GitLab

* 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>

- Fixed incorrect docs layout and a warning

* [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

* [WIP] Added tests on batch of tensors

* Updated tests on batch of images

* All functional transforms can work with (..., C, H, W) format

* Added transforms tests on batch tensors

* Added batch tests for five/ten crop
- updated docs

* add erase function

* add Random Erasing

* Update transforms.py

* Update transforms.py

* add test for random erasing

* Update test_transforms.py

* fix flake8

* Update test_transforms.py

* Update functional.py

* Update test_transforms.py

* fix bug for per-pixel erasing

* Update transforms.py

* specific for coordinate (x, y)

* add raise TypeError for img

* Update transforms.py

* Update transforms.rst

Values from random.random() are always in the range [0.0, 1.0), so the statement `random.random() > 5` will never be true.

* pad updated

* checked pad

* pad checked

* pad checked

* docs changed

Give a short example of how to build a transform pipeline for a
segmentation task.

* Add LinearTransformation to docs
- Arrange the transforms alphabetically

* Add missing transforms to docs

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

* add docs for grayscale + random grayscale transforms

PIL.Image is renamed to 'PIL Image' because PIL.Image is the module and PIL.Image.Image is the class we are referring but is too long to put in docstrings