pypi torchvision 0.10.0
iOS support, GPU image decoding, SSDlite and more
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3 years ago

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

TorchVision 0.10 now provides pre-compiled iOS binaries for its C++ operators, which means you can run Faster R-CNN and Mask R-CNN on iOS. An example app on how to build a program leveraging those ops can be found in here.

[STABLE] Speed optimizations for Tensor transforms

The resize and flip transforms have been optimized and its runtime improved by up to 5x on the CPU. The corresponding PRs were sent to PyTorch in pytorch/pytorch#51653, pytorch/pytorch#54500 and pytorch/pytorch#56713

[STABLE] Documentation improvements

Significant improvements were made to the documentation. In particular, a new gallery of examples is available: see here for the latest version (the stable version is not released at the time of writing). These examples visually illustrate how each transform acts on an image, and also properly documents and illustrate the output of the segmentation models.

The example gallery will be extended in the future to provide more comprehensive examples and serve as a reference for common torchvision tasks.

Backwards Incompatible Changes

  • [transforms] Ensure input type of normalize is float. (#3621)
  • [models] Use PyTorch smooth_l1_loss and remove private custom implementation (#3539)

New Features

  • Added iOS binaries and test app (#3582)(#3629) (#3806)
  • [datasets] Added KITTI dataset (#3640)
  • [utils] Added utility to draw segmentation masks (#3330, #3824)
  • [models] Added the SSD & SSDlite object detection models (#3403, #3757, #3766, #3855, #3896, #3818, #3799)
  • [transforms] Added antialias option to transforms.functional.resize (#3761, #3810, #3842)
  • [transforms] Add new max_size parameter to Resize (#3494)
  • [io] Support for decoding jpegs on GPU with nvjpeg (#3792)
  • [ci, rocm] Add ROCm to builds (#3840) (#3604) (#3575)
  • [ops, models.quantization] Add quantized version of NMS (#3601)
  • [ops, models.quantization] Add quantized version of RoIAlign (#3624, #3904)

Improvement

Code quality

  • Remove inconsistent FB copyright headers (#3741)
  • Keep consistency in classes ConvBNActivation (#3750)
  • Removed unused imports (#3738, #3740, #3639)
  • Fixed floor_divide deprecation warnings seen in pytest output (#3672)
  • Unify onnx and JIT resize implementations (#3654)
  • Cleaned-up imports in test files related to datasets (#3720)
  • [documentation] Remove old css file (#3839)
  • [ci] Fix inconsistent version pinning across yaml files (#3790)
  • [datasets] Remove redundant path.join in Places365 (#3545)
  • [datasets] Remove imprecise error handling in PhotoTour dataset (#3488)
  • [datasets, tests] Remove obsolete test_datasets_transforms.py (#3867)
  • [models] Making protected params of MobileNetV3 public (#3828)
  • [models] Make target argument in transform.py truly optional (#3866)
  • [models] Adding some references on MobileNetV3 implementation. (#3850)
  • [models] Refactored set_cell_anchors() in AnchorGenerator (#3755)
  • [ops] Minor cleanup of roi_align_forward_kernel_impl (#3619)
  • [ops] Replace deprecated AutoNonVariableTypeMode with AutoDispatchBelowADInplaceOrView. (#3786, #3897)
  • [tests] Port tests to use pytest (#3852, #3845, #3697, #3907, #3749)
  • [ops, tests] simplify get_script_fn (#3541)
  • [tests] Use torch.testing.assert_close in out test suite (#3886) (#3885) (#3883) (#3882) (#3881) (#3887) (#3880) (#3878) (#3877) (#3875) (#3888) (#3874) (#3884) (#3876) (#3879) (#3873)
  • [tests] Clean up test accept behaviour (#3759)
  • [tests] Remove unused masks variable in test_image.py (#3910)
  • [transforms] use ternary if in resize (#3533)
  • [transforms] replaced deprecated call to ByteTensor with from_numpy (#3813)
  • [transforms] Remove unnecessary casting in adjust_gamma (#3472)

Bugfixes

  • [ci] set empty cxx flags as default (#3474)
  • [android][test_app] Cleanup duplicate dependency (#3428)
  • Remove leftover exception (#3717)
  • Corrected spelling in a TypeError (#3659)
  • Add missing device info. (#3651)
  • Moving tensors to the right device (#3870)
  • Proper error message (#3725)
  • [ci, io] Pin JPEG version to resolve the size_t issue on windows (#3787)
  • [datasets] Make LSUN OS agnostic (#3455)
  • [datasets] Update squeezenet urls (#3581)
  • [datasets] Add .item() to the target variable in fakedataset.py (#3587)
  • [datasets] Fix VOC datasets for 2007 (#3572)
  • [datasets] Add custom user agent for download_url (#3498)
  • [datasets] Fix LSUN dataset tests flakyness (#3703)
  • [datasets] Fix (Fashion|K)MNIST download and MNIST download test (#3557)
  • [datasets] fix check for exceeded quota on Google Drive (#3710)
  • [datasets] Fix redirect behavior of datasets.utils.download_url (#3564)
  • [datasets] Update EMNIST url (#3567)
  • [datasets] Redirect datasets to correct urls (#3574)
  • [datasets] Prevent potential bug in DatasetFolder.make_dataset (#3733)
  • [datasets, tests] Fix redirection in download tests (#3568)
  • [documentation] Correct the size of returned tensor in comments of ps_roi_pool.py and ps_roi_align.py (#3849)
  • [io] Fix ternary operator to decide to store an image in Grayscale or RGB (#3553)
  • [io] Fixed audio-video synchronisation problem in read_video() when using pts as unit (#3791)
  • [models] Fix bug on detection backbones when trainable_layers == 0 (#3906)
  • [models] Removed caching of anchors from AnchorGenerator (#3745)
  • [models] Update weights of classification models with new serialization format to allow proper unpickling (#3620, #3851)
  • [onnx, ops] Fix roi_align ONNX export (#3355)
  • [referencescripts] Only sync cuda ifn cuda available (#3674)
  • [referencescripts] Add checkpoints used for preemption. (#3789)
  • [transforms] Fix to_tensor for accimage backend (#3439)
  • [transforms] Make crop work the same for PIL and Tensor (#3770)
  • [transforms, models, tests] Fix some tests in fbcode (#3686)
  • [transforms, tests] Fix test_random_autocontrast flakyness (#3699)
  • [utils] Fix the spacing of labels on draw_bounding_boxes (#3895)
  • [utils, tests] Fix test_draw_boxes (#3631)

Deprecation

  • [transforms] Deprecate _transforms_video and _functional_video in favor of transforms (#3441)

Performance

  • [ops] Improve performance of batched_nms when number of boxes is large (#3426)
  • [transforms] Speed up equalize transform by using bincount instead of histc (#3493)

Contributors

We're grateful for our community, which helps us improving torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:

Aditya Oke, Akshay Kumar, Alessandro Melis, Avijit Dasgupta, Bruno Korbar, Caroline Chen, chengjuzhou, Edgar Andrés Margffoy Tuay, Eli Uriegas, Francisco Massa, Guillem Orellana Trullols, harishsdev, Ivan Kobzarev, Jaesun Park, James Thewlis, Jeff Daily, Jeff Yang, Jithendra Paruchuri, Jon Janzen, KAI ZHAO, Ksenija Stanojevic, Lewis Patten, Matti Picus, moto, Mustafa Bal, Nicolas Hug, Nikhil Kumar, Nikita Shulga, Philip Meier, Prabhat Roy, Sanket Thakur, scott-vsi, Sofiane Abbar, t-rutten, urmi22, Vasilis Vryniotis, vfdev, Yuchen Huang, Zhengyang Feng, Zhiqiang Wang

Thank you!

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