[tune](deps): Bump torchvision from 0.10.0 to 0.12.0 in /python/requirements/ml

[dependabot]

Bumps torchvision from 0.10.0 to 0.12.0.

Release notes

Sourced from torchvision's releases.

TorchVision 0.12, including new Models, Datasets, GPU Video Decoding, and more

Highlights

New Models

Four new model families have been released in the latest version along with pre-trained weights for their variants: FCOS, RAFT, Vision Transformer (ViT) and ConvNeXt.

Object Detection

FCOS is a popular, fully convolutional, anchor-free model for object detection. In this release we include a community-contributed model implementation as well as pre-trained weights. The model was trained on COCO train2017 and can be used as follows:

import torch
from torchvision import models
x = [torch.rand(3, 224, 224)]
fcos = models.detection.fcos_resnet50_fpn(pretrained=True).eval()
predictions =  fcos(x)

The box AP of the pre-trained model on COCO val2017 is 39.2 (see #4961 for more details).

We would like to thank Hu Ye and Zhiqiang Wang for contributing to the model implementation and initial training. This was the first community-contributed model in a long while, and given its success, we decided to use the learnings from this process and create a new model contribution guidelines.

Optical Flow support and RAFT model

Torchvision now supports optical flow! Optical flow models try to predict movement in a video: given two consecutive frames, the model predicts where each pixel of the first frame ends up in the second frame. Check out our new tutorial on Optical Flow!

We implemented a torchscript-compatible RAFT model with pre-trained weights (both normal and “small” versions), and added support for training and evaluating optical flow models. Our training scripts support distributed training across processes and nodes, leading to much faster training time than the original implementation. We also added 5 new optical flow datasets: Flying Chairs, Flying Things, Sintel, Kitti, and HD1K.

Image Classification

Vision Transformer (ViT) and ConvNeXt are two popular architectures which can be used as image classifiers or as backbones for downstream vision tasks. In this release we include 8 pre-trained weights for their classification variants. The models were trained on ImageNet and can be used as follows:

import torch
from torchvision import models
x = torch.rand(1, 3, 224, 224)
vit = models.vit_b_16(pretrained=True).eval()
convnext = models.convnext_tiny(pretrained=True).eval()
predictions1 = vit(x)
predictions2 = convnext(x)

The accuracies of the pre-trained models obtained on ImageNet val are seen below:

|Model |Acc@1 |Acc@5 |

... (truncated)

Commits

• 9b5a3fe Add Python-3.10 (build and test) (#5420) (#5579)
• c6c1fb6 Disable CUDA 11.1 builds (#5477) (#5478)
• 9648e07 Revert exposing convNormActivation in ops (#5472) (#5474)
• 211a442 Removing Win CU11.1 support (Cherrypick #5470) (#5471)
• abe5baf Update hardcoded WIN-CUDA from 11.1 with 11.3 (#5451) (#5467)
• cb534ea Delete stale packaging scripts (#5433) (#5437)
• 471a134 Fix functional.adjust_gamma (#5427) (#5434)
• 9cfb0b7 Fixes device mismatch issue while building docs (#5428) (#5429)
• 2662797 [v0.12.0] .circleci: Bump version for pytorch 1.11.0 (#5400)
• d6b0e1c Remove prototype release (#5394)
• Additional commits viewable in compare view

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