GitLab

* Add classes metadata to MNIST and FashionMNIST

* Add `targets` property to MNIST and FashionMNIST

* Add class metadata to CIFAR10/CIFAR100

* Add `targets` property to CIFAR10/CIFAR100

* Add targets attribute to DatasetFolder

This reverts commit 972b80c9.

* cifar10.meta['label_names']

* cifar100.meta['fine_label_names']

* cifar100.meta['coarse_label_names']

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]

* Added Progress Bar to the dataset downloading utility

* Updated progressbar to update by blcoks

* Added progressbar2 to the requirements

* fixed style issues

* Shifted from progressbar to tqdm

nit: replace 4 by expansion

Remove test with variables

* fix for PyTorch v0.4 (remove .data and add _ for the initializations in nn.init)

* fix m.**.**() style to nn.init.**(**) style

* remove .idea

* fix lines and indents

* fix lines and indents

* change to use `kaming_normal_`

* add `.data` for safety

* add nonlinearity='relu' for sure

* fix indents

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

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

* Fix pep8 issues

* Add LinearTransformation to docs
- Arrange the transforms alphabetically

* Add missing transforms to docs

* 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.

Both conditional branches in `__init__` were executed, since `classes` for test
becomes a list and `if` was used instead of `elif`.

* Added reflect, symmetric and edge padding

* Updated padding docs, added tests

Add testing case with Pillow-SIMD, fix bug in RandomAffine if Pillow<5.0.0, add missing docs for affine (#439)

* Fix uninitialized instance variables

* Maintain consistency with other dataset classes

* Fix double assignment

* Fix initialization of self.classes

The URL to download EMNIST seems to have changed. I fixed this.

* 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

It was pointing to an old version where the validation pre-processing was the same as the training pre-processing, and thus had randomness in it (instead of CenterCrop).

* Add random affine transformation

* Rewrite __repr__

* Refactor affine transform and update tests

if on dtype -> elif on dtype

* Update transforms __repr__

* Add _pil_interpolation_to_str map for obtaining strings for PIL.Image interpolation options

* Fix typo

* Add RandomApply, RandomChoice, RandomOrder transformations

* Rename argument `proba` to `p`

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

* Fix documentation