[Fix] Fix bug in rotated_reppoints_head

configs/sasm_reppoints/README.md

@@ -1,10 +1,25 @@
 # SASM
-> SASM-RepPoints
+> [Shape-Adaptive Selection and Measurement for Oriented Object Detection](https://www.aaai.org/AAAI22Papers/AAAI-2171.HouL.pdf)
 
 <!-- [ALGORITHM] -->
 ## Abstract
 
-Coming soon!
+<div align=center>
+<img src="https://raw.githubusercontent.com/zytx121/image-host/main/imgs/sasm.jpg" width="800"/>
+</div>
+
+The development of detection methods for oriented object detection remains a challenging task. A considerable obstacle
+is the wide variation in the shape (e.g., aspect ratio) of objects. Sample selection in general object detection has been
+widely studied as it plays a crucial role in the performance of the detection method and has achieved great progress.
+However, existing sample selection strategies still overlook some issues: (1) most of them ignore the object shape information;
+(2) they do not make a potential distinction between selected positive samples; and (3) some of them can only be applied
+to either anchor-free or anchor-based methods and cannot be used for both of them simultaneously. In this paper, we
+propose novel flexible shape-adaptive selection (SA-S) and shape-adaptive measurement (SA-M) strategies for oriented
+object detection, which comprise an SA-S strategy for sample selection and SA-M strategy for the quality estimation of
+positive samples. Specifically, the SA-S strategy dynamically selects samples according to the shape information and
+characteristics distribution of objects. The SA-M strategy measures the localization potential and adds quality information
+on the selected positive samples. The experimental results on both anchor-free and anchor-based baselines and four publicly
+available oriented datasets (DOTA, HRSC2016, UCASAOD, and ICDAR2015) demonstrate the effectiveness of the proposed method
 
 ## Results and models
 
@@ -18,5 +33,12 @@ DOTA1.0
 
 
 ## Citation
-AAAI 2022
-Coming soon!
+```
+@inproceedings{hou2022shape,
+    title={Shape-Adaptive Selection and Measurement for Oriented Object Detection},
+    author={Hou, Liping and Lu, Ke and Xue, Jian and Li, Yuqiu},
+    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+    year={2022}
+}
+
+```

mmrotate/models/dense_heads/rotated_reppoints_head.py

@@ -429,7 +429,7 @@ def get_targets(self,
             gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
         if gt_labels_list is None:
             gt_labels_list = [None for _ in range(num_imgs)]
-        all_overlaps_rotate_list = [None] * 4
+        all_overlaps_rotate_list = [None] * len(proposals_list)
         (all_labels, all_label_weights, all_bbox_gt, all_proposals,
          all_proposal_weights, pos_inds_list, neg_inds_list,
          sampling_result) = multi_apply(
@@ -518,7 +518,7 @@ def get_cfa_targets(self,
             gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
         if gt_labels_list is None:
             gt_labels_list = [None for _ in range(num_imgs)]
-        all_overlaps_rotate_list = [None] * 4
+        all_overlaps_rotate_list = [None] * len(proposals_list)
         (all_labels, all_label_weights, all_bbox_gt, all_proposals,
          all_proposal_weights, pos_inds_list, neg_inds_list,
          sampling_result) = multi_apply(

mmrotate/models/roi_heads/roi_extractors/rotate_single_level_roi_extractor.py

@@ -99,7 +99,9 @@ def forward(self, feats, rois, roi_scale_factor=None):
         Returns:
             torch.Tensor: Scaled RoI features.
         """
-        if isinstance(self.roi_layers[0], ops.RiRoIAlignRotated):
+        from mmrotate import digit_version, mmcv_version
+        if isinstance(self.roi_layers[0], ops.RiRoIAlignRotated
+                      ) or mmcv_version == digit_version('1.4.5'):
             out_size = nn.modules.utils._pair(self.roi_layers[0].out_size)
         else:
             out_size = self.roi_layers[0].output_size