GitLab

* modes added

* tests_added

* Update test_transforms.py

* Update test_transforms.py

* Update test_transforms.py

* bug fixes to_tensor

* tensor checked

* pad updated

* checked pad

* pad checked

* pad checked

* added separate checks for dimensionality in to_pil_image and added tests

* updated to_pil_image to use both 2D ndarrays and tensors, as well as refactored the tests

* move area calculation out of loop

No need to calculate area 10 times when it doesn't change.

* lint

* 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

Check if `lambd` is callable instead of checking if its a `types.Lambda` object.

* ColorJitter Enhancement

* reduce redundancy

* improve functional

* check input in init

* fix ci fail

* Fix PIL link

* Make normalize more efficient

* fix a bug described in issue #488

* improve doc described in issue #488

* add arguments in RandomCrop as proposed by vfdev-5 in PR #489

This reverts commit 11da4f17.

* 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

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

* 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

Fixes https://github.com/pytorch/vision/issues/399.

The only case of floating point supported by PIL seems to be `F`, so this should fix it.

Updated the doc of RandomResizedCrop.__call__() from 'Image to be flipped' to 'Image to be cropped and resized'

* add __repr__ for all transform objects

* add tests for __repr__