GitLab

* Adding multiweight support for mobilenetv3 prototype

* adding efficientnet to prototype

* Removing _efficientnet_conf from prototype and main efficientnet classification models

* fixing merge conflicts

* fixing merge conflicts

* fixing lint errors

* fixing lint errors

* fixing lint errors

* adding efficientnet to prototype

* renaming _efficientnet_model in torchvision.model to be aligned with prototype implementation

* adding specific cop sizes to ech model

* adding specific cop and resize transforms

* fixing lint errors

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

* rename pretrained model versions
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* rename pretrained model versions
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* rename pretrained model versions
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* rename pretrained model versions
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* rename pretrained model versions
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* rename pretrained model versions
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* rename pretrained model versions
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* rename pretrained model versions
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

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

* Refactoring resnet_fpn backbone building.

* Passing the change to *_rcnn and retinanet.

* Applying for faster_rcnn + mobilenetv3

* Applying for ssdlite + mobilenetv3

* Applying for ssd + vgg16

* Update the expected file of retinanet_resnet50_fpn to fix order of initialization.

* Adding full model weights for the VGG16 features.

* Move FCN methods to itsown package.

* Fix lint.

* Move LRASPP methods to their own package.

* Move DeepLabV3 methods to their own package.

* Adding deprecation warning for torchvision.models.segmentation.segmentation.

* Refactoring deeplab.

* Setting aux default to false.

* Fixing imports.

* Passing backbones instead of backbone names to builders.

* Fixing mypy

* Addressing review comments.

* Correcting typing.

* Restoring special handling for references.

* Start adding types

* add typing

* Type prototype models

* fix optional type bug

* transient import

* Fix weights type

* fix import

* Apply suggestions from code review

Address nits
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
Co-authored-by: Khushi Agrawal <khushiagrawal411@gmail.com>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Fixing minor issue on typing.

* Sample implementation for quantized resnet50.

* Annotate rpn

* Small fix

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

* Adding FasterRCNN ResNet50.

* Refactoring to remove duplicate code.

* Adding typing info.

* Setting weights_backbone=None as default value.

* Overwrite eps only for specific weights.

* Added Dropout parameter of Models

* Added argument description for dropout in MobileNet v2 and v3
Updated quantization/googlenet.py as per the changes in constructor in googlenet

* Moved new dropout parameter n the end

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

Co-authored-by: deepsource-autofix[bot] <62050782+deepsource-autofix[bot]@users.noreply.github.com>
Co-authored-by: deepsource-autofix[bot] <62050782+deepsource-autofix[bot]@users.noreply.github.com>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Add typing for imagelist, move params to docstring

* Add docs

* Start annotating utils

* checking

* Add annotations at _utils.py

* Remove unnecessary comments.

* re-checked typings

* Update typing

* Ignore small error

* Use optional tensor

* Ignore for JIT
Co-authored-by: Khushi Agrawal <khushiagrawal411@gmail.com>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Use FX feature extractor for segm model

* Removed use_fe option

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

* add best weights and x_1_6, x_3_2, y_1_6, y_3_2, y_32 weights

* add best weights and x_1_6, x_3_2, y_1_6, y_3_2, y_32 weights

* add weights for x_16gf, x_32gf, y_16gf

* 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>

* draft commit

* Polish and add corresponding test

* Update docs

* Update torchvision/models/feature_extraction.py

* Update docs/source/feature_extraction.rst
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Moving _make_divisible to utils.

* Replace the old ConvBNReLU and ConvBNActivation layers

* Fix minor bug.

* Moving SE layer to ops.

* Adding deprecation warnings on old layers.

* Apply changes to regnets.

* initial code

* add SqueezeExcitation

* initial code

* add SqueezeExcitation

* add SqueezeExcitation

* regnet blocks, stems and model definition

* nit

* add fc layer

* use Callable instead of Enum for block, stem and activation

* add regnet_x and regnet_y model build functions, add docs

* remove unused depth

* use BN/activation constructor and ConvBNActivation

* add expected test pkl files

* allow custom activation in SqueezeExcitation

* use ReLU as the default activation

* initial code

* add SqueezeExcitation

* initial code

* add SqueezeExcitation

* add SqueezeExcitation

* regnet blocks, stems and model definition

* nit

* add fc layer

* use Callable instead of Enum for block, stem and activation

* add regnet_x and regnet_y model build functions, add docs

* remove unused depth

* use BN/activation constructor and ConvBNActivation

* reuse SqueezeExcitation from efficientnet

* refactor RegNetParams into BlockParams

* use nn.init, replace np with torch

* update README

* construct model with stem, block, classifier instances

* Revert "construct model with stem, block, classifier instances"

This reverts commit 850f5f3ed01a2a9b36fcbf8405afd6e41d2e58ef.

* remove unused blocks

* support scaled model

* fuse into ConvBNActivation

* make reset_parameters private

* fix type errors

* fix for unit test

* add pretrained weights for 6 variant models, update docs

* Reuse EfficientNet SE layer.

* Deprecating the mobilenetv3.SqueezeExcitation layer.

* Passing the right activation on quantization.

* Making strict named param.

* Set default params if missing.

* Fixing typos.

* allow custom activation in SqueezeExcitation

* use ReLU as the default activation

* make scale activation parameterizable
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Allow gradient backpropagation through GeneralizedRCNNTransform to inputs
Signed-off-by: Beat Buesser <beat.buesser@ie.ibm.com>

* Add unit tests for gradient backpropagation to inputs
Signed-off-by: Beat Buesser <beat.buesser@ie.ibm.com>

* Update torchvision/models/detection/transform.py
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Update _check_input_backprop
Signed-off-by: Beat Buesser <beat.buesser@ie.ibm.com>

* Account for tests requiring cuda
Signed-off-by: Beat Buesser <beat.buesser@ie.ibm.com>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

Co-authored-by: Julien RIPOCHE <ripoche@magic-lemp.com>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Added paper references to detection models

* Ignore linter warning

* Break long line into two

* add fx feature extraction util

* Make it possible to use train and eval mode

* FX feature extraction - Tweaks and small bug fixes

* FX feature extraction - add tests

* move to feature_extraction.py, add LeafModuleAwareTracer, add docs

* Tweaks to docs

* addressing latest round of feedback

* undo line spacing changes

* change type hints in docstrings

* fix sphinx indentation

* expose feature_extraction

* add maskrcnn example

* add api refernce subheading

* address latest review notes, refactor names, fix regex, cosmetics

* Add back efficientnet to models

* fix tests for effnet

* fix linting issue

* fix test tracer kwargs
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Add types in transform.

* Trace on eval mode.
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Explicitely store a distance value that is reused 

I don't see a reason to calculate the value twice for each distance. 

Knowing that this code is going to be called at every epoch and that probably there is not a compiler that optimizes this (+ also code clarity), I think is best to store the value for x and y.

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

* removing spaces
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* fix

* add typings

* fixup some more types

* Type more

* remove mypy ignore

* add missing typings

* fix a few mypy errors

* fix mypy errors

* fix mypy

* ignore types

* fixup annotation

* fix remaining types

* cleanup #TODO comments
Co-authored-by: Philip Meier <github.pmeier@posteo.de>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Adding code skeleton

* Adding MBConvConfig.

* Extend SqueezeExcitation to support custom min_value and activation.

* Implement MBConv.

* Replace stochastic_depth with operator.

* Adding the rest of the EfficientNet implementation

* Update torchvision/models/efficientnet.py

* Replacing 1st activation of SE with SiLU.

* Adding efficientnet_b3.

* Replace mobilenetv3 assets with custom.

* Switch to standard sigmoid and reconfiguring BN.

* Reconfiguration of efficientnet.

* Add repr

* Add weights.

* Update weights.

* Adding B5-B7 weights.

* Update docs and hubconf.

* Fix doc link.

* Fix typo on comment.

* style: Added typing to models/video

* style: Fixed typing

* style: Fixed typing

* style: Fixed typing

* refactor: Removed default value for stem

* docs: Fixed docstring of VideoResNet

* style: Refactored typing

* docs: Fixed docstring

* style: Fixed typing

* docs: Specified docstring

* typing: Fixed tying

* docs: Fixed docstring

* Undoing change.

* style: Added typing annotations to segmentation/_utils

* style: Added typing annotations to segmentation/segmentation

* style: Added typing annotations to remaining segmentation models

* style: Fixed typing of DeepLab

* style: Fixed typing

* fix: Fixed typing annotations & default values

* Fixing python_type_check