GitLab

* Renamed original method to test center crop

* Added test method, docs and added padding when imgsize < cropsize.

* BugFix - keep odd_crop_size odd

* Do not crop when image size after padding matches crop size; updated test.
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* initial fix

* fill=0

* docstrings

* fill type check

* fill type check

* set None to zero

* unit tests

* set instead of NotImplemented

* fix W293
Co-authored-by: vfdev <vfdev.5@gmail.com>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Invert Transform (#3104)

* Adding invert operator.

* Make use of the _assert_channels().

* Update upper bound value.

* Remove private doc from invert, create or reuse generic testing methods to avoid duplication of code in the tests. (#3106)

* Create posterize transformation and refactor common methods to assist reuse. (#3108)

* Implement the solarize transform. (#3112)

* Implement the adjust_sharpness transform (#3114)

* Adding functional operator for sharpness.

* Adding transforms for sharpness.

* Handling tiny images and adding a test.

* Implement the autocontrast transform. (#3117)

* Implement the equalize transform (#3119)

* Implement the equalize transform.

* Turn off deterministic for histogram.

* Fixing test. (#3126)

* Force ratio to be float to avoid numeric overflows on blend. (#3127)

* Separate the tests of Adjust Sharpness from ColorJitter. (#3128)

* Add AutoAugment Policies and main Transform (#3142)

* Separate the tests of Adjust Sharpness from ColorJitter.

* Initial implementation, not-jitable.

* AutoAugment passing JIT.

* Adding tests/docs, changing formatting.

* Update test.

* Fix formats

* Fix documentation and imports.

* Apply changes from code review:
- Move the transformations outside of AutoAugment on a separate method.
- Renamed degenerate method for sharpness for better clarity.

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

* Apply more changes from code review:
- Add InterpolationMode parameter.
- Move all declarations away from AutoAugment constructor and into the private method.

* Update documentation.

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

* Apply changes from code review:
- Refactor code to eliminate as any to() and clamp() as possible.
- Reuse methods where possible.
- Apply speed ups.

* Replacing pad.
Co-authored-by: vfdev <vfdev.5@gmail.com>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

It's not used currently and can lead to exceptions such as runtime error: 5.7896e+76 is outside the range of representable values of type 'float'

[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>

* Adding output channels implementation for pngs.

* Adding tests for png.

* Adding channels in the API and documentation.

* Fixing formatting.

* Refactoring test_image.py to remove huge grace_hopper_517x606.pth file from assets and reduce duplicate code. Moving jpeg assets used by encode and write unit-tests on their separate folders.

* Adding output channels implementation for jpegs. Fix asset locations.

* Add tests for JPEG, adding the channels in the API and documentation and adding checks for inputs.

* Changing folder for unit-test.

* Fixing windows flakiness, removing duplicate test.

* Replacing components to channels.

* Adding reference for supporting CMYK.

* Minor changes: num_components to output_components, adding comments, fixing variable name etc.

* Reverting output_components to num_components.

* Replacing decoding with generic method on tests.

* Palette converted to Gray.

* 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

* Added CPU/CUDA+batch tests

* Fixed tests according to review
Co-authored-by: Brian <nairbv@yahoo.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>

* 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>

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

* 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

* fix fill parameter for PIL pad

* add test

* fix

* lint

* 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

* Related to #2292
- RandomErasing is not scriptable

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

* 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>

* 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

* remove sys.version_info == 2

* remove sys.version_info < 3

* remove from __future__ imports

* Add fill option to RandomPerspective #1972

* Minor fix to docstring syntax

* Add _parse_fill() to get fillcolor (#1972)

* Minor refactoring as per comments.

* Added test for RandomPerspective with fillcolor.

* Force perspective transform in test.

* F.normalize unsqueeze mean & std if necessary

* added tests to F.normalize for 3d mean & std tensors

* initial fix

* outsourced num bands lookup

* fix doc

* added pillow version requirement

* simplify number of bands extraction

* remove unrelated change

* remove indirect dependency on pillow>=5.2.0

* extend docstring to transform

* bug fix

* added test

* Testing CI

* Disable tests for Pillow 7

* Improve readability of affine transformation code

* Make shear transformation area preserving

The previous shear implementation did not preserve area, and we
implement a version that does.

The formula used was verified with the following sympy code:

from sympy import Matrix, cos, sin, tan, simplify
from sympy.abc import x, y, phi

Xs = Matrix(
        [[1, -tan(x)],
         [0, 1]]
        )

Ys = Matrix(
        [[1, 0],
         [-tan(y), 1]]
        )

R = Matrix(
        [[cos(phi), -sin(phi)],
         [sin(phi), cos(phi)]]
        )

RSS = Matrix(
        [[cos(phi - y)/cos(y), -cos(phi - y)*tan(x)/cos(y) - sin(phi)],
         [sin(phi - y)/cos(y), -sin(phi - y)*tan(x)/cos(y) + cos(phi)]])

print(simplify(R * Ys * Xs - RSS))

One thing that is not clear (and could be tested) is whether avoiding
the explicit products and calculations in _get_inverse_affine_matrix
really gives performance benefits - compared to doing the explicit
calculation done in _test_transformation.

* Use np.matmul instead of @

The @ syntax is not supported in Python 2.

* test: Updated assert in test_transforms

Updated all raw asserts to corresponding unittest.TestCase.assert. See #1483

* test: Fixed linter on test_transforms

* Fix flakiness of test_randomresized_params

* Real fix

* Reduce number of iters

* 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

* initial commit

* add more checks, fix lint, fix doc

* updates on normalize

* test fixes

* Update test_transforms.py