GitLab

* make convert_image_dtype scriptable

* move convert dtype to functional_tensor since only works on tensors

* retain availability of convert_image_dtype in functional.py

* Update code and tests

* Replaced int by torch.dtype

* int -> torch.dtype and use F instead of F_t

* Update functional_tensor.py
Co-authored-by: vfdev-5 <vfdev.5@gmail.com>

* Negative padding for functional_pil #2381

* Tests for PIL negative padding #2381

* Move PIL vs tensor test inside test_pad

* Adapt test_pad from test_transforms_tensor.py
Co-authored-by: vfdev <vfdev.5@gmail.com>

* adds boxes conversion

* adds documentation

* adds xywh tests

* fixes small typo

* adds tests

* Remove sphinx theme

* corrects assertions

* cleans code as per suggestion
Signed-off-by: Aditya Oke <okeaditya315@gmail.com>

* reverts assertion

* fixes to assertEqual

* fixes inplace operations

* Adds docstrings

* added documentation

* changes tests

* moves code to box_convert

* adds more tests

* Apply suggestions from code review

Let's leave those changes to a separate PR

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

- Updated code and tests to support batch of tensors, e.g. input of shape (B, C, H, W).

* add download tests for Caltech(101|256)

* lint

Otherwise, torch jit scripted function raises exception on save

* Add encode/write_png functions

* Do not redefine

* Style issues correction

* Comply with low-level interface

* Minor comment correction

* Add python frontend functions

* Add encode_png test

* Pass compession level to encode_png

* Do not compare output buffers

* Convert to bytes

* Compare pil image instead of buffer

* Add error tests

* Add test_write_png

* Remove png test assets

* Register writePNG correctly

* Update write_png docstring

* Do not preserve PIL image beyond the scope
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Add decode_image op

* Fix lint

* More lint

* Add C10_EXPORT

* fix triggers for scheduled workflow

* more fix

* add missing repository checkout

* try fix label in template

* rewrite test infrastructure

* trigger issue generation

* try fix issue template

* try remove quotes

* remove buggy label

* try fix title

* cleanup

* add more test details

* reenable issue creation
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Add decode and write JPEG ops

* Fix styling issues

* Use int64_t instead of int

* Use std::string

* Use jpegcommon.h for read_jpeg

* Minor updates to error handling in read

* Include header only once

* Reverse header inclusion

* Update common header

* Add common definitions

* Include string

* Include header?

* Include header?

* Add Python frontend calls

* Use unsigned long directly

* Fix style issues

* Include cstddef

* Ignore clang-format on cstddef

* Also include stdio

* Add JPEG and PNG include dirs

* Use C10_EXPORT

* Add JPEG encoding test

* Set quality to 75 by default and add write jpeg test

* Minor error correction

* Use assertEquals by assertEqual

* Remove test results

* Use pre-saved PIL output

* Remove extra PIL call

* Use read_jpeg instead of PIL

* Add error tests

* Address review comments

* Fix style issues

* Set test case to uint8

* Update test error check

* Apply suggestions from code review

* Fix clang-format

* Fix lint

* Fix test

* Remove unused file

* Fix regex error message

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

* tries adding genaralized_iou

* fixes linting

* Adds docs for giou, iou and box area

* fixes lint

* removes docs to fixup in other PR

* linter fix

* Cleans comments

* Adds tests for box area, iou and giou

* typo fix for testCase

* fixes typo

* fixes box area test

* fixes implementation

* updates tests to tolerance

* [WIP] All transforms are now derived from torch.nn.Module
- Compose, RandomApply, Normalize can be jit scripted

* Fixed flake8

* Updated code and docs
- added getattr to Lambda and tests
- updated code and docs of Compose
- added failing test with append/extend on Composed.transforms

* Fixed flake8

* Updated code, tests and docs

* 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

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

* Removed float16/cpu case

* 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>

* 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

* [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

* 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>

- Updated docs
- Put warning in the code
- Updated tests

* Unified input for RandomPerspective

* Updated docs

* Fixed failing test and bug with torch.randint

* Update test_functional_tensor.py

* [WIP] Added unified input perspective transformation code

* Unified input for F.perspective
- added tests
- updated docs

* Added more random test configs

* Fixed the code according to PR's review

* Added code for F_t.rotate with test
- updated F.affine tests

* Rotate test tolerance to 2%

* Fixes failing test

* [WIP] RandomRotation

* Unified RandomRotation with tests

* Added code for F_t.rotate with test
- updated F.affine tests

* Rotate test tolerance to 2%

* Fixes failing test

* Optimized _expanded_affine_grid with a single matmul op

* Recoded _compute_output_size

* [WIP] recoded F_t.rotate internal methods

* [WIP] Fixed F.affine to support rectangular images

* Recoded _gen_affine_grid to optimized version ~ affine_grid
- Fixes flake8

* [WIP] Use _gen_affine_grid for affine and rotate

* Fixed tests on square / rectangular images for affine and rotate ops

* Removed redefinition of F.rotate
- due to bad merge

* Added code for F_t.rotate with test
- updated F.affine tests

* Rotate test tolerance to 2%

* Fixes failing test

* Optimized _expanded_affine_grid with a single matmul op

* Recoded _compute_output_size

* Add fake data generator for UCF101

* Minor error correction

* Reduce total number of categories

* Fix naming

* Increase length

* Store in uint8

* Close fds

* Add assertGreater

* Add dimension tests

* Use numel instead of size

* Iterate over folds and splits

* Fixed resize to only match the smaller edge when an int is given instead of always

* Added test cases for resize()

* fix fill parameter for PIL pad

* add test

* fix

* lint

* [WIP] Unified input for T.RandomAffine

* Unified inputs for T.RandomAffine transformation

* Update transforms.py

* Updated docs of F.affine fillcolor

* Update transforms.py

* add torchscriptable adjust_gamma transform

https://github.com/pytorch/vision/issues/1375



* changes based on code-review

* Apply suggested change to add type hint

Required by mypy, even thought technically incorrect due to possible Image parameter. torchscript doesn't support a union based type hint.
Co-authored-by: vfdev <vfdev.5@gmail.com>
Co-authored-by: vfdev <vfdev.5@gmail.com>