GitLab

* 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.

* 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.

* Minor refactoring of a private method to make it reusuable.

* Adding a FasterRCNN + MobileNetV3 with & w/o FPN models.

* Reducing Resolution to 320-640 and anchor sizes to 16-256.

* Increase anchor sizes.

* Adding rpn score threshold param on the train script.

* Adding trainable_backbone_layers param on the train script.

* Adding rpn_score_thresh param directly in fasterrcnn_mobilenet_v3_large_fpn.

* Remove fasterrcnn_mobilenet_v3_large prototype and update expected file.

* Update documentation and adding weights.

* Use buildin Identity.

* Fix spelling.

* Add MobileNetV3 Architecture in TorchVision (#3182)

* Adding implementation of network architecture

* Adding rmsprop support on the train.py

* Adding auto-augment and random-erase in the training scripts.

* Adding support for reduced tail on MobileNetV3.

* Tagging blocks with comments.

* Adding documentation, pre-trained model URL and a minor refactoring.

* Handling better untrained supported models.

* Add rough implementation of RetinaNet.

* Move AnchorGenerator to a seperate file.

* Move box similarity to Matcher.

* Expose extra blocks in FPN.

* Expose retinanet in __init__.py.

* Use P6 and P7 in FPN for retinanet.

* Use parameters from retinanet for anchor generation.

* General fixes for retinanet model.

* Implement loss for retinanet heads.

* Output reshaped outputs from retinanet heads.

* Add postprocessing of detections.

* Small fixes.

* Remove unused argument.

* Remove python2 invocation of super.

* Add postprocessing for additional outputs.

* Add missing import of ImageList.

* Remove redundant import.

* Simplify class correction.

* Fix pylint warnings.

* Remove the label adjustment for background class.

* Set default score threshold to 0.05.

* Add weight initialization for regression layer.

* Allow training on images with no annotations.

* Use smooth_l1_loss with beta value.

* Add more typehints for TorchScript conversions.

* Fix linting issues.

* Fix type hints in postprocess_detections.

* Fix type annotations for TorchScript.

* Fix inconsistency with matched_idxs.

* Add retinanet model test.

* Add missing JIT annotations.

* Remove redundant model construction

Make tests pass

* Fix bugs during training on newer PyTorch and unused params in DDP

Needs cleanup and to add back support for images with no annotations

* Cleanup resnet_fpn_backbone

* Use L1 loss for regression

Gives 1mAP improvement over smooth l1

* Disable support for images with no annotations

Need to fix distributed first

* Fix retinanet tests

Need to deduplicate those box checks

* Fix Lint

* Add pretrained model

* Add training info for retinanet
Co-authored-by: Hans Gaiser <hansg91@gmail.com>
Co-authored-by: Hans Gaiser <hans.gaiser@robovalley.com>
Co-authored-by: Hans Gaiser <hans.gaiser@robohouse.com>

* guess documentation location

* document mean/std origins

* fix artifact

* update for clarity

* fix typo

* Add docs for video models

* Fix docstrings for resnet and vgg

* add wide resnet

* add docstring for wide resnet

* update WRN-50-2 model

* add docs

* extend WRN docstring

* use pytorch storage for WRN

* fix rebase

* fix typo in docs

* Add initial mnasnet impl

* Remove all type hints, comply with PyTorch overall style

* Expose models

* Remove avgpool from features() and add separately

* Fix python3-only stuff, replace subclasses with functions

* fix __all__

* Fix typo

* Remove conditional dropout

* Make dropout functional

* Addressing @fmassa's feedback, round 1

* Replaced adaptive avgpool with mean on H and W to prevent collapsing the batch dimension

* Partially address feedback

* YAPF

* Removed redundant class vars

* Update urls to releases

* Add information to models.rst

* Replace init with kaiming_normal_ in fan-out mode

* Use load_state_dict_from_url

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

* 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

* Add GoogLeNet (Inception v1)

* Fix missing padding

* Add missing ReLu to aux classifier

* Add Batch normalized version of GoogLeNet

* Use ceil_mode instead of padding and initialize weights using "xavier"

* Match BVLC GoogLeNet zero initialization of classifier

* Small cleanup

* use adaptive avg pool

* adjust network to match TensorFlow

* Update url of pre-trained model and add classification results on ImageNet

* Bugfix that improves performance by 1 point