GitLab

* Extend the EfficientNet class to support v1 and v2.

* Refactor config/builder methods and add prototype builders

* Refactoring weight info.

* Update dropouts based on TF config ref

* Update BN eps on TF base_config

* Use Conv2dNormActivation.

* Adding pre-trained weights for EfficientNetV2-s

* Add Medium and Large weights

* Update stats with single batch run.

* Add accuracies in the docs.

Co-authored-by: Dbhasin1 <drishti_b@me.iitr.c.in>

* Improve test_video_reader

* Fix linter error

* Move VideoReader out of __init__

* Fix linter error

* fix grayscale to RGB for batches

* make unsupported types in auto augment a parameter

* make auto augment kwargs explicit

* add missing error message

* add support for specifying probabilites on RandomChoice

* remove TODO for deprecating p on random transforms

* streamline sample type checking

* address comments

* split image_size into height and width in auto augment
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* expand prototype functional scriptability tests

* remove obsolete skips

* add prototype AugMix transform

* cleanup

* refactor auto augment subclasses to only trnasform a single image

* address review comments

Co-authored-by: Prabhat Roy <prabhatroy@fb.com>

* Replace get_image_size/num_channels with get_image_dims

* Reduce verbosity

* Fix JIT-scriptability

* Refactoring

* More refactoring

* Replace all _FP/_FT direct calls.

* Remove usages of get_image_size and get_image_num_channels from code-base.

* Fix JIT issues

* Adding missing assertion.

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

* Create dropblock.py

* add dropblock2d

* fix pylint

* refactor dropblock

* add dropblock

* Rename dropblock.py to drop_block.py

* fix pylint

* add dropblock

* add dropblock3d

* add drop_block3d

* add dropblock

* Update drop_block.py

* Update torchvision/ops/drop_block.py
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Update torchvision/ops/drop_block.py
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Update torchvision/ops/drop_block.py
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Update torchvision/ops/drop_block.py
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Update drop_block.py

* Update drop_block.py

* import torch.fx

* fix lint

* fix lint

* Update drop_block.py

* improve dropblock

* add dropblock

* refactor dropblock

* fix doc

* remove the limitation of block_size

* Update torchvision/ops/drop_block.py
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* fix lint

* fix lint

* add dropblock

* Fix linter

* add dropblock random check

* reduce test time

* Update test_ops.py

* speed the dropblock test

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

* add prototype transforms that don't need dispatchers

* cleanup

* remove legacy_transform decorator

* remove legacy classes

* remove explicit param passing

* streamline extra_repr

* remove obsolete ._supports() method

* cleanup

* remove Query

* cleanup

* fix tests

* kernels -> functional

* move image size and num channels extraction to functional

* extend legacy function to extract image size and num channels

* implement dispatching for auto augment

* fix auto augment dispatch

* revert some naming changes

* remove ability to pass params to autoaugment

* fix legacy image size extraction

* align prototype.transforms.functional with transforms.functional

* cleanup

* fix image size and channels extraction

* fix affine and rotate

* revert image size to (width, height)

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

* Add ops.conv3d

* Refactor for conv2d and 3d

* Refactor

* Fix bug

* Addres review

* Fix bug

* nit fix

* Fix flake

* Final fix

* remove documentation

* fix linter

* Update all the implementations to use new Conv

* Small doc fix
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
Co-authored-by: Joao Gomes <jdsgomes@fb.com>

* Use onnx function only in tracing mode

* Add missing import

* Address review comments

* Fix type annotation

* Ignore return type error

* Remove unused import

* Add fake cast

* Fix mypy error

* Fix mypy error

* Update torchvision/models/detection/_utils.py
Co-authored-by: Shubham Bhokare <32080845+shubhambhokare1@users.noreply.github.com>

* _fake_cast_onnx approach
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
Co-authored-by: Shubham Bhokare <32080845+shubhambhokare1@users.noreply.github.com>

Also, remove reference to conda-forge, all CUDA toolchain should be
available from NVIDIA channel

Install h5py from pip on Windows and skip failing gaussian_blur tests if Win+CUDA11.3 is used

* Add topk minimizer function to _utils

* Apply ufmt formatting

* Apply min function for tracing and scripting

* Add type ignore to avoid cast

* fix flake

* Fix python_type_check
Co-authored-by: Prabhat Roy <prabhatroy@fb.com>

* Make small improvements to the release notes scripts

Add release tags as parameters, exclude prototype stuff from release notes and
automate contributors list

* add tensor kernels for normalize and erase

* add image tensor assertion

* Revert "disable ufmt pre-commit hook (#5454)"

This reverts commit 9301685a.

* upgrade ufmt to 1.3.2

* disable ufmt pre-commit hook

* run ufmt outside of pre-commit

* fix prototype resource loading

* revert unrelated change

* Add Python-3.10 (build and test)

* Fix functional.adjust_gamma
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
Co-authored-by: Joao Gomes <joaopsgomes@gmail.com>

* Fix bbox scaling estimation for Large Scale Jitter

* Fix linter

* Adding Scale Jitter in references.

* Update documentation.

* Address review comments.

* Adding basic augmix implementation.

* Finish the implementation.

* Add tests and documentation.

* Fix tests.

* Simplify code.

* Speed optimizations.

* Per image weights instead of per batch.

* Fix tests.

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

* Changing the default severity value to get by default the same strength as RandAugment.
Co-authored-by: vfdev <vfdev.5@gmail.com>

* add option to fail a transform on certain types rather than passthrough

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

* Delete stale packaging scripts

`osx_wheel.sh` and `linux_manywheel.sh` has not been functional for a
while and are not referenced from any of the CI scripts

Updated `README.md` to reflect how release binary builds are currently run

* add prototype transforms that use the prototype dispatchers

Conflicts:
	torchvision/prototype/transforms/__init__.py

* simplify

* add logger

* remove legacy classes

Conflicts:
	torchvision/prototype/transforms/_augment.py
	torchvision/prototype/transforms/_auto_augment.py
	torchvision/prototype/transforms/_geometry.py

* make get_params private

* remove randbool method

* remove AutoAugmentDispatcher

* add high level kernels for meta conversion

* remove transforms meta abstraction from auto augment transforms

* appease mypy

* add smoke tests for transforms

* remove Query object

* remove extra_repr helper

* fix tests

* appease mypy

* revert some changes on the kernel tests

* fix dispatcher annotations

* remove float cast for torch.rand

* add helper to query image

* fix imports

* address auto augment comments

* cleanup