GitLab

* Add new weights for efficientnet_b1 model

* Update accuracies

* Updated the model url and metadata

* Updated default

* [Windows] Workaround for loading bundled DLLs

Python-3.8+ adds `add_dll_directory` call, see https://docs.python.org/3/whatsnew/3.8.html#ctypes
Simulate this behaviour on older versions of Python runtime by calling
`LoadLibraryExW` with the appropriate flags

Fixes https://github.com/pytorch/vision/issues/4787

* Add API usage calls to utils

* Update to the new api

* add from_checkpoint method for vit

* remove useless change

* Making interpolate_embeddings a utility function

* remove logging

* fix type hint

* fix return type check

* ad  retuurns in docsting & unify type hint

* remove useless import

* fix issue: 'type' object is not subscriptable

* Fixing typing issues

* Making interpolation mode configurable

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

* revamp log api usage method

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

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

* Adding test that verifies meta schema.

* Adding missing interpolation to keypoint model.

* Change assertion.

* Adding map metric in meta schema for detection and renaming metrics for kp and mask rcnn.

* add download functionality to prototype datasets

* fix annotation

* fix test

* remove iopath

* add comments

* Revert "[FBcode->GH] remove unused requests functionality (#5014)"

This reverts commit 33123bee.

* Revert "replace requests with urllib (#4973)"

This reverts commit 8d25de7b.

* add requests as hard dependency

* install library stubs in CI

* fix syntax

* add requests to conda dependencies

* fix mypy CI

* Adding logging calls for raft and vit

* Linter fix

* simplify model builders

* cleanup

* refactor kwonly to pos or kw handling

* put weight verification back

* revert num categories checks

* fix default weights

* cleanup

* remove manual parameter map

* refactor decorator interface

* address review comments

* cleanup

* refactor callable default

* fix type annotation

* process ungrouped models

* cleanup

* mroe cleanup

* use decorator for detection models

* add decorator for quantization models

* add decorator for segmentation  models

* add decorator for video  models

* remove old helpers

* fix resnet50

* Adding verification back on InceptionV3

* Add kwargs in DeeplabeV3

* Add kwargs on FCN

* Fix typing on Deeplab

* Fix typing on FCN
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* exclude windows from mmap

* add comment for windows

* appease mypy

According to the benchmark link https://github.com/pytorch/vision/issues/1311#issuecomment-781329339

, for GPU the threshold should be higher (which is why detectron2 used 40k).
This PR changes the threshold to be device dependent.
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Add bias parameter to ConvNormActivation

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

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

making torchvision ops leaf nodes by default

* improve COCO prototype

* test 2017 annotations

* add option to include captions

* fix categories and add tests

* cleanup

* add correct image size to bounding boxes

* fix annotation collation

* appease mypy

* add benchmark

* always use image as reference

* another refactor

* add support for segmentations

* add support for segmentations

* fix CI dependencies

* Change the `default` weights mechanism to sue Enum aliases.

* Change `get_weights` to work with full Enum names and make it public.

* Applying improvements from code review.

* Fix bug on autocontrast when `min==max`

* Adding PIL vs TorchVision test for min==max

* Rename classes Weights => WeightsEnum and WeightEntry => Weights.

* Make enum values follow the naming convention `_V1`, `_V2` etc

* Cleanup the Enum class naming conventions.

* Add a test to check naming conventions.

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

* Update functional_tensor.py

To resolve issue #4818
Add assert function and logic after checking bound of image

* Update functional_tensor.py

* Update test

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Update test_functional_tensor.py

* Update test_functional_tensor.py

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

* [vit] Adding ViT to torchvision/models

* adding pre-logits layer + resolving comments

* Fix the model attribute bug

* Change version to arch

* fix failing unittests

* remove useless prints

* reduce input size to fix unittests

* Increase windows-cpu executor to 2xlarge

* Use `batch_first=True` and remove classifier

* Change resource_class back to xlarge

* Remove vit_h_14

* Remove vit_h_14 from __all__

* Move vision_transformer.py into prototype

* Fix formatting issue

* remove arch in builder

* Fix type err in model builder

* address comments and trigger unittests

* remove the prototype import in torchvision.models

* Adding vit back to models to trigger CircleCI test

* fix test_jit_forward_backward

* Move all to prototype.

* Adopt new helper methods and fix prototype tests.

* Remove unused import.
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
Co-authored-by: Vasilis Vryniotis <vvryniotis@fb.com>