Argparse defaults with defaults specifier (#84)

* Defaults for _base

* Defaults for gan

* Defaults for image

* Periods

* Update READMEs

Author: trsvchn

templates/_base/_argparse.py

@@ -6,123 +6,123 @@
 DEFAULTS = {
     "use_amp": {
         "action": "store_true",
-        "help": "use torch.cuda.amp for automatic mixed precision"
+        "help": "use torch.cuda.amp for automatic mixed precision. Default: %(default)s"
     },
     "resume_from": {
         "default": None,
         "type": str,
-        "help": "path to the checkpoint file to resume, can also url starting with https (None)"
+        "help": "path to the checkpoint file to resume, can also url starting with https. Default: %(default)s"
     },
     "seed": {
         "default": 666,
         "type": int,
-        "help": "seed to use in ignite.utils.manual_seed() (666)"
+        "help": "seed to use in ignite.utils.manual_seed(). Default: %(default)s"
     },
     "verbose": {
         "action": "store_true",
-        "help": "use logging.INFO in ignite.utils.setup_logger",
+        "help": "use logging.INFO in ignite.utils.setup_logger. Default: %(default)s",
     },
 
     # distributed training options
     "backend": {
         "default": None,
         "type": str,
-        "help": "backend to use for distributed training (None)",
+        "help": "backend to use for distributed training. Default: %(default)s",
     },
     "nproc_per_node": {
         "default": {{nproc_per_node}},
         "type": int,
         "help": """number of processes to launch on each node, for GPU training
                 this is recommended to be set to the number of GPUs in your system
-                so that each process can be bound to a single GPU ({{ nproc_per_node }})""",
+                so that each process can be bound to a single GPU. Default: %(default)s""",
     },
     "node_rank": {
         "default": {{node_rank}},
         "type": int,
-        "help": "rank of the node for multi-node distributed training ({{ node_rank }})",
+        "help": "rank of the node for multi-node distributed training. Default: %(default)s",
     },
     "nnodes": {
         "default": {{nnodes}},
         "type": int,
-        "help": "number of nodes to use for distributed training ({{ nnodes }})",
+        "help": "number of nodes to use for distributed training. Default: %(default)s",
     },
     "master_addr": {
         "default": {{master_addr}},
         "type": str,
-        "help": "master node TCP/IP address for torch native backends ({{ master_addr }})",
+        "help": "master node TCP/IP address for torch native backends. Default: %(default)s",
     },
     "master_port": {
         "default": {{master_port}},
         "type": int,
-        "help": "master node port for torch native backends ({{ master_port }})",
+        "help": "master node port for torch native backends. Default: %(default)s",
     },
     "epoch_length": {
         "default": None,
         "type": int,
-        "help": "epoch_length of Engine.run()"
+        "help": "epoch_length of Engine.run(). Default: %(default)s"
     },
     # ignite handlers options
     "save_every_iters": {
         "default": {{save_every_iters}},
         "type": int,
-        "help": "Saving iteration interval ({{save_every_iters}})",
+        "help": "Saving iteration interval. Default: %(default)s",
     },
     "n_saved": {
         "default": {{n_saved}},
         "type": int,
-        "help": "number of best models to store ({{ n_saved }})",
+        "help": "number of best models to store. Default: %(default)s",
     },
     "log_every_iters": {
         "default": {{log_every_iters}},
         "type": int,
-        "help": "logging interval for iteration progress bar ({{log_every_iters}})",
+        "help": "logging interval for iteration progress bar. Default: %(default)s",
     },
     "with_pbars": {
         "default": {{with_pbars}},
         "type": bool,
-        "help": "show epoch-wise and iteration-wise progress bars ({{with_pbars}})",
+        "help": "show epoch-wise and iteration-wise progress bars. Default: %(default)s",
     },
     "with_pbar_on_iters": {
         "default": {{with_pbar_on_iters}},
         "type": bool,
-        "help": "show iteration progress bar or not ({{with_pbar_on_iters}})",
+        "help": "show iteration progress bar or not. Default: %(default)s",
     },
     "stop_on_nan": {
         "default": {{stop_on_nan}},
         "type": bool,
-        "help": "stop the training if engine output contains NaN/inf values ({{stop_on_nan}})",
+        "help": "stop the training if engine output contains NaN/inf values. Default: %(default)s",
     },
     "clear_cuda_cache": {
         "default": {{clear_cuda_cache}},
         "type": bool,
-        "help": "clear cuda cache every end of epoch ({{clear_cuda_cache}})",
+        "help": "clear cuda cache every end of epoch. Default: %(default)s",
     },
     "with_gpu_stats": {
         "default": {{with_gpu_stats}},
         "type": bool,
-        "help": "show gpu information, requires pynvml ({{with_gpu_stats}})",
+        "help": "show gpu information, requires pynvml. Default: %(default)s",
     },
     "patience": {
         "default": {{patience}},
         "type": int,
-        "help": "number of events to wait if no improvement and then stop the training ({{patience}})"
+        "help": "number of events to wait if no improvement and then stop the training. Default: %(default)s"
     },
     "limit_sec": {
         "default": {{limit_sec}},
         "type": int,
-        "help": "maximum time before training terminates in seconds ({{limit_sec}})"
+        "help": "maximum time before training terminates in seconds. Default: %(default)s"
     },
 
     # ignite logger options
     "output_dir": {
         "default": "{{ output_dir }}",
         "type": str,
-        "help": "directory to save all outputs ({{ output_dir }})",
+        "help": "directory to save all outputs. Default: %(default)s",
     },
     "logger_log_every_iters": {
         "default": {{logger_log_every_iters}},
         "type": int,
-        "help": "logging interval for experiment tracking system ({{logger_log_every_iters}})",
+        "help": "logging interval for experiment tracking system. Default: %(default)s",
     },
 }
 {% endblock %}

templates/gan/README.md

@@ -130,92 +130,77 @@ Let's start training on {{nnodes}} nodes with {{nproc_per_node}} gpus each:
 ## Configurations
 
 ```sh
-usage: main.py [-h] [--use_amp] [--resume_from RESUME_FROM] [--seed SEED]
-               [--verbose] [--backend BACKEND]
-               [--nproc_per_node NPROC_PER_NODE] [--nnodes NNODES]
-               [--node_rank NODE_RANK] [--master_addr MASTER_ADDR]
-               [--master_port MASTER_PORT]
-               [--save_every_iters SAVE_EVERY_ITERS] [--n_saved N_SAVED]
-               [--log_every_iters LOG_EVERY_ITERS] [--with_pbars WITH_PBARS]
-               [--with_pbar_on_iters WITH_PBAR_ON_ITERS]
-               [--stop_on_nan STOP_ON_NAN]
-               [--clear_cuda_cache CLEAR_CUDA_CACHE]
-               [--with_gpu_stats WITH_GPU_STATS] [--patience PATIENCE]
-               [--limit_sec LIMIT_SEC] [--output_dir OUTPUT_DIR]
-               [--logger_log_every_iters LOGGER_LOG_EVERY_ITERS]
-               [--dataset {cifar10,lsun,imagenet,folder,lfw,fake,mnist}]
-               [--data_path DATA_PATH] [--batch_size BATCH_SIZE]
-               [--num_workers NUM_WORKERS] [--beta_1 BETA_1] [--lr LR]
-               [--max_epochs MAX_EPOCHS] [--z_dim Z_DIM]
-               [--g_filters G_FILTERS] [--d_filters D_FILTERS]
+usage: main.py [-h] [--use_amp] [--resume_from RESUME_FROM] [--seed SEED] [--verbose] [--backend BACKEND]
+               [--nproc_per_node NPROC_PER_NODE] [--node_rank NODE_RANK] [--nnodes NNODES]
+               [--master_addr MASTER_ADDR] [--master_port MASTER_PORT] [--epoch_length EPOCH_LENGTH]
+               [--save_every_iters SAVE_EVERY_ITERS] [--n_saved N_SAVED] [--log_every_iters LOG_EVERY_ITERS]
+               [--with_pbars WITH_PBARS] [--with_pbar_on_iters WITH_PBAR_ON_ITERS]
+               [--stop_on_nan STOP_ON_NAN] [--clear_cuda_cache CLEAR_CUDA_CACHE]
+               [--with_gpu_stats WITH_GPU_STATS] [--patience PATIENCE] [--limit_sec LIMIT_SEC]
+               [--output_dir OUTPUT_DIR] [--logger_log_every_iters LOGGER_LOG_EVERY_ITERS]
+               [--dataset {cifar10,lsun,imagenet,folder,lfw,fake,mnist}] [--data_path DATA_PATH]
+               [--batch_size BATCH_SIZE] [--num_workers NUM_WORKERS] [--beta_1 BETA_1] [--lr LR]
+               [--max_epochs MAX_EPOCHS] [--z_dim Z_DIM] [--g_filters G_FILTERS] [--d_filters D_FILTERS]
 
 optional arguments:
   -h, --help            show this help message and exit
-  --use_amp             use torch.cuda.amp for automatic mixed precision
+  --use_amp             use torch.cuda.amp for automatic mixed precision. Default: False
   --resume_from RESUME_FROM
-                        path to the checkpoint file to resume, can also url
-                        starting with https (None)
-  --seed SEED           seed to use in ignite.utils.manual_seed() (666)
-  --verbose             use logging.INFO in ignite.utils.setup_logger
-  --backend BACKEND     backend to use for distributed training (None)
+                        path to the checkpoint file to resume, can also url starting with https. Default:
+                        None
+  --seed SEED           seed to use in ignite.utils.manual_seed(). Default: 666
+  --verbose             use logging.INFO in ignite.utils.setup_logger. Default: False
+  --backend BACKEND     backend to use for distributed training. Default: None
   --nproc_per_node NPROC_PER_NODE
-                        number of processes to launch on each node, for GPU
-                        training this is recommended to be set to the number
-                        of GPUs in your system so that each process can be
-                        bound to a single GPU (None)
-  --nnodes NNODES       number of nodes to use for distributed training (None)
+                        number of processes to launch on each node, for GPU training this is recommended to
+                        be set to the number of GPUs in your system so that each process can be bound to a
+                        single GPU. Default: None
   --node_rank NODE_RANK
-                        rank of the node for multi-node distributed training
-                        (None)
+                        rank of the node for multi-node distributed training. Default: None
+  --nnodes NNODES       number of nodes to use for distributed training. Default: None
   --master_addr MASTER_ADDR
-                        master node TCP/IP address for torch native backends
-                        (None)
+                        master node TCP/IP address for torch native backends. Default: None
   --master_port MASTER_PORT
-                        master node port for torch native backends (None)
+                        master node port for torch native backends. Default: None
+  --epoch_length EPOCH_LENGTH
+                        epoch_length of Engine.run(). Default: None
   --save_every_iters SAVE_EVERY_ITERS
-                        Saving iteration interval (1000)
-  --n_saved N_SAVED     number of best models to store (2)
+                        Saving iteration interval. Default: 1000
+  --n_saved N_SAVED     number of best models to store. Default: 2
   --log_every_iters LOG_EVERY_ITERS
-                        logging interval for iteration progress bar (100)
+                        logging interval for iteration progress bar. Default: 100
   --with_pbars WITH_PBARS
-                        show epoch-wise and iteration-wise progress bars
-                        (True)
+                        show epoch-wise and iteration-wise progress bars. Default: False
   --with_pbar_on_iters WITH_PBAR_ON_ITERS
-                        show iteration progress bar or not (True)
+                        show iteration progress bar or not. Default: True
   --stop_on_nan STOP_ON_NAN
-                        stop the training if engine output contains NaN/inf
-                        values (True)
+                        stop the training if engine output contains NaN/inf values. Default: True
   --clear_cuda_cache CLEAR_CUDA_CACHE
-                        clear cuda cache every end of epoch (True)
+                        clear cuda cache every end of epoch. Default: True
   --with_gpu_stats WITH_GPU_STATS
-                        show gpu information, requires pynvml (False)
-  --patience PATIENCE   number of events to wait if no improvement and then
-                        stop the training (None)
+                        show gpu information, requires pynvml. Default: False
+  --patience PATIENCE   number of events to wait if no improvement and then stop the training. Default: None
   --limit_sec LIMIT_SEC
-                        maximum time before training terminates in seconds
-                        (None)
+                        maximum time before training terminates in seconds. Default: None
   --output_dir OUTPUT_DIR
-                        directory to save all outputs (./logs)
+                        directory to save all outputs. Default: ./logs
   --logger_log_every_iters LOGGER_LOG_EVERY_ITERS
-                        logging interval for experiment tracking system (100)
+                        logging interval for experiment tracking system. Default: 100
   --dataset {cifar10,lsun,imagenet,folder,lfw,fake,mnist}
-                        dataset to use (cifar10)
+                        dataset to use. Default: cifar10
   --data_path DATA_PATH
-                        datasets path (./)
+                        datasets path. Default: ./
   --batch_size BATCH_SIZE
-                        will be equally divided by number of GPUs if in
-                        distributed (4)
+                        will be equally divided by number of GPUs if in distributed. Default: 16
   --num_workers NUM_WORKERS
-                        num_workers for DataLoader (2)
-  --beta_1 BETA_1       beta_1 for Adam optimizer (0.5)
-  --lr LR               learning rate used by torch.optim.* (0.001)
+                        num_workers for DataLoader. Default: 2
+  --beta_1 BETA_1       beta_1 for Adam optimizer. Default: 0.5
+  --lr LR               learning rate used by torch.optim.*. Default: 0.001
   --max_epochs MAX_EPOCHS
-                        max_epochs of ignite.Engine.run() for training (2)
-  --z_dim Z_DIM         size of the latent z vector (100)
+                        max_epochs of ignite.Engine.run() for training. Default: 5
+  --z_dim Z_DIM         size of the latent z vector. Default: 100
   --g_filters G_FILTERS
-                        number of filters in the second-to-last generator
-                        deconv layer (64)
+                        number of filters in the second-to-last generator deconv layer. Default: 64
   --d_filters D_FILTERS
-                        number of filters in first discriminator conv layer
-                        (64)
+                        number of filters in first discriminator conv layer. Default: 64
 ```

templates/gan/config.py

@@ -6,56 +6,56 @@
         "default": "{{ dataset }}",
         "type": str,
         "choices": ["cifar10", "lsun", "imagenet", "folder", "lfw", "fake", "mnist"],
-        "help": "dataset to use ({{ dataset }})",
+        "help": "dataset to use. Default: %(default)s",
     },
     "data_path": {
         "default": "{{ data_path }}",
         "type": str,
-        "help": "datasets path ({{ data_path }})",
+        "help": "datasets path. Default: %(default)s",
     },
     # dataloader options
     "batch_size": {
         "default": {{batch_size}},
         "type": int,
-        "help": "will be equally divided by number of GPUs if in distributed ({{ batch_size }})",
+        "help": "will be equally divided by number of GPUs if in distributed. Default: %(default)s",
     },
     "num_workers": {
         "default": {{num_workers}},
         "type": int,
-        "help": "num_workers for DataLoader ({{ num_workers }})",
+        "help": "num_workers for DataLoader. Default: %(default)s",
     },
     # optimizer options
     "beta_1": {
         "default": {{beta_1}},
         "type": float,
-        "help": "beta_1 for Adam optimizer ({{ beta_1 }})",
+        "help": "beta_1 for Adam optimizer. Default: %(default)s",
     },
     "lr": {
         "default": {{lr}},
         "type": float,
-        "help": "learning rate used by torch.optim.* ({{ lr }})",
+        "help": "learning rate used by torch.optim.*. Default: %(default)s",
     },
     # training options
     "max_epochs": {
         "default": {{max_epochs}},
         "type": int,
-        "help": "max_epochs of ignite.Engine.run() for training ({{ max_epochs }})",
+        "help": "max_epochs of ignite.Engine.run() for training. Default: %(default)s",
     },
     # model options
     "z_dim": {
         "default": {{z_dim}},
         "type": int,
-        "help": "size of the latent z vector ({{ z_dim }})",
+        "help": "size of the latent z vector. Default: %(default)s",
     },
     "g_filters": {
         "default": {{g_filters}},
         "type": int,
-        "help": "number of filters in the second-to-last generator deconv layer ({{ g_filters }})",
+        "help": "number of filters in the second-to-last generator deconv layer. Default: %(default)s",
     },
     "d_filters": {
         "default": {{d_filters}},
         "type": int,
-        "help": "number of filters in first discriminator conv layer ({{ d_filters }})",
+        "help": "number of filters in first discriminator conv layer. Default: %(default)s",
     },
 }