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- # 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]
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