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pcdet/models/detectors/second_net_iou.py

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+import torch
+from .detector3d_template import Detector3DTemplate
+from ..model_utils.model_nms_utils import class_agnostic_nms
+from ...ops.roiaware_pool3d import roiaware_pool3d_utils
+
+
+class SECONDNetIoU(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        batch_dict['dataset_cfg'] = self.dataset.dataset_cfg
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}
+
+        loss_rpn, tb_dict = self.dense_head.get_loss()
+        loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict)
+
+        loss = loss_rpn + loss_rcnn
+        return loss, tb_dict, disp_dict
+
+    @staticmethod
+    def cal_scores_by_npoints(cls_scores, iou_scores, num_points_in_gt, cls_thresh=10, iou_thresh=100):
+        """
+        Args:
+            cls_scores: (N)
+            iou_scores: (N)
+            num_points_in_gt: (N, 7+c)
+            cls_thresh: scalar
+            iou_thresh: scalar
+        """
+        assert iou_thresh >= cls_thresh
+        alpha = torch.zeros(cls_scores.shape, dtype=torch.float32).cuda()
+        alpha[num_points_in_gt <= cls_thresh] = 0
+        alpha[num_points_in_gt >= iou_thresh] = 1
+        
+        mask = ((num_points_in_gt > cls_thresh) & (num_points_in_gt < iou_thresh))
+        alpha[mask] = (num_points_in_gt[mask] - 10) / (iou_thresh - cls_thresh)
+        
+        scores = (1 - alpha) * cls_scores + alpha * iou_scores
+
+        return scores
+
+    def set_nms_score_by_class(self, iou_preds, cls_preds, label_preds, score_by_class):
+        n_classes = torch.unique(label_preds).shape[0]
+        nms_scores = torch.zeros(iou_preds.shape, dtype=torch.float32).cuda()
+        for i in range(n_classes):
+            mask = label_preds == (i + 1)
+            class_name = self.class_names[i]
+            score_type = score_by_class[class_name]
+            if score_type == 'iou':
+                nms_scores[mask] = iou_preds[mask]
+            elif score_type == 'cls':
+                nms_scores[mask] = cls_preds[mask]
+            else:
+                raise NotImplementedError
+
+        return nms_scores
+
+    def post_processing(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                batch_cls_preds: (B, num_boxes, num_classes | 1) or (N1+N2+..., num_classes | 1)
+                batch_box_preds: (B, num_boxes, 7+C) or (N1+N2+..., 7+C)
+                cls_preds_normalized: indicate whether batch_cls_preds is normalized
+                batch_index: optional (N1+N2+...)
+                roi_labels: (B, num_rois)  1 .. num_classes
+        Returns:
+
+        """
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        batch_size = batch_dict['batch_size']
+        recall_dict = {}
+        pred_dicts = []
+        for index in range(batch_size):
+            if batch_dict.get('batch_index', None) is not None:
+                assert batch_dict['batch_cls_preds'].shape.__len__() == 2
+                batch_mask = (batch_dict['batch_index'] == index)
+            else:
+                assert batch_dict['batch_cls_preds'].shape.__len__() == 3
+                batch_mask = index
+
+            box_preds = batch_dict['batch_box_preds'][batch_mask]
+            iou_preds = batch_dict['batch_cls_preds'][batch_mask]
+            cls_preds = batch_dict['roi_scores'][batch_mask]
+
+            src_iou_preds = iou_preds
+            src_box_preds = box_preds
+            src_cls_preds = cls_preds
+            assert iou_preds.shape[1] in [1, self.num_class]
+
+            if not batch_dict['cls_preds_normalized']:
+                iou_preds = torch.sigmoid(iou_preds)
+                cls_preds = torch.sigmoid(cls_preds)
+
+            if post_process_cfg.NMS_CONFIG.MULTI_CLASSES_NMS:
+                raise NotImplementedError
+            else:
+                iou_preds, label_preds = torch.max(iou_preds, dim=-1)
+                label_preds = batch_dict['roi_labels'][index] if batch_dict.get('has_class_labels', False) else label_preds + 1
+
+                if post_process_cfg.NMS_CONFIG.get('SCORE_BY_CLASS', None) and \
+                        post_process_cfg.NMS_CONFIG.SCORE_TYPE == 'score_by_class':
+                    nms_scores = self.set_nms_score_by_class(
+                        iou_preds, cls_preds, label_preds, post_process_cfg.NMS_CONFIG.SCORE_BY_CLASS
+                    )
+                elif post_process_cfg.NMS_CONFIG.get('SCORE_TYPE', None) == 'iou' or \
+                        post_process_cfg.NMS_CONFIG.get('SCORE_TYPE', None) is None:
+                    nms_scores = iou_preds
+                elif post_process_cfg.NMS_CONFIG.SCORE_TYPE == 'cls':
+                    nms_scores = cls_preds
+                elif post_process_cfg.NMS_CONFIG.SCORE_TYPE == 'weighted_iou_cls':
+                    nms_scores = post_process_cfg.NMS_CONFIG.SCORE_WEIGHTS.iou * iou_preds + \
+                                 post_process_cfg.NMS_CONFIG.SCORE_WEIGHTS.cls * cls_preds
+                elif post_process_cfg.NMS_CONFIG.SCORE_TYPE == 'num_pts_iou_cls':
+                    point_mask = (batch_dict['points'][:, 0] == batch_mask)
+                    batch_points = batch_dict['points'][point_mask][:, 1:4]
+
+                    num_pts_in_gt = roiaware_pool3d_utils.points_in_boxes_cpu(
+                        batch_points.cpu(), box_preds[:, 0:7].cpu()
+                    ).sum(dim=1).float().cuda()
+                    
+                    score_thresh_cfg = post_process_cfg.NMS_CONFIG.SCORE_THRESH
+                    nms_scores = self.cal_scores_by_npoints(
+                        cls_preds, iou_preds, num_pts_in_gt, 
+                        score_thresh_cfg.cls, score_thresh_cfg.iou
+                    )
+                else:
+                    raise NotImplementedError
+
+                selected, selected_scores = class_agnostic_nms(
+                    box_scores=nms_scores, box_preds=box_preds,
+                    nms_config=post_process_cfg.NMS_CONFIG,
+                    score_thresh=post_process_cfg.SCORE_THRESH
+                )
+
+                if post_process_cfg.OUTPUT_RAW_SCORE:
+                    raise NotImplementedError
+
+                final_scores = selected_scores
+                final_labels = label_preds[selected]
+                final_boxes = box_preds[selected]
+
+            recall_dict = self.generate_recall_record(
+                box_preds=final_boxes if 'rois' not in batch_dict else src_box_preds,
+                recall_dict=recall_dict, batch_index=index, data_dict=batch_dict,
+                thresh_list=post_process_cfg.RECALL_THRESH_LIST
+            )
+
+            record_dict = {
+                'pred_boxes': final_boxes,
+                'pred_scores': final_scores,
+                'pred_labels': final_labels,
+                'pred_cls_scores': cls_preds[selected],
+                'pred_iou_scores': iou_preds[selected]
+            }
+
+            pred_dicts.append(record_dict)
+
+        return pred_dicts, recall_dict