GitLab

* Support Exporting GeneralizedRCNNTransform

* refactor code to address comments

* update tests

* address comments

* revert min_size to test CI

* re-revert min_size

* Make custom ops differentiable

and replace autograd.Function. Use ops unconditionally.

We may consider removing the extension functions in a follow-up.

The code-path is tested by the exisitng tests for differentiability.

* add scripting gradchecks tests and use intlist

* fix implicit tuple conversion for gcc-5

* fix merge

* Revert "Revert "Register Torchvision Ops as Cutom Ops (#1267)" (#1316)"

This reverts commit fe234fc8.

* Make import of C++ extensions lazy

* define python initialization functions for extension

* Fix lint

This reverts commit 78f169b5.

* [WIP] Add CircleCI for CI

* Make jobs only run on master

* Add initial CI

* [wip] testing if works

* Trying some basic GPU tests

* [WIP] maybe it will work?

* [WIP] One more try

* Pin versions

* Simplify and reuse

* Fix

* [WIP] testing windows

* [WIP] testing windows

* Try windows

* Try Windows

* Try windows

* Try windows

* Try windows

* Try windows

* Try windows

* Try windows

* Windows speedup

* Windows multicores

* Add parallel flags for Windows

* Skip some tests on Windows

* Sync config.yml and config.yml.in

* Regenerate

* Run all tests

* Limit python and cuda version for finding pytorch

* Skip darwin for previous check

* Add description

* Fix logic

* Remove space

* Add CUDA test back

* Add back .travis.yml for now and remove duplicate test

* Add newline

* Register torchvision ops

* install ORT only with python 3

* remane lib + address other comments

* fix lint

* fix lib copy

* find file with pattern instead of suffix

* use relative path

* revert rename and use imp to find lib

* fix typo

* make shufflenet scriptable

* make resnet18 scriptable

* set downsample to identity instead of __constants__ api

* use __constants__ for downsample instead of identity

* import tensor to fix flake

* use torch.Tensor type annotation instead of import

* Fix flakiness of test_randomresized_params

* Real fix

* Reduce number of iters

* test that torchhub models are scriptable

* fix lint

Following a similar line of inquiry to pytorch/audio#217

* [WIP] Minor cleanups on R3d

* Move all models to video/resnet.py

* Remove old files

* Make tests less memory intensive

* Lint

* Fix typo and add pretraing arg to training script

* Move RandomClipSampler to references

* Lint and bugfix

* Optimize read_video_timestamps for some formats

* Add some tests

* Miscellaneous fixes and improvements

* Guard against videos without video stream

* Fix lint

* Add test for packed b-frames videos

* Fix missing import

* [0.4_video] models - initial commit

* addressing fmassas inline comments

* pep8 and flake8

* simplify "hacks"

* sorting out latest comments

* nitpick

* Updated tests and constructors

* Added docstrings - ready to merge

* introduced function to verify str arguments

* flake8

* added FIXME to VOC

* Fixed error message

* added test for verify_str_arg

* cleanup todos

* added option for custom error message

* fix VOC

* fixed Caltech

Also extend video saving to support different codecs and options. Notably, we can now save with lossless compression

* in_channels_stage2 from backbone.inplanes

* remove type for backward compatible

* Add VideoClips and Kinetics dataset

* Lint + add back missing line

* Adds ClipSampler following Bruno comment

* Change name following Bruno's suggestion

* Enable specifying a target framerate

* Fix test_io for new interface

* Add comment mentioning drop_last behavior

* Make compute_clips more robust

* Flake8

* Fix for Python2

* Added mnasnet

* Fixed some stuff

* Fixed some stuff

* Finished MNASNet

* Fixed format error

* Fixed format error

* cast images to PIL at instantiation instead of runtime

* add test for svhn

* added tests for remaining SVHN splits

* flake8

* rolled back changes to datasets

* to_pil_image updates

* lint

* Update test_transforms.py

* Update test_transforms.py

* Fix bug to Random Erasing

1. Avoid forever loop for getting parameters of erase.
2. replace' img_b' by 'img_c', because it indicates the channel.
3. replace v = torch.rand([img_c, h, w]) by v = torch.empty([img_c, h, w], dtype=torch.float32).normal_(). Normally distributed achieves better performance.

* add test

* Update test_transforms.py

* Update transforms.py

* Update test_transforms.py

* Update transforms.py

* Update functional.py

* Update test for detection model to test input list unmodified

Update test for detection model to test input list unmodified according to suggestion in a previous PR

* test input unchaged

* Cityscapes test; semantic segmentation getitem test

* Add multiple targets test for Cityscapes

* flake8 fix for test_datasets

* initial commit

* add more checks, fix lint, fix doc

* WIP

* WIP

* Add some documentation

* Improve tests and add GC collection

* [WIP] add timestamp getter

* Bugfixes

* Improvements and travis

* Add audio fine-grained alignment

* More doc

* Remove unecessary file

* Remove comment

* Lazy import av

* Remove hard-coded constants for the test

* Return info stats from read

* Fix for Python-2

* updates on normalize

* test fixes

* Update test_transforms.py

* test improved

* Update test_transforms.py

* behaviour changes RandomErasing

* test fixes

* linter fix

* fix save_image when height or width == 1

* test_save_image_single_pixel

* add erase function

* add Random Erasing

* Update transforms.py

* Update transforms.py

* add test for random erasing

* Update test_transforms.py

* fix flake8

* Update test_transforms.py

* Update functional.py

* Update test_transforms.py

* fix bug for per-pixel erasing

* Update transforms.py

* specific for coordinate (x, y)

* add raise TypeError for img

* Update transforms.py

* Update transforms.rst

* fix:error message of to_tensor

The error "pic should be PIL Image or ndarray. Got '<numpy.ndarray>'" is confusing.

* fix:a clearer function name

_is_numpy_image is clearer than _is_numpy_image_dim

* fix:add a test case

Add a test case in test/test_transforms.py to test the error message

* fix:pass ci check

* fix:wrong random matrix

* added a generic test for the datasets

* addressed requested changes

- renamed generic*() to generic_classification*()
- moved function inside Tester
- test class_to_idx attribute outside of generic_classification*()

* Fix normalize for different dtype than float32

* Fix lint