Torchvision

This is a patch release, which is compatible with [PyTorch 2.2.2](https://github.com/pytorch/pytorch/releases/tag/v2.2.2). There are no new features added.

This is a patch release, which is compatible with [PyTorch 2.2.1](https://github.com/pytorch/pytorch/releases/tag/v2.2.1).

Bug Fixes

* Add `gdown` dependency to support downloading datasets from Google Drive (https://github.com/pytorch/vision/pull/8237)
* Fix silent correctness with `convert_bounding_box_format` when passing string parameters (https://github.com/pytorch/vision/issues/8258)

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](https://pytorch.org/vision/stable/auto_examples/transforms/plot_transforms_getting_started.html#sphx-glr-auto-examples-transforms-plot-transforms-getting-started-py) in order to learn more about what can be done with the new v2 transforms.

Browse our [main docs](https://pytorch.org/vision/stable/transforms.html#) for general information and performance tips. The available transforms and functionals are listed in the [API reference](https://pytorch.org/vision/stable/transforms.html#v2-api-ref). Additional information and tutorials can also be found in our [example gallery](https://pytorch.org/vision/stable/auto_examples/index.html#gallery), e.g. [Transforms v2: End-to-end object detection/segmentation example](https://pytorch.org/vision/stable/auto_examples/transforms/plot_transforms_e2e.html#sphx-glr-auto-examples-transforms-plot-transforms-e2e-py) or [How to write your own v2 transforms](https://pytorch.org/vision/stable/auto_examples/transforms/plot_custom_transforms.html#sphx-glr-auto-examples-transforms-plot-custom-transforms-py).

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](https://github.com/pytorch/vision/blob/main/torchvision/transforms/v2/functional/__init__.py) (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.

---------


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, 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.

This is a patch release, which is compatible with [PyTorch 2.1.2](https://github.com/pytorch/pytorch/releases/tag/v2.1.2). There are no new features added.

This is a minor release that only contains bug-fixes

Bug Fixes

* [models] Fix download of efficientnet weights (8036)
* [transforms] Fix v2 transforms in spawn multi-processing context (8067)

Highlights

[BETA] Transforms and augmentations



Major speedups

The new transforms in `torchvision.transforms.v2` support image classification, segmentation, detection, and video tasks. They are now [10%-40% faster](https://github.com/pytorch/vision/issues/7497#issuecomment-1557478635) than before! This is mostly achieved thanks to 2X-4X improvements made to `v2.Resize()`, which now supports native `uint8` tensors for Bilinear and Bicubic mode. Output results are also now closer to PIL's! Check out our [performance recommendations](https://pytorch.org/vision/stable/transforms.html#performance-considerations) to learn more.

Additionally, `torchvision` now ships with `libjpeg-turbo` instead of `libjpeg`, which should significantly speed-up the jpeg decoding utilities ([`read_image`](https://pytorch.org/vision/stable/generated/torchvision.io.read_image.html#torchvision.io.read_image), [`decode_jpeg`](https://pytorch.org/vision/stable/generated/torchvision.io.read_image.html#torchvision.io.decode_jpeg)), and avoid compatibility issues with PIL.

CutMix and MixUp

Long-awaited support for the `CutMix` and `MixUp` augmentations is now here! Check [our tutorial](https://pytorch.org/vision/stable/auto_examples/transforms/plot_cutmix_mixup.html#sphx-glr-auto-examples-transforms-plot-cutmix-mixup-py) to learn how to use them.

Towards stable V2 transforms

In the [previous release 0.15](https://github.com/pytorch/vision/releases/tag/v0.15.1) we BETA-released a new set of transforms in `torchvision.transforms.v2` with 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.v2` and `torchvision.tv_tensors` namespaces 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](https://pytorch.org/vision/stable/auto_examples/transforms/plot_transforms_getting_started.html#sphx-glr-auto-examples-transforms-plot-transforms-getting-started-py) in order to learn more about what can be done with the new v2 transforms.

Browse our [main docs](https://pytorch.org/vision/stable/transforms.html#) for general information and performance tips. The available transforms and functionals are listed in the [API reference](https://pytorch.org/vision/stable/transforms.html#v2-api-ref). Additional information and tutorials can also be found in our [example gallery](https://pytorch.org/vision/stable/auto_examples/index.html#gallery), e.g. [Transforms v2: End-to-end object detection/segmentation example](https://pytorch.org/vision/stable/auto_examples/transforms/plot_transforms_e2e.html#sphx-glr-auto-examples-transforms-plot-transforms-e2e-py) or [How to write your own v2 transforms](https://pytorch.org/vision/stable/auto_examples/transforms/plot_custom_transforms.html#sphx-glr-auto-examples-transforms-plot-custom-transforms-py).

[BETA] MPS support

The `nms` and roi-align kernels (`roi_align`, `roi_pool`, `ps_roi_align`, `ps_roi_pool`) now support MPS. Thanks to [Li-Huai (Allan) Lin](https://github.com/qqaatw) for this contribution!


---------


Detailed Changes

Deprecations / Breaking changes

All changes below happened in the `transforms.v2` and `datapoints` namespaces, 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 `TVTensor` classes (previously: `Datapoint` classes) were removed
[transforms.v2] `transforms.v2.utils` is now private. (7863)
[transforms.v2] Remove `wrap_like` class method and add `tv_tensors.wrap()` function (7832)

New Features

[transforms.v2] Add support for `MixUp` and `CutMix` (7731, 7784)
[transforms.v2] Add `PermuteChannels` transform (7624)
[transforms.v2] Add `ToPureTensor` transform (7823)
[ops] Add MPS kernels for `nms` and `roi` ops (7643)

Improvements

[io] Added support for CMYK images in `decode_jpeg` (7741)
[io] Package torchvision with `libjpeg-turbo` instead of `libjpeg` (7672, 7840)
[models] Downloaded weights are now sha256-validated (7219)
[transforms.v2] Massive `Resize` speed-up by adding native `uint8` support 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 `fill` dicts. (7779)
[transforms.v2] Allow passthrough for `Resize` (7521)
[transforms.v2] Add `scale` option to `ToDtype`. Remove `ConvertDtype`. (7759, 7862)
[transforms.v2] Improve UX for `Compose` (7758)
[transforms.v2] Allow users to choose whether to return `TVTensor` subclasses or pure `Tensor` (7825)
[transforms.v2] Remove import-time warning for v2 namespaces (7853, 7897)
[transforms.v2] Speedup `hsv2rgb` (7754)
[models] Add `filter` parameters to `list_models()` (7718)
[models] Assert `RAFT` input resolution is 128 x 128 or higher (7339)
[ops] Replaced `gpuAtomicAdd` by `fastAtomicAdd` (7596)
[utils] Add GPU support for `draw_segmentation_masks` (7684)
[ops] Add deterministic, pure-Python `roi_align` implementation (7587)
[tv_tensors] Make `TVTensors` deepcopyable (7701)
[datasets] Only return small set of targets by default from dataset wrapper (7488)
[references] Added support for v2 transforms and `tensors` / `tv_tensors` backends (7732, 7511, 7869, 7665, 7629, 7743, 7724, 7742)
[doc] A lot of documentation improvements (7503, 7843, 7845, 7836, 7830, 7826, 7484, 7795, 7480, 7772, 7847, 7695, 7655, 7906, 7889, 7883, 7881, 7867, 7755, 7870, 7849, 7854, 7858, 7621, 7857, 7864, 7487, 7859, 7877, 7536, 7886, 7679, 7793, 7514, 7789, 7688, 7576, 7600, 7580, 7567, 7459, 7516, 7851, 7730, 7565, 7777)

Bug Fixes

[datasets] Fix `split=None` in `MovingMNIST` (7449)
[io] Fix heap buffer overflow in `decode_png` (7691)
[io] Fix blurry screen in video decoder (7552)
[models] Fix weight download URLs for some models (7898)
[models] Fix `ShuffleNet` ONNX export (7686)
[models] Fix detection models with pytorch 2.0 (7592, 7448)
[ops] Fix segfault in `DeformConv2d` when `mask` is None (7632)
[transforms.v2] Stricter `SanitizeBoundingBoxes` `labels_getter` heuristic (7880)
[transforms.v2] Make sure `RandomPhotometricDistort` transforms all images the same (7442)
[transforms.v2] Fix `v2.Lambda`’s transformed types (7566)
[transforms.v2] Don't call `round()` on float images for `Resize` (7669)
[transforms.v2] Let `SanitizeBoundingBoxes` preserve output type (7446)
[transforms.v2] Fixed int type support for sigma in `GaussianBlur` (7887)
[transforms.v2] Fixed issue with jitted `AutoAugment` transforms (7839)
[transforms] Fix `Resize` pass-through logic (7519)
[utils] Fix color in `draw_segmentation_masks` (7520)



Others

[tests] Various test improvements / fixes (7693, 7816, 7477, 7783, 7716, 7355, 7879, 7874, 7882, 7447, 7856, 7892, 7902, 7884, 7562, 7713, 7708, 7712, 7703, 7641, 7855, 7842, 7717, 7905, 7553, 7678, 7908, 7812, 7646, 7841, 7768, 7828, 7820, 7550, 7546, 7833, 7583, 7810, 7625, 7651)
[CI] Various CI improvements (7485, 7417, 7526, 7834, 7622, 7611, 7872, 7628, 7499, 7616, 7475, 7639, 7498, 7467, 7466, 7441, 7524, 7648, 7640, 7551, 7479, 7634, 7645, 7578, 7572, 7571, 7591, 7470, 7574, 7569, 7435, 7635, 7590, 7589, 7582, 7656, 7900, 7815, 7555, 7694, 7558, 7533, 7547, 7505, 7502, 7540, 7573)
[Code Quality] Various code quality improvements (7559, 7673, 7677, 7771, 7770, 7710, 7709, 7687, 7454, 7464, 7527, 7462, 7662, 7593, 7797, 7805, 7786, 7831, 7829, 7846, 7806, 7814, 7606, 7613, 7608, 7597, 7792, 7781, 7685, 7702, 7500, 7804, 7747, 7835, 7726, 7796)

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, Aditya Oke , Andrey Talman, Camilo De La Torre, Christoph Reich, Danylo Baibak, David Chiu, David Garcia, Dennis M. Pöpperl, Dhuige, Duc Mguyen, Edward Z. Yang, Eric Sauser , Fansure Grin, Huy Do, Illia Vysochyn, Johannes, Kai Wana, Kobrin Eli, kurtamohler, Li-Huai (Allan) Lin, Liron Ilouz, Masahiro Hiramori, Mateusz Guzek, Max Chuprov, Minh-Long Luu (刘明龙), Minliang Lin, mpearce25, Nicolas Granger, Nicolas Hug , Nikita Shulga, Omkar Salpekar, Paul Mulders, Philip Meier , ptrblck, puhuk, Radek Bartoň, Richard Barnes , Riza Velioglu, Sahil Goyal, Shu, Sim Sun, SvenDS9, Tommaso Bianconcini, Vadim Zubov, vfdev-5