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[Feature] Support RTMDet-Ins and improve RTMDet test config. #9494
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mmdet/models/task_modules/assigners/dynamic_soft_label_assigner.py
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for gt_instances in batch_gt_instances: | ||
gt_instances.masks = gt_instances.masks.to_tensor( | ||
dtype=torch.bool, device=device) |
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The conversion overhead here is not negligible and it will slow down training. How about moving this to the data pipeline?
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You are right. We are considering moving this to PackDetInputs. But this modification is related to all instance segmentation methods. We will open another PR for the refactoring.
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LGTM
train_cfg=dict( | ||
assigner=dict(type='DynamicSoftLabelAssigner', topk=13), | ||
allowed_border=-1, | ||
pos_weight=-1, | ||
debug=False), | ||
test_cfg=dict( | ||
nms_pre=1000, | ||
nms_pre=30000, |
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We can add comments in the config to indicate why and its outcome so that users can better understand it.
Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.
Motivation
For a fair comparison, we update the test_cfg in RTMDet.
Modification
Add RTMDet-Ins
Update test_cfg
Support mask mosaic and mixup
BC-breaking (Optional)
Does the modification introduce changes that break the backward-compatibility of the downstream repos?
If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.
Use cases (Optional)
If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.
Checklist