Update torchvision requirement from <0.10.0,>=0.8.1 to >=0.8.1,<0.11.0

[dependabot]

Updates the requirements on torchvision to permit the latest version.

Release notes

Sourced from torchvision's releases.

iOS support, GPU image decoding, SSDlite and more

This release improves support for mobile, with new mobile-friendly detection models based on SSD and SSDlite, CPU kernels for quantized NMS and quantized RoIAlign, pre-compiled binaries for iOS available in cocoapods and an iOS demo app. It also improves image IO by providing JPEG decoding on the GPU, and many more.

Highlights

[BETA] New models for detection

SSD and SSDlite are two popular object detection architectures which are efficient in terms of speed and provide good results for low resolution pictures. In this release, we provide implementations for the original SSD model with VGG16 backbone and for its mobile-friendly variant SSDlite with MobileNetV3-Large backbone. The models were pre-trained on COCO train2017 and can be used as follows:

import torch
import torchvision
Original SSD variant
x = [torch.rand(3, 300, 300), torch.rand(3, 500, 400)]
m_detector = torchvision.models.detection.ssd300_vgg16(pretrained=True)
m_detector.eval()
predictions = m_detector(x)
Mobile-friendly SSDlite variant
x = [torch.rand(3, 320, 320), torch.rand(3, 500, 400)]
m_detector = torchvision.models.detection.ssdlite320_mobilenet_v3_large(pretrained=True)
m_detector.eval()
predictions = m_detector(x)

The following accuracies can be obtained on COCO val2017 (full results available in #3403 and #3757):

Model	mAP	mAP@50	mAP@75
SSD300 VGG16	25.1	41.5	26.2
SSDlite320 MobileNetV3-Large	21.3	34.3	22.1

[STABLE] Quantized kernels for object detection

The forward pass of the nms and roi_align operators now support tensors with a quantized dtype, which can help lowering the memory footprint of object detection models, particularly on mobile environments.

[BETA] JPEG decoding on the GPU

Decoding jpegs is now possible on GPUs with the use of nvjpeg, which should be readily available in your CUDA setup. The decoding time of a single image should be about 2 to 3 times faster than with libjpeg on CPU. While the resulting tensor will be stored on the GPU device, the input raw tensor still needs to reside on the host (CPU), because the first stages of the decoding process take place on the host:

from torchvision.io.image import read_file, decode_jpeg
data = read_file('path_to_image.jpg')  # raw data is on CPU
img = decode_jpeg(data, device='cuda')  # decoded image in on GPU

[BETA] iOS support

... (truncated)

Commits

• 300a8a4 backport fixes to build/push docs on tags (#4060)
• 9d5561b Set documentation version to 0.10.0 (#3927)
• 026a125 Update the our_upload_channel to test.
• cc851b9 Set the versions.
• fc852f3 Fix installation instructions in contributing guide (#3923)
• 6d1a084 Reenable tests for r2plus1d_18. (#3919)
• 48de528 [FBcode->GH] Add check_stride=False (#3920)
• f584309 Fixed audio-video synchronisation problem in read_video() when using pts as...
• 154283b Port test/test_utils.py to pytest (#3917)
• 1b6fe68 remove masks (#3910)
• Additional commits viewable in compare view

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[epwalsh]

TODO