Revert "add RepPoints support (#1256)" (#1263)

This reverts commit ea9b20af.

mmdet/models/anchor_heads/__init__.py

@@ -3,13 +3,11 @@ from .fcos_head import FCOSHead
 from .ga_retina_head import GARetinaHead
 from .ga_rpn_head import GARPNHead
 from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
-from .reppoints_head import RepPointsHead
 from .retina_head import RetinaHead
 from .rpn_head import RPNHead
 from .ssd_head import SSDHead
 
 __all__ = [
     'AnchorHead', 'GuidedAnchorHead', 'FeatureAdaption', 'RPNHead',
-    'GARPNHead', 'RetinaHead', 'GARetinaHead', 'SSDHead', 'FCOSHead',
-    'RepPointsHead'
+    'GARPNHead', 'RetinaHead', 'GARetinaHead', 'SSDHead', 'FCOSHead'
 ]

mmdet/models/detectors/__init__.py

@@ -8,7 +8,6 @@ from .grid_rcnn import GridRCNN
 from .htc import HybridTaskCascade
 from .mask_rcnn import MaskRCNN
 from .mask_scoring_rcnn import MaskScoringRCNN
-from .reppoints_detector import RepPointsDetector
 from .retinanet import RetinaNet
 from .rpn import RPN
 from .single_stage import SingleStageDetector
@@ -17,6 +16,5 @@ from .two_stage import TwoStageDetector
 __all__ = [
     'BaseDetector', 'SingleStageDetector', 'TwoStageDetector', 'RPN',
     'FastRCNN', 'FasterRCNN', 'MaskRCNN', 'CascadeRCNN', 'HybridTaskCascade',
-    'DoubleHeadRCNN', 'RetinaNet', 'FCOS', 'GridRCNN', 'MaskScoringRCNN',
-    'RepPointsDetector'
+    'DoubleHeadRCNN', 'RetinaNet', 'FCOS', 'GridRCNN', 'MaskScoringRCNN'
 ]

mmdet/models/detectors/reppoints_detector.py

-import torch
-
-from mmdet.core import bbox2result, bbox_mapping_back, multiclass_nms
-from ..registry import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module
-class RepPointsDetector(SingleStageDetector):
-    """RepPoints: Point Set Representation for Object Detection.
-
-        This detector is the implementation of:
-        - RepPoints detector (https://arxiv.org/pdf/1904.11490)
-    """
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(RepPointsDetector,
-              self).__init__(backbone, neck, bbox_head, train_cfg, test_cfg,
-                             pretrained)
-
-    def merge_aug_results(self, aug_bboxes, aug_scores, img_metas):
-        """Merge augmented detection bboxes and scores.
-
-        Args:
-            aug_bboxes (list[Tensor]): shape (n, 4*#class)
-            aug_scores (list[Tensor] or None): shape (n, #class)
-            img_shapes (list[Tensor]): shape (3, ).
-
-        Returns:
-            tuple: (bboxes, scores)
-        """
-        recovered_bboxes = []
-        for bboxes, img_info in zip(aug_bboxes, img_metas):
-            img_shape = img_info[0]['img_shape']
-            scale_factor = img_info[0]['scale_factor']
-            flip = img_info[0]['flip']
-            bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip)
-            recovered_bboxes.append(bboxes)
-        bboxes = torch.cat(recovered_bboxes, dim=0)
-        if aug_scores is None:
-            return bboxes
-        else:
-            scores = torch.cat(aug_scores, dim=0)
-            return bboxes, scores
-
-    def aug_test(self, imgs, img_metas, rescale=False):
-        # recompute feats to save memory
-        feats = self.extract_feats(imgs)
-
-        aug_bboxes = []
-        aug_scores = []
-        for x, img_meta in zip(feats, img_metas):
-            # only one image in the batch
-            outs = self.bbox_head(x)
-            bbox_inputs = outs + (img_meta, self.test_cfg, False, False)
-            det_bboxes, det_scores = self.bbox_head.get_bboxes(*bbox_inputs)[0]
-            aug_bboxes.append(det_bboxes)
-            aug_scores.append(det_scores)
-
-        # after merging, bboxes will be rescaled to the original image size
-        merged_bboxes, merged_scores = self.merge_aug_results(
-            aug_bboxes, aug_scores, img_metas)
-        det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
-                                                self.test_cfg.score_thr,
-                                                self.test_cfg.nms,
-                                                self.test_cfg.max_per_img)
-
-        if rescale:
-            _det_bboxes = det_bboxes
-        else:
-            _det_bboxes = det_bboxes.clone()
-            _det_bboxes[:, :4] *= img_metas[0][0]['scale_factor']
-        bbox_results = bbox2result(_det_bboxes, det_labels,
-                                   self.bbox_head.num_classes)
-        return bbox_results