Sourced from torchvision's releases.

TorchVision 0.15.2 Release

This is a minor release, which is compatible with PyTorch 2.0.1 and contains some minor bug fixes.

Highlights

Bug Fixes

• Move parameter sampling of v2.RandomPhotometricDistort into _get_params pytorch/vision#7442
• Fix split parameter for MovingMNIST pytorch/vision#7449
• Prevent unwrapping in v2.SanitizeBoundingBoxes pytorch/vision#7446

TorchVision 0.15 - New transforms API!

Highlights

[BETA] New transforms API

TorchVision is extending its Transforms API! Here is what’s new:

• You can use them not only for Image Classification but also for Object Detection, Instance & Semantic Segmentation and Video Classification.
• You can use new functional transforms for transforming Videos, Bounding Boxes and Segmentation Masks.

The API is completely backward compatible with the previous one, and remains the same to assist the migration and adoption. We are now releasing this new API as Beta in the torchvision.transforms.v2 namespace, and we would love to get early feedback from you to improve its functionality. Please reach out to us if you have any questions or suggestions.

import torchvision.transforms.v2 as transforms
Exactly the same interface as V1:
trans = transforms.Compose([
transforms.ColorJitter(contrast=0.5),
transforms.RandomRotation(30),
transforms.CenterCrop(480),
])
imgs, bboxes, masks, labels = trans(imgs, bboxes, masks, labels)

You can read more about these new transforms in our docs, and you can also check out our examples:

• End-to-end object detection example
• Getting started with transforms v2

Note that this API is still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in pytorch/vision#6753, and you can also check out pytorch/vision#7319 to learn more about the APIs that we suspect might involve future changes.

[BETA] New Video Swin Transformer

We added a Video SwinTransformer model is based on the Video Swin Transformer paper.

import torch
from torchvision.models.video import swin3d_t
video = torch.rand(1, 3, 32, 800, 600)
or swin3d_b, swin3d_s
</tr></table>

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• fa99a53 Remove temporary channel for python 3.11 (#7505) (#7532)
• 3a17f14 [Release only change] Advance version for minor release (#7531)
• 83ef3a6 [CHERRY-PICK] prevent unwrapping in SanitizeBoundingBoxes (#7446) (#7450)
• 2bda93b [CHERRY-PICK] MovingMNIST split fix (#7449) (#7451)
• e872006 [Cherry-pick for 0.15.2] move parameter sampling of RandomPhotometricDistort ...
• 42759b1 Version number bump for vision (#7419)
• 0ca0735 [Cherry-pick for 0.15] Fix warning message (#7395)
• a2c033c Remove prototype code / tests / CI on release branch (#7390)
• 96bdbe0 Skip some warning test on 3.10 to avoid MKL-related warning (and failure) (#7...
• 23e3ede [Cherry-pick for 0.15] Use python 3.9 conda environment for cmake circleci wo...
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