GP: 0.040 D_real: 0.778 D_fake: 0.317 +(epoch: 473, iters: 830, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.868 G_ID: 0.103 G_Rec: 0.309 D_GP: 0.092 D_real: 0.787 D_fake: 0.647 +(epoch: 473, iters: 1230, time: 0.063) G_GAN: 0.447 G_GAN_Feat: 0.934 G_ID: 0.111 G_Rec: 0.472 D_GP: 0.031 D_real: 1.143 D_fake: 0.567 +(epoch: 473, iters: 1630, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.662 G_ID: 0.100 G_Rec: 0.302 D_GP: 0.026 D_real: 1.151 D_fake: 0.808 +(epoch: 473, iters: 2030, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.880 G_ID: 0.104 G_Rec: 0.419 D_GP: 0.033 D_real: 1.145 D_fake: 0.628 +(epoch: 473, iters: 2430, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.703 G_ID: 0.105 G_Rec: 0.318 D_GP: 0.040 D_real: 0.983 D_fake: 0.790 +(epoch: 473, iters: 2830, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.922 G_ID: 0.120 G_Rec: 0.463 D_GP: 0.049 D_real: 0.808 D_fake: 0.726 +(epoch: 473, iters: 3230, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.671 G_ID: 0.085 G_Rec: 0.322 D_GP: 0.040 D_real: 1.131 D_fake: 0.786 +(epoch: 473, iters: 3630, time: 0.063) G_GAN: 0.372 G_GAN_Feat: 0.973 G_ID: 0.106 G_Rec: 0.433 D_GP: 0.052 D_real: 0.906 D_fake: 0.639 +(epoch: 473, iters: 4030, time: 0.064) G_GAN: 0.047 G_GAN_Feat: 0.662 G_ID: 0.099 G_Rec: 0.292 D_GP: 0.039 D_real: 0.934 D_fake: 0.953 +(epoch: 473, iters: 4430, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.006 G_ID: 0.119 G_Rec: 0.473 D_GP: 0.074 D_real: 0.939 D_fake: 0.661 +(epoch: 473, iters: 4830, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.702 G_ID: 0.103 G_Rec: 0.290 D_GP: 0.040 D_real: 0.895 D_fake: 0.900 +(epoch: 473, iters: 5230, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.951 G_ID: 0.122 G_Rec: 0.410 D_GP: 0.054 D_real: 0.901 D_fake: 0.689 +(epoch: 473, iters: 5630, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.716 G_ID: 0.085 G_Rec: 0.285 D_GP: 0.050 D_real: 0.993 D_fake: 0.762 +(epoch: 473, iters: 6030, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 1.015 G_ID: 0.108 G_Rec: 0.421 D_GP: 0.064 D_real: 0.855 D_fake: 0.592 +(epoch: 473, iters: 6430, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.768 G_ID: 0.109 G_Rec: 0.302 D_GP: 0.048 D_real: 0.963 D_fake: 0.830 +(epoch: 473, iters: 6830, time: 0.063) G_GAN: 0.101 G_GAN_Feat: 1.208 G_ID: 0.109 G_Rec: 0.494 D_GP: 4.292 D_real: 0.437 D_fake: 0.930 +(epoch: 473, iters: 7230, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.626 G_ID: 0.098 G_Rec: 0.286 D_GP: 0.028 D_real: 1.154 D_fake: 0.804 +(epoch: 473, iters: 7630, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.793 G_ID: 0.118 G_Rec: 0.391 D_GP: 0.036 D_real: 0.812 D_fake: 0.886 +(epoch: 473, iters: 8030, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 0.636 G_ID: 0.086 G_Rec: 0.300 D_GP: 0.027 D_real: 1.040 D_fake: 0.944 +(epoch: 473, iters: 8430, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.873 G_ID: 0.101 G_Rec: 0.427 D_GP: 0.054 D_real: 0.569 D_fake: 0.941 +(epoch: 474, iters: 222, time: 0.064) G_GAN: -0.104 G_GAN_Feat: 0.715 G_ID: 0.094 G_Rec: 0.315 D_GP: 0.072 D_real: 0.684 D_fake: 1.105 +(epoch: 474, iters: 622, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.896 G_ID: 0.113 G_Rec: 0.417 D_GP: 0.036 D_real: 0.687 D_fake: 0.868 +(epoch: 474, iters: 1022, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 0.706 G_ID: 0.097 G_Rec: 0.321 D_GP: 0.041 D_real: 0.850 D_fake: 0.904 +(epoch: 474, iters: 1422, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.901 G_ID: 0.104 G_Rec: 0.418 D_GP: 0.048 D_real: 0.759 D_fake: 0.738 +(epoch: 474, iters: 1822, time: 0.064) G_GAN: -0.061 G_GAN_Feat: 0.790 G_ID: 0.121 G_Rec: 0.322 D_GP: 0.067 D_real: 0.579 D_fake: 1.061 +(epoch: 474, iters: 2222, time: 0.064) G_GAN: 0.613 G_GAN_Feat: 0.973 G_ID: 0.122 G_Rec: 0.467 D_GP: 0.039 D_real: 1.159 D_fake: 0.412 +(epoch: 474, iters: 2622, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.689 G_ID: 0.123 G_Rec: 0.272 D_GP: 0.043 D_real: 1.072 D_fake: 0.739 +(epoch: 474, iters: 3022, time: 0.063) G_GAN: 0.549 G_GAN_Feat: 0.944 G_ID: 0.139 G_Rec: 0.442 D_GP: 0.036 D_real: 1.018 D_fake: 0.459 +(epoch: 474, iters: 3422, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.739 G_ID: 0.090 G_Rec: 0.323 D_GP: 0.042 D_real: 1.121 D_fake: 0.591 +(epoch: 474, iters: 3822, time: 0.063) G_GAN: 0.345 G_GAN_Feat: 1.057 G_ID: 0.120 G_Rec: 0.506 D_GP: 0.042 D_real: 1.081 D_fake: 0.679 +(epoch: 474, iters: 4222, time: 0.064) G_GAN: -0.026 G_GAN_Feat: 0.686 G_ID: 0.103 G_Rec: 0.324 D_GP: 0.031 D_real: 0.919 D_fake: 1.026 +(epoch: 474, iters: 4622, time: 0.063) G_GAN: -0.059 G_GAN_Feat: 0.939 G_ID: 0.138 G_Rec: 0.451 D_GP: 0.050 D_real: 0.665 D_fake: 1.059 +(epoch: 474, iters: 5022, time: 0.064) G_GAN: -0.027 G_GAN_Feat: 0.731 G_ID: 0.097 G_Rec: 0.325 D_GP: 0.065 D_real: 0.716 D_fake: 1.028 +(epoch: 474, iters: 5422, time: 0.063) G_GAN: 0.069 G_GAN_Feat: 0.822 G_ID: 0.162 G_Rec: 0.370 D_GP: 0.040 D_real: 0.668 D_fake: 0.931 +(epoch: 474, iters: 5822, time: 0.063) G_GAN: 0.012 G_GAN_Feat: 0.730 G_ID: 0.091 G_Rec: 0.303 D_GP: 0.042 D_real: 0.923 D_fake: 0.988 +(epoch: 474, iters: 6222, time: 0.064) G_GAN: 0.529 G_GAN_Feat: 0.952 G_ID: 0.107 G_Rec: 0.407 D_GP: 0.046 D_real: 1.174 D_fake: 0.517 +(epoch: 474, iters: 6622, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.792 G_ID: 0.107 G_Rec: 0.346 D_GP: 0.050 D_real: 0.996 D_fake: 0.762 +(epoch: 474, iters: 7022, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.892 G_ID: 0.103 G_Rec: 0.417 D_GP: 0.033 D_real: 1.050 D_fake: 0.510 +(epoch: 474, iters: 7422, time: 0.064) G_GAN: -0.088 G_GAN_Feat: 0.853 G_ID: 0.105 G_Rec: 0.355 D_GP: 0.105 D_real: 0.372 D_fake: 1.088 +(epoch: 474, iters: 7822, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.983 G_ID: 0.111 G_Rec: 0.403 D_GP: 0.037 D_real: 0.882 D_fake: 0.549 +(epoch: 474, iters: 8222, time: 0.064) G_GAN: 0.049 G_GAN_Feat: 0.976 G_ID: 0.097 G_Rec: 0.365 D_GP: 1.100 D_real: 0.371 D_fake: 0.955 +(epoch: 474, iters: 8622, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.956 G_ID: 0.108 G_Rec: 0.498 D_GP: 0.028 D_real: 1.058 D_fake: 0.549 +(epoch: 475, iters: 414, time: 0.064) G_GAN: -0.010 G_GAN_Feat: 0.752 G_ID: 0.092 G_Rec: 0.296 D_GP: 0.042 D_real: 0.700 D_fake: 1.010 +(epoch: 475, iters: 814, time: 0.064) G_GAN: -0.160 G_GAN_Feat: 0.994 G_ID: 0.125 G_Rec: 0.433 D_GP: 0.146 D_real: 0.351 D_fake: 1.161 +(epoch: 475, iters: 1214, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.847 G_ID: 0.088 G_Rec: 0.363 D_GP: 0.132 D_real: 0.580 D_fake: 0.840 +(epoch: 475, iters: 1614, time: 0.063) G_GAN: 0.579 G_GAN_Feat: 0.999 G_ID: 0.120 G_Rec: 0.427 D_GP: 0.039 D_real: 0.931 D_fake: 0.443 +(epoch: 475, iters: 2014, time: 0.063) G_GAN: 0.226 G_GAN_Feat: 0.771 G_ID: 0.095 G_Rec: 0.284 D_GP: 0.033 D_real: 0.853 D_fake: 0.774 +(epoch: 475, iters: 2414, time: 0.064) G_GAN: 0.774 G_GAN_Feat: 1.023 G_ID: 0.136 G_Rec: 0.419 D_GP: 0.054 D_real: 1.159 D_fake: 0.287 +(epoch: 475, iters: 2814, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.998 G_ID: 0.117 G_Rec: 0.367 D_GP: 0.685 D_real: 0.275 D_fake: 0.942 +(epoch: 475, iters: 3214, time: 0.063) G_GAN: 1.014 G_GAN_Feat: 1.042 G_ID: 0.127 G_Rec: 0.445 D_GP: 0.074 D_real: 1.303 D_fake: 0.113 +(epoch: 475, iters: 3614, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.692 G_ID: 0.104 G_Rec: 0.303 D_GP: 0.032 D_real: 0.972 D_fake: 0.963 +(epoch: 475, iters: 4014, time: 0.063) G_GAN: -0.029 G_GAN_Feat: 0.915 G_ID: 0.143 G_Rec: 0.426 D_GP: 0.036 D_real: 0.667 D_fake: 1.031 +(epoch: 475, iters: 4414, time: 0.064) G_GAN: -0.022 G_GAN_Feat: 0.717 G_ID: 0.088 G_Rec: 0.319 D_GP: 0.069 D_real: 0.819 D_fake: 1.022 +(epoch: 475, iters: 4814, time: 0.063) G_GAN: -0.041 G_GAN_Feat: 0.963 G_ID: 0.117 G_Rec: 0.445 D_GP: 0.047 D_real: 0.593 D_fake: 1.041 +(epoch: 475, iters: 5214, time: 0.063) G_GAN: 0.008 G_GAN_Feat: 0.894 G_ID: 0.104 G_Rec: 0.342 D_GP: 0.059 D_real: 0.496 D_fake: 0.992 +(epoch: 475, iters: 5614, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.928 G_ID: 0.157 G_Rec: 0.416 D_GP: 0.039 D_real: 0.965 D_fake: 0.671 +(epoch: 475, iters: 6014, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.691 G_ID: 0.091 G_Rec: 0.276 D_GP: 0.037 D_real: 1.232 D_fake: 0.634 +(epoch: 475, iters: 6414, time: 0.064) G_GAN: 0.748 G_GAN_Feat: 0.953 G_ID: 0.117 G_Rec: 0.425 D_GP: 0.031 D_real: 1.278 D_fake: 0.301 +(epoch: 475, iters: 6814, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.710 G_ID: 0.088 G_Rec: 0.291 D_GP: 0.029 D_real: 1.176 D_fake: 0.720 +(epoch: 475, iters: 7214, time: 0.064) G_GAN: 0.528 G_GAN_Feat: 1.020 G_ID: 0.112 G_Rec: 0.437 D_GP: 0.060 D_real: 1.020 D_fake: 0.478 +(epoch: 475, iters: 7614, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.804 G_ID: 0.103 G_Rec: 0.343 D_GP: 0.041 D_real: 1.021 D_fake: 0.713 +(epoch: 475, iters: 8014, time: 0.064) G_GAN: 0.588 G_GAN_Feat: 0.969 G_ID: 0.097 G_Rec: 0.462 D_GP: 0.032 D_real: 1.213 D_fake: 0.441 +(epoch: 475, iters: 8414, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.712 G_ID: 0.112 G_Rec: 0.286 D_GP: 0.030 D_real: 0.978 D_fake: 0.941 +(epoch: 476, iters: 206, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.994 G_ID: 0.132 G_Rec: 0.419 D_GP: 0.039 D_real: 0.910 D_fake: 0.546 +(epoch: 476, iters: 606, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.657 G_ID: 0.093 G_Rec: 0.277 D_GP: 0.027 D_real: 1.176 D_fake: 0.773 +(epoch: 476, iters: 1006, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 1.029 G_ID: 0.110 G_Rec: 0.496 D_GP: 0.037 D_real: 1.066 D_fake: 0.526 +(epoch: 476, iters: 1406, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.651 G_ID: 0.098 G_Rec: 0.273 D_GP: 0.029 D_real: 1.062 D_fake: 0.837 +(epoch: 476, iters: 1806, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 1.023 G_ID: 0.110 G_Rec: 0.479 D_GP: 0.068 D_real: 0.710 D_fake: 0.667 +(epoch: 476, iters: 2206, time: 0.064) G_GAN: -0.134 G_GAN_Feat: 0.749 G_ID: 0.080 G_Rec: 0.309 D_GP: 0.194 D_real: 0.504 D_fake: 1.134 +(epoch: 476, iters: 2606, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 1.013 G_ID: 0.110 G_Rec: 0.445 D_GP: 0.033 D_real: 0.822 D_fake: 0.726 +(epoch: 476, iters: 3006, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.667 G_ID: 0.095 G_Rec: 0.263 D_GP: 0.033 D_real: 0.995 D_fake: 0.912 +(epoch: 476, iters: 3406, time: 0.064) G_GAN: 0.894 G_GAN_Feat: 0.981 G_ID: 0.109 G_Rec: 0.435 D_GP: 0.041 D_real: 1.557 D_fake: 0.272 +(epoch: 476, iters: 3806, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.736 G_ID: 0.097 G_Rec: 0.299 D_GP: 0.040 D_real: 0.865 D_fake: 0.941 +(epoch: 476, iters: 4206, time: 0.064) G_GAN: 0.434 G_GAN_Feat: 0.950 G_ID: 0.129 G_Rec: 0.424 D_GP: 0.037 D_real: 1.120 D_fake: 0.569 +(epoch: 476, iters: 4606, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.748 G_ID: 0.095 G_Rec: 0.280 D_GP: 0.039 D_real: 1.056 D_fake: 0.742 +(epoch: 476, iters: 5006, time: 0.064) G_GAN: 0.785 G_GAN_Feat: 1.064 G_ID: 0.101 G_Rec: 0.460 D_GP: 0.061 D_real: 0.905 D_fake: 0.254 +(epoch: 476, iters: 5406, time: 0.064) G_GAN: 0.396 G_GAN_Feat: 0.910 G_ID: 0.108 G_Rec: 0.359 D_GP: 0.168 D_real: 0.714 D_fake: 0.718 +(epoch: 476, iters: 5806, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.866 G_ID: 0.127 G_Rec: 0.396 D_GP: 0.030 D_real: 0.844 D_fake: 0.943 +(epoch: 476, iters: 6206, time: 0.064) G_GAN: -0.141 G_GAN_Feat: 0.754 G_ID: 0.095 G_Rec: 0.321 D_GP: 0.047 D_real: 0.594 D_fake: 1.141 +(epoch: 476, iters: 6606, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 1.067 G_ID: 0.117 G_Rec: 0.478 D_GP: 0.101 D_real: 0.482 D_fake: 0.701 +(epoch: 476, iters: 7006, time: 0.064) G_GAN: 0.624 G_GAN_Feat: 0.870 G_ID: 0.088 G_Rec: 0.349 D_GP: 0.045 D_real: 1.102 D_fake: 0.549 +(epoch: 476, iters: 7406, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.998 G_ID: 0.116 G_Rec: 0.427 D_GP: 0.052 D_real: 1.289 D_fake: 0.560 +(epoch: 476, iters: 7806, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.734 G_ID: 0.079 G_Rec: 0.328 D_GP: 0.036 D_real: 1.199 D_fake: 0.709 +(epoch: 476, iters: 8206, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.987 G_ID: 0.113 G_Rec: 0.424 D_GP: 0.041 D_real: 0.956 D_fake: 0.556 +(epoch: 476, iters: 8606, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.824 G_ID: 0.088 G_Rec: 0.317 D_GP: 0.034 D_real: 1.082 D_fake: 0.653 +(epoch: 477, iters: 398, time: 0.064) G_GAN: 0.628 G_GAN_Feat: 0.951 G_ID: 0.125 G_Rec: 0.446 D_GP: 0.043 D_real: 1.211 D_fake: 0.386 +(epoch: 477, iters: 798, time: 0.064) G_GAN: 0.067 G_GAN_Feat: 0.814 G_ID: 0.089 G_Rec: 0.335 D_GP: 0.085 D_real: 0.826 D_fake: 0.933 +(epoch: 477, iters: 1198, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 0.953 G_ID: 0.101 G_Rec: 0.401 D_GP: 0.035 D_real: 1.220 D_fake: 0.425 +(epoch: 477, iters: 1598, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.849 G_ID: 0.083 G_Rec: 0.333 D_GP: 0.035 D_real: 0.679 D_fake: 0.721 +(epoch: 477, iters: 1998, time: 0.064) G_GAN: 0.635 G_GAN_Feat: 1.021 G_ID: 0.106 G_Rec: 0.462 D_GP: 0.035 D_real: 1.171 D_fake: 0.433 +(epoch: 477, iters: 2398, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.871 G_ID: 0.096 G_Rec: 0.301 D_GP: 0.057 D_real: 0.953 D_fake: 0.533 +(epoch: 477, iters: 2798, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 1.052 G_ID: 0.104 G_Rec: 0.452 D_GP: 0.056 D_real: 0.860 D_fake: 0.525 +(epoch: 477, iters: 3198, time: 0.064) G_GAN: 0.495 G_GAN_Feat: 1.013 G_ID: 0.091 G_Rec: 0.359 D_GP: 0.897 D_real: 0.578 D_fake: 0.563 +(epoch: 477, iters: 3598, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.988 G_ID: 0.105 G_Rec: 0.461 D_GP: 0.038 D_real: 0.829 D_fake: 0.648 +(epoch: 477, iters: 3998, time: 0.064) G_GAN: 0.071 G_GAN_Feat: 0.901 G_ID: 0.103 G_Rec: 0.334 D_GP: 0.050 D_real: 0.768 D_fake: 0.934 +(epoch: 477, iters: 4398, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.945 G_ID: 0.136 G_Rec: 0.403 D_GP: 0.035 D_real: 0.959 D_fake: 0.746 +(epoch: 477, iters: 4798, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.685 G_ID: 0.098 G_Rec: 0.306 D_GP: 0.038 D_real: 0.868 D_fake: 0.959 +(epoch: 477, iters: 5198, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 1.078 G_ID: 0.120 G_Rec: 0.441 D_GP: 0.041 D_real: 0.604 D_fake: 0.677 +(epoch: 477, iters: 5598, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.908 G_ID: 0.092 G_Rec: 0.343 D_GP: 0.074 D_real: 1.086 D_fake: 0.700 +(epoch: 477, iters: 5998, time: 0.064) G_GAN: 1.046 G_GAN_Feat: 1.147 G_ID: 0.108 G_Rec: 0.477 D_GP: 0.071 D_real: 0.990 D_fake: 0.167 +(epoch: 477, iters: 6398, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.694 G_ID: 0.091 G_Rec: 0.294 D_GP: 0.032 D_real: 1.116 D_fake: 0.849 +(epoch: 477, iters: 6798, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.921 G_ID: 0.131 G_Rec: 0.424 D_GP: 0.042 D_real: 0.641 D_fake: 0.842 +(epoch: 477, iters: 7198, time: 0.064) G_GAN: -0.195 G_GAN_Feat: 0.824 G_ID: 0.105 G_Rec: 0.328 D_GP: 0.088 D_real: 0.569 D_fake: 1.195 +(epoch: 477, iters: 7598, time: 0.064) G_GAN: 0.832 G_GAN_Feat: 0.941 G_ID: 0.127 G_Rec: 0.392 D_GP: 0.036 D_real: 1.493 D_fake: 0.251 +(epoch: 477, iters: 7998, time: 0.063) G_GAN: 0.318 G_GAN_Feat: 0.835 G_ID: 0.100 G_Rec: 0.379 D_GP: 0.053 D_real: 1.157 D_fake: 0.683 +(epoch: 477, iters: 8398, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 1.017 G_ID: 0.110 G_Rec: 0.403 D_GP: 0.099 D_real: 0.593 D_fake: 0.542 +(epoch: 478, iters: 190, time: 0.063) G_GAN: 0.175 G_GAN_Feat: 0.900 G_ID: 0.091 G_Rec: 0.313 D_GP: 0.034 D_real: 0.844 D_fake: 0.825 +(epoch: 478, iters: 590, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.862 G_ID: 0.121 G_Rec: 0.451 D_GP: 0.027 D_real: 1.156 D_fake: 0.587 +(epoch: 478, iters: 990, time: 0.063) G_GAN: 0.221 G_GAN_Feat: 0.646 G_ID: 0.077 G_Rec: 0.311 D_GP: 0.023 D_real: 1.148 D_fake: 0.779 +(epoch: 478, iters: 1390, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.891 G_ID: 0.110 G_Rec: 0.462 D_GP: 0.035 D_real: 0.836 D_fake: 0.724 +(epoch: 478, iters: 1790, time: 0.063) G_GAN: 0.144 G_GAN_Feat: 0.603 G_ID: 0.105 G_Rec: 0.288 D_GP: 0.029 D_real: 1.013 D_fake: 0.856 +(epoch: 478, iters: 2190, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.908 G_ID: 0.132 G_Rec: 0.453 D_GP: 0.047 D_real: 0.744 D_fake: 0.833 +(epoch: 478, iters: 2590, time: 0.063) G_GAN: 0.027 G_GAN_Feat: 0.690 G_ID: 0.104 G_Rec: 0.328 D_GP: 0.043 D_real: 0.839 D_fake: 0.973 +(epoch: 478, iters: 2990, time: 0.063) G_GAN: 0.339 G_GAN_Feat: 0.927 G_ID: 0.115 G_Rec: 0.470 D_GP: 0.039 D_real: 0.924 D_fake: 0.678 +(epoch: 478, iters: 3390, time: 0.064) G_GAN: 0.006 G_GAN_Feat: 0.732 G_ID: 0.107 G_Rec: 0.366 D_GP: 0.042 D_real: 0.788 D_fake: 0.995 +(epoch: 478, iters: 3790, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.905 G_ID: 0.134 G_Rec: 0.465 D_GP: 0.034 D_real: 0.654 D_fake: 0.888 +(epoch: 478, iters: 4190, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.678 G_ID: 0.095 G_Rec: 0.313 D_GP: 0.043 D_real: 0.887 D_fake: 0.916 +(epoch: 478, iters: 4590, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.877 G_ID: 0.112 G_Rec: 0.402 D_GP: 0.040 D_real: 0.863 D_fake: 0.867 +(epoch: 478, iters: 4990, time: 0.063) G_GAN: -0.141 G_GAN_Feat: 0.734 G_ID: 0.097 G_Rec: 0.334 D_GP: 0.058 D_real: 0.583 D_fake: 1.141 +(epoch: 478, iters: 5390, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.907 G_ID: 0.118 G_Rec: 0.439 D_GP: 0.065 D_real: 0.777 D_fake: 0.849 +(epoch: 478, iters: 5790, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.753 G_ID: 0.093 G_Rec: 0.312 D_GP: 0.085 D_real: 0.747 D_fake: 0.937 +(epoch: 478, iters: 6190, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.909 G_ID: 0.104 G_Rec: 0.415 D_GP: 0.048 D_real: 1.077 D_fake: 0.603 +(epoch: 478, iters: 6590, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.654 G_ID: 0.095 G_Rec: 0.289 D_GP: 0.033 D_real: 0.999 D_fake: 0.830 +(epoch: 478, iters: 6990, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.900 G_ID: 0.105 G_Rec: 0.383 D_GP: 0.039 D_real: 1.194 D_fake: 0.632 +(epoch: 478, iters: 7390, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.694 G_ID: 0.120 G_Rec: 0.326 D_GP: 0.030 D_real: 0.955 D_fake: 0.909 +(epoch: 478, iters: 7790, time: 0.063) G_GAN: 0.382 G_GAN_Feat: 0.933 G_ID: 0.116 G_Rec: 0.455 D_GP: 0.043 D_real: 0.927 D_fake: 0.626 +(epoch: 478, iters: 8190, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.648 G_ID: 0.104 G_Rec: 0.279 D_GP: 0.037 D_real: 1.049 D_fake: 0.784 +(epoch: 478, iters: 8590, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 0.884 G_ID: 0.123 G_Rec: 0.402 D_GP: 0.049 D_real: 0.704 D_fake: 0.928 +(epoch: 479, iters: 382, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.725 G_ID: 0.093 G_Rec: 0.307 D_GP: 0.044 D_real: 1.020 D_fake: 0.857 +(epoch: 479, iters: 782, time: 0.064) G_GAN: 0.636 G_GAN_Feat: 0.957 G_ID: 0.108 G_Rec: 0.434 D_GP: 0.043 D_real: 1.126 D_fake: 0.402 +(epoch: 479, iters: 1182, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.762 G_ID: 0.094 G_Rec: 0.321 D_GP: 0.053 D_real: 0.803 D_fake: 0.889 +(epoch: 479, iters: 1582, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 1.079 G_ID: 0.119 G_Rec: 0.484 D_GP: 0.640 D_real: 0.501 D_fake: 0.702 +(epoch: 479, iters: 1982, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.743 G_ID: 0.105 G_Rec: 0.294 D_GP: 0.035 D_real: 1.122 D_fake: 0.794 +(epoch: 479, iters: 2382, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.822 G_ID: 0.126 G_Rec: 0.373 D_GP: 0.032 D_real: 1.088 D_fake: 0.728 +(epoch: 479, iters: 2782, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.740 G_ID: 0.089 G_Rec: 0.324 D_GP: 0.036 D_real: 1.008 D_fake: 0.788 +(epoch: 479, iters: 3182, time: 0.064) G_GAN: 0.834 G_GAN_Feat: 1.053 G_ID: 0.135 G_Rec: 0.481 D_GP: 0.050 D_real: 1.405 D_fake: 0.280 +(epoch: 479, iters: 3582, time: 0.063) G_GAN: 0.076 G_GAN_Feat: 0.869 G_ID: 0.091 G_Rec: 0.323 D_GP: 0.128 D_real: 0.548 D_fake: 0.924 +(epoch: 479, iters: 3982, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 1.002 G_ID: 0.115 G_Rec: 0.486 D_GP: 0.064 D_real: 0.902 D_fake: 0.562 +(epoch: 479, iters: 4382, time: 0.064) G_GAN: -0.057 G_GAN_Feat: 0.723 G_ID: 0.109 G_Rec: 0.309 D_GP: 0.034 D_real: 0.904 D_fake: 1.057 +(epoch: 479, iters: 4782, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.903 G_ID: 0.122 G_Rec: 0.391 D_GP: 0.037 D_real: 1.132 D_fake: 0.425 +(epoch: 479, iters: 5182, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.709 G_ID: 0.101 G_Rec: 0.288 D_GP: 0.030 D_real: 1.079 D_fake: 0.814 +(epoch: 479, iters: 5582, time: 0.064) G_GAN: 0.633 G_GAN_Feat: 1.028 G_ID: 0.099 G_Rec: 0.447 D_GP: 0.088 D_real: 0.876 D_fake: 0.389 +(epoch: 479, iters: 5982, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.713 G_ID: 0.102 G_Rec: 0.296 D_GP: 0.032 D_real: 0.973 D_fake: 0.883 +(epoch: 479, iters: 6382, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 0.957 G_ID: 0.128 G_Rec: 0.442 D_GP: 0.035 D_real: 1.079 D_fake: 0.458 +(epoch: 479, iters: 6782, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.727 G_ID: 0.104 G_Rec: 0.301 D_GP: 0.034 D_real: 1.237 D_fake: 0.687 +(epoch: 479, iters: 7182, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.980 G_ID: 0.131 G_Rec: 0.400 D_GP: 0.036 D_real: 1.112 D_fake: 0.530 +(epoch: 479, iters: 7582, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.734 G_ID: 0.089 G_Rec: 0.288 D_GP: 0.034 D_real: 1.112 D_fake: 0.714 +(epoch: 479, iters: 7982, time: 0.064) G_GAN: 0.978 G_GAN_Feat: 1.002 G_ID: 0.114 G_Rec: 0.432 D_GP: 0.040 D_real: 1.573 D_fake: 0.190 +(epoch: 479, iters: 8382, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.698 G_ID: 0.092 G_Rec: 0.277 D_GP: 0.032 D_real: 1.152 D_fake: 0.725 +(epoch: 480, iters: 174, time: 0.064) G_GAN: 1.056 G_GAN_Feat: 1.016 G_ID: 0.125 G_Rec: 0.426 D_GP: 0.062 D_real: 1.402 D_fake: 0.373 +(epoch: 480, iters: 574, time: 0.063) G_GAN: 0.076 G_GAN_Feat: 0.655 G_ID: 0.100 G_Rec: 0.270 D_GP: 0.032 D_real: 0.996 D_fake: 0.925 +(epoch: 480, iters: 974, time: 0.064) G_GAN: 0.629 G_GAN_Feat: 1.076 G_ID: 0.108 G_Rec: 0.462 D_GP: 0.094 D_real: 0.732 D_fake: 0.416 +(epoch: 480, iters: 1374, time: 0.064) G_GAN: 0.063 G_GAN_Feat: 0.693 G_ID: 0.114 G_Rec: 0.264 D_GP: 0.033 D_real: 0.940 D_fake: 0.937 +(epoch: 480, iters: 1774, time: 0.064) G_GAN: -0.048 G_GAN_Feat: 1.182 G_ID: 0.140 G_Rec: 0.483 D_GP: 0.070 D_real: 0.102 D_fake: 1.048 +(epoch: 480, iters: 2174, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 0.779 G_ID: 0.093 G_Rec: 0.314 D_GP: 0.041 D_real: 0.700 D_fake: 0.988 +(epoch: 480, iters: 2574, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.966 G_ID: 0.106 G_Rec: 0.432 D_GP: 0.032 D_real: 0.893 D_fake: 0.712 +(epoch: 480, iters: 2974, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.799 G_ID: 0.102 G_Rec: 0.320 D_GP: 0.044 D_real: 1.123 D_fake: 0.734 +(epoch: 480, iters: 3374, time: 0.063) G_GAN: 0.727 G_GAN_Feat: 1.080 G_ID: 0.113 G_Rec: 0.434 D_GP: 0.041 D_real: 0.927 D_fake: 0.374 +(epoch: 480, iters: 3774, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 1.055 G_ID: 0.086 G_Rec: 0.333 D_GP: 0.116 D_real: 0.496 D_fake: 0.711 +(epoch: 480, iters: 4174, time: 0.064) G_GAN: 0.640 G_GAN_Feat: 1.003 G_ID: 0.118 G_Rec: 0.459 D_GP: 0.036 D_real: 1.256 D_fake: 0.440 +(epoch: 480, iters: 4574, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.647 G_ID: 0.104 G_Rec: 0.285 D_GP: 0.028 D_real: 1.025 D_fake: 0.906 +(epoch: 480, iters: 4974, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.891 G_ID: 0.097 G_Rec: 0.430 D_GP: 0.032 D_real: 1.090 D_fake: 0.573 +(epoch: 480, iters: 5374, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.696 G_ID: 0.098 G_Rec: 0.293 D_GP: 0.034 D_real: 0.864 D_fake: 0.987 +(epoch: 480, iters: 5774, time: 0.063) G_GAN: 0.187 G_GAN_Feat: 0.865 G_ID: 0.110 G_Rec: 0.406 D_GP: 0.047 D_real: 0.789 D_fake: 0.819 +(epoch: 480, iters: 6174, time: 0.063) G_GAN: 0.116 G_GAN_Feat: 0.700 G_ID: 0.094 G_Rec: 0.305 D_GP: 0.051 D_real: 0.942 D_fake: 0.887 +(epoch: 480, iters: 6574, time: 0.064) G_GAN: 0.075 G_GAN_Feat: 0.969 G_ID: 0.112 G_Rec: 0.444 D_GP: 0.048 D_real: 0.639 D_fake: 0.925 +(epoch: 480, iters: 6974, time: 0.064) G_GAN: -0.141 G_GAN_Feat: 0.622 G_ID: 0.075 G_Rec: 0.249 D_GP: 0.032 D_real: 0.868 D_fake: 1.141 +(epoch: 480, iters: 7374, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.864 G_ID: 0.132 G_Rec: 0.373 D_GP: 0.039 D_real: 0.795 D_fake: 0.826 +(epoch: 480, iters: 7774, time: 0.063) G_GAN: -0.057 G_GAN_Feat: 0.659 G_ID: 0.085 G_Rec: 0.265 D_GP: 0.044 D_real: 0.933 D_fake: 1.057 +(epoch: 480, iters: 8174, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.956 G_ID: 0.123 G_Rec: 0.415 D_GP: 0.038 D_real: 0.850 D_fake: 0.812 +(epoch: 480, iters: 8574, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.717 G_ID: 0.099 G_Rec: 0.315 D_GP: 0.036 D_real: 0.968 D_fake: 0.845 +(epoch: 481, iters: 366, time: 0.063) G_GAN: 0.430 G_GAN_Feat: 1.047 G_ID: 0.122 G_Rec: 0.476 D_GP: 0.077 D_real: 0.760 D_fake: 0.580 +(epoch: 481, iters: 766, time: 0.063) G_GAN: -0.508 G_GAN_Feat: 1.083 G_ID: 0.102 G_Rec: 0.384 D_GP: 0.050 D_real: 1.081 D_fake: 1.508 +(epoch: 481, iters: 1166, time: 0.063) G_GAN: 0.226 G_GAN_Feat: 0.832 G_ID: 0.116 G_Rec: 0.417 D_GP: 0.023 D_real: 1.010 D_fake: 0.787 +(epoch: 481, iters: 1566, time: 0.064) G_GAN: -0.196 G_GAN_Feat: 0.636 G_ID: 0.099 G_Rec: 0.285 D_GP: 0.023 D_real: 0.769 D_fake: 1.200 +(epoch: 481, iters: 1966, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.886 G_ID: 0.119 G_Rec: 0.438 D_GP: 0.029 D_real: 0.850 D_fake: 0.822 +(epoch: 481, iters: 2366, time: 0.064) G_GAN: -0.035 G_GAN_Feat: 0.664 G_ID: 0.087 G_Rec: 0.295 D_GP: 0.031 D_real: 0.847 D_fake: 1.036 +(epoch: 481, iters: 2766, time: 0.064) G_GAN: -0.042 G_GAN_Feat: 0.894 G_ID: 0.129 G_Rec: 0.436 D_GP: 0.055 D_real: 0.587 D_fake: 1.043 +(epoch: 481, iters: 3166, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.697 G_ID: 0.113 G_Rec: 0.301 D_GP: 0.036 D_real: 0.995 D_fake: 0.831 +(epoch: 481, iters: 3566, time: 0.063) G_GAN: 0.561 G_GAN_Feat: 0.896 G_ID: 0.099 G_Rec: 0.396 D_GP: 0.035 D_real: 1.235 D_fake: 0.476 +(epoch: 481, iters: 3966, time: 0.063) G_GAN: 0.014 G_GAN_Feat: 0.802 G_ID: 0.094 G_Rec: 0.315 D_GP: 0.099 D_real: 0.626 D_fake: 0.986 +(epoch: 481, iters: 4366, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.961 G_ID: 0.118 G_Rec: 0.443 D_GP: 0.036 D_real: 0.932 D_fake: 0.712 +(epoch: 481, iters: 4766, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.858 G_ID: 0.107 G_Rec: 0.327 D_GP: 0.039 D_real: 0.758 D_fake: 0.784 +(epoch: 481, iters: 5166, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 0.982 G_ID: 0.103 G_Rec: 0.424 D_GP: 0.042 D_real: 0.808 D_fake: 0.631 +(epoch: 481, iters: 5566, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.813 G_ID: 0.099 G_Rec: 0.312 D_GP: 0.045 D_real: 0.877 D_fake: 0.809 +(epoch: 481, iters: 5966, time: 0.063) G_GAN: 0.324 G_GAN_Feat: 0.924 G_ID: 0.109 G_Rec: 0.392 D_GP: 0.030 D_real: 1.027 D_fake: 0.683 +(epoch: 481, iters: 6366, time: 0.064) G_GAN: -0.083 G_GAN_Feat: 0.799 G_ID: 0.109 G_Rec: 0.327 D_GP: 0.040 D_real: 0.577 D_fake: 1.083 +(epoch: 481, iters: 6766, time: 0.064) G_GAN: 0.632 G_GAN_Feat: 0.999 G_ID: 0.106 G_Rec: 0.458 D_GP: 0.037 D_real: 1.214 D_fake: 0.378 +(epoch: 481, iters: 7166, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 0.801 G_ID: 0.091 G_Rec: 0.340 D_GP: 0.033 D_real: 1.134 D_fake: 0.561 +(epoch: 481, iters: 7566, time: 0.063) G_GAN: 0.720 G_GAN_Feat: 0.985 G_ID: 0.101 G_Rec: 0.431 D_GP: 0.077 D_real: 1.300 D_fake: 0.341 +(epoch: 481, iters: 7966, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.844 G_ID: 0.087 G_Rec: 0.350 D_GP: 0.046 D_real: 0.826 D_fake: 0.827 +(epoch: 481, iters: 8366, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 0.983 G_ID: 0.106 G_Rec: 0.407 D_GP: 0.056 D_real: 1.018 D_fake: 0.490 +(epoch: 482, iters: 158, time: 0.063) G_GAN: 0.245 G_GAN_Feat: 0.928 G_ID: 0.099 G_Rec: 0.333 D_GP: 0.040 D_real: 0.558 D_fake: 0.755 +(epoch: 482, iters: 558, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.929 G_ID: 0.131 G_Rec: 0.444 D_GP: 0.037 D_real: 0.777 D_fake: 0.841 +(epoch: 482, iters: 958, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.917 G_ID: 0.081 G_Rec: 0.324 D_GP: 0.064 D_real: 0.845 D_fake: 0.723 +(epoch: 482, iters: 1358, time: 0.064) G_GAN: 0.713 G_GAN_Feat: 0.996 G_ID: 0.108 G_Rec: 0.377 D_GP: 0.030 D_real: 1.085 D_fake: 0.403 +(epoch: 482, iters: 1758, time: 0.063) G_GAN: -0.066 G_GAN_Feat: 0.778 G_ID: 0.091 G_Rec: 0.322 D_GP: 0.029 D_real: 0.826 D_fake: 1.067 +(epoch: 482, iters: 2158, time: 0.063) G_GAN: 0.162 G_GAN_Feat: 0.896 G_ID: 0.135 G_Rec: 0.408 D_GP: 0.028 D_real: 0.741 D_fake: 0.843 +(epoch: 482, iters: 2558, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.784 G_ID: 0.110 G_Rec: 0.327 D_GP: 0.075 D_real: 0.768 D_fake: 0.865 +(epoch: 482, iters: 2958, time: 0.063) G_GAN: 0.565 G_GAN_Feat: 1.084 G_ID: 0.117 G_Rec: 0.426 D_GP: 0.065 D_real: 0.499 D_fake: 0.446 +(epoch: 482, iters: 3358, time: 0.063) G_GAN: 0.127 G_GAN_Feat: 0.733 G_ID: 0.099 G_Rec: 0.334 D_GP: 0.039 D_real: 0.983 D_fake: 0.874 +(epoch: 482, iters: 3758, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.939 G_ID: 0.112 G_Rec: 0.443 D_GP: 0.033 D_real: 1.190 D_fake: 0.435 +(epoch: 482, iters: 4158, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.982 G_ID: 0.090 G_Rec: 0.349 D_GP: 0.574 D_real: 0.486 D_fake: 0.658 +(epoch: 482, iters: 4558, time: 0.064) G_GAN: 0.575 G_GAN_Feat: 1.140 G_ID: 0.120 G_Rec: 0.436 D_GP: 0.055 D_real: 0.383 D_fake: 0.443 +(epoch: 482, iters: 4958, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.981 G_ID: 0.086 G_Rec: 0.338 D_GP: 0.100 D_real: 0.524 D_fake: 0.517 +(epoch: 482, iters: 5358, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.965 G_ID: 0.109 G_Rec: 0.436 D_GP: 0.031 D_real: 1.010 D_fake: 0.647 +(epoch: 482, iters: 5758, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.871 G_ID: 0.097 G_Rec: 0.313 D_GP: 0.072 D_real: 0.597 D_fake: 0.710 +(epoch: 482, iters: 6158, time: 0.063) G_GAN: 0.556 G_GAN_Feat: 1.191 G_ID: 0.094 G_Rec: 0.452 D_GP: 0.321 D_real: 0.371 D_fake: 0.565 +(epoch: 482, iters: 6558, time: 0.063) G_GAN: -0.132 G_GAN_Feat: 0.774 G_ID: 0.083 G_Rec: 0.326 D_GP: 0.030 D_real: 0.790 D_fake: 1.132 +(epoch: 482, iters: 6958, time: 0.063) G_GAN: 0.481 G_GAN_Feat: 1.156 G_ID: 0.129 G_Rec: 0.440 D_GP: 0.098 D_real: 0.508 D_fake: 0.530 +(epoch: 482, iters: 7358, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.898 G_ID: 0.104 G_Rec: 0.321 D_GP: 0.063 D_real: 0.309 D_fake: 0.717 +(epoch: 482, iters: 7758, time: 0.064) G_GAN: 0.579 G_GAN_Feat: 0.982 G_ID: 0.129 G_Rec: 0.449 D_GP: 0.034 D_real: 1.322 D_fake: 0.458 +(epoch: 482, iters: 8158, time: 0.063) G_GAN: 0.054 G_GAN_Feat: 0.773 G_ID: 0.092 G_Rec: 0.301 D_GP: 0.058 D_real: 0.795 D_fake: 0.947 +(epoch: 482, iters: 8558, time: 0.063) G_GAN: 0.516 G_GAN_Feat: 1.008 G_ID: 0.107 G_Rec: 0.475 D_GP: 0.038 D_real: 0.851 D_fake: 0.494 +(epoch: 483, iters: 350, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.911 G_ID: 0.099 G_Rec: 0.342 D_GP: 0.336 D_real: 0.545 D_fake: 0.610 +(epoch: 483, iters: 750, time: 0.063) G_GAN: 0.633 G_GAN_Feat: 1.062 G_ID: 0.107 G_Rec: 0.447 D_GP: 0.068 D_real: 0.763 D_fake: 0.378 +(epoch: 483, iters: 1150, time: 0.063) G_GAN: 0.256 G_GAN_Feat: 0.809 G_ID: 0.099 G_Rec: 0.318 D_GP: 0.033 D_real: 0.848 D_fake: 0.755 +(epoch: 483, iters: 1550, time: 0.063) G_GAN: 0.841 G_GAN_Feat: 1.119 G_ID: 0.126 G_Rec: 0.473 D_GP: 0.085 D_real: 1.125 D_fake: 0.287 +(epoch: 483, iters: 1950, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.758 G_ID: 0.098 G_Rec: 0.334 D_GP: 0.037 D_real: 1.112 D_fake: 0.690 +(epoch: 483, iters: 2350, time: 0.063) G_GAN: 0.268 G_GAN_Feat: 0.991 G_ID: 0.115 G_Rec: 0.441 D_GP: 0.033 D_real: 0.780 D_fake: 0.732 +(epoch: 483, iters: 2750, time: 0.063) G_GAN: 0.279 G_GAN_Feat: 0.995 G_ID: 0.099 G_Rec: 0.339 D_GP: 0.167 D_real: 0.318 D_fake: 0.733 +(epoch: 483, iters: 3150, time: 0.063) G_GAN: 0.422 G_GAN_Feat: 0.890 G_ID: 0.112 G_Rec: 0.435 D_GP: 0.027 D_real: 1.118 D_fake: 0.585 +(epoch: 483, iters: 3550, time: 0.064) G_GAN: -0.070 G_GAN_Feat: 0.683 G_ID: 0.123 G_Rec: 0.294 D_GP: 0.033 D_real: 0.814 D_fake: 1.070 +(epoch: 483, iters: 3950, time: 0.063) G_GAN: 0.283 G_GAN_Feat: 0.896 G_ID: 0.111 G_Rec: 0.387 D_GP: 0.034 D_real: 0.960 D_fake: 0.718 +(epoch: 483, iters: 4350, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.810 G_ID: 0.123 G_Rec: 0.343 D_GP: 0.036 D_real: 1.063 D_fake: 0.712 +(epoch: 483, iters: 4750, time: 0.063) G_GAN: 0.685 G_GAN_Feat: 1.125 G_ID: 0.121 G_Rec: 0.509 D_GP: 0.039 D_real: 0.996 D_fake: 0.385 +(epoch: 483, iters: 5150, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.796 G_ID: 0.094 G_Rec: 0.293 D_GP: 0.034 D_real: 0.968 D_fake: 0.785 +(epoch: 483, iters: 5550, time: 0.063) G_GAN: 0.567 G_GAN_Feat: 1.157 G_ID: 0.126 G_Rec: 0.483 D_GP: 0.047 D_real: 1.049 D_fake: 0.456 +(epoch: 483, iters: 5950, time: 0.063) G_GAN: 0.286 G_GAN_Feat: 0.835 G_ID: 0.102 G_Rec: 0.312 D_GP: 0.033 D_real: 0.790 D_fake: 0.715 +(epoch: 483, iters: 6350, time: 0.063) G_GAN: 0.584 G_GAN_Feat: 1.101 G_ID: 0.114 G_Rec: 0.442 D_GP: 0.965 D_real: 0.698 D_fake: 0.461 +(epoch: 483, iters: 6750, time: 0.064) G_GAN: 0.024 G_GAN_Feat: 0.772 G_ID: 0.114 G_Rec: 0.313 D_GP: 0.044 D_real: 0.789 D_fake: 0.976 +(epoch: 483, iters: 7150, time: 0.063) G_GAN: 0.390 G_GAN_Feat: 1.128 G_ID: 0.109 G_Rec: 0.486 D_GP: 0.034 D_real: 0.680 D_fake: 0.611 +(epoch: 483, iters: 7550, time: 0.063) G_GAN: 0.277 G_GAN_Feat: 0.770 G_ID: 0.115 G_Rec: 0.299 D_GP: 0.031 D_real: 1.072 D_fake: 0.724 +(epoch: 483, iters: 7950, time: 0.063) G_GAN: 0.304 G_GAN_Feat: 1.008 G_ID: 0.116 G_Rec: 0.433 D_GP: 0.032 D_real: 0.924 D_fake: 0.697 +(epoch: 483, iters: 8350, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.742 G_ID: 0.094 G_Rec: 0.276 D_GP: 0.031 D_real: 1.173 D_fake: 0.632 +(epoch: 484, iters: 142, time: 0.063) G_GAN: 0.475 G_GAN_Feat: 1.036 G_ID: 0.119 G_Rec: 0.441 D_GP: 0.036 D_real: 1.073 D_fake: 0.530 +(epoch: 484, iters: 542, time: 0.063) G_GAN: 0.262 G_GAN_Feat: 0.734 G_ID: 0.093 G_Rec: 0.327 D_GP: 0.029 D_real: 1.174 D_fake: 0.738 +(epoch: 484, iters: 942, time: 0.063) G_GAN: 0.164 G_GAN_Feat: 0.857 G_ID: 0.136 G_Rec: 0.380 D_GP: 0.033 D_real: 0.829 D_fake: 0.837 +(epoch: 484, iters: 1342, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.804 G_ID: 0.101 G_Rec: 0.351 D_GP: 0.037 D_real: 0.829 D_fake: 0.938 +(epoch: 484, iters: 1742, time: 0.063) G_GAN: 0.489 G_GAN_Feat: 0.941 G_ID: 0.105 G_Rec: 0.541 D_GP: 0.030 D_real: 1.072 D_fake: 0.531 +(epoch: 484, iters: 2142, time: 0.063) G_GAN: -0.288 G_GAN_Feat: 0.771 G_ID: 0.109 G_Rec: 0.336 D_GP: 0.046 D_real: 0.487 D_fake: 1.288 +(epoch: 484, iters: 2542, time: 0.063) G_GAN: 0.380 G_GAN_Feat: 1.020 G_ID: 0.105 G_Rec: 0.454 D_GP: 0.048 D_real: 0.879 D_fake: 0.624 +(epoch: 484, iters: 2942, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.732 G_ID: 0.097 G_Rec: 0.291 D_GP: 0.039 D_real: 1.136 D_fake: 0.704 +(epoch: 484, iters: 3342, time: 0.063) G_GAN: 0.833 G_GAN_Feat: 0.974 G_ID: 0.105 G_Rec: 0.387 D_GP: 0.039 D_real: 1.487 D_fake: 0.212 +(epoch: 484, iters: 3742, time: 0.063) G_GAN: 0.204 G_GAN_Feat: 0.832 G_ID: 0.105 G_Rec: 0.337 D_GP: 0.149 D_real: 0.566 D_fake: 0.801 +(epoch: 484, iters: 4142, time: 0.063) G_GAN: 0.526 G_GAN_Feat: 1.038 G_ID: 0.123 G_Rec: 0.467 D_GP: 0.044 D_real: 0.914 D_fake: 0.477 +(epoch: 484, iters: 4542, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.851 G_ID: 0.099 G_Rec: 0.318 D_GP: 0.042 D_real: 1.235 D_fake: 0.564 +(epoch: 484, iters: 4942, time: 0.063) G_GAN: 0.526 G_GAN_Feat: 1.047 G_ID: 0.106 G_Rec: 0.476 D_GP: 0.037 D_real: 1.114 D_fake: 0.493 +(epoch: 484, iters: 5342, time: 0.063) G_GAN: 0.021 G_GAN_Feat: 0.700 G_ID: 0.097 G_Rec: 0.285 D_GP: 0.034 D_real: 0.937 D_fake: 0.979 +(epoch: 484, iters: 5742, time: 0.063) G_GAN: 0.415 G_GAN_Feat: 1.058 G_ID: 0.126 G_Rec: 0.443 D_GP: 0.044 D_real: 0.701 D_fake: 0.591 +(epoch: 484, iters: 6142, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.965 G_ID: 0.095 G_Rec: 0.375 D_GP: 0.113 D_real: 0.468 D_fake: 0.746 +(epoch: 484, iters: 6542, time: 0.063) G_GAN: 0.287 G_GAN_Feat: 1.123 G_ID: 0.114 G_Rec: 0.436 D_GP: 0.038 D_real: 0.670 D_fake: 0.714 +(epoch: 484, iters: 6942, time: 0.063) G_GAN: 0.349 G_GAN_Feat: 0.910 G_ID: 0.096 G_Rec: 0.336 D_GP: 0.058 D_real: 1.329 D_fake: 0.655 +(epoch: 484, iters: 7342, time: 0.063) G_GAN: 0.200 G_GAN_Feat: 1.109 G_ID: 0.126 G_Rec: 0.436 D_GP: 0.033 D_real: 0.498 D_fake: 0.801 +(epoch: 484, iters: 7742, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.711 G_ID: 0.086 G_Rec: 0.276 D_GP: 0.033 D_real: 1.178 D_fake: 0.759 +(epoch: 484, iters: 8142, time: 0.063) G_GAN: 0.655 G_GAN_Feat: 1.234 G_ID: 0.114 G_Rec: 0.494 D_GP: 0.249 D_real: 0.552 D_fake: 0.373 +(epoch: 484, iters: 8542, time: 0.063) G_GAN: 0.357 G_GAN_Feat: 1.039 G_ID: 0.112 G_Rec: 0.355 D_GP: 0.123 D_real: 0.236 D_fake: 0.687 +(epoch: 485, iters: 334, time: 0.063) G_GAN: 0.423 G_GAN_Feat: 0.947 G_ID: 0.115 G_Rec: 0.434 D_GP: 0.029 D_real: 1.091 D_fake: 0.583 +(epoch: 485, iters: 734, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.643 G_ID: 0.096 G_Rec: 0.307 D_GP: 0.027 D_real: 1.116 D_fake: 0.817 +(epoch: 485, iters: 1134, time: 0.063) G_GAN: -0.054 G_GAN_Feat: 0.883 G_ID: 0.146 G_Rec: 0.441 D_GP: 0.033 D_real: 0.768 D_fake: 1.054 +(epoch: 485, iters: 1534, time: 0.063) G_GAN: 0.278 G_GAN_Feat: 0.699 G_ID: 0.092 G_Rec: 0.302 D_GP: 0.034 D_real: 1.211 D_fake: 0.724 +(epoch: 485, iters: 1934, time: 0.063) G_GAN: 0.292 G_GAN_Feat: 0.978 G_ID: 0.122 G_Rec: 0.443 D_GP: 0.049 D_real: 0.854 D_fake: 0.713 +(epoch: 485, iters: 2334, time: 0.063) G_GAN: 0.252 G_GAN_Feat: 0.627 G_ID: 0.093 G_Rec: 0.275 D_GP: 0.027 D_real: 1.178 D_fake: 0.749 +(epoch: 485, iters: 2734, time: 0.063) G_GAN: 0.301 G_GAN_Feat: 0.907 G_ID: 0.117 G_Rec: 0.432 D_GP: 0.033 D_real: 0.895 D_fake: 0.707 +(epoch: 485, iters: 3134, time: 0.063) G_GAN: 0.103 G_GAN_Feat: 0.724 G_ID: 0.104 G_Rec: 0.313 D_GP: 0.043 D_real: 0.925 D_fake: 0.897 +(epoch: 485, iters: 3534, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.956 G_ID: 0.131 G_Rec: 0.457 D_GP: 0.042 D_real: 0.861 D_fake: 0.688 +(epoch: 485, iters: 3934, time: 0.063) G_GAN: 0.065 G_GAN_Feat: 0.697 G_ID: 0.101 G_Rec: 0.305 D_GP: 0.029 D_real: 1.052 D_fake: 0.935 +(epoch: 485, iters: 4334, time: 0.063) G_GAN: 0.389 G_GAN_Feat: 1.010 G_ID: 0.105 G_Rec: 0.460 D_GP: 0.051 D_real: 0.819 D_fake: 0.616 +(epoch: 485, iters: 4734, time: 0.063) G_GAN: 0.133 G_GAN_Feat: 0.737 G_ID: 0.092 G_Rec: 0.298 D_GP: 0.032 D_real: 1.025 D_fake: 0.867 +(epoch: 485, iters: 5134, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.991 G_ID: 0.131 G_Rec: 0.423 D_GP: 0.065 D_real: 0.784 D_fake: 0.631 +(epoch: 485, iters: 5534, time: 0.063) G_GAN: 0.113 G_GAN_Feat: 0.711 G_ID: 0.102 G_Rec: 0.295 D_GP: 0.029 D_real: 1.008 D_fake: 0.887 +(epoch: 485, iters: 5934, time: 0.063) G_GAN: 0.548 G_GAN_Feat: 1.038 G_ID: 0.106 G_Rec: 0.463 D_GP: 0.049 D_real: 0.918 D_fake: 0.464 +(epoch: 485, iters: 6334, time: 0.063) G_GAN: 0.138 G_GAN_Feat: 0.741 G_ID: 0.108 G_Rec: 0.312 D_GP: 0.033 D_real: 0.887 D_fake: 0.862 +(epoch: 485, iters: 6734, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.839 G_ID: 0.110 G_Rec: 0.382 D_GP: 0.031 D_real: 1.213 D_fake: 0.604 +(epoch: 485, iters: 7134, time: 0.063) G_GAN: 0.323 G_GAN_Feat: 0.660 G_ID: 0.088 G_Rec: 0.301 D_GP: 0.034 D_real: 1.221 D_fake: 0.678 +(epoch: 485, iters: 7534, time: 0.063) G_GAN: 0.427 G_GAN_Feat: 0.996 G_ID: 0.115 G_Rec: 0.434 D_GP: 0.085 D_real: 0.778 D_fake: 0.575 +(epoch: 485, iters: 7934, time: 0.063) G_GAN: 0.180 G_GAN_Feat: 0.841 G_ID: 0.116 G_Rec: 0.413 D_GP: 0.056 D_real: 0.939 D_fake: 0.822 +(epoch: 485, iters: 8334, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.975 G_ID: 0.136 G_Rec: 0.427 D_GP: 0.037 D_real: 1.199 D_fake: 0.555 +(epoch: 486, iters: 126, time: 0.063) G_GAN: 0.425 G_GAN_Feat: 0.649 G_ID: 0.087 G_Rec: 0.323 D_GP: 0.029 D_real: 1.351 D_fake: 0.575 +(epoch: 486, iters: 526, time: 0.063) G_GAN: 0.652 G_GAN_Feat: 0.893 G_ID: 0.104 G_Rec: 0.437 D_GP: 0.033 D_real: 1.373 D_fake: 0.393 +(epoch: 486, iters: 926, time: 0.063) G_GAN: 0.224 G_GAN_Feat: 0.724 G_ID: 0.105 G_Rec: 0.323 D_GP: 0.036 D_real: 0.998 D_fake: 0.777 +(epoch: 486, iters: 1326, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.897 G_ID: 0.121 G_Rec: 0.389 D_GP: 0.043 D_real: 0.941 D_fake: 0.689 +(epoch: 486, iters: 1726, time: 0.063) G_GAN: -0.064 G_GAN_Feat: 0.784 G_ID: 0.108 G_Rec: 0.300 D_GP: 0.070 D_real: 0.560 D_fake: 1.064 +(epoch: 486, iters: 2126, time: 0.063) G_GAN: 0.496 G_GAN_Feat: 1.028 G_ID: 0.107 G_Rec: 0.415 D_GP: 0.040 D_real: 0.883 D_fake: 0.507 +(epoch: 486, iters: 2526, time: 0.063) G_GAN: 0.200 G_GAN_Feat: 0.855 G_ID: 0.117 G_Rec: 0.343 D_GP: 0.057 D_real: 1.034 D_fake: 0.811 +(epoch: 486, iters: 2926, time: 0.064) G_GAN: 0.697 G_GAN_Feat: 0.937 G_ID: 0.100 G_Rec: 0.383 D_GP: 0.034 D_real: 1.359 D_fake: 0.329 +(epoch: 486, iters: 3326, time: 0.063) G_GAN: 0.181 G_GAN_Feat: 0.942 G_ID: 0.099 G_Rec: 0.338 D_GP: 0.226 D_real: 0.142 D_fake: 1.005 +(epoch: 486, iters: 3726, time: 0.063) G_GAN: 0.814 G_GAN_Feat: 1.116 G_ID: 0.102 G_Rec: 0.440 D_GP: 0.133 D_real: 0.816 D_fake: 0.253 +(epoch: 486, iters: 4126, time: 0.063) G_GAN: 0.036 G_GAN_Feat: 0.999 G_ID: 0.108 G_Rec: 0.383 D_GP: 0.539 D_real: 0.214 D_fake: 0.971 +(epoch: 486, iters: 4526, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 1.030 G_ID: 0.121 G_Rec: 0.471 D_GP: 0.036 D_real: 1.188 D_fake: 0.567 +(epoch: 486, iters: 4926, time: 0.063) G_GAN: 0.145 G_GAN_Feat: 0.708 G_ID: 0.099 G_Rec: 0.320 D_GP: 0.031 D_real: 0.925 D_fake: 0.859 +(epoch: 486, iters: 5326, time: 0.063) G_GAN: 0.442 G_GAN_Feat: 0.922 G_ID: 0.125 G_Rec: 0.438 D_GP: 0.034 D_real: 1.109 D_fake: 0.584 +(epoch: 486, iters: 5726, time: 0.063) G_GAN: 0.213 G_GAN_Feat: 0.843 G_ID: 0.101 G_Rec: 0.364 D_GP: 0.038 D_real: 0.732 D_fake: 0.787 +(epoch: 486, iters: 6126, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 1.057 G_ID: 0.110 G_Rec: 0.403 D_GP: 0.041 D_real: 0.848 D_fake: 0.503 +(epoch: 486, iters: 6526, time: 0.063) G_GAN: 0.641 G_GAN_Feat: 0.813 G_ID: 0.100 G_Rec: 0.301 D_GP: 0.036 D_real: 1.368 D_fake: 0.372 +(epoch: 486, iters: 6926, time: 0.063) G_GAN: 0.341 G_GAN_Feat: 1.255 G_ID: 0.114 G_Rec: 0.485 D_GP: 0.885 D_real: 0.329 D_fake: 0.661 +(epoch: 486, iters: 7326, time: 0.063) G_GAN: 0.110 G_GAN_Feat: 0.766 G_ID: 0.078 G_Rec: 0.309 D_GP: 0.032 D_real: 0.798 D_fake: 0.890 +(epoch: 486, iters: 7726, time: 0.064) G_GAN: 0.737 G_GAN_Feat: 1.005 G_ID: 0.114 G_Rec: 0.427 D_GP: 0.059 D_real: 1.191 D_fake: 0.290 +(epoch: 486, iters: 8126, time: 0.063) G_GAN: 0.492 G_GAN_Feat: 0.938 G_ID: 0.110 G_Rec: 0.341 D_GP: 0.041 D_real: 0.883 D_fake: 0.516 +(epoch: 486, iters: 8526, time: 0.063) G_GAN: 0.545 G_GAN_Feat: 0.945 G_ID: 0.147 G_Rec: 0.455 D_GP: 0.034 D_real: 1.256 D_fake: 0.469 +(epoch: 487, iters: 318, time: 0.063) G_GAN: 0.374 G_GAN_Feat: 0.731 G_ID: 0.106 G_Rec: 0.297 D_GP: 0.040 D_real: 1.270 D_fake: 0.627 +(epoch: 487, iters: 718, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 1.037 G_ID: 0.100 G_Rec: 0.424 D_GP: 0.040 D_real: 0.618 D_fake: 0.649 +(epoch: 487, iters: 1118, time: 0.063) G_GAN: 0.226 G_GAN_Feat: 0.649 G_ID: 0.082 G_Rec: 0.285 D_GP: 0.031 D_real: 1.199 D_fake: 0.776 +(epoch: 487, iters: 1518, time: 0.063) G_GAN: 0.459 G_GAN_Feat: 0.875 G_ID: 0.113 G_Rec: 0.407 D_GP: 0.032 D_real: 1.110 D_fake: 0.568 +(epoch: 487, iters: 1918, time: 0.063) G_GAN: -0.023 G_GAN_Feat: 0.732 G_ID: 0.086 G_Rec: 0.321 D_GP: 0.042 D_real: 0.785 D_fake: 1.023 +(epoch: 487, iters: 2318, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.917 G_ID: 0.150 G_Rec: 0.374 D_GP: 0.043 D_real: 0.740 D_fake: 0.821 +(epoch: 487, iters: 2718, time: 0.063) G_GAN: 0.270 G_GAN_Feat: 0.739 G_ID: 0.106 G_Rec: 0.312 D_GP: 0.040 D_real: 1.162 D_fake: 0.734 +(epoch: 487, iters: 3118, time: 0.063) G_GAN: 0.478 G_GAN_Feat: 1.032 G_ID: 0.123 G_Rec: 0.467 D_GP: 0.041 D_real: 1.008 D_fake: 0.534 +(epoch: 487, iters: 3518, time: 0.063) G_GAN: 0.430 G_GAN_Feat: 0.746 G_ID: 0.093 G_Rec: 0.316 D_GP: 0.030 D_real: 1.426 D_fake: 0.573 +(epoch: 487, iters: 3918, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 1.059 G_ID: 0.109 G_Rec: 0.444 D_GP: 0.042 D_real: 0.619 D_fake: 0.735 +(epoch: 487, iters: 4318, time: 0.063) G_GAN: 0.688 G_GAN_Feat: 0.883 G_ID: 0.092 G_Rec: 0.325 D_GP: 0.056 D_real: 1.289 D_fake: 0.570 +(epoch: 487, iters: 4718, time: 0.063) G_GAN: 0.674 G_GAN_Feat: 1.163 G_ID: 0.124 G_Rec: 0.468 D_GP: 0.093 D_real: 0.458 D_fake: 0.363 +(epoch: 487, iters: 5118, time: 0.063) G_GAN: -0.061 G_GAN_Feat: 0.788 G_ID: 0.100 G_Rec: 0.279 D_GP: 0.032 D_real: 0.618 D_fake: 1.061 +(epoch: 487, iters: 5518, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.904 G_ID: 0.122 G_Rec: 0.422 D_GP: 0.029 D_real: 0.963 D_fake: 0.730 +(epoch: 487, iters: 5918, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.729 G_ID: 0.083 G_Rec: 0.285 D_GP: 0.039 D_real: 0.964 D_fake: 0.810 +(epoch: 487, iters: 6318, time: 0.063) G_GAN: 0.443 G_GAN_Feat: 0.989 G_ID: 0.106 G_Rec: 0.381 D_GP: 0.040 D_real: 1.167 D_fake: 0.562 +(epoch: 487, iters: 6718, time: 0.063) G_GAN: 0.198 G_GAN_Feat: 0.735 G_ID: 0.102 G_Rec: 0.304 D_GP: 0.030 D_real: 1.115 D_fake: 0.802 +(epoch: 487, iters: 7118, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.972 G_ID: 0.108 G_Rec: 0.432 D_GP: 0.044 D_real: 0.973 D_fake: 0.591 +(epoch: 487, iters: 7518, time: 0.063) G_GAN: 0.321 G_GAN_Feat: 0.786 G_ID: 0.092 G_Rec: 0.345 D_GP: 0.038 D_real: 0.938 D_fake: 0.681 +(epoch: 487, iters: 7918, time: 0.063) G_GAN: 0.629 G_GAN_Feat: 0.940 G_ID: 0.109 G_Rec: 0.421 D_GP: 0.031 D_real: 1.276 D_fake: 0.398 +(epoch: 487, iters: 8318, time: 0.063) G_GAN: 0.423 G_GAN_Feat: 0.709 G_ID: 0.082 G_Rec: 0.295 D_GP: 0.033 D_real: 1.379 D_fake: 0.580 +(epoch: 488, iters: 110, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.991 G_ID: 0.094 G_Rec: 0.455 D_GP: 0.036 D_real: 1.045 D_fake: 0.593 +(epoch: 488, iters: 510, time: 0.063) G_GAN: 0.042 G_GAN_Feat: 0.728 G_ID: 0.108 G_Rec: 0.315 D_GP: 0.034 D_real: 0.865 D_fake: 0.959 +(epoch: 488, iters: 910, time: 0.063) G_GAN: 0.275 G_GAN_Feat: 0.932 G_ID: 0.117 G_Rec: 0.424 D_GP: 0.041 D_real: 0.884 D_fake: 0.728 +(epoch: 488, iters: 1310, time: 0.063) G_GAN: 0.150 G_GAN_Feat: 0.690 G_ID: 0.122 G_Rec: 0.293 D_GP: 0.031 D_real: 0.965 D_fake: 0.850 +(epoch: 488, iters: 1710, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 1.097 G_ID: 0.128 G_Rec: 0.476 D_GP: 0.379 D_real: 0.408 D_fake: 0.666 +(epoch: 488, iters: 2110, time: 0.063) G_GAN: 0.067 G_GAN_Feat: 0.755 G_ID: 0.107 G_Rec: 0.315 D_GP: 0.041 D_real: 0.810 D_fake: 0.933 +(epoch: 488, iters: 2510, time: 0.063) G_GAN: 0.666 G_GAN_Feat: 1.091 G_ID: 0.117 G_Rec: 0.466 D_GP: 0.058 D_real: 0.756 D_fake: 0.358 +(epoch: 488, iters: 2910, time: 0.063) G_GAN: 0.176 G_GAN_Feat: 0.744 G_ID: 0.108 G_Rec: 0.271 D_GP: 0.037 D_real: 0.864 D_fake: 0.824 +(epoch: 488, iters: 3310, time: 0.064) G_GAN: 0.938 G_GAN_Feat: 1.050 G_ID: 0.123 G_Rec: 0.509 D_GP: 0.031 D_real: 1.410 D_fake: 0.277 +(epoch: 488, iters: 3710, time: 0.063) G_GAN: 0.187 G_GAN_Feat: 0.741 G_ID: 0.086 G_Rec: 0.277 D_GP: 0.037 D_real: 1.124 D_fake: 0.814 +(epoch: 488, iters: 4110, time: 0.063) G_GAN: 0.662 G_GAN_Feat: 1.027 G_ID: 0.114 G_Rec: 0.433 D_GP: 0.030 D_real: 1.183 D_fake: 0.361 +(epoch: 488, iters: 4510, time: 0.063) G_GAN: 0.435 G_GAN_Feat: 0.827 G_ID: 0.096 G_Rec: 0.310 D_GP: 0.041 D_real: 1.158 D_fake: 0.608 +(epoch: 488, iters: 4910, time: 0.064) G_GAN: 0.598 G_GAN_Feat: 1.177 G_ID: 0.129 G_Rec: 0.469 D_GP: 0.111 D_real: 0.482 D_fake: 0.508 +(epoch: 488, iters: 5310, time: 0.063) G_GAN: 0.160 G_GAN_Feat: 0.905 G_ID: 0.110 G_Rec: 0.364 D_GP: 0.100 D_real: 0.511 D_fake: 0.843 +(epoch: 488, iters: 5710, time: 0.063) G_GAN: 0.596 G_GAN_Feat: 1.242 G_ID: 0.127 G_Rec: 0.465 D_GP: 1.181 D_real: 0.366 D_fake: 0.467 +(epoch: 488, iters: 6110, time: 0.063) G_GAN: 0.145 G_GAN_Feat: 0.829 G_ID: 0.096 G_Rec: 0.320 D_GP: 0.038 D_real: 0.802 D_fake: 0.856 +(epoch: 488, iters: 6510, time: 0.064) G_GAN: 0.781 G_GAN_Feat: 0.982 G_ID: 0.112 G_Rec: 0.422 D_GP: 0.039 D_real: 1.312 D_fake: 0.270 +(epoch: 488, iters: 6910, time: 0.063) G_GAN: 0.060 G_GAN_Feat: 1.035 G_ID: 0.108 G_Rec: 0.389 D_GP: 0.047 D_real: 0.897 D_fake: 0.959 +(epoch: 488, iters: 7310, time: 0.063) G_GAN: 0.301 G_GAN_Feat: 1.058 G_ID: 0.124 G_Rec: 0.455 D_GP: 0.030 D_real: 0.950 D_fake: 0.705 +(epoch: 488, iters: 7710, time: 0.063) G_GAN: -0.017 G_GAN_Feat: 0.760 G_ID: 0.109 G_Rec: 0.312 D_GP: 0.042 D_real: 0.741 D_fake: 1.017 +(epoch: 488, iters: 8110, time: 0.064) G_GAN: 0.609 G_GAN_Feat: 0.978 G_ID: 0.119 G_Rec: 0.410 D_GP: 0.037 D_real: 1.072 D_fake: 0.407 +(epoch: 488, iters: 8510, time: 0.063) G_GAN: 0.562 G_GAN_Feat: 0.834 G_ID: 0.105 G_Rec: 0.321 D_GP: 0.072 D_real: 1.383 D_fake: 0.523 +(epoch: 489, iters: 302, time: 0.063) G_GAN: 0.295 G_GAN_Feat: 1.036 G_ID: 0.116 G_Rec: 0.433 D_GP: 0.052 D_real: 0.309 D_fake: 0.736 +(epoch: 489, iters: 702, time: 0.063) G_GAN: 0.340 G_GAN_Feat: 0.765 G_ID: 0.110 G_Rec: 0.294 D_GP: 0.031 D_real: 1.113 D_fake: 0.661 +(epoch: 489, iters: 1102, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 1.124 G_ID: 0.102 G_Rec: 0.441 D_GP: 0.036 D_real: 0.540 D_fake: 0.464 +(epoch: 489, iters: 1502, time: 0.063) G_GAN: -0.085 G_GAN_Feat: 0.768 G_ID: 0.116 G_Rec: 0.349 D_GP: 0.033 D_real: 0.800 D_fake: 1.086 +(epoch: 489, iters: 1902, time: 0.063) G_GAN: 0.549 G_GAN_Feat: 0.912 G_ID: 0.106 G_Rec: 0.418 D_GP: 0.028 D_real: 1.235 D_fake: 0.470 +(epoch: 489, iters: 2302, time: 0.063) G_GAN: 0.070 G_GAN_Feat: 0.735 G_ID: 0.115 G_Rec: 0.321 D_GP: 0.039 D_real: 0.981 D_fake: 0.931 +(epoch: 489, iters: 2702, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.889 G_ID: 0.097 G_Rec: 0.423 D_GP: 0.031 D_real: 1.095 D_fake: 0.624 +(epoch: 489, iters: 3102, time: 0.063) G_GAN: 0.262 G_GAN_Feat: 0.710 G_ID: 0.090 G_Rec: 0.299 D_GP: 0.033 D_real: 1.117 D_fake: 0.739 +(epoch: 489, iters: 3502, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 1.008 G_ID: 0.119 G_Rec: 0.473 D_GP: 0.067 D_real: 0.711 D_fake: 0.813 +(epoch: 489, iters: 3902, time: 0.063) G_GAN: -0.144 G_GAN_Feat: 0.781 G_ID: 0.114 G_Rec: 0.346 D_GP: 0.034 D_real: 0.651 D_fake: 1.144 +(epoch: 489, iters: 4302, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.923 G_ID: 0.122 G_Rec: 0.415 D_GP: 0.034 D_real: 1.032 D_fake: 0.593 +(epoch: 489, iters: 4702, time: 0.063) G_GAN: 0.230 G_GAN_Feat: 0.796 G_ID: 0.093 G_Rec: 0.327 D_GP: 0.041 D_real: 0.959 D_fake: 0.771 +(epoch: 489, iters: 5102, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 1.069 G_ID: 0.115 G_Rec: 0.477 D_GP: 0.084 D_real: 0.604 D_fake: 0.730 +(epoch: 489, iters: 5502, time: 0.063) G_GAN: 0.196 G_GAN_Feat: 0.762 G_ID: 0.090 G_Rec: 0.289 D_GP: 0.040 D_real: 0.770 D_fake: 0.804 +(epoch: 489, iters: 5902, time: 0.064) G_GAN: 0.740 G_GAN_Feat: 1.083 G_ID: 0.120 G_Rec: 0.481 D_GP: 0.051 D_real: 0.814 D_fake: 0.331 +(epoch: 489, iters: 6302, time: 0.063) G_GAN: 0.219 G_GAN_Feat: 0.814 G_ID: 0.089 G_Rec: 0.298 D_GP: 0.035 D_real: 0.939 D_fake: 0.781 +(epoch: 489, iters: 6702, time: 0.063) G_GAN: 0.518 G_GAN_Feat: 1.055 G_ID: 0.108 G_Rec: 0.408 D_GP: 0.070 D_real: 0.610 D_fake: 0.492 +(epoch: 489, iters: 7102, time: 0.063) G_GAN: 0.169 G_GAN_Feat: 0.868 G_ID: 0.093 G_Rec: 0.289 D_GP: 0.035 D_real: 0.645 D_fake: 0.831 +(epoch: 489, iters: 7502, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.786 G_ID: 0.122 G_Rec: 0.392 D_GP: 0.028 D_real: 1.042 D_fake: 0.763 +(epoch: 489, iters: 7902, time: 0.063) G_GAN: 0.020 G_GAN_Feat: 0.646 G_ID: 0.101 G_Rec: 0.307 D_GP: 0.031 D_real: 0.896 D_fake: 0.981 +(epoch: 489, iters: 8302, time: 0.063) G_GAN: 0.362 G_GAN_Feat: 0.808 G_ID: 0.115 G_Rec: 0.396 D_GP: 0.032 D_real: 1.128 D_fake: 0.640 +(epoch: 489, iters: 8702, time: 0.064) G_GAN: -0.050 G_GAN_Feat: 0.632 G_ID: 0.086 G_Rec: 0.272 D_GP: 0.032 D_real: 0.869 D_fake: 1.050 +(epoch: 490, iters: 494, time: 0.063) G_GAN: 0.242 G_GAN_Feat: 0.869 G_ID: 0.130 G_Rec: 0.412 D_GP: 0.044 D_real: 0.739 D_fake: 0.760 +(epoch: 490, iters: 894, time: 0.063) G_GAN: 0.038 G_GAN_Feat: 0.692 G_ID: 0.092 G_Rec: 0.313 D_GP: 0.038 D_real: 0.988 D_fake: 0.962 +(epoch: 490, iters: 1294, time: 0.063) G_GAN: 0.514 G_GAN_Feat: 0.975 G_ID: 0.114 G_Rec: 0.446 D_GP: 0.040 D_real: 1.014 D_fake: 0.513 +(epoch: 490, iters: 1694, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.706 G_ID: 0.082 G_Rec: 0.323 D_GP: 0.049 D_real: 1.063 D_fake: 0.778 +(epoch: 490, iters: 2094, time: 0.063) G_GAN: 0.347 G_GAN_Feat: 0.981 G_ID: 0.129 G_Rec: 0.453 D_GP: 0.053 D_real: 0.739 D_fake: 0.657 +(epoch: 490, iters: 2494, time: 0.063) G_GAN: 0.309 G_GAN_Feat: 0.745 G_ID: 0.109 G_Rec: 0.313 D_GP: 0.058 D_real: 0.988 D_fake: 0.692 +(epoch: 490, iters: 2894, time: 0.063) G_GAN: 0.637 G_GAN_Feat: 0.925 G_ID: 0.117 G_Rec: 0.414 D_GP: 0.037 D_real: 1.333 D_fake: 0.379 +(epoch: 490, iters: 3294, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.861 G_ID: 0.109 G_Rec: 0.336 D_GP: 0.126 D_real: 0.293 D_fake: 0.922 +(epoch: 490, iters: 3694, time: 0.063) G_GAN: 0.398 G_GAN_Feat: 0.984 G_ID: 0.113 G_Rec: 0.389 D_GP: 0.037 D_real: 1.004 D_fake: 0.614 +(epoch: 490, iters: 4094, time: 0.063) G_GAN: 0.390 G_GAN_Feat: 0.753 G_ID: 0.086 G_Rec: 0.304 D_GP: 0.039 D_real: 1.216 D_fake: 0.611 +(epoch: 490, iters: 4494, time: 0.063) G_GAN: 0.730 G_GAN_Feat: 1.030 G_ID: 0.120 G_Rec: 0.444 D_GP: 0.043 D_real: 1.261 D_fake: 0.332 +(epoch: 490, iters: 4894, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.763 G_ID: 0.099 G_Rec: 0.301 D_GP: 0.043 D_real: 1.116 D_fake: 0.630 +(epoch: 490, iters: 5294, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.941 G_ID: 0.120 G_Rec: 0.461 D_GP: 0.025 D_real: 1.019 D_fake: 0.762 +(epoch: 490, iters: 5694, time: 0.063) G_GAN: 0.148 G_GAN_Feat: 0.716 G_ID: 0.114 G_Rec: 0.300 D_GP: 0.035 D_real: 0.957 D_fake: 0.852 +(epoch: 490, iters: 6094, time: 0.063) G_GAN: 0.377 G_GAN_Feat: 0.915 G_ID: 0.104 G_Rec: 0.408 D_GP: 0.037 D_real: 1.091 D_fake: 0.624 +(epoch: 490, iters: 6494, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.814 G_ID: 0.096 G_Rec: 0.316 D_GP: 0.092 D_real: 1.053 D_fake: 0.649 +(epoch: 490, iters: 6894, time: 0.063) G_GAN: 0.503 G_GAN_Feat: 1.103 G_ID: 0.123 G_Rec: 0.451 D_GP: 0.084 D_real: 0.506 D_fake: 0.501 +(epoch: 490, iters: 7294, time: 0.063) G_GAN: 0.414 G_GAN_Feat: 0.848 G_ID: 0.098 G_Rec: 0.320 D_GP: 0.076 D_real: 0.754 D_fake: 0.617 +(epoch: 490, iters: 7694, time: 0.063) G_GAN: 0.463 G_GAN_Feat: 1.001 G_ID: 0.142 G_Rec: 0.428 D_GP: 0.062 D_real: 0.831 D_fake: 0.549 +(epoch: 490, iters: 8094, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.827 G_ID: 0.107 G_Rec: 0.313 D_GP: 0.052 D_real: 0.745 D_fake: 0.689 +(epoch: 490, iters: 8494, time: 0.063) G_GAN: 0.461 G_GAN_Feat: 0.945 G_ID: 0.118 G_Rec: 0.433 D_GP: 0.028 D_real: 1.237 D_fake: 0.542 +(epoch: 491, iters: 286, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 0.699 G_ID: 0.081 G_Rec: 0.294 D_GP: 0.034 D_real: 0.984 D_fake: 0.875 +(epoch: 491, iters: 686, time: 0.063) G_GAN: 0.302 G_GAN_Feat: 0.950 G_ID: 0.115 G_Rec: 0.416 D_GP: 0.040 D_real: 0.913 D_fake: 0.707 +(epoch: 491, iters: 1086, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.839 G_ID: 0.088 G_Rec: 0.357 D_GP: 0.059 D_real: 0.849 D_fake: 0.781 +(epoch: 491, iters: 1486, time: 0.063) G_GAN: 0.794 G_GAN_Feat: 1.043 G_ID: 0.125 G_Rec: 0.426 D_GP: 0.053 D_real: 1.051 D_fake: 0.279 +(epoch: 491, iters: 1886, time: 0.063) G_GAN: 0.106 G_GAN_Feat: 0.948 G_ID: 0.083 G_Rec: 0.347 D_GP: 0.360 D_real: 0.236 D_fake: 0.910 +(epoch: 491, iters: 2286, time: 0.063) G_GAN: 0.696 G_GAN_Feat: 1.123 G_ID: 0.116 G_Rec: 0.448 D_GP: 0.053 D_real: 0.997 D_fake: 0.363 +(epoch: 491, iters: 2686, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.962 G_ID: 0.092 G_Rec: 0.351 D_GP: 0.208 D_real: 0.488 D_fake: 0.704 +(epoch: 491, iters: 3086, time: 0.063) G_GAN: 0.466 G_GAN_Feat: 0.963 G_ID: 0.104 G_Rec: 0.453 D_GP: 0.031 D_real: 1.006 D_fake: 0.564 +(epoch: 491, iters: 3486, time: 0.063) G_GAN: 0.252 G_GAN_Feat: 0.738 G_ID: 0.118 G_Rec: 0.295 D_GP: 0.039 D_real: 1.039 D_fake: 0.748 +(epoch: 491, iters: 3886, time: 0.063) G_GAN: 0.375 G_GAN_Feat: 1.025 G_ID: 0.153 G_Rec: 0.421 D_GP: 0.058 D_real: 0.633 D_fake: 0.638 +(epoch: 491, iters: 4286, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.903 G_ID: 0.107 G_Rec: 0.336 D_GP: 0.272 D_real: 0.302 D_fake: 0.881 +(epoch: 491, iters: 4686, time: 0.063) G_GAN: 0.488 G_GAN_Feat: 0.977 G_ID: 0.128 G_Rec: 0.462 D_GP: 0.042 D_real: 1.142 D_fake: 0.516 +(epoch: 491, iters: 5086, time: 0.063) G_GAN: 0.217 G_GAN_Feat: 0.686 G_ID: 0.087 G_Rec: 0.312 D_GP: 0.034 D_real: 1.060 D_fake: 0.784 +(epoch: 491, iters: 5486, time: 0.063) G_GAN: 0.280 G_GAN_Feat: 1.025 G_ID: 0.122 G_Rec: 0.472 D_GP: 0.068 D_real: 0.730 D_fake: 0.722 +(epoch: 491, iters: 5886, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 0.797 G_ID: 0.090 G_Rec: 0.339 D_GP: 0.040 D_real: 0.761 D_fake: 0.990 +(epoch: 491, iters: 6286, time: 0.063) G_GAN: 0.430 G_GAN_Feat: 0.891 G_ID: 0.155 G_Rec: 0.410 D_GP: 0.036 D_real: 1.041 D_fake: 0.580 +(epoch: 491, iters: 6686, time: 0.063) G_GAN: 0.008 G_GAN_Feat: 0.814 G_ID: 0.101 G_Rec: 0.345 D_GP: 0.044 D_real: 0.611 D_fake: 0.992 +(epoch: 491, iters: 7086, time: 0.063) G_GAN: 0.459 G_GAN_Feat: 1.068 G_ID: 0.122 G_Rec: 0.468 D_GP: 0.215 D_real: 0.515 D_fake: 0.548 +(epoch: 491, iters: 7486, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.840 G_ID: 0.099 G_Rec: 0.372 D_GP: 0.060 D_real: 0.654 D_fake: 0.840 +(epoch: 491, iters: 7886, time: 0.063) G_GAN: 0.373 G_GAN_Feat: 1.348 G_ID: 0.117 G_Rec: 0.484 D_GP: 0.061 D_real: 1.202 D_fake: 0.647 +(epoch: 491, iters: 8286, time: 0.063) G_GAN: 0.002 G_GAN_Feat: 0.649 G_ID: 0.088 G_Rec: 0.305 D_GP: 0.024 D_real: 0.994 D_fake: 0.998 +(epoch: 491, iters: 8686, time: 0.063) G_GAN: 0.321 G_GAN_Feat: 0.925 G_ID: 0.121 G_Rec: 0.458 D_GP: 0.032 D_real: 0.933 D_fake: 0.688 +(epoch: 492, iters: 478, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.741 G_ID: 0.081 G_Rec: 0.337 D_GP: 0.042 D_real: 1.025 D_fake: 0.854 +(epoch: 492, iters: 878, time: 0.063) G_GAN: 0.076 G_GAN_Feat: 1.001 G_ID: 0.126 G_Rec: 0.489 D_GP: 0.072 D_real: 0.599 D_fake: 0.925 +(epoch: 492, iters: 1278, time: 0.063) G_GAN: 0.011 G_GAN_Feat: 0.725 G_ID: 0.086 G_Rec: 0.306 D_GP: 0.060 D_real: 0.835 D_fake: 0.989 +(epoch: 492, iters: 1678, time: 0.063) G_GAN: 0.361 G_GAN_Feat: 0.902 G_ID: 0.106 G_Rec: 0.404 D_GP: 0.040 D_real: 0.947 D_fake: 0.647 +(epoch: 492, iters: 2078, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.791 G_ID: 0.094 G_Rec: 0.306 D_GP: 0.050 D_real: 0.865 D_fake: 0.827 +(epoch: 492, iters: 2478, time: 0.063) G_GAN: 0.652 G_GAN_Feat: 0.963 G_ID: 0.105 G_Rec: 0.426 D_GP: 0.035 D_real: 1.286 D_fake: 0.380 +(epoch: 492, iters: 2878, time: 0.063) G_GAN: 0.323 G_GAN_Feat: 0.844 G_ID: 0.099 G_Rec: 0.333 D_GP: 0.078 D_real: 0.729 D_fake: 0.677 +(epoch: 492, iters: 3278, time: 0.063) G_GAN: 0.064 G_GAN_Feat: 0.949 G_ID: 0.129 G_Rec: 0.453 D_GP: 0.037 D_real: 0.893 D_fake: 0.937 +(epoch: 492, iters: 3678, time: 0.064) G_GAN: -0.023 G_GAN_Feat: 0.700 G_ID: 0.111 G_Rec: 0.287 D_GP: 0.029 D_real: 0.859 D_fake: 1.023 +(epoch: 492, iters: 4078, time: 0.063) G_GAN: 0.362 G_GAN_Feat: 1.028 G_ID: 0.121 G_Rec: 0.431 D_GP: 0.084 D_real: 0.717 D_fake: 0.646 +(epoch: 492, iters: 4478, time: 0.063) G_GAN: 0.342 G_GAN_Feat: 0.648 G_ID: 0.103 G_Rec: 0.269 D_GP: 0.033 D_real: 1.262 D_fake: 0.659 +(epoch: 492, iters: 4878, time: 0.063) G_GAN: 0.398 G_GAN_Feat: 1.145 G_ID: 0.128 G_Rec: 0.435 D_GP: 0.770 D_real: 0.119 D_fake: 0.711 +(epoch: 492, iters: 5278, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.884 G_ID: 0.093 G_Rec: 0.342 D_GP: 0.239 D_real: 0.311 D_fake: 0.988 +(epoch: 492, iters: 5678, time: 0.063) G_GAN: 0.536 G_GAN_Feat: 1.070 G_ID: 0.115 G_Rec: 0.489 D_GP: 0.034 D_real: 1.039 D_fake: 0.471 +(epoch: 492, iters: 6078, time: 0.063) G_GAN: 0.028 G_GAN_Feat: 0.897 G_ID: 0.129 G_Rec: 0.304 D_GP: 0.033 D_real: 0.447 D_fake: 0.972 +(epoch: 492, iters: 6478, time: 0.063) G_GAN: 0.418 G_GAN_Feat: 1.356 G_ID: 0.125 G_Rec: 0.462 D_GP: 0.105 D_real: 0.071 D_fake: 0.602 +(epoch: 492, iters: 6878, time: 0.064) G_GAN: 0.720 G_GAN_Feat: 0.858 G_ID: 0.095 G_Rec: 0.332 D_GP: 0.065 D_real: 1.349 D_fake: 0.654 +(epoch: 492, iters: 7278, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 1.224 G_ID: 0.109 G_Rec: 0.466 D_GP: 0.076 D_real: 0.135 D_fake: 0.766 +(epoch: 492, iters: 7678, time: 0.063) G_GAN: 0.496 G_GAN_Feat: 0.838 G_ID: 0.091 G_Rec: 0.318 D_GP: 0.057 D_real: 1.113 D_fake: 0.510 +(epoch: 492, iters: 8078, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 1.084 G_ID: 0.123 G_Rec: 0.559 D_GP: 0.035 D_real: 0.979 D_fake: 0.632 +(epoch: 492, iters: 8478, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.794 G_ID: 0.115 G_Rec: 0.327 D_GP: 0.030 D_real: 1.123 D_fake: 0.740 +(epoch: 493, iters: 270, time: 0.063) G_GAN: 0.690 G_GAN_Feat: 1.012 G_ID: 0.114 G_Rec: 0.438 D_GP: 0.034 D_real: 1.082 D_fake: 0.378 +(epoch: 493, iters: 670, time: 0.063) G_GAN: 0.064 G_GAN_Feat: 0.807 G_ID: 0.084 G_Rec: 0.331 D_GP: 0.078 D_real: 0.733 D_fake: 0.936 +(epoch: 493, iters: 1070, time: 0.063) G_GAN: 0.579 G_GAN_Feat: 1.063 G_ID: 0.138 G_Rec: 0.465 D_GP: 0.050 D_real: 0.843 D_fake: 0.428 +(epoch: 493, iters: 1470, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.828 G_ID: 0.104 G_Rec: 0.323 D_GP: 0.081 D_real: 0.831 D_fake: 0.640 +(epoch: 493, iters: 1870, time: 0.063) G_GAN: 0.697 G_GAN_Feat: 1.058 G_ID: 0.114 G_Rec: 0.442 D_GP: 0.065 D_real: 1.052 D_fake: 0.345 +(epoch: 493, iters: 2270, time: 0.063) G_GAN: 0.498 G_GAN_Feat: 0.922 G_ID: 0.124 G_Rec: 0.307 D_GP: 0.059 D_real: 0.951 D_fake: 0.681 +(epoch: 493, iters: 2670, time: 0.063) G_GAN: 0.858 G_GAN_Feat: 0.988 G_ID: 0.110 G_Rec: 0.440 D_GP: 0.031 D_real: 1.446 D_fake: 0.206 +(epoch: 493, iters: 3070, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.887 G_ID: 0.105 G_Rec: 0.309 D_GP: 0.050 D_real: 1.212 D_fake: 0.524 +(epoch: 493, iters: 3470, time: 0.063) G_GAN: 0.985 G_GAN_Feat: 1.131 G_ID: 0.115 G_Rec: 0.472 D_GP: 0.039 D_real: 1.577 D_fake: 0.206 +(epoch: 493, iters: 3870, time: 0.063) G_GAN: 0.329 G_GAN_Feat: 0.840 G_ID: 0.099 G_Rec: 0.323 D_GP: 0.030 D_real: 1.109 D_fake: 0.671 +(epoch: 493, iters: 4270, time: 0.063) G_GAN: 0.387 G_GAN_Feat: 1.070 G_ID: 0.126 G_Rec: 0.509 D_GP: 0.032 D_real: 1.034 D_fake: 0.622 +(epoch: 493, iters: 4670, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.709 G_ID: 0.100 G_Rec: 0.287 D_GP: 0.035 D_real: 1.169 D_fake: 0.742 +(epoch: 493, iters: 5070, time: 0.063) G_GAN: 0.360 G_GAN_Feat: 0.968 G_ID: 0.109 G_Rec: 0.419 D_GP: 0.079 D_real: 0.768 D_fake: 0.642 +(epoch: 493, iters: 5470, time: 0.063) G_GAN: 0.345 G_GAN_Feat: 0.813 G_ID: 0.096 G_Rec: 0.292 D_GP: 0.127 D_real: 0.784 D_fake: 0.656 +(epoch: 493, iters: 5870, time: 0.063) G_GAN: 0.804 G_GAN_Feat: 0.976 G_ID: 0.120 G_Rec: 0.395 D_GP: 0.041 D_real: 1.347 D_fake: 0.254 +(epoch: 493, iters: 6270, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.729 G_ID: 0.094 G_Rec: 0.311 D_GP: 0.032 D_real: 1.359 D_fake: 0.577 +(epoch: 493, iters: 6670, time: 0.063) G_GAN: 0.724 G_GAN_Feat: 1.152 G_ID: 0.149 G_Rec: 0.497 D_GP: 0.037 D_real: 1.048 D_fake: 0.376 +(epoch: 493, iters: 7070, time: 0.063) G_GAN: 0.531 G_GAN_Feat: 0.787 G_ID: 0.109 G_Rec: 0.283 D_GP: 0.038 D_real: 1.233 D_fake: 0.553 +(epoch: 493, iters: 7470, time: 0.063) G_GAN: 0.397 G_GAN_Feat: 0.843 G_ID: 0.123 G_Rec: 0.405 D_GP: 0.031 D_real: 1.164 D_fake: 0.611 +(epoch: 493, iters: 7870, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.740 G_ID: 0.095 G_Rec: 0.330 D_GP: 0.033 D_real: 1.025 D_fake: 0.849 +(epoch: 493, iters: 8270, time: 0.063) G_GAN: 0.207 G_GAN_Feat: 0.884 G_ID: 0.121 G_Rec: 0.413 D_GP: 0.032 D_real: 0.985 D_fake: 0.794 +(epoch: 493, iters: 8670, time: 0.063) G_GAN: -0.003 G_GAN_Feat: 0.803 G_ID: 0.099 G_Rec: 0.325 D_GP: 0.081 D_real: 0.617 D_fake: 1.003 +(epoch: 494, iters: 462, time: 0.063) G_GAN: 0.633 G_GAN_Feat: 1.021 G_ID: 0.107 G_Rec: 0.481 D_GP: 0.035 D_real: 1.196 D_fake: 0.404 +(epoch: 494, iters: 862, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.656 G_ID: 0.102 G_Rec: 0.284 D_GP: 0.030 D_real: 1.124 D_fake: 0.811 +(epoch: 494, iters: 1262, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 0.950 G_ID: 0.121 G_Rec: 0.437 D_GP: 0.034 D_real: 0.859 D_fake: 0.792 +(epoch: 494, iters: 1662, time: 0.063) G_GAN: 0.082 G_GAN_Feat: 0.779 G_ID: 0.107 G_Rec: 0.318 D_GP: 0.063 D_real: 0.683 D_fake: 0.921 +(epoch: 494, iters: 2062, time: 0.063) G_GAN: 0.532 G_GAN_Feat: 1.008 G_ID: 0.144 G_Rec: 0.460 D_GP: 0.053 D_real: 1.003 D_fake: 0.486 +(epoch: 494, iters: 2462, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.771 G_ID: 0.094 G_Rec: 0.305 D_GP: 0.029 D_real: 1.060 D_fake: 0.731 +(epoch: 494, iters: 2862, time: 0.063) G_GAN: 0.478 G_GAN_Feat: 1.028 G_ID: 0.117 G_Rec: 0.445 D_GP: 0.050 D_real: 0.687 D_fake: 0.538 +(epoch: 494, iters: 3262, time: 0.063) G_GAN: -0.159 G_GAN_Feat: 0.765 G_ID: 0.096 G_Rec: 0.293 D_GP: 0.103 D_real: 0.571 D_fake: 1.159 +(epoch: 494, iters: 3662, time: 0.063) G_GAN: 0.641 G_GAN_Feat: 1.048 G_ID: 0.127 G_Rec: 0.481 D_GP: 0.053 D_real: 1.055 D_fake: 0.384 +(epoch: 494, iters: 4062, time: 0.063) G_GAN: 0.313 G_GAN_Feat: 0.643 G_ID: 0.086 G_Rec: 0.326 D_GP: 0.025 D_real: 1.242 D_fake: 0.688 +(epoch: 494, iters: 4462, time: 0.063) G_GAN: 0.579 G_GAN_Feat: 0.904 G_ID: 0.117 G_Rec: 0.448 D_GP: 0.028 D_real: 1.324 D_fake: 0.429 +(epoch: 494, iters: 4862, time: 0.063) G_GAN: 0.245 G_GAN_Feat: 0.660 G_ID: 0.102 G_Rec: 0.276 D_GP: 0.028 D_real: 1.190 D_fake: 0.755 +(epoch: 494, iters: 5262, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.951 G_ID: 0.112 G_Rec: 0.419 D_GP: 0.054 D_real: 0.988 D_fake: 0.600 +(epoch: 494, iters: 5662, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.733 G_ID: 0.089 G_Rec: 0.290 D_GP: 0.045 D_real: 1.120 D_fake: 0.705 +(epoch: 494, iters: 6062, time: 0.063) G_GAN: 0.505 G_GAN_Feat: 0.954 G_ID: 0.125 G_Rec: 0.435 D_GP: 0.036 D_real: 1.129 D_fake: 0.503 +(epoch: 494, iters: 6462, time: 0.063) G_GAN: 0.437 G_GAN_Feat: 0.744 G_ID: 0.085 G_Rec: 0.309 D_GP: 0.038 D_real: 1.212 D_fake: 0.565 +(epoch: 494, iters: 6862, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.941 G_ID: 0.108 G_Rec: 0.429 D_GP: 0.035 D_real: 1.113 D_fake: 0.541 +(epoch: 494, iters: 7262, time: 0.063) G_GAN: 0.290 G_GAN_Feat: 0.720 G_ID: 0.092 G_Rec: 0.310 D_GP: 0.040 D_real: 1.078 D_fake: 0.711 +(epoch: 494, iters: 7662, time: 0.063) G_GAN: 0.308 G_GAN_Feat: 0.973 G_ID: 0.119 G_Rec: 0.400 D_GP: 0.056 D_real: 0.795 D_fake: 0.693 +(epoch: 494, iters: 8062, time: 0.063) G_GAN: 0.304 G_GAN_Feat: 0.771 G_ID: 0.092 G_Rec: 0.304 D_GP: 0.057 D_real: 0.987 D_fake: 0.703 +(epoch: 494, iters: 8462, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 1.012 G_ID: 0.119 G_Rec: 0.475 D_GP: 0.031 D_real: 0.870 D_fake: 0.638 +(epoch: 495, iters: 254, time: 0.063) G_GAN: 0.114 G_GAN_Feat: 0.757 G_ID: 0.097 G_Rec: 0.282 D_GP: 0.028 D_real: 0.919 D_fake: 0.886 +(epoch: 495, iters: 654, time: 0.063) G_GAN: 0.613 G_GAN_Feat: 1.002 G_ID: 0.105 G_Rec: 0.414 D_GP: 0.034 D_real: 1.305 D_fake: 0.410 +(epoch: 495, iters: 1054, time: 0.063) G_GAN: 0.263 G_GAN_Feat: 0.864 G_ID: 0.087 G_Rec: 0.315 D_GP: 0.052 D_real: 0.577 D_fake: 0.739 +(epoch: 495, iters: 1454, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 1.079 G_ID: 0.114 G_Rec: 0.471 D_GP: 0.044 D_real: 0.980 D_fake: 0.569 +(epoch: 495, iters: 1854, time: 0.063) G_GAN: 0.358 G_GAN_Feat: 0.839 G_ID: 0.100 G_Rec: 0.314 D_GP: 0.096 D_real: 0.701 D_fake: 0.646 +(epoch: 495, iters: 2254, time: 0.063) G_GAN: 0.430 G_GAN_Feat: 0.928 G_ID: 0.106 G_Rec: 0.414 D_GP: 0.033 D_real: 1.122 D_fake: 0.580 +(epoch: 495, iters: 2654, time: 0.063) G_GAN: 0.339 G_GAN_Feat: 0.761 G_ID: 0.111 G_Rec: 0.323 D_GP: 0.036 D_real: 1.138 D_fake: 0.662 +(epoch: 495, iters: 3054, time: 0.064) G_GAN: 0.557 G_GAN_Feat: 1.147 G_ID: 0.122 G_Rec: 0.458 D_GP: 0.262 D_real: 0.395 D_fake: 0.465 +(epoch: 495, iters: 3454, time: 0.063) G_GAN: 0.141 G_GAN_Feat: 0.946 G_ID: 0.090 G_Rec: 0.308 D_GP: 0.055 D_real: 0.222 D_fake: 0.862 +(epoch: 495, iters: 3854, time: 0.063) G_GAN: 0.448 G_GAN_Feat: 0.881 G_ID: 0.118 G_Rec: 0.441 D_GP: 0.029 D_real: 1.198 D_fake: 0.559 +(epoch: 495, iters: 4254, time: 0.063) G_GAN: 0.143 G_GAN_Feat: 0.644 G_ID: 0.101 G_Rec: 0.324 D_GP: 0.028 D_real: 1.057 D_fake: 0.857 +(epoch: 495, iters: 4654, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.861 G_ID: 0.112 G_Rec: 0.436 D_GP: 0.034 D_real: 1.042 D_fake: 0.512 +(epoch: 495, iters: 5054, time: 0.063) G_GAN: 0.197 G_GAN_Feat: 0.666 G_ID: 0.104 G_Rec: 0.304 D_GP: 0.033 D_real: 1.058 D_fake: 0.803 +(epoch: 495, iters: 5454, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.893 G_ID: 0.117 G_Rec: 0.424 D_GP: 0.042 D_real: 1.062 D_fake: 0.644 +(epoch: 495, iters: 5854, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.686 G_ID: 0.104 G_Rec: 0.314 D_GP: 0.034 D_real: 0.967 D_fake: 0.873 +(epoch: 495, iters: 6254, time: 0.064) G_GAN: 0.124 G_GAN_Feat: 0.861 G_ID: 0.118 G_Rec: 0.403 D_GP: 0.038 D_real: 0.899 D_fake: 0.876 +(epoch: 495, iters: 6654, time: 0.064) G_GAN: -0.079 G_GAN_Feat: 0.752 G_ID: 0.104 G_Rec: 0.341 D_GP: 0.046 D_real: 0.662 D_fake: 1.079 +(epoch: 495, iters: 7054, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.914 G_ID: 0.124 G_Rec: 0.420 D_GP: 0.048 D_real: 0.907 D_fake: 0.670 +(epoch: 495, iters: 7454, time: 0.064) G_GAN: -0.182 G_GAN_Feat: 0.740 G_ID: 0.108 G_Rec: 0.319 D_GP: 0.039 D_real: 0.605 D_fake: 1.182 +(epoch: 495, iters: 7854, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.975 G_ID: 0.115 G_Rec: 0.454 D_GP: 0.063 D_real: 0.789 D_fake: 0.615 +(epoch: 495, iters: 8254, time: 0.064) G_GAN: -0.126 G_GAN_Feat: 0.756 G_ID: 0.099 G_Rec: 0.355 D_GP: 0.108 D_real: 0.550 D_fake: 1.126 +(epoch: 495, iters: 8654, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 1.027 G_ID: 0.113 G_Rec: 0.448 D_GP: 0.089 D_real: 0.641 D_fake: 0.691 +(epoch: 496, iters: 446, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.726 G_ID: 0.112 G_Rec: 0.306 D_GP: 0.049 D_real: 0.906 D_fake: 0.878 +(epoch: 496, iters: 846, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.955 G_ID: 0.113 G_Rec: 0.398 D_GP: 0.050 D_real: 1.036 D_fake: 0.508 +(epoch: 496, iters: 1246, time: 0.064) G_GAN: 0.035 G_GAN_Feat: 0.739 G_ID: 0.095 G_Rec: 0.308 D_GP: 0.053 D_real: 0.950 D_fake: 0.965 +(epoch: 496, iters: 1646, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.865 G_ID: 0.107 G_Rec: 0.391 D_GP: 0.036 D_real: 1.251 D_fake: 0.524 +(epoch: 496, iters: 2046, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.845 G_ID: 0.100 G_Rec: 0.357 D_GP: 0.077 D_real: 0.487 D_fake: 0.894 +(epoch: 496, iters: 2446, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 1.054 G_ID: 0.140 G_Rec: 0.488 D_GP: 0.110 D_real: 0.452 D_fake: 0.681 +(epoch: 496, iters: 2846, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.773 G_ID: 0.099 G_Rec: 0.313 D_GP: 0.074 D_real: 1.015 D_fake: 0.792 +(epoch: 496, iters: 3246, time: 0.063) G_GAN: 0.624 G_GAN_Feat: 0.936 G_ID: 0.116 G_Rec: 0.441 D_GP: 0.070 D_real: 1.160 D_fake: 0.401 +(epoch: 496, iters: 3646, time: 0.063) G_GAN: 0.282 G_GAN_Feat: 0.796 G_ID: 0.092 G_Rec: 0.327 D_GP: 0.050 D_real: 0.868 D_fake: 0.720 +(epoch: 496, iters: 4046, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.964 G_ID: 0.132 G_Rec: 0.407 D_GP: 0.050 D_real: 0.837 D_fake: 0.599 +(epoch: 496, iters: 4446, time: 0.064) G_GAN: 0.040 G_GAN_Feat: 0.899 G_ID: 0.093 G_Rec: 0.333 D_GP: 0.117 D_real: 0.413 D_fake: 0.961 +(epoch: 496, iters: 4846, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.930 G_ID: 0.109 G_Rec: 0.450 D_GP: 0.026 D_real: 1.146 D_fake: 0.635 +(epoch: 496, iters: 5246, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.708 G_ID: 0.095 G_Rec: 0.314 D_GP: 0.031 D_real: 0.904 D_fake: 0.941 +(epoch: 496, iters: 5646, time: 0.064) G_GAN: 0.607 G_GAN_Feat: 0.944 G_ID: 0.109 G_Rec: 0.506 D_GP: 0.046 D_real: 1.106 D_fake: 0.431 +(epoch: 496, iters: 6046, time: 0.063) G_GAN: -0.131 G_GAN_Feat: 0.826 G_ID: 0.105 G_Rec: 0.325 D_GP: 0.115 D_real: 0.415 D_fake: 1.132 +(epoch: 496, iters: 6446, time: 0.063) G_GAN: 0.561 G_GAN_Feat: 0.956 G_ID: 0.103 G_Rec: 0.416 D_GP: 0.041 D_real: 1.115 D_fake: 0.448 +(epoch: 496, iters: 6846, time: 0.063) G_GAN: 0.166 G_GAN_Feat: 0.739 G_ID: 0.106 G_Rec: 0.309 D_GP: 0.036 D_real: 1.014 D_fake: 0.835 +(epoch: 496, iters: 7246, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.932 G_ID: 0.117 G_Rec: 0.396 D_GP: 0.039 D_real: 1.177 D_fake: 0.517 +(epoch: 496, iters: 7646, time: 0.063) G_GAN: 0.297 G_GAN_Feat: 0.802 G_ID: 0.097 G_Rec: 0.291 D_GP: 0.039 D_real: 1.088 D_fake: 0.703 +(epoch: 496, iters: 8046, time: 0.063) G_GAN: 0.501 G_GAN_Feat: 1.032 G_ID: 0.129 G_Rec: 0.479 D_GP: 0.078 D_real: 0.903 D_fake: 0.520 +(epoch: 496, iters: 8446, time: 0.063) G_GAN: 0.098 G_GAN_Feat: 0.862 G_ID: 0.109 G_Rec: 0.325 D_GP: 0.057 D_real: 0.582 D_fake: 0.902 +(epoch: 497, iters: 238, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.937 G_ID: 0.123 G_Rec: 0.379 D_GP: 0.068 D_real: 1.017 D_fake: 0.583 +(epoch: 497, iters: 638, time: 0.063) G_GAN: 0.251 G_GAN_Feat: 0.721 G_ID: 0.093 G_Rec: 0.314 D_GP: 0.035 D_real: 1.019 D_fake: 0.749 +(epoch: 497, iters: 1038, time: 0.063) G_GAN: 0.638 G_GAN_Feat: 0.963 G_ID: 0.112 G_Rec: 0.428 D_GP: 0.038 D_real: 1.276 D_fake: 0.384 +(epoch: 497, iters: 1438, time: 0.063) G_GAN: 0.373 G_GAN_Feat: 0.717 G_ID: 0.101 G_Rec: 0.289 D_GP: 0.032 D_real: 1.278 D_fake: 0.628 +(epoch: 497, iters: 1838, time: 0.064) G_GAN: 0.575 G_GAN_Feat: 1.022 G_ID: 0.119 G_Rec: 0.459 D_GP: 0.096 D_real: 1.108 D_fake: 0.442 +(epoch: 497, iters: 2238, time: 0.063) G_GAN: 0.196 G_GAN_Feat: 0.802 G_ID: 0.095 G_Rec: 0.295 D_GP: 0.035 D_real: 0.827 D_fake: 0.807 +(epoch: 497, iters: 2638, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 1.019 G_ID: 0.120 G_Rec: 0.403 D_GP: 0.039 D_real: 0.622 D_fake: 0.639 +(epoch: 497, iters: 3038, time: 0.063) G_GAN: 0.088 G_GAN_Feat: 0.783 G_ID: 0.100 G_Rec: 0.289 D_GP: 0.032 D_real: 0.900 D_fake: 0.912 +(epoch: 497, iters: 3438, time: 0.064) G_GAN: 0.858 G_GAN_Feat: 1.025 G_ID: 0.113 G_Rec: 0.430 D_GP: 0.056 D_real: 1.212 D_fake: 0.209 +(epoch: 497, iters: 3838, time: 0.063) G_GAN: -0.051 G_GAN_Feat: 0.969 G_ID: 0.098 G_Rec: 0.371 D_GP: 0.205 D_real: 0.223 D_fake: 1.052 +(epoch: 497, iters: 4238, time: 0.063) G_GAN: 0.387 G_GAN_Feat: 0.927 G_ID: 0.126 G_Rec: 0.408 D_GP: 0.036 D_real: 1.054 D_fake: 0.646 +(epoch: 497, iters: 4638, time: 0.063) G_GAN: 0.384 G_GAN_Feat: 0.772 G_ID: 0.088 G_Rec: 0.287 D_GP: 0.037 D_real: 1.199 D_fake: 0.618 +(epoch: 497, iters: 5038, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 1.063 G_ID: 0.119 G_Rec: 0.461 D_GP: 0.036 D_real: 0.829 D_fake: 0.723 +(epoch: 497, iters: 5438, time: 0.063) G_GAN: 0.103 G_GAN_Feat: 0.841 G_ID: 0.098 G_Rec: 0.305 D_GP: 0.058 D_real: 0.611 D_fake: 0.898 +(epoch: 497, iters: 5838, time: 0.063) G_GAN: 0.773 G_GAN_Feat: 1.073 G_ID: 0.114 G_Rec: 0.428 D_GP: 0.037 D_real: 0.833 D_fake: 0.252 +(epoch: 497, iters: 6238, time: 0.063) G_GAN: 0.376 G_GAN_Feat: 0.630 G_ID: 0.097 G_Rec: 0.290 D_GP: 0.025 D_real: 1.344 D_fake: 0.625 +(epoch: 497, iters: 6638, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.845 G_ID: 0.109 G_Rec: 0.383 D_GP: 0.031 D_real: 1.157 D_fake: 0.578 +(epoch: 497, iters: 7038, time: 0.063) G_GAN: 0.139 G_GAN_Feat: 0.785 G_ID: 0.092 G_Rec: 0.348 D_GP: 0.050 D_real: 0.946 D_fake: 0.864 +(epoch: 497, iters: 7438, time: 0.063) G_GAN: 0.635 G_GAN_Feat: 1.005 G_ID: 0.108 G_Rec: 0.429 D_GP: 0.043 D_real: 1.141 D_fake: 0.383 +(epoch: 497, iters: 7838, time: 0.063) G_GAN: 0.278 G_GAN_Feat: 0.764 G_ID: 0.082 G_Rec: 0.316 D_GP: 0.045 D_real: 0.981 D_fake: 0.723 +(epoch: 497, iters: 8238, time: 0.064) G_GAN: 0.656 G_GAN_Feat: 1.051 G_ID: 0.108 G_Rec: 0.462 D_GP: 0.048 D_real: 1.188 D_fake: 0.386 +(epoch: 497, iters: 8638, time: 0.063) G_GAN: 0.452 G_GAN_Feat: 0.668 G_ID: 0.092 G_Rec: 0.281 D_GP: 0.027 D_real: 1.371 D_fake: 0.548 +(epoch: 498, iters: 430, time: 0.063) G_GAN: 0.531 G_GAN_Feat: 1.017 G_ID: 0.110 G_Rec: 0.456 D_GP: 0.062 D_real: 1.048 D_fake: 0.478 +(epoch: 498, iters: 830, time: 0.063) G_GAN: 0.148 G_GAN_Feat: 0.787 G_ID: 0.091 G_Rec: 0.325 D_GP: 0.096 D_real: 0.922 D_fake: 0.853 +(epoch: 498, iters: 1230, time: 0.064) G_GAN: 0.929 G_GAN_Feat: 0.970 G_ID: 0.110 G_Rec: 0.405 D_GP: 0.038 D_real: 1.544 D_fake: 0.147 +(epoch: 498, iters: 1630, time: 0.063) G_GAN: 0.108 G_GAN_Feat: 0.795 G_ID: 0.104 G_Rec: 0.323 D_GP: 0.032 D_real: 0.693 D_fake: 0.892 +(epoch: 498, iters: 2030, time: 0.063) G_GAN: 0.594 G_GAN_Feat: 0.988 G_ID: 0.124 G_Rec: 0.416 D_GP: 0.040 D_real: 1.075 D_fake: 0.431 +(epoch: 498, iters: 2430, time: 0.063) G_GAN: 0.154 G_GAN_Feat: 0.781 G_ID: 0.093 G_Rec: 0.296 D_GP: 0.034 D_real: 0.928 D_fake: 0.846 +(epoch: 498, iters: 2830, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 1.035 G_ID: 0.107 G_Rec: 0.412 D_GP: 0.035 D_real: 0.649 D_fake: 0.665 +(epoch: 498, iters: 3230, time: 0.063) G_GAN: 0.020 G_GAN_Feat: 0.748 G_ID: 0.097 G_Rec: 0.286 D_GP: 0.032 D_real: 0.796 D_fake: 0.980 +(epoch: 498, iters: 3630, time: 0.063) G_GAN: 0.804 G_GAN_Feat: 0.951 G_ID: 0.134 G_Rec: 0.441 D_GP: 0.042 D_real: 1.472 D_fake: 0.289 +(epoch: 498, iters: 4030, time: 0.063) G_GAN: 0.121 G_GAN_Feat: 0.777 G_ID: 0.090 G_Rec: 0.340 D_GP: 0.029 D_real: 1.153 D_fake: 0.880 +(epoch: 498, iters: 4430, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.821 G_ID: 0.112 G_Rec: 0.392 D_GP: 0.027 D_real: 1.129 D_fake: 0.701 +(epoch: 498, iters: 4830, time: 0.063) G_GAN: 0.007 G_GAN_Feat: 0.619 G_ID: 0.085 G_Rec: 0.288 D_GP: 0.025 D_real: 0.934 D_fake: 0.994 +(epoch: 498, iters: 5230, time: 0.063) G_GAN: 0.253 G_GAN_Feat: 0.852 G_ID: 0.106 G_Rec: 0.404 D_GP: 0.037 D_real: 0.918 D_fake: 0.752 +(epoch: 498, iters: 5630, time: 0.063) G_GAN: -0.196 G_GAN_Feat: 0.791 G_ID: 0.096 G_Rec: 0.358 D_GP: 0.033 D_real: 0.628 D_fake: 1.196 +(epoch: 498, iters: 6030, time: 0.064) G_GAN: 0.025 G_GAN_Feat: 0.829 G_ID: 0.127 G_Rec: 0.387 D_GP: 0.041 D_real: 0.575 D_fake: 0.976 +(epoch: 498, iters: 6430, time: 0.063) G_GAN: -0.163 G_GAN_Feat: 0.611 G_ID: 0.090 G_Rec: 0.290 D_GP: 0.029 D_real: 0.699 D_fake: 1.163 +(epoch: 498, iters: 6830, time: 0.063) G_GAN: 0.274 G_GAN_Feat: 0.871 G_ID: 0.113 G_Rec: 0.425 D_GP: 0.035 D_real: 0.926 D_fake: 0.748 +(epoch: 498, iters: 7230, time: 0.063) G_GAN: -0.364 G_GAN_Feat: 0.713 G_ID: 0.109 G_Rec: 0.316 D_GP: 0.040 D_real: 0.488 D_fake: 1.364 +(epoch: 498, iters: 7630, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.945 G_ID: 0.113 G_Rec: 0.440 D_GP: 0.039 D_real: 0.986 D_fake: 0.632 +(epoch: 498, iters: 8030, time: 0.063) G_GAN: -0.089 G_GAN_Feat: 0.759 G_ID: 0.101 G_Rec: 0.311 D_GP: 0.097 D_real: 0.585 D_fake: 1.089 +(epoch: 498, iters: 8430, time: 0.063) G_GAN: 0.519 G_GAN_Feat: 1.044 G_ID: 0.116 G_Rec: 0.465 D_GP: 0.040 D_real: 1.035 D_fake: 0.497 +(epoch: 499, iters: 222, time: 0.063) G_GAN: 0.299 G_GAN_Feat: 0.680 G_ID: 0.090 G_Rec: 0.295 D_GP: 0.042 D_real: 1.174 D_fake: 0.701 +(epoch: 499, iters: 622, time: 0.063) G_GAN: 0.548 G_GAN_Feat: 0.915 G_ID: 0.117 G_Rec: 0.393 D_GP: 0.032 D_real: 1.210 D_fake: 0.454 +(epoch: 499, iters: 1022, time: 0.063) G_GAN: 0.248 G_GAN_Feat: 0.741 G_ID: 0.111 G_Rec: 0.291 D_GP: 0.030 D_real: 1.092 D_fake: 0.752 +(epoch: 499, iters: 1422, time: 0.063) G_GAN: 0.504 G_GAN_Feat: 1.030 G_ID: 0.114 G_Rec: 0.441 D_GP: 0.036 D_real: 0.938 D_fake: 0.500 +(epoch: 499, iters: 1822, time: 0.064) G_GAN: -0.153 G_GAN_Feat: 0.760 G_ID: 0.097 G_Rec: 0.311 D_GP: 0.033 D_real: 0.845 D_fake: 1.153 +(epoch: 499, iters: 2222, time: 0.063) G_GAN: 0.336 G_GAN_Feat: 1.036 G_ID: 0.107 G_Rec: 0.467 D_GP: 0.057 D_real: 0.848 D_fake: 0.670 +(epoch: 499, iters: 2622, time: 0.063) G_GAN: 0.070 G_GAN_Feat: 0.780 G_ID: 0.111 G_Rec: 0.341 D_GP: 0.047 D_real: 0.834 D_fake: 0.933 +(epoch: 499, iters: 3022, time: 0.063) G_GAN: 0.282 G_GAN_Feat: 0.934 G_ID: 0.126 G_Rec: 0.427 D_GP: 0.037 D_real: 0.857 D_fake: 0.722 +(epoch: 499, iters: 3422, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.787 G_ID: 0.098 G_Rec: 0.313 D_GP: 0.067 D_real: 0.788 D_fake: 0.884 +(epoch: 499, iters: 3822, time: 0.063) G_GAN: 0.532 G_GAN_Feat: 1.041 G_ID: 0.123 G_Rec: 0.456 D_GP: 0.060 D_real: 0.887 D_fake: 0.497 +(epoch: 499, iters: 4222, time: 0.063) G_GAN: -0.197 G_GAN_Feat: 0.811 G_ID: 0.115 G_Rec: 0.306 D_GP: 0.077 D_real: 0.553 D_fake: 1.198 +(epoch: 499, iters: 4622, time: 0.063) G_GAN: 0.461 G_GAN_Feat: 1.094 G_ID: 0.108 G_Rec: 0.457 D_GP: 0.063 D_real: 0.883 D_fake: 0.547 +(epoch: 499, iters: 5022, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.827 G_ID: 0.087 G_Rec: 0.304 D_GP: 0.060 D_real: 0.839 D_fake: 0.753 +(epoch: 499, iters: 5422, time: 0.063) G_GAN: 0.360 G_GAN_Feat: 0.838 G_ID: 0.108 G_Rec: 0.385 D_GP: 0.033 D_real: 1.073 D_fake: 0.641 +(epoch: 499, iters: 5822, time: 0.063) G_GAN: 0.037 G_GAN_Feat: 0.665 G_ID: 0.097 G_Rec: 0.294 D_GP: 0.032 D_real: 0.985 D_fake: 0.963 +(epoch: 499, iters: 6222, time: 0.063) G_GAN: 0.364 G_GAN_Feat: 0.912 G_ID: 0.113 G_Rec: 0.421 D_GP: 0.049 D_real: 1.049 D_fake: 0.645 +(epoch: 499, iters: 6622, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.676 G_ID: 0.080 G_Rec: 0.297 D_GP: 0.032 D_real: 1.083 D_fake: 0.795 +(epoch: 499, iters: 7022, time: 0.063) G_GAN: 0.386 G_GAN_Feat: 0.902 G_ID: 0.103 G_Rec: 0.435 D_GP: 0.029 D_real: 1.020 D_fake: 0.628 +(epoch: 499, iters: 7422, time: 0.063) G_GAN: 0.111 G_GAN_Feat: 0.708 G_ID: 0.103 G_Rec: 0.309 D_GP: 0.033 D_real: 0.997 D_fake: 0.889 +(epoch: 499, iters: 7822, time: 0.063) G_GAN: 0.301 G_GAN_Feat: 0.860 G_ID: 0.115 G_Rec: 0.415 D_GP: 0.031 D_real: 1.046 D_fake: 0.703 +(epoch: 499, iters: 8222, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.732 G_ID: 0.085 G_Rec: 0.313 D_GP: 0.035 D_real: 1.033 D_fake: 0.780 +(epoch: 499, iters: 8622, time: 0.063) G_GAN: 0.985 G_GAN_Feat: 1.019 G_ID: 0.104 G_Rec: 0.466 D_GP: 0.045 D_real: 1.403 D_fake: 0.288 +(epoch: 500, iters: 414, time: 0.063) G_GAN: 0.171 G_GAN_Feat: 0.739 G_ID: 0.095 G_Rec: 0.315 D_GP: 0.038 D_real: 1.006 D_fake: 0.829 +(epoch: 500, iters: 814, time: 0.063) G_GAN: 0.383 G_GAN_Feat: 1.007 G_ID: 0.121 G_Rec: 0.411 D_GP: 0.043 D_real: 0.834 D_fake: 0.620 +(epoch: 500, iters: 1214, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.775 G_ID: 0.091 G_Rec: 0.286 D_GP: 0.039 D_real: 1.113 D_fake: 0.683 +(epoch: 500, iters: 1614, time: 0.063) G_GAN: 0.360 G_GAN_Feat: 0.888 G_ID: 0.114 G_Rec: 0.383 D_GP: 0.031 D_real: 1.095 D_fake: 0.641 +(epoch: 500, iters: 2014, time: 0.063) G_GAN: 0.261 G_GAN_Feat: 0.677 G_ID: 0.100 G_Rec: 0.289 D_GP: 0.030 D_real: 1.114 D_fake: 0.740 +(epoch: 500, iters: 2414, time: 0.063) G_GAN: 0.539 G_GAN_Feat: 0.982 G_ID: 0.122 G_Rec: 0.390 D_GP: 0.039 D_real: 0.984 D_fake: 0.468 +(epoch: 500, iters: 2814, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.898 G_ID: 0.123 G_Rec: 0.306 D_GP: 0.064 D_real: 0.607 D_fake: 0.832 +(epoch: 500, iters: 3214, time: 0.063) G_GAN: 0.628 G_GAN_Feat: 1.152 G_ID: 0.115 G_Rec: 0.439 D_GP: 1.355 D_real: 0.337 D_fake: 0.444 +(epoch: 500, iters: 3614, time: 0.063) G_GAN: 0.118 G_GAN_Feat: 0.673 G_ID: 0.082 G_Rec: 0.286 D_GP: 0.026 D_real: 1.173 D_fake: 0.882 +(epoch: 500, iters: 4014, time: 0.063) G_GAN: 0.391 G_GAN_Feat: 0.989 G_ID: 0.113 G_Rec: 0.427 D_GP: 0.035 D_real: 0.859 D_fake: 0.609 +(epoch: 500, iters: 4414, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.765 G_ID: 0.081 G_Rec: 0.291 D_GP: 0.038 D_real: 1.280 D_fake: 0.669 +(epoch: 500, iters: 4814, time: 0.063) G_GAN: 0.602 G_GAN_Feat: 0.998 G_ID: 0.116 G_Rec: 0.490 D_GP: 0.039 D_real: 1.107 D_fake: 0.426 +(epoch: 500, iters: 5214, time: 0.063) G_GAN: 0.256 G_GAN_Feat: 0.785 G_ID: 0.092 G_Rec: 0.288 D_GP: 0.032 D_real: 1.012 D_fake: 0.745 +(epoch: 500, iters: 5614, time: 0.063) G_GAN: 0.795 G_GAN_Feat: 1.131 G_ID: 0.114 G_Rec: 0.525 D_GP: 0.038 D_real: 1.323 D_fake: 0.262 +(epoch: 500, iters: 6014, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.826 G_ID: 0.098 G_Rec: 0.294 D_GP: 0.045 D_real: 0.476 D_fake: 0.886 +(epoch: 500, iters: 6414, time: 0.063) G_GAN: 0.318 G_GAN_Feat: 1.043 G_ID: 0.098 G_Rec: 0.414 D_GP: 0.044 D_real: 0.891 D_fake: 0.683 +(epoch: 500, iters: 6814, time: 0.063) G_GAN: 0.361 G_GAN_Feat: 0.713 G_ID: 0.090 G_Rec: 0.266 D_GP: 0.029 D_real: 1.273 D_fake: 0.708 +(epoch: 500, iters: 7214, time: 0.063) G_GAN: 0.339 G_GAN_Feat: 0.934 G_ID: 0.119 G_Rec: 0.413 D_GP: 0.033 D_real: 0.988 D_fake: 0.675 +(epoch: 500, iters: 7614, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.669 G_ID: 0.093 G_Rec: 0.281 D_GP: 0.029 D_real: 0.989 D_fake: 0.926 +(epoch: 500, iters: 8014, time: 0.063) G_GAN: 0.255 G_GAN_Feat: 1.055 G_ID: 0.108 G_Rec: 0.509 D_GP: 0.033 D_real: 0.635 D_fake: 0.759 +(epoch: 500, iters: 8414, time: 0.063) G_GAN: 0.030 G_GAN_Feat: 0.729 G_ID: 0.099 G_Rec: 0.300 D_GP: 0.045 D_real: 0.925 D_fake: 0.970 +(epoch: 501, iters: 206, time: 0.063) G_GAN: 0.113 G_GAN_Feat: 0.980 G_ID: 0.114 G_Rec: 0.404 D_GP: 0.092 D_real: 0.449 D_fake: 0.888 +(epoch: 501, iters: 606, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.883 G_ID: 0.097 G_Rec: 0.323 D_GP: 0.220 D_real: 0.596 D_fake: 0.634 +(epoch: 501, iters: 1006, time: 0.063) G_GAN: 0.712 G_GAN_Feat: 1.246 G_ID: 0.111 G_Rec: 0.519 D_GP: 0.156 D_real: 0.793 D_fake: 0.348 +(epoch: 501, iters: 1406, time: 0.063) G_GAN: 0.450 G_GAN_Feat: 0.717 G_ID: 0.092 G_Rec: 0.317 D_GP: 0.033 D_real: 1.344 D_fake: 0.553 +(epoch: 501, iters: 1806, time: 0.063) G_GAN: 0.337 G_GAN_Feat: 1.019 G_ID: 0.126 G_Rec: 0.448 D_GP: 0.090 D_real: 0.788 D_fake: 0.664 +(epoch: 501, iters: 2206, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.708 G_ID: 0.109 G_Rec: 0.281 D_GP: 0.028 D_real: 1.368 D_fake: 0.568 +(epoch: 501, iters: 2606, time: 0.063) G_GAN: 0.466 G_GAN_Feat: 0.974 G_ID: 0.101 G_Rec: 0.430 D_GP: 0.032 D_real: 1.161 D_fake: 0.545 +(epoch: 501, iters: 3006, time: 0.063) G_GAN: 0.157 G_GAN_Feat: 0.870 G_ID: 0.092 G_Rec: 0.286 D_GP: 0.384 D_real: 0.369 D_fake: 0.844 +(epoch: 501, iters: 3406, time: 0.063) G_GAN: 0.775 G_GAN_Feat: 1.242 G_ID: 0.117 G_Rec: 0.517 D_GP: 0.137 D_real: 0.297 D_fake: 0.293 +(epoch: 501, iters: 3806, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 0.984 G_ID: 0.096 G_Rec: 0.355 D_GP: 0.417 D_real: 0.294 D_fake: 0.619 +(epoch: 501, iters: 4206, time: 0.063) G_GAN: 0.789 G_GAN_Feat: 1.115 G_ID: 0.106 G_Rec: 0.471 D_GP: 0.054 D_real: 0.917 D_fake: 0.291 +(epoch: 501, iters: 4606, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.695 G_ID: 0.085 G_Rec: 0.306 D_GP: 0.028 D_real: 1.006 D_fake: 0.978 +(epoch: 501, iters: 5006, time: 0.063) G_GAN: 0.368 G_GAN_Feat: 0.945 G_ID: 0.118 G_Rec: 0.431 D_GP: 0.029 D_real: 1.019 D_fake: 0.643 +(epoch: 501, iters: 5406, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.666 G_ID: 0.086 G_Rec: 0.283 D_GP: 0.029 D_real: 0.998 D_fake: 0.913 +(epoch: 501, iters: 5806, time: 0.063) G_GAN: 0.217 G_GAN_Feat: 0.904 G_ID: 0.114 G_Rec: 0.410 D_GP: 0.035 D_real: 0.974 D_fake: 0.787 +(epoch: 501, iters: 6206, time: 0.063) G_GAN: -0.161 G_GAN_Feat: 0.765 G_ID: 0.095 G_Rec: 0.301 D_GP: 0.054 D_real: 0.613 D_fake: 1.161 +(epoch: 501, iters: 6606, time: 0.063) G_GAN: 0.103 G_GAN_Feat: 0.941 G_ID: 0.116 G_Rec: 0.459 D_GP: 0.049 D_real: 0.656 D_fake: 0.897 +(epoch: 501, iters: 7006, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.715 G_ID: 0.087 G_Rec: 0.298 D_GP: 0.037 D_real: 0.919 D_fake: 0.920 +(epoch: 501, iters: 7406, time: 0.063) G_GAN: 0.542 G_GAN_Feat: 1.144 G_ID: 0.112 G_Rec: 0.481 D_GP: 0.170 D_real: 0.589 D_fake: 0.483 +(epoch: 501, iters: 7806, time: 0.063) G_GAN: 0.464 G_GAN_Feat: 0.791 G_ID: 0.091 G_Rec: 0.350 D_GP: 0.041 D_real: 1.248 D_fake: 0.537 +(epoch: 501, iters: 8206, time: 0.063) G_GAN: 0.478 G_GAN_Feat: 1.042 G_ID: 0.123 G_Rec: 0.441 D_GP: 0.158 D_real: 0.644 D_fake: 0.529 +(epoch: 501, iters: 8606, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.824 G_ID: 0.094 G_Rec: 0.305 D_GP: 0.132 D_real: 0.726 D_fake: 0.701 +(epoch: 502, iters: 398, time: 0.063) G_GAN: 0.542 G_GAN_Feat: 0.906 G_ID: 0.110 G_Rec: 0.379 D_GP: 0.036 D_real: 1.171 D_fake: 0.468 +(epoch: 502, iters: 798, time: 0.063) G_GAN: 0.527 G_GAN_Feat: 0.782 G_ID: 0.084 G_Rec: 0.312 D_GP: 0.046 D_real: 1.259 D_fake: 0.480 +(epoch: 502, iters: 1198, time: 0.063) G_GAN: 0.584 G_GAN_Feat: 1.168 G_ID: 0.130 G_Rec: 0.484 D_GP: 0.095 D_real: 0.449 D_fake: 0.440 +(epoch: 502, iters: 1598, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.828 G_ID: 0.095 G_Rec: 0.302 D_GP: 0.136 D_real: 0.642 D_fake: 0.737 +(epoch: 502, iters: 1998, time: 0.063) G_GAN: 0.749 G_GAN_Feat: 1.213 G_ID: 0.103 G_Rec: 0.490 D_GP: 0.068 D_real: 0.525 D_fake: 0.380 +(epoch: 502, iters: 2398, time: 0.063) G_GAN: 0.004 G_GAN_Feat: 1.121 G_ID: 0.113 G_Rec: 0.406 D_GP: 2.753 D_real: 0.450 D_fake: 0.999 +(epoch: 502, iters: 2798, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 0.953 G_ID: 0.105 G_Rec: 0.497 D_GP: 0.030 D_real: 1.049 D_fake: 0.765 +(epoch: 502, iters: 3198, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.668 G_ID: 0.086 G_Rec: 0.307 D_GP: 0.027 D_real: 1.150 D_fake: 0.769 +(epoch: 502, iters: 3598, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 0.731 G_ID: 0.110 G_Rec: 0.349 D_GP: 0.028 D_real: 1.011 D_fake: 0.792 +(epoch: 502, iters: 3998, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 0.677 G_ID: 0.096 G_Rec: 0.302 D_GP: 0.053 D_real: 1.105 D_fake: 0.765 +(epoch: 502, iters: 4398, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 0.890 G_ID: 0.115 G_Rec: 0.420 D_GP: 0.035 D_real: 1.054 D_fake: 0.631 +(epoch: 502, iters: 4798, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.724 G_ID: 0.093 G_Rec: 0.331 D_GP: 0.040 D_real: 1.130 D_fake: 0.745 +(epoch: 502, iters: 5198, time: 0.063) G_GAN: 0.225 G_GAN_Feat: 0.963 G_ID: 0.120 G_Rec: 0.461 D_GP: 0.045 D_real: 0.759 D_fake: 0.776 +(epoch: 502, iters: 5598, time: 0.063) G_GAN: 0.055 G_GAN_Feat: 0.794 G_ID: 0.094 G_Rec: 0.391 D_GP: 0.122 D_real: 0.620 D_fake: 0.946 +(epoch: 502, iters: 5998, time: 0.063) G_GAN: 0.283 G_GAN_Feat: 0.928 G_ID: 0.128 G_Rec: 0.422 D_GP: 0.044 D_real: 0.833 D_fake: 0.717 +(epoch: 502, iters: 6398, time: 0.064) G_GAN: 0.674 G_GAN_Feat: 0.693 G_ID: 0.097 G_Rec: 0.404 D_GP: 0.027 D_real: 1.561 D_fake: 0.359 +(epoch: 502, iters: 6798, time: 0.063) G_GAN: 0.447 G_GAN_Feat: 0.836 G_ID: 0.113 G_Rec: 0.393 D_GP: 0.030 D_real: 1.152 D_fake: 0.556 +(epoch: 502, iters: 7198, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.747 G_ID: 0.089 G_Rec: 0.330 D_GP: 0.039 D_real: 0.922 D_fake: 0.876 +(epoch: 502, iters: 7598, time: 0.063) G_GAN: 0.384 G_GAN_Feat: 0.932 G_ID: 0.115 G_Rec: 0.459 D_GP: 0.037 D_real: 1.066 D_fake: 0.638 +(epoch: 502, iters: 7998, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.738 G_ID: 0.084 G_Rec: 0.314 D_GP: 0.062 D_real: 0.871 D_fake: 0.989 +(epoch: 502, iters: 8398, time: 0.063) G_GAN: 0.293 G_GAN_Feat: 0.989 G_ID: 0.114 G_Rec: 0.441 D_GP: 0.041 D_real: 0.886 D_fake: 0.707 +(epoch: 503, iters: 190, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 0.743 G_ID: 0.102 G_Rec: 0.309 D_GP: 0.078 D_real: 0.910 D_fake: 0.792 +(epoch: 503, iters: 590, time: 0.063) G_GAN: 0.504 G_GAN_Feat: 0.956 G_ID: 0.115 G_Rec: 0.394 D_GP: 0.069 D_real: 0.995 D_fake: 0.516 +(epoch: 503, iters: 990, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.865 G_ID: 0.109 G_Rec: 0.341 D_GP: 0.043 D_real: 0.775 D_fake: 0.880 +(epoch: 503, iters: 1390, time: 0.063) G_GAN: 0.396 G_GAN_Feat: 0.929 G_ID: 0.102 G_Rec: 0.459 D_GP: 0.028 D_real: 1.196 D_fake: 0.605 +(epoch: 503, iters: 1790, time: 0.063) G_GAN: 0.161 G_GAN_Feat: 0.796 G_ID: 0.100 G_Rec: 0.321 D_GP: 0.043 D_real: 0.967 D_fake: 0.839 +(epoch: 503, iters: 2190, time: 0.063) G_GAN: 0.511 G_GAN_Feat: 0.980 G_ID: 0.111 G_Rec: 0.457 D_GP: 0.041 D_real: 1.062 D_fake: 0.510 +(epoch: 503, iters: 2590, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.726 G_ID: 0.095 G_Rec: 0.306 D_GP: 0.035 D_real: 0.982 D_fake: 0.809 +(epoch: 503, iters: 2990, time: 0.063) G_GAN: 0.081 G_GAN_Feat: 0.936 G_ID: 0.114 G_Rec: 0.428 D_GP: 0.047 D_real: 0.648 D_fake: 0.919 +(epoch: 503, iters: 3390, time: 0.063) G_GAN: 0.044 G_GAN_Feat: 0.737 G_ID: 0.100 G_Rec: 0.290 D_GP: 0.046 D_real: 0.909 D_fake: 0.956 +(epoch: 503, iters: 3790, time: 0.063) G_GAN: 0.628 G_GAN_Feat: 0.912 G_ID: 0.106 G_Rec: 0.376 D_GP: 0.034 D_real: 1.443 D_fake: 0.382 +(epoch: 503, iters: 4190, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.830 G_ID: 0.125 G_Rec: 0.399 D_GP: 0.042 D_real: 1.127 D_fake: 0.936 +(epoch: 503, iters: 4590, time: 0.063) G_GAN: 0.337 G_GAN_Feat: 0.909 G_ID: 0.118 G_Rec: 0.466 D_GP: 0.024 D_real: 1.117 D_fake: 0.667 +(epoch: 503, iters: 4990, time: 0.063) G_GAN: -0.047 G_GAN_Feat: 0.708 G_ID: 0.105 G_Rec: 0.345 D_GP: 0.027 D_real: 0.900 D_fake: 1.047 +(epoch: 503, iters: 5390, time: 0.063) G_GAN: 0.444 G_GAN_Feat: 0.813 G_ID: 0.111 G_Rec: 0.393 D_GP: 0.030 D_real: 1.195 D_fake: 0.560 +(epoch: 503, iters: 5790, time: 0.063) G_GAN: 0.141 G_GAN_Feat: 0.662 G_ID: 0.090 G_Rec: 0.353 D_GP: 0.028 D_real: 1.028 D_fake: 0.860 +(epoch: 503, iters: 6190, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 0.890 G_ID: 0.127 G_Rec: 0.458 D_GP: 0.040 D_real: 0.920 D_fake: 0.718 +(epoch: 503, iters: 6590, time: 0.063) G_GAN: 0.055 G_GAN_Feat: 0.642 G_ID: 0.094 G_Rec: 0.310 D_GP: 0.045 D_real: 0.896 D_fake: 0.945 +(epoch: 503, iters: 6990, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.914 G_ID: 0.131 G_Rec: 0.441 D_GP: 0.054 D_real: 0.769 D_fake: 0.829 +(epoch: 503, iters: 7390, time: 0.063) G_GAN: 0.215 G_GAN_Feat: 0.658 G_ID: 0.096 G_Rec: 0.312 D_GP: 0.039 D_real: 1.161 D_fake: 0.786 +(epoch: 503, iters: 7790, time: 0.063) G_GAN: 0.304 G_GAN_Feat: 0.885 G_ID: 0.124 G_Rec: 0.424 D_GP: 0.044 D_real: 1.026 D_fake: 0.698 +(epoch: 503, iters: 8190, time: 0.063) G_GAN: 0.052 G_GAN_Feat: 0.668 G_ID: 0.094 G_Rec: 0.300 D_GP: 0.040 D_real: 0.933 D_fake: 0.948 +(epoch: 503, iters: 8590, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.867 G_ID: 0.100 G_Rec: 0.416 D_GP: 0.037 D_real: 1.056 D_fake: 0.656 +(epoch: 504, iters: 382, time: 0.063) G_GAN: 0.186 G_GAN_Feat: 0.670 G_ID: 0.082 G_Rec: 0.302 D_GP: 0.064 D_real: 1.061 D_fake: 0.815 +(epoch: 504, iters: 782, time: 0.063) G_GAN: 0.170 G_GAN_Feat: 0.904 G_ID: 0.113 G_Rec: 0.443 D_GP: 0.040 D_real: 0.699 D_fake: 0.830 +(epoch: 504, iters: 1182, time: 0.063) G_GAN: 0.007 G_GAN_Feat: 0.719 G_ID: 0.094 G_Rec: 0.310 D_GP: 0.046 D_real: 0.737 D_fake: 0.993 +(epoch: 504, iters: 1582, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.888 G_ID: 0.128 G_Rec: 0.417 D_GP: 0.047 D_real: 0.774 D_fake: 0.688 +(epoch: 504, iters: 1982, time: 0.063) G_GAN: 0.040 G_GAN_Feat: 0.696 G_ID: 0.083 G_Rec: 0.296 D_GP: 0.042 D_real: 0.817 D_fake: 0.960 +(epoch: 504, iters: 2382, time: 0.063) G_GAN: 0.320 G_GAN_Feat: 0.961 G_ID: 0.101 G_Rec: 0.462 D_GP: 0.046 D_real: 0.896 D_fake: 0.684 +(epoch: 504, iters: 2782, time: 0.063) G_GAN: 0.155 G_GAN_Feat: 0.760 G_ID: 0.095 G_Rec: 0.339 D_GP: 0.069 D_real: 0.873 D_fake: 0.846 +(epoch: 504, iters: 3182, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.819 G_ID: 0.113 G_Rec: 0.367 D_GP: 0.035 D_real: 1.043 D_fake: 0.661 +(epoch: 504, iters: 3582, time: 0.063) G_GAN: 0.255 G_GAN_Feat: 0.715 G_ID: 0.106 G_Rec: 0.309 D_GP: 0.045 D_real: 1.079 D_fake: 0.747 +(epoch: 504, iters: 3982, time: 0.063) G_GAN: 0.646 G_GAN_Feat: 1.013 G_ID: 0.102 G_Rec: 0.434 D_GP: 0.059 D_real: 1.049 D_fake: 0.418 +(epoch: 504, iters: 4382, time: 0.063) G_GAN: 0.145 G_GAN_Feat: 0.818 G_ID: 0.104 G_Rec: 0.366 D_GP: 0.092 D_real: 0.712 D_fake: 0.855 +(epoch: 504, iters: 4782, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.986 G_ID: 0.131 G_Rec: 0.465 D_GP: 0.042 D_real: 0.797 D_fake: 0.636 +(epoch: 504, iters: 5182, time: 0.063) G_GAN: 0.072 G_GAN_Feat: 0.792 G_ID: 0.119 G_Rec: 0.318 D_GP: 0.211 D_real: 0.656 D_fake: 0.930 +(epoch: 504, iters: 5582, time: 0.063) G_GAN: 0.442 G_GAN_Feat: 1.085 G_ID: 0.116 G_Rec: 0.502 D_GP: 0.375 D_real: 0.667 D_fake: 0.583 +(epoch: 504, iters: 5982, time: 0.063) G_GAN: -0.199 G_GAN_Feat: 0.921 G_ID: 0.118 G_Rec: 0.393 D_GP: 0.352 D_real: 0.425 D_fake: 1.200 +(epoch: 504, iters: 6382, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.966 G_ID: 0.115 G_Rec: 0.422 D_GP: 0.041 D_real: 1.195 D_fake: 0.513 +(epoch: 504, iters: 6782, time: 0.063) G_GAN: -0.129 G_GAN_Feat: 0.770 G_ID: 0.095 G_Rec: 0.314 D_GP: 0.044 D_real: 0.610 D_fake: 1.129 +(epoch: 504, iters: 7182, time: 0.063) G_GAN: 0.541 G_GAN_Feat: 0.944 G_ID: 0.105 G_Rec: 0.392 D_GP: 0.038 D_real: 1.195 D_fake: 0.464 +(epoch: 504, iters: 7582, time: 0.063) G_GAN: 0.340 G_GAN_Feat: 0.798 G_ID: 0.088 G_Rec: 0.287 D_GP: 0.055 D_real: 0.928 D_fake: 0.661 +(epoch: 504, iters: 7982, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.977 G_ID: 0.121 G_Rec: 0.445 D_GP: 0.032 D_real: 0.904 D_fake: 0.711 +(epoch: 504, iters: 8382, time: 0.063) G_GAN: 0.167 G_GAN_Feat: 0.743 G_ID: 0.092 G_Rec: 0.301 D_GP: 0.032 D_real: 0.951 D_fake: 0.834 +(epoch: 505, iters: 174, time: 0.063) G_GAN: 1.143 G_GAN_Feat: 1.145 G_ID: 0.114 G_Rec: 0.448 D_GP: 0.061 D_real: 1.485 D_fake: 0.122 +(epoch: 505, iters: 574, time: 0.063) G_GAN: 0.635 G_GAN_Feat: 0.892 G_ID: 0.086 G_Rec: 0.324 D_GP: 0.054 D_real: 0.752 D_fake: 0.416 +(epoch: 505, iters: 974, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.953 G_ID: 0.118 G_Rec: 0.447 D_GP: 0.033 D_real: 0.986 D_fake: 0.663 +(epoch: 505, iters: 1374, time: 0.063) G_GAN: 0.195 G_GAN_Feat: 0.657 G_ID: 0.099 G_Rec: 0.288 D_GP: 0.030 D_real: 1.102 D_fake: 0.807 +(epoch: 505, iters: 1774, time: 0.063) G_GAN: 0.148 G_GAN_Feat: 0.861 G_ID: 0.115 G_Rec: 0.425 D_GP: 0.031 D_real: 0.859 D_fake: 0.854 +(epoch: 505, iters: 2174, time: 0.063) G_GAN: 0.073 G_GAN_Feat: 0.665 G_ID: 0.079 G_Rec: 0.304 D_GP: 0.030 D_real: 0.933 D_fake: 0.927 +(epoch: 505, iters: 2574, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.879 G_ID: 0.119 G_Rec: 0.423 D_GP: 0.044 D_real: 0.950 D_fake: 0.692 +(epoch: 505, iters: 2974, time: 0.063) G_GAN: -0.209 G_GAN_Feat: 0.681 G_ID: 0.113 G_Rec: 0.296 D_GP: 0.036 D_real: 0.665 D_fake: 1.209 +(epoch: 505, iters: 3374, time: 0.063) G_GAN: 0.400 G_GAN_Feat: 0.934 G_ID: 0.109 G_Rec: 0.510 D_GP: 0.050 D_real: 0.884 D_fake: 0.614 +(epoch: 505, iters: 3774, time: 0.063) G_GAN: 0.057 G_GAN_Feat: 0.751 G_ID: 0.101 G_Rec: 0.314 D_GP: 0.070 D_real: 0.801 D_fake: 0.943 +(epoch: 505, iters: 4174, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.994 G_ID: 0.117 G_Rec: 0.450 D_GP: 0.092 D_real: 0.639 D_fake: 0.783 +(epoch: 505, iters: 4574, time: 0.063) G_GAN: 0.064 G_GAN_Feat: 0.789 G_ID: 0.095 G_Rec: 0.338 D_GP: 0.048 D_real: 0.967 D_fake: 0.937 +(epoch: 505, iters: 4974, time: 0.063) G_GAN: 0.454 G_GAN_Feat: 1.077 G_ID: 0.142 G_Rec: 0.472 D_GP: 0.301 D_real: 0.618 D_fake: 0.560 +(epoch: 505, iters: 5374, time: 0.063) G_GAN: 0.252 G_GAN_Feat: 0.741 G_ID: 0.129 G_Rec: 0.297 D_GP: 0.093 D_real: 0.849 D_fake: 0.748 +(epoch: 505, iters: 5774, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 1.036 G_ID: 0.102 G_Rec: 0.470 D_GP: 0.045 D_real: 0.780 D_fake: 0.623 +(epoch: 505, iters: 6174, time: 0.063) G_GAN: 0.615 G_GAN_Feat: 0.721 G_ID: 0.104 G_Rec: 0.295 D_GP: 0.039 D_real: 1.398 D_fake: 0.402 +(epoch: 505, iters: 6574, time: 0.063) G_GAN: 0.728 G_GAN_Feat: 1.079 G_ID: 0.122 G_Rec: 0.526 D_GP: 0.059 D_real: 0.996 D_fake: 0.300 +(epoch: 505, iters: 6974, time: 0.063) G_GAN: 0.244 G_GAN_Feat: 0.881 G_ID: 0.112 G_Rec: 0.342 D_GP: 0.032 D_real: 0.967 D_fake: 0.757 +(epoch: 505, iters: 7374, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 1.016 G_ID: 0.125 G_Rec: 0.465 D_GP: 0.038 D_real: 0.864 D_fake: 0.837 +(epoch: 505, iters: 7774, time: 0.063) G_GAN: 0.051 G_GAN_Feat: 0.673 G_ID: 0.088 G_Rec: 0.307 D_GP: 0.028 D_real: 0.976 D_fake: 0.949 +(epoch: 505, iters: 8174, time: 0.063) G_GAN: 0.155 G_GAN_Feat: 0.972 G_ID: 0.120 G_Rec: 0.447 D_GP: 0.042 D_real: 0.808 D_fake: 0.847 +(epoch: 505, iters: 8574, time: 0.063) G_GAN: 0.005 G_GAN_Feat: 0.781 G_ID: 0.115 G_Rec: 0.313 D_GP: 0.050 D_real: 0.696 D_fake: 0.996 +(epoch: 506, iters: 366, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.960 G_ID: 0.132 G_Rec: 0.413 D_GP: 0.045 D_real: 0.940 D_fake: 0.613 +(epoch: 506, iters: 766, time: 0.063) G_GAN: 0.102 G_GAN_Feat: 0.786 G_ID: 0.125 G_Rec: 0.326 D_GP: 0.042 D_real: 0.806 D_fake: 0.898 +(epoch: 506, iters: 1166, time: 0.063) G_GAN: 0.674 G_GAN_Feat: 1.018 G_ID: 0.110 G_Rec: 0.461 D_GP: 0.062 D_real: 1.021 D_fake: 0.344 +(epoch: 506, iters: 1566, time: 0.063) G_GAN: 0.207 G_GAN_Feat: 0.811 G_ID: 0.103 G_Rec: 0.298 D_GP: 0.038 D_real: 0.811 D_fake: 0.794 +(epoch: 506, iters: 1966, time: 0.064) G_GAN: 0.805 G_GAN_Feat: 1.186 G_ID: 0.106 G_Rec: 0.465 D_GP: 0.052 D_real: 0.656 D_fake: 0.268 +(epoch: 506, iters: 2366, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 0.927 G_ID: 0.109 G_Rec: 0.315 D_GP: 0.048 D_real: 0.563 D_fake: 0.602 +(epoch: 506, iters: 2766, time: 0.063) G_GAN: 0.317 G_GAN_Feat: 0.904 G_ID: 0.110 G_Rec: 0.464 D_GP: 0.031 D_real: 1.081 D_fake: 0.688 +(epoch: 506, iters: 3166, time: 0.063) G_GAN: 0.145 G_GAN_Feat: 0.663 G_ID: 0.098 G_Rec: 0.310 D_GP: 0.028 D_real: 1.092 D_fake: 0.855 +(epoch: 506, iters: 3566, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.802 G_ID: 0.117 G_Rec: 0.384 D_GP: 0.028 D_real: 0.946 D_fake: 0.781 +(epoch: 506, iters: 3966, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.682 G_ID: 0.092 G_Rec: 0.313 D_GP: 0.039 D_real: 1.059 D_fake: 0.818 +(epoch: 506, iters: 4366, time: 0.063) G_GAN: 0.153 G_GAN_Feat: 0.863 G_ID: 0.118 G_Rec: 0.405 D_GP: 0.043 D_real: 0.887 D_fake: 0.847 +(epoch: 506, iters: 4766, time: 0.063) G_GAN: -0.088 G_GAN_Feat: 0.762 G_ID: 0.125 G_Rec: 0.361 D_GP: 0.040 D_real: 0.755 D_fake: 1.088 +(epoch: 506, iters: 5166, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.874 G_ID: 0.112 G_Rec: 0.425 D_GP: 0.036 D_real: 1.007 D_fake: 0.662 +(epoch: 506, iters: 5566, time: 0.063) G_GAN: -0.049 G_GAN_Feat: 0.676 G_ID: 0.100 G_Rec: 0.336 D_GP: 0.030 D_real: 0.821 D_fake: 1.049 +(epoch: 506, iters: 5966, time: 0.063) G_GAN: 0.121 G_GAN_Feat: 0.856 G_ID: 0.122 G_Rec: 0.400 D_GP: 0.034 D_real: 0.761 D_fake: 0.880 +(epoch: 506, iters: 6366, time: 0.063) G_GAN: 0.063 G_GAN_Feat: 0.645 G_ID: 0.087 G_Rec: 0.302 D_GP: 0.042 D_real: 0.925 D_fake: 0.937 +(epoch: 506, iters: 6766, time: 0.064) G_GAN: -0.033 G_GAN_Feat: 0.941 G_ID: 0.133 G_Rec: 0.445 D_GP: 0.042 D_real: 0.580 D_fake: 1.033 +(epoch: 506, iters: 7166, time: 0.063) G_GAN: -0.125 G_GAN_Feat: 0.662 G_ID: 0.117 G_Rec: 0.287 D_GP: 0.037 D_real: 0.767 D_fake: 1.125 +(epoch: 506, iters: 7566, time: 0.063) G_GAN: 0.372 G_GAN_Feat: 0.875 G_ID: 0.113 G_Rec: 0.422 D_GP: 0.036 D_real: 1.148 D_fake: 0.636 +(epoch: 506, iters: 7966, time: 0.063) G_GAN: -0.113 G_GAN_Feat: 0.703 G_ID: 0.103 G_Rec: 0.302 D_GP: 0.040 D_real: 0.720 D_fake: 1.113 +(epoch: 506, iters: 8366, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.877 G_ID: 0.101 G_Rec: 0.393 D_GP: 0.034 D_real: 1.064 D_fake: 0.633 +(epoch: 507, iters: 158, time: 0.063) G_GAN: 0.378 G_GAN_Feat: 0.712 G_ID: 0.111 G_Rec: 0.323 D_GP: 0.046 D_real: 1.184 D_fake: 0.636 +(epoch: 507, iters: 558, time: 0.063) G_GAN: 0.140 G_GAN_Feat: 0.935 G_ID: 0.123 G_Rec: 0.453 D_GP: 0.070 D_real: 0.605 D_fake: 0.862 +(epoch: 507, iters: 958, time: 0.063) G_GAN: -0.099 G_GAN_Feat: 0.710 G_ID: 0.082 G_Rec: 0.298 D_GP: 0.050 D_real: 0.710 D_fake: 1.099 +(epoch: 507, iters: 1358, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.929 G_ID: 0.113 G_Rec: 0.452 D_GP: 0.040 D_real: 1.001 D_fake: 0.639 +(epoch: 507, iters: 1758, time: 0.063) G_GAN: 0.137 G_GAN_Feat: 0.771 G_ID: 0.100 G_Rec: 0.344 D_GP: 0.057 D_real: 0.822 D_fake: 0.864 +(epoch: 507, iters: 2158, time: 0.063) G_GAN: 0.268 G_GAN_Feat: 0.876 G_ID: 0.094 G_Rec: 0.394 D_GP: 0.056 D_real: 0.984 D_fake: 0.735 +(epoch: 507, iters: 2558, time: 0.063) G_GAN: 0.082 G_GAN_Feat: 0.672 G_ID: 0.088 G_Rec: 0.287 D_GP: 0.039 D_real: 1.087 D_fake: 0.919 +(epoch: 507, iters: 2958, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.905 G_ID: 0.106 G_Rec: 0.442 D_GP: 0.032 D_real: 1.172 D_fake: 0.545 +(epoch: 507, iters: 3358, time: 0.063) G_GAN: 0.315 G_GAN_Feat: 0.673 G_ID: 0.092 G_Rec: 0.294 D_GP: 0.031 D_real: 1.172 D_fake: 0.687 +(epoch: 507, iters: 3758, time: 0.063) G_GAN: 0.255 G_GAN_Feat: 1.055 G_ID: 0.108 G_Rec: 0.476 D_GP: 0.108 D_real: 0.580 D_fake: 0.747 +(epoch: 507, iters: 4158, time: 0.063) G_GAN: 0.087 G_GAN_Feat: 0.690 G_ID: 0.098 G_Rec: 0.320 D_GP: 0.044 D_real: 0.865 D_fake: 0.913 +(epoch: 507, iters: 4558, time: 0.064) G_GAN: 0.770 G_GAN_Feat: 0.973 G_ID: 0.112 G_Rec: 0.478 D_GP: 0.049 D_real: 1.291 D_fake: 0.313 +(epoch: 507, iters: 4958, time: 0.063) G_GAN: 0.078 G_GAN_Feat: 0.727 G_ID: 0.104 G_Rec: 0.328 D_GP: 0.050 D_real: 0.848 D_fake: 0.922 +(epoch: 507, iters: 5358, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.970 G_ID: 0.114 G_Rec: 0.482 D_GP: 0.105 D_real: 0.653 D_fake: 0.672 +(epoch: 507, iters: 5758, time: 0.063) G_GAN: 0.196 G_GAN_Feat: 0.864 G_ID: 0.106 G_Rec: 0.360 D_GP: 0.070 D_real: 0.605 D_fake: 0.806 +(epoch: 507, iters: 6158, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 1.021 G_ID: 0.118 G_Rec: 0.464 D_GP: 0.114 D_real: 0.697 D_fake: 0.594 +(epoch: 507, iters: 6558, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.842 G_ID: 0.109 G_Rec: 0.344 D_GP: 0.119 D_real: 0.647 D_fake: 0.776 +(epoch: 507, iters: 6958, time: 0.063) G_GAN: 0.025 G_GAN_Feat: 1.013 G_ID: 0.106 G_Rec: 0.484 D_GP: 0.069 D_real: 0.541 D_fake: 0.976 +(epoch: 507, iters: 7358, time: 0.063) G_GAN: 0.103 G_GAN_Feat: 0.677 G_ID: 0.089 G_Rec: 0.315 D_GP: 0.031 D_real: 1.017 D_fake: 0.897 +(epoch: 507, iters: 7758, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.946 G_ID: 0.111 G_Rec: 0.462 D_GP: 0.038 D_real: 1.011 D_fake: 0.584 +(epoch: 507, iters: 8158, time: 0.063) G_GAN: 0.013 G_GAN_Feat: 0.815 G_ID: 0.093 G_Rec: 0.348 D_GP: 0.129 D_real: 0.421 D_fake: 0.989 +(epoch: 507, iters: 8558, time: 0.063) G_GAN: 0.356 G_GAN_Feat: 1.043 G_ID: 0.120 G_Rec: 0.456 D_GP: 0.211 D_real: 0.767 D_fake: 0.656 +(epoch: 508, iters: 350, time: 0.063) G_GAN: 0.061 G_GAN_Feat: 0.674 G_ID: 0.088 G_Rec: 0.275 D_GP: 0.036 D_real: 0.906 D_fake: 0.939 +(epoch: 508, iters: 750, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 1.028 G_ID: 0.122 G_Rec: 0.464 D_GP: 0.046 D_real: 0.826 D_fake: 0.596 +(epoch: 508, iters: 1150, time: 0.063) G_GAN: -0.104 G_GAN_Feat: 0.895 G_ID: 0.105 G_Rec: 0.354 D_GP: 0.749 D_real: 0.223 D_fake: 1.105 +(epoch: 508, iters: 1550, time: 0.063) G_GAN: 0.324 G_GAN_Feat: 0.918 G_ID: 0.117 G_Rec: 0.421 D_GP: 0.043 D_real: 0.893 D_fake: 0.680 +(epoch: 508, iters: 1950, time: 0.063) G_GAN: 0.297 G_GAN_Feat: 0.770 G_ID: 0.088 G_Rec: 0.313 D_GP: 0.105 D_real: 0.818 D_fake: 0.707 +(epoch: 508, iters: 2350, time: 0.063) G_GAN: 0.470 G_GAN_Feat: 1.096 G_ID: 0.091 G_Rec: 0.518 D_GP: 0.027 D_real: 1.159 D_fake: 0.544 +(epoch: 508, iters: 2750, time: 0.063) G_GAN: 0.098 G_GAN_Feat: 0.716 G_ID: 0.100 G_Rec: 0.321 D_GP: 0.027 D_real: 0.924 D_fake: 0.902 +(epoch: 508, iters: 3150, time: 0.063) G_GAN: 0.265 G_GAN_Feat: 0.884 G_ID: 0.110 G_Rec: 0.419 D_GP: 0.034 D_real: 0.975 D_fake: 0.737 +(epoch: 508, iters: 3550, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.737 G_ID: 0.083 G_Rec: 0.355 D_GP: 0.057 D_real: 0.825 D_fake: 0.990 +(epoch: 508, iters: 3950, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.832 G_ID: 0.116 G_Rec: 0.382 D_GP: 0.036 D_real: 0.974 D_fake: 0.771 +(epoch: 508, iters: 4350, time: 0.063) G_GAN: 0.097 G_GAN_Feat: 0.687 G_ID: 0.097 G_Rec: 0.297 D_GP: 0.049 D_real: 0.948 D_fake: 0.904 +(epoch: 508, iters: 4750, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 0.922 G_ID: 0.109 G_Rec: 0.423 D_GP: 0.056 D_real: 0.718 D_fake: 0.876 +(epoch: 508, iters: 5150, time: 0.064) G_GAN: -0.177 G_GAN_Feat: 0.728 G_ID: 0.080 G_Rec: 0.307 D_GP: 0.052 D_real: 0.608 D_fake: 1.177 +(epoch: 508, iters: 5550, time: 0.063) G_GAN: 0.449 G_GAN_Feat: 1.037 G_ID: 0.105 G_Rec: 0.475 D_GP: 0.174 D_real: 0.660 D_fake: 0.559 +(epoch: 508, iters: 5950, time: 0.063) G_GAN: -0.175 G_GAN_Feat: 0.804 G_ID: 0.115 G_Rec: 0.355 D_GP: 0.115 D_real: 0.477 D_fake: 1.177 +(epoch: 508, iters: 6350, time: 0.063) G_GAN: 0.719 G_GAN_Feat: 1.063 G_ID: 0.121 G_Rec: 0.463 D_GP: 0.045 D_real: 1.108 D_fake: 0.315 +(epoch: 508, iters: 6750, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.783 G_ID: 0.082 G_Rec: 0.295 D_GP: 0.049 D_real: 1.122 D_fake: 0.555 +(epoch: 508, iters: 7150, time: 0.063) G_GAN: 0.197 G_GAN_Feat: 0.959 G_ID: 0.129 G_Rec: 0.408 D_GP: 0.044 D_real: 0.859 D_fake: 0.804 +(epoch: 508, iters: 7550, time: 0.063) G_GAN: 0.042 G_GAN_Feat: 0.784 G_ID: 0.115 G_Rec: 0.320 D_GP: 0.032 D_real: 0.951 D_fake: 0.958 +(epoch: 508, iters: 7950, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.930 G_ID: 0.116 G_Rec: 0.453 D_GP: 0.044 D_real: 0.667 D_fake: 0.878 +(epoch: 508, iters: 8350, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.710 G_ID: 0.078 G_Rec: 0.284 D_GP: 0.068 D_real: 0.736 D_fake: 0.912 +(epoch: 509, iters: 142, time: 0.063) G_GAN: 0.557 G_GAN_Feat: 0.879 G_ID: 0.105 G_Rec: 0.373 D_GP: 0.035 D_real: 1.236 D_fake: 0.477 +(epoch: 509, iters: 542, time: 0.063) G_GAN: 0.122 G_GAN_Feat: 0.737 G_ID: 0.081 G_Rec: 0.287 D_GP: 0.034 D_real: 0.931 D_fake: 0.878 +(epoch: 509, iters: 942, time: 0.063) G_GAN: 0.535 G_GAN_Feat: 1.165 G_ID: 0.124 G_Rec: 0.517 D_GP: 0.080 D_real: 0.495 D_fake: 0.479 +(epoch: 509, iters: 1342, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.874 G_ID: 0.092 G_Rec: 0.337 D_GP: 0.056 D_real: 0.586 D_fake: 0.755 +(epoch: 509, iters: 1742, time: 0.063) G_GAN: 0.374 G_GAN_Feat: 1.080 G_ID: 0.122 G_Rec: 0.452 D_GP: 0.054 D_real: 0.792 D_fake: 0.633 +(epoch: 509, iters: 2142, time: 0.063) G_GAN: -0.009 G_GAN_Feat: 0.881 G_ID: 0.124 G_Rec: 0.360 D_GP: 0.048 D_real: 0.659 D_fake: 1.010 +(epoch: 509, iters: 2542, time: 0.063) G_GAN: 0.440 G_GAN_Feat: 0.965 G_ID: 0.133 G_Rec: 0.410 D_GP: 0.043 D_real: 0.843 D_fake: 0.564 +(epoch: 509, iters: 2942, time: 0.064) G_GAN: 0.054 G_GAN_Feat: 0.763 G_ID: 0.117 G_Rec: 0.333 D_GP: 0.037 D_real: 0.991 D_fake: 0.946 +(epoch: 509, iters: 3342, time: 0.063) G_GAN: 0.180 G_GAN_Feat: 1.005 G_ID: 0.109 G_Rec: 0.493 D_GP: 0.033 D_real: 0.894 D_fake: 0.820 +(epoch: 509, iters: 3742, time: 0.063) G_GAN: 0.043 G_GAN_Feat: 0.609 G_ID: 0.088 G_Rec: 0.262 D_GP: 0.027 D_real: 1.061 D_fake: 0.957 +(epoch: 509, iters: 4142, time: 0.063) G_GAN: 0.455 G_GAN_Feat: 0.919 G_ID: 0.111 G_Rec: 0.424 D_GP: 0.035 D_real: 1.048 D_fake: 0.582 +(epoch: 509, iters: 4542, time: 0.064) G_GAN: -0.082 G_GAN_Feat: 0.739 G_ID: 0.117 G_Rec: 0.303 D_GP: 0.045 D_real: 0.670 D_fake: 1.082 +(epoch: 509, iters: 4942, time: 0.063) G_GAN: 0.525 G_GAN_Feat: 0.927 G_ID: 0.108 G_Rec: 0.388 D_GP: 0.055 D_real: 1.084 D_fake: 0.486 +(epoch: 509, iters: 5342, time: 0.063) G_GAN: 0.185 G_GAN_Feat: 0.719 G_ID: 0.099 G_Rec: 0.302 D_GP: 0.036 D_real: 1.106 D_fake: 0.815 +(epoch: 509, iters: 5742, time: 0.063) G_GAN: 0.427 G_GAN_Feat: 0.861 G_ID: 0.118 G_Rec: 0.388 D_GP: 0.043 D_real: 1.092 D_fake: 0.588 +(epoch: 509, iters: 6142, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.789 G_ID: 0.093 G_Rec: 0.333 D_GP: 0.037 D_real: 0.968 D_fake: 0.696 +(epoch: 509, iters: 6542, time: 0.063) G_GAN: 0.535 G_GAN_Feat: 0.920 G_ID: 0.106 G_Rec: 0.382 D_GP: 0.037 D_real: 1.199 D_fake: 0.488 +(epoch: 509, iters: 6942, time: 0.063) G_GAN: -0.160 G_GAN_Feat: 0.746 G_ID: 0.106 G_Rec: 0.339 D_GP: 0.032 D_real: 0.792 D_fake: 1.160 +(epoch: 509, iters: 7342, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.917 G_ID: 0.106 G_Rec: 0.392 D_GP: 0.031 D_real: 0.897 D_fake: 0.766 +(epoch: 509, iters: 7742, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.802 G_ID: 0.085 G_Rec: 0.331 D_GP: 0.039 D_real: 0.813 D_fake: 0.942 +(epoch: 509, iters: 8142, time: 0.063) G_GAN: 0.292 G_GAN_Feat: 0.990 G_ID: 0.110 G_Rec: 0.408 D_GP: 0.036 D_real: 0.917 D_fake: 0.709 +(epoch: 509, iters: 8542, time: 0.063) G_GAN: 0.275 G_GAN_Feat: 0.859 G_ID: 0.087 G_Rec: 0.311 D_GP: 0.086 D_real: 0.740 D_fake: 0.728 +(epoch: 510, iters: 334, time: 0.063) G_GAN: 0.535 G_GAN_Feat: 1.059 G_ID: 0.116 G_Rec: 0.440 D_GP: 0.118 D_real: 0.552 D_fake: 0.487 +(epoch: 510, iters: 734, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.979 G_ID: 0.102 G_Rec: 0.325 D_GP: 0.042 D_real: 0.845 D_fake: 0.626 +(epoch: 510, iters: 1134, time: 0.063) G_GAN: 0.563 G_GAN_Feat: 1.039 G_ID: 0.108 G_Rec: 0.443 D_GP: 0.057 D_real: 1.132 D_fake: 0.529 +(epoch: 510, iters: 1534, time: 0.063) G_GAN: 0.246 G_GAN_Feat: 0.763 G_ID: 0.133 G_Rec: 0.298 D_GP: 0.048 D_real: 1.056 D_fake: 0.755 +(epoch: 510, iters: 1934, time: 0.063) G_GAN: 0.268 G_GAN_Feat: 1.093 G_ID: 0.115 G_Rec: 0.430 D_GP: 0.469 D_real: 0.389 D_fake: 0.737 +(epoch: 510, iters: 2334, time: 0.064) G_GAN: 0.660 G_GAN_Feat: 0.713 G_ID: 0.080 G_Rec: 0.283 D_GP: 0.055 D_real: 1.518 D_fake: 0.474 +(epoch: 510, iters: 2734, time: 0.063) G_GAN: 0.428 G_GAN_Feat: 1.022 G_ID: 0.124 G_Rec: 0.455 D_GP: 0.060 D_real: 0.942 D_fake: 0.578 +(epoch: 510, iters: 3134, time: 0.063) G_GAN: 0.060 G_GAN_Feat: 0.752 G_ID: 0.096 G_Rec: 0.318 D_GP: 0.053 D_real: 0.801 D_fake: 0.940 +(epoch: 510, iters: 3534, time: 0.063) G_GAN: 0.319 G_GAN_Feat: 0.899 G_ID: 0.108 G_Rec: 0.408 D_GP: 0.040 D_real: 1.024 D_fake: 0.688 +(epoch: 510, iters: 3934, time: 0.064) G_GAN: -0.123 G_GAN_Feat: 0.822 G_ID: 0.092 G_Rec: 0.331 D_GP: 0.077 D_real: 0.883 D_fake: 1.123 +(epoch: 510, iters: 4334, time: 0.063) G_GAN: 0.568 G_GAN_Feat: 1.035 G_ID: 0.107 G_Rec: 0.481 D_GP: 0.051 D_real: 1.038 D_fake: 0.443 +(epoch: 510, iters: 4734, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 0.824 G_ID: 0.132 G_Rec: 0.335 D_GP: 0.071 D_real: 0.961 D_fake: 0.616 +(epoch: 510, iters: 5134, time: 0.063) G_GAN: 0.454 G_GAN_Feat: 1.032 G_ID: 0.113 G_Rec: 0.473 D_GP: 0.046 D_real: 1.147 D_fake: 0.562 +(epoch: 510, iters: 5534, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.789 G_ID: 0.095 G_Rec: 0.290 D_GP: 0.048 D_real: 0.893 D_fake: 0.735 +(epoch: 510, iters: 5934, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 0.970 G_ID: 0.113 G_Rec: 0.431 D_GP: 0.052 D_real: 0.892 D_fake: 0.596 +(epoch: 510, iters: 6334, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.881 G_ID: 0.100 G_Rec: 0.342 D_GP: 0.043 D_real: 0.756 D_fake: 0.705 +(epoch: 510, iters: 6734, time: 0.063) G_GAN: 0.581 G_GAN_Feat: 1.077 G_ID: 0.109 G_Rec: 0.466 D_GP: 0.036 D_real: 0.832 D_fake: 0.478 +(epoch: 510, iters: 7134, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.856 G_ID: 0.091 G_Rec: 0.311 D_GP: 0.037 D_real: 1.121 D_fake: 0.707 +(epoch: 510, iters: 7534, time: 0.063) G_GAN: 0.575 G_GAN_Feat: 1.111 G_ID: 0.131 G_Rec: 0.456 D_GP: 0.047 D_real: 0.686 D_fake: 0.433 +(epoch: 510, iters: 7934, time: 0.063) G_GAN: -0.049 G_GAN_Feat: 0.859 G_ID: 0.092 G_Rec: 0.328 D_GP: 0.102 D_real: 0.526 D_fake: 1.049 +(epoch: 510, iters: 8334, time: 0.063) G_GAN: 0.311 G_GAN_Feat: 1.026 G_ID: 0.139 G_Rec: 0.476 D_GP: 0.033 D_real: 0.868 D_fake: 0.689 +(epoch: 511, iters: 126, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.768 G_ID: 0.087 G_Rec: 0.293 D_GP: 0.032 D_real: 1.156 D_fake: 0.569 +(epoch: 511, iters: 526, time: 0.063) G_GAN: 0.920 G_GAN_Feat: 1.157 G_ID: 0.109 G_Rec: 0.474 D_GP: 0.135 D_real: 0.765 D_fake: 0.183 +(epoch: 511, iters: 926, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 0.825 G_ID: 0.081 G_Rec: 0.327 D_GP: 0.032 D_real: 1.176 D_fake: 0.697 +(epoch: 511, iters: 1326, time: 0.063) G_GAN: 0.636 G_GAN_Feat: 1.069 G_ID: 0.096 G_Rec: 0.474 D_GP: 0.041 D_real: 0.835 D_fake: 0.399 +(epoch: 511, iters: 1726, time: 0.064) G_GAN: 0.007 G_GAN_Feat: 0.858 G_ID: 0.116 G_Rec: 0.318 D_GP: 0.105 D_real: 0.379 D_fake: 0.993 +(epoch: 511, iters: 2126, time: 0.063) G_GAN: 0.835 G_GAN_Feat: 1.212 G_ID: 0.094 G_Rec: 0.474 D_GP: 0.368 D_real: 0.539 D_fake: 0.241 +(epoch: 511, iters: 2526, time: 0.063) G_GAN: 0.326 G_GAN_Feat: 0.900 G_ID: 0.096 G_Rec: 0.332 D_GP: 0.058 D_real: 1.151 D_fake: 0.723 +(epoch: 511, iters: 2926, time: 0.063) G_GAN: 0.419 G_GAN_Feat: 0.994 G_ID: 0.105 G_Rec: 0.435 D_GP: 0.035 D_real: 1.095 D_fake: 0.586 +(epoch: 511, iters: 3326, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.716 G_ID: 0.095 G_Rec: 0.317 D_GP: 0.031 D_real: 1.255 D_fake: 0.631 +(epoch: 511, iters: 3726, time: 0.063) G_GAN: 0.256 G_GAN_Feat: 0.916 G_ID: 0.119 G_Rec: 0.451 D_GP: 0.031 D_real: 0.934 D_fake: 0.750 +(epoch: 511, iters: 4126, time: 0.063) G_GAN: 0.079 G_GAN_Feat: 0.778 G_ID: 0.091 G_Rec: 0.335 D_GP: 0.053 D_real: 0.905 D_fake: 0.922 +(epoch: 511, iters: 4526, time: 0.063) G_GAN: 0.268 G_GAN_Feat: 0.891 G_ID: 0.122 G_Rec: 0.396 D_GP: 0.037 D_real: 0.814 D_fake: 0.735 +(epoch: 511, iters: 4926, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.708 G_ID: 0.089 G_Rec: 0.283 D_GP: 0.033 D_real: 1.299 D_fake: 0.619 +(epoch: 511, iters: 5326, time: 0.063) G_GAN: 0.567 G_GAN_Feat: 0.962 G_ID: 0.108 G_Rec: 0.446 D_GP: 0.042 D_real: 1.231 D_fake: 0.446 +(epoch: 511, iters: 5726, time: 0.063) G_GAN: 0.540 G_GAN_Feat: 0.845 G_ID: 0.087 G_Rec: 0.311 D_GP: 0.054 D_real: 1.103 D_fake: 0.471 +(epoch: 511, iters: 6126, time: 0.063) G_GAN: 0.910 G_GAN_Feat: 1.030 G_ID: 0.105 G_Rec: 0.460 D_GP: 0.040 D_real: 1.410 D_fake: 0.219 +(epoch: 511, iters: 6526, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.992 G_ID: 0.129 G_Rec: 0.368 D_GP: 0.041 D_real: 1.246 D_fake: 0.721 +(epoch: 511, iters: 6926, time: 0.063) G_GAN: 0.425 G_GAN_Feat: 1.031 G_ID: 0.113 G_Rec: 0.482 D_GP: 0.038 D_real: 1.062 D_fake: 0.578 +(epoch: 511, iters: 7326, time: 0.063) G_GAN: 0.376 G_GAN_Feat: 0.962 G_ID: 0.113 G_Rec: 0.342 D_GP: 0.079 D_real: 0.381 D_fake: 0.639 +(epoch: 511, iters: 7726, time: 0.063) G_GAN: 0.692 G_GAN_Feat: 1.029 G_ID: 0.112 G_Rec: 0.431 D_GP: 0.048 D_real: 1.165 D_fake: 0.464 +(epoch: 511, iters: 8126, time: 0.064) G_GAN: -0.064 G_GAN_Feat: 0.736 G_ID: 0.101 G_Rec: 0.323 D_GP: 0.037 D_real: 0.752 D_fake: 1.064 +(epoch: 511, iters: 8526, time: 0.063) G_GAN: 0.653 G_GAN_Feat: 1.027 G_ID: 0.119 G_Rec: 0.451 D_GP: 0.081 D_real: 0.903 D_fake: 0.383 +(epoch: 512, iters: 318, time: 0.063) G_GAN: 0.213 G_GAN_Feat: 0.815 G_ID: 0.104 G_Rec: 0.321 D_GP: 0.077 D_real: 0.640 D_fake: 0.791 +(epoch: 512, iters: 718, time: 0.063) G_GAN: 0.289 G_GAN_Feat: 1.149 G_ID: 0.115 G_Rec: 0.487 D_GP: 0.250 D_real: 0.380 D_fake: 0.723 +(epoch: 512, iters: 1118, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.848 G_ID: 0.114 G_Rec: 0.323 D_GP: 0.051 D_real: 1.523 D_fake: 0.500 +(epoch: 512, iters: 1518, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.987 G_ID: 0.125 G_Rec: 0.465 D_GP: 0.031 D_real: 0.878 D_fake: 0.763 +(epoch: 512, iters: 1918, time: 0.063) G_GAN: 0.068 G_GAN_Feat: 0.728 G_ID: 0.107 G_Rec: 0.316 D_GP: 0.048 D_real: 0.876 D_fake: 0.932 +(epoch: 512, iters: 2318, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 1.017 G_ID: 0.112 G_Rec: 0.459 D_GP: 0.049 D_real: 0.926 D_fake: 0.536 +(epoch: 512, iters: 2718, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.670 G_ID: 0.089 G_Rec: 0.315 D_GP: 0.028 D_real: 1.411 D_fake: 0.553 +(epoch: 512, iters: 3118, time: 0.063) G_GAN: 0.404 G_GAN_Feat: 0.966 G_ID: 0.100 G_Rec: 0.480 D_GP: 0.031 D_real: 0.973 D_fake: 0.606 +(epoch: 512, iters: 3518, time: 0.063) G_GAN: 0.053 G_GAN_Feat: 0.719 G_ID: 0.129 G_Rec: 0.320 D_GP: 0.035 D_real: 0.904 D_fake: 0.947 +(epoch: 512, iters: 3918, time: 0.063) G_GAN: 0.363 G_GAN_Feat: 1.017 G_ID: 0.118 G_Rec: 0.467 D_GP: 0.050 D_real: 0.792 D_fake: 0.647 +(epoch: 512, iters: 4318, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.728 G_ID: 0.092 G_Rec: 0.310 D_GP: 0.041 D_real: 0.919 D_fake: 0.863 +(epoch: 512, iters: 4718, time: 0.063) G_GAN: 0.239 G_GAN_Feat: 1.038 G_ID: 0.108 G_Rec: 0.457 D_GP: 0.047 D_real: 0.610 D_fake: 0.762 +(epoch: 512, iters: 5118, time: 0.063) G_GAN: 0.277 G_GAN_Feat: 0.831 G_ID: 0.101 G_Rec: 0.324 D_GP: 0.044 D_real: 0.863 D_fake: 0.723 +(epoch: 512, iters: 5518, time: 0.063) G_GAN: 0.503 G_GAN_Feat: 1.035 G_ID: 0.106 G_Rec: 0.450 D_GP: 0.047 D_real: 0.729 D_fake: 0.507 +(epoch: 512, iters: 5918, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.807 G_ID: 0.085 G_Rec: 0.332 D_GP: 0.031 D_real: 1.457 D_fake: 0.646 +(epoch: 512, iters: 6318, time: 0.063) G_GAN: 0.538 G_GAN_Feat: 0.932 G_ID: 0.112 G_Rec: 0.425 D_GP: 0.038 D_real: 1.234 D_fake: 0.474 +(epoch: 512, iters: 6718, time: 0.063) G_GAN: 0.216 G_GAN_Feat: 0.687 G_ID: 0.093 G_Rec: 0.279 D_GP: 0.030 D_real: 1.152 D_fake: 0.784 +(epoch: 512, iters: 7118, time: 0.063) G_GAN: 0.322 G_GAN_Feat: 1.023 G_ID: 0.117 G_Rec: 0.420 D_GP: 0.036 D_real: 0.695 D_fake: 0.679 +(epoch: 512, iters: 7518, time: 0.063) G_GAN: 0.417 G_GAN_Feat: 0.753 G_ID: 0.096 G_Rec: 0.317 D_GP: 0.045 D_real: 1.223 D_fake: 0.589 +(epoch: 512, iters: 7918, time: 0.063) G_GAN: 0.315 G_GAN_Feat: 0.985 G_ID: 0.111 G_Rec: 0.424 D_GP: 0.056 D_real: 0.878 D_fake: 0.686 +(epoch: 512, iters: 8318, time: 0.063) G_GAN: 0.206 G_GAN_Feat: 0.862 G_ID: 0.099 G_Rec: 0.320 D_GP: 0.072 D_real: 0.566 D_fake: 0.795 +(epoch: 513, iters: 110, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.854 G_ID: 0.120 G_Rec: 0.376 D_GP: 0.036 D_real: 1.096 D_fake: 0.691 +(epoch: 513, iters: 510, time: 0.063) G_GAN: 0.084 G_GAN_Feat: 0.671 G_ID: 0.108 G_Rec: 0.297 D_GP: 0.033 D_real: 0.933 D_fake: 0.916 +(epoch: 513, iters: 910, time: 0.063) G_GAN: 0.185 G_GAN_Feat: 0.945 G_ID: 0.109 G_Rec: 0.418 D_GP: 0.037 D_real: 0.801 D_fake: 0.817 +(epoch: 513, iters: 1310, time: 0.063) G_GAN: 0.006 G_GAN_Feat: 0.772 G_ID: 0.095 G_Rec: 0.292 D_GP: 0.065 D_real: 0.779 D_fake: 0.995 +(epoch: 513, iters: 1710, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 1.056 G_ID: 0.112 G_Rec: 0.461 D_GP: 0.040 D_real: 0.625 D_fake: 0.813 +(epoch: 513, iters: 2110, time: 0.063) G_GAN: 0.435 G_GAN_Feat: 0.723 G_ID: 0.096 G_Rec: 0.318 D_GP: 0.030 D_real: 1.341 D_fake: 0.565 +(epoch: 513, iters: 2510, time: 0.063) G_GAN: 0.829 G_GAN_Feat: 0.984 G_ID: 0.109 G_Rec: 0.408 D_GP: 0.044 D_real: 1.353 D_fake: 0.254 +(epoch: 513, iters: 2910, time: 0.063) G_GAN: 0.498 G_GAN_Feat: 0.728 G_ID: 0.098 G_Rec: 0.309 D_GP: 0.029 D_real: 1.454 D_fake: 0.513 +(epoch: 513, iters: 3310, time: 0.064) G_GAN: 0.051 G_GAN_Feat: 0.915 G_ID: 0.125 G_Rec: 0.412 D_GP: 0.030 D_real: 0.816 D_fake: 0.949 +(epoch: 513, iters: 3710, time: 0.063) G_GAN: 0.071 G_GAN_Feat: 0.663 G_ID: 0.093 G_Rec: 0.293 D_GP: 0.028 D_real: 0.992 D_fake: 0.931 +(epoch: 513, iters: 4110, time: 0.063) G_GAN: 0.520 G_GAN_Feat: 0.853 G_ID: 0.110 G_Rec: 0.395 D_GP: 0.030 D_real: 1.205 D_fake: 0.503 +(epoch: 513, iters: 4510, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.748 G_ID: 0.093 G_Rec: 0.310 D_GP: 0.031 D_real: 1.145 D_fake: 0.767 +(epoch: 513, iters: 4910, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.950 G_ID: 0.135 G_Rec: 0.411 D_GP: 0.048 D_real: 0.689 D_fake: 0.917 +(epoch: 513, iters: 5310, time: 0.063) G_GAN: 0.154 G_GAN_Feat: 0.734 G_ID: 0.091 G_Rec: 0.291 D_GP: 0.031 D_real: 0.981 D_fake: 0.846 +(epoch: 513, iters: 5710, time: 0.063) G_GAN: 0.067 G_GAN_Feat: 1.060 G_ID: 0.117 G_Rec: 0.467 D_GP: 0.037 D_real: 0.798 D_fake: 0.933 +(epoch: 513, iters: 6110, time: 0.063) G_GAN: 0.331 G_GAN_Feat: 0.942 G_ID: 0.079 G_Rec: 0.314 D_GP: 0.049 D_real: 0.739 D_fake: 0.670 +(epoch: 513, iters: 6510, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 1.089 G_ID: 0.105 G_Rec: 0.452 D_GP: 0.070 D_real: 0.484 D_fake: 0.899 +(epoch: 513, iters: 6910, time: 0.063) G_GAN: 0.070 G_GAN_Feat: 0.783 G_ID: 0.083 G_Rec: 0.307 D_GP: 0.037 D_real: 0.883 D_fake: 0.930 +(epoch: 513, iters: 7310, time: 0.063) G_GAN: 0.732 G_GAN_Feat: 1.016 G_ID: 0.106 G_Rec: 0.442 D_GP: 0.033 D_real: 1.248 D_fake: 0.303 +(epoch: 513, iters: 7710, time: 0.063) G_GAN: 0.360 G_GAN_Feat: 0.865 G_ID: 0.086 G_Rec: 0.299 D_GP: 0.039 D_real: 0.920 D_fake: 0.641 +(epoch: 513, iters: 8110, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.908 G_ID: 0.099 G_Rec: 0.403 D_GP: 0.027 D_real: 1.179 D_fake: 0.600 +(epoch: 513, iters: 8510, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 0.691 G_ID: 0.090 G_Rec: 0.307 D_GP: 0.031 D_real: 1.161 D_fake: 0.768 +(epoch: 514, iters: 302, time: 0.063) G_GAN: 0.212 G_GAN_Feat: 1.016 G_ID: 0.123 G_Rec: 0.456 D_GP: 0.042 D_real: 0.677 D_fake: 0.788 +(epoch: 514, iters: 702, time: 0.063) G_GAN: -0.032 G_GAN_Feat: 0.778 G_ID: 0.106 G_Rec: 0.309 D_GP: 0.049 D_real: 0.684 D_fake: 1.032 +(epoch: 514, iters: 1102, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.960 G_ID: 0.111 G_Rec: 0.421 D_GP: 0.055 D_real: 0.924 D_fake: 0.681 +(epoch: 514, iters: 1502, time: 0.063) G_GAN: 0.113 G_GAN_Feat: 0.793 G_ID: 0.099 G_Rec: 0.330 D_GP: 0.054 D_real: 0.896 D_fake: 0.887 +(epoch: 514, iters: 1902, time: 0.063) G_GAN: 0.371 G_GAN_Feat: 0.982 G_ID: 0.116 G_Rec: 0.448 D_GP: 0.050 D_real: 0.918 D_fake: 0.638 +(epoch: 514, iters: 2302, time: 0.063) G_GAN: -0.108 G_GAN_Feat: 0.734 G_ID: 0.103 G_Rec: 0.299 D_GP: 0.044 D_real: 0.650 D_fake: 1.108 +(epoch: 514, iters: 2702, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 1.023 G_ID: 0.114 G_Rec: 0.450 D_GP: 0.048 D_real: 0.895 D_fake: 0.562 +(epoch: 514, iters: 3102, time: 0.063) G_GAN: 0.083 G_GAN_Feat: 0.919 G_ID: 0.119 G_Rec: 0.324 D_GP: 0.255 D_real: 0.203 D_fake: 0.917 +(epoch: 514, iters: 3502, time: 0.063) G_GAN: 0.659 G_GAN_Feat: 1.133 G_ID: 0.138 G_Rec: 0.501 D_GP: 0.117 D_real: 0.868 D_fake: 0.398 +(epoch: 514, iters: 3902, time: 0.063) G_GAN: 0.400 G_GAN_Feat: 0.819 G_ID: 0.097 G_Rec: 0.304 D_GP: 0.057 D_real: 0.742 D_fake: 0.631 +(epoch: 514, iters: 4302, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 1.027 G_ID: 0.125 G_Rec: 0.436 D_GP: 0.039 D_real: 0.861 D_fake: 0.550 +(epoch: 514, iters: 4702, time: 0.063) G_GAN: 0.356 G_GAN_Feat: 0.921 G_ID: 0.117 G_Rec: 0.313 D_GP: 0.253 D_real: 0.452 D_fake: 0.647 +(epoch: 514, iters: 5102, time: 0.063) G_GAN: 0.310 G_GAN_Feat: 1.100 G_ID: 0.120 G_Rec: 0.433 D_GP: 0.039 D_real: 0.575 D_fake: 0.691 +(epoch: 514, iters: 5502, time: 0.063) G_GAN: 0.225 G_GAN_Feat: 0.931 G_ID: 0.104 G_Rec: 0.319 D_GP: 0.070 D_real: 0.403 D_fake: 0.796 +(epoch: 514, iters: 5902, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.854 G_ID: 0.098 G_Rec: 0.456 D_GP: 0.027 D_real: 1.116 D_fake: 0.638 +(epoch: 514, iters: 6302, time: 0.063) G_GAN: 0.126 G_GAN_Feat: 0.589 G_ID: 0.104 G_Rec: 0.277 D_GP: 0.029 D_real: 0.998 D_fake: 0.874 +(epoch: 514, iters: 6702, time: 0.063) G_GAN: 0.275 G_GAN_Feat: 0.863 G_ID: 0.121 G_Rec: 0.433 D_GP: 0.041 D_real: 0.932 D_fake: 0.725 +(epoch: 514, iters: 7102, time: 0.063) G_GAN: 0.137 G_GAN_Feat: 0.575 G_ID: 0.091 G_Rec: 0.285 D_GP: 0.029 D_real: 1.071 D_fake: 0.863 +(epoch: 514, iters: 7502, time: 0.064) G_GAN: 0.124 G_GAN_Feat: 0.812 G_ID: 0.110 G_Rec: 0.403 D_GP: 0.033 D_real: 0.924 D_fake: 0.876 +(epoch: 514, iters: 7902, time: 0.063) G_GAN: -0.118 G_GAN_Feat: 0.670 G_ID: 0.098 G_Rec: 0.342 D_GP: 0.037 D_real: 0.823 D_fake: 1.118 +(epoch: 514, iters: 8302, time: 0.063) G_GAN: 0.202 G_GAN_Feat: 0.835 G_ID: 0.123 G_Rec: 0.411 D_GP: 0.038 D_real: 0.882 D_fake: 0.803 +(epoch: 514, iters: 8702, time: 0.063) G_GAN: 0.071 G_GAN_Feat: 0.649 G_ID: 0.088 G_Rec: 0.341 D_GP: 0.032 D_real: 0.941 D_fake: 0.930 +(epoch: 515, iters: 494, time: 0.064) G_GAN: 0.028 G_GAN_Feat: 0.901 G_ID: 0.116 G_Rec: 0.435 D_GP: 0.066 D_real: 0.686 D_fake: 0.972 +(epoch: 515, iters: 894, time: 0.063) G_GAN: 0.085 G_GAN_Feat: 0.591 G_ID: 0.082 G_Rec: 0.287 D_GP: 0.028 D_real: 1.019 D_fake: 0.915 +(epoch: 515, iters: 1294, time: 0.063) G_GAN: 0.096 G_GAN_Feat: 0.902 G_ID: 0.102 G_Rec: 0.444 D_GP: 0.042 D_real: 0.708 D_fake: 0.909 +(epoch: 515, iters: 1694, time: 0.063) G_GAN: -0.418 G_GAN_Feat: 0.742 G_ID: 0.112 G_Rec: 0.362 D_GP: 0.062 D_real: 0.463 D_fake: 1.418 +(epoch: 515, iters: 2094, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.802 G_ID: 0.114 G_Rec: 0.390 D_GP: 0.035 D_real: 0.890 D_fake: 0.735 +(epoch: 515, iters: 2494, time: 0.063) G_GAN: 0.012 G_GAN_Feat: 0.657 G_ID: 0.089 G_Rec: 0.298 D_GP: 0.039 D_real: 0.872 D_fake: 0.988 +(epoch: 515, iters: 2894, time: 0.063) G_GAN: 0.002 G_GAN_Feat: 0.922 G_ID: 0.104 G_Rec: 0.444 D_GP: 0.037 D_real: 0.588 D_fake: 0.999 +(epoch: 515, iters: 3294, time: 0.063) G_GAN: -0.062 G_GAN_Feat: 0.730 G_ID: 0.106 G_Rec: 0.353 D_GP: 0.057 D_real: 0.669 D_fake: 1.064 +(epoch: 515, iters: 3694, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.898 G_ID: 0.123 G_Rec: 0.430 D_GP: 0.039 D_real: 0.903 D_fake: 0.805 +(epoch: 515, iters: 4094, time: 0.063) G_GAN: -0.121 G_GAN_Feat: 0.683 G_ID: 0.084 G_Rec: 0.302 D_GP: 0.047 D_real: 0.725 D_fake: 1.121 +(epoch: 515, iters: 4494, time: 0.063) G_GAN: 0.244 G_GAN_Feat: 0.858 G_ID: 0.118 G_Rec: 0.414 D_GP: 0.034 D_real: 0.884 D_fake: 0.757 +(epoch: 515, iters: 4894, time: 0.063) G_GAN: 0.029 G_GAN_Feat: 0.759 G_ID: 0.099 G_Rec: 0.317 D_GP: 0.066 D_real: 0.742 D_fake: 0.971 +(epoch: 515, iters: 5294, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.912 G_ID: 0.107 G_Rec: 0.411 D_GP: 0.042 D_real: 0.922 D_fake: 0.657 +(epoch: 515, iters: 5694, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.636 G_ID: 0.092 G_Rec: 0.279 D_GP: 0.050 D_real: 1.208 D_fake: 0.668 +(epoch: 515, iters: 6094, time: 0.063) G_GAN: 0.287 G_GAN_Feat: 0.916 G_ID: 0.112 G_Rec: 0.407 D_GP: 0.064 D_real: 0.843 D_fake: 0.715 +(epoch: 515, iters: 6494, time: 0.063) G_GAN: 0.338 G_GAN_Feat: 0.671 G_ID: 0.103 G_Rec: 0.282 D_GP: 0.036 D_real: 1.256 D_fake: 0.665 +(epoch: 515, iters: 6894, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.909 G_ID: 0.117 G_Rec: 0.419 D_GP: 0.042 D_real: 1.068 D_fake: 0.595 +(epoch: 515, iters: 7294, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 0.742 G_ID: 0.093 G_Rec: 0.405 D_GP: 0.064 D_real: 0.872 D_fake: 0.875 +(epoch: 515, iters: 7694, time: 0.063) G_GAN: 0.302 G_GAN_Feat: 0.981 G_ID: 0.136 G_Rec: 0.440 D_GP: 0.047 D_real: 0.739 D_fake: 0.702 +(epoch: 515, iters: 8094, time: 0.063) G_GAN: 0.118 G_GAN_Feat: 0.744 G_ID: 0.110 G_Rec: 0.313 D_GP: 0.075 D_real: 0.759 D_fake: 0.884 +(epoch: 515, iters: 8494, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.991 G_ID: 0.124 G_Rec: 0.434 D_GP: 0.085 D_real: 0.613 D_fake: 0.642 +(epoch: 516, iters: 286, time: 0.063) G_GAN: 0.356 G_GAN_Feat: 0.693 G_ID: 0.092 G_Rec: 0.281 D_GP: 0.040 D_real: 1.304 D_fake: 0.645 +(epoch: 516, iters: 686, time: 0.063) G_GAN: 0.720 G_GAN_Feat: 0.910 G_ID: 0.109 G_Rec: 0.382 D_GP: 0.038 D_real: 1.330 D_fake: 0.337 +(epoch: 516, iters: 1086, time: 0.063) G_GAN: 0.129 G_GAN_Feat: 0.752 G_ID: 0.088 G_Rec: 0.290 D_GP: 0.067 D_real: 0.815 D_fake: 0.872 +(epoch: 516, iters: 1486, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.915 G_ID: 0.105 G_Rec: 0.413 D_GP: 0.037 D_real: 1.208 D_fake: 0.457 +(epoch: 516, iters: 1886, time: 0.063) G_GAN: 0.100 G_GAN_Feat: 0.781 G_ID: 0.113 G_Rec: 0.315 D_GP: 0.045 D_real: 0.765 D_fake: 0.900 +(epoch: 516, iters: 2286, time: 0.063) G_GAN: 0.301 G_GAN_Feat: 0.842 G_ID: 0.122 G_Rec: 0.403 D_GP: 0.025 D_real: 1.161 D_fake: 0.707 +(epoch: 516, iters: 2686, time: 0.063) G_GAN: -0.025 G_GAN_Feat: 0.611 G_ID: 0.092 G_Rec: 0.298 D_GP: 0.025 D_real: 0.957 D_fake: 1.025 +(epoch: 516, iters: 3086, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.900 G_ID: 0.123 G_Rec: 0.437 D_GP: 0.037 D_real: 0.809 D_fake: 0.782 +(epoch: 516, iters: 3486, time: 0.063) G_GAN: -0.204 G_GAN_Feat: 0.693 G_ID: 0.085 G_Rec: 0.321 D_GP: 0.041 D_real: 0.647 D_fake: 1.204 +(epoch: 516, iters: 3886, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 0.892 G_ID: 0.105 G_Rec: 0.419 D_GP: 0.036 D_real: 1.018 D_fake: 0.721 +(epoch: 516, iters: 4286, time: 0.063) G_GAN: -0.084 G_GAN_Feat: 0.729 G_ID: 0.108 G_Rec: 0.345 D_GP: 0.050 D_real: 0.687 D_fake: 1.084 +(epoch: 516, iters: 4686, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.870 G_ID: 0.108 G_Rec: 0.410 D_GP: 0.034 D_real: 1.061 D_fake: 0.621 +(epoch: 516, iters: 5086, time: 0.063) G_GAN: -0.043 G_GAN_Feat: 0.757 G_ID: 0.096 G_Rec: 0.326 D_GP: 0.038 D_real: 0.823 D_fake: 1.043 +(epoch: 516, iters: 5486, time: 0.063) G_GAN: 0.451 G_GAN_Feat: 0.986 G_ID: 0.113 G_Rec: 0.460 D_GP: 0.062 D_real: 0.789 D_fake: 0.562 +(epoch: 516, iters: 5886, time: 0.063) G_GAN: 0.139 G_GAN_Feat: 0.687 G_ID: 0.085 G_Rec: 0.300 D_GP: 0.034 D_real: 1.086 D_fake: 0.862 +(epoch: 516, iters: 6286, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.959 G_ID: 0.108 G_Rec: 0.436 D_GP: 0.048 D_real: 0.801 D_fake: 0.737 +(epoch: 516, iters: 6686, time: 0.063) G_GAN: -0.054 G_GAN_Feat: 0.849 G_ID: 0.089 G_Rec: 0.370 D_GP: 0.276 D_real: 0.285 D_fake: 1.054 +(epoch: 516, iters: 7086, time: 0.063) G_GAN: 0.435 G_GAN_Feat: 0.927 G_ID: 0.118 G_Rec: 0.390 D_GP: 0.036 D_real: 1.121 D_fake: 0.565 +(epoch: 516, iters: 7486, time: 0.063) G_GAN: 0.119 G_GAN_Feat: 0.837 G_ID: 0.089 G_Rec: 0.332 D_GP: 0.354 D_real: 0.484 D_fake: 0.881 +(epoch: 516, iters: 7886, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 1.018 G_ID: 0.139 G_Rec: 0.456 D_GP: 0.071 D_real: 0.866 D_fake: 0.430 +(epoch: 516, iters: 8286, time: 0.063) G_GAN: 0.163 G_GAN_Feat: 0.959 G_ID: 0.103 G_Rec: 0.381 D_GP: 0.566 D_real: 0.369 D_fake: 0.841 +(epoch: 516, iters: 8686, time: 0.063) G_GAN: 0.593 G_GAN_Feat: 1.053 G_ID: 0.117 G_Rec: 0.512 D_GP: 0.052 D_real: 1.083 D_fake: 0.511 +(epoch: 517, iters: 478, time: 0.063) G_GAN: 0.446 G_GAN_Feat: 0.821 G_ID: 0.096 G_Rec: 0.316 D_GP: 0.060 D_real: 0.968 D_fake: 0.600 +(epoch: 517, iters: 878, time: 0.064) G_GAN: 0.660 G_GAN_Feat: 0.916 G_ID: 0.092 G_Rec: 0.450 D_GP: 0.029 D_real: 1.410 D_fake: 0.357 +(epoch: 517, iters: 1278, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.680 G_ID: 0.078 G_Rec: 0.309 D_GP: 0.028 D_real: 1.094 D_fake: 0.817 +(epoch: 517, iters: 1678, time: 0.063) G_GAN: 0.359 G_GAN_Feat: 0.890 G_ID: 0.108 G_Rec: 0.400 D_GP: 0.032 D_real: 1.141 D_fake: 0.643 +(epoch: 517, iters: 2078, time: 0.063) G_GAN: 0.249 G_GAN_Feat: 0.651 G_ID: 0.090 G_Rec: 0.294 D_GP: 0.028 D_real: 1.132 D_fake: 0.751 +(epoch: 517, iters: 2478, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.826 G_ID: 0.099 G_Rec: 0.387 D_GP: 0.033 D_real: 1.102 D_fake: 0.654 +(epoch: 517, iters: 2878, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.695 G_ID: 0.086 G_Rec: 0.308 D_GP: 0.044 D_real: 0.914 D_fake: 0.878 +(epoch: 517, iters: 3278, time: 0.063) G_GAN: 0.420 G_GAN_Feat: 0.883 G_ID: 0.109 G_Rec: 0.403 D_GP: 0.039 D_real: 1.117 D_fake: 0.582 +(epoch: 517, iters: 3678, time: 0.063) G_GAN: 0.147 G_GAN_Feat: 0.649 G_ID: 0.087 G_Rec: 0.284 D_GP: 0.035 D_real: 1.010 D_fake: 0.853 +(epoch: 517, iters: 4078, time: 0.063) G_GAN: 0.373 G_GAN_Feat: 0.916 G_ID: 0.100 G_Rec: 0.446 D_GP: 0.034 D_real: 1.105 D_fake: 0.633 +(epoch: 517, iters: 4478, time: 0.063) G_GAN: 0.132 G_GAN_Feat: 0.702 G_ID: 0.091 G_Rec: 0.304 D_GP: 0.041 D_real: 0.948 D_fake: 0.870 +(epoch: 517, iters: 4878, time: 0.063) G_GAN: 0.534 G_GAN_Feat: 0.939 G_ID: 0.118 G_Rec: 0.418 D_GP: 0.050 D_real: 1.174 D_fake: 0.496 +(epoch: 517, iters: 5278, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.763 G_ID: 0.085 G_Rec: 0.286 D_GP: 0.038 D_real: 0.952 D_fake: 0.823 +(epoch: 517, iters: 5678, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 1.057 G_ID: 0.112 G_Rec: 0.454 D_GP: 0.064 D_real: 0.698 D_fake: 0.669 +(epoch: 517, iters: 6078, time: 0.063) G_GAN: 0.127 G_GAN_Feat: 0.824 G_ID: 0.105 G_Rec: 0.363 D_GP: 0.244 D_real: 0.693 D_fake: 0.885 +(epoch: 517, iters: 6478, time: 0.063) G_GAN: 0.260 G_GAN_Feat: 1.073 G_ID: 0.116 G_Rec: 0.489 D_GP: 0.157 D_real: 0.398 D_fake: 0.741 +(epoch: 517, iters: 6878, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.689 G_ID: 0.088 G_Rec: 0.286 D_GP: 0.035 D_real: 0.903 D_fake: 0.931 +(epoch: 517, iters: 7278, time: 0.063) G_GAN: 0.569 G_GAN_Feat: 0.872 G_ID: 0.118 G_Rec: 0.417 D_GP: 0.032 D_real: 1.194 D_fake: 0.450 +(epoch: 517, iters: 7678, time: 0.063) G_GAN: 0.166 G_GAN_Feat: 0.793 G_ID: 0.095 G_Rec: 0.294 D_GP: 0.049 D_real: 0.779 D_fake: 0.834 +(epoch: 517, iters: 8078, time: 0.063) G_GAN: 0.362 G_GAN_Feat: 0.931 G_ID: 0.107 G_Rec: 0.444 D_GP: 0.029 D_real: 1.042 D_fake: 0.639 +(epoch: 517, iters: 8478, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.798 G_ID: 0.104 G_Rec: 0.337 D_GP: 0.062 D_real: 0.988 D_fake: 0.699 +(epoch: 518, iters: 270, time: 0.063) G_GAN: 0.765 G_GAN_Feat: 0.913 G_ID: 0.120 G_Rec: 0.416 D_GP: 0.036 D_real: 1.386 D_fake: 0.312 +(epoch: 518, iters: 670, time: 0.063) G_GAN: 0.358 G_GAN_Feat: 0.852 G_ID: 0.129 G_Rec: 0.335 D_GP: 0.116 D_real: 0.780 D_fake: 0.715 +(epoch: 518, iters: 1070, time: 0.063) G_GAN: 0.431 G_GAN_Feat: 1.048 G_ID: 0.116 G_Rec: 0.422 D_GP: 0.041 D_real: 0.726 D_fake: 0.573 +(epoch: 518, iters: 1470, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.829 G_ID: 0.106 G_Rec: 0.293 D_GP: 0.073 D_real: 0.574 D_fake: 0.802 +(epoch: 518, iters: 1870, time: 0.063) G_GAN: 0.074 G_GAN_Feat: 0.915 G_ID: 0.130 G_Rec: 0.419 D_GP: 0.032 D_real: 0.800 D_fake: 0.927 +(epoch: 518, iters: 2270, time: 0.063) G_GAN: 0.422 G_GAN_Feat: 0.835 G_ID: 0.086 G_Rec: 0.334 D_GP: 0.073 D_real: 0.676 D_fake: 0.597 +(epoch: 518, iters: 2670, time: 0.063) G_GAN: 0.484 G_GAN_Feat: 1.032 G_ID: 0.103 G_Rec: 0.447 D_GP: 0.035 D_real: 0.969 D_fake: 0.525 +(epoch: 518, iters: 3070, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.802 G_ID: 0.093 G_Rec: 0.307 D_GP: 0.036 D_real: 1.041 D_fake: 0.603 +(epoch: 518, iters: 3470, time: 0.063) G_GAN: 0.611 G_GAN_Feat: 1.158 G_ID: 0.107 G_Rec: 0.455 D_GP: 0.122 D_real: 0.511 D_fake: 0.407 +(epoch: 518, iters: 3870, time: 0.063) G_GAN: 0.049 G_GAN_Feat: 0.874 G_ID: 0.098 G_Rec: 0.322 D_GP: 0.047 D_real: 0.553 D_fake: 0.951 +(epoch: 518, iters: 4270, time: 0.063) G_GAN: 0.559 G_GAN_Feat: 0.893 G_ID: 0.108 G_Rec: 0.415 D_GP: 0.037 D_real: 1.297 D_fake: 0.469 +(epoch: 518, iters: 4670, time: 0.064) G_GAN: -0.035 G_GAN_Feat: 0.726 G_ID: 0.106 G_Rec: 0.330 D_GP: 0.046 D_real: 0.846 D_fake: 1.035 +(epoch: 518, iters: 5070, time: 0.063) G_GAN: 0.163 G_GAN_Feat: 0.944 G_ID: 0.107 G_Rec: 0.457 D_GP: 0.048 D_real: 0.771 D_fake: 0.838 +(epoch: 518, iters: 5470, time: 0.063) G_GAN: -0.036 G_GAN_Feat: 0.723 G_ID: 0.091 G_Rec: 0.318 D_GP: 0.040 D_real: 0.904 D_fake: 1.036 +(epoch: 518, iters: 5870, time: 0.063) G_GAN: 0.400 G_GAN_Feat: 0.936 G_ID: 0.107 G_Rec: 0.413 D_GP: 0.040 D_real: 0.958 D_fake: 0.604 +(epoch: 518, iters: 6270, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.654 G_ID: 0.095 G_Rec: 0.266 D_GP: 0.029 D_real: 1.066 D_fake: 0.810 +(epoch: 518, iters: 6670, time: 0.063) G_GAN: 0.356 G_GAN_Feat: 0.933 G_ID: 0.118 G_Rec: 0.431 D_GP: 0.032 D_real: 0.905 D_fake: 0.644 +(epoch: 518, iters: 7070, time: 0.063) G_GAN: 0.416 G_GAN_Feat: 0.782 G_ID: 0.095 G_Rec: 0.300 D_GP: 0.044 D_real: 1.109 D_fake: 0.588 +(epoch: 518, iters: 7470, time: 0.063) G_GAN: 0.812 G_GAN_Feat: 1.093 G_ID: 0.111 G_Rec: 0.467 D_GP: 0.101 D_real: 1.049 D_fake: 0.316 +(epoch: 518, iters: 7870, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.924 G_ID: 0.097 G_Rec: 0.370 D_GP: 1.121 D_real: 0.810 D_fake: 0.511 +(epoch: 518, iters: 8270, time: 0.063) G_GAN: 0.311 G_GAN_Feat: 0.954 G_ID: 0.120 G_Rec: 0.464 D_GP: 0.029 D_real: 0.942 D_fake: 0.690 +(epoch: 518, iters: 8670, time: 0.063) G_GAN: 0.223 G_GAN_Feat: 0.665 G_ID: 0.097 G_Rec: 0.293 D_GP: 0.026 D_real: 1.128 D_fake: 0.778 +(epoch: 519, iters: 462, time: 0.063) G_GAN: 0.269 G_GAN_Feat: 0.942 G_ID: 0.100 G_Rec: 0.403 D_GP: 0.045 D_real: 0.932 D_fake: 0.731 +(epoch: 519, iters: 862, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.719 G_ID: 0.096 G_Rec: 0.274 D_GP: 0.032 D_real: 1.080 D_fake: 0.772 +(epoch: 519, iters: 1262, time: 0.063) G_GAN: 0.521 G_GAN_Feat: 0.953 G_ID: 0.099 G_Rec: 0.454 D_GP: 0.046 D_real: 1.063 D_fake: 0.486 +(epoch: 519, iters: 1662, time: 0.063) G_GAN: 0.493 G_GAN_Feat: 0.717 G_ID: 0.094 G_Rec: 0.293 D_GP: 0.029 D_real: 1.397 D_fake: 0.512 +(epoch: 519, iters: 2062, time: 0.063) G_GAN: 0.554 G_GAN_Feat: 0.929 G_ID: 0.139 G_Rec: 0.394 D_GP: 0.035 D_real: 1.066 D_fake: 0.453 +(epoch: 519, iters: 2462, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.803 G_ID: 0.090 G_Rec: 0.316 D_GP: 0.040 D_real: 0.930 D_fake: 0.773 +(epoch: 519, iters: 2862, time: 0.063) G_GAN: 0.438 G_GAN_Feat: 0.982 G_ID: 0.124 G_Rec: 0.421 D_GP: 0.033 D_real: 1.037 D_fake: 0.601 +(epoch: 519, iters: 3262, time: 0.063) G_GAN: 0.046 G_GAN_Feat: 0.880 G_ID: 0.100 G_Rec: 0.368 D_GP: 0.046 D_real: 0.724 D_fake: 0.955 +(epoch: 519, iters: 3662, time: 0.063) G_GAN: 0.197 G_GAN_Feat: 0.973 G_ID: 0.123 G_Rec: 0.445 D_GP: 0.060 D_real: 0.703 D_fake: 0.803 +(epoch: 519, iters: 4062, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.694 G_ID: 0.101 G_Rec: 0.291 D_GP: 0.037 D_real: 1.088 D_fake: 0.838 +(epoch: 519, iters: 4462, time: 0.063) G_GAN: 0.743 G_GAN_Feat: 1.089 G_ID: 0.100 G_Rec: 0.470 D_GP: 0.155 D_real: 0.759 D_fake: 0.312 +(epoch: 519, iters: 4862, time: 0.063) G_GAN: 0.018 G_GAN_Feat: 0.770 G_ID: 0.113 G_Rec: 0.329 D_GP: 0.031 D_real: 0.829 D_fake: 0.982 +(epoch: 519, iters: 5262, time: 0.063) G_GAN: 0.506 G_GAN_Feat: 0.995 G_ID: 0.106 G_Rec: 0.445 D_GP: 0.030 D_real: 1.144 D_fake: 0.495 +(epoch: 519, iters: 5662, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.729 G_ID: 0.080 G_Rec: 0.296 D_GP: 0.037 D_real: 1.238 D_fake: 0.586 +(epoch: 519, iters: 6062, time: 0.063) G_GAN: 0.781 G_GAN_Feat: 1.083 G_ID: 0.102 G_Rec: 0.471 D_GP: 0.042 D_real: 1.125 D_fake: 0.250 +(epoch: 519, iters: 6462, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.864 G_ID: 0.108 G_Rec: 0.334 D_GP: 0.052 D_real: 0.838 D_fake: 0.827 +(epoch: 519, iters: 6862, time: 0.063) G_GAN: 0.782 G_GAN_Feat: 1.019 G_ID: 0.120 G_Rec: 0.433 D_GP: 0.047 D_real: 1.197 D_fake: 0.267 +(epoch: 519, iters: 7262, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.803 G_ID: 0.088 G_Rec: 0.334 D_GP: 0.038 D_real: 1.170 D_fake: 0.717 +(epoch: 519, iters: 7662, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.998 G_ID: 0.141 G_Rec: 0.473 D_GP: 0.044 D_real: 0.620 D_fake: 0.822 +(epoch: 519, iters: 8062, time: 0.063) G_GAN: 0.102 G_GAN_Feat: 0.680 G_ID: 0.098 G_Rec: 0.283 D_GP: 0.032 D_real: 1.063 D_fake: 0.898 +(epoch: 519, iters: 8462, time: 0.063) G_GAN: 0.655 G_GAN_Feat: 0.900 G_ID: 0.109 G_Rec: 0.429 D_GP: 0.041 D_real: 1.281 D_fake: 0.431 +(epoch: 520, iters: 254, time: 0.064) G_GAN: -0.002 G_GAN_Feat: 0.753 G_ID: 0.097 G_Rec: 0.321 D_GP: 0.034 D_real: 0.779 D_fake: 1.003 diff --git a/SimSwap/checkpoints/people/opt.txt b/SimSwap/checkpoints/people/opt.txt new file mode 100644 index 0000000..3cc4ed4 --- /dev/null +++ b/SimSwap/checkpoints/people/opt.txt @@ -0,0 +1,72 @@ +------------ Options ------------- +batchSize: 8 +beta1: 0.5 +checkpoints_dir: ./checkpoints +continue_train: False +data_type: 32 +dataroot: ./datasets/cityscapes/ +debug: False +display_freq: 99 +display_winsize: 512 +feat_num: 3 +fineSize: 512 +fp16: False +gan_mode: hinge +gpu_ids: [0] +image_size: 224 +input_nc: 3 +instance_feat: False +isTrain: True +label_feat: False +label_nc: 0 +lambda_GP: 10.0 +lambda_feat: 10.0 +lambda_id: 20.0 +lambda_rec: 10.0 +latent_size: 512 +loadSize: 1024 +load_features: False +load_pretrain: +local_rank: 0 +lr: 0.0002 +max_dataset_size: inf +model: pix2pixHD +nThreads: 2 +n_blocks_global: 6 +n_blocks_local: 3 +n_clusters: 10 +n_downsample_E: 4 +n_downsample_global: 3 +n_layers_D: 4 +n_local_enhancers: 1 +name: people +ndf: 64 +nef: 16 +netG: global +ngf: 64 +niter: 10000 +niter_decay: 10000 +niter_fix_global: 0 +no_flip: False +no_ganFeat_loss: False +no_html: False +no_instance: False +no_vgg_loss: False +norm: batch +norm_G: spectralspadesyncbatch3x3 +num_D: 2 +output_nc: 3 +phase: train +pool_size: 0 +print_freq: 100 +resize_or_crop: scale_width +save_epoch_freq: 10000 +save_latest_freq: 10000 +semantic_nc: 3 +serial_batches: False +tf_log: False +times_G: 1 +use_dropout: False +verbose: False +which_epoch: latest +-------------- End ---------------- diff --git a/SimSwap/checkpoints/simswap/opt.txt b/SimSwap/checkpoints/simswap/opt.txt new file mode 100644 index 0000000..78f646f --- /dev/null +++ b/SimSwap/checkpoints/simswap/opt.txt @@ -0,0 +1,26 @@ +------------ Options ------------- +Arc_path: arcface_model/arcface_checkpoint.tar +Gdeep: False +batchSize: 4 +beta1: 0.0 +checkpoints_dir: ./checkpoints +continue_train: False +dataset: /path/to/VGGFace2 +gpu_ids: 0 +isTrain: True +lambda_feat: 10.0 +lambda_id: 30.0 +lambda_rec: 10.0 +load_pretrain: ./checkpoints/simswap224_test +log_frep: 200 +lr: 0.0004 +model_freq: 10000 +name: simswap +niter: 10000 +niter_decay: 10000 +phase: train +sample_freq: 1000 +total_step: 1000000 +use_tensorboard: False +which_epoch: 10000 +-------------- End ---------------- diff --git a/SimSwap/cog.yaml b/SimSwap/cog.yaml new file mode 100644 index 0000000..280c369 --- /dev/null +++ b/SimSwap/cog.yaml @@ -0,0 +1,20 @@ +build: + gpu: true + python_version: "3.8" + system_packages: + - "libgl1-mesa-glx" + - "libglib2.0-0" + python_packages: + - "imageio==2.9.0" + - "torch==1.8.0" + - "torchvision==0.9.0" + - "numpy==1.21.1" + - "insightface==0.2.1" + - "ipython==7.21.0" + - "Pillow==8.3.1" + - "opencv-python==4.5.3.56" + - "Fraction==1.5.1" + - "onnxruntime-gpu==1.8.1" + - "moviepy==1.0.3" + +predict: "predict.py:Predictor" diff --git a/SimSwap/crop_224/1_source.jpg b/SimSwap/crop_224/1_source.jpg new file mode 100644 index 0000000..de06097 Binary files /dev/null and b/SimSwap/crop_224/1_source.jpg differ diff --git a/SimSwap/crop_224/2.jpg b/SimSwap/crop_224/2.jpg new file mode 100644 index 0000000..1a5fbf1 Binary files /dev/null and b/SimSwap/crop_224/2.jpg differ diff --git a/SimSwap/crop_224/6.jpg b/SimSwap/crop_224/6.jpg new file mode 100644 index 0000000..958c554 Binary files /dev/null and b/SimSwap/crop_224/6.jpg differ diff --git a/SimSwap/crop_224/cage.jpg b/SimSwap/crop_224/cage.jpg new file mode 100644 index 0000000..5ee0f57 Binary files /dev/null and b/SimSwap/crop_224/cage.jpg differ diff --git a/SimSwap/crop_224/dnl.jpg b/SimSwap/crop_224/dnl.jpg new file mode 100644 index 0000000..e502d08 Binary files /dev/null and b/SimSwap/crop_224/dnl.jpg differ diff --git a/SimSwap/crop_224/ds.jpg b/SimSwap/crop_224/ds.jpg new file mode 100644 index 0000000..ba3b965 Binary files /dev/null and b/SimSwap/crop_224/ds.jpg differ diff --git a/SimSwap/crop_224/gdg.jpg b/SimSwap/crop_224/gdg.jpg new file mode 100644 index 0000000..8e15a7b Binary files /dev/null and b/SimSwap/crop_224/gdg.jpg differ diff --git a/SimSwap/crop_224/gy.jpg b/SimSwap/crop_224/gy.jpg new file mode 100644 index 0000000..0be6964 Binary files /dev/null and b/SimSwap/crop_224/gy.jpg differ diff --git a/SimSwap/crop_224/hzc.jpg b/SimSwap/crop_224/hzc.jpg new file mode 100644 index 0000000..b76e509 Binary files /dev/null and b/SimSwap/crop_224/hzc.jpg differ diff --git a/SimSwap/crop_224/hzxc.jpg b/SimSwap/crop_224/hzxc.jpg new file mode 100644 index 0000000..abc5581 Binary files /dev/null and b/SimSwap/crop_224/hzxc.jpg differ diff --git a/SimSwap/crop_224/james.jpg b/SimSwap/crop_224/james.jpg new file mode 100644 index 0000000..a36e305 Binary files /dev/null and b/SimSwap/crop_224/james.jpg differ diff --git a/SimSwap/crop_224/jl.jpg b/SimSwap/crop_224/jl.jpg new file mode 100644 index 0000000..dbe942f Binary files /dev/null and b/SimSwap/crop_224/jl.jpg differ diff --git a/SimSwap/crop_224/lcw.jpg b/SimSwap/crop_224/lcw.jpg new file mode 100644 index 0000000..30955c1 Binary files /dev/null and b/SimSwap/crop_224/lcw.jpg differ diff --git a/SimSwap/crop_224/ljm.jpg b/SimSwap/crop_224/ljm.jpg new file mode 100644 index 0000000..d452733 Binary files /dev/null and b/SimSwap/crop_224/ljm.jpg differ diff --git a/SimSwap/crop_224/ljm2.jpg b/SimSwap/crop_224/ljm2.jpg new file mode 100644 index 0000000..cdab34d Binary files /dev/null and b/SimSwap/crop_224/ljm2.jpg differ diff --git a/SimSwap/crop_224/ljm3.jpg b/SimSwap/crop_224/ljm3.jpg new file mode 100644 index 0000000..e977708 Binary files /dev/null and b/SimSwap/crop_224/ljm3.jpg differ diff --git a/SimSwap/crop_224/mars2.jpg b/SimSwap/crop_224/mars2.jpg new file mode 100644 index 0000000..19088a5 Binary files /dev/null and b/SimSwap/crop_224/mars2.jpg differ diff --git a/SimSwap/crop_224/mouth_open.jpg b/SimSwap/crop_224/mouth_open.jpg new file mode 100644 index 0000000..241eac1 Binary files /dev/null and b/SimSwap/crop_224/mouth_open.jpg differ diff --git a/SimSwap/crop_224/mtdm.jpg b/SimSwap/crop_224/mtdm.jpg new file mode 100644 index 0000000..dca6be5 Binary files /dev/null and b/SimSwap/crop_224/mtdm.jpg differ diff --git a/SimSwap/crop_224/trump.jpg b/SimSwap/crop_224/trump.jpg new file mode 100644 index 0000000..442fe0e Binary files /dev/null and b/SimSwap/crop_224/trump.jpg differ diff --git a/SimSwap/crop_224/wlh.jpg b/SimSwap/crop_224/wlh.jpg new file mode 100644 index 0000000..a22ab0b Binary files /dev/null and b/SimSwap/crop_224/wlh.jpg differ diff --git a/SimSwap/crop_224/zjl.jpg b/SimSwap/crop_224/zjl.jpg new file mode 100644 index 0000000..e3719dc Binary files /dev/null and b/SimSwap/crop_224/zjl.jpg differ diff --git a/SimSwap/crop_224/zrf.jpg b/SimSwap/crop_224/zrf.jpg new file mode 100644 index 0000000..0166ae1 Binary files /dev/null and b/SimSwap/crop_224/zrf.jpg differ diff --git a/SimSwap/crop_224/zxy.jpg b/SimSwap/crop_224/zxy.jpg new file mode 100644 index 0000000..c696347 Binary files /dev/null and b/SimSwap/crop_224/zxy.jpg differ diff --git a/SimSwap/data/__pycache__/data_loader_Swapping.cpython-310.pyc b/SimSwap/data/__pycache__/data_loader_Swapping.cpython-310.pyc new file mode 100644 index 0000000..bf27f78 Binary files /dev/null and b/SimSwap/data/__pycache__/data_loader_Swapping.cpython-310.pyc differ diff --git a/SimSwap/data/data_loader_Swapping.py b/SimSwap/data/data_loader_Swapping.py new file mode 100644 index 0000000..8788b95 --- /dev/null +++ b/SimSwap/data/data_loader_Swapping.py @@ -0,0 +1,125 @@ +import os +import glob +import torch +import random +from PIL import Image +from torch.utils import data +from torchvision import transforms as T + +class data_prefetcher(): + def __init__(self, loader): + self.loader = loader + self.dataiter = iter(loader) + self.stream = torch.cuda.Stream() + self.mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(1,3,1,1) + self.std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(1,3,1,1) + # With Amp, it isn't necessary to manually convert data to half. + # if args.fp16: + # self.mean = self.mean.half() + # self.std = self.std.half() + self.num_images = len(loader) + self.preload() + + def preload(self): + try: + self.src_image1, self.src_image2 = next(self.dataiter) + except StopIteration: + self.dataiter = iter(self.loader) + self.src_image1, self.src_image2 = next(self.dataiter) + + with torch.cuda.stream(self.stream): + self.src_image1 = self.src_image1.cuda(non_blocking=True) + self.src_image1 = self.src_image1.sub_(self.mean).div_(self.std) + self.src_image2 = self.src_image2.cuda(non_blocking=True) + self.src_image2 = self.src_image2.sub_(self.mean).div_(self.std) + + def next(self): + torch.cuda.current_stream().wait_stream(self.stream) + src_image1 = self.src_image1 + src_image2 = self.src_image2 + self.preload() + return src_image1, src_image2 + + def __len__(self): + """Return the number of images.""" + return self.num_images + +class SwappingDataset(data.Dataset): + """Dataset class for the Artworks dataset and content dataset.""" + + def __init__(self, + image_dir, + img_transform, + subffix='jpg', + random_seed=1234): + """Initialize and preprocess the Swapping dataset.""" + self.image_dir = image_dir + self.img_transform = img_transform + self.subffix = subffix + self.dataset = [] + self.random_seed = random_seed + self.preprocess() + self.num_images = len(self.dataset) + + def preprocess(self): + """Preprocess the Swapping dataset.""" + print("processing Swapping dataset images...") + + temp_path = os.path.join(self.image_dir,'*/') + pathes = glob.glob(temp_path) + self.dataset = [] + for dir_item in pathes: + join_path = glob.glob(os.path.join(dir_item,'*.jpg')) + print("processing %s"%dir_item,end='\r') + temp_list = [] + for item in join_path: + temp_list.append(item) + self.dataset.append(temp_list) + random.seed(self.random_seed) + random.shuffle(self.dataset) + print('Finished preprocessing the Swapping dataset, total dirs number: %d...'%len(self.dataset)) + + def __getitem__(self, index): + """Return two src domain images and two dst domain images.""" + dir_tmp1 = self.dataset[index] + dir_tmp1_len = len(dir_tmp1) + + filename1 = dir_tmp1[random.randint(0,dir_tmp1_len-1)] + filename2 = dir_tmp1[random.randint(0,dir_tmp1_len-1)] + image1 = self.img_transform(Image.open(filename1)) + image2 = self.img_transform(Image.open(filename2)) + return image1, image2 + + def __len__(self): + """Return the number of images.""" + return self.num_images + +def GetLoader( dataset_roots, + batch_size=16, + dataloader_workers=8, + random_seed = 1234 + ): + """Build and return a data loader.""" + + num_workers = dataloader_workers + data_root = dataset_roots + random_seed = random_seed + + c_transforms = [] + + c_transforms.append(T.ToTensor()) + c_transforms = T.Compose(c_transforms) + + content_dataset = SwappingDataset( + data_root, + c_transforms, + "jpg", + random_seed) + content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, + drop_last=True,shuffle=True,num_workers=num_workers,pin_memory=True) + prefetcher = data_prefetcher(content_data_loader) + return prefetcher + +def denorm(x): + out = (x + 1) / 2 + return out.clamp_(0, 1) \ No newline at end of file diff --git a/SimSwap/demo_file/Iron_man.jpg b/SimSwap/demo_file/Iron_man.jpg new file mode 100644 index 0000000..77a0f20 Binary files /dev/null and b/SimSwap/demo_file/Iron_man.jpg differ diff --git a/SimSwap/demo_file/multi_people.jpg b/SimSwap/demo_file/multi_people.jpg new file mode 100644 index 0000000..d6aef17 Binary files /dev/null and b/SimSwap/demo_file/multi_people.jpg differ diff --git a/SimSwap/demo_file/multi_people_1080p.mp4 b/SimSwap/demo_file/multi_people_1080p.mp4 new file mode 100644 index 0000000..c796b81 Binary files /dev/null and b/SimSwap/demo_file/multi_people_1080p.mp4 differ diff --git a/SimSwap/demo_file/multispecific/DST_01.jpg b/SimSwap/demo_file/multispecific/DST_01.jpg new file mode 100644 index 0000000..8bbe4ea Binary files /dev/null and b/SimSwap/demo_file/multispecific/DST_01.jpg differ diff --git a/SimSwap/demo_file/multispecific/DST_02.jpg b/SimSwap/demo_file/multispecific/DST_02.jpg new file mode 100644 index 0000000..ff42e9e Binary files /dev/null and b/SimSwap/demo_file/multispecific/DST_02.jpg differ diff --git a/SimSwap/demo_file/multispecific/DST_03.jpg b/SimSwap/demo_file/multispecific/DST_03.jpg new file mode 100644 index 0000000..77a0f20 Binary files /dev/null and b/SimSwap/demo_file/multispecific/DST_03.jpg differ diff --git a/SimSwap/demo_file/multispecific/SRC_01.png b/SimSwap/demo_file/multispecific/SRC_01.png new file mode 100644 index 0000000..f4bd195 Binary files /dev/null and b/SimSwap/demo_file/multispecific/SRC_01.png differ diff --git a/SimSwap/demo_file/multispecific/SRC_02.png b/SimSwap/demo_file/multispecific/SRC_02.png new file mode 100644 index 0000000..ccb48d4 Binary files /dev/null and b/SimSwap/demo_file/multispecific/SRC_02.png differ diff --git a/SimSwap/demo_file/multispecific/SRC_03.png b/SimSwap/demo_file/multispecific/SRC_03.png new file mode 100644 index 0000000..cef7e5b Binary files /dev/null and b/SimSwap/demo_file/multispecific/SRC_03.png differ diff --git a/SimSwap/demo_file/specific1.png b/SimSwap/demo_file/specific1.png new file mode 100644 index 0000000..f4bd195 Binary files /dev/null and b/SimSwap/demo_file/specific1.png differ diff --git a/SimSwap/demo_file/specific2.png b/SimSwap/demo_file/specific2.png new file mode 100644 index 0000000..ccb48d4 Binary files /dev/null and b/SimSwap/demo_file/specific2.png differ diff --git a/SimSwap/demo_file/specific3.png b/SimSwap/demo_file/specific3.png new file mode 100644 index 0000000..cef7e5b Binary files /dev/null and b/SimSwap/demo_file/specific3.png differ diff --git a/SimSwap/docs/css/bootstrap-theme.min.css b/SimSwap/docs/css/bootstrap-theme.min.css new file mode 100644 index 0000000..5e39401 --- /dev/null +++ b/SimSwap/docs/css/bootstrap-theme.min.css @@ -0,0 +1,6 @@ +/*! + * Bootstrap v3.3.7 (http://getbootstrap.com) + * Copyright 2011-2016 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) + */.btn-danger,.btn-default,.btn-info,.btn-primary,.btn-success,.btn-warning{text-shadow:0 -1px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075)}.btn-danger.active,.btn-danger:active,.btn-default.active,.btn-default:active,.btn-info.active,.btn-info:active,.btn-primary.active,.btn-primary:active,.btn-success.active,.btn-success:active,.btn-warning.active,.btn-warning:active{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-danger.disabled,.btn-danger[disabled],.btn-default.disabled,.btn-default[disabled],.btn-info.disabled,.btn-info[disabled],.btn-primary.disabled,.btn-primary[disabled],.btn-success.disabled,.btn-success[disabled],.btn-warning.disabled,.btn-warning[disabled],fieldset[disabled] .btn-danger,fieldset[disabled] .btn-default,fieldset[disabled] .btn-info,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-success,fieldset[disabled] .btn-warning{-webkit-box-shadow:none;box-shadow:none}.btn-danger .badge,.btn-default .badge,.btn-info .badge,.btn-primary .badge,.btn-success .badge,.btn-warning .badge{text-shadow:none}.btn.active,.btn:active{background-image:none}.btn-default{text-shadow:0 1px 0 #fff;background-image:-webkit-linear-gradient(top,#fff 0,#e0e0e0 100%);background-image:-o-linear-gradient(top,#fff 0,#e0e0e0 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fff),to(#e0e0e0));background-image:linear-gradient(to bottom,#fff 0,#e0e0e0 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#dbdbdb;border-color:#ccc}.btn-default:focus,.btn-default:hover{background-color:#e0e0e0;background-position:0 -15px}.btn-default.active,.btn-default:active{background-color:#e0e0e0;border-color:#dbdbdb}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#e0e0e0;background-image:none}.btn-primary{background-image:-webkit-linear-gradient(top,#337ab7 0,#265a88 100%);background-image:-o-linear-gradient(top,#337ab7 0,#265a88 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#265a88));background-image:linear-gradient(to bottom,#337ab7 0,#265a88 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#245580}.btn-primary:focus,.btn-primary:hover{background-color:#265a88;background-position:0 -15px}.btn-primary.active,.btn-primary:active{background-color:#265a88;border-color:#245580}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#265a88;background-image:none}.btn-success{background-image:-webkit-linear-gradient(top,#5cb85c 0,#419641 100%);background-image:-o-linear-gradient(top,#5cb85c 0,#419641 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5cb85c),to(#419641));background-image:linear-gradient(to bottom,#5cb85c 0,#419641 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#3e8f3e}.btn-success:focus,.btn-success:hover{background-color:#419641;background-position:0 -15px}.btn-success.active,.btn-success:active{background-color:#419641;border-color:#3e8f3e}.btn-success.disabled,.btn-success.disabled.active,.btn-success.disabled.focus,.btn-success.disabled:active,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled],.btn-success[disabled].active,.btn-success[disabled].focus,.btn-success[disabled]:active,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success,fieldset[disabled] .btn-success.active,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:active,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#419641;background-image:none}.btn-info{background-image:-webkit-linear-gradient(top,#5bc0de 0,#2aabd2 100%);background-image:-o-linear-gradient(top,#5bc0de 0,#2aabd2 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5bc0de),to(#2aabd2));background-image:linear-gradient(to bottom,#5bc0de 0,#2aabd2 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#28a4c9}.btn-info:focus,.btn-info:hover{background-color:#2aabd2;background-position:0 -15px}.btn-info.active,.btn-info:active{background-color:#2aabd2;border-color:#28a4c9}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#2aabd2;background-image:none}.btn-warning{background-image:-webkit-linear-gradient(top,#f0ad4e 0,#eb9316 100%);background-image:-o-linear-gradient(top,#f0ad4e 0,#eb9316 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f0ad4e),to(#eb9316));background-image:linear-gradient(to bottom,#f0ad4e 0,#eb9316 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#e38d13}.btn-warning:focus,.btn-warning:hover{background-color:#eb9316;background-position:0 -15px}.btn-warning.active,.btn-warning:active{background-color:#eb9316;border-color:#e38d13}.btn-warning.disabled,.btn-warning.disabled.active,.btn-warning.disabled.focus,.btn-warning.disabled:active,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled],.btn-warning[disabled].active,.btn-warning[disabled].focus,.btn-warning[disabled]:active,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning,fieldset[disabled] .btn-warning.active,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:active,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#eb9316;background-image:none}.btn-danger{background-image:-webkit-linear-gradient(top,#d9534f 0,#c12e2a 100%);background-image:-o-linear-gradient(top,#d9534f 0,#c12e2a 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9534f),to(#c12e2a));background-image:linear-gradient(to bottom,#d9534f 0,#c12e2a 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#b92c28}.btn-danger:focus,.btn-danger:hover{background-color:#c12e2a;background-position:0 -15px}.btn-danger.active,.btn-danger:active{background-color:#c12e2a;border-color:#b92c28}.btn-danger.disabled,.btn-danger.disabled.active,.btn-danger.disabled.focus,.btn-danger.disabled:active,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled],.btn-danger[disabled].active,.btn-danger[disabled].focus,.btn-danger[disabled]:active,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger,fieldset[disabled] .btn-danger.active,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:active,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#c12e2a;background-image:none}.img-thumbnail,.thumbnail{-webkit-box-shadow:0 1px 2px rgba(0,0,0,.075);box-shadow:0 1px 2px rgba(0,0,0,.075)}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{background-color:#e8e8e8;background-image:-webkit-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-o-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f5f5f5),to(#e8e8e8));background-image:linear-gradient(to bottom,#f5f5f5 0,#e8e8e8 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);background-repeat:repeat-x}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{background-color:#2e6da4;background-image:-webkit-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2e6da4));background-image:linear-gradient(to bottom,#337ab7 0,#2e6da4 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);background-repeat:repeat-x}.navbar-default{background-image:-webkit-linear-gradient(top,#fff 0,#f8f8f8 100%);background-image:-o-linear-gradient(top,#fff 0,#f8f8f8 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fff),to(#f8f8f8));background-image:linear-gradient(to bottom,#fff 0,#f8f8f8 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-radius:4px;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 5px rgba(0,0,0,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 5px rgba(0,0,0,.075)}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.open>a{background-image:-webkit-linear-gradient(top,#dbdbdb 0,#e2e2e2 100%);background-image:-o-linear-gradient(top,#dbdbdb 0,#e2e2e2 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#dbdbdb),to(#e2e2e2));background-image:linear-gradient(to bottom,#dbdbdb 0,#e2e2e2 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);background-repeat:repeat-x;-webkit-box-shadow:inset 0 3px 9px rgba(0,0,0,.075);box-shadow:inset 0 3px 9px rgba(0,0,0,.075)}.navbar-brand,.navbar-nav>li>a{text-shadow:0 1px 0 rgba(255,255,255,.25)}.navbar-inverse{background-image:-webkit-linear-gradient(top,#3c3c3c 0,#222 100%);background-image:-o-linear-gradient(top,#3c3c3c 0,#222 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#3c3c3c),to(#222));background-image:linear-gradient(to bottom,#3c3c3c 0,#222 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-radius:4px}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.open>a{background-image:-webkit-linear-gradient(top,#080808 0,#0f0f0f 100%);background-image:-o-linear-gradient(top,#080808 0,#0f0f0f 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#080808),to(#0f0f0f));background-image:linear-gradient(to bottom,#080808 0,#0f0f0f 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);background-repeat:repeat-x;-webkit-box-shadow:inset 0 3px 9px rgba(0,0,0,.25);box-shadow:inset 0 3px 9px rgba(0,0,0,.25)}.navbar-inverse .navbar-brand,.navbar-inverse .navbar-nav>li>a{text-shadow:0 -1px 0 rgba(0,0,0,.25)}.navbar-fixed-bottom,.navbar-fixed-top,.navbar-static-top{border-radius:0}@media (max-width:767px){.navbar .navbar-nav .open .dropdown-menu>.active>a,.navbar .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-image:-webkit-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2e6da4));background-image:linear-gradient(to bottom,#337ab7 0,#2e6da4 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);background-repeat:repeat-x}}.alert{text-shadow:0 1px 0 rgba(255,255,255,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.25),0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 1px 0 rgba(255,255,255,.25),0 1px 2px rgba(0,0,0,.05)}.alert-success{background-image:-webkit-linear-gradient(top,#dff0d8 0,#c8e5bc 100%);background-image:-o-linear-gradient(top,#dff0d8 0,#c8e5bc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#dff0d8),to(#c8e5bc));background-image:linear-gradient(to bottom,#dff0d8 0,#c8e5bc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);background-repeat:repeat-x;border-color:#b2dba1}.alert-info{background-image:-webkit-linear-gradient(top,#d9edf7 0,#b9def0 100%);background-image:-o-linear-gradient(top,#d9edf7 0,#b9def0 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9edf7),to(#b9def0));background-image:linear-gradient(to bottom,#d9edf7 0,#b9def0 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);background-repeat:repeat-x;border-color:#9acfea}.alert-warning{background-image:-webkit-linear-gradient(top,#fcf8e3 0,#f8efc0 100%);background-image:-o-linear-gradient(top,#fcf8e3 0,#f8efc0 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fcf8e3),to(#f8efc0));background-image:linear-gradient(to bottom,#fcf8e3 0,#f8efc0 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);background-repeat:repeat-x;border-color:#f5e79e}.alert-danger{background-image:-webkit-linear-gradient(top,#f2dede 0,#e7c3c3 100%);background-image:-o-linear-gradient(top,#f2dede 0,#e7c3c3 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f2dede),to(#e7c3c3));background-image:linear-gradient(to bottom,#f2dede 0,#e7c3c3 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);background-repeat:repeat-x;border-color:#dca7a7}.progress{background-image:-webkit-linear-gradient(top,#ebebeb 0,#f5f5f5 100%);background-image:-o-linear-gradient(top,#ebebeb 0,#f5f5f5 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#ebebeb),to(#f5f5f5));background-image:linear-gradient(to bottom,#ebebeb 0,#f5f5f5 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);background-repeat:repeat-x}.progress-bar{background-image:-webkit-linear-gradient(top,#337ab7 0,#286090 100%);background-image:-o-linear-gradient(top,#337ab7 0,#286090 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#286090));background-image:linear-gradient(to bottom,#337ab7 0,#286090 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);background-repeat:repeat-x}.progress-bar-success{background-image:-webkit-linear-gradient(top,#5cb85c 0,#449d44 100%);background-image:-o-linear-gradient(top,#5cb85c 0,#449d44 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5cb85c),to(#449d44));background-image:linear-gradient(to bottom,#5cb85c 0,#449d44 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);background-repeat:repeat-x}.progress-bar-info{background-image:-webkit-linear-gradient(top,#5bc0de 0,#31b0d5 100%);background-image:-o-linear-gradient(top,#5bc0de 0,#31b0d5 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5bc0de),to(#31b0d5));background-image:linear-gradient(to bottom,#5bc0de 0,#31b0d5 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);background-repeat:repeat-x}.progress-bar-warning{background-image:-webkit-linear-gradient(top,#f0ad4e 0,#ec971f 100%);background-image:-o-linear-gradient(top,#f0ad4e 0,#ec971f 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f0ad4e),to(#ec971f));background-image:linear-gradient(to bottom,#f0ad4e 0,#ec971f 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);background-repeat:repeat-x}.progress-bar-danger{background-image:-webkit-linear-gradient(top,#d9534f 0,#c9302c 100%);background-image:-o-linear-gradient(top,#d9534f 0,#c9302c 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9534f),to(#c9302c));background-image:linear-gradient(to bottom,#d9534f 0,#c9302c 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);background-repeat:repeat-x}.progress-bar-striped{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.list-group{border-radius:4px;-webkit-box-shadow:0 1px 2px rgba(0,0,0,.075);box-shadow:0 1px 2px rgba(0,0,0,.075)}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{text-shadow:0 -1px 0 #286090;background-image:-webkit-linear-gradient(top,#337ab7 0,#2b669a 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2b669a 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2b669a));background-image:linear-gradient(to bottom,#337ab7 0,#2b669a 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);background-repeat:repeat-x;border-color:#2b669a}.list-group-item.active .badge,.list-group-item.active:focus .badge,.list-group-item.active:hover .badge{text-shadow:none}.panel{-webkit-box-shadow:0 1px 2px rgba(0,0,0,.05);box-shadow:0 1px 2px rgba(0,0,0,.05)}.panel-default>.panel-heading{background-image:-webkit-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-o-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f5f5f5),to(#e8e8e8));background-image:linear-gradient(to bottom,#f5f5f5 0,#e8e8e8 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);background-repeat:repeat-x}.panel-primary>.panel-heading{background-image:-webkit-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2e6da4));background-image:linear-gradient(to bottom,#337ab7 0,#2e6da4 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);background-repeat:repeat-x}.panel-success>.panel-heading{background-image:-webkit-linear-gradient(top,#dff0d8 0,#d0e9c6 100%);background-image:-o-linear-gradient(top,#dff0d8 0,#d0e9c6 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#dff0d8),to(#d0e9c6));background-image:linear-gradient(to bottom,#dff0d8 0,#d0e9c6 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);background-repeat:repeat-x}.panel-info>.panel-heading{background-image:-webkit-linear-gradient(top,#d9edf7 0,#c4e3f3 100%);background-image:-o-linear-gradient(top,#d9edf7 0,#c4e3f3 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9edf7),to(#c4e3f3));background-image:linear-gradient(to bottom,#d9edf7 0,#c4e3f3 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);background-repeat:repeat-x}.panel-warning>.panel-heading{background-image:-webkit-linear-gradient(top,#fcf8e3 0,#faf2cc 100%);background-image:-o-linear-gradient(top,#fcf8e3 0,#faf2cc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fcf8e3),to(#faf2cc));background-image:linear-gradient(to bottom,#fcf8e3 0,#faf2cc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);background-repeat:repeat-x}.panel-danger>.panel-heading{background-image:-webkit-linear-gradient(top,#f2dede 0,#ebcccc 100%);background-image:-o-linear-gradient(top,#f2dede 0,#ebcccc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f2dede),to(#ebcccc));background-image:linear-gradient(to bottom,#f2dede 0,#ebcccc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);background-repeat:repeat-x}.well{background-image:-webkit-linear-gradient(top,#e8e8e8 0,#f5f5f5 100%);background-image:-o-linear-gradient(top,#e8e8e8 0,#f5f5f5 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#e8e8e8),to(#f5f5f5));background-image:linear-gradient(to bottom,#e8e8e8 0,#f5f5f5 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);background-repeat:repeat-x;border-color:#dcdcdc;-webkit-box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1)} +/*# sourceMappingURL=bootstrap-theme.min.css.map */ \ No newline at end of file diff --git a/SimSwap/docs/css/bootstrap.min.css b/SimSwap/docs/css/bootstrap.min.css new file mode 100644 index 0000000..ed3905e --- /dev/null +++ b/SimSwap/docs/css/bootstrap.min.css @@ -0,0 +1,6 @@ +/*! + * Bootstrap v3.3.7 (http://getbootstrap.com) + * Copyright 2011-2016 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) + *//*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{margin:.67em 0;font-size:2em}mark{color:#000;background:#ff0}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{height:0;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{margin:0;font:inherit;color:inherit}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{padding:.35em .625em .75em;margin:0 2px;border:1px solid silver}legend{padding:0;border:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */@media print{*,:after,:before{color:#000!important;text-shadow:none!important;background:0 0!important;-webkit-box-shadow:none!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}a[href^="javascript:"]:after,a[href^="#"]:after{content:""}blockquote,pre{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}img{max-width:100%!important}h2,h3,p{orphans:3;widows:3}h2,h3{page-break-after:avoid}.navbar{display:none}.btn>.caret,.dropup>.btn>.caret{border-top-color:#000!important}.label{border:1px solid #000}.table{border-collapse:collapse!important}.table td,.table th{background-color:#fff!important}.table-bordered td,.table-bordered th{border:1px solid #ddd!important}}@font-face{font-family:'Glyphicons Halflings';src:url(../fonts/glyphicons-halflings-regular.eot);src:url(../fonts/glyphicons-halflings-regular.eot?#iefix) format('embedded-opentype'),url(../fonts/glyphicons-halflings-regular.woff2) format('woff2'),url(../fonts/glyphicons-halflings-regular.woff) format('woff'),url(../fonts/glyphicons-halflings-regular.ttf) format('truetype'),url(../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular) format('svg')}.glyphicon{position:relative;top:1px;display:inline-block;font-family:'Glyphicons Halflings';font-style:normal;font-weight:400;line-height:1;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.glyphicon-asterisk:before{content:"\002a"}.glyphicon-plus:before{content:"\002b"}.glyphicon-eur:before,.glyphicon-euro:before{content:"\20ac"}.glyphicon-minus:before{content:"\2212"}.glyphicon-cloud:before{content:"\2601"}.glyphicon-envelope:before{content:"\2709"}.glyphicon-pencil:before{content:"\270f"}.glyphicon-glass:before{content:"\e001"}.glyphicon-music:before{content:"\e002"}.glyphicon-search:before{content:"\e003"}.glyphicon-heart:before{content:"\e005"}.glyphicon-star:before{content:"\e006"}.glyphicon-star-empty:before{content:"\e007"}.glyphicon-user:before{content:"\e008"}.glyphicon-film:before{content:"\e009"}.glyphicon-th-large:before{content:"\e010"}.glyphicon-th:before{content:"\e011"}.glyphicon-th-list:before{content:"\e012"}.glyphicon-ok:before{content:"\e013"}.glyphicon-remove:before{content:"\e014"}.glyphicon-zoom-in:before{content:"\e015"}.glyphicon-zoom-out:before{content:"\e016"}.glyphicon-off:before{content:"\e017"}.glyphicon-signal:before{content:"\e018"}.glyphicon-cog:before{content:"\e019"}.glyphicon-trash:before{content:"\e020"}.glyphicon-home:before{content:"\e021"}.glyphicon-file:before{content:"\e022"}.glyphicon-time:before{content:"\e023"}.glyphicon-road:before{content:"\e024"}.glyphicon-download-alt:before{content:"\e025"}.glyphicon-download:before{content:"\e026"}.glyphicon-upload:before{content:"\e027"}.glyphicon-inbox:before{content:"\e028"}.glyphicon-play-circle:before{content:"\e029"}.glyphicon-repeat:before{content:"\e030"}.glyphicon-refresh:before{content:"\e031"}.glyphicon-list-alt:before{content:"\e032"}.glyphicon-lock:before{content:"\e033"}.glyphicon-flag:before{content:"\e034"}.glyphicon-headphones:before{content:"\e035"}.glyphicon-volume-off:before{content:"\e036"}.glyphicon-volume-down:before{content:"\e037"}.glyphicon-volume-up:before{content:"\e038"}.glyphicon-qrcode:before{content:"\e039"}.glyphicon-barcode:before{content:"\e040"}.glyphicon-tag:before{content:"\e041"}.glyphicon-tags:before{content:"\e042"}.glyphicon-book:before{content:"\e043"}.glyphicon-bookmark:before{content:"\e044"}.glyphicon-print:before{content:"\e045"}.glyphicon-camera:before{content:"\e046"}.glyphicon-font:before{content:"\e047"}.glyphicon-bold:before{content:"\e048"}.glyphicon-italic:before{content:"\e049"}.glyphicon-text-height:before{content:"\e050"}.glyphicon-text-width:before{content:"\e051"}.glyphicon-align-left:before{content:"\e052"}.glyphicon-align-center:before{content:"\e053"}.glyphicon-align-right:before{content:"\e054"}.glyphicon-align-justify:before{content:"\e055"}.glyphicon-list:before{content:"\e056"}.glyphicon-indent-left:before{content:"\e057"}.glyphicon-indent-right:before{content:"\e058"}.glyphicon-facetime-video:before{content:"\e059"}.glyphicon-picture:before{content:"\e060"}.glyphicon-map-marker:before{content:"\e062"}.glyphicon-adjust:before{content:"\e063"}.glyphicon-tint:before{content:"\e064"}.glyphicon-edit:before{content:"\e065"}.glyphicon-share:before{content:"\e066"}.glyphicon-check:before{content:"\e067"}.glyphicon-move:before{content:"\e068"}.glyphicon-step-backward:before{content:"\e069"}.glyphicon-fast-backward:before{content:"\e070"}.glyphicon-backward:before{content:"\e071"}.glyphicon-play:before{content:"\e072"}.glyphicon-pause:before{content:"\e073"}.glyphicon-stop:before{content:"\e074"}.glyphicon-forward:before{content:"\e075"}.glyphicon-fast-forward:before{content:"\e076"}.glyphicon-step-forward:before{content:"\e077"}.glyphicon-eject:before{content:"\e078"}.glyphicon-chevron-left:before{content:"\e079"}.glyphicon-chevron-right:before{content:"\e080"}.glyphicon-plus-sign:before{content:"\e081"}.glyphicon-minus-sign:before{content:"\e082"}.glyphicon-remove-sign:before{content:"\e083"}.glyphicon-ok-sign:before{content:"\e084"}.glyphicon-question-sign:before{content:"\e085"}.glyphicon-info-sign:before{content:"\e086"}.glyphicon-screenshot:before{content:"\e087"}.glyphicon-remove-circle:before{content:"\e088"}.glyphicon-ok-circle:before{content:"\e089"}.glyphicon-ban-circle:before{content:"\e090"}.glyphicon-arrow-left:before{content:"\e091"}.glyphicon-arrow-right:before{content:"\e092"}.glyphicon-arrow-up:before{content:"\e093"}.glyphicon-arrow-down:before{content:"\e094"}.glyphicon-share-alt:before{content:"\e095"}.glyphicon-resize-full:before{content:"\e096"}.glyphicon-resize-small:before{content:"\e097"}.glyphicon-exclamation-sign:before{content:"\e101"}.glyphicon-gift:before{content:"\e102"}.glyphicon-leaf:before{content:"\e103"}.glyphicon-fire:before{content:"\e104"}.glyphicon-eye-open:before{content:"\e105"}.glyphicon-eye-close:before{content:"\e106"}.glyphicon-warning-sign:before{content:"\e107"}.glyphicon-plane:before{content:"\e108"}.glyphicon-calendar:before{content:"\e109"}.glyphicon-random:before{content:"\e110"}.glyphicon-comment:before{content:"\e111"}.glyphicon-magnet:before{content:"\e112"}.glyphicon-chevron-up:before{content:"\e113"}.glyphicon-chevron-down:before{content:"\e114"}.glyphicon-retweet:before{content:"\e115"}.glyphicon-shopping-cart:before{content:"\e116"}.glyphicon-folder-close:before{content:"\e117"}.glyphicon-folder-open:before{content:"\e118"}.glyphicon-resize-vertical:before{content:"\e119"}.glyphicon-resize-horizontal:before{content:"\e120"}.glyphicon-hdd:before{content:"\e121"}.glyphicon-bullhorn:before{content:"\e122"}.glyphicon-bell:before{content:"\e123"}.glyphicon-certificate:before{content:"\e124"}.glyphicon-thumbs-up:before{content:"\e125"}.glyphicon-thumbs-down:before{content:"\e126"}.glyphicon-hand-right:before{content:"\e127"}.glyphicon-hand-left:before{content:"\e128"}.glyphicon-hand-up:before{content:"\e129"}.glyphicon-hand-down:before{content:"\e130"}.glyphicon-circle-arrow-right:before{content:"\e131"}.glyphicon-circle-arrow-left:before{content:"\e132"}.glyphicon-circle-arrow-up:before{content:"\e133"}.glyphicon-circle-arrow-down:before{content:"\e134"}.glyphicon-globe:before{content:"\e135"}.glyphicon-wrench:before{content:"\e136"}.glyphicon-tasks:before{content:"\e137"}.glyphicon-filter:before{content:"\e138"}.glyphicon-briefcase:before{content:"\e139"}.glyphicon-fullscreen:before{content:"\e140"}.glyphicon-dashboard:before{content:"\e141"}.glyphicon-paperclip:before{content:"\e142"}.glyphicon-heart-empty:before{content:"\e143"}.glyphicon-link:before{content:"\e144"}.glyphicon-phone:before{content:"\e145"}.glyphicon-pushpin:before{content:"\e146"}.glyphicon-usd:before{content:"\e148"}.glyphicon-gbp:before{content:"\e149"}.glyphicon-sort:before{content:"\e150"}.glyphicon-sort-by-alphabet:before{content:"\e151"}.glyphicon-sort-by-alphabet-alt:before{content:"\e152"}.glyphicon-sort-by-order:before{content:"\e153"}.glyphicon-sort-by-order-alt:before{content:"\e154"}.glyphicon-sort-by-attributes:before{content:"\e155"}.glyphicon-sort-by-attributes-alt:before{content:"\e156"}.glyphicon-unchecked:before{content:"\e157"}.glyphicon-expand:before{content:"\e158"}.glyphicon-collapse-down:before{content:"\e159"}.glyphicon-collapse-up:before{content:"\e160"}.glyphicon-log-in:before{content:"\e161"}.glyphicon-flash:before{content:"\e162"}.glyphicon-log-out:before{content:"\e163"}.glyphicon-new-window:before{content:"\e164"}.glyphicon-record:before{content:"\e165"}.glyphicon-save:before{content:"\e166"}.glyphicon-open:before{content:"\e167"}.glyphicon-saved:before{content:"\e168"}.glyphicon-import:before{content:"\e169"}.glyphicon-export:before{content:"\e170"}.glyphicon-send:before{content:"\e171"}.glyphicon-floppy-disk:before{content:"\e172"}.glyphicon-floppy-saved:before{content:"\e173"}.glyphicon-floppy-remove:before{content:"\e174"}.glyphicon-floppy-save:before{content:"\e175"}.glyphicon-floppy-open:before{content:"\e176"}.glyphicon-credit-card:before{content:"\e177"}.glyphicon-transfer:before{content:"\e178"}.glyphicon-cutlery:before{content:"\e179"}.glyphicon-header:before{content:"\e180"}.glyphicon-compressed:before{content:"\e181"}.glyphicon-earphone:before{content:"\e182"}.glyphicon-phone-alt:before{content:"\e183"}.glyphicon-tower:before{content:"\e184"}.glyphicon-stats:before{content:"\e185"}.glyphicon-sd-video:before{content:"\e186"}.glyphicon-hd-video:before{content:"\e187"}.glyphicon-subtitles:before{content:"\e188"}.glyphicon-sound-stereo:before{content:"\e189"}.glyphicon-sound-dolby:before{content:"\e190"}.glyphicon-sound-5-1:before{content:"\e191"}.glyphicon-sound-6-1:before{content:"\e192"}.glyphicon-sound-7-1:before{content:"\e193"}.glyphicon-copyright-mark:before{content:"\e194"}.glyphicon-registration-mark:before{content:"\e195"}.glyphicon-cloud-download:before{content:"\e197"}.glyphicon-cloud-upload:before{content:"\e198"}.glyphicon-tree-conifer:before{content:"\e199"}.glyphicon-tree-deciduous:before{content:"\e200"}.glyphicon-cd:before{content:"\e201"}.glyphicon-save-file:before{content:"\e202"}.glyphicon-open-file:before{content:"\e203"}.glyphicon-level-up:before{content:"\e204"}.glyphicon-copy:before{content:"\e205"}.glyphicon-paste:before{content:"\e206"}.glyphicon-alert:before{content:"\e209"}.glyphicon-equalizer:before{content:"\e210"}.glyphicon-king:before{content:"\e211"}.glyphicon-queen:before{content:"\e212"}.glyphicon-pawn:before{content:"\e213"}.glyphicon-bishop:before{content:"\e214"}.glyphicon-knight:before{content:"\e215"}.glyphicon-baby-formula:before{content:"\e216"}.glyphicon-tent:before{content:"\26fa"}.glyphicon-blackboard:before{content:"\e218"}.glyphicon-bed:before{content:"\e219"}.glyphicon-apple:before{content:"\f8ff"}.glyphicon-erase:before{content:"\e221"}.glyphicon-hourglass:before{content:"\231b"}.glyphicon-lamp:before{content:"\e223"}.glyphicon-duplicate:before{content:"\e224"}.glyphicon-piggy-bank:before{content:"\e225"}.glyphicon-scissors:before{content:"\e226"}.glyphicon-bitcoin:before{content:"\e227"}.glyphicon-btc:before{content:"\e227"}.glyphicon-xbt:before{content:"\e227"}.glyphicon-yen:before{content:"\00a5"}.glyphicon-jpy:before{content:"\00a5"}.glyphicon-ruble:before{content:"\20bd"}.glyphicon-rub:before{content:"\20bd"}.glyphicon-scale:before{content:"\e230"}.glyphicon-ice-lolly:before{content:"\e231"}.glyphicon-ice-lolly-tasted:before{content:"\e232"}.glyphicon-education:before{content:"\e233"}.glyphicon-option-horizontal:before{content:"\e234"}.glyphicon-option-vertical:before{content:"\e235"}.glyphicon-menu-hamburger:before{content:"\e236"}.glyphicon-modal-window:before{content:"\e237"}.glyphicon-oil:before{content:"\e238"}.glyphicon-grain:before{content:"\e239"}.glyphicon-sunglasses:before{content:"\e240"}.glyphicon-text-size:before{content:"\e241"}.glyphicon-text-color:before{content:"\e242"}.glyphicon-text-background:before{content:"\e243"}.glyphicon-object-align-top:before{content:"\e244"}.glyphicon-object-align-bottom:before{content:"\e245"}.glyphicon-object-align-horizontal:before{content:"\e246"}.glyphicon-object-align-left:before{content:"\e247"}.glyphicon-object-align-vertical:before{content:"\e248"}.glyphicon-object-align-right:before{content:"\e249"}.glyphicon-triangle-right:before{content:"\e250"}.glyphicon-triangle-left:before{content:"\e251"}.glyphicon-triangle-bottom:before{content:"\e252"}.glyphicon-triangle-top:before{content:"\e253"}.glyphicon-console:before{content:"\e254"}.glyphicon-superscript:before{content:"\e255"}.glyphicon-subscript:before{content:"\e256"}.glyphicon-menu-left:before{content:"\e257"}.glyphicon-menu-right:before{content:"\e258"}.glyphicon-menu-down:before{content:"\e259"}.glyphicon-menu-up:before{content:"\e260"}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:10px;-webkit-tap-highlight-color:rgba(0,0,0,0)}body{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:1.42857143;color:#333;background-color:#fff}button,input,select,textarea{font-family:inherit;font-size:inherit;line-height:inherit}a{color:#337ab7;text-decoration:none}a:focus,a:hover{color:#23527c;text-decoration:underline}a:focus{outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}figure{margin:0}img{vertical-align:middle}.carousel-inner>.item>a>img,.carousel-inner>.item>img,.img-responsive,.thumbnail a>img,.thumbnail>img{display:block;max-width:100%;height:auto}.img-rounded{border-radius:6px}.img-thumbnail{display:inline-block;max-width:100%;height:auto;padding:4px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.img-circle{border-radius:50%}hr{margin-top:20px;margin-bottom:20px;border:0;border-top:1px solid #eee}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto}[role=button]{cursor:pointer}.h1,.h2,.h3,.h4,.h5,.h6,h1,h2,h3,h4,h5,h6{font-family:inherit;font-weight:500;line-height:1.1;color:inherit}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-weight:400;line-height:1;color:#777}.h1,.h2,.h3,h1,h2,h3{margin-top:20px;margin-bottom:10px}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small{font-size:65%}.h4,.h5,.h6,h4,h5,h6{margin-top:10px;margin-bottom:10px}.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-size:75%}.h1,h1{font-size:36px}.h2,h2{font-size:30px}.h3,h3{font-size:24px}.h4,h4{font-size:18px}.h5,h5{font-size:14px}.h6,h6{font-size:12px}p{margin:0 0 10px}.lead{margin-bottom:20px;font-size:16px;font-weight:300;line-height:1.4}@media (min-width:768px){.lead{font-size:21px}}.small,small{font-size:85%}.mark,mark{padding:.2em;background-color:#fcf8e3}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}.text-justify{text-align:justify}.text-nowrap{white-space:nowrap}.text-lowercase{text-transform:lowercase}.text-uppercase{text-transform:uppercase}.text-capitalize{text-transform:capitalize}.text-muted{color:#777}.text-primary{color:#337ab7}a.text-primary:focus,a.text-primary:hover{color:#286090}.text-success{color:#3c763d}a.text-success:focus,a.text-success:hover{color:#2b542c}.text-info{color:#31708f}a.text-info:focus,a.text-info:hover{color:#245269}.text-warning{color:#8a6d3b}a.text-warning:focus,a.text-warning:hover{color:#66512c}.text-danger{color:#a94442}a.text-danger:focus,a.text-danger:hover{color:#843534}.bg-primary{color:#fff;background-color:#337ab7}a.bg-primary:focus,a.bg-primary:hover{background-color:#286090}.bg-success{background-color:#dff0d8}a.bg-success:focus,a.bg-success:hover{background-color:#c1e2b3}.bg-info{background-color:#d9edf7}a.bg-info:focus,a.bg-info:hover{background-color:#afd9ee}.bg-warning{background-color:#fcf8e3}a.bg-warning:focus,a.bg-warning:hover{background-color:#f7ecb5}.bg-danger{background-color:#f2dede}a.bg-danger:focus,a.bg-danger:hover{background-color:#e4b9b9}.page-header{padding-bottom:9px;margin:40px 0 20px;border-bottom:1px solid #eee}ol,ul{margin-top:0;margin-bottom:10px}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;margin-left:-5px;list-style:none}.list-inline>li{display:inline-block;padding-right:5px;padding-left:5px}dl{margin-top:0;margin-bottom:20px}dd,dt{line-height:1.42857143}dt{font-weight:700}dd{margin-left:0}@media (min-width:768px){.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}}abbr[data-original-title],abbr[title]{cursor:help;border-bottom:1px dotted #777}.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:10px 20px;margin:0 0 20px;font-size:17.5px;border-left:5px solid #eee}blockquote ol:last-child,blockquote p:last-child,blockquote ul:last-child{margin-bottom:0}blockquote .small,blockquote footer,blockquote small{display:block;font-size:80%;line-height:1.42857143;color:#777}blockquote .small:before,blockquote footer:before,blockquote small:before{content:'\2014 \00A0'}.blockquote-reverse,blockquote.pull-right{padding-right:15px;padding-left:0;text-align:right;border-right:5px solid #eee;border-left:0}.blockquote-reverse .small:before,.blockquote-reverse footer:before,.blockquote-reverse small:before,blockquote.pull-right .small:before,blockquote.pull-right footer:before,blockquote.pull-right small:before{content:''}.blockquote-reverse .small:after,.blockquote-reverse footer:after,.blockquote-reverse small:after,blockquote.pull-right .small:after,blockquote.pull-right footer:after,blockquote.pull-right small:after{content:'\00A0 \2014'}address{margin-bottom:20px;font-style:normal;line-height:1.42857143}code,kbd,pre,samp{font-family:Menlo,Monaco,Consolas,"Courier New",monospace}code{padding:2px 4px;font-size:90%;color:#c7254e;background-color:#f9f2f4;border-radius:4px}kbd{padding:2px 4px;font-size:90%;color:#fff;background-color:#333;border-radius:3px;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.25);box-shadow:inset 0 -1px 0 rgba(0,0,0,.25)}kbd kbd{padding:0;font-size:100%;font-weight:700;-webkit-box-shadow:none;box-shadow:none}pre{display:block;padding:9.5px;margin:0 0 10px;font-size:13px;line-height:1.42857143;color:#333;word-break:break-all;word-wrap:break-word;background-color:#f5f5f5;border:1px solid #ccc;border-radius:4px}pre code{padding:0;font-size:inherit;color:inherit;white-space:pre-wrap;background-color:transparent;border-radius:0}.pre-scrollable{max-height:340px;overflow-y:scroll}.container{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}@media (min-width:768px){.container{width:750px}}@media (min-width:992px){.container{width:970px}}@media (min-width:1200px){.container{width:1170px}}.container-fluid{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}.row{margin-right:-15px;margin-left:-15px}.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9,.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9,.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9,.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{position:relative;min-height:1px;padding-right:15px;padding-left:15px}.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{float:left}.col-xs-12{width:100%}.col-xs-11{width:91.66666667%}.col-xs-10{width:83.33333333%}.col-xs-9{width:75%}.col-xs-8{width:66.66666667%}.col-xs-7{width:58.33333333%}.col-xs-6{width:50%}.col-xs-5{width:41.66666667%}.col-xs-4{width:33.33333333%}.col-xs-3{width:25%}.col-xs-2{width:16.66666667%}.col-xs-1{width:8.33333333%}.col-xs-pull-12{right:100%}.col-xs-pull-11{right:91.66666667%}.col-xs-pull-10{right:83.33333333%}.col-xs-pull-9{right:75%}.col-xs-pull-8{right:66.66666667%}.col-xs-pull-7{right:58.33333333%}.col-xs-pull-6{right:50%}.col-xs-pull-5{right:41.66666667%}.col-xs-pull-4{right:33.33333333%}.col-xs-pull-3{right:25%}.col-xs-pull-2{right:16.66666667%}.col-xs-pull-1{right:8.33333333%}.col-xs-pull-0{right:auto}.col-xs-push-12{left:100%}.col-xs-push-11{left:91.66666667%}.col-xs-push-10{left:83.33333333%}.col-xs-push-9{left:75%}.col-xs-push-8{left:66.66666667%}.col-xs-push-7{left:58.33333333%}.col-xs-push-6{left:50%}.col-xs-push-5{left:41.66666667%}.col-xs-push-4{left:33.33333333%}.col-xs-push-3{left:25%}.col-xs-push-2{left:16.66666667%}.col-xs-push-1{left:8.33333333%}.col-xs-push-0{left:auto}.col-xs-offset-12{margin-left:100%}.col-xs-offset-11{margin-left:91.66666667%}.col-xs-offset-10{margin-left:83.33333333%}.col-xs-offset-9{margin-left:75%}.col-xs-offset-8{margin-left:66.66666667%}.col-xs-offset-7{margin-left:58.33333333%}.col-xs-offset-6{margin-left:50%}.col-xs-offset-5{margin-left:41.66666667%}.col-xs-offset-4{margin-left:33.33333333%}.col-xs-offset-3{margin-left:25%}.col-xs-offset-2{margin-left:16.66666667%}.col-xs-offset-1{margin-left:8.33333333%}.col-xs-offset-0{margin-left:0}@media (min-width:768px){.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9{float:left}.col-sm-12{width:100%}.col-sm-11{width:91.66666667%}.col-sm-10{width:83.33333333%}.col-sm-9{width:75%}.col-sm-8{width:66.66666667%}.col-sm-7{width:58.33333333%}.col-sm-6{width:50%}.col-sm-5{width:41.66666667%}.col-sm-4{width:33.33333333%}.col-sm-3{width:25%}.col-sm-2{width:16.66666667%}.col-sm-1{width:8.33333333%}.col-sm-pull-12{right:100%}.col-sm-pull-11{right:91.66666667%}.col-sm-pull-10{right:83.33333333%}.col-sm-pull-9{right:75%}.col-sm-pull-8{right:66.66666667%}.col-sm-pull-7{right:58.33333333%}.col-sm-pull-6{right:50%}.col-sm-pull-5{right:41.66666667%}.col-sm-pull-4{right:33.33333333%}.col-sm-pull-3{right:25%}.col-sm-pull-2{right:16.66666667%}.col-sm-pull-1{right:8.33333333%}.col-sm-pull-0{right:auto}.col-sm-push-12{left:100%}.col-sm-push-11{left:91.66666667%}.col-sm-push-10{left:83.33333333%}.col-sm-push-9{left:75%}.col-sm-push-8{left:66.66666667%}.col-sm-push-7{left:58.33333333%}.col-sm-push-6{left:50%}.col-sm-push-5{left:41.66666667%}.col-sm-push-4{left:33.33333333%}.col-sm-push-3{left:25%}.col-sm-push-2{left:16.66666667%}.col-sm-push-1{left:8.33333333%}.col-sm-push-0{left:auto}.col-sm-offset-12{margin-left:100%}.col-sm-offset-11{margin-left:91.66666667%}.col-sm-offset-10{margin-left:83.33333333%}.col-sm-offset-9{margin-left:75%}.col-sm-offset-8{margin-left:66.66666667%}.col-sm-offset-7{margin-left:58.33333333%}.col-sm-offset-6{margin-left:50%}.col-sm-offset-5{margin-left:41.66666667%}.col-sm-offset-4{margin-left:33.33333333%}.col-sm-offset-3{margin-left:25%}.col-sm-offset-2{margin-left:16.66666667%}.col-sm-offset-1{margin-left:8.33333333%}.col-sm-offset-0{margin-left:0}}@media (min-width:992px){.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9{float:left}.col-md-12{width:100%}.col-md-11{width:91.66666667%}.col-md-10{width:83.33333333%}.col-md-9{width:75%}.col-md-8{width:66.66666667%}.col-md-7{width:58.33333333%}.col-md-6{width:50%}.col-md-5{width:41.66666667%}.col-md-4{width:33.33333333%}.col-md-3{width:25%}.col-md-2{width:16.66666667%}.col-md-1{width:8.33333333%}.col-md-pull-12{right:100%}.col-md-pull-11{right:91.66666667%}.col-md-pull-10{right:83.33333333%}.col-md-pull-9{right:75%}.col-md-pull-8{right:66.66666667%}.col-md-pull-7{right:58.33333333%}.col-md-pull-6{right:50%}.col-md-pull-5{right:41.66666667%}.col-md-pull-4{right:33.33333333%}.col-md-pull-3{right:25%}.col-md-pull-2{right:16.66666667%}.col-md-pull-1{right:8.33333333%}.col-md-pull-0{right:auto}.col-md-push-12{left:100%}.col-md-push-11{left:91.66666667%}.col-md-push-10{left:83.33333333%}.col-md-push-9{left:75%}.col-md-push-8{left:66.66666667%}.col-md-push-7{left:58.33333333%}.col-md-push-6{left:50%}.col-md-push-5{left:41.66666667%}.col-md-push-4{left:33.33333333%}.col-md-push-3{left:25%}.col-md-push-2{left:16.66666667%}.col-md-push-1{left:8.33333333%}.col-md-push-0{left:auto}.col-md-offset-12{margin-left:100%}.col-md-offset-11{margin-left:91.66666667%}.col-md-offset-10{margin-left:83.33333333%}.col-md-offset-9{margin-left:75%}.col-md-offset-8{margin-left:66.66666667%}.col-md-offset-7{margin-left:58.33333333%}.col-md-offset-6{margin-left:50%}.col-md-offset-5{margin-left:41.66666667%}.col-md-offset-4{margin-left:33.33333333%}.col-md-offset-3{margin-left:25%}.col-md-offset-2{margin-left:16.66666667%}.col-md-offset-1{margin-left:8.33333333%}.col-md-offset-0{margin-left:0}}@media (min-width:1200px){.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9{float:left}.col-lg-12{width:100%}.col-lg-11{width:91.66666667%}.col-lg-10{width:83.33333333%}.col-lg-9{width:75%}.col-lg-8{width:66.66666667%}.col-lg-7{width:58.33333333%}.col-lg-6{width:50%}.col-lg-5{width:41.66666667%}.col-lg-4{width:33.33333333%}.col-lg-3{width:25%}.col-lg-2{width:16.66666667%}.col-lg-1{width:8.33333333%}.col-lg-pull-12{right:100%}.col-lg-pull-11{right:91.66666667%}.col-lg-pull-10{right:83.33333333%}.col-lg-pull-9{right:75%}.col-lg-pull-8{right:66.66666667%}.col-lg-pull-7{right:58.33333333%}.col-lg-pull-6{right:50%}.col-lg-pull-5{right:41.66666667%}.col-lg-pull-4{right:33.33333333%}.col-lg-pull-3{right:25%}.col-lg-pull-2{right:16.66666667%}.col-lg-pull-1{right:8.33333333%}.col-lg-pull-0{right:auto}.col-lg-push-12{left:100%}.col-lg-push-11{left:91.66666667%}.col-lg-push-10{left:83.33333333%}.col-lg-push-9{left:75%}.col-lg-push-8{left:66.66666667%}.col-lg-push-7{left:58.33333333%}.col-lg-push-6{left:50%}.col-lg-push-5{left:41.66666667%}.col-lg-push-4{left:33.33333333%}.col-lg-push-3{left:25%}.col-lg-push-2{left:16.66666667%}.col-lg-push-1{left:8.33333333%}.col-lg-push-0{left:auto}.col-lg-offset-12{margin-left:100%}.col-lg-offset-11{margin-left:91.66666667%}.col-lg-offset-10{margin-left:83.33333333%}.col-lg-offset-9{margin-left:75%}.col-lg-offset-8{margin-left:66.66666667%}.col-lg-offset-7{margin-left:58.33333333%}.col-lg-offset-6{margin-left:50%}.col-lg-offset-5{margin-left:41.66666667%}.col-lg-offset-4{margin-left:33.33333333%}.col-lg-offset-3{margin-left:25%}.col-lg-offset-2{margin-left:16.66666667%}.col-lg-offset-1{margin-left:8.33333333%}.col-lg-offset-0{margin-left:0}}table{background-color:transparent}caption{padding-top:8px;padding-bottom:8px;color:#777;text-align:left}th{text-align:left}.table{width:100%;max-width:100%;margin-bottom:20px}.table>tbody>tr>td,.table>tbody>tr>th,.table>tfoot>tr>td,.table>tfoot>tr>th,.table>thead>tr>td,.table>thead>tr>th{padding:8px;line-height:1.42857143;vertical-align:top;border-top:1px solid #ddd}.table>thead>tr>th{vertical-align:bottom;border-bottom:2px solid #ddd}.table>caption+thead>tr:first-child>td,.table>caption+thead>tr:first-child>th,.table>colgroup+thead>tr:first-child>td,.table>colgroup+thead>tr:first-child>th,.table>thead:first-child>tr:first-child>td,.table>thead:first-child>tr:first-child>th{border-top:0}.table>tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed>tbody>tr>td,.table-condensed>tbody>tr>th,.table-condensed>tfoot>tr>td,.table-condensed>tfoot>tr>th,.table-condensed>thead>tr>td,.table-condensed>thead>tr>th{padding:5px}.table-bordered{border:1px solid #ddd}.table-bordered>tbody>tr>td,.table-bordered>tbody>tr>th,.table-bordered>tfoot>tr>td,.table-bordered>tfoot>tr>th,.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border:1px solid #ddd}.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border-bottom-width:2px}.table-striped>tbody>tr:nth-of-type(odd){background-color:#f9f9f9}.table-hover>tbody>tr:hover{background-color:#f5f5f5}table col[class*=col-]{position:static;display:table-column;float:none}table td[class*=col-],table th[class*=col-]{position:static;display:table-cell;float:none}.table>tbody>tr.active>td,.table>tbody>tr.active>th,.table>tbody>tr>td.active,.table>tbody>tr>th.active,.table>tfoot>tr.active>td,.table>tfoot>tr.active>th,.table>tfoot>tr>td.active,.table>tfoot>tr>th.active,.table>thead>tr.active>td,.table>thead>tr.active>th,.table>thead>tr>td.active,.table>thead>tr>th.active{background-color:#f5f5f5}.table-hover>tbody>tr.active:hover>td,.table-hover>tbody>tr.active:hover>th,.table-hover>tbody>tr:hover>.active,.table-hover>tbody>tr>td.active:hover,.table-hover>tbody>tr>th.active:hover{background-color:#e8e8e8}.table>tbody>tr.success>td,.table>tbody>tr.success>th,.table>tbody>tr>td.success,.table>tbody>tr>th.success,.table>tfoot>tr.success>td,.table>tfoot>tr.success>th,.table>tfoot>tr>td.success,.table>tfoot>tr>th.success,.table>thead>tr.success>td,.table>thead>tr.success>th,.table>thead>tr>td.success,.table>thead>tr>th.success{background-color:#dff0d8}.table-hover>tbody>tr.success:hover>td,.table-hover>tbody>tr.success:hover>th,.table-hover>tbody>tr:hover>.success,.table-hover>tbody>tr>td.success:hover,.table-hover>tbody>tr>th.success:hover{background-color:#d0e9c6}.table>tbody>tr.info>td,.table>tbody>tr.info>th,.table>tbody>tr>td.info,.table>tbody>tr>th.info,.table>tfoot>tr.info>td,.table>tfoot>tr.info>th,.table>tfoot>tr>td.info,.table>tfoot>tr>th.info,.table>thead>tr.info>td,.table>thead>tr.info>th,.table>thead>tr>td.info,.table>thead>tr>th.info{background-color:#d9edf7}.table-hover>tbody>tr.info:hover>td,.table-hover>tbody>tr.info:hover>th,.table-hover>tbody>tr:hover>.info,.table-hover>tbody>tr>td.info:hover,.table-hover>tbody>tr>th.info:hover{background-color:#c4e3f3}.table>tbody>tr.warning>td,.table>tbody>tr.warning>th,.table>tbody>tr>td.warning,.table>tbody>tr>th.warning,.table>tfoot>tr.warning>td,.table>tfoot>tr.warning>th,.table>tfoot>tr>td.warning,.table>tfoot>tr>th.warning,.table>thead>tr.warning>td,.table>thead>tr.warning>th,.table>thead>tr>td.warning,.table>thead>tr>th.warning{background-color:#fcf8e3}.table-hover>tbody>tr.warning:hover>td,.table-hover>tbody>tr.warning:hover>th,.table-hover>tbody>tr:hover>.warning,.table-hover>tbody>tr>td.warning:hover,.table-hover>tbody>tr>th.warning:hover{background-color:#faf2cc}.table>tbody>tr.danger>td,.table>tbody>tr.danger>th,.table>tbody>tr>td.danger,.table>tbody>tr>th.danger,.table>tfoot>tr.danger>td,.table>tfoot>tr.danger>th,.table>tfoot>tr>td.danger,.table>tfoot>tr>th.danger,.table>thead>tr.danger>td,.table>thead>tr.danger>th,.table>thead>tr>td.danger,.table>thead>tr>th.danger{background-color:#f2dede}.table-hover>tbody>tr.danger:hover>td,.table-hover>tbody>tr.danger:hover>th,.table-hover>tbody>tr:hover>.danger,.table-hover>tbody>tr>td.danger:hover,.table-hover>tbody>tr>th.danger:hover{background-color:#ebcccc}.table-responsive{min-height:.01%;overflow-x:auto}@media screen and (max-width:767px){.table-responsive{width:100%;margin-bottom:15px;overflow-y:hidden;-ms-overflow-style:-ms-autohiding-scrollbar;border:1px solid #ddd}.table-responsive>.table{margin-bottom:0}.table-responsive>.table>tbody>tr>td,.table-responsive>.table>tbody>tr>th,.table-responsive>.table>tfoot>tr>td,.table-responsive>.table>tfoot>tr>th,.table-responsive>.table>thead>tr>td,.table-responsive>.table>thead>tr>th{white-space:nowrap}.table-responsive>.table-bordered{border:0}.table-responsive>.table-bordered>tbody>tr>td:first-child,.table-responsive>.table-bordered>tbody>tr>th:first-child,.table-responsive>.table-bordered>tfoot>tr>td:first-child,.table-responsive>.table-bordered>tfoot>tr>th:first-child,.table-responsive>.table-bordered>thead>tr>td:first-child,.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.table-responsive>.table-bordered>tbody>tr>td:last-child,.table-responsive>.table-bordered>tbody>tr>th:last-child,.table-responsive>.table-bordered>tfoot>tr>td:last-child,.table-responsive>.table-bordered>tfoot>tr>th:last-child,.table-responsive>.table-bordered>thead>tr>td:last-child,.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.table-responsive>.table-bordered>tbody>tr:last-child>td,.table-responsive>.table-bordered>tbody>tr:last-child>th,.table-responsive>.table-bordered>tfoot>tr:last-child>td,.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:21px;line-height:inherit;color:#333;border:0;border-bottom:1px solid #e5e5e5}label{display:inline-block;max-width:100%;margin-bottom:5px;font-weight:700}input[type=search]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}input[type=checkbox],input[type=radio]{margin:4px 0 0;margin-top:1px\9;line-height:normal}input[type=file]{display:block}input[type=range]{display:block;width:100%}select[multiple],select[size]{height:auto}input[type=file]:focus,input[type=checkbox]:focus,input[type=radio]:focus{outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}output{display:block;padding-top:7px;font-size:14px;line-height:1.42857143;color:#555}.form-control{display:block;width:100%;height:34px;padding:6px 12px;font-size:14px;line-height:1.42857143;color:#555;background-color:#fff;background-image:none;border:1px solid #ccc;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075);-webkit-transition:border-color ease-in-out .15s,-webkit-box-shadow ease-in-out .15s;-o-transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s;transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s}.form-control:focus{border-color:#66afe9;outline:0;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6)}.form-control::-moz-placeholder{color:#999;opacity:1}.form-control:-ms-input-placeholder{color:#999}.form-control::-webkit-input-placeholder{color:#999}.form-control::-ms-expand{background-color:transparent;border:0}.form-control[disabled],.form-control[readonly],fieldset[disabled] .form-control{background-color:#eee;opacity:1}.form-control[disabled],fieldset[disabled] .form-control{cursor:not-allowed}textarea.form-control{height:auto}input[type=search]{-webkit-appearance:none}@media screen and (-webkit-min-device-pixel-ratio:0){input[type=date].form-control,input[type=time].form-control,input[type=datetime-local].form-control,input[type=month].form-control{line-height:34px}.input-group-sm input[type=date],.input-group-sm input[type=time],.input-group-sm input[type=datetime-local],.input-group-sm input[type=month],input[type=date].input-sm,input[type=time].input-sm,input[type=datetime-local].input-sm,input[type=month].input-sm{line-height:30px}.input-group-lg input[type=date],.input-group-lg input[type=time],.input-group-lg input[type=datetime-local],.input-group-lg input[type=month],input[type=date].input-lg,input[type=time].input-lg,input[type=datetime-local].input-lg,input[type=month].input-lg{line-height:46px}}.form-group{margin-bottom:15px}.checkbox,.radio{position:relative;display:block;margin-top:10px;margin-bottom:10px}.checkbox label,.radio label{min-height:20px;padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.checkbox input[type=checkbox],.checkbox-inline input[type=checkbox],.radio input[type=radio],.radio-inline input[type=radio]{position:absolute;margin-top:4px\9;margin-left:-20px}.checkbox+.checkbox,.radio+.radio{margin-top:-5px}.checkbox-inline,.radio-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.checkbox-inline+.checkbox-inline,.radio-inline+.radio-inline{margin-top:0;margin-left:10px}fieldset[disabled] input[type=checkbox],fieldset[disabled] input[type=radio],input[type=checkbox].disabled,input[type=checkbox][disabled],input[type=radio].disabled,input[type=radio][disabled]{cursor:not-allowed}.checkbox-inline.disabled,.radio-inline.disabled,fieldset[disabled] .checkbox-inline,fieldset[disabled] .radio-inline{cursor:not-allowed}.checkbox.disabled label,.radio.disabled label,fieldset[disabled] .checkbox label,fieldset[disabled] .radio label{cursor:not-allowed}.form-control-static{min-height:34px;padding-top:7px;padding-bottom:7px;margin-bottom:0}.form-control-static.input-lg,.form-control-static.input-sm{padding-right:0;padding-left:0}.input-sm{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-sm{height:30px;line-height:30px}select[multiple].input-sm,textarea.input-sm{height:auto}.form-group-sm .form-control{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.form-group-sm select.form-control{height:30px;line-height:30px}.form-group-sm select[multiple].form-control,.form-group-sm textarea.form-control{height:auto}.form-group-sm .form-control-static{height:30px;min-height:32px;padding:6px 10px;font-size:12px;line-height:1.5}.input-lg{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-lg{height:46px;line-height:46px}select[multiple].input-lg,textarea.input-lg{height:auto}.form-group-lg .form-control{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.form-group-lg select.form-control{height:46px;line-height:46px}.form-group-lg select[multiple].form-control,.form-group-lg textarea.form-control{height:auto}.form-group-lg .form-control-static{height:46px;min-height:38px;padding:11px 16px;font-size:18px;line-height:1.3333333}.has-feedback{position:relative}.has-feedback .form-control{padding-right:42.5px}.form-control-feedback{position:absolute;top:0;right:0;z-index:2;display:block;width:34px;height:34px;line-height:34px;text-align:center;pointer-events:none}.form-group-lg .form-control+.form-control-feedback,.input-group-lg+.form-control-feedback,.input-lg+.form-control-feedback{width:46px;height:46px;line-height:46px}.form-group-sm .form-control+.form-control-feedback,.input-group-sm+.form-control-feedback,.input-sm+.form-control-feedback{width:30px;height:30px;line-height:30px}.has-success .checkbox,.has-success .checkbox-inline,.has-success .control-label,.has-success .help-block,.has-success .radio,.has-success .radio-inline,.has-success.checkbox label,.has-success.checkbox-inline label,.has-success.radio label,.has-success.radio-inline label{color:#3c763d}.has-success .form-control{border-color:#3c763d;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-success .form-control:focus{border-color:#2b542c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168}.has-success .input-group-addon{color:#3c763d;background-color:#dff0d8;border-color:#3c763d}.has-success .form-control-feedback{color:#3c763d}.has-warning .checkbox,.has-warning .checkbox-inline,.has-warning .control-label,.has-warning .help-block,.has-warning .radio,.has-warning .radio-inline,.has-warning.checkbox label,.has-warning.checkbox-inline label,.has-warning.radio label,.has-warning.radio-inline label{color:#8a6d3b}.has-warning .form-control{border-color:#8a6d3b;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-warning .form-control:focus{border-color:#66512c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b}.has-warning .input-group-addon{color:#8a6d3b;background-color:#fcf8e3;border-color:#8a6d3b}.has-warning .form-control-feedback{color:#8a6d3b}.has-error .checkbox,.has-error .checkbox-inline,.has-error .control-label,.has-error .help-block,.has-error .radio,.has-error .radio-inline,.has-error.checkbox label,.has-error.checkbox-inline label,.has-error.radio label,.has-error.radio-inline label{color:#a94442}.has-error .form-control{border-color:#a94442;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-error .form-control:focus{border-color:#843534;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483}.has-error .input-group-addon{color:#a94442;background-color:#f2dede;border-color:#a94442}.has-error .form-control-feedback{color:#a94442}.has-feedback label~.form-control-feedback{top:25px}.has-feedback label.sr-only~.form-control-feedback{top:0}.help-block{display:block;margin-top:5px;margin-bottom:10px;color:#737373}@media (min-width:768px){.form-inline .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.form-inline .form-control{display:inline-block;width:auto;vertical-align:middle}.form-inline .form-control-static{display:inline-block}.form-inline .input-group{display:inline-table;vertical-align:middle}.form-inline .input-group .form-control,.form-inline .input-group .input-group-addon,.form-inline .input-group .input-group-btn{width:auto}.form-inline .input-group>.form-control{width:100%}.form-inline .control-label{margin-bottom:0;vertical-align:middle}.form-inline .checkbox,.form-inline .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.form-inline .checkbox label,.form-inline .radio label{padding-left:0}.form-inline .checkbox input[type=checkbox],.form-inline .radio input[type=radio]{position:relative;margin-left:0}.form-inline .has-feedback .form-control-feedback{top:0}}.form-horizontal .checkbox,.form-horizontal .checkbox-inline,.form-horizontal .radio,.form-horizontal .radio-inline{padding-top:7px;margin-top:0;margin-bottom:0}.form-horizontal .checkbox,.form-horizontal .radio{min-height:27px}.form-horizontal .form-group{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.form-horizontal .control-label{padding-top:7px;margin-bottom:0;text-align:right}}.form-horizontal .has-feedback .form-control-feedback{right:15px}@media (min-width:768px){.form-horizontal .form-group-lg .control-label{padding-top:11px;font-size:18px}}@media (min-width:768px){.form-horizontal .form-group-sm .control-label{padding-top:6px;font-size:12px}}.btn{display:inline-block;padding:6px 12px;margin-bottom:0;font-size:14px;font-weight:400;line-height:1.42857143;text-align:center;white-space:nowrap;vertical-align:middle;-ms-touch-action:manipulation;touch-action:manipulation;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background-image:none;border:1px solid transparent;border-radius:4px}.btn.active.focus,.btn.active:focus,.btn.focus,.btn:active.focus,.btn:active:focus,.btn:focus{outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.focus,.btn:focus,.btn:hover{color:#333;text-decoration:none}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn.disabled,.btn[disabled],fieldset[disabled] .btn{cursor:not-allowed;filter:alpha(opacity=65);-webkit-box-shadow:none;box-shadow:none;opacity:.65}a.btn.disabled,fieldset[disabled] a.btn{pointer-events:none}.btn-default{color:#333;background-color:#fff;border-color:#ccc}.btn-default.focus,.btn-default:focus{color:#333;background-color:#e6e6e6;border-color:#8c8c8c}.btn-default:hover{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active.focus,.btn-default.active:focus,.btn-default.active:hover,.btn-default:active.focus,.btn-default:active:focus,.btn-default:active:hover,.open>.dropdown-toggle.btn-default.focus,.open>.dropdown-toggle.btn-default:focus,.open>.dropdown-toggle.btn-default:hover{color:#333;background-color:#d4d4d4;border-color:#8c8c8c}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{background-image:none}.btn-default.disabled.focus,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled].focus,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#fff;border-color:#ccc}.btn-default .badge{color:#fff;background-color:#333}.btn-primary{color:#fff;background-color:#337ab7;border-color:#2e6da4}.btn-primary.focus,.btn-primary:focus{color:#fff;background-color:#286090;border-color:#122b40}.btn-primary:hover{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active.focus,.btn-primary.active:focus,.btn-primary.active:hover,.btn-primary:active.focus,.btn-primary:active:focus,.btn-primary:active:hover,.open>.dropdown-toggle.btn-primary.focus,.open>.dropdown-toggle.btn-primary:focus,.open>.dropdown-toggle.btn-primary:hover{color:#fff;background-color:#204d74;border-color:#122b40}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{background-image:none}.btn-primary.disabled.focus,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled].focus,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#337ab7;border-color:#2e6da4}.btn-primary .badge{color:#337ab7;background-color:#fff}.btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c}.btn-success.focus,.btn-success:focus{color:#fff;background-color:#449d44;border-color:#255625}.btn-success:hover{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active.focus,.btn-success.active:focus,.btn-success.active:hover,.btn-success:active.focus,.btn-success:active:focus,.btn-success:active:hover,.open>.dropdown-toggle.btn-success.focus,.open>.dropdown-toggle.btn-success:focus,.open>.dropdown-toggle.btn-success:hover{color:#fff;background-color:#398439;border-color:#255625}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{background-image:none}.btn-success.disabled.focus,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled].focus,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#5cb85c;border-color:#4cae4c}.btn-success .badge{color:#5cb85c;background-color:#fff}.btn-info{color:#fff;background-color:#5bc0de;border-color:#46b8da}.btn-info.focus,.btn-info:focus{color:#fff;background-color:#31b0d5;border-color:#1b6d85}.btn-info:hover{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active.focus,.btn-info.active:focus,.btn-info.active:hover,.btn-info:active.focus,.btn-info:active:focus,.btn-info:active:hover,.open>.dropdown-toggle.btn-info.focus,.open>.dropdown-toggle.btn-info:focus,.open>.dropdown-toggle.btn-info:hover{color:#fff;background-color:#269abc;border-color:#1b6d85}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{background-image:none}.btn-info.disabled.focus,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled].focus,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#5bc0de;border-color:#46b8da}.btn-info .badge{color:#5bc0de;background-color:#fff}.btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236}.btn-warning.focus,.btn-warning:focus{color:#fff;background-color:#ec971f;border-color:#985f0d}.btn-warning:hover{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active.focus,.btn-warning.active:focus,.btn-warning.active:hover,.btn-warning:active.focus,.btn-warning:active:focus,.btn-warning:active:hover,.open>.dropdown-toggle.btn-warning.focus,.open>.dropdown-toggle.btn-warning:focus,.open>.dropdown-toggle.btn-warning:hover{color:#fff;background-color:#d58512;border-color:#985f0d}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{background-image:none}.btn-warning.disabled.focus,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled].focus,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#f0ad4e;border-color:#eea236}.btn-warning .badge{color:#f0ad4e;background-color:#fff}.btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a}.btn-danger.focus,.btn-danger:focus{color:#fff;background-color:#c9302c;border-color:#761c19}.btn-danger:hover{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active.focus,.btn-danger.active:focus,.btn-danger.active:hover,.btn-danger:active.focus,.btn-danger:active:focus,.btn-danger:active:hover,.open>.dropdown-toggle.btn-danger.focus,.open>.dropdown-toggle.btn-danger:focus,.open>.dropdown-toggle.btn-danger:hover{color:#fff;background-color:#ac2925;border-color:#761c19}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{background-image:none}.btn-danger.disabled.focus,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled].focus,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#d9534f;border-color:#d43f3a}.btn-danger .badge{color:#d9534f;background-color:#fff}.btn-link{font-weight:400;color:#337ab7;border-radius:0}.btn-link,.btn-link.active,.btn-link:active,.btn-link[disabled],fieldset[disabled] .btn-link{background-color:transparent;-webkit-box-shadow:none;box-shadow:none}.btn-link,.btn-link:active,.btn-link:focus,.btn-link:hover{border-color:transparent}.btn-link:focus,.btn-link:hover{color:#23527c;text-decoration:underline;background-color:transparent}.btn-link[disabled]:focus,.btn-link[disabled]:hover,fieldset[disabled] .btn-link:focus,fieldset[disabled] .btn-link:hover{color:#777;text-decoration:none}.btn-group-lg>.btn,.btn-lg{padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.btn-group-sm>.btn,.btn-sm{padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.btn-group-xs>.btn,.btn-xs{padding:1px 5px;font-size:12px;line-height:1.5;border-radius:3px}.btn-block{display:block;width:100%}.btn-block+.btn-block{margin-top:5px}input[type=button].btn-block,input[type=reset].btn-block,input[type=submit].btn-block{width:100%}.fade{opacity:0;-webkit-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{display:none}.collapse.in{display:block}tr.collapse.in{display:table-row}tbody.collapse.in{display:table-row-group}.collapsing{position:relative;height:0;overflow:hidden;-webkit-transition-timing-function:ease;-o-transition-timing-function:ease;transition-timing-function:ease;-webkit-transition-duration:.35s;-o-transition-duration:.35s;transition-duration:.35s;-webkit-transition-property:height,visibility;-o-transition-property:height,visibility;transition-property:height,visibility}.caret{display:inline-block;width:0;height:0;margin-left:2px;vertical-align:middle;border-top:4px dashed;border-top:4px solid\9;border-right:4px solid transparent;border-left:4px solid transparent}.dropdown,.dropup{position:relative}.dropdown-toggle:focus{outline:0}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;font-size:14px;text-align:left;list-style:none;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.15);border-radius:4px;-webkit-box-shadow:0 6px 12px rgba(0,0,0,.175);box-shadow:0 6px 12px rgba(0,0,0,.175)}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:400;line-height:1.42857143;color:#333;white-space:nowrap}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{color:#262626;text-decoration:none;background-color:#f5f5f5}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{color:#fff;text-decoration:none;background-color:#337ab7;outline:0}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{color:#777}.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{text-decoration:none;cursor:not-allowed;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open>.dropdown-menu{display:block}.open>a{outline:0}.dropdown-menu-right{right:0;left:auto}.dropdown-menu-left{right:auto;left:0}.dropdown-header{display:block;padding:3px 20px;font-size:12px;line-height:1.42857143;color:#777;white-space:nowrap}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{content:"";border-top:0;border-bottom:4px dashed;border-bottom:4px solid\9}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:2px}@media (min-width:768px){.navbar-right .dropdown-menu{right:0;left:auto}.navbar-right .dropdown-menu-left{right:auto;left:0}}.btn-group,.btn-group-vertical{position:relative;display:inline-block;vertical-align:middle}.btn-group-vertical>.btn,.btn-group>.btn{position:relative;float:left}.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:hover,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus,.btn-group>.btn:hover{z-index:2}.btn-group .btn+.btn,.btn-group .btn+.btn-group,.btn-group .btn-group+.btn,.btn-group .btn-group+.btn-group{margin-left:-1px}.btn-toolbar{margin-left:-5px}.btn-toolbar .btn,.btn-toolbar .btn-group,.btn-toolbar .input-group{float:left}.btn-toolbar>.btn,.btn-toolbar>.btn-group,.btn-toolbar>.input-group{margin-left:5px}.btn-group>.btn:not(:first-child):not(:last-child):not(.dropdown-toggle){border-radius:0}.btn-group>.btn:first-child{margin-left:0}.btn-group>.btn:first-child:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:last-child:not(:first-child),.btn-group>.dropdown-toggle:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.btn-group>.btn-group{float:left}.btn-group>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-bottom-left-radius:0}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{padding-right:8px;padding-left:8px}.btn-group>.btn-lg+.dropdown-toggle{padding-right:12px;padding-left:12px}.btn-group.open .dropdown-toggle{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-group.open .dropdown-toggle.btn-link{-webkit-box-shadow:none;box-shadow:none}.btn .caret{margin-left:0}.btn-lg .caret{border-width:5px 5px 0;border-bottom-width:0}.dropup .btn-lg .caret{border-width:0 5px 5px}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group,.btn-group-vertical>.btn-group>.btn{display:block;float:none;width:100%;max-width:100%}.btn-group-vertical>.btn-group>.btn{float:none}.btn-group-vertical>.btn+.btn,.btn-group-vertical>.btn+.btn-group,.btn-group-vertical>.btn-group+.btn,.btn-group-vertical>.btn-group+.btn-group{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:not(:first-child):not(:last-child){border-radius:0}.btn-group-vertical>.btn:first-child:not(:last-child){border-top-left-radius:4px;border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn:last-child:not(:first-child){border-top-left-radius:0;border-top-right-radius:0;border-bottom-right-radius:4px;border-bottom-left-radius:4px}.btn-group-vertical>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group-vertical>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group-vertical>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-top-right-radius:0}.btn-group-justified{display:table;width:100%;table-layout:fixed;border-collapse:separate}.btn-group-justified>.btn,.btn-group-justified>.btn-group{display:table-cell;float:none;width:1%}.btn-group-justified>.btn-group .btn{width:100%}.btn-group-justified>.btn-group .dropdown-menu{left:auto}[data-toggle=buttons]>.btn input[type=checkbox],[data-toggle=buttons]>.btn input[type=radio],[data-toggle=buttons]>.btn-group>.btn input[type=checkbox],[data-toggle=buttons]>.btn-group>.btn input[type=radio]{position:absolute;clip:rect(0,0,0,0);pointer-events:none}.input-group{position:relative;display:table;border-collapse:separate}.input-group[class*=col-]{float:none;padding-right:0;padding-left:0}.input-group .form-control{position:relative;z-index:2;float:left;width:100%;margin-bottom:0}.input-group .form-control:focus{z-index:3}.input-group-lg>.form-control,.input-group-lg>.input-group-addon,.input-group-lg>.input-group-btn>.btn{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-group-lg>.form-control,select.input-group-lg>.input-group-addon,select.input-group-lg>.input-group-btn>.btn{height:46px;line-height:46px}select[multiple].input-group-lg>.form-control,select[multiple].input-group-lg>.input-group-addon,select[multiple].input-group-lg>.input-group-btn>.btn,textarea.input-group-lg>.form-control,textarea.input-group-lg>.input-group-addon,textarea.input-group-lg>.input-group-btn>.btn{height:auto}.input-group-sm>.form-control,.input-group-sm>.input-group-addon,.input-group-sm>.input-group-btn>.btn{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-group-sm>.form-control,select.input-group-sm>.input-group-addon,select.input-group-sm>.input-group-btn>.btn{height:30px;line-height:30px}select[multiple].input-group-sm>.form-control,select[multiple].input-group-sm>.input-group-addon,select[multiple].input-group-sm>.input-group-btn>.btn,textarea.input-group-sm>.form-control,textarea.input-group-sm>.input-group-addon,textarea.input-group-sm>.input-group-btn>.btn{height:auto}.input-group .form-control,.input-group-addon,.input-group-btn{display:table-cell}.input-group .form-control:not(:first-child):not(:last-child),.input-group-addon:not(:first-child):not(:last-child),.input-group-btn:not(:first-child):not(:last-child){border-radius:0}.input-group-addon,.input-group-btn{width:1%;white-space:nowrap;vertical-align:middle}.input-group-addon{padding:6px 12px;font-size:14px;font-weight:400;line-height:1;color:#555;text-align:center;background-color:#eee;border:1px solid #ccc;border-radius:4px}.input-group-addon.input-sm{padding:5px 10px;font-size:12px;border-radius:3px}.input-group-addon.input-lg{padding:10px 16px;font-size:18px;border-radius:6px}.input-group-addon input[type=checkbox],.input-group-addon input[type=radio]{margin-top:0}.input-group .form-control:first-child,.input-group-addon:first-child,.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group>.btn,.input-group-btn:first-child>.dropdown-toggle,.input-group-btn:last-child>.btn-group:not(:last-child)>.btn,.input-group-btn:last-child>.btn:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.input-group-addon:first-child{border-right:0}.input-group .form-control:last-child,.input-group-addon:last-child,.input-group-btn:first-child>.btn-group:not(:first-child)>.btn,.input-group-btn:first-child>.btn:not(:first-child),.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group>.btn,.input-group-btn:last-child>.dropdown-toggle{border-top-left-radius:0;border-bottom-left-radius:0}.input-group-addon:last-child{border-left:0}.input-group-btn{position:relative;font-size:0;white-space:nowrap}.input-group-btn>.btn{position:relative}.input-group-btn>.btn+.btn{margin-left:-1px}.input-group-btn>.btn:active,.input-group-btn>.btn:focus,.input-group-btn>.btn:hover{z-index:2}.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group{margin-right:-1px}.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group{z-index:2;margin-left:-1px}.nav{padding-left:0;margin-bottom:0;list-style:none}.nav>li{position:relative;display:block}.nav>li>a{position:relative;display:block;padding:10px 15px}.nav>li>a:focus,.nav>li>a:hover{text-decoration:none;background-color:#eee}.nav>li.disabled>a{color:#777}.nav>li.disabled>a:focus,.nav>li.disabled>a:hover{color:#777;text-decoration:none;cursor:not-allowed;background-color:transparent}.nav .open>a,.nav .open>a:focus,.nav .open>a:hover{background-color:#eee;border-color:#337ab7}.nav .nav-divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.nav>li>a>img{max-width:none}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{float:left;margin-bottom:-1px}.nav-tabs>li>a{margin-right:2px;line-height:1.42857143;border:1px solid transparent;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover{border-color:#eee #eee #ddd}.nav-tabs>li.active>a,.nav-tabs>li.active>a:focus,.nav-tabs>li.active>a:hover{color:#555;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-tabs.nav-justified{width:100%;border-bottom:0}.nav-tabs.nav-justified>li{float:none}.nav-tabs.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-tabs.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-tabs.nav-justified>li{display:table-cell;width:1%}.nav-tabs.nav-justified>li>a{margin-bottom:0}}.nav-tabs.nav-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs.nav-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border-bottom-color:#fff}}.nav-pills>li{float:left}.nav-pills>li>a{border-radius:4px}.nav-pills>li+li{margin-left:2px}.nav-pills>li.active>a,.nav-pills>li.active>a:focus,.nav-pills>li.active>a:hover{color:#fff;background-color:#337ab7}.nav-stacked>li{float:none}.nav-stacked>li+li{margin-top:2px;margin-left:0}.nav-justified{width:100%}.nav-justified>li{float:none}.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-justified>li{display:table-cell;width:1%}.nav-justified>li>a{margin-bottom:0}}.nav-tabs-justified{border-bottom:0}.nav-tabs-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border-bottom-color:#fff}}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.nav-tabs .dropdown-menu{margin-top:-1px;border-top-left-radius:0;border-top-right-radius:0}.navbar{position:relative;min-height:50px;margin-bottom:20px;border:1px solid transparent}@media (min-width:768px){.navbar{border-radius:4px}}@media (min-width:768px){.navbar-header{float:left}}.navbar-collapse{padding-right:15px;padding-left:15px;overflow-x:visible;-webkit-overflow-scrolling:touch;border-top:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1)}.navbar-collapse.in{overflow-y:auto}@media (min-width:768px){.navbar-collapse{width:auto;border-top:0;-webkit-box-shadow:none;box-shadow:none}.navbar-collapse.collapse{display:block!important;height:auto!important;padding-bottom:0;overflow:visible!important}.navbar-collapse.in{overflow-y:visible}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse,.navbar-static-top .navbar-collapse{padding-right:0;padding-left:0}}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:340px}@media (max-device-width:480px) and (orientation:landscape){.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:200px}}.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:0;margin-left:0}}.navbar-static-top{z-index:1000;border-width:0 0 1px}@media (min-width:768px){.navbar-static-top{border-radius:0}}.navbar-fixed-bottom,.navbar-fixed-top{position:fixed;right:0;left:0;z-index:1030}@media (min-width:768px){.navbar-fixed-bottom,.navbar-fixed-top{border-radius:0}}.navbar-fixed-top{top:0;border-width:0 0 1px}.navbar-fixed-bottom{bottom:0;margin-bottom:0;border-width:1px 0 0}.navbar-brand{float:left;height:50px;padding:15px 15px;font-size:18px;line-height:20px}.navbar-brand:focus,.navbar-brand:hover{text-decoration:none}.navbar-brand>img{display:block}@media (min-width:768px){.navbar>.container .navbar-brand,.navbar>.container-fluid .navbar-brand{margin-left:-15px}}.navbar-toggle{position:relative;float:right;padding:9px 10px;margin-top:8px;margin-right:15px;margin-bottom:8px;background-color:transparent;background-image:none;border:1px solid transparent;border-radius:4px}.navbar-toggle:focus{outline:0}.navbar-toggle .icon-bar{display:block;width:22px;height:2px;border-radius:1px}.navbar-toggle .icon-bar+.icon-bar{margin-top:4px}@media (min-width:768px){.navbar-toggle{display:none}}.navbar-nav{margin:7.5px -15px}.navbar-nav>li>a{padding-top:10px;padding-bottom:10px;line-height:20px}@media (max-width:767px){.navbar-nav .open .dropdown-menu{position:static;float:none;width:auto;margin-top:0;background-color:transparent;border:0;-webkit-box-shadow:none;box-shadow:none}.navbar-nav .open .dropdown-menu .dropdown-header,.navbar-nav .open .dropdown-menu>li>a{padding:5px 15px 5px 25px}.navbar-nav .open .dropdown-menu>li>a{line-height:20px}.navbar-nav .open .dropdown-menu>li>a:focus,.navbar-nav .open .dropdown-menu>li>a:hover{background-image:none}}@media (min-width:768px){.navbar-nav{float:left;margin:0}.navbar-nav>li{float:left}.navbar-nav>li>a{padding-top:15px;padding-bottom:15px}}.navbar-form{padding:10px 15px;margin-top:8px;margin-right:-15px;margin-bottom:8px;margin-left:-15px;border-top:1px solid transparent;border-bottom:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1)}@media (min-width:768px){.navbar-form .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.navbar-form .form-control{display:inline-block;width:auto;vertical-align:middle}.navbar-form .form-control-static{display:inline-block}.navbar-form .input-group{display:inline-table;vertical-align:middle}.navbar-form .input-group .form-control,.navbar-form .input-group .input-group-addon,.navbar-form .input-group .input-group-btn{width:auto}.navbar-form .input-group>.form-control{width:100%}.navbar-form .control-label{margin-bottom:0;vertical-align:middle}.navbar-form .checkbox,.navbar-form .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.navbar-form .checkbox label,.navbar-form .radio label{padding-left:0}.navbar-form .checkbox input[type=checkbox],.navbar-form .radio input[type=radio]{position:relative;margin-left:0}.navbar-form .has-feedback .form-control-feedback{top:0}}@media (max-width:767px){.navbar-form .form-group{margin-bottom:5px}.navbar-form .form-group:last-child{margin-bottom:0}}@media (min-width:768px){.navbar-form{width:auto;padding-top:0;padding-bottom:0;margin-right:0;margin-left:0;border:0;-webkit-box-shadow:none;box-shadow:none}}.navbar-nav>li>.dropdown-menu{margin-top:0;border-top-left-radius:0;border-top-right-radius:0}.navbar-fixed-bottom .navbar-nav>li>.dropdown-menu{margin-bottom:0;border-top-left-radius:4px;border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.navbar-btn{margin-top:8px;margin-bottom:8px}.navbar-btn.btn-sm{margin-top:10px;margin-bottom:10px}.navbar-btn.btn-xs{margin-top:14px;margin-bottom:14px}.navbar-text{margin-top:15px;margin-bottom:15px}@media (min-width:768px){.navbar-text{float:left;margin-right:15px;margin-left:15px}}@media (min-width:768px){.navbar-left{float:left!important}.navbar-right{float:right!important;margin-right:-15px}.navbar-right~.navbar-right{margin-right:0}}.navbar-default{background-color:#f8f8f8;border-color:#e7e7e7}.navbar-default .navbar-brand{color:#777}.navbar-default .navbar-brand:focus,.navbar-default .navbar-brand:hover{color:#5e5e5e;background-color:transparent}.navbar-default .navbar-text{color:#777}.navbar-default .navbar-nav>li>a{color:#777}.navbar-default .navbar-nav>li>a:focus,.navbar-default .navbar-nav>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.active>a:focus,.navbar-default .navbar-nav>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav>.disabled>a,.navbar-default .navbar-nav>.disabled>a:focus,.navbar-default .navbar-nav>.disabled>a:hover{color:#ccc;background-color:transparent}.navbar-default .navbar-toggle{border-color:#ddd}.navbar-default .navbar-toggle:focus,.navbar-default .navbar-toggle:hover{background-color:#ddd}.navbar-default .navbar-toggle .icon-bar{background-color:#888}.navbar-default .navbar-collapse,.navbar-default .navbar-form{border-color:#e7e7e7}.navbar-default .navbar-nav>.open>a,.navbar-default .navbar-nav>.open>a:focus,.navbar-default .navbar-nav>.open>a:hover{color:#555;background-color:#e7e7e7}@media (max-width:767px){.navbar-default .navbar-nav .open .dropdown-menu>li>a{color:#777}.navbar-default .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav .open .dropdown-menu>.active>a,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#ccc;background-color:transparent}}.navbar-default .navbar-link{color:#777}.navbar-default .navbar-link:hover{color:#333}.navbar-default .btn-link{color:#777}.navbar-default .btn-link:focus,.navbar-default .btn-link:hover{color:#333}.navbar-default .btn-link[disabled]:focus,.navbar-default .btn-link[disabled]:hover,fieldset[disabled] .navbar-default .btn-link:focus,fieldset[disabled] .navbar-default .btn-link:hover{color:#ccc}.navbar-inverse{background-color:#222;border-color:#080808}.navbar-inverse .navbar-brand{color:#9d9d9d}.navbar-inverse .navbar-brand:focus,.navbar-inverse .navbar-brand:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-text{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a:focus,.navbar-inverse .navbar-nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.active>a:focus,.navbar-inverse .navbar-nav>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav>.disabled>a,.navbar-inverse .navbar-nav>.disabled>a:focus,.navbar-inverse .navbar-nav>.disabled>a:hover{color:#444;background-color:transparent}.navbar-inverse .navbar-toggle{border-color:#333}.navbar-inverse .navbar-toggle:focus,.navbar-inverse .navbar-toggle:hover{background-color:#333}.navbar-inverse .navbar-toggle .icon-bar{background-color:#fff}.navbar-inverse .navbar-collapse,.navbar-inverse .navbar-form{border-color:#101010}.navbar-inverse .navbar-nav>.open>a,.navbar-inverse .navbar-nav>.open>a:focus,.navbar-inverse .navbar-nav>.open>a:hover{color:#fff;background-color:#080808}@media (max-width:767px){.navbar-inverse .navbar-nav .open .dropdown-menu>.dropdown-header{border-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu .divider{background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#444;background-color:transparent}}.navbar-inverse .navbar-link{color:#9d9d9d}.navbar-inverse .navbar-link:hover{color:#fff}.navbar-inverse .btn-link{color:#9d9d9d}.navbar-inverse .btn-link:focus,.navbar-inverse .btn-link:hover{color:#fff}.navbar-inverse .btn-link[disabled]:focus,.navbar-inverse .btn-link[disabled]:hover,fieldset[disabled] .navbar-inverse .btn-link:focus,fieldset[disabled] .navbar-inverse .btn-link:hover{color:#444}.breadcrumb{padding:8px 15px;margin-bottom:20px;list-style:none;background-color:#f5f5f5;border-radius:4px}.breadcrumb>li{display:inline-block}.breadcrumb>li+li:before{padding:0 5px;color:#ccc;content:"/\00a0"}.breadcrumb>.active{color:#777}.pagination{display:inline-block;padding-left:0;margin:20px 0;border-radius:4px}.pagination>li{display:inline}.pagination>li>a,.pagination>li>span{position:relative;float:left;padding:6px 12px;margin-left:-1px;line-height:1.42857143;color:#337ab7;text-decoration:none;background-color:#fff;border:1px solid #ddd}.pagination>li:first-child>a,.pagination>li:first-child>span{margin-left:0;border-top-left-radius:4px;border-bottom-left-radius:4px}.pagination>li:last-child>a,.pagination>li:last-child>span{border-top-right-radius:4px;border-bottom-right-radius:4px}.pagination>li>a:focus,.pagination>li>a:hover,.pagination>li>span:focus,.pagination>li>span:hover{z-index:2;color:#23527c;background-color:#eee;border-color:#ddd}.pagination>.active>a,.pagination>.active>a:focus,.pagination>.active>a:hover,.pagination>.active>span,.pagination>.active>span:focus,.pagination>.active>span:hover{z-index:3;color:#fff;cursor:default;background-color:#337ab7;border-color:#337ab7}.pagination>.disabled>a,.pagination>.disabled>a:focus,.pagination>.disabled>a:hover,.pagination>.disabled>span,.pagination>.disabled>span:focus,.pagination>.disabled>span:hover{color:#777;cursor:not-allowed;background-color:#fff;border-color:#ddd}.pagination-lg>li>a,.pagination-lg>li>span{padding:10px 16px;font-size:18px;line-height:1.3333333}.pagination-lg>li:first-child>a,.pagination-lg>li:first-child>span{border-top-left-radius:6px;border-bottom-left-radius:6px}.pagination-lg>li:last-child>a,.pagination-lg>li:last-child>span{border-top-right-radius:6px;border-bottom-right-radius:6px}.pagination-sm>li>a,.pagination-sm>li>span{padding:5px 10px;font-size:12px;line-height:1.5}.pagination-sm>li:first-child>a,.pagination-sm>li:first-child>span{border-top-left-radius:3px;border-bottom-left-radius:3px}.pagination-sm>li:last-child>a,.pagination-sm>li:last-child>span{border-top-right-radius:3px;border-bottom-right-radius:3px}.pager{padding-left:0;margin:20px 0;text-align:center;list-style:none}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px}.pager li>a:focus,.pager li>a:hover{text-decoration:none;background-color:#eee}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:focus,.pager .disabled>a:hover,.pager .disabled>span{color:#777;cursor:not-allowed;background-color:#fff}.label{display:inline;padding:.2em .6em .3em;font-size:75%;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:.25em}a.label:focus,a.label:hover{color:#fff;text-decoration:none;cursor:pointer}.label:empty{display:none}.btn .label{position:relative;top:-1px}.label-default{background-color:#777}.label-default[href]:focus,.label-default[href]:hover{background-color:#5e5e5e}.label-primary{background-color:#337ab7}.label-primary[href]:focus,.label-primary[href]:hover{background-color:#286090}.label-success{background-color:#5cb85c}.label-success[href]:focus,.label-success[href]:hover{background-color:#449d44}.label-info{background-color:#5bc0de}.label-info[href]:focus,.label-info[href]:hover{background-color:#31b0d5}.label-warning{background-color:#f0ad4e}.label-warning[href]:focus,.label-warning[href]:hover{background-color:#ec971f}.label-danger{background-color:#d9534f}.label-danger[href]:focus,.label-danger[href]:hover{background-color:#c9302c}.badge{display:inline-block;min-width:10px;padding:3px 7px;font-size:12px;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:middle;background-color:#777;border-radius:10px}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.btn-group-xs>.btn .badge,.btn-xs .badge{top:0;padding:1px 5px}a.badge:focus,a.badge:hover{color:#fff;text-decoration:none;cursor:pointer}.list-group-item.active>.badge,.nav-pills>.active>a>.badge{color:#337ab7;background-color:#fff}.list-group-item>.badge{float:right}.list-group-item>.badge+.badge{margin-right:5px}.nav-pills>li>a>.badge{margin-left:3px}.jumbotron{padding-top:30px;padding-bottom:30px;margin-bottom:30px;color:inherit;background-color:#eee}.jumbotron .h1,.jumbotron h1{color:inherit}.jumbotron p{margin-bottom:15px;font-size:21px;font-weight:200}.jumbotron>hr{border-top-color:#d5d5d5}.container .jumbotron,.container-fluid .jumbotron{padding-right:15px;padding-left:15px;border-radius:6px}.jumbotron .container{max-width:100%}@media screen and (min-width:768px){.jumbotron{padding-top:48px;padding-bottom:48px}.container .jumbotron,.container-fluid .jumbotron{padding-right:60px;padding-left:60px}.jumbotron .h1,.jumbotron h1{font-size:63px}}.thumbnail{display:block;padding:4px;margin-bottom:20px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:border .2s ease-in-out;-o-transition:border .2s ease-in-out;transition:border .2s ease-in-out}.thumbnail a>img,.thumbnail>img{margin-right:auto;margin-left:auto}a.thumbnail.active,a.thumbnail:focus,a.thumbnail:hover{border-color:#337ab7}.thumbnail .caption{padding:9px;color:#333}.alert{padding:15px;margin-bottom:20px;border:1px solid transparent;border-radius:4px}.alert h4{margin-top:0;color:inherit}.alert .alert-link{font-weight:700}.alert>p,.alert>ul{margin-bottom:0}.alert>p+p{margin-top:5px}.alert-dismissable,.alert-dismissible{padding-right:35px}.alert-dismissable .close,.alert-dismissible .close{position:relative;top:-2px;right:-21px;color:inherit}.alert-success{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.alert-success hr{border-top-color:#c9e2b3}.alert-success .alert-link{color:#2b542c}.alert-info{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.alert-info hr{border-top-color:#a6e1ec}.alert-info .alert-link{color:#245269}.alert-warning{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.alert-warning hr{border-top-color:#f7e1b5}.alert-warning .alert-link{color:#66512c}.alert-danger{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.alert-danger hr{border-top-color:#e4b9c0}.alert-danger .alert-link{color:#843534}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:20px;margin-bottom:20px;overflow:hidden;background-color:#f5f5f5;border-radius:4px;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1);box-shadow:inset 0 1px 2px rgba(0,0,0,.1)}.progress-bar{float:left;width:0;height:100%;font-size:12px;line-height:20px;color:#fff;text-align:center;background-color:#337ab7;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);-webkit-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress-bar-striped,.progress-striped .progress-bar{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;background-size:40px 40px}.progress-bar.active,.progress.active .progress-bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-bar-success{background-color:#5cb85c}.progress-striped .progress-bar-success{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-info{background-color:#5bc0de}.progress-striped .progress-bar-info{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-warning{background-color:#f0ad4e}.progress-striped .progress-bar-warning{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-danger{background-color:#d9534f}.progress-striped .progress-bar-danger{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.media{margin-top:15px}.media:first-child{margin-top:0}.media,.media-body{overflow:hidden;zoom:1}.media-body{width:10000px}.media-object{display:block}.media-object.img-thumbnail{max-width:none}.media-right,.media>.pull-right{padding-left:10px}.media-left,.media>.pull-left{padding-right:10px}.media-body,.media-left,.media-right{display:table-cell;vertical-align:top}.media-middle{vertical-align:middle}.media-bottom{vertical-align:bottom}.media-heading{margin-top:0;margin-bottom:5px}.media-list{padding-left:0;list-style:none}.list-group{padding-left:0;margin-bottom:20px}.list-group-item{position:relative;display:block;padding:10px 15px;margin-bottom:-1px;background-color:#fff;border:1px solid #ddd}.list-group-item:first-child{border-top-left-radius:4px;border-top-right-radius:4px}.list-group-item:last-child{margin-bottom:0;border-bottom-right-radius:4px;border-bottom-left-radius:4px}a.list-group-item,button.list-group-item{color:#555}a.list-group-item .list-group-item-heading,button.list-group-item .list-group-item-heading{color:#333}a.list-group-item:focus,a.list-group-item:hover,button.list-group-item:focus,button.list-group-item:hover{color:#555;text-decoration:none;background-color:#f5f5f5}button.list-group-item{width:100%;text-align:left}.list-group-item.disabled,.list-group-item.disabled:focus,.list-group-item.disabled:hover{color:#777;cursor:not-allowed;background-color:#eee}.list-group-item.disabled .list-group-item-heading,.list-group-item.disabled:focus .list-group-item-heading,.list-group-item.disabled:hover .list-group-item-heading{color:inherit}.list-group-item.disabled .list-group-item-text,.list-group-item.disabled:focus .list-group-item-text,.list-group-item.disabled:hover .list-group-item-text{color:#777}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{z-index:2;color:#fff;background-color:#337ab7;border-color:#337ab7}.list-group-item.active .list-group-item-heading,.list-group-item.active .list-group-item-heading>.small,.list-group-item.active .list-group-item-heading>small,.list-group-item.active:focus .list-group-item-heading,.list-group-item.active:focus .list-group-item-heading>.small,.list-group-item.active:focus .list-group-item-heading>small,.list-group-item.active:hover .list-group-item-heading,.list-group-item.active:hover .list-group-item-heading>.small,.list-group-item.active:hover .list-group-item-heading>small{color:inherit}.list-group-item.active .list-group-item-text,.list-group-item.active:focus .list-group-item-text,.list-group-item.active:hover .list-group-item-text{color:#c7ddef}.list-group-item-success{color:#3c763d;background-color:#dff0d8}a.list-group-item-success,button.list-group-item-success{color:#3c763d}a.list-group-item-success .list-group-item-heading,button.list-group-item-success .list-group-item-heading{color:inherit}a.list-group-item-success:focus,a.list-group-item-success:hover,button.list-group-item-success:focus,button.list-group-item-success:hover{color:#3c763d;background-color:#d0e9c6}a.list-group-item-success.active,a.list-group-item-success.active:focus,a.list-group-item-success.active:hover,button.list-group-item-success.active,button.list-group-item-success.active:focus,button.list-group-item-success.active:hover{color:#fff;background-color:#3c763d;border-color:#3c763d}.list-group-item-info{color:#31708f;background-color:#d9edf7}a.list-group-item-info,button.list-group-item-info{color:#31708f}a.list-group-item-info .list-group-item-heading,button.list-group-item-info .list-group-item-heading{color:inherit}a.list-group-item-info:focus,a.list-group-item-info:hover,button.list-group-item-info:focus,button.list-group-item-info:hover{color:#31708f;background-color:#c4e3f3}a.list-group-item-info.active,a.list-group-item-info.active:focus,a.list-group-item-info.active:hover,button.list-group-item-info.active,button.list-group-item-info.active:focus,button.list-group-item-info.active:hover{color:#fff;background-color:#31708f;border-color:#31708f}.list-group-item-warning{color:#8a6d3b;background-color:#fcf8e3}a.list-group-item-warning,button.list-group-item-warning{color:#8a6d3b}a.list-group-item-warning .list-group-item-heading,button.list-group-item-warning .list-group-item-heading{color:inherit}a.list-group-item-warning:focus,a.list-group-item-warning:hover,button.list-group-item-warning:focus,button.list-group-item-warning:hover{color:#8a6d3b;background-color:#faf2cc}a.list-group-item-warning.active,a.list-group-item-warning.active:focus,a.list-group-item-warning.active:hover,button.list-group-item-warning.active,button.list-group-item-warning.active:focus,button.list-group-item-warning.active:hover{color:#fff;background-color:#8a6d3b;border-color:#8a6d3b}.list-group-item-danger{color:#a94442;background-color:#f2dede}a.list-group-item-danger,button.list-group-item-danger{color:#a94442}a.list-group-item-danger .list-group-item-heading,button.list-group-item-danger .list-group-item-heading{color:inherit}a.list-group-item-danger:focus,a.list-group-item-danger:hover,button.list-group-item-danger:focus,button.list-group-item-danger:hover{color:#a94442;background-color:#ebcccc}a.list-group-item-danger.active,a.list-group-item-danger.active:focus,a.list-group-item-danger.active:hover,button.list-group-item-danger.active,button.list-group-item-danger.active:focus,button.list-group-item-danger.active:hover{color:#fff;background-color:#a94442;border-color:#a94442}.list-group-item-heading{margin-top:0;margin-bottom:5px}.list-group-item-text{margin-bottom:0;line-height:1.3}.panel{margin-bottom:20px;background-color:#fff;border:1px solid transparent;border-radius:4px;-webkit-box-shadow:0 1px 1px rgba(0,0,0,.05);box-shadow:0 1px 1px rgba(0,0,0,.05)}.panel-body{padding:15px}.panel-heading{padding:10px 15px;border-bottom:1px solid transparent;border-top-left-radius:3px;border-top-right-radius:3px}.panel-heading>.dropdown .dropdown-toggle{color:inherit}.panel-title{margin-top:0;margin-bottom:0;font-size:16px;color:inherit}.panel-title>.small,.panel-title>.small>a,.panel-title>a,.panel-title>small,.panel-title>small>a{color:inherit}.panel-footer{padding:10px 15px;background-color:#f5f5f5;border-top:1px solid #ddd;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.list-group,.panel>.panel-collapse>.list-group{margin-bottom:0}.panel>.list-group .list-group-item,.panel>.panel-collapse>.list-group .list-group-item{border-width:1px 0;border-radius:0}.panel>.list-group:first-child .list-group-item:first-child,.panel>.panel-collapse>.list-group:first-child .list-group-item:first-child{border-top:0;border-top-left-radius:3px;border-top-right-radius:3px}.panel>.list-group:last-child .list-group-item:last-child,.panel>.panel-collapse>.list-group:last-child .list-group-item:last-child{border-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.panel-heading+.panel-collapse>.list-group .list-group-item:first-child{border-top-left-radius:0;border-top-right-radius:0}.panel-heading+.list-group .list-group-item:first-child{border-top-width:0}.list-group+.panel-footer{border-top-width:0}.panel>.panel-collapse>.table,.panel>.table,.panel>.table-responsive>.table{margin-bottom:0}.panel>.panel-collapse>.table caption,.panel>.table caption,.panel>.table-responsive>.table caption{padding-right:15px;padding-left:15px}.panel>.table-responsive:first-child>.table:first-child,.panel>.table:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child,.panel>.table:first-child>thead:first-child>tr:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table:first-child>thead:first-child>tr:first-child th:first-child{border-top-left-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table:first-child>thead:first-child>tr:first-child th:last-child{border-top-right-radius:3px}.panel>.table-responsive:last-child>.table:last-child,.panel>.table:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:first-child{border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:last-child{border-bottom-right-radius:3px}.panel>.panel-body+.table,.panel>.panel-body+.table-responsive,.panel>.table+.panel-body,.panel>.table-responsive+.panel-body{border-top:1px solid #ddd}.panel>.table>tbody:first-child>tr:first-child td,.panel>.table>tbody:first-child>tr:first-child th{border-top:0}.panel>.table-bordered,.panel>.table-responsive>.table-bordered{border:0}.panel>.table-bordered>tbody>tr>td:first-child,.panel>.table-bordered>tbody>tr>th:first-child,.panel>.table-bordered>tfoot>tr>td:first-child,.panel>.table-bordered>tfoot>tr>th:first-child,.panel>.table-bordered>thead>tr>td:first-child,.panel>.table-bordered>thead>tr>th:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:first-child,.panel>.table-responsive>.table-bordered>thead>tr>td:first-child,.panel>.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.panel>.table-bordered>tbody>tr>td:last-child,.panel>.table-bordered>tbody>tr>th:last-child,.panel>.table-bordered>tfoot>tr>td:last-child,.panel>.table-bordered>tfoot>tr>th:last-child,.panel>.table-bordered>thead>tr>td:last-child,.panel>.table-bordered>thead>tr>th:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:last-child,.panel>.table-responsive>.table-bordered>thead>tr>td:last-child,.panel>.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.panel>.table-bordered>tbody>tr:first-child>td,.panel>.table-bordered>tbody>tr:first-child>th,.panel>.table-bordered>thead>tr:first-child>td,.panel>.table-bordered>thead>tr:first-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>th,.panel>.table-responsive>.table-bordered>thead>tr:first-child>td,.panel>.table-responsive>.table-bordered>thead>tr:first-child>th{border-bottom:0}.panel>.table-bordered>tbody>tr:last-child>td,.panel>.table-bordered>tbody>tr:last-child>th,.panel>.table-bordered>tfoot>tr:last-child>td,.panel>.table-bordered>tfoot>tr:last-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>th,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>td,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}.panel>.table-responsive{margin-bottom:0;border:0}.panel-group{margin-bottom:20px}.panel-group .panel{margin-bottom:0;border-radius:4px}.panel-group .panel+.panel{margin-top:5px}.panel-group .panel-heading{border-bottom:0}.panel-group .panel-heading+.panel-collapse>.list-group,.panel-group .panel-heading+.panel-collapse>.panel-body{border-top:1px solid #ddd}.panel-group .panel-footer{border-top:0}.panel-group .panel-footer+.panel-collapse .panel-body{border-bottom:1px solid #ddd}.panel-default{border-color:#ddd}.panel-default>.panel-heading{color:#333;background-color:#f5f5f5;border-color:#ddd}.panel-default>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ddd}.panel-default>.panel-heading .badge{color:#f5f5f5;background-color:#333}.panel-default>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ddd}.panel-primary{border-color:#337ab7}.panel-primary>.panel-heading{color:#fff;background-color:#337ab7;border-color:#337ab7}.panel-primary>.panel-heading+.panel-collapse>.panel-body{border-top-color:#337ab7}.panel-primary>.panel-heading .badge{color:#337ab7;background-color:#fff}.panel-primary>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#337ab7}.panel-success{border-color:#d6e9c6}.panel-success>.panel-heading{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.panel-success>.panel-heading+.panel-collapse>.panel-body{border-top-color:#d6e9c6}.panel-success>.panel-heading .badge{color:#dff0d8;background-color:#3c763d}.panel-success>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#d6e9c6}.panel-info{border-color:#bce8f1}.panel-info>.panel-heading{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.panel-info>.panel-heading+.panel-collapse>.panel-body{border-top-color:#bce8f1}.panel-info>.panel-heading .badge{color:#d9edf7;background-color:#31708f}.panel-info>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#bce8f1}.panel-warning{border-color:#faebcc}.panel-warning>.panel-heading{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.panel-warning>.panel-heading+.panel-collapse>.panel-body{border-top-color:#faebcc}.panel-warning>.panel-heading .badge{color:#fcf8e3;background-color:#8a6d3b}.panel-warning>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#faebcc}.panel-danger{border-color:#ebccd1}.panel-danger>.panel-heading{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.panel-danger>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ebccd1}.panel-danger>.panel-heading .badge{color:#f2dede;background-color:#a94442}.panel-danger>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ebccd1}.embed-responsive{position:relative;display:block;height:0;padding:0;overflow:hidden}.embed-responsive .embed-responsive-item,.embed-responsive embed,.embed-responsive iframe,.embed-responsive object,.embed-responsive video{position:absolute;top:0;bottom:0;left:0;width:100%;height:100%;border:0}.embed-responsive-16by9{padding-bottom:56.25%}.embed-responsive-4by3{padding-bottom:75%}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.05);box-shadow:inset 0 1px 1px rgba(0,0,0,.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,.15)}.well-lg{padding:24px;border-radius:6px}.well-sm{padding:9px;border-radius:3px}.close{float:right;font-size:21px;font-weight:700;line-height:1;color:#000;text-shadow:0 1px 0 #fff;filter:alpha(opacity=20);opacity:.2}.close:focus,.close:hover{color:#000;text-decoration:none;cursor:pointer;filter:alpha(opacity=50);opacity:.5}button.close{-webkit-appearance:none;padding:0;cursor:pointer;background:0 0;border:0}.modal-open{overflow:hidden}.modal{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1050;display:none;overflow:hidden;-webkit-overflow-scrolling:touch;outline:0}.modal.fade .modal-dialog{-webkit-transition:-webkit-transform .3s ease-out;-o-transition:-o-transform .3s ease-out;transition:transform .3s ease-out;-webkit-transform:translate(0,-25%);-ms-transform:translate(0,-25%);-o-transform:translate(0,-25%);transform:translate(0,-25%)}.modal.in .modal-dialog{-webkit-transform:translate(0,0);-ms-transform:translate(0,0);-o-transform:translate(0,0);transform:translate(0,0)}.modal-open .modal{overflow-x:hidden;overflow-y:auto}.modal-dialog{position:relative;width:auto;margin:10px}.modal-content{position:relative;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #999;border:1px solid rgba(0,0,0,.2);border-radius:6px;outline:0;-webkit-box-shadow:0 3px 9px rgba(0,0,0,.5);box-shadow:0 3px 9px rgba(0,0,0,.5)}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{filter:alpha(opacity=0);opacity:0}.modal-backdrop.in{filter:alpha(opacity=50);opacity:.5}.modal-header{padding:15px;border-bottom:1px solid #e5e5e5}.modal-header .close{margin-top:-2px}.modal-title{margin:0;line-height:1.42857143}.modal-body{position:relative;padding:15px}.modal-footer{padding:15px;text-align:right;border-top:1px solid #e5e5e5}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.modal-scrollbar-measure{position:absolute;top:-9999px;width:50px;height:50px;overflow:scroll}@media (min-width:768px){.modal-dialog{width:600px;margin:30px auto}.modal-content{-webkit-box-shadow:0 5px 15px rgba(0,0,0,.5);box-shadow:0 5px 15px rgba(0,0,0,.5)}.modal-sm{width:300px}}@media (min-width:992px){.modal-lg{width:900px}}.tooltip{position:absolute;z-index:1070;display:block;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:12px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;filter:alpha(opacity=0);opacity:0;line-break:auto}.tooltip.in{filter:alpha(opacity=90);opacity:.9}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:3px 8px;color:#fff;text-align:center;background-color:#000;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-left .tooltip-arrow{right:5px;bottom:0;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-right .tooltip-arrow{bottom:0;left:5px;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-left .tooltip-arrow{top:0;right:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-right .tooltip-arrow{top:0;left:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.popover{position:absolute;top:0;left:0;z-index:1060;display:none;max-width:276px;padding:1px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.2);border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,.2);box-shadow:0 5px 10px rgba(0,0,0,.2);line-break:auto}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0}.popover-content{padding:9px 14px}.popover>.arrow,.popover>.arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover>.arrow{border-width:11px}.popover>.arrow:after{content:"";border-width:10px}.popover.top>.arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,.25);border-bottom-width:0}.popover.top>.arrow:after{bottom:1px;margin-left:-10px;content:" ";border-top-color:#fff;border-bottom-width:0}.popover.right>.arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,.25);border-left-width:0}.popover.right>.arrow:after{bottom:-10px;left:1px;content:" ";border-right-color:#fff;border-left-width:0}.popover.bottom>.arrow{top:-11px;left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,.25)}.popover.bottom>.arrow:after{top:1px;margin-left:-10px;content:" ";border-top-width:0;border-bottom-color:#fff}.popover.left>.arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,.25)}.popover.left>.arrow:after{right:1px;bottom:-10px;content:" ";border-right-width:0;border-left-color:#fff}.carousel{position:relative}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>a>img,.carousel-inner>.item>img{line-height:1}@media all and (transform-3d),(-webkit-transform-3d){.carousel-inner>.item{-webkit-transition:-webkit-transform .6s ease-in-out;-o-transition:-o-transform .6s ease-in-out;transition:transform .6s ease-in-out;-webkit-backface-visibility:hidden;backface-visibility:hidden;-webkit-perspective:1000px;perspective:1000px}.carousel-inner>.item.active.right,.carousel-inner>.item.next{left:0;-webkit-transform:translate3d(100%,0,0);transform:translate3d(100%,0,0)}.carousel-inner>.item.active.left,.carousel-inner>.item.prev{left:0;-webkit-transform:translate3d(-100%,0,0);transform:translate3d(-100%,0,0)}.carousel-inner>.item.active,.carousel-inner>.item.next.left,.carousel-inner>.item.prev.right{left:0;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:0;bottom:0;left:0;width:15%;font-size:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6);background-color:rgba(0,0,0,0);filter:alpha(opacity=50);opacity:.5}.carousel-control.left{background-image:-webkit-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.5)),to(rgba(0,0,0,.0001)));background-image:linear-gradient(to right,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);background-repeat:repeat-x}.carousel-control.right{right:0;left:auto;background-image:-webkit-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.0001)),to(rgba(0,0,0,.5)));background-image:linear-gradient(to right,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);background-repeat:repeat-x}.carousel-control:focus,.carousel-control:hover{color:#fff;text-decoration:none;filter:alpha(opacity=90);outline:0;opacity:.9}.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{position:absolute;top:50%;z-index:5;display:inline-block;margin-top:-10px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{left:50%;margin-left:-10px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{right:50%;margin-right:-10px}.carousel-control .icon-next,.carousel-control .icon-prev{width:20px;height:20px;font-family:serif;line-height:1}.carousel-control .icon-prev:before{content:'\2039'}.carousel-control .icon-next:before{content:'\203a'}.carousel-indicators{position:absolute;bottom:10px;left:50%;z-index:15;width:60%;padding-left:0;margin-left:-30%;text-align:center;list-style:none}.carousel-indicators li{display:inline-block;width:10px;height:10px;margin:1px;text-indent:-999px;cursor:pointer;background-color:#000\9;background-color:rgba(0,0,0,0);border:1px solid #fff;border-radius:10px}.carousel-indicators .active{width:12px;height:12px;margin:0;background-color:#fff}.carousel-caption{position:absolute;right:15%;bottom:20px;left:15%;z-index:10;padding-top:20px;padding-bottom:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6)}.carousel-caption .btn{text-shadow:none}@media screen and (min-width:768px){.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{width:30px;height:30px;margin-top:-10px;font-size:30px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{margin-left:-10px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{margin-right:-10px}.carousel-caption{right:20%;left:20%;padding-bottom:30px}.carousel-indicators{bottom:20px}}.btn-group-vertical>.btn-group:after,.btn-group-vertical>.btn-group:before,.btn-toolbar:after,.btn-toolbar:before,.clearfix:after,.clearfix:before,.container-fluid:after,.container-fluid:before,.container:after,.container:before,.dl-horizontal dd:after,.dl-horizontal dd:before,.form-horizontal .form-group:after,.form-horizontal .form-group:before,.modal-footer:after,.modal-footer:before,.modal-header:after,.modal-header:before,.nav:after,.nav:before,.navbar-collapse:after,.navbar-collapse:before,.navbar-header:after,.navbar-header:before,.navbar:after,.navbar:before,.pager:after,.pager:before,.panel-body:after,.panel-body:before,.row:after,.row:before{display:table;content:" "}.btn-group-vertical>.btn-group:after,.btn-toolbar:after,.clearfix:after,.container-fluid:after,.container:after,.dl-horizontal dd:after,.form-horizontal .form-group:after,.modal-footer:after,.modal-header:after,.nav:after,.navbar-collapse:after,.navbar-header:after,.navbar:after,.pager:after,.panel-body:after,.row:after{clear:both}.center-block{display:block;margin-right:auto;margin-left:auto}.pull-right{float:right!important}.pull-left{float:left!important}.hide{display:none!important}.show{display:block!important}.invisible{visibility:hidden}.text-hide{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.hidden{display:none!important}.affix{position:fixed}@-ms-viewport{width:device-width}.visible-lg,.visible-md,.visible-sm,.visible-xs{display:none!important}.visible-lg-block,.visible-lg-inline,.visible-lg-inline-block,.visible-md-block,.visible-md-inline,.visible-md-inline-block,.visible-sm-block,.visible-sm-inline,.visible-sm-inline-block,.visible-xs-block,.visible-xs-inline,.visible-xs-inline-block{display:none!important}@media (max-width:767px){.visible-xs{display:block!important}table.visible-xs{display:table!important}tr.visible-xs{display:table-row!important}td.visible-xs,th.visible-xs{display:table-cell!important}}@media (max-width:767px){.visible-xs-block{display:block!important}}@media (max-width:767px){.visible-xs-inline{display:inline!important}}@media (max-width:767px){.visible-xs-inline-block{display:inline-block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm{display:block!important}table.visible-sm{display:table!important}tr.visible-sm{display:table-row!important}td.visible-sm,th.visible-sm{display:table-cell!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-block{display:block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline{display:inline!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline-block{display:inline-block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md{display:block!important}table.visible-md{display:table!important}tr.visible-md{display:table-row!important}td.visible-md,th.visible-md{display:table-cell!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-block{display:block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline{display:inline!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline-block{display:inline-block!important}}@media (min-width:1200px){.visible-lg{display:block!important}table.visible-lg{display:table!important}tr.visible-lg{display:table-row!important}td.visible-lg,th.visible-lg{display:table-cell!important}}@media (min-width:1200px){.visible-lg-block{display:block!important}}@media (min-width:1200px){.visible-lg-inline{display:inline!important}}@media (min-width:1200px){.visible-lg-inline-block{display:inline-block!important}}@media (max-width:767px){.hidden-xs{display:none!important}}@media (min-width:768px) and (max-width:991px){.hidden-sm{display:none!important}}@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none!important}}@media (min-width:1200px){.hidden-lg{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}} +/*# sourceMappingURL=bootstrap.min.css.map */ \ No newline at end of file diff --git a/SimSwap/docs/css/ie10-viewport-bug-workaround.css b/SimSwap/docs/css/ie10-viewport-bug-workaround.css new file mode 100644 index 0000000..4b9518e --- /dev/null +++ b/SimSwap/docs/css/ie10-viewport-bug-workaround.css @@ -0,0 +1,13 @@ +/*! + * IE10 viewport hack for Surface/desktop Windows 8 bug + * Copyright 2014-2015 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) + */ + +/* + * See the Getting Started docs for more information: + * http://getbootstrap.com/getting-started/#support-ie10-width + */ +@-ms-viewport { width: device-width; } +@-o-viewport { width: device-width; } +@viewport { width: device-width; } diff --git a/SimSwap/docs/css/jumbotron.css b/SimSwap/docs/css/jumbotron.css new file mode 100644 index 0000000..179d408 --- /dev/null +++ b/SimSwap/docs/css/jumbotron.css @@ -0,0 +1,5 @@ +/* Move down content because we have a fixed navbar that is 50px tall */ +body { + padding-top: 50px; + padding-bottom: 20px; +} diff --git a/SimSwap/docs/css/page.css b/SimSwap/docs/css/page.css new file mode 100644 index 0000000..614330a --- /dev/null +++ b/SimSwap/docs/css/page.css @@ -0,0 +1,49 @@ +.which-image-container { + display: flex; + flex-wrap: wrap; + align-items: center; + flex-direction: column; + justify-content: space-between; + height: 100%; +} + +.which-image-container :nth-child(2) { + display: flex; + flex-wrap: wrap; + align-items: center; + flex-direction: column; + justify-content: flex-end; +} + +.which-image { + display: inline-block; + flex: 1; + flex-basis: 45%; + margin: 1% 1% 1% 1%; + width: 100%; + height: auto; + +} + +.which-image img { + float: right; + width: 100%; +} + +.image-display2 img { + float: right; + width: 100%; +} +/* .image-display{ + align-items: center; +} +.image-display2{ + align-items: center; +} */ +.select-show { + border-style: dashed; + border-width: 2px; + border-color: purple; + + /* padding: 2px; */ +} \ No newline at end of file diff --git a/SimSwap/docs/favicon.ico b/SimSwap/docs/favicon.ico new file mode 100644 index 0000000..dd0e8bd Binary files /dev/null and b/SimSwap/docs/favicon.ico differ diff --git a/SimSwap/docs/fonts/glyphicons-halflings-regular.eot b/SimSwap/docs/fonts/glyphicons-halflings-regular.eot new file mode 100644 index 0000000..b93a495 Binary files /dev/null and b/SimSwap/docs/fonts/glyphicons-halflings-regular.eot differ diff --git a/SimSwap/docs/fonts/glyphicons-halflings-regular.svg b/SimSwap/docs/fonts/glyphicons-halflings-regular.svg new file mode 100644 index 0000000..94fb549 --- /dev/null +++ b/SimSwap/docs/fonts/glyphicons-halflings-regular.svg @@ -0,0 +1,288 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/SimSwap/docs/fonts/glyphicons-halflings-regular.ttf b/SimSwap/docs/fonts/glyphicons-halflings-regular.ttf new file mode 100644 index 0000000..1413fc6 Binary files /dev/null and b/SimSwap/docs/fonts/glyphicons-halflings-regular.ttf differ diff --git a/SimSwap/docs/fonts/glyphicons-halflings-regular.woff b/SimSwap/docs/fonts/glyphicons-halflings-regular.woff new file mode 100644 index 0000000..9e61285 Binary files /dev/null and b/SimSwap/docs/fonts/glyphicons-halflings-regular.woff differ diff --git a/SimSwap/docs/fonts/glyphicons-halflings-regular.woff2 b/SimSwap/docs/fonts/glyphicons-halflings-regular.woff2 new file mode 100644 index 0000000..64539b5 Binary files /dev/null and b/SimSwap/docs/fonts/glyphicons-halflings-regular.woff2 differ diff --git a/SimSwap/docs/guidance/preparation.md b/SimSwap/docs/guidance/preparation.md new file mode 100644 index 0000000..56dca98 --- /dev/null +++ b/SimSwap/docs/guidance/preparation.md @@ -0,0 +1,37 @@ + +# Preparation + +### Installation +**We highly recommand that you use Anaconda for Installation** +``` +conda create -n simswap python=3.6 +conda activate simswap +conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch +(option): pip install --ignore-installed imageio +pip install insightface==0.2.1 onnxruntime moviepy +(option): pip install onnxruntime-gpu (If you want to reduce the inference time)(It will be diffcult to install onnxruntime-gpu , the specify version of onnxruntime-gpu may depends on your machine and cuda version.) +``` +- ***We have now updated the prepare document. The main change gpu version of onnx is supported now. If you have configured the environment before, now use pip install onnxruntime-gpu ,You can increase the computing speed.*** +- We use the face detection and alignment methods from **[insightface](https://github.com/deepinsight/insightface)** for image preprocessing. Please download the relative files and unzip them to ./insightface_func/models from [this link](https://onedrive.live.com/?authkey=%21ADJ0aAOSsc90neY&cid=4A83B6B633B029CC&id=4A83B6B633B029CC%215837&parId=4A83B6B633B029CC%215834&action=locate). +- We use the face parsing from **[face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch)** for image postprocessing. Please download the relative file and place it in ./parsing_model/checkpoint from [this link](https://drive.google.com/file/d/154JgKpzCPW82qINcVieuPH3fZ2e0P812/view). +- The pytorch and cuda versions above are most recommanded. They may vary. +- Using insightface with different versions is not recommanded. Please use this specific version. +- These settings are tested valid on both Windows and Ubuntu. + +### Pretrained model +There are two archive files in the drive: **checkpoints.zip** and **arcface_checkpoint.tar** + +- **Copy the arcface_checkpoint.tar into ./arcface_model** +- **Unzip checkpoints.zip, place it in the root dir ./** + +[[Google Drive]](https://drive.google.com/drive/folders/1jV6_0FIMPC53FZ2HzZNJZGMe55bbu17R?usp=sharing) +[[Baidu Drive]](https://pan.baidu.com/s/1wFV11RVZMHqd-ky4YpLdcA) Password: ```jd2v``` + +**Simswap 512 (optional)** + +The checkpoint of **Simswap 512 beta version** has been uploaded in [Github release](https://github.com/neuralchen/SimSwap/releases/download/512_beta/512.zip).If you want to experience Simswap 512, feel free to try. +- **Unzip 512.zip, place it in the root dir ./checkpoints**. + + +### Note +We expect users to have GPU with at least 3G memory. For those who do not, we provide [[Colab Notebook implementation]](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/SimSwap%20colab.ipynb). diff --git a/SimSwap/docs/guidance/usage.md b/SimSwap/docs/guidance/usage.md new file mode 100644 index 0000000..fd60869 --- /dev/null +++ b/SimSwap/docs/guidance/usage.md @@ -0,0 +1,115 @@ + + +# Usage + +###### Before running, please make sure you have installed the environment and downloaded requested files according to the [preparation guidance](./preparation.md). +###### The below example command lines are using mask by default. + +### Simple face swapping for already face-aligned images +``` +python test_one_image.py --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path crop_224/6.jpg --pic_b_path crop_224/ds.jpg --output_path output/ +``` + +### Face swapping for video + +- Swap only one face within the video(the one with highest confidence by face detection). +``` +python test_video_swapsingle.py --crop_size 224 --use_mask --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_swapsingle.mp4 --temp_path ./temp_results +``` +- Swap all faces within the video. +``` +python test_video_swapmulti.py --crop_size 224 --use_mask --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_swapmulti.mp4 --temp_path ./temp_results +``` +- Swap the ***specific*** face within the video. +``` +python test_video_swapspecific.py --crop_size 224 --use_mask --pic_specific_path ./demo_file/specific1.png --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_specific.mp4 --temp_path ./temp_results +``` +When changing the specified face, you need to give a picture of the person whose face is to be changed. Then assign the picture path to the argument "***--pic_specific_path***". This picture should be a front face and show the entire head and neck, which can help accurately change the face (if you still don’t know how to choose the picture, you can refer to the specific*.png of [./demo_file/](https://github.com/neuralchen/SimSwap/tree/main/demo_file)). It would be better if this picture was taken from the video to be changed. + +- Swap ***multi specific*** face with **multi specific id** within the video. +``` +python test_video_swap_multispecific.py --crop_size 224 --use_mask --name people --Arc_path arcface_model/arcface_checkpoint.tar --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_multispecific.mp4 --temp_path ./temp_results --multisepcific_dir ./demo_file/multispecific +``` +The folder you assign to ***"--multisepcific_dir"*** should be looked like: +``` +$Your folder name$ + +├── DST_01.jpg(png) +└── DST_02.jpg(png) +└──... +└── SRC_01.jpg(png) +└── SRC_02.jpg(png) +└──... +``` +The result is that the face corresponding to SRC_01.jpg (png) in the video will be replaced by the face corresponding to DST_01.jpg (png). Then the character corresponding to SRC_02.jpg(png) will be replaced by the face of DST_02.jpg(png), and so on. Note that when using your own data and naming it, do not remove the **0** in SRC_(DST_)**0**1.jpg(png), etc. + + + +### Face swapping for Arbitrary images + +- Swap only one face within one image(the one with highest confidence by face detection). The result would be saved to ./output/result_whole_swapsingle.jpg +``` +python test_wholeimage_swapsingle.py --crop_size 224 --use_mask --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ +``` +- Swap all faces within one image. The result would be saved to ./output/result_whole_swapmulti.jpg +``` +python test_wholeimage_swapmulti.py --crop_size 224 --use_mask --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ +``` +- Swap **specific** face within one image. The result would be saved to ./output/result_whole_swapspecific.jpg +``` +python test_wholeimage_swapspecific.py --crop_size 224 --use_mask --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ --pic_specific_path ./demo_file/specific2.png +``` +- Swap **multi specific** face with **multi specific id** within one image. The result would be saved to ./output/result_whole_swap_multispecific.jpg +``` +python test_wholeimage_swap_multispecific.py --crop_size 224 --use_mask --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ --multisepcific_dir ./demo_file/multispecific +``` +### About using Simswap 512 (beta version) +We trained a beta version of Simswap 512 on [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) and open sourced the model (if you think the Simswap 512 is cool, please star our [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) repo). + +The usage of applying Simswap 512 is to modify the value of the argument: "***--crop_size***" to 512 , take the command line of "Swap **multi specific** face with **multi specific id** within one image." as an example, the following command line can get the result without watermark: +``` +python test_wholeimage_swap_multispecific.py --crop_size 512 --use_mask --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ --multisepcific_dir ./demo_file/multispecific +``` +The effect of Simswap 512 is shown below. + + +### About watermark of simswap logo +The above example command lines are to add the simswap logo as the watermark by default. After our discussion, we have added a hyper parameter to control whether to remove watermark. + +The usage of removing the watermark is to add an argument: "***--no_simswaplogo***" to the command line, take the command line of "Swap all faces within one image" as an example, the following command line can get the result without watermark: +``` +python test_wholeimage_swapmulti.py --no_simswaplogo --crop_size 224 --use_mask --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ +``` +### About using mask for better result +We provide two methods to paste the face back to the original image after changing the face: Using mask or using bounding box. At present, the effect of using mask is the best. All the above code examples are using mask. If you want to use the bounding box, you only need to remove the --use_mask in the code example. +Difference between using mask and not using mask can be found [here](https://imgsli.com/NjE3OTA). + +### Difference between single face swapping and all face swapping are shown below. + + + + + +### Parameters +| Parameters | Function | +| :---- | :---- | +| --name | The SimSwap training logs name | +| --pic_a_path | Path of image with the target face | +| --pic_b_path | Path of image with the source face to swap | +| --pic_specific_path | Path of image with the specific face to be swapped | +|--multisepcific_dir |Path of image folder for multi specific face swapping| +| --video_path | Path of video with the source face to swap | +| --temp_path | Path to store intermediate files | +| --output_path | Path of directory to store the face swapping result | +| --no_simswaplogo |The hyper parameter to control whether to remove watermark | +| --use_mask |The hyper parameter to control whether to use face parsing for the better visual effects(I recommend to use)| + +### Note +We expect users to have GPU with at least 3G memory.the For those who do not, we will provide Colab Notebook implementation in the future. diff --git a/SimSwap/docs/img/LRGT_201110059_201110091.webp b/SimSwap/docs/img/LRGT_201110059_201110091.webp new file mode 100644 index 0000000..84aadd3 Binary files /dev/null and b/SimSwap/docs/img/LRGT_201110059_201110091.webp differ diff --git a/SimSwap/docs/img/anni.webp b/SimSwap/docs/img/anni.webp new file mode 100644 index 0000000..1ecc37a Binary files /dev/null and b/SimSwap/docs/img/anni.webp differ diff --git a/SimSwap/docs/img/chenglong.webp b/SimSwap/docs/img/chenglong.webp new file mode 100644 index 0000000..0c1906f Binary files /dev/null and b/SimSwap/docs/img/chenglong.webp differ diff --git a/SimSwap/docs/img/girl2-RGB.png b/SimSwap/docs/img/girl2-RGB.png new file mode 100644 index 0000000..7dfee55 Binary files /dev/null and b/SimSwap/docs/img/girl2-RGB.png differ diff --git a/SimSwap/docs/img/girl2.gif b/SimSwap/docs/img/girl2.gif new file mode 100644 index 0000000..fc5c5e0 Binary files /dev/null and b/SimSwap/docs/img/girl2.gif differ diff --git a/SimSwap/docs/img/id/Iron_man.jpg b/SimSwap/docs/img/id/Iron_man.jpg new file mode 100644 index 0000000..77a0f20 Binary files /dev/null and b/SimSwap/docs/img/id/Iron_man.jpg differ diff --git a/SimSwap/docs/img/id/anni.jpg b/SimSwap/docs/img/id/anni.jpg new file mode 100644 index 0000000..ff42e9e Binary files /dev/null and b/SimSwap/docs/img/id/anni.jpg differ diff --git a/SimSwap/docs/img/id/chenglong.jpg b/SimSwap/docs/img/id/chenglong.jpg new file mode 100644 index 0000000..8bbe4ea Binary files /dev/null and b/SimSwap/docs/img/id/chenglong.jpg differ diff --git a/SimSwap/docs/img/id/wuyifan.png b/SimSwap/docs/img/id/wuyifan.png new file mode 100644 index 0000000..f2c2e8a Binary files /dev/null and b/SimSwap/docs/img/id/wuyifan.png differ diff --git a/SimSwap/docs/img/id/zhoujielun.jpg b/SimSwap/docs/img/id/zhoujielun.jpg new file mode 100644 index 0000000..795d14b Binary files /dev/null and b/SimSwap/docs/img/id/zhoujielun.jpg differ diff --git a/SimSwap/docs/img/id/zhuyin.jpg b/SimSwap/docs/img/id/zhuyin.jpg new file mode 100644 index 0000000..6a812ba Binary files /dev/null and b/SimSwap/docs/img/id/zhuyin.jpg differ diff --git a/SimSwap/docs/img/logo.png b/SimSwap/docs/img/logo.png new file mode 100644 index 0000000..7b88904 Binary files /dev/null and b/SimSwap/docs/img/logo.png differ diff --git a/SimSwap/docs/img/logo1.png b/SimSwap/docs/img/logo1.png new file mode 100644 index 0000000..36b993e Binary files /dev/null and b/SimSwap/docs/img/logo1.png differ diff --git a/SimSwap/docs/img/logo2.png b/SimSwap/docs/img/logo2.png new file mode 100644 index 0000000..884da60 Binary files /dev/null and b/SimSwap/docs/img/logo2.png differ diff --git a/SimSwap/docs/img/mama_mask_short.webp b/SimSwap/docs/img/mama_mask_short.webp new file mode 100644 index 0000000..cc7c210 Binary files /dev/null and b/SimSwap/docs/img/mama_mask_short.webp differ diff --git a/SimSwap/docs/img/mama_mask_wuyifan_short.webp b/SimSwap/docs/img/mama_mask_wuyifan_short.webp new file mode 100644 index 0000000..c423572 Binary files /dev/null and b/SimSwap/docs/img/mama_mask_wuyifan_short.webp differ diff --git a/SimSwap/docs/img/multi_face_comparison.png b/SimSwap/docs/img/multi_face_comparison.png new file mode 100644 index 0000000..b3e814b Binary files /dev/null and b/SimSwap/docs/img/multi_face_comparison.png differ diff --git a/SimSwap/docs/img/new.gif b/SimSwap/docs/img/new.gif new file mode 100644 index 0000000..a5fe07d Binary files /dev/null and b/SimSwap/docs/img/new.gif differ diff --git a/SimSwap/docs/img/nrsig.png b/SimSwap/docs/img/nrsig.png new file mode 100644 index 0000000..eb8b42c Binary files /dev/null and b/SimSwap/docs/img/nrsig.png differ diff --git a/SimSwap/docs/img/result_whole_swap_multispecific_512.jpg b/SimSwap/docs/img/result_whole_swap_multispecific_512.jpg new file mode 100644 index 0000000..37560d3 Binary files /dev/null and b/SimSwap/docs/img/result_whole_swap_multispecific_512.jpg differ diff --git a/SimSwap/docs/img/results1.PNG b/SimSwap/docs/img/results1.PNG new file mode 100644 index 0000000..64b77ae Binary files /dev/null and b/SimSwap/docs/img/results1.PNG differ diff --git a/SimSwap/docs/img/title.png b/SimSwap/docs/img/title.png new file mode 100644 index 0000000..452e80e Binary files /dev/null and b/SimSwap/docs/img/title.png differ diff --git a/SimSwap/docs/img/total.PNG b/SimSwap/docs/img/total.PNG new file mode 100644 index 0000000..6af9a5e Binary files /dev/null and b/SimSwap/docs/img/total.PNG differ diff --git a/SimSwap/docs/img/vggface2_hq_compare.png b/SimSwap/docs/img/vggface2_hq_compare.png new file mode 100644 index 0000000..4f0b427 Binary files /dev/null and b/SimSwap/docs/img/vggface2_hq_compare.png differ diff --git a/SimSwap/docs/img/video.webp b/SimSwap/docs/img/video.webp new file mode 100644 index 0000000..f7cc006 Binary files /dev/null and b/SimSwap/docs/img/video.webp differ diff --git a/SimSwap/docs/img/zhoujielun.webp b/SimSwap/docs/img/zhoujielun.webp new file mode 100644 index 0000000..679f404 Binary files /dev/null and b/SimSwap/docs/img/zhoujielun.webp differ diff --git a/SimSwap/docs/img/zhuyin.webp b/SimSwap/docs/img/zhuyin.webp new file mode 100644 index 0000000..7ab6512 Binary files /dev/null and b/SimSwap/docs/img/zhuyin.webp differ diff --git a/SimSwap/docs/index.html b/SimSwap/docs/index.html new file mode 100644 index 0000000..2b48b1a --- /dev/null +++ b/SimSwap/docs/index.html @@ -0,0 +1,345 @@ + + + + + + + + + + + + + SimSwap + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+
+
+ SimSwap +
+
+ +
+
+

SimSwap: An Efficient Framework For High Fidelity Face Swapping

+
+
+ +
+
+

Renwang Chen*, Xuanhong Chen*, Bingbing Ni, Yanhao Ge

+
+
+
+
+

Shanghai Jiao Tong University, Tencent, China

+
+
+
+
+
*Equal contribution.
+
+
+
+
+
+
+
+
+
+

+ Code » +

+
+
+

Download »

+
+
+

Paper »

+
+
+
+
+ +
+
+ +
+ +
+
+

Abstract

+
+
+
+
+

+ We propose an efficient framework, called Simple Swap (SimSwap), aiming for generalized and high fidelity face swapping. In contrast to previous approaches that either lack the ability to generalize to arbitrary + identity or fail to preserve attributes like facial expression and gaze direction, our framework is capable of transferring the identity of an arbitrary source face into an arbitrary target face while preserving the attributes of the + target face. We overcome the above defects in the following two ways. First, we present the ID Injection Module (IIM) which transfers the identity information of the source face into the target face at feature level. By using + this module, we extend the architecture of an identityspecific face swapping algorithm to a framework for arbitrary face swapping. Second, we propose the Weak Feature Matching Loss which efficiently helps our framework to preserve + the facial attributes in an implicit way. Extensive experiments on wild faces demonstrate that our SimSwap is able to achieve competitive identity performance while preserving attributes better than previous state-of-the-art + methods. The code is already available on github: https://github.com/neuralchen/SimSwap. +

+
+
+
+
+
+ +
+
+

SimSwap Demo

+
+
+
+
+

Single Face Video Swap

+
+
+
+
+
+
+ +
+
+ +
+
+
+
+ +
+
+ +
+
+
+ +
+
+
+
+
+
+
+ +
+
+
+
+ +
+
+
+ +
+
+
+
+
+

Multiple Faces Video Swap

+
+
+
+ [TODO] +
+ +
+
+

An overview of model capacity

+
+
+
+
+ +
+ +
+
+
+ +
+
+ +
+
+
+
+
+

Downloads

+
+
+ + +
+
+
+
+
+

Citation

+
+
+
+
+
+ + @article{Chen_2020, +
  title={SimSwap}, +
  ISBN={9781450379885}, +
  url={http://dx.doi.org/10.1145/3394171.3413630}, +
  DOI={10.1145/3394171.3413630}, +
  journal={Proceedings of the 28th ACM International Conference on Multimedia}, +
  publisher={ACM}, +
  author={Chen, Renwang and Chen, Xuanhong and Ni, Bingbing and Ge, Yanhao}, +
  year={2020}, +
  month={Oct} +
} +
+
+
+ +
+
+
+
+
+

Contact

+
+
+
+
+

Please concat Renwang Chen applebananac@sjtu.edu.cn and Xuanhong Chen xuanhongchenzju@outlook.com for questions about the paper.

+
+
+ + +
+ + +
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/SimSwap/docs/js/bootstrap.min.js b/SimSwap/docs/js/bootstrap.min.js new file mode 100644 index 0000000..9bcd2fc --- /dev/null +++ b/SimSwap/docs/js/bootstrap.min.js @@ -0,0 +1,7 @@ +/*! + * Bootstrap v3.3.7 (http://getbootstrap.com) + * Copyright 2011-2016 Twitter, Inc. + * Licensed under the MIT license + */ +if("undefined"==typeof jQuery)throw new Error("Bootstrap's JavaScript requires jQuery");+function(a){"use strict";var b=a.fn.jquery.split(" ")[0].split(".");if(b[0]<2&&b[1]<9||1==b[0]&&9==b[1]&&b[2]<1||b[0]>3)throw new Error("Bootstrap's JavaScript requires jQuery version 1.9.1 or higher, but lower than version 4")}(jQuery),+function(a){"use strict";function b(){var a=document.createElement("bootstrap"),b={WebkitTransition:"webkitTransitionEnd",MozTransition:"transitionend",OTransition:"oTransitionEnd otransitionend",transition:"transitionend"};for(var c in b)if(void 0!==a.style[c])return{end:b[c]};return!1}a.fn.emulateTransitionEnd=function(b){var c=!1,d=this;a(this).one("bsTransitionEnd",function(){c=!0});var e=function(){c||a(d).trigger(a.support.transition.end)};return setTimeout(e,b),this},a(function(){a.support.transition=b(),a.support.transition&&(a.event.special.bsTransitionEnd={bindType:a.support.transition.end,delegateType:a.support.transition.end,handle:function(b){if(a(b.target).is(this))return b.handleObj.handler.apply(this,arguments)}})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var c=a(this),e=c.data("bs.alert");e||c.data("bs.alert",e=new d(this)),"string"==typeof b&&e[b].call(c)})}var c='[data-dismiss="alert"]',d=function(b){a(b).on("click",c,this.close)};d.VERSION="3.3.7",d.TRANSITION_DURATION=150,d.prototype.close=function(b){function c(){g.detach().trigger("closed.bs.alert").remove()}var e=a(this),f=e.attr("data-target");f||(f=e.attr("href"),f=f&&f.replace(/.*(?=#[^\s]*$)/,""));var g=a("#"===f?[]:f);b&&b.preventDefault(),g.length||(g=e.closest(".alert")),g.trigger(b=a.Event("close.bs.alert")),b.isDefaultPrevented()||(g.removeClass("in"),a.support.transition&&g.hasClass("fade")?g.one("bsTransitionEnd",c).emulateTransitionEnd(d.TRANSITION_DURATION):c())};var e=a.fn.alert;a.fn.alert=b,a.fn.alert.Constructor=d,a.fn.alert.noConflict=function(){return a.fn.alert=e,this},a(document).on("click.bs.alert.data-api",c,d.prototype.close)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.button"),f="object"==typeof b&&b;e||d.data("bs.button",e=new c(this,f)),"toggle"==b?e.toggle():b&&e.setState(b)})}var c=function(b,d){this.$element=a(b),this.options=a.extend({},c.DEFAULTS,d),this.isLoading=!1};c.VERSION="3.3.7",c.DEFAULTS={loadingText:"loading..."},c.prototype.setState=function(b){var c="disabled",d=this.$element,e=d.is("input")?"val":"html",f=d.data();b+="Text",null==f.resetText&&d.data("resetText",d[e]()),setTimeout(a.proxy(function(){d[e](null==f[b]?this.options[b]:f[b]),"loadingText"==b?(this.isLoading=!0,d.addClass(c).attr(c,c).prop(c,!0)):this.isLoading&&(this.isLoading=!1,d.removeClass(c).removeAttr(c).prop(c,!1))},this),0)},c.prototype.toggle=function(){var a=!0,b=this.$element.closest('[data-toggle="buttons"]');if(b.length){var c=this.$element.find("input");"radio"==c.prop("type")?(c.prop("checked")&&(a=!1),b.find(".active").removeClass("active"),this.$element.addClass("active")):"checkbox"==c.prop("type")&&(c.prop("checked")!==this.$element.hasClass("active")&&(a=!1),this.$element.toggleClass("active")),c.prop("checked",this.$element.hasClass("active")),a&&c.trigger("change")}else this.$element.attr("aria-pressed",!this.$element.hasClass("active")),this.$element.toggleClass("active")};var d=a.fn.button;a.fn.button=b,a.fn.button.Constructor=c,a.fn.button.noConflict=function(){return a.fn.button=d,this},a(document).on("click.bs.button.data-api",'[data-toggle^="button"]',function(c){var d=a(c.target).closest(".btn");b.call(d,"toggle"),a(c.target).is('input[type="radio"], input[type="checkbox"]')||(c.preventDefault(),d.is("input,button")?d.trigger("focus"):d.find("input:visible,button:visible").first().trigger("focus"))}).on("focus.bs.button.data-api blur.bs.button.data-api",'[data-toggle^="button"]',function(b){a(b.target).closest(".btn").toggleClass("focus",/^focus(in)?$/.test(b.type))})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.carousel"),f=a.extend({},c.DEFAULTS,d.data(),"object"==typeof b&&b),g="string"==typeof b?b:f.slide;e||d.data("bs.carousel",e=new c(this,f)),"number"==typeof b?e.to(b):g?e[g]():f.interval&&e.pause().cycle()})}var c=function(b,c){this.$element=a(b),this.$indicators=this.$element.find(".carousel-indicators"),this.options=c,this.paused=null,this.sliding=null,this.interval=null,this.$active=null,this.$items=null,this.options.keyboard&&this.$element.on("keydown.bs.carousel",a.proxy(this.keydown,this)),"hover"==this.options.pause&&!("ontouchstart"in document.documentElement)&&this.$element.on("mouseenter.bs.carousel",a.proxy(this.pause,this)).on("mouseleave.bs.carousel",a.proxy(this.cycle,this))};c.VERSION="3.3.7",c.TRANSITION_DURATION=600,c.DEFAULTS={interval:5e3,pause:"hover",wrap:!0,keyboard:!0},c.prototype.keydown=function(a){if(!/input|textarea/i.test(a.target.tagName)){switch(a.which){case 37:this.prev();break;case 39:this.next();break;default:return}a.preventDefault()}},c.prototype.cycle=function(b){return b||(this.paused=!1),this.interval&&clearInterval(this.interval),this.options.interval&&!this.paused&&(this.interval=setInterval(a.proxy(this.next,this),this.options.interval)),this},c.prototype.getItemIndex=function(a){return this.$items=a.parent().children(".item"),this.$items.index(a||this.$active)},c.prototype.getItemForDirection=function(a,b){var c=this.getItemIndex(b),d="prev"==a&&0===c||"next"==a&&c==this.$items.length-1;if(d&&!this.options.wrap)return b;var e="prev"==a?-1:1,f=(c+e)%this.$items.length;return this.$items.eq(f)},c.prototype.to=function(a){var b=this,c=this.getItemIndex(this.$active=this.$element.find(".item.active"));if(!(a>this.$items.length-1||a<0))return this.sliding?this.$element.one("slid.bs.carousel",function(){b.to(a)}):c==a?this.pause().cycle():this.slide(a>c?"next":"prev",this.$items.eq(a))},c.prototype.pause=function(b){return b||(this.paused=!0),this.$element.find(".next, .prev").length&&a.support.transition&&(this.$element.trigger(a.support.transition.end),this.cycle(!0)),this.interval=clearInterval(this.interval),this},c.prototype.next=function(){if(!this.sliding)return this.slide("next")},c.prototype.prev=function(){if(!this.sliding)return this.slide("prev")},c.prototype.slide=function(b,d){var e=this.$element.find(".item.active"),f=d||this.getItemForDirection(b,e),g=this.interval,h="next"==b?"left":"right",i=this;if(f.hasClass("active"))return this.sliding=!1;var j=f[0],k=a.Event("slide.bs.carousel",{relatedTarget:j,direction:h});if(this.$element.trigger(k),!k.isDefaultPrevented()){if(this.sliding=!0,g&&this.pause(),this.$indicators.length){this.$indicators.find(".active").removeClass("active");var l=a(this.$indicators.children()[this.getItemIndex(f)]);l&&l.addClass("active")}var m=a.Event("slid.bs.carousel",{relatedTarget:j,direction:h});return a.support.transition&&this.$element.hasClass("slide")?(f.addClass(b),f[0].offsetWidth,e.addClass(h),f.addClass(h),e.one("bsTransitionEnd",function(){f.removeClass([b,h].join(" ")).addClass("active"),e.removeClass(["active",h].join(" ")),i.sliding=!1,setTimeout(function(){i.$element.trigger(m)},0)}).emulateTransitionEnd(c.TRANSITION_DURATION)):(e.removeClass("active"),f.addClass("active"),this.sliding=!1,this.$element.trigger(m)),g&&this.cycle(),this}};var d=a.fn.carousel;a.fn.carousel=b,a.fn.carousel.Constructor=c,a.fn.carousel.noConflict=function(){return a.fn.carousel=d,this};var e=function(c){var d,e=a(this),f=a(e.attr("data-target")||(d=e.attr("href"))&&d.replace(/.*(?=#[^\s]+$)/,""));if(f.hasClass("carousel")){var g=a.extend({},f.data(),e.data()),h=e.attr("data-slide-to");h&&(g.interval=!1),b.call(f,g),h&&f.data("bs.carousel").to(h),c.preventDefault()}};a(document).on("click.bs.carousel.data-api","[data-slide]",e).on("click.bs.carousel.data-api","[data-slide-to]",e),a(window).on("load",function(){a('[data-ride="carousel"]').each(function(){var c=a(this);b.call(c,c.data())})})}(jQuery),+function(a){"use strict";function b(b){var c,d=b.attr("data-target")||(c=b.attr("href"))&&c.replace(/.*(?=#[^\s]+$)/,"");return a(d)}function c(b){return this.each(function(){var c=a(this),e=c.data("bs.collapse"),f=a.extend({},d.DEFAULTS,c.data(),"object"==typeof b&&b);!e&&f.toggle&&/show|hide/.test(b)&&(f.toggle=!1),e||c.data("bs.collapse",e=new d(this,f)),"string"==typeof b&&e[b]()})}var d=function(b,c){this.$element=a(b),this.options=a.extend({},d.DEFAULTS,c),this.$trigger=a('[data-toggle="collapse"][href="#'+b.id+'"],[data-toggle="collapse"][data-target="#'+b.id+'"]'),this.transitioning=null,this.options.parent?this.$parent=this.getParent():this.addAriaAndCollapsedClass(this.$element,this.$trigger),this.options.toggle&&this.toggle()};d.VERSION="3.3.7",d.TRANSITION_DURATION=350,d.DEFAULTS={toggle:!0},d.prototype.dimension=function(){var a=this.$element.hasClass("width");return a?"width":"height"},d.prototype.show=function(){if(!this.transitioning&&!this.$element.hasClass("in")){var b,e=this.$parent&&this.$parent.children(".panel").children(".in, .collapsing");if(!(e&&e.length&&(b=e.data("bs.collapse"),b&&b.transitioning))){var f=a.Event("show.bs.collapse");if(this.$element.trigger(f),!f.isDefaultPrevented()){e&&e.length&&(c.call(e,"hide"),b||e.data("bs.collapse",null));var g=this.dimension();this.$element.removeClass("collapse").addClass("collapsing")[g](0).attr("aria-expanded",!0),this.$trigger.removeClass("collapsed").attr("aria-expanded",!0),this.transitioning=1;var h=function(){this.$element.removeClass("collapsing").addClass("collapse in")[g](""),this.transitioning=0,this.$element.trigger("shown.bs.collapse")};if(!a.support.transition)return h.call(this);var i=a.camelCase(["scroll",g].join("-"));this.$element.one("bsTransitionEnd",a.proxy(h,this)).emulateTransitionEnd(d.TRANSITION_DURATION)[g](this.$element[0][i])}}}},d.prototype.hide=function(){if(!this.transitioning&&this.$element.hasClass("in")){var b=a.Event("hide.bs.collapse");if(this.$element.trigger(b),!b.isDefaultPrevented()){var c=this.dimension();this.$element[c](this.$element[c]())[0].offsetHeight,this.$element.addClass("collapsing").removeClass("collapse in").attr("aria-expanded",!1),this.$trigger.addClass("collapsed").attr("aria-expanded",!1),this.transitioning=1;var e=function(){this.transitioning=0,this.$element.removeClass("collapsing").addClass("collapse").trigger("hidden.bs.collapse")};return a.support.transition?void this.$element[c](0).one("bsTransitionEnd",a.proxy(e,this)).emulateTransitionEnd(d.TRANSITION_DURATION):e.call(this)}}},d.prototype.toggle=function(){this[this.$element.hasClass("in")?"hide":"show"]()},d.prototype.getParent=function(){return a(this.options.parent).find('[data-toggle="collapse"][data-parent="'+this.options.parent+'"]').each(a.proxy(function(c,d){var e=a(d);this.addAriaAndCollapsedClass(b(e),e)},this)).end()},d.prototype.addAriaAndCollapsedClass=function(a,b){var c=a.hasClass("in");a.attr("aria-expanded",c),b.toggleClass("collapsed",!c).attr("aria-expanded",c)};var e=a.fn.collapse;a.fn.collapse=c,a.fn.collapse.Constructor=d,a.fn.collapse.noConflict=function(){return a.fn.collapse=e,this},a(document).on("click.bs.collapse.data-api",'[data-toggle="collapse"]',function(d){var e=a(this);e.attr("data-target")||d.preventDefault();var f=b(e),g=f.data("bs.collapse"),h=g?"toggle":e.data();c.call(f,h)})}(jQuery),+function(a){"use strict";function b(b){var c=b.attr("data-target");c||(c=b.attr("href"),c=c&&/#[A-Za-z]/.test(c)&&c.replace(/.*(?=#[^\s]*$)/,""));var d=c&&a(c);return d&&d.length?d:b.parent()}function c(c){c&&3===c.which||(a(e).remove(),a(f).each(function(){var d=a(this),e=b(d),f={relatedTarget:this};e.hasClass("open")&&(c&&"click"==c.type&&/input|textarea/i.test(c.target.tagName)&&a.contains(e[0],c.target)||(e.trigger(c=a.Event("hide.bs.dropdown",f)),c.isDefaultPrevented()||(d.attr("aria-expanded","false"),e.removeClass("open").trigger(a.Event("hidden.bs.dropdown",f)))))}))}function d(b){return this.each(function(){var c=a(this),d=c.data("bs.dropdown");d||c.data("bs.dropdown",d=new g(this)),"string"==typeof b&&d[b].call(c)})}var e=".dropdown-backdrop",f='[data-toggle="dropdown"]',g=function(b){a(b).on("click.bs.dropdown",this.toggle)};g.VERSION="3.3.7",g.prototype.toggle=function(d){var e=a(this);if(!e.is(".disabled, :disabled")){var f=b(e),g=f.hasClass("open");if(c(),!g){"ontouchstart"in document.documentElement&&!f.closest(".navbar-nav").length&&a(document.createElement("div")).addClass("dropdown-backdrop").insertAfter(a(this)).on("click",c);var h={relatedTarget:this};if(f.trigger(d=a.Event("show.bs.dropdown",h)),d.isDefaultPrevented())return;e.trigger("focus").attr("aria-expanded","true"),f.toggleClass("open").trigger(a.Event("shown.bs.dropdown",h))}return!1}},g.prototype.keydown=function(c){if(/(38|40|27|32)/.test(c.which)&&!/input|textarea/i.test(c.target.tagName)){var d=a(this);if(c.preventDefault(),c.stopPropagation(),!d.is(".disabled, :disabled")){var e=b(d),g=e.hasClass("open");if(!g&&27!=c.which||g&&27==c.which)return 27==c.which&&e.find(f).trigger("focus"),d.trigger("click");var h=" li:not(.disabled):visible a",i=e.find(".dropdown-menu"+h);if(i.length){var j=i.index(c.target);38==c.which&&j>0&&j--,40==c.which&&jdocument.documentElement.clientHeight;this.$element.css({paddingLeft:!this.bodyIsOverflowing&&a?this.scrollbarWidth:"",paddingRight:this.bodyIsOverflowing&&!a?this.scrollbarWidth:""})},c.prototype.resetAdjustments=function(){this.$element.css({paddingLeft:"",paddingRight:""})},c.prototype.checkScrollbar=function(){var a=window.innerWidth;if(!a){var b=document.documentElement.getBoundingClientRect();a=b.right-Math.abs(b.left)}this.bodyIsOverflowing=document.body.clientWidth
',trigger:"hover focus",title:"",delay:0,html:!1,container:!1,viewport:{selector:"body",padding:0}},c.prototype.init=function(b,c,d){if(this.enabled=!0,this.type=b,this.$element=a(c),this.options=this.getOptions(d),this.$viewport=this.options.viewport&&a(a.isFunction(this.options.viewport)?this.options.viewport.call(this,this.$element):this.options.viewport.selector||this.options.viewport),this.inState={click:!1,hover:!1,focus:!1},this.$element[0]instanceof document.constructor&&!this.options.selector)throw new Error("`selector` option must be specified when initializing "+this.type+" on the window.document object!");for(var e=this.options.trigger.split(" "),f=e.length;f--;){var g=e[f];if("click"==g)this.$element.on("click."+this.type,this.options.selector,a.proxy(this.toggle,this));else if("manual"!=g){var h="hover"==g?"mouseenter":"focusin",i="hover"==g?"mouseleave":"focusout";this.$element.on(h+"."+this.type,this.options.selector,a.proxy(this.enter,this)),this.$element.on(i+"."+this.type,this.options.selector,a.proxy(this.leave,this))}}this.options.selector?this._options=a.extend({},this.options,{trigger:"manual",selector:""}):this.fixTitle()},c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.getOptions=function(b){return b=a.extend({},this.getDefaults(),this.$element.data(),b),b.delay&&"number"==typeof b.delay&&(b.delay={show:b.delay,hide:b.delay}),b},c.prototype.getDelegateOptions=function(){var b={},c=this.getDefaults();return this._options&&a.each(this._options,function(a,d){c[a]!=d&&(b[a]=d)}),b},c.prototype.enter=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusin"==b.type?"focus":"hover"]=!0),c.tip().hasClass("in")||"in"==c.hoverState?void(c.hoverState="in"):(clearTimeout(c.timeout),c.hoverState="in",c.options.delay&&c.options.delay.show?void(c.timeout=setTimeout(function(){"in"==c.hoverState&&c.show()},c.options.delay.show)):c.show())},c.prototype.isInStateTrue=function(){for(var a in this.inState)if(this.inState[a])return!0;return!1},c.prototype.leave=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);if(c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusout"==b.type?"focus":"hover"]=!1),!c.isInStateTrue())return clearTimeout(c.timeout),c.hoverState="out",c.options.delay&&c.options.delay.hide?void(c.timeout=setTimeout(function(){"out"==c.hoverState&&c.hide()},c.options.delay.hide)):c.hide()},c.prototype.show=function(){var b=a.Event("show.bs."+this.type);if(this.hasContent()&&this.enabled){this.$element.trigger(b);var d=a.contains(this.$element[0].ownerDocument.documentElement,this.$element[0]);if(b.isDefaultPrevented()||!d)return;var e=this,f=this.tip(),g=this.getUID(this.type);this.setContent(),f.attr("id",g),this.$element.attr("aria-describedby",g),this.options.animation&&f.addClass("fade");var h="function"==typeof this.options.placement?this.options.placement.call(this,f[0],this.$element[0]):this.options.placement,i=/\s?auto?\s?/i,j=i.test(h);j&&(h=h.replace(i,"")||"top"),f.detach().css({top:0,left:0,display:"block"}).addClass(h).data("bs."+this.type,this),this.options.container?f.appendTo(this.options.container):f.insertAfter(this.$element),this.$element.trigger("inserted.bs."+this.type);var k=this.getPosition(),l=f[0].offsetWidth,m=f[0].offsetHeight;if(j){var n=h,o=this.getPosition(this.$viewport);h="bottom"==h&&k.bottom+m>o.bottom?"top":"top"==h&&k.top-mo.width?"left":"left"==h&&k.left-lg.top+g.height&&(e.top=g.top+g.height-i)}else{var j=b.left-f,k=b.left+f+c;jg.right&&(e.left=g.left+g.width-k)}return e},c.prototype.getTitle=function(){var a,b=this.$element,c=this.options;return a=b.attr("data-original-title")||("function"==typeof c.title?c.title.call(b[0]):c.title)},c.prototype.getUID=function(a){do a+=~~(1e6*Math.random());while(document.getElementById(a));return a},c.prototype.tip=function(){if(!this.$tip&&(this.$tip=a(this.options.template),1!=this.$tip.length))throw new Error(this.type+" `template` option must consist of exactly 1 top-level element!");return this.$tip},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".tooltip-arrow")},c.prototype.enable=function(){this.enabled=!0},c.prototype.disable=function(){this.enabled=!1},c.prototype.toggleEnabled=function(){this.enabled=!this.enabled},c.prototype.toggle=function(b){var c=this;b&&(c=a(b.currentTarget).data("bs."+this.type),c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c))),b?(c.inState.click=!c.inState.click,c.isInStateTrue()?c.enter(c):c.leave(c)):c.tip().hasClass("in")?c.leave(c):c.enter(c)},c.prototype.destroy=function(){var a=this;clearTimeout(this.timeout),this.hide(function(){a.$element.off("."+a.type).removeData("bs."+a.type),a.$tip&&a.$tip.detach(),a.$tip=null,a.$arrow=null,a.$viewport=null,a.$element=null})};var d=a.fn.tooltip;a.fn.tooltip=b,a.fn.tooltip.Constructor=c,a.fn.tooltip.noConflict=function(){return a.fn.tooltip=d,this}}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.popover"),f="object"==typeof b&&b;!e&&/destroy|hide/.test(b)||(e||d.data("bs.popover",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.init("popover",a,b)};if(!a.fn.tooltip)throw new Error("Popover requires tooltip.js");c.VERSION="3.3.7",c.DEFAULTS=a.extend({},a.fn.tooltip.Constructor.DEFAULTS,{placement:"right",trigger:"click",content:"",template:''}),c.prototype=a.extend({},a.fn.tooltip.Constructor.prototype),c.prototype.constructor=c,c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle(),c=this.getContent();a.find(".popover-title")[this.options.html?"html":"text"](b),a.find(".popover-content").children().detach().end()[this.options.html?"string"==typeof c?"html":"append":"text"](c),a.removeClass("fade top bottom left right in"),a.find(".popover-title").html()||a.find(".popover-title").hide()},c.prototype.hasContent=function(){return this.getTitle()||this.getContent()},c.prototype.getContent=function(){var a=this.$element,b=this.options;return a.attr("data-content")||("function"==typeof b.content?b.content.call(a[0]):b.content)},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".arrow")};var d=a.fn.popover;a.fn.popover=b,a.fn.popover.Constructor=c,a.fn.popover.noConflict=function(){return a.fn.popover=d,this}}(jQuery),+function(a){"use strict";function b(c,d){this.$body=a(document.body),this.$scrollElement=a(a(c).is(document.body)?window:c),this.options=a.extend({},b.DEFAULTS,d),this.selector=(this.options.target||"")+" .nav li > a",this.offsets=[],this.targets=[],this.activeTarget=null,this.scrollHeight=0,this.$scrollElement.on("scroll.bs.scrollspy",a.proxy(this.process,this)),this.refresh(),this.process()}function c(c){return this.each(function(){var d=a(this),e=d.data("bs.scrollspy"),f="object"==typeof c&&c;e||d.data("bs.scrollspy",e=new b(this,f)),"string"==typeof c&&e[c]()})}b.VERSION="3.3.7",b.DEFAULTS={offset:10},b.prototype.getScrollHeight=function(){return this.$scrollElement[0].scrollHeight||Math.max(this.$body[0].scrollHeight,document.documentElement.scrollHeight)},b.prototype.refresh=function(){var b=this,c="offset",d=0;this.offsets=[],this.targets=[],this.scrollHeight=this.getScrollHeight(),a.isWindow(this.$scrollElement[0])||(c="position",d=this.$scrollElement.scrollTop()),this.$body.find(this.selector).map(function(){var b=a(this),e=b.data("target")||b.attr("href"),f=/^#./.test(e)&&a(e);return f&&f.length&&f.is(":visible")&&[[f[c]().top+d,e]]||null}).sort(function(a,b){return a[0]-b[0]}).each(function(){b.offsets.push(this[0]),b.targets.push(this[1])})},b.prototype.process=function(){var a,b=this.$scrollElement.scrollTop()+this.options.offset,c=this.getScrollHeight(),d=this.options.offset+c-this.$scrollElement.height(),e=this.offsets,f=this.targets,g=this.activeTarget;if(this.scrollHeight!=c&&this.refresh(),b>=d)return g!=(a=f[f.length-1])&&this.activate(a);if(g&&b=e[a]&&(void 0===e[a+1]||b .dropdown-menu > .active").removeClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!1),b.addClass("active").find('[data-toggle="tab"]').attr("aria-expanded",!0),h?(b[0].offsetWidth,b.addClass("in")):b.removeClass("fade"),b.parent(".dropdown-menu").length&&b.closest("li.dropdown").addClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!0),e&&e()}var g=d.find("> .active"),h=e&&a.support.transition&&(g.length&&g.hasClass("fade")||!!d.find("> .fade").length);g.length&&h?g.one("bsTransitionEnd",f).emulateTransitionEnd(c.TRANSITION_DURATION):f(),g.removeClass("in")};var d=a.fn.tab;a.fn.tab=b,a.fn.tab.Constructor=c,a.fn.tab.noConflict=function(){return a.fn.tab=d,this};var e=function(c){c.preventDefault(),b.call(a(this),"show")};a(document).on("click.bs.tab.data-api",'[data-toggle="tab"]',e).on("click.bs.tab.data-api",'[data-toggle="pill"]',e)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.affix"),f="object"==typeof b&&b;e||d.data("bs.affix",e=new c(this,f)),"string"==typeof b&&e[b]()})}var c=function(b,d){this.options=a.extend({},c.DEFAULTS,d),this.$target=a(this.options.target).on("scroll.bs.affix.data-api",a.proxy(this.checkPosition,this)).on("click.bs.affix.data-api",a.proxy(this.checkPositionWithEventLoop,this)),this.$element=a(b),this.affixed=null,this.unpin=null,this.pinnedOffset=null,this.checkPosition()};c.VERSION="3.3.7",c.RESET="affix affix-top affix-bottom",c.DEFAULTS={offset:0,target:window},c.prototype.getState=function(a,b,c,d){var e=this.$target.scrollTop(),f=this.$element.offset(),g=this.$target.height();if(null!=c&&"top"==this.affixed)return e=a-d&&"bottom"},c.prototype.getPinnedOffset=function(){if(this.pinnedOffset)return this.pinnedOffset;this.$element.removeClass(c.RESET).addClass("affix");var a=this.$target.scrollTop(),b=this.$element.offset();return this.pinnedOffset=b.top-a},c.prototype.checkPositionWithEventLoop=function(){setTimeout(a.proxy(this.checkPosition,this),1)},c.prototype.checkPosition=function(){if(this.$element.is(":visible")){var b=this.$element.height(),d=this.options.offset,e=d.top,f=d.bottom,g=Math.max(a(document).height(),a(document.body).height());"object"!=typeof d&&(f=e=d),"function"==typeof e&&(e=d.top(this.$element)),"function"==typeof f&&(f=d.bottom(this.$element));var h=this.getState(g,b,e,f);if(this.affixed!=h){null!=this.unpin&&this.$element.css("top","");var i="affix"+(h?"-"+h:""),j=a.Event(i+".bs.affix");if(this.$element.trigger(j),j.isDefaultPrevented())return;this.affixed=h,this.unpin="bottom"==h?this.getPinnedOffset():null,this.$element.removeClass(c.RESET).addClass(i).trigger(i.replace("affix","affixed")+".bs.affix")}"bottom"==h&&this.$element.offset({top:g-b-f})}};var d=a.fn.affix;a.fn.affix=b,a.fn.affix.Constructor=c,a.fn.affix.noConflict=function(){return a.fn.affix=d,this},a(window).on("load",function(){a('[data-spy="affix"]').each(function(){var c=a(this),d=c.data();d.offset=d.offset||{},null!=d.offsetBottom&&(d.offset.bottom=d.offsetBottom),null!=d.offsetTop&&(d.offset.top=d.offsetTop),b.call(c,d)})})}(jQuery); \ No newline at end of file diff --git a/SimSwap/docs/js/ie-emulation-modes-warning.js b/SimSwap/docs/js/ie-emulation-modes-warning.js new file mode 100644 index 0000000..3f97ba5 --- /dev/null +++ b/SimSwap/docs/js/ie-emulation-modes-warning.js @@ -0,0 +1,51 @@ +// NOTICE!! DO NOT USE ANY OF THIS JAVASCRIPT +// IT'S JUST JUNK FOR OUR DOCS! +// ++++++++++++++++++++++++++++++++++++++++++ +/*! + * Copyright 2014-2015 Twitter, Inc. + * + * Licensed under the Creative Commons Attribution 3.0 Unported License. For + * details, see https://creativecommons.org/licenses/by/3.0/. + */ +// Intended to prevent false-positive bug reports about Bootstrap not working properly in old versions of IE due to folks testing using IE's unreliable emulation modes. +(function () { + 'use strict'; + + function emulatedIEMajorVersion() { + var groups = /MSIE ([0-9.]+)/.exec(window.navigator.userAgent) + if (groups === null) { + return null + } + var ieVersionNum = parseInt(groups[1], 10) + var ieMajorVersion = Math.floor(ieVersionNum) + return ieMajorVersion + } + + function actualNonEmulatedIEMajorVersion() { + // Detects the actual version of IE in use, even if it's in an older-IE emulation mode. + // IE JavaScript conditional compilation docs: https://msdn.microsoft.com/library/121hztk3%28v=vs.94%29.aspx + // @cc_on docs: https://msdn.microsoft.com/library/8ka90k2e%28v=vs.94%29.aspx + var jscriptVersion = new Function('/*@cc_on return @_jscript_version; @*/')() // jshint ignore:line + if (jscriptVersion === undefined) { + return 11 // IE11+ not in emulation mode + } + if (jscriptVersion < 9) { + return 8 // IE8 (or lower; haven't tested on IE<8) + } + return jscriptVersion // IE9 or IE10 in any mode, or IE11 in non-IE11 mode + } + + var ua = window.navigator.userAgent + if (ua.indexOf('Opera') > -1 || ua.indexOf('Presto') > -1) { + return // Opera, which might pretend to be IE + } + var emulated = emulatedIEMajorVersion() + if (emulated === null) { + return // Not IE + } + var nonEmulated = actualNonEmulatedIEMajorVersion() + + if (emulated !== nonEmulated) { + window.alert('WARNING: You appear to be using IE' + nonEmulated + ' in IE' + emulated + ' emulation mode.\nIE emulation modes can behave significantly differently from ACTUAL older versions of IE.\nPLEASE DON\'T FILE BOOTSTRAP BUGS based on testing in IE emulation modes!') + } +})(); diff --git a/SimSwap/docs/js/ie10-viewport-bug-workaround.js b/SimSwap/docs/js/ie10-viewport-bug-workaround.js new file mode 100644 index 0000000..479a6eb --- /dev/null +++ b/SimSwap/docs/js/ie10-viewport-bug-workaround.js @@ -0,0 +1,23 @@ +/*! + * IE10 viewport hack for Surface/desktop Windows 8 bug + * Copyright 2014-2015 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) + */ + +// See the Getting Started docs for more information: +// http://getbootstrap.com/getting-started/#support-ie10-width + +(function () { + 'use strict'; + + if (navigator.userAgent.match(/IEMobile\/10\.0/)) { + var msViewportStyle = document.createElement('style') + msViewportStyle.appendChild( + document.createTextNode( + '@-ms-viewport{width:auto!important}' + ) + ) + document.querySelector('head').appendChild(msViewportStyle) + } + +})(); diff --git a/SimSwap/docs/js/vendor/jquery.min.js b/SimSwap/docs/js/vendor/jquery.min.js new file mode 100644 index 0000000..e836475 --- /dev/null +++ b/SimSwap/docs/js/vendor/jquery.min.js @@ -0,0 +1,5 @@ +/*! jQuery v1.12.4 | (c) jQuery Foundation | jquery.org/license */ +!function(a,b){"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){var c=[],d=a.document,e=c.slice,f=c.concat,g=c.push,h=c.indexOf,i={},j=i.toString,k=i.hasOwnProperty,l={},m="1.12.4",n=function(a,b){return new n.fn.init(a,b)},o=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,p=/^-ms-/,q=/-([\da-z])/gi,r=function(a,b){return b.toUpperCase()};n.fn=n.prototype={jquery:m,constructor:n,selector:"",length:0,toArray:function(){return e.call(this)},get:function(a){return null!=a?0>a?this[a+this.length]:this[a]:e.call(this)},pushStack:function(a){var b=n.merge(this.constructor(),a);return b.prevObject=this,b.context=this.context,b},each:function(a){return n.each(this,a)},map:function(a){return this.pushStack(n.map(this,function(b,c){return a.call(b,c,b)}))},slice:function(){return this.pushStack(e.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},eq:function(a){var b=this.length,c=+a+(0>a?b:0);return this.pushStack(c>=0&&b>c?[this[c]]:[])},end:function(){return this.prevObject||this.constructor()},push:g,sort:c.sort,splice:c.splice},n.extend=n.fn.extend=function(){var a,b,c,d,e,f,g=arguments[0]||{},h=1,i=arguments.length,j=!1;for("boolean"==typeof g&&(j=g,g=arguments[h]||{},h++),"object"==typeof g||n.isFunction(g)||(g={}),h===i&&(g=this,h--);i>h;h++)if(null!=(e=arguments[h]))for(d in e)a=g[d],c=e[d],g!==c&&(j&&c&&(n.isPlainObject(c)||(b=n.isArray(c)))?(b?(b=!1,f=a&&n.isArray(a)?a:[]):f=a&&n.isPlainObject(a)?a:{},g[d]=n.extend(j,f,c)):void 0!==c&&(g[d]=c));return g},n.extend({expando:"jQuery"+(m+Math.random()).replace(/\D/g,""),isReady:!0,error:function(a){throw new Error(a)},noop:function(){},isFunction:function(a){return"function"===n.type(a)},isArray:Array.isArray||function(a){return"array"===n.type(a)},isWindow:function(a){return null!=a&&a==a.window},isNumeric:function(a){var b=a&&a.toString();return!n.isArray(a)&&b-parseFloat(b)+1>=0},isEmptyObject:function(a){var b;for(b in a)return!1;return!0},isPlainObject:function(a){var b;if(!a||"object"!==n.type(a)||a.nodeType||n.isWindow(a))return!1;try{if(a.constructor&&!k.call(a,"constructor")&&!k.call(a.constructor.prototype,"isPrototypeOf"))return!1}catch(c){return!1}if(!l.ownFirst)for(b in a)return k.call(a,b);for(b in a);return void 0===b||k.call(a,b)},type:function(a){return null==a?a+"":"object"==typeof a||"function"==typeof a?i[j.call(a)]||"object":typeof a},globalEval:function(b){b&&n.trim(b)&&(a.execScript||function(b){a.eval.call(a,b)})(b)},camelCase:function(a){return a.replace(p,"ms-").replace(q,r)},nodeName:function(a,b){return a.nodeName&&a.nodeName.toLowerCase()===b.toLowerCase()},each:function(a,b){var c,d=0;if(s(a)){for(c=a.length;c>d;d++)if(b.call(a[d],d,a[d])===!1)break}else for(d in a)if(b.call(a[d],d,a[d])===!1)break;return a},trim:function(a){return null==a?"":(a+"").replace(o,"")},makeArray:function(a,b){var c=b||[];return null!=a&&(s(Object(a))?n.merge(c,"string"==typeof a?[a]:a):g.call(c,a)),c},inArray:function(a,b,c){var d;if(b){if(h)return h.call(b,a,c);for(d=b.length,c=c?0>c?Math.max(0,d+c):c:0;d>c;c++)if(c in b&&b[c]===a)return c}return-1},merge:function(a,b){var c=+b.length,d=0,e=a.length;while(c>d)a[e++]=b[d++];if(c!==c)while(void 0!==b[d])a[e++]=b[d++];return a.length=e,a},grep:function(a,b,c){for(var d,e=[],f=0,g=a.length,h=!c;g>f;f++)d=!b(a[f],f),d!==h&&e.push(a[f]);return e},map:function(a,b,c){var d,e,g=0,h=[];if(s(a))for(d=a.length;d>g;g++)e=b(a[g],g,c),null!=e&&h.push(e);else for(g in a)e=b(a[g],g,c),null!=e&&h.push(e);return f.apply([],h)},guid:1,proxy:function(a,b){var c,d,f;return"string"==typeof b&&(f=a[b],b=a,a=f),n.isFunction(a)?(c=e.call(arguments,2),d=function(){return a.apply(b||this,c.concat(e.call(arguments)))},d.guid=a.guid=a.guid||n.guid++,d):void 0},now:function(){return+new Date},support:l}),"function"==typeof Symbol&&(n.fn[Symbol.iterator]=c[Symbol.iterator]),n.each("Boolean Number String Function Array Date RegExp Object Error Symbol".split(" "),function(a,b){i["[object "+b+"]"]=b.toLowerCase()});function s(a){var b=!!a&&"length"in a&&a.length,c=n.type(a);return"function"===c||n.isWindow(a)?!1:"array"===c||0===b||"number"==typeof b&&b>0&&b-1 in a}var t=function(a){var b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u="sizzle"+1*new Date,v=a.document,w=0,x=0,y=ga(),z=ga(),A=ga(),B=function(a,b){return a===b&&(l=!0),0},C=1<<31,D={}.hasOwnProperty,E=[],F=E.pop,G=E.push,H=E.push,I=E.slice,J=function(a,b){for(var c=0,d=a.length;d>c;c++)if(a[c]===b)return c;return-1},K="checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",L="[\\x20\\t\\r\\n\\f]",M="(?:\\\\.|[\\w-]|[^\\x00-\\xa0])+",N="\\["+L+"*("+M+")(?:"+L+"*([*^$|!~]?=)"+L+"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|("+M+"))|)"+L+"*\\]",O=":("+M+")(?:\\((('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|((?:\\\\.|[^\\\\()[\\]]|"+N+")*)|.*)\\)|)",P=new RegExp(L+"+","g"),Q=new RegExp("^"+L+"+|((?:^|[^\\\\])(?:\\\\.)*)"+L+"+$","g"),R=new RegExp("^"+L+"*,"+L+"*"),S=new RegExp("^"+L+"*([>+~]|"+L+")"+L+"*"),T=new RegExp("="+L+"*([^\\]'\"]*?)"+L+"*\\]","g"),U=new RegExp(O),V=new RegExp("^"+M+"$"),W={ID:new RegExp("^#("+M+")"),CLASS:new RegExp("^\\.("+M+")"),TAG:new RegExp("^("+M+"|[*])"),ATTR:new RegExp("^"+N),PSEUDO:new RegExp("^"+O),CHILD:new RegExp("^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\("+L+"*(even|odd|(([+-]|)(\\d*)n|)"+L+"*(?:([+-]|)"+L+"*(\\d+)|))"+L+"*\\)|)","i"),bool:new RegExp("^(?:"+K+")$","i"),needsContext:new RegExp("^"+L+"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\("+L+"*((?:-\\d)?\\d*)"+L+"*\\)|)(?=[^-]|$)","i")},X=/^(?:input|select|textarea|button)$/i,Y=/^h\d$/i,Z=/^[^{]+\{\s*\[native \w/,$=/^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,_=/[+~]/,aa=/'|\\/g,ba=new RegExp("\\\\([\\da-f]{1,6}"+L+"?|("+L+")|.)","ig"),ca=function(a,b,c){var d="0x"+b-65536;return d!==d||c?b:0>d?String.fromCharCode(d+65536):String.fromCharCode(d>>10|55296,1023&d|56320)},da=function(){m()};try{H.apply(E=I.call(v.childNodes),v.childNodes),E[v.childNodes.length].nodeType}catch(ea){H={apply:E.length?function(a,b){G.apply(a,I.call(b))}:function(a,b){var c=a.length,d=0;while(a[c++]=b[d++]);a.length=c-1}}}function fa(a,b,d,e){var f,h,j,k,l,o,r,s,w=b&&b.ownerDocument,x=b?b.nodeType:9;if(d=d||[],"string"!=typeof a||!a||1!==x&&9!==x&&11!==x)return d;if(!e&&((b?b.ownerDocument||b:v)!==n&&m(b),b=b||n,p)){if(11!==x&&(o=$.exec(a)))if(f=o[1]){if(9===x){if(!(j=b.getElementById(f)))return d;if(j.id===f)return d.push(j),d}else if(w&&(j=w.getElementById(f))&&t(b,j)&&j.id===f)return d.push(j),d}else{if(o[2])return H.apply(d,b.getElementsByTagName(a)),d;if((f=o[3])&&c.getElementsByClassName&&b.getElementsByClassName)return H.apply(d,b.getElementsByClassName(f)),d}if(c.qsa&&!A[a+" "]&&(!q||!q.test(a))){if(1!==x)w=b,s=a;else if("object"!==b.nodeName.toLowerCase()){(k=b.getAttribute("id"))?k=k.replace(aa,"\\$&"):b.setAttribute("id",k=u),r=g(a),h=r.length,l=V.test(k)?"#"+k:"[id='"+k+"']";while(h--)r[h]=l+" "+qa(r[h]);s=r.join(","),w=_.test(a)&&oa(b.parentNode)||b}if(s)try{return H.apply(d,w.querySelectorAll(s)),d}catch(y){}finally{k===u&&b.removeAttribute("id")}}}return i(a.replace(Q,"$1"),b,d,e)}function ga(){var a=[];function b(c,e){return a.push(c+" ")>d.cacheLength&&delete b[a.shift()],b[c+" "]=e}return b}function ha(a){return a[u]=!0,a}function ia(a){var b=n.createElement("div");try{return!!a(b)}catch(c){return!1}finally{b.parentNode&&b.parentNode.removeChild(b),b=null}}function ja(a,b){var c=a.split("|"),e=c.length;while(e--)d.attrHandle[c[e]]=b}function ka(a,b){var c=b&&a,d=c&&1===a.nodeType&&1===b.nodeType&&(~b.sourceIndex||C)-(~a.sourceIndex||C);if(d)return d;if(c)while(c=c.nextSibling)if(c===b)return-1;return a?1:-1}function la(a){return function(b){var c=b.nodeName.toLowerCase();return"input"===c&&b.type===a}}function ma(a){return function(b){var c=b.nodeName.toLowerCase();return("input"===c||"button"===c)&&b.type===a}}function na(a){return ha(function(b){return b=+b,ha(function(c,d){var e,f=a([],c.length,b),g=f.length;while(g--)c[e=f[g]]&&(c[e]=!(d[e]=c[e]))})})}function oa(a){return a&&"undefined"!=typeof a.getElementsByTagName&&a}c=fa.support={},f=fa.isXML=function(a){var b=a&&(a.ownerDocument||a).documentElement;return b?"HTML"!==b.nodeName:!1},m=fa.setDocument=function(a){var b,e,g=a?a.ownerDocument||a:v;return g!==n&&9===g.nodeType&&g.documentElement?(n=g,o=n.documentElement,p=!f(n),(e=n.defaultView)&&e.top!==e&&(e.addEventListener?e.addEventListener("unload",da,!1):e.attachEvent&&e.attachEvent("onunload",da)),c.attributes=ia(function(a){return a.className="i",!a.getAttribute("className")}),c.getElementsByTagName=ia(function(a){return a.appendChild(n.createComment("")),!a.getElementsByTagName("*").length}),c.getElementsByClassName=Z.test(n.getElementsByClassName),c.getById=ia(function(a){return o.appendChild(a).id=u,!n.getElementsByName||!n.getElementsByName(u).length}),c.getById?(d.find.ID=function(a,b){if("undefined"!=typeof b.getElementById&&p){var c=b.getElementById(a);return c?[c]:[]}},d.filter.ID=function(a){var b=a.replace(ba,ca);return function(a){return a.getAttribute("id")===b}}):(delete d.find.ID,d.filter.ID=function(a){var b=a.replace(ba,ca);return function(a){var c="undefined"!=typeof a.getAttributeNode&&a.getAttributeNode("id");return c&&c.value===b}}),d.find.TAG=c.getElementsByTagName?function(a,b){return"undefined"!=typeof b.getElementsByTagName?b.getElementsByTagName(a):c.qsa?b.querySelectorAll(a):void 0}:function(a,b){var c,d=[],e=0,f=b.getElementsByTagName(a);if("*"===a){while(c=f[e++])1===c.nodeType&&d.push(c);return d}return f},d.find.CLASS=c.getElementsByClassName&&function(a,b){return"undefined"!=typeof b.getElementsByClassName&&p?b.getElementsByClassName(a):void 0},r=[],q=[],(c.qsa=Z.test(n.querySelectorAll))&&(ia(function(a){o.appendChild(a).innerHTML="",a.querySelectorAll("[msallowcapture^='']").length&&q.push("[*^$]="+L+"*(?:''|\"\")"),a.querySelectorAll("[selected]").length||q.push("\\["+L+"*(?:value|"+K+")"),a.querySelectorAll("[id~="+u+"-]").length||q.push("~="),a.querySelectorAll(":checked").length||q.push(":checked"),a.querySelectorAll("a#"+u+"+*").length||q.push(".#.+[+~]")}),ia(function(a){var b=n.createElement("input");b.setAttribute("type","hidden"),a.appendChild(b).setAttribute("name","D"),a.querySelectorAll("[name=d]").length&&q.push("name"+L+"*[*^$|!~]?="),a.querySelectorAll(":enabled").length||q.push(":enabled",":disabled"),a.querySelectorAll("*,:x"),q.push(",.*:")})),(c.matchesSelector=Z.test(s=o.matches||o.webkitMatchesSelector||o.mozMatchesSelector||o.oMatchesSelector||o.msMatchesSelector))&&ia(function(a){c.disconnectedMatch=s.call(a,"div"),s.call(a,"[s!='']:x"),r.push("!=",O)}),q=q.length&&new RegExp(q.join("|")),r=r.length&&new RegExp(r.join("|")),b=Z.test(o.compareDocumentPosition),t=b||Z.test(o.contains)?function(a,b){var c=9===a.nodeType?a.documentElement:a,d=b&&b.parentNode;return a===d||!(!d||1!==d.nodeType||!(c.contains?c.contains(d):a.compareDocumentPosition&&16&a.compareDocumentPosition(d)))}:function(a,b){if(b)while(b=b.parentNode)if(b===a)return!0;return!1},B=b?function(a,b){if(a===b)return l=!0,0;var d=!a.compareDocumentPosition-!b.compareDocumentPosition;return d?d:(d=(a.ownerDocument||a)===(b.ownerDocument||b)?a.compareDocumentPosition(b):1,1&d||!c.sortDetached&&b.compareDocumentPosition(a)===d?a===n||a.ownerDocument===v&&t(v,a)?-1:b===n||b.ownerDocument===v&&t(v,b)?1:k?J(k,a)-J(k,b):0:4&d?-1:1)}:function(a,b){if(a===b)return l=!0,0;var c,d=0,e=a.parentNode,f=b.parentNode,g=[a],h=[b];if(!e||!f)return a===n?-1:b===n?1:e?-1:f?1:k?J(k,a)-J(k,b):0;if(e===f)return ka(a,b);c=a;while(c=c.parentNode)g.unshift(c);c=b;while(c=c.parentNode)h.unshift(c);while(g[d]===h[d])d++;return d?ka(g[d],h[d]):g[d]===v?-1:h[d]===v?1:0},n):n},fa.matches=function(a,b){return fa(a,null,null,b)},fa.matchesSelector=function(a,b){if((a.ownerDocument||a)!==n&&m(a),b=b.replace(T,"='$1']"),c.matchesSelector&&p&&!A[b+" "]&&(!r||!r.test(b))&&(!q||!q.test(b)))try{var d=s.call(a,b);if(d||c.disconnectedMatch||a.document&&11!==a.document.nodeType)return d}catch(e){}return fa(b,n,null,[a]).length>0},fa.contains=function(a,b){return(a.ownerDocument||a)!==n&&m(a),t(a,b)},fa.attr=function(a,b){(a.ownerDocument||a)!==n&&m(a);var e=d.attrHandle[b.toLowerCase()],f=e&&D.call(d.attrHandle,b.toLowerCase())?e(a,b,!p):void 0;return void 0!==f?f:c.attributes||!p?a.getAttribute(b):(f=a.getAttributeNode(b))&&f.specified?f.value:null},fa.error=function(a){throw new Error("Syntax error, unrecognized expression: "+a)},fa.uniqueSort=function(a){var b,d=[],e=0,f=0;if(l=!c.detectDuplicates,k=!c.sortStable&&a.slice(0),a.sort(B),l){while(b=a[f++])b===a[f]&&(e=d.push(f));while(e--)a.splice(d[e],1)}return k=null,a},e=fa.getText=function(a){var b,c="",d=0,f=a.nodeType;if(f){if(1===f||9===f||11===f){if("string"==typeof a.textContent)return a.textContent;for(a=a.firstChild;a;a=a.nextSibling)c+=e(a)}else if(3===f||4===f)return a.nodeValue}else while(b=a[d++])c+=e(b);return c},d=fa.selectors={cacheLength:50,createPseudo:ha,match:W,attrHandle:{},find:{},relative:{">":{dir:"parentNode",first:!0}," ":{dir:"parentNode"},"+":{dir:"previousSibling",first:!0},"~":{dir:"previousSibling"}},preFilter:{ATTR:function(a){return a[1]=a[1].replace(ba,ca),a[3]=(a[3]||a[4]||a[5]||"").replace(ba,ca),"~="===a[2]&&(a[3]=" "+a[3]+" "),a.slice(0,4)},CHILD:function(a){return a[1]=a[1].toLowerCase(),"nth"===a[1].slice(0,3)?(a[3]||fa.error(a[0]),a[4]=+(a[4]?a[5]+(a[6]||1):2*("even"===a[3]||"odd"===a[3])),a[5]=+(a[7]+a[8]||"odd"===a[3])):a[3]&&fa.error(a[0]),a},PSEUDO:function(a){var b,c=!a[6]&&a[2];return W.CHILD.test(a[0])?null:(a[3]?a[2]=a[4]||a[5]||"":c&&U.test(c)&&(b=g(c,!0))&&(b=c.indexOf(")",c.length-b)-c.length)&&(a[0]=a[0].slice(0,b),a[2]=c.slice(0,b)),a.slice(0,3))}},filter:{TAG:function(a){var b=a.replace(ba,ca).toLowerCase();return"*"===a?function(){return!0}:function(a){return a.nodeName&&a.nodeName.toLowerCase()===b}},CLASS:function(a){var b=y[a+" "];return b||(b=new RegExp("(^|"+L+")"+a+"("+L+"|$)"))&&y(a,function(a){return b.test("string"==typeof a.className&&a.className||"undefined"!=typeof a.getAttribute&&a.getAttribute("class")||"")})},ATTR:function(a,b,c){return function(d){var e=fa.attr(d,a);return null==e?"!="===b:b?(e+="","="===b?e===c:"!="===b?e!==c:"^="===b?c&&0===e.indexOf(c):"*="===b?c&&e.indexOf(c)>-1:"$="===b?c&&e.slice(-c.length)===c:"~="===b?(" "+e.replace(P," ")+" ").indexOf(c)>-1:"|="===b?e===c||e.slice(0,c.length+1)===c+"-":!1):!0}},CHILD:function(a,b,c,d,e){var f="nth"!==a.slice(0,3),g="last"!==a.slice(-4),h="of-type"===b;return 1===d&&0===e?function(a){return!!a.parentNode}:function(b,c,i){var j,k,l,m,n,o,p=f!==g?"nextSibling":"previousSibling",q=b.parentNode,r=h&&b.nodeName.toLowerCase(),s=!i&&!h,t=!1;if(q){if(f){while(p){m=b;while(m=m[p])if(h?m.nodeName.toLowerCase()===r:1===m.nodeType)return!1;o=p="only"===a&&!o&&"nextSibling"}return!0}if(o=[g?q.firstChild:q.lastChild],g&&s){m=q,l=m[u]||(m[u]={}),k=l[m.uniqueID]||(l[m.uniqueID]={}),j=k[a]||[],n=j[0]===w&&j[1],t=n&&j[2],m=n&&q.childNodes[n];while(m=++n&&m&&m[p]||(t=n=0)||o.pop())if(1===m.nodeType&&++t&&m===b){k[a]=[w,n,t];break}}else if(s&&(m=b,l=m[u]||(m[u]={}),k=l[m.uniqueID]||(l[m.uniqueID]={}),j=k[a]||[],n=j[0]===w&&j[1],t=n),t===!1)while(m=++n&&m&&m[p]||(t=n=0)||o.pop())if((h?m.nodeName.toLowerCase()===r:1===m.nodeType)&&++t&&(s&&(l=m[u]||(m[u]={}),k=l[m.uniqueID]||(l[m.uniqueID]={}),k[a]=[w,t]),m===b))break;return t-=e,t===d||t%d===0&&t/d>=0}}},PSEUDO:function(a,b){var c,e=d.pseudos[a]||d.setFilters[a.toLowerCase()]||fa.error("unsupported pseudo: "+a);return e[u]?e(b):e.length>1?(c=[a,a,"",b],d.setFilters.hasOwnProperty(a.toLowerCase())?ha(function(a,c){var d,f=e(a,b),g=f.length;while(g--)d=J(a,f[g]),a[d]=!(c[d]=f[g])}):function(a){return e(a,0,c)}):e}},pseudos:{not:ha(function(a){var b=[],c=[],d=h(a.replace(Q,"$1"));return d[u]?ha(function(a,b,c,e){var f,g=d(a,null,e,[]),h=a.length;while(h--)(f=g[h])&&(a[h]=!(b[h]=f))}):function(a,e,f){return b[0]=a,d(b,null,f,c),b[0]=null,!c.pop()}}),has:ha(function(a){return function(b){return fa(a,b).length>0}}),contains:ha(function(a){return a=a.replace(ba,ca),function(b){return(b.textContent||b.innerText||e(b)).indexOf(a)>-1}}),lang:ha(function(a){return V.test(a||"")||fa.error("unsupported lang: "+a),a=a.replace(ba,ca).toLowerCase(),function(b){var c;do if(c=p?b.lang:b.getAttribute("xml:lang")||b.getAttribute("lang"))return c=c.toLowerCase(),c===a||0===c.indexOf(a+"-");while((b=b.parentNode)&&1===b.nodeType);return!1}}),target:function(b){var c=a.location&&a.location.hash;return c&&c.slice(1)===b.id},root:function(a){return a===o},focus:function(a){return a===n.activeElement&&(!n.hasFocus||n.hasFocus())&&!!(a.type||a.href||~a.tabIndex)},enabled:function(a){return a.disabled===!1},disabled:function(a){return a.disabled===!0},checked:function(a){var b=a.nodeName.toLowerCase();return"input"===b&&!!a.checked||"option"===b&&!!a.selected},selected:function(a){return a.parentNode&&a.parentNode.selectedIndex,a.selected===!0},empty:function(a){for(a=a.firstChild;a;a=a.nextSibling)if(a.nodeType<6)return!1;return!0},parent:function(a){return!d.pseudos.empty(a)},header:function(a){return Y.test(a.nodeName)},input:function(a){return X.test(a.nodeName)},button:function(a){var b=a.nodeName.toLowerCase();return"input"===b&&"button"===a.type||"button"===b},text:function(a){var b;return"input"===a.nodeName.toLowerCase()&&"text"===a.type&&(null==(b=a.getAttribute("type"))||"text"===b.toLowerCase())},first:na(function(){return[0]}),last:na(function(a,b){return[b-1]}),eq:na(function(a,b,c){return[0>c?c+b:c]}),even:na(function(a,b){for(var c=0;b>c;c+=2)a.push(c);return a}),odd:na(function(a,b){for(var c=1;b>c;c+=2)a.push(c);return a}),lt:na(function(a,b,c){for(var d=0>c?c+b:c;--d>=0;)a.push(d);return a}),gt:na(function(a,b,c){for(var d=0>c?c+b:c;++db;b++)d+=a[b].value;return d}function ra(a,b,c){var d=b.dir,e=c&&"parentNode"===d,f=x++;return b.first?function(b,c,f){while(b=b[d])if(1===b.nodeType||e)return a(b,c,f)}:function(b,c,g){var h,i,j,k=[w,f];if(g){while(b=b[d])if((1===b.nodeType||e)&&a(b,c,g))return!0}else while(b=b[d])if(1===b.nodeType||e){if(j=b[u]||(b[u]={}),i=j[b.uniqueID]||(j[b.uniqueID]={}),(h=i[d])&&h[0]===w&&h[1]===f)return k[2]=h[2];if(i[d]=k,k[2]=a(b,c,g))return!0}}}function sa(a){return a.length>1?function(b,c,d){var e=a.length;while(e--)if(!a[e](b,c,d))return!1;return!0}:a[0]}function ta(a,b,c){for(var d=0,e=b.length;e>d;d++)fa(a,b[d],c);return c}function ua(a,b,c,d,e){for(var f,g=[],h=0,i=a.length,j=null!=b;i>h;h++)(f=a[h])&&(c&&!c(f,d,e)||(g.push(f),j&&b.push(h)));return g}function va(a,b,c,d,e,f){return d&&!d[u]&&(d=va(d)),e&&!e[u]&&(e=va(e,f)),ha(function(f,g,h,i){var j,k,l,m=[],n=[],o=g.length,p=f||ta(b||"*",h.nodeType?[h]:h,[]),q=!a||!f&&b?p:ua(p,m,a,h,i),r=c?e||(f?a:o||d)?[]:g:q;if(c&&c(q,r,h,i),d){j=ua(r,n),d(j,[],h,i),k=j.length;while(k--)(l=j[k])&&(r[n[k]]=!(q[n[k]]=l))}if(f){if(e||a){if(e){j=[],k=r.length;while(k--)(l=r[k])&&j.push(q[k]=l);e(null,r=[],j,i)}k=r.length;while(k--)(l=r[k])&&(j=e?J(f,l):m[k])>-1&&(f[j]=!(g[j]=l))}}else r=ua(r===g?r.splice(o,r.length):r),e?e(null,g,r,i):H.apply(g,r)})}function wa(a){for(var b,c,e,f=a.length,g=d.relative[a[0].type],h=g||d.relative[" "],i=g?1:0,k=ra(function(a){return a===b},h,!0),l=ra(function(a){return J(b,a)>-1},h,!0),m=[function(a,c,d){var e=!g&&(d||c!==j)||((b=c).nodeType?k(a,c,d):l(a,c,d));return b=null,e}];f>i;i++)if(c=d.relative[a[i].type])m=[ra(sa(m),c)];else{if(c=d.filter[a[i].type].apply(null,a[i].matches),c[u]){for(e=++i;f>e;e++)if(d.relative[a[e].type])break;return va(i>1&&sa(m),i>1&&qa(a.slice(0,i-1).concat({value:" "===a[i-2].type?"*":""})).replace(Q,"$1"),c,e>i&&wa(a.slice(i,e)),f>e&&wa(a=a.slice(e)),f>e&&qa(a))}m.push(c)}return sa(m)}function xa(a,b){var c=b.length>0,e=a.length>0,f=function(f,g,h,i,k){var l,o,q,r=0,s="0",t=f&&[],u=[],v=j,x=f||e&&d.find.TAG("*",k),y=w+=null==v?1:Math.random()||.1,z=x.length;for(k&&(j=g===n||g||k);s!==z&&null!=(l=x[s]);s++){if(e&&l){o=0,g||l.ownerDocument===n||(m(l),h=!p);while(q=a[o++])if(q(l,g||n,h)){i.push(l);break}k&&(w=y)}c&&((l=!q&&l)&&r--,f&&t.push(l))}if(r+=s,c&&s!==r){o=0;while(q=b[o++])q(t,u,g,h);if(f){if(r>0)while(s--)t[s]||u[s]||(u[s]=F.call(i));u=ua(u)}H.apply(i,u),k&&!f&&u.length>0&&r+b.length>1&&fa.uniqueSort(i)}return k&&(w=y,j=v),t};return c?ha(f):f}return h=fa.compile=function(a,b){var c,d=[],e=[],f=A[a+" "];if(!f){b||(b=g(a)),c=b.length;while(c--)f=wa(b[c]),f[u]?d.push(f):e.push(f);f=A(a,xa(e,d)),f.selector=a}return f},i=fa.select=function(a,b,e,f){var i,j,k,l,m,n="function"==typeof a&&a,o=!f&&g(a=n.selector||a);if(e=e||[],1===o.length){if(j=o[0]=o[0].slice(0),j.length>2&&"ID"===(k=j[0]).type&&c.getById&&9===b.nodeType&&p&&d.relative[j[1].type]){if(b=(d.find.ID(k.matches[0].replace(ba,ca),b)||[])[0],!b)return e;n&&(b=b.parentNode),a=a.slice(j.shift().value.length)}i=W.needsContext.test(a)?0:j.length;while(i--){if(k=j[i],d.relative[l=k.type])break;if((m=d.find[l])&&(f=m(k.matches[0].replace(ba,ca),_.test(j[0].type)&&oa(b.parentNode)||b))){if(j.splice(i,1),a=f.length&&qa(j),!a)return H.apply(e,f),e;break}}}return(n||h(a,o))(f,b,!p,e,!b||_.test(a)&&oa(b.parentNode)||b),e},c.sortStable=u.split("").sort(B).join("")===u,c.detectDuplicates=!!l,m(),c.sortDetached=ia(function(a){return 1&a.compareDocumentPosition(n.createElement("div"))}),ia(function(a){return a.innerHTML="","#"===a.firstChild.getAttribute("href")})||ja("type|href|height|width",function(a,b,c){return c?void 0:a.getAttribute(b,"type"===b.toLowerCase()?1:2)}),c.attributes&&ia(function(a){return a.innerHTML="",a.firstChild.setAttribute("value",""),""===a.firstChild.getAttribute("value")})||ja("value",function(a,b,c){return c||"input"!==a.nodeName.toLowerCase()?void 0:a.defaultValue}),ia(function(a){return null==a.getAttribute("disabled")})||ja(K,function(a,b,c){var d;return c?void 0:a[b]===!0?b.toLowerCase():(d=a.getAttributeNode(b))&&d.specified?d.value:null}),fa}(a);n.find=t,n.expr=t.selectors,n.expr[":"]=n.expr.pseudos,n.uniqueSort=n.unique=t.uniqueSort,n.text=t.getText,n.isXMLDoc=t.isXML,n.contains=t.contains;var u=function(a,b,c){var d=[],e=void 0!==c;while((a=a[b])&&9!==a.nodeType)if(1===a.nodeType){if(e&&n(a).is(c))break;d.push(a)}return d},v=function(a,b){for(var c=[];a;a=a.nextSibling)1===a.nodeType&&a!==b&&c.push(a);return c},w=n.expr.match.needsContext,x=/^<([\w-]+)\s*\/?>(?:<\/\1>|)$/,y=/^.[^:#\[\.,]*$/;function z(a,b,c){if(n.isFunction(b))return n.grep(a,function(a,d){return!!b.call(a,d,a)!==c});if(b.nodeType)return n.grep(a,function(a){return a===b!==c});if("string"==typeof b){if(y.test(b))return n.filter(b,a,c);b=n.filter(b,a)}return n.grep(a,function(a){return n.inArray(a,b)>-1!==c})}n.filter=function(a,b,c){var d=b[0];return c&&(a=":not("+a+")"),1===b.length&&1===d.nodeType?n.find.matchesSelector(d,a)?[d]:[]:n.find.matches(a,n.grep(b,function(a){return 1===a.nodeType}))},n.fn.extend({find:function(a){var b,c=[],d=this,e=d.length;if("string"!=typeof a)return this.pushStack(n(a).filter(function(){for(b=0;e>b;b++)if(n.contains(d[b],this))return!0}));for(b=0;e>b;b++)n.find(a,d[b],c);return c=this.pushStack(e>1?n.unique(c):c),c.selector=this.selector?this.selector+" "+a:a,c},filter:function(a){return this.pushStack(z(this,a||[],!1))},not:function(a){return this.pushStack(z(this,a||[],!0))},is:function(a){return!!z(this,"string"==typeof a&&w.test(a)?n(a):a||[],!1).length}});var A,B=/^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]*))$/,C=n.fn.init=function(a,b,c){var e,f;if(!a)return this;if(c=c||A,"string"==typeof a){if(e="<"===a.charAt(0)&&">"===a.charAt(a.length-1)&&a.length>=3?[null,a,null]:B.exec(a),!e||!e[1]&&b)return!b||b.jquery?(b||c).find(a):this.constructor(b).find(a);if(e[1]){if(b=b instanceof n?b[0]:b,n.merge(this,n.parseHTML(e[1],b&&b.nodeType?b.ownerDocument||b:d,!0)),x.test(e[1])&&n.isPlainObject(b))for(e in b)n.isFunction(this[e])?this[e](b[e]):this.attr(e,b[e]);return this}if(f=d.getElementById(e[2]),f&&f.parentNode){if(f.id!==e[2])return A.find(a);this.length=1,this[0]=f}return this.context=d,this.selector=a,this}return a.nodeType?(this.context=this[0]=a,this.length=1,this):n.isFunction(a)?"undefined"!=typeof c.ready?c.ready(a):a(n):(void 0!==a.selector&&(this.selector=a.selector,this.context=a.context),n.makeArray(a,this))};C.prototype=n.fn,A=n(d);var D=/^(?:parents|prev(?:Until|All))/,E={children:!0,contents:!0,next:!0,prev:!0};n.fn.extend({has:function(a){var b,c=n(a,this),d=c.length;return this.filter(function(){for(b=0;d>b;b++)if(n.contains(this,c[b]))return!0})},closest:function(a,b){for(var c,d=0,e=this.length,f=[],g=w.test(a)||"string"!=typeof a?n(a,b||this.context):0;e>d;d++)for(c=this[d];c&&c!==b;c=c.parentNode)if(c.nodeType<11&&(g?g.index(c)>-1:1===c.nodeType&&n.find.matchesSelector(c,a))){f.push(c);break}return this.pushStack(f.length>1?n.uniqueSort(f):f)},index:function(a){return a?"string"==typeof a?n.inArray(this[0],n(a)):n.inArray(a.jquery?a[0]:a,this):this[0]&&this[0].parentNode?this.first().prevAll().length:-1},add:function(a,b){return this.pushStack(n.uniqueSort(n.merge(this.get(),n(a,b))))},addBack:function(a){return this.add(null==a?this.prevObject:this.prevObject.filter(a))}});function F(a,b){do a=a[b];while(a&&1!==a.nodeType);return a}n.each({parent:function(a){var b=a.parentNode;return b&&11!==b.nodeType?b:null},parents:function(a){return u(a,"parentNode")},parentsUntil:function(a,b,c){return u(a,"parentNode",c)},next:function(a){return F(a,"nextSibling")},prev:function(a){return F(a,"previousSibling")},nextAll:function(a){return u(a,"nextSibling")},prevAll:function(a){return u(a,"previousSibling")},nextUntil:function(a,b,c){return u(a,"nextSibling",c)},prevUntil:function(a,b,c){return u(a,"previousSibling",c)},siblings:function(a){return v((a.parentNode||{}).firstChild,a)},children:function(a){return v(a.firstChild)},contents:function(a){return n.nodeName(a,"iframe")?a.contentDocument||a.contentWindow.document:n.merge([],a.childNodes)}},function(a,b){n.fn[a]=function(c,d){var e=n.map(this,b,c);return"Until"!==a.slice(-5)&&(d=c),d&&"string"==typeof d&&(e=n.filter(d,e)),this.length>1&&(E[a]||(e=n.uniqueSort(e)),D.test(a)&&(e=e.reverse())),this.pushStack(e)}});var G=/\S+/g;function H(a){var b={};return n.each(a.match(G)||[],function(a,c){b[c]=!0}),b}n.Callbacks=function(a){a="string"==typeof a?H(a):n.extend({},a);var b,c,d,e,f=[],g=[],h=-1,i=function(){for(e=a.once,d=b=!0;g.length;h=-1){c=g.shift();while(++h-1)f.splice(c,1),h>=c&&h--}),this},has:function(a){return a?n.inArray(a,f)>-1:f.length>0},empty:function(){return f&&(f=[]),this},disable:function(){return e=g=[],f=c="",this},disabled:function(){return!f},lock:function(){return e=!0,c||j.disable(),this},locked:function(){return!!e},fireWith:function(a,c){return e||(c=c||[],c=[a,c.slice?c.slice():c],g.push(c),b||i()),this},fire:function(){return j.fireWith(this,arguments),this},fired:function(){return!!d}};return j},n.extend({Deferred:function(a){var b=[["resolve","done",n.Callbacks("once memory"),"resolved"],["reject","fail",n.Callbacks("once memory"),"rejected"],["notify","progress",n.Callbacks("memory")]],c="pending",d={state:function(){return c},always:function(){return e.done(arguments).fail(arguments),this},then:function(){var a=arguments;return n.Deferred(function(c){n.each(b,function(b,f){var g=n.isFunction(a[b])&&a[b];e[f[1]](function(){var a=g&&g.apply(this,arguments);a&&n.isFunction(a.promise)?a.promise().progress(c.notify).done(c.resolve).fail(c.reject):c[f[0]+"With"](this===d?c.promise():this,g?[a]:arguments)})}),a=null}).promise()},promise:function(a){return null!=a?n.extend(a,d):d}},e={};return d.pipe=d.then,n.each(b,function(a,f){var g=f[2],h=f[3];d[f[1]]=g.add,h&&g.add(function(){c=h},b[1^a][2].disable,b[2][2].lock),e[f[0]]=function(){return e[f[0]+"With"](this===e?d:this,arguments),this},e[f[0]+"With"]=g.fireWith}),d.promise(e),a&&a.call(e,e),e},when:function(a){var b=0,c=e.call(arguments),d=c.length,f=1!==d||a&&n.isFunction(a.promise)?d:0,g=1===f?a:n.Deferred(),h=function(a,b,c){return function(d){b[a]=this,c[a]=arguments.length>1?e.call(arguments):d,c===i?g.notifyWith(b,c):--f||g.resolveWith(b,c)}},i,j,k;if(d>1)for(i=new Array(d),j=new Array(d),k=new Array(d);d>b;b++)c[b]&&n.isFunction(c[b].promise)?c[b].promise().progress(h(b,j,i)).done(h(b,k,c)).fail(g.reject):--f;return f||g.resolveWith(k,c),g.promise()}});var I;n.fn.ready=function(a){return n.ready.promise().done(a),this},n.extend({isReady:!1,readyWait:1,holdReady:function(a){a?n.readyWait++:n.ready(!0)},ready:function(a){(a===!0?--n.readyWait:n.isReady)||(n.isReady=!0,a!==!0&&--n.readyWait>0||(I.resolveWith(d,[n]),n.fn.triggerHandler&&(n(d).triggerHandler("ready"),n(d).off("ready"))))}});function J(){d.addEventListener?(d.removeEventListener("DOMContentLoaded",K),a.removeEventListener("load",K)):(d.detachEvent("onreadystatechange",K),a.detachEvent("onload",K))}function K(){(d.addEventListener||"load"===a.event.type||"complete"===d.readyState)&&(J(),n.ready())}n.ready.promise=function(b){if(!I)if(I=n.Deferred(),"complete"===d.readyState||"loading"!==d.readyState&&!d.documentElement.doScroll)a.setTimeout(n.ready);else if(d.addEventListener)d.addEventListener("DOMContentLoaded",K),a.addEventListener("load",K);else{d.attachEvent("onreadystatechange",K),a.attachEvent("onload",K);var c=!1;try{c=null==a.frameElement&&d.documentElement}catch(e){}c&&c.doScroll&&!function f(){if(!n.isReady){try{c.doScroll("left")}catch(b){return a.setTimeout(f,50)}J(),n.ready()}}()}return I.promise(b)},n.ready.promise();var L;for(L in n(l))break;l.ownFirst="0"===L,l.inlineBlockNeedsLayout=!1,n(function(){var a,b,c,e;c=d.getElementsByTagName("body")[0],c&&c.style&&(b=d.createElement("div"),e=d.createElement("div"),e.style.cssText="position:absolute;border:0;width:0;height:0;top:0;left:-9999px",c.appendChild(e).appendChild(b),"undefined"!=typeof b.style.zoom&&(b.style.cssText="display:inline;margin:0;border:0;padding:1px;width:1px;zoom:1",l.inlineBlockNeedsLayout=a=3===b.offsetWidth,a&&(c.style.zoom=1)),c.removeChild(e))}),function(){var a=d.createElement("div");l.deleteExpando=!0;try{delete a.test}catch(b){l.deleteExpando=!1}a=null}();var M=function(a){var b=n.noData[(a.nodeName+" ").toLowerCase()],c=+a.nodeType||1;return 1!==c&&9!==c?!1:!b||b!==!0&&a.getAttribute("classid")===b},N=/^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,O=/([A-Z])/g;function P(a,b,c){if(void 0===c&&1===a.nodeType){var d="data-"+b.replace(O,"-$1").toLowerCase();if(c=a.getAttribute(d),"string"==typeof c){try{c="true"===c?!0:"false"===c?!1:"null"===c?null:+c+""===c?+c:N.test(c)?n.parseJSON(c):c}catch(e){}n.data(a,b,c)}else c=void 0; +}return c}function Q(a){var b;for(b in a)if(("data"!==b||!n.isEmptyObject(a[b]))&&"toJSON"!==b)return!1;return!0}function R(a,b,d,e){if(M(a)){var f,g,h=n.expando,i=a.nodeType,j=i?n.cache:a,k=i?a[h]:a[h]&&h;if(k&&j[k]&&(e||j[k].data)||void 0!==d||"string"!=typeof b)return k||(k=i?a[h]=c.pop()||n.guid++:h),j[k]||(j[k]=i?{}:{toJSON:n.noop}),"object"!=typeof b&&"function"!=typeof b||(e?j[k]=n.extend(j[k],b):j[k].data=n.extend(j[k].data,b)),g=j[k],e||(g.data||(g.data={}),g=g.data),void 0!==d&&(g[n.camelCase(b)]=d),"string"==typeof b?(f=g[b],null==f&&(f=g[n.camelCase(b)])):f=g,f}}function S(a,b,c){if(M(a)){var d,e,f=a.nodeType,g=f?n.cache:a,h=f?a[n.expando]:n.expando;if(g[h]){if(b&&(d=c?g[h]:g[h].data)){n.isArray(b)?b=b.concat(n.map(b,n.camelCase)):b in d?b=[b]:(b=n.camelCase(b),b=b in d?[b]:b.split(" ")),e=b.length;while(e--)delete d[b[e]];if(c?!Q(d):!n.isEmptyObject(d))return}(c||(delete g[h].data,Q(g[h])))&&(f?n.cleanData([a],!0):l.deleteExpando||g!=g.window?delete g[h]:g[h]=void 0)}}}n.extend({cache:{},noData:{"applet ":!0,"embed ":!0,"object ":"clsid:D27CDB6E-AE6D-11cf-96B8-444553540000"},hasData:function(a){return a=a.nodeType?n.cache[a[n.expando]]:a[n.expando],!!a&&!Q(a)},data:function(a,b,c){return R(a,b,c)},removeData:function(a,b){return S(a,b)},_data:function(a,b,c){return R(a,b,c,!0)},_removeData:function(a,b){return S(a,b,!0)}}),n.fn.extend({data:function(a,b){var c,d,e,f=this[0],g=f&&f.attributes;if(void 0===a){if(this.length&&(e=n.data(f),1===f.nodeType&&!n._data(f,"parsedAttrs"))){c=g.length;while(c--)g[c]&&(d=g[c].name,0===d.indexOf("data-")&&(d=n.camelCase(d.slice(5)),P(f,d,e[d])));n._data(f,"parsedAttrs",!0)}return e}return"object"==typeof a?this.each(function(){n.data(this,a)}):arguments.length>1?this.each(function(){n.data(this,a,b)}):f?P(f,a,n.data(f,a)):void 0},removeData:function(a){return this.each(function(){n.removeData(this,a)})}}),n.extend({queue:function(a,b,c){var d;return a?(b=(b||"fx")+"queue",d=n._data(a,b),c&&(!d||n.isArray(c)?d=n._data(a,b,n.makeArray(c)):d.push(c)),d||[]):void 0},dequeue:function(a,b){b=b||"fx";var c=n.queue(a,b),d=c.length,e=c.shift(),f=n._queueHooks(a,b),g=function(){n.dequeue(a,b)};"inprogress"===e&&(e=c.shift(),d--),e&&("fx"===b&&c.unshift("inprogress"),delete f.stop,e.call(a,g,f)),!d&&f&&f.empty.fire()},_queueHooks:function(a,b){var c=b+"queueHooks";return n._data(a,c)||n._data(a,c,{empty:n.Callbacks("once memory").add(function(){n._removeData(a,b+"queue"),n._removeData(a,c)})})}}),n.fn.extend({queue:function(a,b){var c=2;return"string"!=typeof a&&(b=a,a="fx",c--),arguments.lengthh;h++)b(a[h],c,g?d:d.call(a[h],h,b(a[h],c)));return e?a:j?b.call(a):i?b(a[0],c):f},Z=/^(?:checkbox|radio)$/i,$=/<([\w:-]+)/,_=/^$|\/(?:java|ecma)script/i,aa=/^\s+/,ba="abbr|article|aside|audio|bdi|canvas|data|datalist|details|dialog|figcaption|figure|footer|header|hgroup|main|mark|meter|nav|output|picture|progress|section|summary|template|time|video";function ca(a){var b=ba.split("|"),c=a.createDocumentFragment();if(c.createElement)while(b.length)c.createElement(b.pop());return c}!function(){var a=d.createElement("div"),b=d.createDocumentFragment(),c=d.createElement("input");a.innerHTML="
a",l.leadingWhitespace=3===a.firstChild.nodeType,l.tbody=!a.getElementsByTagName("tbody").length,l.htmlSerialize=!!a.getElementsByTagName("link").length,l.html5Clone="<:nav>"!==d.createElement("nav").cloneNode(!0).outerHTML,c.type="checkbox",c.checked=!0,b.appendChild(c),l.appendChecked=c.checked,a.innerHTML="",l.noCloneChecked=!!a.cloneNode(!0).lastChild.defaultValue,b.appendChild(a),c=d.createElement("input"),c.setAttribute("type","radio"),c.setAttribute("checked","checked"),c.setAttribute("name","t"),a.appendChild(c),l.checkClone=a.cloneNode(!0).cloneNode(!0).lastChild.checked,l.noCloneEvent=!!a.addEventListener,a[n.expando]=1,l.attributes=!a.getAttribute(n.expando)}();var da={option:[1,""],legend:[1,"
","
"],area:[1,"",""],param:[1,"",""],thead:[1,"","
"],tr:[2,"","
"],col:[2,"","
"],td:[3,"","
"],_default:l.htmlSerialize?[0,"",""]:[1,"X
","
"]};da.optgroup=da.option,da.tbody=da.tfoot=da.colgroup=da.caption=da.thead,da.th=da.td;function ea(a,b){var c,d,e=0,f="undefined"!=typeof a.getElementsByTagName?a.getElementsByTagName(b||"*"):"undefined"!=typeof a.querySelectorAll?a.querySelectorAll(b||"*"):void 0;if(!f)for(f=[],c=a.childNodes||a;null!=(d=c[e]);e++)!b||n.nodeName(d,b)?f.push(d):n.merge(f,ea(d,b));return void 0===b||b&&n.nodeName(a,b)?n.merge([a],f):f}function fa(a,b){for(var c,d=0;null!=(c=a[d]);d++)n._data(c,"globalEval",!b||n._data(b[d],"globalEval"))}var ga=/<|&#?\w+;/,ha=/r;r++)if(g=a[r],g||0===g)if("object"===n.type(g))n.merge(q,g.nodeType?[g]:g);else if(ga.test(g)){i=i||p.appendChild(b.createElement("div")),j=($.exec(g)||["",""])[1].toLowerCase(),m=da[j]||da._default,i.innerHTML=m[1]+n.htmlPrefilter(g)+m[2],f=m[0];while(f--)i=i.lastChild;if(!l.leadingWhitespace&&aa.test(g)&&q.push(b.createTextNode(aa.exec(g)[0])),!l.tbody){g="table"!==j||ha.test(g)?""!==m[1]||ha.test(g)?0:i:i.firstChild,f=g&&g.childNodes.length;while(f--)n.nodeName(k=g.childNodes[f],"tbody")&&!k.childNodes.length&&g.removeChild(k)}n.merge(q,i.childNodes),i.textContent="";while(i.firstChild)i.removeChild(i.firstChild);i=p.lastChild}else q.push(b.createTextNode(g));i&&p.removeChild(i),l.appendChecked||n.grep(ea(q,"input"),ia),r=0;while(g=q[r++])if(d&&n.inArray(g,d)>-1)e&&e.push(g);else if(h=n.contains(g.ownerDocument,g),i=ea(p.appendChild(g),"script"),h&&fa(i),c){f=0;while(g=i[f++])_.test(g.type||"")&&c.push(g)}return i=null,p}!function(){var b,c,e=d.createElement("div");for(b in{submit:!0,change:!0,focusin:!0})c="on"+b,(l[b]=c in a)||(e.setAttribute(c,"t"),l[b]=e.attributes[c].expando===!1);e=null}();var ka=/^(?:input|select|textarea)$/i,la=/^key/,ma=/^(?:mouse|pointer|contextmenu|drag|drop)|click/,na=/^(?:focusinfocus|focusoutblur)$/,oa=/^([^.]*)(?:\.(.+)|)/;function pa(){return!0}function qa(){return!1}function ra(){try{return d.activeElement}catch(a){}}function sa(a,b,c,d,e,f){var g,h;if("object"==typeof b){"string"!=typeof c&&(d=d||c,c=void 0);for(h in b)sa(a,h,c,d,b[h],f);return a}if(null==d&&null==e?(e=c,d=c=void 0):null==e&&("string"==typeof c?(e=d,d=void 0):(e=d,d=c,c=void 0)),e===!1)e=qa;else if(!e)return a;return 1===f&&(g=e,e=function(a){return n().off(a),g.apply(this,arguments)},e.guid=g.guid||(g.guid=n.guid++)),a.each(function(){n.event.add(this,b,e,d,c)})}n.event={global:{},add:function(a,b,c,d,e){var f,g,h,i,j,k,l,m,o,p,q,r=n._data(a);if(r){c.handler&&(i=c,c=i.handler,e=i.selector),c.guid||(c.guid=n.guid++),(g=r.events)||(g=r.events={}),(k=r.handle)||(k=r.handle=function(a){return"undefined"==typeof n||a&&n.event.triggered===a.type?void 0:n.event.dispatch.apply(k.elem,arguments)},k.elem=a),b=(b||"").match(G)||[""],h=b.length;while(h--)f=oa.exec(b[h])||[],o=q=f[1],p=(f[2]||"").split(".").sort(),o&&(j=n.event.special[o]||{},o=(e?j.delegateType:j.bindType)||o,j=n.event.special[o]||{},l=n.extend({type:o,origType:q,data:d,handler:c,guid:c.guid,selector:e,needsContext:e&&n.expr.match.needsContext.test(e),namespace:p.join(".")},i),(m=g[o])||(m=g[o]=[],m.delegateCount=0,j.setup&&j.setup.call(a,d,p,k)!==!1||(a.addEventListener?a.addEventListener(o,k,!1):a.attachEvent&&a.attachEvent("on"+o,k))),j.add&&(j.add.call(a,l),l.handler.guid||(l.handler.guid=c.guid)),e?m.splice(m.delegateCount++,0,l):m.push(l),n.event.global[o]=!0);a=null}},remove:function(a,b,c,d,e){var f,g,h,i,j,k,l,m,o,p,q,r=n.hasData(a)&&n._data(a);if(r&&(k=r.events)){b=(b||"").match(G)||[""],j=b.length;while(j--)if(h=oa.exec(b[j])||[],o=q=h[1],p=(h[2]||"").split(".").sort(),o){l=n.event.special[o]||{},o=(d?l.delegateType:l.bindType)||o,m=k[o]||[],h=h[2]&&new RegExp("(^|\\.)"+p.join("\\.(?:.*\\.|)")+"(\\.|$)"),i=f=m.length;while(f--)g=m[f],!e&&q!==g.origType||c&&c.guid!==g.guid||h&&!h.test(g.namespace)||d&&d!==g.selector&&("**"!==d||!g.selector)||(m.splice(f,1),g.selector&&m.delegateCount--,l.remove&&l.remove.call(a,g));i&&!m.length&&(l.teardown&&l.teardown.call(a,p,r.handle)!==!1||n.removeEvent(a,o,r.handle),delete k[o])}else for(o in k)n.event.remove(a,o+b[j],c,d,!0);n.isEmptyObject(k)&&(delete r.handle,n._removeData(a,"events"))}},trigger:function(b,c,e,f){var g,h,i,j,l,m,o,p=[e||d],q=k.call(b,"type")?b.type:b,r=k.call(b,"namespace")?b.namespace.split("."):[];if(i=m=e=e||d,3!==e.nodeType&&8!==e.nodeType&&!na.test(q+n.event.triggered)&&(q.indexOf(".")>-1&&(r=q.split("."),q=r.shift(),r.sort()),h=q.indexOf(":")<0&&"on"+q,b=b[n.expando]?b:new n.Event(q,"object"==typeof b&&b),b.isTrigger=f?2:3,b.namespace=r.join("."),b.rnamespace=b.namespace?new RegExp("(^|\\.)"+r.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,b.result=void 0,b.target||(b.target=e),c=null==c?[b]:n.makeArray(c,[b]),l=n.event.special[q]||{},f||!l.trigger||l.trigger.apply(e,c)!==!1)){if(!f&&!l.noBubble&&!n.isWindow(e)){for(j=l.delegateType||q,na.test(j+q)||(i=i.parentNode);i;i=i.parentNode)p.push(i),m=i;m===(e.ownerDocument||d)&&p.push(m.defaultView||m.parentWindow||a)}o=0;while((i=p[o++])&&!b.isPropagationStopped())b.type=o>1?j:l.bindType||q,g=(n._data(i,"events")||{})[b.type]&&n._data(i,"handle"),g&&g.apply(i,c),g=h&&i[h],g&&g.apply&&M(i)&&(b.result=g.apply(i,c),b.result===!1&&b.preventDefault());if(b.type=q,!f&&!b.isDefaultPrevented()&&(!l._default||l._default.apply(p.pop(),c)===!1)&&M(e)&&h&&e[q]&&!n.isWindow(e)){m=e[h],m&&(e[h]=null),n.event.triggered=q;try{e[q]()}catch(s){}n.event.triggered=void 0,m&&(e[h]=m)}return b.result}},dispatch:function(a){a=n.event.fix(a);var b,c,d,f,g,h=[],i=e.call(arguments),j=(n._data(this,"events")||{})[a.type]||[],k=n.event.special[a.type]||{};if(i[0]=a,a.delegateTarget=this,!k.preDispatch||k.preDispatch.call(this,a)!==!1){h=n.event.handlers.call(this,a,j),b=0;while((f=h[b++])&&!a.isPropagationStopped()){a.currentTarget=f.elem,c=0;while((g=f.handlers[c++])&&!a.isImmediatePropagationStopped())a.rnamespace&&!a.rnamespace.test(g.namespace)||(a.handleObj=g,a.data=g.data,d=((n.event.special[g.origType]||{}).handle||g.handler).apply(f.elem,i),void 0!==d&&(a.result=d)===!1&&(a.preventDefault(),a.stopPropagation()))}return k.postDispatch&&k.postDispatch.call(this,a),a.result}},handlers:function(a,b){var c,d,e,f,g=[],h=b.delegateCount,i=a.target;if(h&&i.nodeType&&("click"!==a.type||isNaN(a.button)||a.button<1))for(;i!=this;i=i.parentNode||this)if(1===i.nodeType&&(i.disabled!==!0||"click"!==a.type)){for(d=[],c=0;h>c;c++)f=b[c],e=f.selector+" ",void 0===d[e]&&(d[e]=f.needsContext?n(e,this).index(i)>-1:n.find(e,this,null,[i]).length),d[e]&&d.push(f);d.length&&g.push({elem:i,handlers:d})}return h]","i"),va=/<(?!area|br|col|embed|hr|img|input|link|meta|param)(([\w:-]+)[^>]*)\/>/gi,wa=/\s*$/g,Aa=ca(d),Ba=Aa.appendChild(d.createElement("div"));function Ca(a,b){return n.nodeName(a,"table")&&n.nodeName(11!==b.nodeType?b:b.firstChild,"tr")?a.getElementsByTagName("tbody")[0]||a.appendChild(a.ownerDocument.createElement("tbody")):a}function Da(a){return a.type=(null!==n.find.attr(a,"type"))+"/"+a.type,a}function Ea(a){var b=ya.exec(a.type);return b?a.type=b[1]:a.removeAttribute("type"),a}function Fa(a,b){if(1===b.nodeType&&n.hasData(a)){var c,d,e,f=n._data(a),g=n._data(b,f),h=f.events;if(h){delete g.handle,g.events={};for(c in h)for(d=0,e=h[c].length;e>d;d++)n.event.add(b,c,h[c][d])}g.data&&(g.data=n.extend({},g.data))}}function Ga(a,b){var c,d,e;if(1===b.nodeType){if(c=b.nodeName.toLowerCase(),!l.noCloneEvent&&b[n.expando]){e=n._data(b);for(d in e.events)n.removeEvent(b,d,e.handle);b.removeAttribute(n.expando)}"script"===c&&b.text!==a.text?(Da(b).text=a.text,Ea(b)):"object"===c?(b.parentNode&&(b.outerHTML=a.outerHTML),l.html5Clone&&a.innerHTML&&!n.trim(b.innerHTML)&&(b.innerHTML=a.innerHTML)):"input"===c&&Z.test(a.type)?(b.defaultChecked=b.checked=a.checked,b.value!==a.value&&(b.value=a.value)):"option"===c?b.defaultSelected=b.selected=a.defaultSelected:"input"!==c&&"textarea"!==c||(b.defaultValue=a.defaultValue)}}function Ha(a,b,c,d){b=f.apply([],b);var e,g,h,i,j,k,m=0,o=a.length,p=o-1,q=b[0],r=n.isFunction(q);if(r||o>1&&"string"==typeof q&&!l.checkClone&&xa.test(q))return a.each(function(e){var f=a.eq(e);r&&(b[0]=q.call(this,e,f.html())),Ha(f,b,c,d)});if(o&&(k=ja(b,a[0].ownerDocument,!1,a,d),e=k.firstChild,1===k.childNodes.length&&(k=e),e||d)){for(i=n.map(ea(k,"script"),Da),h=i.length;o>m;m++)g=k,m!==p&&(g=n.clone(g,!0,!0),h&&n.merge(i,ea(g,"script"))),c.call(a[m],g,m);if(h)for(j=i[i.length-1].ownerDocument,n.map(i,Ea),m=0;h>m;m++)g=i[m],_.test(g.type||"")&&!n._data(g,"globalEval")&&n.contains(j,g)&&(g.src?n._evalUrl&&n._evalUrl(g.src):n.globalEval((g.text||g.textContent||g.innerHTML||"").replace(za,"")));k=e=null}return a}function Ia(a,b,c){for(var d,e=b?n.filter(b,a):a,f=0;null!=(d=e[f]);f++)c||1!==d.nodeType||n.cleanData(ea(d)),d.parentNode&&(c&&n.contains(d.ownerDocument,d)&&fa(ea(d,"script")),d.parentNode.removeChild(d));return a}n.extend({htmlPrefilter:function(a){return a.replace(va,"<$1>")},clone:function(a,b,c){var d,e,f,g,h,i=n.contains(a.ownerDocument,a);if(l.html5Clone||n.isXMLDoc(a)||!ua.test("<"+a.nodeName+">")?f=a.cloneNode(!0):(Ba.innerHTML=a.outerHTML,Ba.removeChild(f=Ba.firstChild)),!(l.noCloneEvent&&l.noCloneChecked||1!==a.nodeType&&11!==a.nodeType||n.isXMLDoc(a)))for(d=ea(f),h=ea(a),g=0;null!=(e=h[g]);++g)d[g]&&Ga(e,d[g]);if(b)if(c)for(h=h||ea(a),d=d||ea(f),g=0;null!=(e=h[g]);g++)Fa(e,d[g]);else Fa(a,f);return d=ea(f,"script"),d.length>0&&fa(d,!i&&ea(a,"script")),d=h=e=null,f},cleanData:function(a,b){for(var d,e,f,g,h=0,i=n.expando,j=n.cache,k=l.attributes,m=n.event.special;null!=(d=a[h]);h++)if((b||M(d))&&(f=d[i],g=f&&j[f])){if(g.events)for(e in g.events)m[e]?n.event.remove(d,e):n.removeEvent(d,e,g.handle);j[f]&&(delete j[f],k||"undefined"==typeof d.removeAttribute?d[i]=void 0:d.removeAttribute(i),c.push(f))}}}),n.fn.extend({domManip:Ha,detach:function(a){return Ia(this,a,!0)},remove:function(a){return Ia(this,a)},text:function(a){return Y(this,function(a){return void 0===a?n.text(this):this.empty().append((this[0]&&this[0].ownerDocument||d).createTextNode(a))},null,a,arguments.length)},append:function(){return Ha(this,arguments,function(a){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var b=Ca(this,a);b.appendChild(a)}})},prepend:function(){return Ha(this,arguments,function(a){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var b=Ca(this,a);b.insertBefore(a,b.firstChild)}})},before:function(){return Ha(this,arguments,function(a){this.parentNode&&this.parentNode.insertBefore(a,this)})},after:function(){return Ha(this,arguments,function(a){this.parentNode&&this.parentNode.insertBefore(a,this.nextSibling)})},empty:function(){for(var a,b=0;null!=(a=this[b]);b++){1===a.nodeType&&n.cleanData(ea(a,!1));while(a.firstChild)a.removeChild(a.firstChild);a.options&&n.nodeName(a,"select")&&(a.options.length=0)}return this},clone:function(a,b){return a=null==a?!1:a,b=null==b?a:b,this.map(function(){return n.clone(this,a,b)})},html:function(a){return Y(this,function(a){var b=this[0]||{},c=0,d=this.length;if(void 0===a)return 1===b.nodeType?b.innerHTML.replace(ta,""):void 0;if("string"==typeof a&&!wa.test(a)&&(l.htmlSerialize||!ua.test(a))&&(l.leadingWhitespace||!aa.test(a))&&!da[($.exec(a)||["",""])[1].toLowerCase()]){a=n.htmlPrefilter(a);try{for(;d>c;c++)b=this[c]||{},1===b.nodeType&&(n.cleanData(ea(b,!1)),b.innerHTML=a);b=0}catch(e){}}b&&this.empty().append(a)},null,a,arguments.length)},replaceWith:function(){var a=[];return Ha(this,arguments,function(b){var c=this.parentNode;n.inArray(this,a)<0&&(n.cleanData(ea(this)),c&&c.replaceChild(b,this))},a)}}),n.each({appendTo:"append",prependTo:"prepend",insertBefore:"before",insertAfter:"after",replaceAll:"replaceWith"},function(a,b){n.fn[a]=function(a){for(var c,d=0,e=[],f=n(a),h=f.length-1;h>=d;d++)c=d===h?this:this.clone(!0),n(f[d])[b](c),g.apply(e,c.get());return this.pushStack(e)}});var Ja,Ka={HTML:"block",BODY:"block"};function La(a,b){var c=n(b.createElement(a)).appendTo(b.body),d=n.css(c[0],"display");return c.detach(),d}function Ma(a){var b=d,c=Ka[a];return c||(c=La(a,b),"none"!==c&&c||(Ja=(Ja||n("