mmdet.core.post_processing.merge_augs 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings

import numpy as np
import torch
from mmcv import ConfigDict
from mmcv.ops import nms

from ..bbox import bbox_mapping_back

[文档]def merge_aug_proposals(aug_proposals, img_metas, cfg): """Merge augmented proposals (multiscale, flip, etc.) Args: aug_proposals (list[Tensor]): proposals from different testing schemes, shape (n, 5). Note that they are not rescaled to the original image size. 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/`. cfg (dict): rpn test config. Returns: Tensor: shape (n, 4), proposals corresponding to original image scale. """ cfg = copy.deepcopy(cfg) # deprecate arguments warning if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg: warnings.warn( 'In rpn_proposal or test_cfg, ' 'nms_thr has been moved to a dict named nms as ' 'iou_threshold, max_num has been renamed as max_per_img, ' 'name of original arguments and the way to specify ' 'iou_threshold of NMS will be deprecated.') if 'nms' not in cfg: cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr)) if 'max_num' in cfg: if 'max_per_img' in cfg: assert cfg.max_num == cfg.max_per_img, f'You set max_num and ' \ f'max_per_img at the same time, but get {cfg.max_num} ' \ f'and {cfg.max_per_img} respectively' \ f'Please delete max_num which will be deprecated.' else: cfg.max_per_img = cfg.max_num if 'nms_thr' in cfg: assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set ' \ f'iou_threshold in nms and ' \ f'nms_thr at the same time, but get ' \ f'{cfg.nms.iou_threshold} and {cfg.nms_thr}' \ f' respectively. Please delete the nms_thr ' \ f'which will be deprecated.' recovered_proposals = [] for proposals, img_info in zip(aug_proposals, img_metas): img_shape = img_info['img_shape'] scale_factor = img_info['scale_factor'] flip = img_info['flip'] flip_direction = img_info['flip_direction'] _proposals = proposals.clone() _proposals[:, :4] = bbox_mapping_back(_proposals[:, :4], img_shape, scale_factor, flip, flip_direction) recovered_proposals.append(_proposals) aug_proposals =, dim=0) merged_proposals, _ = nms(aug_proposals[:, :4].contiguous(), aug_proposals[:, -1].contiguous(), cfg.nms.iou_threshold) scores = merged_proposals[:, 4] _, order = scores.sort(0, descending=True) num = min(cfg.max_per_img, merged_proposals.shape[0]) order = order[:num] merged_proposals = merged_proposals[order, :] return merged_proposals
[文档]def merge_aug_bboxes(aug_bboxes, aug_scores, img_metas, rcnn_test_cfg): """Merge augmented detection bboxes and scores. Args: aug_bboxes (list[Tensor]): shape (n, 4*#class) aug_scores (list[Tensor] or None): shape (n, #class) img_shapes (list[Tensor]): shape (3, ). rcnn_test_cfg (dict): rcnn test config. Returns: tuple: (bboxes, scores) """ recovered_bboxes = [] for bboxes, img_info in zip(aug_bboxes, img_metas): img_shape = img_info[0]['img_shape'] scale_factor = img_info[0]['scale_factor'] flip = img_info[0]['flip'] flip_direction = img_info[0]['flip_direction'] bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip, flip_direction) recovered_bboxes.append(bboxes) bboxes = torch.stack(recovered_bboxes).mean(dim=0) if aug_scores is None: return bboxes else: scores = torch.stack(aug_scores).mean(dim=0) return bboxes, scores
[文档]def merge_aug_scores(aug_scores): """Merge augmented bbox scores.""" if isinstance(aug_scores[0], torch.Tensor): return torch.mean(torch.stack(aug_scores), dim=0) else: return np.mean(aug_scores, axis=0)
[文档]def merge_aug_masks(aug_masks, img_metas, rcnn_test_cfg, weights=None): """Merge augmented mask prediction. Args: aug_masks (list[ndarray]): shape (n, #class, h, w) img_shapes (list[ndarray]): shape (3, ). rcnn_test_cfg (dict): rcnn test config. Returns: tuple: (bboxes, scores) """ recovered_masks = [] for mask, img_info in zip(aug_masks, img_metas): flip = img_info[0]['flip'] if flip: flip_direction = img_info[0]['flip_direction'] if flip_direction == 'horizontal': mask = mask[:, :, :, ::-1] elif flip_direction == 'vertical': mask = mask[:, :, ::-1, :] elif flip_direction == 'diagonal': mask = mask[:, :, :, ::-1] mask = mask[:, :, ::-1, :] else: raise ValueError( f"Invalid flipping direction '{flip_direction}'") recovered_masks.append(mask) if weights is None: merged_masks = np.mean(recovered_masks, axis=0) else: merged_masks = np.average( np.array(recovered_masks), axis=0, weights=np.array(weights)) return merged_masks
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