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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
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/formatting.py:Collect`.
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 = torch.cat(recovered_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
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
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)
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']
flip_direction = img_info[0]['flip_direction']
if flip:
if flip_direction == 'horizontal':
mask = mask[:, :, :, ::-1]
elif flip_direction == 'vertical':
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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