GitLab

* 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>

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

* 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 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

* Fix resize when size == small_edge_size and max_size isn't None

* Better test name

* Removed custom ops for interp with AA

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

* revamp prototype features (#5283)

* remove decoding from prototype datasets (#5287)

* remove decoder from prototype datasets

* remove unused imports

* cleanup

* fix readme

* use OneHotLabel in SEMEION

* improve voc implementation

* revert unrelated changes

* fix semeion mock data

* fix pcam

* readd functional transforms API to prototype (#5295)

* readd functional transforms

* cleanup

* add missing imports

* remove __torch_function__ dispatch

* readd repr

* readd empty line

* add test for scriptability

* remove function copy

* change import from functional tensor transforms to just functional

* fix import

* fix test

* fix prototype features and functional transforms after review (#5377)

* fix prototype functional transforms after review

* address features review

* make mypy more strict on prototype features

* make mypy more strict for prototype transforms

* fix annotation

* fix kernel tests

* add automatic feature type dispatch to functional transforms (#5323)

* add auto dispatch

* fix missing arguments error message

* remove pil kernel for erase

* automate feature specific parameter detection

* fix typos

* cleanup dispatcher call

* remove __torch_function__ from transform dispatch

* remove auto-generation

* revert unrelated changes

* remove implements decorator

* change register parameter order

* change order of transforms for readability

* add documentation for __torch_function__

* fix mypy

* inline check for support

* refactor kernel registering process

* refactor dispatch to be a regular decorator

* split kernels and dispatchers

* remove sentinels

* replace pass with ...

* appease mypy

* make single kernel dispatchers more concise

* make dispatcher signatures more generic

* make kernel checking more strict

* revert doc changes

* address Franciscos comments

* remove inplace

* rename kernel test module

* fix inplace

* remove special casing for pil and vanilla tensors

* address comments

* update docs

* cleanup features / transforms feature branch (#5406)

* mark candidates for removal

* align signature of resize_bounding_box with corresponding image kernel

* fix documentation of Feature

* remove interpolation mode and antialias option from resize_segmentation_mask

* remove or privatize functionality in features / datasets / transforms

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

* Added ref tests for shear X/Y

* Added PIL tests and fixed tan(level) difference

* Updated tests

* Fixed reparam for shear X/Y in autoaugment

* Fixed arc_level -> level as atan is applied internally

* Fixed links

* Refactor BoxOps tests to use parameterize

* Refactor BoxOps tests to use parameterize

* Refactor BoxOps to use parameterize, addressed comments from PR#5380

* Refactor BoxOps to use parameterize, addressed minor styling comments from PR#5380

* Refactor BoxOps to use parameterize, addressed typing errorsfrom PR#5380

* Refactor BoxOps to use parameterize, addressed minor naming comments for PR#5380

* properly deprecate legacy implementation

* cleanup

* use warning over deprecation directive

* remove patch version

* fix link in Kinetics docstring

* Some more

* fix affine functional tests
Co-authored-by: Nicolas Hug <nicolashug@fb.com>

* Remove APIs that were deprecated before 0.8

* More stuff

* Some more

* oops

* Add random colors

* Update error message, pretty the code

* Update edge cases

* Change implementation to tuples

* Fix bugs

* Add tests

* Reuse palette

* small rename fix

* Update tests and code

* Simplify code

* ufmt

* fixed colors -> random colors in docstring

* Actually simplify further

* Silence mypy. Twice. lol.
Co-authored-by: Nicolas Hug <nicolashug@fb.com>

* Add is_qat support using a method getter

* Switch to an internal _fuse_modules

* Fix linter.

* Pass is_qat=False on PTQ

* Fix bug on ra_sampler flag.

* Set is_qat=True for QAT

* Updated gpu decoder test to use parametrize

* Remove list comprehension

* Refactor model builder

* Add 3 more convnext variants.

* Adding weights for convnext_small.

* Fix minor bug.

* Fix number of parameters for small model.

* Adding weights for the base variant.

* Adding weights for the large variant.

* Simplify LayerNorm2d implementation.

* Optimize speed of CNBlock.

* Repackage weights.

* Adding conv_stem support

* fix lint

* bug fix

* address comments

* fix after merge

* adding back checking lines

* fix failing tests

* fix iignore

* add unittest & address comments

* fix memory issue

* address comments

* Some Qs

* Some modifications

* don't need _loader in __init__

* list_names -> list_datasets

* Update torchvision/prototype/datasets/utils/_resource.py
Co-authored-by: Philip Meier <github.pmeier@posteo.de>

* Remove unsued import

* fix tests

* Some missing renames
Co-authored-by: Philip Meier <github.pmeier@posteo.de>

* Add PCAM dataset to prototype area

* use BytesIO instead of writing file to disk

* Apply suggestions from code review
Co-authored-by: Philip Meier <github.pmeier@posteo.de>

* remove noqa

* typos

* Use _Resource namedtuple

* Add h5py to unittest_prototype job

* use .item() on target

* Forgot to call regenerate.py
Co-authored-by: Philip Meier <github.pmeier@posteo.de>

* Start stuff

* Start adding some implementation

* Add implementation

* Use torch for colorwheel

* Add simple test

* Fix small numpy rename

* Adapt the changes

* Fix stuff

* Add suggestions

* Minor fixes for float

* Remove idx logic

* Simplify

* Fix test

* Update code and add test

* Add expected flow asset

* Fix expected flow path

* Doc nits
Co-authored-by: Nicolas Hug <nicolashug@fb.com>
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* fix bug when the target is empty

* Add unittest for empty instance training
Co-authored-by: Zhiqiang Wang <zhiqwang@foxmail.com>
Co-authored-by: Joao Gomes <joaopsgomes@gmail.com>

* expect the CI to fail

* retrigger tests

* rewriting to close the containers and avoid potential clashes

* addressing Prabhat's commetns
Co-authored-by: Bruno Korbar <bkorbar@quansight.com>
Co-authored-by: Prabhat Roy <prabhatroy@fb.com>

* replace assert with valueerror

* pytest should raise ValueError not AssertionError

* minor edit

* raise assert changed to raise valueerror in test

* Update torchvision/models/detection/backbone_utils.py
Co-authored-by: Aditya Oke <47158509+oke-aditya@users.noreply.github.com>

* Update torchvision/models/detection/backbone_utils.py
Co-authored-by: Aditya Oke <47158509+oke-aditya@users.noreply.github.com>

* minor edits

* minor edits

* added one test

* added another test

* added another test

* test for mobilenet

* ufmt formatting

* cant have unused variables

* suggested changes

* minor edit

* corrected bug pointed out by datumbox

* corrected bug pointed out by datumbox

* bug correction and shorten msg

* ufmt stuff

* resolved last comment
Co-authored-by: Abhijit Deo <72816663+abhi-glitchhg@users.noreply.github.com>
Co-authored-by: Aditya Oke <47158509+oke-aditya@users.noreply.github.com>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* use fixture for dataset root in tests

* fix home dir generation

* remove caching
Co-authored-by: Philip Meier <github.pmeier@posteo.de>

* Added center option to F.affine and RandomAffine ops

* Updates according to the review

* Add seek in GPU decoder

* Merge the two tests

* Refine unit test

Co-authored-by: Philip Meier <github.pmeier@posteo.de>

* renamed ImageNet weights

* renamed COCO weights

* renamed COCO with VOC labels weights

* renamed Kinetics 400 weights

* rename default with DEFAULT

* update test

* fix typos

* update test

* update test

* update test

* indent as w was weight_enum

* revert

* Adding back the capitalization test
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* add fcos

* update fcos

* add giou_loss

* add BoxLinearCoder for FCOS

* add full code for FCOS

* add giou loss

* add fcos

* add __all__

* Fixing lint

* Fixing lint in giou_loss.py

* Add typing annotation to fcos

* Add trained checkpoints

* Use partial to replace lambda

* Minor fixes to docstrings

* Apply ufmt format

* Fixing docstrings

* Fixing jit scripting

* Minor fixes to docstrings

* Fixing jit scripting

* Ignore mypy in fcos

* Fixing trained checkpoints

* Fixing unit-test of jit script

* Fixing docstrings

* Add test/expect/ModelTester.test_fcos_resnet50_fpn_expect.pkl

* Fixing test_detection_model_trainable_backbone_layers

* Update test_fcos_resnet50_fpn_expect.pkl

* rename stride to box size

* remove TODO and fix some typo

* merge some code for better

* impove the comments

* remove decode and encode of BoxLinearCoder

* remove some unnecessary hints

* use the default value in detectron2.

* update doc

* Add unittest for BoxLinearCoder

* Add types in FCOS

* Add docstring for BoxLinearCoder

* Minor fix for the docstring

* update doc

* Update fcos_resnet50_fpn_coco pretained weights url

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

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

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

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

* Add FCOS model documentation

* Fix typo in FCOS documentation

* Add fcos to the prototype builder

* Capitalize COCO_V1

* Fix params of fcos

* fix bug for partial

* Fixing docs indentation

* Fixing docs format in giou_loss

* Adopt Reference for GIoU Loss

* Rename giou_loss to generalized_box_iou_loss

* remove overwrite_eps

* Update AP test values

* Minor fixes for the docs

* Minor fixes for the docs

* Update torchvision/models/detection/fcos.py
Co-authored-by: Zhiqiang Wang <zhiqwang@foxmail.com>

* Update torchvision/prototype/models/detection/fcos.py
Co-authored-by: Zhiqiang Wang <zhiqwang@foxmail.com>
Co-authored-by: zhiqiang <zhiqwang@foxmail.com>
Co-authored-by: Joao Gomes <jdsgomes@fb.com>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
Co-authored-by: Joao Gomes <joaopsgomes@gmail.com>

* Adding CNBlock and skeleton architecture

* Completed implementation.

* Adding model in prototypes.

* Add test and minor refactor for JIT.

* Fix mypy.

* Fixing naming conventions.

* Fixing tests.

* Fix stochastic depth percentages.

* Adding stochastic depth to tiny variant.

* Minor refactoring and adding comments.

* Adding weights.

* Update default weights.

* Fix transforms issue

* Move convnext to prototype.

* linter fix

* fix docs

* Addressing code review comments.

* add SVHN prototype dataset

* add test

* refactor prototype dataset tests to use public API for loading

* add explanation

* use loop alternative

* allow single extension as str in make_dataset

* remove test class

* remove regex

* revert collection to tuple

* cleanup

* Adding min_size as a required field.

* Adding min_size to classification models (quantized and not)

* Adding min_size to video models meta.

* Moving min_size to _COMMON_META

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