chore(deps): update dependency torchvision to ^0.22.0 #9
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This PR contains the following updates:
^0.12.0->^0.22.0Release Notes
pytorch/vision (torchvision)
v0.22.0+cpuCompare Source
v0.22.0: Torchvision 0.22 releaseCompare Source
Key info
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_jpegon CUDA (#8929)[transforms]
pin_memory()now preservesTVTensorclass and metadata (#8921)Improvements
[datasets] Most datasets now support a
loaderparameter, which allow you to decode images directly into tensors withtorchvision.io.decode_image(), instead of relying on PIL. This should lead to faster pipelines! (#8945, #8972, #8939, #8922)[datasets] Add
classesattribute to theFlowers102dataset (#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_boxesis high (#8766, #8925)[ops] Add
roi_alignnondeterministic 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+cpuCompare Source
v0.21.0: Torchvision 0.21 releaseCompare Source
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 astorchvision.io.decode_heic()andtorchvision.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_colorsargument todraw_bounding_boxes(#8578)[models] Add
__deepcopy__support forDualGraphModule(#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_hueon 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+cpuCompare Source
v0.20.1Compare Source
v0.20.0+cpuCompare Source
v0.20.0: Torchvision 0.20 releaseCompare Source
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 orpathlib.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_blurbackprop (#8486)[transforms] Expose
transforms.v2utils 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+cpuCompare Source
v0.19.1: TorchVision 0.19.1 ReleaseCompare Source
This is a patch release, which is compatible with PyTorch 2.4.1. There are no new features added.
v0.19.0+cpuCompare Source
v0.19.0: Torchvision 0.19 releaseCompare Source
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), andtorchvision.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 tomax_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_alphain classification training references (#8448)[utils] Allow
K=1indraw_keypoints(#8439)New Features
[io] Add decoder for GIF images (
decode_gif(),decode_image(),read_image()) (#8406, #8419)[transforms] Add
GaussianNoisetransform (#8381)Improvements
[transforms] Allow v2
Resizeto resize longer edge exactly tomax_size(#8459)[transforms] Add
min_areaparameter toSanitizeBoundingBox(#7735)[transforms] Make
adjust_hue()work withnumpy 2.0(#8463)[transforms] Enable one-hot-encoded labels in
MixUpandCutMix(#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: acceptBytesIOobjects onpyavbackend (#8442)[io] Add compatibility with FFMPEG 7.0 (#8408)
[datasets] Add extra to install
gdown(#8430)[datasets] Support encoded
RLEformat in forCOCOsegmentations (#8387)[datasets] Added binary cat vs dog classification target type to Oxford pet dataset (#8388)
[datasets] Return labels for
FER2013if possible (#8452)[ops] Force use of
torch.compileon deterministicroi_alignimplementation (#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+cpuCompare Source
v0.18.1: TorchVision 0.18.1 ReleaseCompare Source
This is a patch release, which is compatible with PyTorch 2.3.1. There are no new features added.
v0.18.0+cpuCompare Source
v0.18.0: TorchVision 0.18 ReleaseCompare Source
BC-Breaking changes
[datasets]
gdownis now a required dependency for downloading datasets that are on Google Drive. This change was actually introduced in0.17.1(repeated here for visibility) (#8237)[datasets] The
StanfordCarsdataset isn’t available for download anymore. Please follow these instructions to manually download it (#8309, #8324)[transforms]
to_grayscaleand corresponding transform now always return 3 channels whennum_output_channels=3(#8229)Bug Fixes
[datasets] Fix download URL of
EMNISTdataset (#8350)[datasets] Fix root path expansion in
Kittidataset (#8164)[models] Fix default momentum value of
BatchNorm2dinMaxViTfrom 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.Pathsupport to datasets and io utilities. (#8196, #8200, #8314, #8321)[datasets] Added
allow_emptyparameter toImageFolderand related utils to support empty classes during image discovery (#8311)[datasets] Raise proper error in
CocoDetectionwhen 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.VideoReaderwithpyavbackend (#8173)[transforms] Allow
SanitizeBoundingBoxesto sanitize more than labels (#8319)[transforms] Add
sanitize_bounding_boxeskernel/functional (#8308)[transforms] Make
perspectivemore numerically stable (#8249)[transforms] Allow 2D numpy arrays as inputs for
to_image(#8256)[transforms] Speed-up
rotatefor 90, 180, 270 degrees (#8295)[transforms] Enabled torch compile on
affinetransform (#8218)[transforms] Avoid some graph breaks in transforms (#8171)
[utils] Add float support to
draw_keypoints(#8276)[utils] Add
visibilityparameter todraw_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+cpuCompare Source
v0.17.2: TorchVision 0.17.2 ReleaseCompare Source
This is a patch release, which is compatible with PyTorch 2.2.2. There are no new features added.
v0.17.1+cpuCompare Source
v0.17.1: TorchVision 0.17.1 ReleaseCompare Source
This is a patch release, which is compatible with PyTorch 2.2.1.
Bug Fixes
gdowndependency to support downloading datasets from Google Drive (https://github.com/pytorch/vision/pull/8237)convert_bounding_box_formatwhen passing string parameters (https://github.com/pytorch/vision/issues/8258)v0.17.0+cpuCompare Source
v0.17.0: TorchVision 0.17 ReleaseCompare Source
Highlights
The V2 transforms are now stable!
The
torchvision.transforms.v2namespace 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()supportWe 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_poolanddeform_conv_2d) are now compatible withtorch.compileand dynamic shapes.On the transforms side, the majority of low-level kernels (like
resize_image()orcrop_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.Detailed Changes
Breaking changes / Finalizing deprecations
antialiasparameter 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)torchvision.transforms.functional_tensor.pyandtorchvision.transforms.functional_pil.pymodules, as these had been deprecated for a while. Use the public functionals fromtorchvision.transforms.v2.functionalinstead. (#7953)to_pil_imagenow 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 withtorch.compileand dynamic shapes (#8061, #8049, #8062, #8063, #7942, #7944)[models] Allow custom
atrous_ratesfor 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, #8133, #8160)
[Code Quality] (#8077, #8070, #8004, #8113,
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:
Aleksei Nikiforov. Alex Wei, Andrey Talman, Chunyuan WU, CptCaptain, Edward Z. Yang, Gu Wang, Haochen Yu, Huy Do, Jeff Daily, Josh Levy-Kramer, moto, Nicolas Hug, NVS Abhilash, Omkar Salpekar, Philip Meier, Sergii Dymchenko, Siddharth Singh, Thiago Crepaldi, Thomas Fritz, TilmannR, vfdev-5, Zeeshan Khan Suri.
v0.16.2+cpuCompare Source
v0.16.2: TorchVision 0.16.2 ReleaseCompare Source
This is a patch release, which is compatible with PyTorch 2.1.2. There are no new features added.
v0.16.1+cpuCompare Source
v0.16.1: TorchVision 0.16.1 ReleaseCompare Source
This is a minor release that only contains bug-fixes
Bug Fixes
v0.16.0+cpuCompare Source
v0.16.0: TorchVision 0.16 - Transforms speedups, CutMix/MixUp, and MPS support!Compare Source
Highlights
[BETA] Transforms and augmentations
Major speedups
The new transforms in
torchvision.transforms.v2support image classification, segmentation, detection, and video tasks. They are now 10%-40% faster than before! This is mostly achieved thanks to 2X-4X improvements made tov2.Resize(), which now supports nativeuint8tensors for Bilinear and Bicubic mode. Output results are also now closer to PIL's! Check out our performance recommendations to learn more.Additionally,
torchvisionnow ships withlibjpeg-turboinstead oflibjpeg, which should significantly speed-up the jpeg decoding utilities (read_image,decode_jpeg), and avoid compatibility issues with PIL.CutMix and MixUp
Long-awaited support for the
CutMixandMixUpaugmentations is now here! Check our tutorial to learn how to use them.Towards stable V2 transforms
In the previous release 0.15 we BETA-released a new set of transforms in
torchvision.transforms.v2with native support for tasks like segmentation, detection, or videos. We have now stabilized the design decisions of these transforms and made further improvements in terms of speedups, usability, new transforms support, etc.We're keeping the
torchvision.transforms.v2andtorchvision.tv_tensorsnamespaces as BETA until 0.17 out of precaution, but we do not expect disruptive API changes in the future.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.
[BETA] MPS support
The
nmsand roi-align kernels (roi_align,roi_pool,ps_roi_align,ps_roi_pool) now support MPS. Thanks to Li-Huai (Allan) Lin for this contribution!Detailed Changes
Deprecations / Breaking changes
All changes below happened in the
transforms.v2anddatapointsnamespaces, which were BETA and protected with a warning. We do not expect other disruptive changes to these APIs moving forward![transforms.v2]
to_grayscale()is not deprecated anymore (#7707)[transforms.v2] Renaming:
torchvision.datapoints.Datapoint->torchvision.tv_tensors.TVTensor(#7904, #7894)[transforms.v2] Renaming:
BoundingBox->BoundingBoxes(#7778)[transforms.v2] Renaming:
BoundingBoxes.spatial_size->BoundingBoxes.canvas_size(#7734)[transforms.v2] All public method on
TVTensorclasses (previously:Datapointclasses) were removed[transforms.v2]
transforms.v2.utilsis now private. (#7863)[transforms.v2] Remove
wrap_likeclass method and addtv_tensors.wrap()function (#7832)New Features
[transforms.v2] Add support for
MixUpandCutMix(#7731, #7784)[transforms.v2] Add
PermuteChannelstransform (#7624)[transforms.v2] Add
ToPureTensortransform (#7823)[ops] Add MPS kernels for
nmsandroiops (#7643)Improvements
[io] Added support for CMYK images in
decode_jpeg(#7741)[io] Package torchvision with
libjpeg-turboinstead oflibjpeg(#7672, #7840)[models] Downloaded weights are now sha256-validated (#7219)
[transforms.v2] Massive
Resizespeed-up by adding nativeuint8support for bilinear and bicubic modes (#7557, #7668)[transforms.v2] Enforce pickleability for v2 transforms and wrapped datasets (#7860)
[transforms.v2] Allow catch-all "others" key in
filldicts. (#7779)[transforms.v2] Allow passthrough for
Resize(#7521)[transforms.v2] Add
scaleoption toToDtype. RemoveConvertDtype. (#7759, #7862)[transforms.v2] Improve UX for
Compose(#7758)[transforms.v2] Allow users to choose whether to return
TVTensorsubclasses or pureTensor(#7825)[transforms.v2] Remove import-time warning for v2 namespaces (#7853, 7897)
[transforms.v2] Speedup
hsv2rgb(#7754)[models] Add
filterparameters tolist_models()(#7718)[models] Assert
RAFTinput resolution is 128 x 128 or higher (#7339)[ops] Replaced
gpuAtomicAddbyfastAtomicAdd(#7596)[utils] Add GPU support for
draw_segmentation_masks(#7684)[ops] Add deterministic, pure-Python
roi_alignimplementation (#7587)[tv_tensors] Make
TVTensorsdeepcopyable (#7701)[datasets] Only return small set of targets by default from dataset wrapper (#7488)
[references] Added support for v2 transforms and
tensors/tv_tensorsbackends (#7732, #7511, #7869, #7665, #7629, #7743, #7724, #7742)[doc] A lot of documentation improvements (#7503, #7843, #7845, #7836, #7830, #7826, [#
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