GitLab

* Adding docs for RandAugment.

* Fix docs.

* 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.

* Adding operator.

* Adding tests

* switching order of `p` and `mode`.

* Remove seed setting.

* Replace stats import with pytest.importorskip.

* Fix doc

* Apply suggestions from code review
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Fixing indentation.

* Adding operator in the documentation.

* Fixing lint
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Add iNaturalist dataset

* Add download support

* address comments
Co-authored-by: dgenzel <dgenzel@fb.com>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* fix

* add docs for VisionDataset

* add getitem

* add deprecated note to transform

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

* fix docs

* Update torchvision/datasets/vision.py

* flake8
Co-authored-by: Nicolas Hug <nicolashug@fb.com>
Co-authored-by: Philip Meier <github.pmeier@posteo.de>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Fix io imports and expose methods

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

* Partial implementation of SSDlite.

* Add normal init and BN hyperparams.

* Refactor to keep JIT happy

* Completed SSDlite.

* Fix lint

* Update todos

* Add expected file in repo.

* Use C4 expansion instead of C4 output.

* Change scales formula for Default Boxes.

* Add cosine annealing on trainer.

* Make T_max count epochs.

* Fix test and handle corner-case.

* Add support of support width_mult

* Add ssdlite presets.

* Change ReLU6, [-1,1] rescaling, backbone init & no pretraining.

* Use _reduced_tail=True.

* Add sync BN support.

* Adding the best config along with its weights and documentation.

* Make mean/std configurable.

* Fix not implemented for half exception

* Add auto-augment introduced transforms in docs.

* Adding examples in Gallery.

* Make posterize more extreme.

* Early skeleton of API.

* Adding MultiFeatureMap and vgg16 backbone.

* Making vgg16 backbone same as paper.

* Making code generic to support all vggs.

* Moving vgg's extra layers a separate class + L2 scaling.

* Adding header vgg layers.

* Fix maxpool patching.

* Refactoring code to allow for support of different backbones & sizes:
- Skeleton for Default Boxes generator class
- Dynamic estimation of configuration when possible
- Addition of types

* Complete the implementation of DefaultBox generator.

* Replace randn with empty.

* Minor refactoring

* Making clamping between 0 and 1 optional.

* Change xywh to xyxy encoding.

* Adding parameters and reusing objects in constructor.

* Temporarily inherit from Retina to avoid dup code.

* Implement forward methods + temp workarounds to inherit from retina.

* Inherit more methods from retinanet.

* Fix type error.

* Add Regression loss.

* Fixing JIT issues.

* Change JIT workaround to minimize new code.

* Fixing initialization bug.

* Add classification loss.

* Update todos.

* Add weight loading support.

* Support SSD512.

* Change kernel_size to get output size 1x1

* Add xavier init and refactoring.

* Adding unit-tests and fixing JIT issues.

* Add a test for dbox generator.

* Remove unnecessary import.

* Workaround on GeneralizedRCNNTransform to support fixed size input.

* Remove unnecessary random calls from the test.

* Remove more rand calls from the test.

* change mapping and handling of empty labels

* Fix JIT warnings.

* Speed up loss.

* Convert 0-1 dboxes to original size.

* Fix warning.

* Fix tests.

* Update comments.

* Fixing minor bugs.

* Introduce a custom DBoxMatcher.

* Minor refactoring

* Move extra layer definition inside feature extractor.

* handle no bias on init.

* Remove fixed image size limitation

* Change initialization values for bias of classification head.

* Refactoring and update test file.

* Adding ResNet backbone.

* Minor refactoring.

* Remove inheritance of retina and general refactoring.

* SSD should fix the input size.

* Fixing messages and comments.

* Silently ignoring exception if test-only.

* Update comments.

* Update regression loss.

* Restore Xavier init everywhere, update the negative sampling method, change the clipping approach.

* Fixing tests.

* Refactor to move the losses from the Head to the SSD.

* Removing resnet50 ssd version.

* Adding support for best performing backbone and its config.

* Refactor and clean up the API.

* Fix lint

* Update todos and comments.

* Adding RandomHorizontalFlip and RandomIoUCrop transforms.

* Adding necessary checks to our tranforms.

* Adding RandomZoomOut.

* Adding RandomPhotometricDistort.

* Moving Detection transforms to references.

* Update presets

* fix lint

* leave compose and object

* Adding scaling for completeness.

* Adding params in the repr

* Remove unnecessary import.

* minor refactoring

* Remove unnecessary call.

* Give better names to DBox* classes

* Port num_anchors estimation in generator

* Remove rescaling and fix presets

* Add the ability to pass a custom head and refactoring.

* fix lint

* Fix unit-test

* Update todos.

* Change mean values.

* Change the default parameter of SSD to train the full VGG16 and remove the catch of exception for eval only.

* Adding documentation

* Adding weights and updating readmes.

* Update the model weights with a more performing model.

* Adding doc for head.

* Restore import.

* Phew

* not sure CI got properly triggered?

* Try using the pyortch theme

* directly embed script in layout.html instead

* use more mordern script
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Added KITTI dataset

* Addressed review comments

* Changed type of target to List[Dict] and corrected the data types of the returned values.

* Updated unit test to rely on ImageDatasetTestCase

* Added kitti to dataset documentation

* Cleaned up test and some minor changes

* Made data_url a string instead of a list

* Removed unnecessary try and print
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Fixed return docstrings

* Added some refs and corrected some parts

* more refs, and a note about dtypes
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* add draw segm masks

* rewrites with new api

* fix flaky colors

* fix resize bug

* resize for sanity

* cleanup

* project the image

* Minor refactor to adopt num classes

* add uint8 in docstring

* adds alpha and docstring

* move code a bit down

* Minor fix

* fix type check

* Fixing resize bug.

* Fix type of alpha.

* Remove unnecessary RGBA conversions.

* update docs to supported only rgb

* minor edits

* adds tests

* shifts masks up

* change tests and impelementation for bool

* change mode to L

* convert to float

* fixes docs
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
Co-authored-by: Vasilis Vryniotis <vvryniotis@fb.com>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* add missing docs

* tries fixing docs

* fixes docs

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

* add _all for autoaugment

* adds docs

* add docs, test locally

* refactored as per code review

Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Adding TODO placeholders.

* More placeholders.

* Add MobileNetV3 small pre-trained weights.

* Remove placeholders.

* Refactoring mobilenetv3 to make code reusable.

* Adding quantizable MobileNetV3 architecture.

* Fix bug on reference script.

* Moving documentation of quantized models in the right place.

* Update documentation.

* Workaround for loading correct weights of quant model.

* Update weight URL and readme.

* Adding eval.

* Making _segm_resnet() generic and reusable.

* Adding fcn and deeplabv3 directly on mobilenetv3 backbone.

* Adding tests for segmentation models.

* Rename is_strided with _is_cn.

* Add dilation support on MobileNetV3 for Segmentation.

* Add Lite R-ASPP with MobileNetV3 backbone.

* Add pretrained model weights.

* Removing model fcn_mobilenet_v3_large.

* Adding docs and imports.

* Fixing typo and readme.

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

* Tag fasterrcnn mobilenetv3 model with 320, add new inference config that makes it 2x faster sacrificing a bit of mAP.

* Add a high resolution fasterrcnn mobilenetv3 model.

* Update tests and expected values.