[RLlib](deps): Bump torchvision from 0.9.1 to 0.11.3 in /python/requirements/rllib

[dependabot]

Bumps torchvision from 0.9.1 to 0.11.3.

Release notes

Sourced from torchvision's releases.

Minor release

This is a minor release compatible with PyTorch 1.10.2 and a minor bug fix.

Highlights

Bug Fixes

• [CI] Skip jpeg comparison tests with PIL (#5232)

Minor bugfix release

This minor release bumps the pinned PyTorch version to v1.10.1 and contains some minor bug fixes.

Highlights

Bug Fixes

• [CI] Fix clang_format issue (#5061)
• [CI, MOBILE] Fix binary_libtorchvision_ops_android job (#5062)
• [CI] Add numpy as explicit dependency to build_cmake.sh (#5065)
• [MODELS] Amend the weights only if quantize=True. (#5066)
• [TRANSFORMS] Fix augmentation space to be uint8 compatible (#5067)
• [DATASETS] Fix WIDERFace download links (#5068)
• [BUILD, WINDOWS] Workaround for loading bundled DLLs (#5094)

Update dependency on wheels to match version in PyPI

Users were reporting issues installing torchvision on PyPI, this release contains an update to the dependencies for wheels to point directly to torch==0.10.0

RegNet, EfficientNet, FX Feature Extraction and more

This release introduces the RegNet and EfficientNet architectures, a new FX-based utility to perform Feature Extraction, new data augmentation techniques such as RandAugment and TrivialAugment, updated training recipes that support EMA, Label Smoothing, Learning-Rate Warmup, Mixup and Cutmix, and many more.

Highlights

New Models

RegNet and EfficientNet are two popular architectures that can be scaled to different computational budgets. In this release we include 22 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)
regnet = models.regnet_y_400mf(pretrained=True)
regnet.eval()
predictions = regnet(x)
efficientnet = models.efficientnet_b0(pretrained=True)
efficientnet.eval()
predictions = efficientnet(x)

The accuracies of the pre-trained models obtained on ImageNet val are seen below (see #4403, #4530 and #4293 for more details)

... (truncated)

Commits

• 05eae32 ci: Bump pytorch 1.10.1 -> 1.10.2 (#5239)
• 076a0e3 ci: Pin av to 8.0.3 (#5240)
• 34395c7 Skip jpeg comparison tests with PIL (#5169) (#5232)
• e7ec7e2 [Windows] Workaround for loading bundled DLLs (#4893) (#5094)
• 95b26e0 bump pytorch dependency 1.10.0 -> 1.10.1
• 2b3e0a7 fix WIDERFace download links (#4649) (#5068)
• 12f664b Fix augmentation space to be uint8 compatible (#4806) (#5067)
• 2ea7133 Amend the weights only if quantize=True. (#4966) (#5066)
• 73efcc6 Add numpy as explicit dependency to build_cmake.sh (#4987) (#5065)
• 0cac420 Fix binary_libtorchvision_ops_android job (#5062)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #61.