GitLab

Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* [proto] Added few transforms tests, part 1 (#6262)

* Added supported/unsupported data checks in the tests for cutmix/mixup

* Added RandomRotation, RandomAffine transforms tests

* Added tests for RandomZoomOut, Pad

* Update test_prototype_transforms.py

* Added RandomCrop transform and tests (#6271)

* [proto] Added GaussianBlur transform and tests (#6273)

* Added GaussianBlur transform and tests

* Fixing code format

* Copied correctness test

* [proto] Added random color transforms and tests (#6275)

* Added random color transforms and tests

* Disable smoke test for RandomSolarize, RandomAdjustSharpness

* Added RandomPerspective and tests (#6284)

- replaced real image creation by mocks for other tests

* Added more functional tests (#6285)

* [proto] Added elastic transform and tests (#6295)

* WIP [proto] Added functional elastic transform with tests

* Added more functional tests

* WIP on elastic op

* Added elastic transform and tests

* Added tests

* Added tests for ElasticTransform

* Try to format code as in https://github.com/pytorch/vision/pull/5106



* Fixed bug in affine get_params test

* Implemented RandomErase on PIL input as fallback to tensors (#6309)

Added tests

* Added image_size computation for BoundingBox.rotate if expand (#6319)

* Added image_size computation for BoundingBox.rotate if expand

* Added tests

* Added erase_image_pil and eager/jit erase_image_tensor test (#6320)

* Updates according to the review
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* upgrade usort to

* Also update black

* Actually use 1.0.2

* Apply pre-commit
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Another attempt to add transforms

* Fixed padding type hint

* Fixed fill arg for pad and rotate, affine

* code formatting and type hints for affine transformation

* Fixed flake8

* Updated tests to save and load transforms

* Fixed code formatting issue

* Fixed jit loading issue

* Restored fill default value to None
Updated code according to the review

* Added tests for rotation, affine and zoom transforms

* Put back commented code

* Random erase bypass boxes and masks
Go back with if-return/elif-return/else-return

* Fixed acceptable and non-acceptable types for Cutmix/Mixup

* Updated conditions for _BaseMixupCutmix

* Added elastic augment

* ufmt formatting

* updated comments

* fixed circular dependency issue and bare except error

* Fixed three type checking errors in functional_tensor.py

* ufmt formatted

* changed elastic_deformation to a more common implementation

Implementation uses alpha and sigma to control strength and smoothness of the displacement vectors in elastic_deformation instead of control_point_spacings and sigma.

* ufmt formatting

* Some performance updates

Put random offset vectors to device before gaussian_blur is applied speeds it up 3-fold.

* fixed type error

* fixed again a type error

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

* Added some requested changes

- pil image support similar to GaussianBlur
- changed interpolation arg to InterpolationMode
- added a wrapper in torchvision.transforms.functional.py that gets called by the class in transforms.py
-renamed it to ElasticTransform
- handled sigma = 0 case

* added img docstring

* added some tests

* Updated tests and the code

* Added the requested changes to the arguments of F.elastic_transform

Added random_state and displacement as arguments to F.elastic_transform

* fixed the type error

* Fixed tests and docs

* implemented requested changes

Changes:
1) alpha AND sigma OR displacement must be given as arguments to transforms.functional_tensor.elastic_transform instead of alpha AND sigma AND displacement
2) displacements are accepted in transforms.functional.elastic_transform as np.array and torch.Tensor instead of only accepting torch.Tensor

* ufmt formatting

* trochscript error resolved

replaced torch.from_numpy() to torch.Tensor() to make it compatible to torchscript

* revert to torch.from_numpy()

* updated argument checks and errors

- In F.elastic_transform added check to see if both user inputs img and displacement are either of type PIL Image and ndarray or both of type tensor.
- In F_t.elastic_transform added check if alpha and sigma are None if displacement is given or vice versa.

* fixed seed error

changed torch.seed to torch.manual_seed in F_t.elastic_transform

* Reverted displacement type and other cosmetics

* Other minor improvements

* changed gaussian_blur filter size

changed gaussian_blur filter size
from
4 * int(sigma) + 1
to
int(8 * sigma + 1)
to make it consistent with ernestums implementation

* resolved merge error

* Revert "resolved merge error"

This reverts commit 6a4a4e74ff4d078e2c2753d359185f9a81c415d0.

* resolve merge error

* ufmt formatted

* ufmt formated once again..

* fixed unsupported operand error

* Update API and removed random_state from functional part

* Added default values

* Added ElasticTransform to gallery and updated the docstring

* Updated gallery and added _log_api_usage_once
BTW, matplotlib.pylab is deprecated

* Updated gallery transforms code

* Updates according to review
Co-authored-by: vfdev <vfdev.5@gmail.com>

* Requested here https://github.com/pytorch/vision/pull/5898#discussion_r864765799

.

* Fix tests

* ufmt, not black
Co-authored-by: Philip Meier <github.pmeier@posteo.de>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Fixed rotate with expand inconsistency between torch vs PIL on odd-sized images

* Update functional_tensor.py

* Replace get_image_size/num_channels with get_image_dims

* Reduce verbosity

* Fix JIT-scriptability

* Refactoring

* More refactoring

* Replace all _FP/_FT direct calls.

* Remove usages of get_image_size and get_image_num_channels from code-base.

* Fix JIT issues

* Adding missing assertion.

* Removed custom ops for interp with AA

* Fixed umft issues
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* properly deprecate legacy implementation

* cleanup

* use warning over deprecation directive

* remove patch version

* fix link in Kinetics docstring

* Some more

* fix affine functional tests
Co-authored-by: Nicolas Hug <nicolashug@fb.com>

* Added center option to F.affine and RandomAffine ops

* Updates according to the review

* Fix bug on autocontrast when `min==max`

* Adding PIL vs TorchVision test for min==max

* Update functional_tensor.py

To resolve issue #4818
Add assert function and logic after checking bound of image

* Update functional_tensor.py

* Update test

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Fix linter
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

Co-authored-by: Nicolas Hug <nicolashug@fb.com>

* add ufmt as code formatter

* cleanup

* quote ufmt requirement

* split imports into more groups

* regenerate circleci config

* fix CI

* clarify local testing utils section

* use ufmt pre-commit hook

* split relative imports into local category

* Revert "split relative imports into local category"

This reverts commit f2e224cde2008c56c9347c1f69746d39065cdd51.

* pin black and usort dependencies

* fix local test utils detection

* fix ufmt rev

* add reference utils to local category

* fix usort config

* remove custom categories sorting

* Run pre-commit without fixing flake8

* got a double import in merge
Co-authored-by: Nicolas Hug <nicolashug@fb.com>

* update channels parameter to every calling to check_functional_vs_PIL_vs_scripted

* update adjust_saturation

* update docstrings for functional transformations

* parametrize channels

* update docstring of ColorJitter class

* move channels to class's parameter

* remove testing channels for geometric transforms

* revert redundant changes

* revert redundant changes

* update grayscale test cases for randaugment, autoaugment, trivialaugment

* update docstrings of randaugment, autoaugment, trivialaugment

* update docstring of ColorJitter

* fix adjust_hue's docstring

* change test equal tolerance

* refactor grayscale tests

* make get_grayscale_test_image private

Description:
- Removed tests executing deprecated F_t.center/five/ten_crop methods
- Suppressed deprecation warnings while running test/test_functional_tensor.py

* WIP on backward op interpolation with AA

* Removed cuda tests and reformat cpp code

* Fixed clang wrong formatting

* Added channels last test case

* Added CUDA support for backward pass, interpolation with AA

* Removed unused buffers
Co-authored-by: vfdev-5 <vfdev-5@gmail.com>

* WIP on backward op interpolation with AA

* Removed cuda tests and reformat cpp code

* Fixed clang wrong formatting

* Added channels last test case
Co-authored-by: vfdev-5 <vfdev-5@gmail.com>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Co-authored-by: Nicolas Hug <nicolashug@fb.com>

Co-authored-by: Philip Meier <github.pmeier@posteo.de>

Co-authored-by: Philip Meier <github.pmeier@posteo.de>

* Added CUDA support for interpolation AA bilinear/bicubic

* Fixed code formatting error

* Fixed cuda tests on cpu-only builds

* Fixed cuda internal torch check for bicubic mode

* Added support for bicubic mode with AA

* Updated comment in the test

* WIP Added antialias option to transforms.functional.resize

* Updates according to the review

* Excluded these C++ files for iOS build

* Added support for mixed downsampling/upsampling

* Fixed heap overflow caused by explicit loop unrolling

* Applied PR review suggestions
- used pytest parametrize instead unittest
- cast to scalar_t ptr
- removed interpolate aa files for ios/android keeping original cmake version

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/3748

This PR parametrizes the `perspective`-related tests, and avoids having deeply nested for-loops which will help debugging. "What" gets tested is left unchanged.

The newly-introduced `cpu_and_gpu()` generator along with the `dont_collect` mark is a logic that allows to not run CPU tests on GPU machines (and vice versa).

Reviewed By: fmassa

Differential Revision: D27908299

fbshipit-source-id: 24a10a89fe90ae0a9e62de4bc7e768a669ebf212