GitLab

Torchvision includes at least 3 bits of code that calculate
box Intersection over Union values (and usually compare to
a threshold):

- box_iou in torchvision/ops/boxes.py
- devIoU in torchvision/csrc/cuda/nms_cuda.cu
- nms_cpu_kernel in torchvision/csrc/cpu/nms_cpu.cpp

The calculations were performed slightly differently between
those, leading to occasional differences in results.

Update devIoU to use the same method as the others for better
consistency.

This change improves agreement between the CPU and CUDA
calculations but the results can still differ slightly.
Setting NVCC_FLAGS to include "--fmad=true" would provide
still better agreement, but with likely cost to performance.

* Add tests for negative samples for Mask R-CNN and Keypoint R-CNN

* Fix lint

* Replace **.is_cuda() to just is_cuda()

* Replace type to scalar_type

* Fix lint, clang-format

* Fix lint, clang-format

cleanup

* type annotations for torchvision/utils.py

* add missing annotation for make_grid

* fix annotation for save_image

* mirror PIL annotation for fp

* Add clang-format to CircleCI

* Fix for clang-format version

* Fix lint and remove Travis CI

* Seeing if lost commit comes back

* Fix lint

* Re-enable all tests

* Add CircleCI job for python lint

* Break lint

* Fix

* Fix lint

* Re-enable all tests and remove travis python lint

Summary: Fix docstring formatting issues

Reviewed By: fmassa

Differential Revision: D20736644

fbshipit-source-id: 78f66045cfd4c84cb35ca84a1e1fa6aadcd50642
Co-authored-by: Patrick Labatut <plabatut@fb.com>

* Add test for large batches in DeformConv2d

* Clean-up and (try) fix DeformConv2d

* Simplifications and bugfixes

* Try fix CUDA now

Co-authored-by: Andrew Murray <radarhere@users.noreply.github.com>

* fixes and tests for variable input size

* transform test fix

* Fix comment

* Dynamic shape for keypoint_rcnn

* Update test_onnx.py

* Update rpn.py

* Fix for split on RPN

* Fixes for feedbacks

* flake8

* topk fix

* Fix build

* branch on tracing

* fix for scalar tensor

* Fixes for script type annotations

* Update rpn.py

* clean up

* clean up

* Update rpn.py

* Updated for feedback

* Fix for comments

* revert to use tensor

* Added test for box clip

* Fixes for feedback

* Fix for feedback

* ORT version revert

* Update ort

* Update .travis.yml

* Update test_onnx.py

* Update test_onnx.py

* Tensor sizes

* Fix for dynamic split

* Try disable tests

* pytest verbose

* revert one test

* enable tests

* Update .travis.yml

* Update .travis.yml

* Update .travis.yml

* Update test_onnx.py

* Update .travis.yml

* Passing device

* Fixes for test

* Fix for boxes datatype

* clean up
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* remove sys.version_info == 2

* remove sys.version_info < 3

* remove from __future__ imports

* remove six from python code

* remove six from setup.py

* remove six from tests

* remove six from references

* remove six from packaging

* revert str to torch._six._string_classes

* revert str to torch._six._string_classes

* fix shape error for deform conv gpu op

recover shape of columns for next iteration in for loops, previous version will cause error when batch_sz / n_parallel_imgs > 1

* fix shape error for deform conv cpu op

recover shape of columns for next iteration in for loops, previous version will cause error when batch_sz / n_parallel_imgs > 1

* Improve code readability and docstring

* Remove unused argument

* Improve make_dataset() readability

* Fix C++ lint

* More fixes

Summary:
Currently the scale argument can only be of type tuple or integer, this diff allows feeding the input argument `scale` with a list.
Pull Request resolved: https://github.com/pytorch/vision/pull/1997

Test Plan:
Without this diff, launching the following classy vision task causes error:
https://our.intern.facebook.com/intern/fblearner/details/175876950/

With this diff, everything works fine:
https://our.intern.facebook.com/intern/fblearner/details/175913768/



Reviewed By: resonatevision

Differential Revision: D20544904

Pulled By: ymao1993

fbshipit-source-id: a95a2e9ceadec77fffe234756fb3b38b1b9c9cb1
Co-authored-by: Yu Mao <ymao1@fb.com>

* Add fill option to RandomPerspective #1972

* Minor fix to docstring syntax

* Add _parse_fill() to get fillcolor (#1972)

* Minor refactoring as per comments.

* Added test for RandomPerspective with fillcolor.

* Force perspective transform in test.

* F.normalize unsqueeze mean & std if necessary

* added tests to F.normalize for 3d mean & std tensors

* modified FasterRCNN to accept negative samples

* remove debug lines

* Change torch.zeros_like to torch.zerros

* Add unit tests

* take the `device` into account
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Base decoder for video. (#1747)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1747

Pull Request resolved: https://github.com/pytorch/vision/pull/1746

Added the implementation of ffmpeg based decoder with functionality that can be used in VUE and TorchVision.

Reviewed By: fmassa

Differential Revision: D19358914

fbshipit-source-id: abb672f89bfaca6351dda2354f0d35cf8e47fa0f

* Integrated base decoder into VideoReader class and video_utils.py (#1766)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1766

Replaced FfmpegDecoder (incompativle with VUE) by base decoder (compatible with VUE).
Modified python utilities video_utils.py for internal simplification. Public interface got preserved.

Reviewed By: fmassa

Differential Revision: D19415903

fbshipit-source-id: 4d7a0158bd77bac0a18732fe4183fdd9a57f6402

* Optimizating base decoder performance. (#1852)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1852

Changed base decoder internals for a faster clip processing.

Reviewed By: stephenyan1231

Differential Revision: D19748379

fbshipit-source-id: 58a435f0a0b25545e7bd1a3edb0b1d558176a806

* Minor fix and decoder class members access.

Summary:
Found and fix a bug in cropping algorithm (simple mistyping).
Also derived classes need access to some decoder class members, like initialization parameters - make it protected.

Reviewed By: stephenyan1231, fmassa

Differential Revision: D19895076

fbshipit-source-id: 691336c8e18526b085ae5792ac3546bc387a6db9

* Added missing header for less dependencies. (#1898)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1898

Include streams/samplers shouldn't depend on decoder headers. Add dependencies directly to the place where they are required.

Reviewed By: stephenyan1231

Differential Revision: D19911404

fbshipit-source-id: ef322a053708405c02cee4562b456b1602fb12fc

* Implemented VUE Asynchronous Decoder

Summary: For Mothership we have found that asynchronous decoder provides a better performance.

Differential Revision: D20026194

fbshipit-source-id: 627b91844b4e3f917002031dd32cb19c239f4ba8

* fix a bug in API read_video_from_memory (#1942)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1942

In D18720474, it introduces a bug in `read_video_from_memory` API. Thank weiyaowang for reporting it.

Reviewed By: weiyaowang

Differential Revision: D20270179

fbshipit-source-id: 66348c99a5ad1f9129b90e934524ddfaad59de03

* extend decoder to support new video_max_dimension argument (#1924)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1924

Extend `video reader` decoder python API in Torchvision to support a new argument `video_max_dimension`. This enables the new video decoding use cases. When setting `video_width=0`, `video_height=0`, `video_min_dimension != 0`, and `video_max_dimension != 0`, we can rescale the video clips so that its spatial resolution (height, width) becomes
 - (video_min_dimension, video_max_dimension) if original height < original width
 - (video_max_dimension, video_min_dimension) if original height >= original width

This is useful at video model testing stage, where we perform fully convolution evaluation and take entire video frames without cropping as input. Previously, for instance we can only set `video_width=0`, `video_height=0`, `video_min_dimension = 128`, which will preserve aspect ratio. In production dataset, there are a small number of videos where aspect ratio is either extremely large or small, and when the shorter edge is rescaled to 128, the longer edge is still large. This will easily cause GPU memory OOM when we sample multiple video clips, and put them in a single minibatch.

Now, we can set (for instance) `video_width=0`, `video_height=0`, `video_min_dimension = 128` and `video_max_dimension = 171` so that the rescale resolution is either (128, 171) or (171, 128) depending on whether original height is larger than original width. Thus, we are less likely to have gpu OOM because the spatial size of video clips is determined.

Reviewed By: putivsky

Differential Revision: D20182529

fbshipit-source-id: f9c40afb7590e7c45e6908946597141efa35f57c

* Fixing samplers initialization (#1967)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1967



No-ops for torchvision diff, which fixes samplers.

Differential Revision: D20397218

fbshipit-source-id: 6dc4d04364f305fbda7ca4f67a25ceecd73d0f20

* Exclude C++ test files
Co-authored-by: Yuri Putivsky <yuri@fb.com>
Co-authored-by: Zhicheng Yan <zyan3@fb.com>

Co-authored-by: eellison <eellison@fb.com>

Co-authored-by: Charles Pao <dirtybluer@gmail.com>

* add checkout/assert in roi_pool

* add checkout/assert in roi_align

* move check_roi_boxes_shape func to ops/_utils.py

* add tests

* fix CI

* fix CI
Co-authored-by: Guanheng Zhang <zhangguanheng@devfair0197.h2.fair>

https://github.com/pytorch/vision/pull/1949 seems to forget fixing quantized googlenet

* update model path

* remove autologits before loading quantized model