GitLab

* Fix inconsistent version pinning across yaml files.

* Pin pillow to >=5.3.0

* Pin pillow>=5.3.0 everywhere

* WIP Added antialias option to transforms.functional.resize

* Updates according to the review

* Excluded these C++ files for iOS build

* Added support for mixed downsampling/upsampling

* Fixed heap overflow caused by explicit loop unrolling

* Applied PR review suggestions
- used pytest parametrize instead unittest
- cast to scalar_t ptr
- removed interpolate aa files for ios/android keeping original cmake version

* Fixed return docstrings

* Added some refs and corrected some parts

* more refs, and a note about dtypes
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* docs for resize

* address comment: describe antialiasing

Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* WIP, still needs tests and docs

* tests

* flake8

* Docs + fixed some tests

* proper error messages

* aspect ratio must be a sampling from log scale.
reference from: https://github.com/pytorch/vision/blob/8317295c1d272e0ba7b2ce31e3fd2c048235fc73/torchvision/transforms/transforms.py#L833-L836



* add random erasing unittest code

* Increased threshold for difference in sampling rate

* move outside of the loop

* log_ratio move outside of the loop. RandomResizedCrop also
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Renamed original method to test center crop

* Added test method, docs and added padding when imgsize < cropsize.

* BugFix - keep odd_crop_size odd

* Do not crop when image size after padding matches crop size; updated test.
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* initial fix

* fill=0

* docstrings

* fill type check

* fill type check

* set None to zero

* unit tests

* set instead of NotImplemented

* fix W293
Co-authored-by: vfdev <vfdev.5@gmail.com>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* adjust_hue

* adjust_*

* colorjitter

* Initial doc clean-up

* Remove all private docs

* Rename files

* Highlight backend inconsistencies

* Sequence and number

* [Need checking] AutoAugment related doc change

* Revert name changes

* Invert Transform (#3104)

* Adding invert operator.

* Make use of the _assert_channels().

* Update upper bound value.

* Remove private doc from invert, create or reuse generic testing methods to avoid duplication of code in the tests. (#3106)

* Create posterize transformation and refactor common methods to assist reuse. (#3108)

* Implement the solarize transform. (#3112)

* Implement the adjust_sharpness transform (#3114)

* Adding functional operator for sharpness.

* Adding transforms for sharpness.

* Handling tiny images and adding a test.

* Implement the autocontrast transform. (#3117)

* Implement the equalize transform (#3119)

* Implement the equalize transform.

* Turn off deterministic for histogram.

* Fixing test. (#3126)

* Force ratio to be float to avoid numeric overflows on blend. (#3127)

* Separate the tests of Adjust Sharpness from ColorJitter. (#3128)

* Add AutoAugment Policies and main Transform (#3142)

* Separate the tests of Adjust Sharpness from ColorJitter.

* Initial implementation, not-jitable.

* AutoAugment passing JIT.

* Adding tests/docs, changing formatting.

* Update test.

* Fix formats

* Fix documentation and imports.

* Apply changes from code review:
- Move the transformations outside of AutoAugment on a separate method.
- Renamed degenerate method for sharpness for better clarity.

* Update torchvision/transforms/functional.py
Co-authored-by: vfdev <vfdev.5@gmail.com>

* Apply more changes from code review:
- Add InterpolationMode parameter.
- Move all declarations away from AutoAugment constructor and into the private method.

* Update documentation.

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

* Apply changes from code review:
- Refactor code to eliminate as any to() and clamp() as possible.
- Reuse methods where possible.
- Apply speed ups.

* Replacing pad.
Co-authored-by: vfdev <vfdev.5@gmail.com>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Fill color support for tensor affine transforms

* PEP fix

* Docstring changes and float support

* Docstring update for transforms and float type cast

* Cast only for Tensor

* Temporary patch for lack of Union type support, plus an extra unit test

* More plausible bilinear filling for tensors

* Keep things simple & New docstrings

* Fix lint and other issues after merge

* make it in one line

* Docstring and some code modifications

* More tests and corresponding changes for transoforms and docstring changes

* Simplify test configs

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Move assertions
Co-authored-by: vfdev <vfdev.5@gmail.com>

[BC-breaking] Introduced InterpolationModes and deprecated arguments: resample and fillcolor (#2952)

* Deprecated arguments: resample and fillcolor
Replaced by interpolation and fill

* Updates according to the review

* Added tests to check warnings and asserted BC

* [WIP] Interpolation modes

* Added InterpolationModes enum

* Added supported for int values for interpolation for BC

* Removed useless test code

* Fix flake8

* ColorJitter gets its random params by calling get_params().

* Update arguments.

* Styles.

* Add description for Nones.

* Chainging Nones to optional.

* docs: Fixed docstring of RandomResizedCrop

* docs: Updated docstring
Co-authored-by: vfdev <vfdev.5@gmail.com>

* [WIP] Added scriptable transforms python example

* Replaced script file with jupyter notebook

* Updated readme

* Updates according to review
+ updated docstrings

* Updated notebook and docstring according to the review

* torch script -> torchscript

* 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

* make convert_image_dtype scriptable

* move convert dtype to functional_tensor since only works on tensors

* retain availability of convert_image_dtype in functional.py

* Update code and tests

* Replaced int by torch.dtype

* int -> torch.dtype and use F instead of F_t

* Update functional_tensor.py
Co-authored-by: vfdev-5 <vfdev.5@gmail.com>

- Updated code and tests to support batch of tensors, e.g. input of shape (B, C, H, W).

* [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

* Refactor transforms input checks
- added _setup_size to check and setup size argument

* More refactor

* [WIP] Unify ops Grayscale and RandomGrayscale

* Unified inputs for grayscale op and transforms
- deprecated F.to_grayscale in favor of F.rgb_to_grayscale

* Fixes bug with fp input

* [WIP] Updated code according to review

* Removed unused import

* Unified input for RandomPerspective

* Updated docs

* Fixed failing test and bug with torch.randint

* Update test_functional_tensor.py

* 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

* 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>