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* [WIP] Fixes #2598
- Adjusted num_kernels and batch_size according to kMaxGridNum * CUDA_NUM_THREADS
- Tests to add

* - Redefined kMaxGridNum as max grid according to current CUDA device
- Added test to check the code from issue and compared grads CPU/CUDA

* Fixed static kMaxGridNum evaluation to dynamic

offset's 1 dimension should be batch size

* Adapted almost all functional tensor tests on CPU/CUDA
- fixed bug with transforms using generated grid
- remains *_crop, blocked by #2568
- TODO: test_adjustments

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

* Fixed issues according to review

* Split tests into two: cpu and cuda

* Updated test_adjustments to run on CPU and CUDA
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Only pull keys from db in lsun for faster cache.

This pull request inhances the speed of the cache creation for lsun dataset. For the "kitchen_train" the speed was getting slow with cache creation taking more then two hours. This speeds up to cache creation in within minutes. The issue was pulling the large image values each time and dropping them.

For more details on this please refer this issue https://github.com/jnwatson/py-lmdb/issues/195.

* Fixed bug in lsun.py when loading multiple categories

* Make linter happy

- Updated docs
- Put warning in the code
- Updated tests

* Unified input for RandomPerspective

* Updated docs

* Fixed failing test and bug with torch.randint

* Update test_functional_tensor.py

* [WIP] Added unified input perspective transformation code

* Unified input for F.perspective
- added tests
- updated docs

* Added more random test configs

* Fixed the code according to PR's review

* Added code for F_t.rotate with test
- updated F.affine tests

* Rotate test tolerance to 2%

* Fixes failing test

* [WIP] RandomRotation

* Unified RandomRotation with tests

* 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

* [WIP] recoded F_t.rotate internal methods

* [WIP] Fixed F.affine to support rectangular images

* Recoded _gen_affine_grid to optimized version ~ affine_grid
- Fixes flake8

* [WIP] Use _gen_affine_grid for affine and rotate

* Fixed tests on square / rectangular images for affine and rotate ops

* Removed redefinition of F.rotate
- due to bad merge

* 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 type hints and move degenerate boxes to a function in torchvision.models.detection.generalized_rcnn

* format code

* format code

* changed to static method

* revert imports

* changed to method

* revert procedure for degenerating boxes

* Fixed resize to only match the smaller edge when an int is given instead of always

* Added test cases for resize()

* add typehints for torchvision.datasets.stl10

* move annotation from class to instance scope

* Clarify scaling of ToTensor transform for target masks in the documentation

* Add link to the segmentation reference
Co-authored-by: vfdev <vfdev.5@gmail.com>
Co-authored-by: vfdev <vfdev.5@gmail.com>

* enable mypy for torchvision.datasets and ignore existing errors

* imagenet

* utils

* lint

* fix fill parameter for PIL pad

* add test

* fix

* lint

* [WIP] Unified input for T.RandomAffine

* Unified inputs for T.RandomAffine transformation

* Update transforms.py

* Updated docs of F.affine fillcolor

* Update transforms.py

* Update transform.py

* Update transform.py

* 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>