# Copyright (c) OpenMMLab. All rights reserved. import numpy as np import torch import torch.nn.functional as F from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes, merge_aug_masks, multiclass_nms) from ..builder import HEADS, build_head, build_roi_extractor from ..utils.brick_wrappers import adaptive_avg_pool2d from .cascade_roi_head import CascadeRoIHead @HEADS.register_module() class HybridTaskCascadeRoIHead(CascadeRoIHead): """Hybrid task cascade roi head including one bbox head and one mask head. https://arxiv.org/abs/1901.07518 """ def __init__(self, num_stages, stage_loss_weights, semantic_roi_extractor=None, semantic_head=None, semantic_fusion=('bbox', 'mask'), interleaved=True, mask_info_flow=True, **kwargs): super(HybridTaskCascadeRoIHead, self).__init__(num_stages, stage_loss_weights, **kwargs) assert self.with_bbox assert not self.with_shared_head # shared head is not supported if semantic_head is not None: self.semantic_roi_extractor = build_roi_extractor( semantic_roi_extractor) self.semantic_head = build_head(semantic_head) self.semantic_fusion = semantic_fusion self.interleaved = interleaved self.mask_info_flow = mask_info_flow @property def with_semantic(self): """bool: whether the head has semantic head""" if hasattr(self, 'semantic_head') and self.semantic_head is not None: return True else: return False def forward_dummy(self, x, proposals): """Dummy forward function.""" outs = () # semantic head if self.with_semantic: _, semantic_feat = self.semantic_head(x) else: semantic_feat = None # bbox heads rois = bbox2roi([proposals]) for i in range(self.num_stages): bbox_results = self._bbox_forward( i, x, rois, semantic_feat=semantic_feat) outs = outs + (bbox_results['cls_score'], bbox_results['bbox_pred']) # mask heads if self.with_mask: mask_rois = rois[:100] mask_roi_extractor = self.mask_roi_extractor[-1] mask_feats = mask_roi_extractor( x[:len(mask_roi_extractor.featmap_strides)], mask_rois) if self.with_semantic and 'mask' in self.semantic_fusion: mask_semantic_feat = self.semantic_roi_extractor( [semantic_feat], mask_rois) mask_feats += mask_semantic_feat last_feat = None for i in range(self.num_stages): mask_head = self.mask_head[i] if self.mask_info_flow: mask_pred, last_feat = mask_head(mask_feats, last_feat) else: mask_pred = mask_head(mask_feats) outs = outs + (mask_pred, ) return outs def _bbox_forward_train(self, stage, x, sampling_results, gt_bboxes, gt_labels, rcnn_train_cfg, semantic_feat=None): """Run forward function and calculate loss for box head in training.""" bbox_head = self.bbox_head[stage] rois = bbox2roi([res.bboxes for res in sampling_results]) bbox_results = self._bbox_forward( stage, x, rois, semantic_feat=semantic_feat) bbox_targets = bbox_head.get_targets(sampling_results, gt_bboxes, gt_labels, rcnn_train_cfg) loss_bbox = bbox_head.loss(bbox_results['cls_score'], bbox_results['bbox_pred'], rois, *bbox_targets) bbox_results.update( loss_bbox=loss_bbox, rois=rois, bbox_targets=bbox_targets, ) return bbox_results def _mask_forward_train(self, stage, x, sampling_results, gt_masks, rcnn_train_cfg, semantic_feat=None): """Run forward function and calculate loss for mask head in training.""" mask_roi_extractor = self.mask_roi_extractor[stage] mask_head = self.mask_head[stage] pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results]) mask_feats = mask_roi_extractor(x[:mask_roi_extractor.num_inputs], pos_rois) # semantic feature fusion # element-wise sum for original features and pooled semantic features if self.with_semantic and 'mask' in self.semantic_fusion: mask_semantic_feat = self.semantic_roi_extractor([semantic_feat], pos_rois) if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]: mask_semantic_feat = F.adaptive_avg_pool2d( mask_semantic_feat, mask_feats.shape[-2:]) mask_feats += mask_semantic_feat # mask information flow # forward all previous mask heads to obtain last_feat, and fuse it # with the normal mask feature if self.mask_info_flow: last_feat = None for i in range(stage): last_feat = self.mask_head[i]( mask_feats, last_feat, return_logits=False) mask_pred = mask_head(mask_feats, last_feat, return_feat=False) else: mask_pred = mask_head(mask_feats, return_feat=False) mask_targets = mask_head.get_targets(sampling_results, gt_masks, rcnn_train_cfg) pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results]) loss_mask = mask_head.loss(mask_pred, mask_targets, pos_labels) mask_results = dict(loss_mask=loss_mask) return mask_results def _bbox_forward(self, stage, x, rois, semantic_feat=None): """Box head forward function used in both training and testing.""" bbox_roi_extractor = self.bbox_roi_extractor[stage] bbox_head = self.bbox_head[stage] bbox_feats = bbox_roi_extractor( x[:len(bbox_roi_extractor.featmap_strides)], rois) if self.with_semantic and 'bbox' in self.semantic_fusion: bbox_semantic_feat = self.semantic_roi_extractor([semantic_feat], rois) if bbox_semantic_feat.shape[-2:] != bbox_feats.shape[-2:]: bbox_semantic_feat = adaptive_avg_pool2d( bbox_semantic_feat, bbox_feats.shape[-2:]) bbox_feats += bbox_semantic_feat cls_score, bbox_pred = bbox_head(bbox_feats) bbox_results = dict(cls_score=cls_score, bbox_pred=bbox_pred) return bbox_results def _mask_forward_test(self, stage, x, bboxes, semantic_feat=None): """Mask head forward function for testing.""" mask_roi_extractor = self.mask_roi_extractor[stage] mask_head = self.mask_head[stage] mask_rois = bbox2roi([bboxes]) mask_feats = mask_roi_extractor( x[:len(mask_roi_extractor.featmap_strides)], mask_rois) if self.with_semantic and 'mask' in self.semantic_fusion: mask_semantic_feat = self.semantic_roi_extractor([semantic_feat], mask_rois) if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]: mask_semantic_feat = F.adaptive_avg_pool2d( mask_semantic_feat, mask_feats.shape[-2:]) mask_feats += mask_semantic_feat if self.mask_info_flow: last_feat = None last_pred = None for i in range(stage): mask_pred, last_feat = self.mask_head[i](mask_feats, last_feat) if last_pred is not None: mask_pred = mask_pred + last_pred last_pred = mask_pred mask_pred = mask_head(mask_feats, last_feat, return_feat=False) if last_pred is not None: mask_pred = mask_pred + last_pred else: mask_pred = mask_head(mask_feats) return mask_pred def forward_train(self, x, img_metas, proposal_list, gt_bboxes, gt_labels, gt_bboxes_ignore=None, gt_masks=None, gt_semantic_seg=None): """ Args: x (list[Tensor]): list of multi-level img features. img_metas (list[dict]): list of image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys see `mmdet/datasets/pipelines/formatting.py:Collect`. proposal_list (list[Tensors]): list of region proposals. gt_bboxes (list[Tensor]): Ground truth bboxes for each image with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. gt_labels (list[Tensor]): class indices corresponding to each box gt_bboxes_ignore (None, list[Tensor]): specify which bounding boxes can be ignored when computing the loss. gt_masks (None, Tensor) : true segmentation masks for each box used if the architecture supports a segmentation task. gt_semantic_seg (None, list[Tensor]): semantic segmentation masks used if the architecture supports semantic segmentation task. Returns: dict[str, Tensor]: a dictionary of loss components """ # semantic segmentation part # 2 outputs: segmentation prediction and embedded features losses = dict() if self.with_semantic: semantic_pred, semantic_feat = self.semantic_head(x) loss_seg = self.semantic_head.loss(semantic_pred, gt_semantic_seg) losses['loss_semantic_seg'] = loss_seg else: semantic_feat = None for i in range(self.num_stages): self.current_stage = i rcnn_train_cfg = self.train_cfg[i] lw = self.stage_loss_weights[i] # assign gts and sample proposals sampling_results = [] bbox_assigner = self.bbox_assigner[i] bbox_sampler = self.bbox_sampler[i] num_imgs = len(img_metas) if gt_bboxes_ignore is None: gt_bboxes_ignore = [None for _ in range(num_imgs)] for j in range(num_imgs): assign_result = bbox_assigner.assign(proposal_list[j], gt_bboxes[j], gt_bboxes_ignore[j], gt_labels[j]) sampling_result = bbox_sampler.sample( assign_result, proposal_list[j], gt_bboxes[j], gt_labels[j], feats=[lvl_feat[j][None] for lvl_feat in x]) sampling_results.append(sampling_result) # bbox head forward and loss bbox_results = \ self._bbox_forward_train( i, x, sampling_results, gt_bboxes, gt_labels, rcnn_train_cfg, semantic_feat) roi_labels = bbox_results['bbox_targets'][0] for name, value in bbox_results['loss_bbox'].items(): losses[f's{i}.{name}'] = ( value * lw if 'loss' in name else value) # mask head forward and loss if self.with_mask: # interleaved execution: use regressed bboxes by the box branch # to train the mask branch if self.interleaved: pos_is_gts = [res.pos_is_gt for res in sampling_results] with torch.no_grad(): proposal_list = self.bbox_head[i].refine_bboxes( bbox_results['rois'], roi_labels, bbox_results['bbox_pred'], pos_is_gts, img_metas) # re-assign and sample 512 RoIs from 512 RoIs sampling_results = [] for j in range(num_imgs): assign_result = bbox_assigner.assign( proposal_list[j], gt_bboxes[j], gt_bboxes_ignore[j], gt_labels[j]) sampling_result = bbox_sampler.sample( assign_result, proposal_list[j], gt_bboxes[j], gt_labels[j], feats=[lvl_feat[j][None] for lvl_feat in x]) sampling_results.append(sampling_result) mask_results = self._mask_forward_train( i, x, sampling_results, gt_masks, rcnn_train_cfg, semantic_feat) for name, value in mask_results['loss_mask'].items(): losses[f's{i}.{name}'] = ( value * lw if 'loss' in name else value) # refine bboxes (same as Cascade R-CNN) if i < self.num_stages - 1 and not self.interleaved: pos_is_gts = [res.pos_is_gt for res in sampling_results] with torch.no_grad(): proposal_list = self.bbox_head[i].refine_bboxes( bbox_results['rois'], roi_labels, bbox_results['bbox_pred'], pos_is_gts, img_metas) return losses def simple_test(self, x, proposal_list, img_metas, rescale=False): """Test without augmentation. Args: x (tuple[Tensor]): Features from upstream network. Each has shape (batch_size, c, h, w). proposal_list (list(Tensor)): Proposals from rpn head. Each has shape (num_proposals, 5), last dimension 5 represent (x1, y1, x2, y2, score). img_metas (list[dict]): Meta information of images. rescale (bool): Whether to rescale the results to the original image. Default: True. Returns: list[list[np.ndarray]] or list[tuple]: When no mask branch, it is bbox results of each image and classes with type `list[list[np.ndarray]]`. The outer list corresponds to each image. The inner list corresponds to each class. When the model has mask branch, it contains bbox results and mask results. The outer list corresponds to each image, and first element of tuple is bbox results, second element is mask results. """ if self.with_semantic: _, semantic_feat = self.semantic_head(x) else: semantic_feat = None num_imgs = len(proposal_list) img_shapes = tuple(meta['img_shape'] for meta in img_metas) ori_shapes = tuple(meta['ori_shape'] for meta in img_metas) scale_factors = tuple(meta['scale_factor'] for meta in img_metas) # "ms" in variable names means multi-stage ms_bbox_result = {} ms_segm_result = {} ms_scores = [] rcnn_test_cfg = self.test_cfg rois = bbox2roi(proposal_list) if rois.shape[0] == 0: # There is no proposal in the whole batch bbox_results = [[ np.zeros((0, 5), dtype=np.float32) for _ in range(self.bbox_head[-1].num_classes) ]] * num_imgs if self.with_mask: mask_classes = self.mask_head[-1].num_classes segm_results = [[[] for _ in range(mask_classes)] for _ in range(num_imgs)] results = list(zip(bbox_results, segm_results)) else: results = bbox_results return results for i in range(self.num_stages): bbox_head = self.bbox_head[i] bbox_results = self._bbox_forward( i, x, rois, semantic_feat=semantic_feat) # split batch bbox prediction back to each image cls_score = bbox_results['cls_score'] bbox_pred = bbox_results['bbox_pred'] num_proposals_per_img = tuple(len(p) for p in proposal_list) rois = rois.split(num_proposals_per_img, 0) cls_score = cls_score.split(num_proposals_per_img, 0) bbox_pred = bbox_pred.split(num_proposals_per_img, 0) ms_scores.append(cls_score) if i < self.num_stages - 1: refine_rois_list = [] for j in range(num_imgs): if rois[j].shape[0] > 0: bbox_label = cls_score[j][:, :-1].argmax(dim=1) refine_rois = bbox_head.regress_by_class( rois[j], bbox_label, bbox_pred[j], img_metas[j]) refine_rois_list.append(refine_rois) rois = torch.cat(refine_rois_list) # average scores of each image by stages cls_score = [ sum([score[i] for score in ms_scores]) / float(len(ms_scores)) for i in range(num_imgs) ] # apply bbox post-processing to each image individually det_bboxes = [] det_labels = [] for i in range(num_imgs): det_bbox, det_label = self.bbox_head[-1].get_bboxes( rois[i], cls_score[i], bbox_pred[i], img_shapes[i], scale_factors[i], rescale=rescale, cfg=rcnn_test_cfg) det_bboxes.append(det_bbox) det_labels.append(det_label) bbox_result = [ bbox2result(det_bboxes[i], det_labels[i], self.bbox_head[-1].num_classes) for i in range(num_imgs) ] ms_bbox_result['ensemble'] = bbox_result if self.with_mask: if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): mask_classes = self.mask_head[-1].num_classes segm_results = [[[] for _ in range(mask_classes)] for _ in range(num_imgs)] else: if rescale and not isinstance(scale_factors[0], float): scale_factors = [ torch.from_numpy(scale_factor).to(det_bboxes[0].device) for scale_factor in scale_factors ] _bboxes = [ det_bboxes[i][:, :4] * scale_factors[i] if rescale else det_bboxes[i] for i in range(num_imgs) ] mask_rois = bbox2roi(_bboxes) aug_masks = [] mask_roi_extractor = self.mask_roi_extractor[-1] mask_feats = mask_roi_extractor( x[:len(mask_roi_extractor.featmap_strides)], mask_rois) if self.with_semantic and 'mask' in self.semantic_fusion: mask_semantic_feat = self.semantic_roi_extractor( [semantic_feat], mask_rois) mask_feats += mask_semantic_feat last_feat = None num_bbox_per_img = tuple(len(_bbox) for _bbox in _bboxes) for i in range(self.num_stages): mask_head = self.mask_head[i] if self.mask_info_flow: mask_pred, last_feat = mask_head(mask_feats, last_feat) else: mask_pred = mask_head(mask_feats) # split batch mask prediction back to each image mask_pred = mask_pred.split(num_bbox_per_img, 0) aug_masks.append( [mask.sigmoid().cpu().numpy() for mask in mask_pred]) # apply mask post-processing to each image individually segm_results = [] for i in range(num_imgs): if det_bboxes[i].shape[0] == 0: segm_results.append( [[] for _ in range(self.mask_head[-1].num_classes)]) else: aug_mask = [mask[i] for mask in aug_masks] merged_mask = merge_aug_masks( aug_mask, [[img_metas[i]]] * self.num_stages, rcnn_test_cfg) segm_result = self.mask_head[-1].get_seg_masks( merged_mask, _bboxes[i], det_labels[i], rcnn_test_cfg, ori_shapes[i], scale_factors[i], rescale) segm_results.append(segm_result) ms_segm_result['ensemble'] = segm_results if self.with_mask: results = list( zip(ms_bbox_result['ensemble'], ms_segm_result['ensemble'])) else: results = ms_bbox_result['ensemble'] return results def aug_test(self, img_feats, proposal_list, img_metas, rescale=False): """Test with augmentations. If rescale is False, then returned bboxes and masks will fit the scale of imgs[0]. """ if self.with_semantic: semantic_feats = [ self.semantic_head(feat)[1] for feat in img_feats ] else: semantic_feats = [None] * len(img_metas) rcnn_test_cfg = self.test_cfg aug_bboxes = [] aug_scores = [] for x, img_meta, semantic in zip(img_feats, img_metas, semantic_feats): # only one image in the batch img_shape = img_meta[0]['img_shape'] scale_factor = img_meta[0]['scale_factor'] flip = img_meta[0]['flip'] flip_direction = img_meta[0]['flip_direction'] proposals = bbox_mapping(proposal_list[0][:, :4], img_shape, scale_factor, flip, flip_direction) # "ms" in variable names means multi-stage ms_scores = [] rois = bbox2roi([proposals]) if rois.shape[0] == 0: # There is no proposal in the single image aug_bboxes.append(rois.new_zeros(0, 4)) aug_scores.append(rois.new_zeros(0, 1)) continue for i in range(self.num_stages): bbox_head = self.bbox_head[i] bbox_results = self._bbox_forward( i, x, rois, semantic_feat=semantic) ms_scores.append(bbox_results['cls_score']) if i < self.num_stages - 1: bbox_label = bbox_results['cls_score'].argmax(dim=1) rois = bbox_head.regress_by_class( rois, bbox_label, bbox_results['bbox_pred'], img_meta[0]) cls_score = sum(ms_scores) / float(len(ms_scores)) bboxes, scores = self.bbox_head[-1].get_bboxes( rois, cls_score, bbox_results['bbox_pred'], img_shape, scale_factor, rescale=False, cfg=None) aug_bboxes.append(bboxes) aug_scores.append(scores) # after merging, bboxes will be rescaled to the original image size merged_bboxes, merged_scores = merge_aug_bboxes( aug_bboxes, aug_scores, img_metas, rcnn_test_cfg) det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores, rcnn_test_cfg.score_thr, rcnn_test_cfg.nms, rcnn_test_cfg.max_per_img) bbox_result = bbox2result(det_bboxes, det_labels, self.bbox_head[-1].num_classes) if self.with_mask: if det_bboxes.shape[0] == 0: segm_result = [[] for _ in range(self.mask_head[-1].num_classes)] else: aug_masks = [] aug_img_metas = [] for x, img_meta, semantic in zip(img_feats, img_metas, semantic_feats): img_shape = img_meta[0]['img_shape'] scale_factor = img_meta[0]['scale_factor'] flip = img_meta[0]['flip'] flip_direction = img_meta[0]['flip_direction'] _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape, scale_factor, flip, flip_direction) mask_rois = bbox2roi([_bboxes]) mask_feats = self.mask_roi_extractor[-1]( x[:len(self.mask_roi_extractor[-1].featmap_strides)], mask_rois) if self.with_semantic: semantic_feat = semantic mask_semantic_feat = self.semantic_roi_extractor( [semantic_feat], mask_rois) if mask_semantic_feat.shape[-2:] != mask_feats.shape[ -2:]: mask_semantic_feat = F.adaptive_avg_pool2d( mask_semantic_feat, mask_feats.shape[-2:]) mask_feats += mask_semantic_feat last_feat = None for i in range(self.num_stages): mask_head = self.mask_head[i] if self.mask_info_flow: mask_pred, last_feat = mask_head( mask_feats, last_feat) else: mask_pred = mask_head(mask_feats) aug_masks.append(mask_pred.sigmoid().cpu().numpy()) aug_img_metas.append(img_meta) merged_masks = merge_aug_masks(aug_masks, aug_img_metas, self.test_cfg) ori_shape = img_metas[0][0]['ori_shape'] segm_result = self.mask_head[-1].get_seg_masks( merged_masks, det_bboxes, det_labels, rcnn_test_cfg, ori_shape, scale_factor=1.0, rescale=False) return [(bbox_result, segm_result)] else: return [bbox_result]