def loss(self, cls_scores, bbox_preds, gt_bboxes, gt_labels, img_metas, cfg, gt_bboxes_ignore=None): featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] assert len(featmap_sizes) == len(self.anchor_generators) anchor_list, valid_flag_list = self.get_anchors( featmap_sizes, img_metas) cls_reg_targets = anchor_target(anchor_list, valid_flag_list, gt_bboxes, img_metas, self.target_means, self.target_stds, cfg, gt_bboxes_ignore_list=gt_bboxes_ignore, gt_labels_list=gt_labels, label_channels=1, sampling=False, unmap_outputs=False) if cls_reg_targets is None: return None (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets num_images = len(img_metas) all_cls_scores = torch.cat([ s.permute(0, 2, 3, 1).reshape( num_images, -1, self.cls_out_channels) for s in cls_scores ], 1) all_labels = torch.cat(labels_list, -1).view(num_images, -1) all_label_weights = torch.cat(label_weights_list, -1).view(num_images, -1) all_bbox_preds = torch.cat([ b.permute(0, 2, 3, 1).reshape(num_images, -1, 4) for b in bbox_preds ], -2) all_bbox_targets = torch.cat(bbox_targets_list, -2).view(num_images, -1, 4) all_bbox_weights = torch.cat(bbox_weights_list, -2).view(num_images, -1, 4) losses_cls, losses_bbox = multi_apply(self.loss_single, all_cls_scores, all_bbox_preds, all_labels, all_label_weights, all_bbox_targets, all_bbox_weights, num_total_samples=num_total_pos, cfg=cfg) return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
def loss(self, cls_scores, bbox_preds, gt_bboxes, gt_labels, img_metas, cfg, gt_bboxes_ignore=None): featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] assert len(featmap_sizes) == len(self.anchor_generators) anchor_list, valid_flag_list = self.get_anchors( featmap_sizes, img_metas) label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 cls_reg_targets = anchor_target(anchor_list, valid_flag_list, gt_bboxes, img_metas, self.target_means, self.target_stds, cfg, gt_bboxes_ignore_list=gt_bboxes_ignore, gt_labels_list=gt_labels, label_channels=label_channels, sampling=self.sampling) if cls_reg_targets is None: return None (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets num_total_samples = (num_total_pos + num_total_neg if self.sampling else num_total_pos) losses_cls, losses_bbox = multi_apply( self.loss_single, cls_scores, bbox_preds, labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_samples=num_total_samples, cfg=cfg) return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
def fcos_target(self, points, gt_bboxes_list, gt_labels_list): assert len(points) == len(self.regress_ranges) num_levels = len(points) # expand regress ranges to align with points expanded_regress_ranges = [ points[i].new_tensor(self.regress_ranges[i])[None].expand_as( points[i]) for i in range(num_levels) ] # concat all levels points and regress ranges concat_regress_ranges = torch.cat(expanded_regress_ranges, dim=0) concat_points = torch.cat(points, dim=0) # get labels and bbox_targets of each image labels_list, bbox_targets_list = multi_apply( self.fcos_target_single, gt_bboxes_list, gt_labels_list, points=concat_points, regress_ranges=concat_regress_ranges) # split to per img, per level num_points = [center.size(0) for center in points] labels_list = [labels.split(num_points, 0) for labels in labels_list] bbox_targets_list = [ bbox_targets.split(num_points, 0) for bbox_targets in bbox_targets_list ] # concat per level image concat_lvl_labels = [] concat_lvl_bbox_targets = [] for i in range(num_levels): concat_lvl_labels.append( torch.cat([labels[i] for labels in labels_list])) concat_lvl_bbox_targets.append( torch.cat( [bbox_targets[i] for bbox_targets in bbox_targets_list])) return concat_lvl_labels, concat_lvl_bbox_targets
def forward(self, feats): return multi_apply(self.forward_single, feats)