MMDetection V2.5.0 Release

Highlights

• Support new methods: YOLACT, CentripetalNet.
• Add more documentations for easier and more clear usage.

Backwards Incompatible Changes

FP16 related methods are imported from mmcv instead of mmdet. (#3766, #3822)
Mixed precision training utils in mmdet.core.fp16 are moved to mmcv.runner, including force_fp32, auto_fp16, wrap_fp16_model, and Fp16OptimizerHook. A deprecation warning will be raised if users attempt to import those methods from mmdet.core.fp16, and will be finally removed in V2.8.0.

[0, N-1] represents foreground classes and N indicates background classes for all models. (#3221)
Before v2.5.0, the background label for RPN is 0, and N for other heads. Now the behavior is consistent for all models. Thus self.background_labels in dense_heads is removed and all heads use self.num_classes to indicate the class index of background labels.
This change has no effect on the pre-trained models in the v2.x model zoo, but will affect the training of all models with RPN heads. Two-stage detectors whose RPN head uses softmax will be affected because the order of categories is changed.

Only call get_subset_by_classes when test_mode=True and self.filter_empty_gt=True (#3695)
Function get_subset_by_classes in dataset is refactored and only filters out images when test_mode=True and self.filter_empty_gt=True.
In the original implementation, get_subset_by_classes is not related to the flag self.filter_empty_gt and will only be called when the classes is set during initialization no matter test_mode is True or False. This brings ambiguous behavior and potential bugs in many cases. After v2.5.0, if filter_empty_gt=False, no matter whether the classes are specified in a dataset, the dataset will use all the images in the annotations. If filter_empty_gt=True and test_mode=True, no matter whether the classes are specified, the dataset will call ``get_subset_by_classes` to check the images and filter out images containing no GT boxes. Therefore, the users should be responsible for the data filtering/cleaning process for the test dataset.

New Features

• Test time augmentation for single stage detectors (#3844, #3638)
• Support to show the name of experiments during training (#3764)
• Add Shear, Rotate, Translate Augmentation (#3656, #3619, #3687)
• Add image-only transformations including Constrast, Equalize, Color, and Brightness. (#3643)
• Support YOLACT (#3456)
• Support CentripetalNet (#3390)
• Support PyTorch 1.6 in docker (#3905)

Bug Fixes

• Fix the bug of training ATSS when there is no ground truth boxes (#3702)
• Fix the bug of using Focal Loss when there is num_pos is 0 (#3702)
• Fix the label index mapping in dataset browser (#3708)
• Fix Mask R-CNN training stuck problem when ther is no positive rois (#3713)
• Fix the bug of self.rpn_head.test_cfg in RPNTestMixin by using self.rpn_head in rpn head (#3808)
• Fix deprecated Conv2d from mmcv.ops (#3791)
• Fix device bug in RepPoints (#3836)
• Fix SABL validating bug (#3849)
• Use https://download.openmmlab.com/mmcv/dist/index.html for installing MMCV (#3840)
• Fix nonzero in NMS for PyTorch 1.6.0 (#3867)
• Fix the API change bug of PAA (#3883)
• Fix typo in bbox_flip (#3886)
• Fix cv2 import error of ligGL.so.1 in Dockerfile (#3891)

Improvements

• Change to use mmcv.utils.collect_env for collecting environment information to avoid duplicate codes (#3779)
• Update checkpoint file names to v2.0 models in documentation (#3795)
• Update tutorials for changing runtime settings (#3778), modifing loss (#3777)
• Improve the function of simple_test_bboxes in SABL (#3853)
• Convert mask to bool before using it as img's index for robustness and speedup (#3870)
• Improve documentation of modules and dataset customization (#3821)

New Contributors

• @jd730 made their first contribution in #3741
• @yl-1993 made their first contribution in #3840
• @chongzhou96 made their first contribution in #3456
• @LabMemNo003 made their first contribution in #3876

Full Changelog: v2.4.0...v2.5.0