GitLab

* only display extensions if available

* add tests

* fix lint

* For detection model, replace L1 loss with beta smooth L1 loss to achieve better performance.

* Add type annotations for torchscript

* Resolve E226

* fix the use of contiguous() in kernels

* clang-format

* add a contiguous in nms
Co-authored-by: Yuxin Wu <ppwwyyxx@users.noreply.github.com>

* Fixing nms on boxes when no detection

* test

* Fix for scale_factor computation

* remove newline

* Fix for mask_rcnn dynanmic axes

* Clean up

* Update transform.py

* Fix for torchscript

* Fix scripting errors

* Fix annotation

* Fix lint

* Fix annotation

* Fix for interpolate scripting

* Fix for scripting

* refactoring

* refactor the code

* Fix annotation

* Fixed annotations

* Added test for resize

* lint

* format

* bump ORT

* ort-nightly version

* Going to ort 1.1.0

* remove version

* install typing-extension

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/2077

Pull Request resolved: https://github.com/facebookresearch/SlowFast/pull/164

This is a follow-up diff from D18720474

We will be releasing a new version of torchvision soon and the signature of those functions is not ready yet, following my comment in https://our.intern.facebook.com/intern/diff/D18720474/?transaction_id=561239541337402



Reviewed By: stephenyan1231

Differential Revision: D20914571

fbshipit-source-id: 1a7560b8f8e46ab42ef376c50b494a4f73923e94
Co-authored-by: Francisco Massa <fmassa@fb.com>

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>