GitLab

* Restructuring metrics meta-data for detection, segmentation and optical flow.

* Renaming acc to pixel_acc for segmentation

* Restructure video meta-data.

* Restructure classification and quantization meta-data.

* Fix tests.

* Fix documentation

* Removing `task`, `architecture` and `quantization`

* Fix mypy

* Remove size field

* Remove unused import.

* Fix mypy

* Remove size from schema list.

* update todo

* Simplify with assert

* Adding min_size to all models.

* Update RAFT min size to 128

* Remove publication_year and interpolation meta

* Add type to _COMMON_META and _COMMON_SWAG_META to prevent error from mypy check

* Remove test to check interpolation and publication_year meta
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Moving basefiles outside of prototype and porting Alexnet, ConvNext, Densenet and EfficientNet.

* Porting googlenet

* Porting inception

* Porting mnasnet

* Porting mobilenetv2

* Porting mobilenetv3

* Porting regnet

* Porting resnet

* Porting shufflenetv2

* Porting squeezenet

* Porting vgg

* Porting vit

* Fix docstrings

* Fixing imports

* Adding missing import

* Fix mobilenet imports

* Fix tests

* Fix prototype tests

* Exclude get_weight from models on test

* Fix init files

* Porting googlenet

* Porting inception

* porting mobilenetv2

* porting mobilenetv3

* porting resnet

* porting shufflenetv2

* Fix test and linter

* Fixing docs.

* Porting Detection models (#5617)

* fix inits

* fix docs

* Port faster_rcnn

* Port fcos

* Port keypoint_rcnn

* Port mask_rcnn

* Port retinanet

* Port ssd

* Port ssdlite

* Fix linter

* Fixing tests

* Fixing tests

* Fixing vgg test

* Porting Optical Flow, Segmentation, Video models (#5619)

* Porting raft

* Porting video resnet

* Porting deeplabv3

* Porting fcn and lraspp

* Fixing the tests and linter

* Porting docs, examples, tutorials and galleries (#5620)

* Fix examples, tutorials and gallery

* Update gallery/plot_optical_flow.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Fix import

* Revert hardcoded normalization

* fix uncommitted changes

* Fix bug

* Fix more bugs

* Making resize optional for segmentation

* Fixing preset

* Fix mypy

* Fixing documentation strings

* Fix flake8

* minor refactoring
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Resolve conflict

* Porting model tests (#5622)

* Porting tests

* Remove unnecessary variable

* Fix linter

* Move prototype to extended tests

* Fix download models job

* Update CI on Multiweight branch to use the new weight download approach (#5628)

* port Pad to prototype transforms (#5621)

* port Pad to prototype transforms

* use literal

* Bump up LibTorchvision version number for Podspec to release Cocoapods (#5624)
Co-authored-by: Anton Thomma <anton@pri.co.nz>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* pre-download model weights in CI docs build (#5625)

* pre-download model weights in CI docs build

* move changes into template

* change docs image

* Regenerated config.yml
Co-authored-by: Philip Meier <github.pmeier@posteo.de>
Co-authored-by: Anton Thomma <11010310+thommaa@users.noreply.github.com>
Co-authored-by: Anton Thomma <anton@pri.co.nz>

* Porting reference scripts and updating presets (#5629)

* Making _preset.py classes

* Remove support of targets on presets.

* Rewriting the video preset

* Adding tests to check that the bundled transforms are JIT scriptable

* Rename all presets from *Eval to *Inference

* Minor refactoring

* Remove --prototype and --pretrained from reference scripts

* remove  pretained_backbone refs

* Corrections and simplifications

* Fixing bug

* Fixing linter

* Fix flake8

* restore documentation example

* minor fixes

* fix optical flow missing param

* Fixing commands

* Adding weights_backbone support in detection and segmentation

* Updating the commands for InceptionV3

* Setting `weights_backbone` to its fully BC value (#5653)

* Replace default `weights_backbone=None` with its BC values.

* Fixing tests

* Fix linter

* Update docs.

* Update preprocessing on reference scripts.

* Change qat/ptq to their full values.

* Refactoring preprocessing

* Fix video preset

* No initialization on VGG if pretrained

* Fix warning messages for backbone utils.

* Adding star to all preset constructors.

* Fix mypy.
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>
Co-authored-by: Philip Meier <github.pmeier@posteo.de>
Co-authored-by: Anton Thomma <11010310+thommaa@users.noreply.github.com>
Co-authored-by: Anton Thomma <anton@pri.co.nz>

* replace most asserts with exceptions

* fix formating issues

* fix linting and remove more asserts

* fix regresion

* fix regresion

* fix bug

* apply ufmt

* apply ufmt

* fix tests

* fix format

* fix None check

* fix detection models tests

* non scriptable any

* add more checks for None values

* fix retinanet test

* fix retinanet test

* Update references/classification/transforms.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update references/classification/transforms.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update references/optical_flow/transforms.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update references/optical_flow/transforms.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update references/optical_flow/transforms.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* make value checks more pythonic:

* Update references/optical_flow/transforms.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* make value checks more pythonic

* make more checks pythonic

* fix bug

* appy ufmt

* fix tracing issues

* fib typos

* fix lint

* remove unecessary f-strings

* fix bug

* Update torchvision/datasets/mnist.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update torchvision/datasets/mnist.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update torchvision/ops/boxes.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update torchvision/ops/poolers.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update torchvision/utils.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* address PR comments

* Update torchvision/io/_video_opt.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update torchvision/models/detection/generalized_rcnn.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update torchvision/models/feature_extraction.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update torchvision/models/optical_flow/raft.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* address PR comments

* addressing further pr comments

* fix bug

* remove unecessary else

* apply ufmt

* last pr comment

* replace RuntimeErrors
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* log API v3

* make torchscript happy

* make torchscript happy

* add missing logs to constructor

* log ops C++ API as well

* fix type hint

* check function with isinstance
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* revamp log api usage method

* Aligning exception with all other models.

* Adding prototype preprocessing on video references.

* Adding the rest of model builders on faster_rcnn.

Co-authored-by: Nicolas Hug <nicolashug@fb.com>

Summary: We would like to track the internal usage of TorchVision components. We're tracking [datasets usage](https://fburl.com/daiquery/dqpmemn3

) now. This diff expand the tracking to all models.

Reviewed By: fmassa

Differential Revision: D31441632

fbshipit-source-id: e26072e582ac9f832c2056307ebf0eccf2ed6c9c
Co-authored-by: Kai Zhang <kaizh@fb.com>

* add ufmt as code formatter

* cleanup

* quote ufmt requirement

* split imports into more groups

* regenerate circleci config

* fix CI

* clarify local testing utils section

* use ufmt pre-commit hook

* split relative imports into local category

* Revert "split relative imports into local category"

This reverts commit f2e224cde2008c56c9347c1f69746d39065cdd51.

* pin black and usort dependencies

* fix local test utils detection

* fix ufmt rev

* add reference utils to local category

* fix usort config

* remove custom categories sorting

* Run pre-commit without fixing flake8

* got a double import in merge
Co-authored-by: Nicolas Hug <nicolashug@fb.com>

* Fix an issue that ShuffleNetV2 model is exported to a wrong ONNX file if dynamic_axes field was provided.

* Add a ut for the bug fix.

* Fix flake8 issue.

* Don't access each element in x.shape, use x.size() instead.
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* style: Added annotation typing for shufflenet

* fix: Removed duplicate type hint

* refactor: Removed un-necessary import

* fix: Fixed constructor typing

* style: Added black formatting on depthwise_conv

* style: Fixed stage typing in shufflenet

* Add tests for results in script vs eager mode

This copies some logic from `test_jit.py` to check that a TorchScript'ed
model's outputs are the same as outputs from the model in eager mode.

To support differences in TorchScript / eager mode outputs, an
`unwrapper` function can be provided per-model.

* Fix inception, use PYTORCH_TEST_WITH_SLOW

* Update

* Remove assertNestedTensorObjectsEqual

* Add PYTORCH_TEST_WITH_SLOW to CircleCI config

* Add MaskRCNN unwrapper

* fix prec args

* Remove CI changes

* update

* Update

* remove expect changes

* Fix tolerance bug

* Fix breakages

* Fix quantized resnet

* Fix merge errors and simplify code

* DeepLabV3 has been fixed

* Temporarily disable jit compilation

* quantizable googlenet

* Minor improvements

* Rename basic_conv2d with conv_block plus additional fixes

* More renamings and fixes

* Bugfix

* Fix missing import for mypy

* Add pretrained weights

* make shufflenet scriptable

* make resnet18 scriptable

* set downsample to identity instead of __constants__ api

* use __constants__ for downsample instead of identity

* import tensor to fix flake

* use torch.Tensor type annotation instead of import

* Remove dependency from functool in ShuffleNetsV2

This PR removes the dependence of the ShuffleNetV2 code from `functool`.

* flake fix

Also move weights from ShuffleNet to PyTorch bucket. Additionally, rename shufflenet to make it consistent with the other models

* Enhance ShufflenetV2

Class shufflenetv2 receives `stages_repeats` and `stages_out_channels` arguments.

* remove explicit num_classes argument from utility functions

* Minor refactoring of ShuffleNetV2

Added progress flag following #875. Further the following refactoring was also done:

1) added `version` argument in shufflenetv2 method and removed the operations for converting the `width_mult` arg to float and string.
2) removed `num_classes` argument and **kwargs from functions except `ShuffleNetV2`

* removed `version` arg

* Update shufflenetv2.py

* Removed the try except block

* Update shufflenetv2.py

* Changed version from float to str

* Replace `width_mult` with `stages_out_channels`

Removes the need of  `_getStages` function.

* remove 'input_size' parameter from shufflenetv2

* Update shufflenetv2.py

* Add ShuffleNet v2

Added 4 configurations: x0.5, x1, x1.5, x2
Add 2 pretrained models: x0.5, x1

* fix lint

* Change globalpool to torch.mean() call