GitLab

* Make JPEG/PNG return images in CHW format

* Use int array

* [WIP] Added tests on batch of tensors

* Updated tests on batch of images

* All functional transforms can work with (..., C, H, W) format

* Added transforms tests on batch tensors

* Added batch tests for five/ten crop
- updated docs

* Run C++ torchvision example as part of cmake tests

* Enable C++14 support

* Minor error correction

* Add GPU forward on example

* Use arrow operator

* Fixes #2547

- Added warning to the documentation of F_pil and F_t functions

* Update functional_tensor.py

* Improved functional tensor geom transforms to work on floatX dtype
- Fixes #2600
- added tests
- refactored test_affine

* Removed float16/cpu case

* enable typing check for torchvision.io

* fix existing errors

* Update torchvision/io/_video_opt.py
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* add ignores for FileFinder

* use python 3 type hints

* lint

* video_opt

* video

* try quote av type hints

* revert from .dim() to .ndim

* revert changes to _video_opt.py and ignore errors

* fix type hints

* fix type hints for read_video_timestamps

* change offset int to float

* remove unused import
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Add CMake build pipeline

* Add CMake build workflow

* Add executable permissions to script

* Install cmake on Windows/MacOS

* Install conda-build before setting up MSVC

* Install PyTorch from nightly

* Do not use conda-build variables

* Add path to CMake

* Install libpng and libjpeg

* Perform make

* Call msbuild on Windows

* Add missing yq

* Use vc_env_helper

* Use string instruction

* Escape configuration option

* Remove configuration flag

* Try to pass -p

* Use caret to escape equal sign

* Escape string option in Windows

* Try to call other bat

* Remove Windows/GPU CMake

* Add tracing cpp test

* Script model instead of tracing it

* Try to register operators manually

* Use manylinux-cuda102

* Activate conda env on Linux

* Build and run sample tracing test

* Add empty echo

* Remove unnecessary register

* Copy headers on Mac

* Revert to 2xlarge

* Include /usr/local/include on Mac

* Install pillow on Windows

* Install future

* Install torchvision on Windows

* Set include flag

* Add torchlib to PATH

* Normalize path via cygpath

* Register ops on Windows

* Minor error correction

* Register CPU/GPU ops on DLL library and register ops via reference

* Install dataclasses

* Install dataclasses using pip

* Address clang formatting issue

* Try to use an actual GPU instance on Linux

* Remove extra environment section

* Declare environment explicitly

* Regenerate

* Pass env variables to Dokcer

* Regenerate circleci

* Test tracing on GPU

* Use GPU medium

* Regenerate

* Use cuda101

* Regenerate

* Do not use pre-trained weights

Avoids having to download pretrained files, which could cause flaky tests
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* split off tests for dataset downloadability

* ignore download tests during normal test suite

* lint

* add retry mechanic

* adjust_hue now supports inputs of type Tensor

* Added comparison between original adjust_hue and its Tensor and torch.jit.script versions.

* Added a few type checkings related to adjust_hue in functional_tensor.py in hopes to make F_t.adjust_hue scriptable...but to no avail.

* Changed implementation of _rgb2hsv and removed useless type declaration according to PR's review.

* Handled the range of hue_factor in the assertions and temporarily increased the assertLess bound to make sure that no other test fails.

* Fixed some lint issues with CircleCI and added type hints in functional_pil.py as well.

* Corrected type hint mistakes.

* Followed PR review recommendations and added test for class interface with hue.

* Refactored test_functional_tensor.py to match vfdev-5's d016cab branch by simple copy/paste and added the test_adjust_hue and ColorJitter class interface test in the same style (class interface test was removed in vfdev-5's branch for some reason).

* Removed test_adjustments from test_transforms_tensor.py and moved the ColorJitter class interface test in test_transforms_tensor.py.

* Added cuda test cases for test_adjustments and tried to fix conflict.

* Updated tests
- adjust hue
- color jitter

* Fixes incompatible devices

* Increased tol for cuda tests

* Fixes potential issue with inplace op
- fixes irreproducible failing test on Travis CI

* Reverted fmod -> %
Co-authored-by: vfdev-5 <vfdev.5@gmail.com>

* add autocasting on ROCm

* enable ROIAlign autocasting on ROCm

* enable NMS autocasting on ROCm

* fix to use correct torch CUDA APIs

* Added cuda torch.jit tests for rgb-hsv tensor conversion ops

* Reverted deleted test

* [WIP] Unify ops Grayscale and RandomGrayscale

* Unified inputs for grayscale op and transforms
- deprecated F.to_grayscale in favor of F.rgb_to_grayscale

* Fixes bug with fp input

* Rewritten adjust_* tests
- split test_adjustments into 3 separate tests
- unified testing approach with test_adjust_gamma

* Added ColorJitter tests

* Relaxed tolerance for functional adjust-* tests

* Removed wrong merge and commented code

Upstream is on 9.4.1 and we were experiencing issues when conda-build
was attempting to check for overlinking and failed out due to some files
not existing in xcode 9.0
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

* [WIP] Unify ops Grayscale and RandomGrayscale

* Unified inputs for grayscale op and transforms
- deprecated F.to_grayscale in favor of F.rgb_to_grayscale

* Fixes bug with fp input

* [WIP] Updated code according to review

* Removed unused import

* Add support for export ROIAlign

* Fix for feedback

* flake8

* fix images extraction

* remove test split

* fix tests

* be less clever in test data generation

* remove micro optimization

* lint

* Improve transforms test codebase
- refactored compareTensorToPIL, _create_data, approxEqualTensorToPIL methods

* Fixed flake8

* initial draft

* [dirty] progress

* remove inheritance from ImageFolder

* add tests

* lint

* fix type hints

* align getitem with other datasets

* remove unused import

* add docstring

* guard existing image folders from overwrite

* add missing entry in docstring

* make fixpath more legible

* add Places365 to docs

* [WIP] Fixes #2598
- Adjusted num_kernels and batch_size according to kMaxGridNum * CUDA_NUM_THREADS
- Tests to add

* - Redefined kMaxGridNum as max grid according to current CUDA device
- Added test to check the code from issue and compared grads CPU/CUDA

* Fixed static kMaxGridNum evaluation to dynamic

* Update README.rst

Updated the link to the docs..

* Update README.rst

- can be helpful for debugging C++ extensions

offset's 1 dimension should be batch size

* Adapted almost all functional tensor tests on CPU/CUDA
- fixed bug with transforms using generated grid
- remains *_crop, blocked by #2568
- TODO: test_adjustments

* Apply suggestions from code review
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Fixed issues according to review

* Split tests into two: cpu and cuda

* Updated test_adjustments to run on CPU and CUDA
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Only pull keys from db in lsun for faster cache.

This pull request inhances the speed of the cache creation for lsun dataset. For the "kitchen_train" the speed was getting slow with cache creation taking more then two hours. This speeds up to cache creation in within minutes. The issue was pulling the large image values each time and dropping them.

For more details on this please refer this issue https://github.com/jnwatson/py-lmdb/issues/195.

* Fixed bug in lsun.py when loading multiple categories

* Make linter happy