Update dependency torchvision to v0.24.1

[renovate]

Note: This PR body was truncated due to platform limits.

This PR contains the following updates:

Package	Change	Age	Confidence
torchvision	==0.7.0 → ==0.24.1

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Release Notes

pytorch/vision (torchvision)

v0.24.1: TorchVision 0.24.1 Release

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This is a patch release, which is compatible with PyTorch 2.9.1. There are no new features added.

v0.24.0: Torchvision 0.24 release

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Improving KeyPoints and Rotated boxes support!

We are releasing a tutorial on how to use KeyPoint transformations in our Transforms on KeyPoints with a preview below!

[!NOTE]
These features are still in BETA status. The API are unlikely to change, but we may have some rough edges and we may make some slight bug fixes in future releases. Please let us know if you encounter any issue!

Detailed changes

Improvements

[ops] Improve efficiency of the box_area and box_iou functions by eliminating the intermediate to "xyxy" conversion (#​8992)
[ops] Update box operations to support arbitrary batch dimensions (#​9058)
[utils] Add control for the background color of label text boxes (#​9204)
[transforms] Add support for uint8 image format to the GaussianNoise transform (#​9169)
[transforms] Accelerate the resize transform on machines with AVX512 (#​9190)
[transforms] Better error handling in RandomApply for empty list of transforms (#​9130)
[documentation] New tutorial for KeyPoints transforms (#​9209)
[documentation] Various documentation improvements (#​9186, #​9180, #​9172)
[code quality] Various code quality improvements (#​9193, #​9161, #​9201, #​9218, #​9160)

Bug Fixes and deprecations

[transforms] Fix output of some geometric transforms for rotated boxes (#​9181, #​9175)
[transforms] Fix clamping for key points and add sanitization feature (#​9236, #​9235)
[datasets] Update download links to official repo for the Caltech-101 & 256 datasets (#​9205)
[ops] Raise error in drop_block[2,3]d by enforcing odd-sized block sizes (#​9157)
[io] Removed deprecated video_reader video decoding backend. (#​9208)

Contributors

🎉 We're grateful for our community, which helps us improve Torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release: @​alperenunlu, @​AndreiMoraru123, @​atalman, @​AntoineSimoulin, @​5had3z, @​dcasbol, @​GdoongMathew, @​hrsvrn, @​JonasKlotz, @​zklaus, @​NicolasHug, @​rdong8, @​scotts, @​get9, @​diaz-esparza, @​ZainRizvi, @​Callidior, and @​pytorch/xla-devs

v0.23.0: Torchvision 0.23 release

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Highlight - Transforming KeyPoints and Rotated boxes!

📦 This release is introducing two highly popular feature requests: Transforms support for KeyPoints and Rotated Bounding Boxes!

• Rotated Bounding Boxes provide a tighter fit and alignment with rotated and elongated objects, which improves the localization, reduces overlap in densely packed images, and improves isolation of objects in crowded scenes.
• KeyPoints offer a robust and accurate way to identify and locate specific points of interest within an image or video frame. These features aim at improving developer experience to implement use cases, including detecting & tracking objects, estimating pose, analyzing facial expressions, and creating augmented reality experiences.

We illustrated the use of Rotated Bounding Boxes below. You can expect keypoints and rotated boxes to work with all existing torchvision transforms in torchvision.transforms.v2. You can find some examples on how to use those transformations in our Transforms on Rotated Bounding Boxes tutorials.

[!NOTE]
These features are released in BETA status. The API are unlikely to change, but we may have some rough edges and we may make some slight bug fixes in future releases. Please let us know if you encounter any issue!

Detailed changes

New Features

[transforms] Added support for BoundingBoxes formats and transforms (#​9104, #​9084, #​9095, #​9138)
[transforms] Added the KeyPoints to TVTensor and support for transforms (#​8817)

Improvements

[utils] Add label background to draw_bounding_boxes (#​9018)
[MPS] Add deformable conv2d kernel support on MPS (#​9017, #​9115)
[documentation] Various documentation improvements (#​9063, #​9119, #​9083, #​9105, #​9106, #​9145)
[code-quality] Bunch of code quality improvements (#​9087, #​9093, #​8814, #​9035, #​9120, #​9080, #​9027, #​9062, #​9117, #​9024, #​9032)

Bug Fixes

[datasets] Fix COCO dataset to avoid issue when copying the dataset results (#​9107)
[datasets] Raise error when download=True for LFW dataset, which is not available for download anymore #​9040)
[tv_tensors] Add error message when setting 1D tensor ToImage() (#​9114)
[io] Warn when webp is asked to decode into grayscale (#​9101)

Contributors

🎉 We're grateful for our community, which helps us improve Torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release: @​AlannaBurke, @​Alexandre-SCHOEPP, @​atalman, @​AntoineSimoulin, @​BoyuanFeng, @​cyyever, @​elmuz, @​emmanuel-ferdman, @​hmk114, @​Isalia20, @​NicolasHug, @​malfet, @​chengolivia, @​RhutvikH, @​hvaara, @​scotts, @​alinpahontu2912, @​tsahiasher, and @​youcefouadjer.

v0.22.1: TorchVision 0.22.1 Release

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Key info

⚠️ We are updating the areas that TorchVision will be prioritizing in the future. Please take a look at #​9036 for more details.

⚠️ We are deprecating the video decoding and encoding capabilities of TorchVision, and they will be removed soon in version 0.25 (aimed for end of 2025). We encourage users to migrate existing video decoding code to rely on TorchCodec project, where we are consolidating all media decoding/encoding functionalities of PyTorch.

This is a patch release, which is compatible with PyTorch 2.7.1. There are no new features added.

v0.22.0: Torchvision 0.22 release

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Key info

⚠️ We are updating the areas that TorchVision will be prioritizing in the future. Please take a look at #​9036 for more details.

⚠️ We are deprecating the video decoding and encoding capabilities of TorchVision, and they will be removed soon in version 0.25 (aimed for end of 2025). We encourage users to migrate existing video decoding code to rely on TorchCodec project, where we are consolidating all media decoding/encoding functionalities of PyTorch.

Detailed Changes

Deprecations

[io] Video decoding and encoding capabilities are deprecated and will be removed soon in 0.25! Please migrate to TorchCodec! (#​8997)

Bug Fixes

[io] Fix sync bug with encode_jpeg on CUDA (#​8929)
[transforms] pin_memory() now preserves TVTensor class and metadata (#​8921)

Improvements

[datasets] Most datasets now support a loader parameter, which allow you to decode images directly into tensors with torchvision.io.decode_image(), instead of relying on PIL. This should lead to faster pipelines! (#​8945, #​8972, #​8939, #​8922)
[datasets] Add classes attribute to the Flowers102 dataset (#​8838)
[datasets] Added 'test' split support for Places365 dataset (#​8928)
[datasets] Reduce output log on MNIST (#​8865)
[ops] Perf: greatly speed-up NMS on CUDA when num_boxes is high (#​8766, #​8925)
[ops] Add roi_align nondeterministic support for XPU (#​8931)
[all] Improvements on input checks and error messages (#​8959, #​8994, #​8944, #​8995, #​8993, #​8866, #​8882, #​8851, #​8844, #​8991)
[build] Various build improvements / platforms support (#​8913, #​8933, #​8936, #​8792)
[docs] Various documentation improvements (#​8843, #​8860, #​9014, #​9015, #​8932)
[misc] Other non-user-facing changes (#​8872, #​8982, #​8976, #​8935, #​8977, #​8978, #​8963, #​8975, #​8974, #​8950, #​8970, #​8924, #​8964, #​8996, #​8920, #​8873, #​8876, #​8885, #​8890, #​8901, #​8999, #​8998, #​8973, #​8897, #​9007, #​8852)

Contributors

We're grateful for our community, which helps us improve torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:

Aditya Kamath, Alexandre Ghelfi, PhD, Alfredo Tupone, amdfaa, Andrey Talman, Antoine Simoulin, Aurélien Geron, bjarzemb, deekay42, Frost Mitchell, frost-intel , GdoongMathew, Hangxing Wei, Huy Do, Nicolas Hug, Nikita Shulga, Noopur, Ruben, tvukovic-amd, Wenchen Li, Wieland Morgenstern , Yichen Yan, Yonghye Kwon, Zain Rizvi

v0.21.0: Torchvision 0.21 release

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Highlights

Image decoding

Torchvision continues to improve its image decoding capabilities. For this version, we added support for HEIC and AVIF image formats. Things are a bit different this time: to enable it, you'll need to pip install torchvision-extra-decoders, and the decoders are available in torchvision as torchvision.io.decode_heic() and torchvision.io.decode_avif(). This is still experimental / BETA, so let us know if you encounter any issue.

Read more in our docs!

Detailed changes

New Features

[io] Add support for decoding AVIF and HEIC image formats (#​8671)

Improvements

[datasets] Don't error when dataset is already downloaded (#​8691)
[datasets] Don't print when dataset is already downloaded (#​8681)
[datasets] remove printing info in datasets (#​8683)
[utils] Add label_colors argument to draw_bounding_boxes (#​8578)
[models] Add __deepcopy__ support for DualGraphModule (#​8708)
[Docs] Various documentation improvements (#​8798, #​8709, #​8576, #​8620, #​8846, #​8758)
[Code quality] Various code quality improvements (#​8757, #​8755, #​8754, #​8689, #​8719, #​8772, #​8774, #​8791, #​8705)

Bug Fixes

[io] Fix memory leak in decode_webp (#​8712)
[io] Fix pyav 14 compatibility error (#​8776)
[models] Fix order of auxiliary networks in googlenet.py (#​8743)
[transforms] Fix adjust_hue on ARM (#​8618)
[reference scripts] Fix error when loading the cached dataset in video classification reference(#​8727)
[build] fix CUDA build with NVCC_FLAGS in env (#​8692)

Tracked Regressions

[build] aarch64 builds are build with manylinux_2_34_aarch64 tag according to auditwheel check (#​8883)

Contributors

We're grateful for our community, which helps us improve torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:

amdfaa Andreas Floros, Andrey Talman , Beh Chuen Yang, David Miguel Susano Pinto, GdoongMathew, Jason Chou, Li-Huai (Allan) Lin, Maohua Li, Nicolas Hug , pblwk, R. Yao, sclarkson, vfdev, Ștefan Talpalaru

v0.20.1

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v0.20.0: Torchvision 0.20 release

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Highlights

Encoding / Decoding images

Torchvision is further extending its encoding/decoding capabilities. For this version, we added a WEBP decoder, and a batch JPEG decoder on CUDA GPUs, which can lead to 10X speed-ups over CPU decoding.

We have also improved the UX of our decoding APIs to be more user-friendly. The main entry point is now torchvision.io.decode_image(), and it can take as input either a path (as str or pathlib.Path), or a tensor containing the raw encoded data.

Read more on the docs!

We also added support for HEIC and AVIF decoding, but these are currently only available when building from source. We are working on making those available directly in the upcoming releases. Stay tuned!

Detailed changes

Bug Fixes

[datasets] Update URL of SBDataset train_noval (#​8551)
[datasets] EuroSAT: fix SSL certificate issues (#​8563)
[io] Check average_rate availability in video reader (#​8548)

New Features

[io] Add batch JPEG GPU decoding (decode_jpeg()) (#​8496)
[io] Add WEBP image decoder: decode_image(), decode_webp() (#​8527, #​8612, #​8610)
[io] Add HEIC and AVIF decoders, only available when building from source (#​8597, #​8596, #​8647, #​8613, #​8621)

Improvements

[io] Add support for decoding 16bits png (#​8524)
[io] Allow decoding functions to accept the mode parameter as a string (#​8627)
[io] Allow decode_image() to support paths (#​8624)
[io] Automatically send video to CPU in io.write_video (#​8537)
[datasets] Better progress bar for file downloading (#​8556)
[datasets] Add Path type annotation for ImageFolder (#​8526)
[ops] Register nms and roi_align Autocast policy for PyTorch Intel GPU backend (#​8541)
[transforms] Use Sequence for parameters type checking in transforms.RandomErase (#​8615)
[transforms] Support v2.functional.gaussian_blur backprop (#​8486)
[transforms] Expose transforms.v2 utils for writing custom transforms. (#​8670)
[utils] Fix f-string in color error message (#​8639)
[packaging] Revamped and improved debuggability of setup.py build (#​8535, #​8581, #​8581, #​8582, #​8590, #​8533, #​8528, #​8659)
[Documentation] Various documentation improvements (#​8605, #​8611, #​8506, #​8507, #​8539, #​8512, #​8513, #​8583, #​8633)
[tests] Various tests improvements (#​8580, #​8553, #​8523, #​8617, #​8518, #​8579, #​8558, #​8617, #​8641)
[code quality] Various code quality improvements (#​8552, #​8555, #​8516, #​8526, #​8602, #​8615, #​8639, #​8532)
[ci] #​8562, #​8644, #​8592, #​8542, #​8594, #​8530, #​8656

Contributors

We're grateful for our community, which helps us improve torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:

Adam J. Stewart, AJS Payne, Andreas Floros, Andrey Talman, Bhavay Malhotra, Brizar, deekay42, Ehsan, Feng Yuan, Joseph Macaranas, Martin, Masahiro Hiramori, Nicolas Hug, Nikita Shulga , Sergii Dymchenko, Stefan Baumann, venkatram-dev, Wang, Chuanqi

v0.19.1: TorchVision 0.19.1 Release

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This is a patch release, which is compatible with PyTorch 2.4.1. There are no new features added.

v0.19.0: Torchvision 0.19 release

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Highlights

Encoding / Decoding images

Torchvision is extending its encoding/decoding capabilities. For this version, we added a GIF decoder which is available as torchvision.io.decode_gif(raw_tensor), torchvision.io.decode_image(raw_tensor), and torchvision.io.read_image(path_to_image).

We also added support for jpeg GPU encoding in torchvision.io.encode_jpeg(). This is 10X faster than the existing CPU jpeg encoder.

Read more on the docs!

Stay tuned for more improvements coming in the next versions. We plan to improve jpeg GPU decoding, and add more image decoders (webp in particular).

Resizing according to the longest edge of an image

It is now possible to resize images by setting torchvision.transforms.v2.Resize(max_size=N): this will resize the longest edge of the image exactly to max_size, making sure the image dimension don't exceed this value. Read more on the docs!

Detailed changes

Bug Fixes

[datasets] SBDataset: Only download noval file when image_set='train_noval' (#​8475)
[datasets] Update the download url in class EMNIST (#​8350)
[io] Fix compilation error when there is no libjpeg (#​8342)
[reference scripts] Fix use of cutmix_alpha in classification training references (#​8448)
[utils] Allow K=1 in draw_keypoints (#​8439)

New Features

[io] Add decoder for GIF images (decode_gif(), decode_image(),read_image()) (#​8406, #​8419)
[transforms] Add GaussianNoise transform (#​8381)

Improvements

[transforms] Allow v2 Resize to resize longer edge exactly to max_size (#​8459)
[transforms] Add min_area parameter to SanitizeBoundingBox (#​7735)
[transforms] Make adjust_hue() work with numpy 2.0 (#​8463)
[transforms] Enable one-hot-encoded labels in MixUp and CutMix (#​8427)
[transforms] Create kernel on-device for transforms.functional.gaussian_blur (#​8426)
[io] Adding GPU acceleration to encode_jpeg (10X faster than CPU encoder) (#​8391)
[io] read_video: accept BytesIO objects on pyav backend (#​8442)
[io] Add compatibility with FFMPEG 7.0 (#​8408)
[datasets] Add extra to install gdown (#​8430)
[datasets] Support encoded RLE format in for COCO segmentations (#​8387)
[datasets] Added binary cat vs dog classification target type to Oxford pet dataset (#​8388)
[datasets] Return labels for FER2013 if possible (#​8452)
[ops] Force use of torch.compile on deterministic roi_align implementation (#​8436)
[utils] add float support to utils.draw_bounding_boxes() (#​8328)
[feature_extraction] Add concrete_args to feature extraction tracing. (#​8393)
[Docs] Various documentation improvements (#​8429, #​8467, #​8469, #​8332, #​8262, #​8341, #​8392, #​8386, #​8385, #​8411).
[Tests] Various testing improvements (#​8454, #​8418, #​8480, #​8455)
[Code quality] Various code quality improvements (#​8404, #​8402, #​8345, #​8335, #​8481, #​8334, #​8384, #​8451, #​8470, #​8413, #​8414, #​8416, #​8412)

Contributors

We're grateful for our community, which helps us improve torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:

Adam J. Stewart ahmadsharif1, AJS Payne, Andrew Lingg, Andrey Talman, Anner, Antoine Broyelle, cdzhan, deekay42, drhead, Edward Z. Yang, Emin Orhan, Fangjun Kuang, G, haarisr, Huy Do, Jack Newsom, JavaZero, Mahdi Lamb, Mantas, Nicolas Hug, Nicolas Hug , nihui, Richard Barnes , Richard Zou, Richie Bendall, Robert-André Mauchin, Ross Wightman, Siddarth Ijju, vfdev

v0.18.1: TorchVision 0.18.1 Release

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This is a patch release, which is compatible with PyTorch 2.3.1. There are no new features added.

v0.18.0: TorchVision 0.18 Release

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BC-Breaking changes

[datasets] gdown is now a required dependency for downloading datasets that are on Google Drive. This change was actually introduced in 0.17.1 (repeated here for visibility) (#​8237)
[datasets] The StanfordCars dataset isn’t available for download anymore. Please follow these instructions to manually download it (#​8309, #​8324)
[transforms] to_grayscale and corresponding transform now always return 3 channels when num_output_channels=3 (#​8229)

Bug Fixes

[datasets] Fix download URL of EMNIST dataset (#​8350)
[datasets] Fix root path expansion in Kitti dataset (#​8164)
[models] Fix default momentum value of BatchNorm2d in MaxViT from 0.99 to 0.01 (#​8312)
[reference scripts] Fix CutMix and MixUp arguments (#​8287)
[MPS, build] Link essential libraries in cmake (#​8230)
[build] Fix build with ffmpeg 6.0 (#​8096)

New Features

[transforms] New GrayscaleToRgb transform (#​8247)
[transforms] New JPEG augmentation transform (#​8316)

Improvements

[datasets, io] Added pathlib.Path support to datasets and io utilities. (#​8196, #​8200, #​8314, #​8321)
[datasets] Added allow_empty parameter to ImageFolder and related utils to support empty classes during image discovery (#​8311)
[datasets] Raise proper error in CocoDetection when a slice is passed (#​8227)
[io] Added support for EXIF orientation in JPEG and PNG decoders (#​8303, #​8279, #​8342, #​8302)
[io] Avoiding unnecessary copies on io.VideoReader with pyav backend (#​8173)
[transforms] Allow SanitizeBoundingBoxes to sanitize more than labels (#​8319)
[transforms] Add sanitize_bounding_boxes kernel/functional (#​8308)
[transforms] Make perspective more numerically stable (#​8249)
[transforms] Allow 2D numpy arrays as inputs for to_image (#​8256)
[transforms] Speed-up rotate for 90, 180, 270 degrees (#​8295)
[transforms] Enabled torch compile on affine transform (#​8218)
[transforms] Avoid some graph breaks in transforms (#​8171)
[utils] Add float support to draw_keypoints (#​8276)
[utils] Add visibility parameter to draw_keypoints (#​8225)
[utils] Add float support to draw_segmentation_masks (#​8150)
[utils] Better show overlap section of masks in draw_segmentation_masks (#​8213)
[Docs] Various documentation improvements (#​8341, #​8332, #​8198, #​8318, #​8202, #​8246, #​8208, #​8231, #​8300, #​8197)
[code quality] Various code quality improvements (#​8273, #​8335, #​8234, #​8345, #​8334, #​8119, #​8251, #​8329, #​8217, #​8180, #​8105, #​8280, #​8161, #​8313)

Contributors

We're grateful for our community, which helps us improve torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:

Adam Dangoor Ahmad Sharif , ahmadsharif1, Andrey Talman, Anner, anthony-cabacungan, Arun Sathiya, Brizar, Brizar , cdzhan, Danylo Baibak, Huy Do, Ivan Magazinnik, JavaZero, Johan Edstedt, Li-Huai (Allan) Lin, Mantas, Mark Harfouche, Mithra, Nicolas Hug, Nicolas Hug , nihui, Philip Meier, Philip Meier , RazaProdigy , Richard Barnes , Riza Velioglu, sam-watts, Santiago Castro, Sergii Dymchenko, Syed Raza, talcs, Thien Tran, Thien Tran , TilmannR, Tobias Fischer, vfdev, vfdev , Zhu Lin Ch'ng, Zoltán Böszörményi.

v0.17.2: TorchVision 0.17.2 Release

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This is a patch release, which is compatible with PyTorch 2.2.2. There are no new features added.

v0.17.1: TorchVision 0.17.1 Release

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This is a patch release, which is compatible with PyTorch 2.2.1.

Bug Fixes

• Add gdown dependency to support downloading datasets from Google Drive (#​8237)
• Fix silent correctness with convert_bounding_box_format when passing string parameters (#​8258)

v0.17.0: TorchVision 0.17 Release

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Highlights

The V2 transforms are now stable!

The torchvision.transforms.v2 namespace was still in BETA stage until now. It is now stable! Whether you’re new to Torchvision transforms, or you’re already experienced with them, we encourage you to start with Getting started with transforms v2 in order to learn more about what can be done with the new v2 transforms.

Browse our main docs for general information and performance tips. The available transforms and functionals are listed in the API reference. Additional information and tutorials can also be found in our example gallery, e.g. Transforms v2: End-to-end object detection/segmentation example or How to write your own v2 transforms.

Towards torch.compile() support

We are progressively adding support for torch.compile() to torchvision interfaces, reducing graph breaks and allowing dynamic shape.

The torchvision ops (nms, [ps_]roi_align, [ps_]roi_pool and deform_conv_2d) are now compatible with torch.compile and dynamic shapes.

On the transforms side, the majority of low-level kernels (like resize_image() or crop_image()) should compile properly without graph breaks and with dynamic shapes. We are still addressing the remaining edge-cases, moving up towards full functional support and classes, and you should expect more progress on that front with the next release.

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Detailed Changes

Breaking changes / Finalizing deprecations

• [transforms] We changed the default of the antialias parameter from None to True, in all transforms that perform resizing. This change of default has been communicated in previous versions, and should drastically reduce the amount of bugs/surprises as it aligns the tensor backend with the PIL backend. Simply put: from now on, antialias is always applied when resizing (with bilinear or bicubic modes), whether you're using tensors or PIL images. This change only affects the tensor backend, as PIL always applies antialias anyway. (#​7949)
• [transforms] We removed the torchvision.transforms.functional_tensor.py and torchvision.transforms.functional_pil.py modules, as these had been deprecated for a while. Use the public functionals from torchvision.transforms.v2.functional instead. (#​7953)
• [video] Remove deprecated path parameter to VideoReader and made src mandatory (#​8125)
• [transforms] to_pil_image now provides the same output for equivalent numpy arrays and tensor inputs (#​8097)

Bug Fixes

[datasets] Fix root path expansion in datasets.Kitti (#​8165)
[transforms] allow sequence fill for v2 AA scripted (#​7919)
[reference scripts] Fix quantized references (#​8073)
[reference scripts] Fix IoUs reported in segmentation references (#​7916)

New Features

[datasets] add Imagenette dataset (#​8139)

Improvements

[transforms] The v2 transforms are now officially stable and out of BETA stage (#​8111)
[ops] The ops ([ps_]roi_align, ps_[roi_pool], deform_conv_2d) are now compatible with torch.compile and dynamic shapes (#​8061, #​8049, #​8062, #​8063, #​7942, #​7944)
[models] Allow custom atrous_rates for deeplabv3_mobilenet_v3_large (#​8019)
[transforms] allow float fill for integer images in F.pad (#​7950)
[transforms] allow len 1 sequences for fill with PIL (#​7928)
[transforms] allow size to be generic Sequence in Resize (#​7999)
[transforms] Making root parameter optional for Vision Dataset (#​8124)
[transforms] Added support for tv tensors in torch compile for func ops (#​8110)
[transforms] Reduced number of graphs for compiled resize (#​8108)
[misc] Various fixes for S390x support (#​8149)
[Docs] Various Documentation enhancements (#​8007, #​8014, #​7940, #​7989, #​7993, #​8114, #​8117, #​8121, #​7978, #​8002, #​7957, #​7907, #​8000, #​7963)
[Tests] Various test enhancements (#​8032, #​7927, #​7933, #​7934, #​7935, #​7939, #​7946, #​7943, #​7968, #​7967, #​8033, #​7975, #​7954, #​8001, #​7962, #​8003, #​8011, #​8012, #​8013, #​8023, #​7973, #​7970, #​7976, #​8037, #​8052, #​7982, #​8145, #​8148, #​8144, #​8058, #​8057, #​7961, [#​8132](https://redirect.github.com/pytorch/vi

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