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