GitLab

* Fill color support for tensor affine transforms

* PEP fix

* Docstring changes and float support

* Docstring update for transforms and float type cast

* Cast only for Tensor

* Temporary patch for lack of Union type support, plus an extra unit test

* More plausible bilinear filling for tensors

* Keep things simple & New docstrings

* Fix lint and other issues after merge

* make it in one line

* Docstring and some code modifications

* More tests and corresponding changes for transoforms and docstring changes

* Simplify test configs

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Move assertions
Co-authored-by: vfdev <vfdev.5@gmail.com>

[BC-breaking] Introduced InterpolationModes and deprecated arguments: resample and fillcolor (#2952)

* Deprecated arguments: resample and fillcolor
Replaced by interpolation and fill

* Updates according to the review

* Added tests to check warnings and asserted BC

* [WIP] Interpolation modes

* Added InterpolationModes enum

* Added supported for int values for interpolation for BC

* Removed useless test code

* Fix flake8

* Add explicit check for number of channels

Example why you need to check it:
`M = torch.randint(low=0, high=2, size=(6, 64, 64), dtype = torch.float)`
When you put this input through to_pil_image without mode argument, it converts to uint8 here:
```
if pic.is_floating_point() and mode != 'F':
            pic = pic.mul(255).byte()
```
and change the mode to RGB here:
```
if mode is None and npimg.dtype == np.uint8:
            mode = 'RGB'
```
Image.fromarray doesn't raise if provided with mode RGB and just cut number of channels from what you have to 3

* Check number of channels before processing

* Add test for invalid number of channels

* Add explicit check for number of channels

Example why you need to check it:
`M = torch.randint(low=0, high=2, size=(6, 64, 64), dtype = torch.float)`
When you put this input through to_pil_image without mode argument, it converts to uint8 here:
```
if pic.is_floating_point() and mode != 'F':
            pic = pic.mul(255).byte()
```
and change the mode to RGB here:
```
if mode is None and npimg.dtype == np.uint8:
            mode = 'RGB'
```
Image.fromarray doesn't raise if provided with mode RGB and just cut number of channels from what you have to 3

* Check number of channels before processing

* Add test for invalid number of channels

* Put check after channel dim unsqueeze

* Add test if error message is matching

* Delete redundant code

* Bug fix in checking for bad types
Co-authored-by: Demyanchuk <demyanca@mh-hannover.local>
Co-authored-by: vfdev <vfdev.5@gmail.com>

* Added GaussianBlur transform

* fixed linting

* supports fixed radius for kernel

* [WIP] New API for gaussian_blur

* Gaussian blur with kernelsize and sigma API

* Fixed implementation and updated tests

* Added large input case and refactored gt into a file

* Updated docs

* fix kernel dimesnions order while creating kernel

* added tests for exception handling of gaussian blur

* fix linting, bug in tests

* Fixed failing tests, refactored code and other minor fixes
Co-authored-by: vfdev-5 <vfdev.5@gmail.com>

* 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>

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

* [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

Co-authored-by: vfdev <vfdev.5@gmail.com>

* 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>

* [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

* 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>

* [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

* [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>

* [WIP] F.affine

* [WIP] F.affine + tests

* Unified input for F.affine

* Removed commented code

* Removed unused imports

Incoherence is when affine transformation is 90 degrees rotation and output contains a zero line

* Minor docs improvement

* Replaced link by already defined `filters`_

* [WIP] Unify random resized crop

* Unify input for RandomResizedCrop

* Fixed bugs and updated test

* Added resized crop functional test
- fixed bug with size convention

* Fixed incoherent sampling

* Fixed torch randint review remark

* [WIP] F.resize with tensor

* Adapted T.Resize and F.resize with a test

* According to the review, fixed copy-pasted messages and unused imports

* Related to #2292
- RandomErasing is not scriptable

* Fixed code according to review comments
- added additional checking of value vs img num_channels

- RandomCrop: crop with padding using all commonly supported modes

* [WIP] Unified Tensor/PIL crop

* Fixed misplaced type annotation

* Fixed tests
- crop with padding
- other tests using mising private functions: _is_pil_image, _get_image_size

* Unified CenterCrop and F.center_crop
- sorted includes in transforms.py
- used py3 annotations

* Unified FiveCrop and F.five_crop

* Improved tests and docs

* Unified TenCrop and F.ten_crop

* Removed useless typing in functional_pil

* Updated code according to the review
- removed useless torch.jit.export
- added missing typing return type
- fixed F.F_pil._is_pil_image -> F._is_pil_image

* Removed useless torch.jit.export

* Improved code according to the review

* [WIP] Add Tensor implementation for pad

* Unified Pad and F.pad opertion for PIL and Tensor inputs

* Added another test and improved docstring

* Updates according to the review

* Cosmetics and replaced f-string by "".format

* Updated docstring
- added compatibility support for padding as [value, ] for functional_pil.pad
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

I was reading through the code and noticed a few spelling errors
orignal -> original
maintaing -> maintaining

* add convert_image_dtype to functionals

* add ConvertImageDtype transform

* add test

* remove underscores from numbers since they are not compatible with python<3.6

* address review comments 1/3

* fix torch.bool

* use torch.iinfo in test

* fix flake8

* remove double conversion

* fix flake9

* bug fix

* add error messages to test

* disable torch.float16 and torch.half for now

* add docstring

* add test for consistency

* move nested function to top

* test in CI

* dirty progress

* add int to int and cleanup

* lint
Co-authored-by: Philip Meier <meier.philip@posteo.de>

* feat: torchscriptable adjusments

* fix: tensor output type

* feat: ColorJitter torchscriptable

* fix: too many blank lines

* fix: documentation spacing and torchscript annotation

* refactor: list type for _check_input

* refactor: reverting to original syntax
Co-authored-by: clement.joudet <clement.joudet@inventia.life>

* Make RandomHorizontalFlip torchscriptable

* Make _is_tensor_a_torch_image more generic

* Make RandomVerticalFlip torchscriptable (#2283)

* Make RandomVerticalFlip torchscriptable

* Fix lint

* Bugfix in pad

* Address review comments

* Fix lint

* Adds as_tensor to functional.py

Similar functionality to to_tensor without the default conversion to float and division by 255.
Also adds support for Image mode 'L'.

* Adds tests to AsTensor()

Adds tests to AsTensor and removes the conversion to float and division by 255.

* Adds AsTensor to transforms.py

Calls the as_tensor function in functionals and adds the function AsTensor as callable from transforms.

* Removes the pic.mode == 'L'

This was handled by the else condition previously so I'll remove it.

* Fix Lint issue

Adds two line breaks between functions to fix lint issue

* Replace from_numpy with as_tensor

Removes the extra if conditionals and replaces from_numpy with as_tensor.

* Renames as_tensor to pil_to_tensor

Renames the function as_tensor to pil_to_tensor and narrows the scope of the function.  At the same time also creates a flag that defaults to True for swapping to the channels first format.

* Renames AsTensor to PILToImage

Renames the function AsTensor to PILToImage and modifies the description.  Adds the swap_to_channelsfirst boolean variable to indicate if the user wishes to change the shape of the input.

* Add the __init__ function to PILToTensor 

Add the __init__ function to PILToTensor since it contains the swap_to_channelsfirst parameter now.

* fix lint issue

remove trailing white space

* Fix the tests

Reflects the name change to PILToTensor and the parameter to the function as well as the new narrowed scope that the function only accepts PIL images.

* fix tests

Instead of undoing the transpose just create a new tensor and test that one.

* Add the view back

Add img.view(pic.size[1], pic.size[0], len(pic.getbands())) back to outside the if condition.

* fix test

fix conversion from torch tensor to PIL back to torch tensor.

* fix lint issues

* fix lint

remove trailing white space

* Fixed the channel swapping tensor test

Torch tranpose operates differently than numpy transpose.  Changed operation to permute.

* Add mode='F'

Add mode information when converting to PIL Image from Float Tensor.

* Added inline comments to follow shape changes

* ToPILImage converts FloatTensors to uint8

* Remove testing not swapping

* Removes the swap_channelsfirst parameter

Makes the channel swapping the default behavior.

* Remove the swap_channelsfirst argument

Remove the swap_channelsfirst argument and makes the swapping the default functionality.

* 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>