GitLab

* cast mnist targer to int

* fix unused variables

* fix syntax in tests

* remove # noqa; rename l variable to line_split; use except OSError

* add W504

* add W504

* add W503,W504 tox.ini

Improve `test_pad_with_non_constant_padding_modes` to avoid data multiplication in `transforms.ToPILImage()` on float data:
```python
img = torch.zeros(3, 27, 27)  # Float32
img[:, :, 0] = 1   # we add 1 and not 255
img = transforms.ToPILImage()(img)  # This converts 1 to 255 due to [pic = pic.mul(255).byte()](https://github.com/pytorch/vision/blob/master/torchvision/transforms/functional.py#L107)
```
and thus test's correct values are [..., 200, 1, 0]

Remove test with variables

* Add case in test_to_tensor for PIL Images mode '1'

* Add support in ToTensor for PIL Images mode '1'

* Fix pep8 issues

* Made transorms.RandomCrop tolerate images smaller than the given size.

* Extended the tescase for transforms.RandomCrop

* Made the testcase test for owidth=width+1

* Fixed the one pixel pading and the testcase

* Fixed minor lintint errors. flake8 passes.

* Added reflect, symmetric and edge padding

* Updated padding docs, added tests

* Adding tests to ImageFolder

* Adding DatasetFolder class

* Fix tests for pytest and code for lint checker

* Adding mock to requirements for ImageFolder tests

* Remove mocks from requirements

This reverts commit ab03dc43.

* Adding tests to ImageFolder

* Adding DatasetFolder class

* Fix tests for pytest and code for lint checker

* Adding mock to requirements for ImageFolder tests

* Add random affine transformation

* Rewrite __repr__

* Refactor affine transform and update tests

* Add RandomApply, RandomChoice, RandomOrder transformations

* Rename argument `proba` to `p`

* Set probability as configuration parameter in RandomHorizontalFlip and RandomHorizontalFlip (#414)

* Fix documentation

* add __repr__ for all transform objects

* add tests for __repr__

* add to_grayscale + randomGrayscale

add rotate and RandomRotation to transforms

* refactor transforms and add transforms/functional.py

* add __all__ to transforms.py

* add assert for byte tensor to_pil_image

* add mode param to ToPILImage

* add some more tests

* fix formatiing on error messages

* added tests for to_pil_image with mode param

* flake8 fixes

expands coverage on test_to_tensor to 1 and 4 image channels

* added Whiten transform and relative section in README

* added Whiten transform and its section in README

* fixed lint

* fixed lint again

* added linear transformation and whitening test unitary covariance

* fixed lint and test

* commit before rebasing

* before rebase

* added assertion to check matrix size

* fix readme

* added space around operator

* replace assert with raise ValueError, fix spaces and readme

* increase num_samples, reduce alpha and fix seed for hflip and vflip tests

* flake8 fixes

* Add adjust_hue and adjust_saturation

* Add adjust_brightness, adjust_contrast

Also
* Change adjust_saturation to use pillow implementation
* Documentation made clear

* Add adjust_gamma

* Add ColorJitter

* Address review comments

* Fix documentation for ColorJitter

* Address review comments 2

* Fallback to adjust_hue in case of BW images

* Add tests

* fix dtype

rename scaled_crop -> resized_crop + rename RandomSizedCrop -> RandomResizedCrop

TenCrop and FiveCrop refactored

add FiveCrop and TenCrop

add RandomVerticalFlip transform

make size param of Scale (h,w) not (w,h)

It can be enabled by calling torchvision.set_image_backend('accimage')

* Fixed border missing on bottom and right side using make_grid

* Add support for Transforms.Scale([h, w]) with specific height and width

if self.size is a int then scale the image with the shorter side,
otherwise if self.size is a list then scale the image to self.size
directly

* Add assert of size and doc for README

Add assert of size and doc for README

* Fix linter problem

Fix linter problem

* Add test for Scale

Add test for Scale

* Add both tuple and list support for Scale.size

Add both tuple and list support for Scale.size

* Add order of Scale.size in document and test case for list type of Scale.size

Add order of Scale.size in document and test case for list type of
Scale.size