Support AlignDETR

Co-authored-by: huanghaian <[email protected]>

mmdet/models/detectors/deformable_detr.py

@@ -500,7 +500,7 @@ def gen_encoder_output_proposals(
             else:
                 if not isinstance(HW, torch.Tensor):
                     HW = memory.new_tensor(HW)
-                scale = HW.unsqueeze(0).flip(dims=[0, 1]).view(bs, 1, 1, 2)
+                scale = HW.unsqueeze(0).flip(dims=[0, 1]).view(1, 1, 1, 2)
             grid_y, grid_x = torch.meshgrid(
                 torch.linspace(
                     0, H - 1, H, dtype=torch.float32, device=memory.device),

projects/AlignDETR/README.md

@@ -0,0 +1,33 @@
+# AlignDETR
+
+> [Align-DETR: Improving DETR with Simple IoU-aware BCE loss](https://arxiv.org/abs/2304.07527)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+DETR has set up a simple end-to-end pipeline for object detection by formulating this task as a set prediction problem, showing promising potential. However, despite the significant progress in improving DETR, this paper identifies a problem of misalignment in the output distribution, which prevents the best-regressed samples from being assigned with high confidence, hindering the model's accuracy. We propose a metric, recall of best-regressed samples, to quantitively evaluate the misalignment problem. Observing its importance, we propose a novel Align-DETR that incorporates a localization precision-aware classification loss in optimization. The proposed loss, IA-BCE, guides the training of DETR to build a strong correlation between classification score and localization precision. We also adopt the mixed-matching strategy, to facilitate DETR-based detectors with faster training convergence while keeping an end-to-end scheme. Moreover, to overcome the dramatic decrease in sample quality induced by the sparsity of queries, we introduce a prime sample weighting mechanism to suppress the interference of unimportant samples. Extensive experiments are conducted with very competitive results reported. In particular, it delivers a 46 (+3.8)% AP on the DAB-DETR baseline with the ResNet-50 backbone and reaches a new SOTA performance of 50.2% AP in the 1x setting on the COCO validation set when employing the strong baseline DINO.
+
+![image](https://github.com/open-mmlab/mmdetection/assets/33146359/5a4fa664-b4c6-487d-b6d8-22be9d59a2bc)
+
+## Results and Models
+
+| Backbone |    Model    | Lr schd | box AP |                       Config                       |                                                                                                                                                                    Download                                                                                                                                                                    |
+| :------: | :---------: | :-----: | :----: | :------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | DINO-4scale |   12e   |  50.5  | [config](./align_detr-4scale_r50_8xb2-12e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/align_detr/align_detr-4scale_r50_8xb2-12e_coco/align_detr-4scale_r50_8xb2-12e_coco_20230914_095734-61f921af.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/align_detr/align_detr-4scale_r50_8xb2-12e_coco/align_detr-4scale_r50_8xb2-12e_coco_20230914_095734.log.json) |
+|   R-50   | DINO-4scale |   24e   |  51.4  | [config](./align_detr-4scale_r50_8xb2-24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/align_detr/align_detr-4scale_r50_8xb2-24e_coco/align_detr-4scale_r50_8xb2-24e_coco_20230919_152414-f4b6cf76.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/align_detr/align_detr-4scale_r50_8xb2-24e_coco/align_detr-4scale_r50_8xb2-24e_coco_20230919_152414.log.json) |
+
+## Citation
+
+We provide the config files for AlignDETR: [Align-DETR: Improving DETR with Simple IoU-aware BCE loss](https://arxiv.org/abs/2304.07527).
+
+```latex
+@misc{cai2023aligndetr,
+      title={Align-DETR: Improving DETR with Simple IoU-aware BCE loss},
+      author={Zhi Cai and Songtao Liu and Guodong Wang and Zheng Ge and Xiangyu Zhang and Di Huang},
+      year={2023},
+      eprint={2304.07527},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

projects/AlignDETR/align_detr/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .align_detr_head import AlignDETRHead
+from .mixed_hungarian_assigner import MixedHungarianAssigner
+
+__all__ = ['AlignDETRHead', 'MixedHungarianAssigner']