312 lines
13 KiB
Python
312 lines
13 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import sys
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import warnings
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import numpy as np
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import torch
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from mmdet.core import (bbox2roi, bbox_mapping, merge_aug_bboxes,
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merge_aug_masks, multiclass_nms)
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if sys.version_info >= (3, 7):
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from mmdet.utils.contextmanagers import completed
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class BBoxTestMixin:
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if sys.version_info >= (3, 7):
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async def async_test_bboxes(self,
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x,
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img_metas,
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proposals,
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rcnn_test_cfg,
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rescale=False,
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**kwargs):
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"""Asynchronized test for box head without augmentation."""
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rois = bbox2roi(proposals)
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roi_feats = self.bbox_roi_extractor(
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x[:len(self.bbox_roi_extractor.featmap_strides)], rois)
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if self.with_shared_head:
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roi_feats = self.shared_head(roi_feats)
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sleep_interval = rcnn_test_cfg.get('async_sleep_interval', 0.017)
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async with completed(
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__name__, 'bbox_head_forward',
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sleep_interval=sleep_interval):
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cls_score, bbox_pred = self.bbox_head(roi_feats)
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img_shape = img_metas[0]['img_shape']
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scale_factor = img_metas[0]['scale_factor']
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det_bboxes, det_labels = self.bbox_head.get_bboxes(
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rois,
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cls_score,
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bbox_pred,
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img_shape,
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scale_factor,
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rescale=rescale,
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cfg=rcnn_test_cfg)
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return det_bboxes, det_labels
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def simple_test_bboxes(self,
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x,
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img_metas,
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proposals,
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rcnn_test_cfg,
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rescale=False):
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"""Test only det bboxes without augmentation.
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Args:
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x (tuple[Tensor]): Feature maps of all scale level.
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img_metas (list[dict]): Image meta info.
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proposals (List[Tensor]): Region proposals.
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rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of R-CNN.
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rescale (bool): If True, return boxes in original image space.
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Default: False.
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Returns:
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tuple[list[Tensor], list[Tensor]]: The first list contains
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the boxes of the corresponding image in a batch, each
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tensor has the shape (num_boxes, 5) and last dimension
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5 represent (tl_x, tl_y, br_x, br_y, score). Each Tensor
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in the second list is the labels with shape (num_boxes, ).
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The length of both lists should be equal to batch_size.
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"""
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rois = bbox2roi(proposals)
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if rois.shape[0] == 0:
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batch_size = len(proposals)
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det_bbox = rois.new_zeros(0, 5)
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det_label = rois.new_zeros((0, ), dtype=torch.long)
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if rcnn_test_cfg is None:
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det_bbox = det_bbox[:, :4]
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det_label = rois.new_zeros(
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(0, self.bbox_head.fc_cls.out_features))
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# There is no proposal in the whole batch
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return [det_bbox] * batch_size, [det_label] * batch_size
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bbox_results = self._bbox_forward(x, rois)
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img_shapes = tuple(meta['img_shape'] for meta in img_metas)
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scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
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# split batch bbox prediction back to each image
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cls_score = bbox_results['cls_score']
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bbox_pred = bbox_results['bbox_pred']
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num_proposals_per_img = tuple(len(p) for p in proposals)
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rois = rois.split(num_proposals_per_img, 0)
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cls_score = cls_score.split(num_proposals_per_img, 0)
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# some detector with_reg is False, bbox_pred will be None
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if bbox_pred is not None:
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# TODO move this to a sabl_roi_head
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# the bbox prediction of some detectors like SABL is not Tensor
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if isinstance(bbox_pred, torch.Tensor):
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bbox_pred = bbox_pred.split(num_proposals_per_img, 0)
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else:
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bbox_pred = self.bbox_head.bbox_pred_split(
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bbox_pred, num_proposals_per_img)
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else:
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bbox_pred = (None, ) * len(proposals)
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# apply bbox post-processing to each image individually
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det_bboxes = []
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det_labels = []
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for i in range(len(proposals)):
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if rois[i].shape[0] == 0:
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# There is no proposal in the single image
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det_bbox = rois[i].new_zeros(0, 5)
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det_label = rois[i].new_zeros((0, ), dtype=torch.long)
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if rcnn_test_cfg is None:
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det_bbox = det_bbox[:, :4]
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det_label = rois[i].new_zeros(
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(0, self.bbox_head.fc_cls.out_features))
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else:
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det_bbox, det_label = self.bbox_head.get_bboxes(
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rois[i],
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cls_score[i],
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bbox_pred[i],
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img_shapes[i],
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scale_factors[i],
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rescale=rescale,
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cfg=rcnn_test_cfg)
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det_bboxes.append(det_bbox)
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det_labels.append(det_label)
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return det_bboxes, det_labels
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def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg):
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"""Test det bboxes with test time augmentation."""
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aug_bboxes = []
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aug_scores = []
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for x, img_meta in zip(feats, img_metas):
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# only one image in the batch
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img_shape = img_meta[0]['img_shape']
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scale_factor = img_meta[0]['scale_factor']
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flip = img_meta[0]['flip']
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flip_direction = img_meta[0]['flip_direction']
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# TODO more flexible
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proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
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scale_factor, flip, flip_direction)
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rois = bbox2roi([proposals])
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bbox_results = self._bbox_forward(x, rois)
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bboxes, scores = self.bbox_head.get_bboxes(
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rois,
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bbox_results['cls_score'],
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bbox_results['bbox_pred'],
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img_shape,
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scale_factor,
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rescale=False,
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cfg=None)
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aug_bboxes.append(bboxes)
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aug_scores.append(scores)
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# after merging, bboxes will be rescaled to the original image size
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merged_bboxes, merged_scores = merge_aug_bboxes(
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aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
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if merged_bboxes.shape[0] == 0:
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# There is no proposal in the single image
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det_bboxes = merged_bboxes.new_zeros(0, 5)
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det_labels = merged_bboxes.new_zeros((0, ), dtype=torch.long)
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else:
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det_bboxes, det_labels = multiclass_nms(merged_bboxes,
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merged_scores,
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rcnn_test_cfg.score_thr,
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rcnn_test_cfg.nms,
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rcnn_test_cfg.max_per_img)
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return det_bboxes, det_labels
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class MaskTestMixin:
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if sys.version_info >= (3, 7):
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async def async_test_mask(self,
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x,
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img_metas,
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det_bboxes,
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det_labels,
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rescale=False,
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mask_test_cfg=None):
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"""Asynchronized test for mask head without augmentation."""
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# image shape of the first image in the batch (only one)
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ori_shape = img_metas[0]['ori_shape']
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scale_factor = img_metas[0]['scale_factor']
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if det_bboxes.shape[0] == 0:
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segm_result = [[] for _ in range(self.mask_head.num_classes)]
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else:
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if rescale and not isinstance(scale_factor,
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(float, torch.Tensor)):
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scale_factor = det_bboxes.new_tensor(scale_factor)
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_bboxes = (
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det_bboxes[:, :4] *
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scale_factor if rescale else det_bboxes)
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mask_rois = bbox2roi([_bboxes])
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mask_feats = self.mask_roi_extractor(
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x[:len(self.mask_roi_extractor.featmap_strides)],
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mask_rois)
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if self.with_shared_head:
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mask_feats = self.shared_head(mask_feats)
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if mask_test_cfg and mask_test_cfg.get('async_sleep_interval'):
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sleep_interval = mask_test_cfg['async_sleep_interval']
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else:
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sleep_interval = 0.035
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async with completed(
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__name__,
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'mask_head_forward',
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sleep_interval=sleep_interval):
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mask_pred = self.mask_head(mask_feats)
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segm_result = self.mask_head.get_seg_masks(
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mask_pred, _bboxes, det_labels, self.test_cfg, ori_shape,
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scale_factor, rescale)
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return segm_result
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def simple_test_mask(self,
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x,
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img_metas,
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det_bboxes,
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det_labels,
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rescale=False):
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"""Simple test for mask head without augmentation."""
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# image shapes of images in the batch
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ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
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scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
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if isinstance(scale_factors[0], float):
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warnings.warn(
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'Scale factor in img_metas should be a '
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'ndarray with shape (4,) '
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'arrange as (factor_w, factor_h, factor_w, factor_h), '
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'The scale_factor with float type has been deprecated. ')
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scale_factors = np.array([scale_factors] * 4, dtype=np.float32)
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num_imgs = len(det_bboxes)
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if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
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segm_results = [[[] for _ in range(self.mask_head.num_classes)]
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for _ in range(num_imgs)]
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else:
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# if det_bboxes is rescaled to the original image size, we need to
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# rescale it back to the testing scale to obtain RoIs.
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if rescale:
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scale_factors = [
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torch.from_numpy(scale_factor).to(det_bboxes[0].device)
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for scale_factor in scale_factors
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]
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_bboxes = [
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det_bboxes[i][:, :4] *
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scale_factors[i] if rescale else det_bboxes[i][:, :4]
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for i in range(len(det_bboxes))
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]
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mask_rois = bbox2roi(_bboxes)
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mask_results = self._mask_forward(x, mask_rois)
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mask_pred = mask_results['mask_pred']
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# split batch mask prediction back to each image
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num_mask_roi_per_img = [len(det_bbox) for det_bbox in det_bboxes]
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mask_preds = mask_pred.split(num_mask_roi_per_img, 0)
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# apply mask post-processing to each image individually
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segm_results = []
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for i in range(num_imgs):
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if det_bboxes[i].shape[0] == 0:
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segm_results.append(
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[[] for _ in range(self.mask_head.num_classes)])
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else:
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segm_result = self.mask_head.get_seg_masks(
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mask_preds[i], _bboxes[i], det_labels[i],
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self.test_cfg, ori_shapes[i], scale_factors[i],
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rescale)
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segm_results.append(segm_result)
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return segm_results
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def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
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"""Test for mask head with test time augmentation."""
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if det_bboxes.shape[0] == 0:
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segm_result = [[] for _ in range(self.mask_head.num_classes)]
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else:
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aug_masks = []
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for x, img_meta in zip(feats, img_metas):
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img_shape = img_meta[0]['img_shape']
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scale_factor = img_meta[0]['scale_factor']
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flip = img_meta[0]['flip']
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flip_direction = img_meta[0]['flip_direction']
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_bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
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scale_factor, flip, flip_direction)
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mask_rois = bbox2roi([_bboxes])
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mask_results = self._mask_forward(x, mask_rois)
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# convert to numpy array to save memory
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aug_masks.append(
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mask_results['mask_pred'].sigmoid().cpu().numpy())
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merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg)
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ori_shape = img_metas[0][0]['ori_shape']
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scale_factor = det_bboxes.new_ones(4)
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segm_result = self.mask_head.get_seg_masks(
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merged_masks,
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det_bboxes,
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det_labels,
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self.test_cfg,
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ori_shape,
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scale_factor=scale_factor,
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rescale=False)
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return segm_result
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