本文整理汇总了Python中mmdet.ops.nms方法的典型用法代码示例。如果您正苦于以下问题:Python ops.nms方法的具体用法?Python ops.nms怎么用?Python ops.nms使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mmdet.ops
的用法示例。
在下文中一共展示了ops.nms方法的6个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: merge_aug_proposals
# 需要导入模块: from mmdet import ops [as 别名]
# 或者: from mmdet.ops import nms [as 别名]
def merge_aug_proposals(aug_proposals, img_metas, rpn_test_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 my 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`.
rpn_test_cfg (dict): rpn test config.
Returns:
Tensor: shape (n, 4), proposals corresponding to original image scale.
"""
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, rpn_test_cfg.nms_thr)
scores = merged_proposals[:, 4]
_, order = scores.sort(0, descending=True)
num = min(rpn_test_cfg.max_num, merged_proposals.shape[0])
order = order[:num]
merged_proposals = merged_proposals[order, :]
return merged_proposals
示例2: merge_rotate_aug_proposals
# 需要导入模块: from mmdet import ops [as 别名]
# 或者: from mmdet.ops import nms [as 别名]
def merge_rotate_aug_proposals(aug_proposals, img_metas, rpn_test_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]): image info including "shape_scale" and "flip".
rpn_test_cfg (dict): rpn test config.
Returns:
Tensor: shape (n, 4), proposals corresponding to original image scale.
"""
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']
_proposals = proposals.clone()
_proposals[:, :4] = bbox_mapping_back(_proposals[:, :4], img_shape,
scale_factor, flip)
# rotation mapping
angle = img_info['angle']
if angle != 0:
# TODO: check the angle
_proposals[:, :4] = bbox_rotate_mapping(_proposals[:, :4], img_shape, -angle)
recovered_proposals.append(_proposals)
aug_proposals = torch.cat(recovered_proposals, dim=0)
merged_proposals, _ = nms(aug_proposals, rpn_test_cfg.nms_thr)
scores = merged_proposals[:, 4]
_, order = scores.sort(0, descending=True)
num = min(rpn_test_cfg.max_num, merged_proposals.shape[0])
order = order[:num]
merged_proposals = merged_proposals[order, :]
return merged_proposals
示例3: merge_aug_proposals
# 需要导入模块: from mmdet import ops [as 别名]
# 或者: from mmdet.ops import nms [as 别名]
def merge_aug_proposals(aug_proposals, img_metas, rpn_test_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]): image info including "shape_scale" and "flip".
rpn_test_cfg (dict): rpn test config.
Returns:
Tensor: shape (n, 4), proposals corresponding to original image scale.
"""
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']
_proposals = proposals.clone()
_proposals[:, :4] = bbox_mapping_back(_proposals[:, :4], img_shape,
scale_factor, flip)
recovered_proposals.append(_proposals)
aug_proposals = torch.cat(recovered_proposals, dim=0)
merged_proposals, _ = nms(aug_proposals, rpn_test_cfg.nms_thr)
scores = merged_proposals[:, 4]
_, order = scores.sort(0, descending=True)
num = min(rpn_test_cfg.max_num, merged_proposals.shape[0])
order = order[:num]
merged_proposals = merged_proposals[order, :]
return merged_proposals
示例4: post_process
# 需要导入模块: from mmdet import ops [as 别名]
# 或者: from mmdet.ops import nms [as 别名]
def post_process(preds, num_classes=4787, iou_thr=0.3, score_thr=0.3):
ret = []
for pred in tqdm(preds):
bboxes = np.vstack(pred)
labels = np.concatenate([[i] * len(bb) for i, bb in enumerate(pred)])
# nms
_, inds = nms(bboxes, iou_thr)
bboxes, labels = bboxes[inds], labels[inds]
# score filtering
inds = bboxes[:, 4] > score_thr
bboxes, labels = bboxes[inds], labels[inds]
#
ret.append([bboxes[labels == i] for i in range(num_classes)])
return ret
示例5: merge_aug_proposals
# 需要导入模块: from mmdet import ops [as 别名]
# 或者: from mmdet.ops import nms [as 别名]
def merge_aug_proposals(aug_proposals, img_metas, rpn_test_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 my 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`.
rpn_test_cfg (dict): rpn test config.
Returns:
Tensor: shape (n, 4), proposals corresponding to original image scale.
"""
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']
_proposals = proposals.clone()
_proposals[:, :4] = bbox_mapping_back(_proposals[:, :4], img_shape,
scale_factor, flip)
recovered_proposals.append(_proposals)
aug_proposals = torch.cat(recovered_proposals, dim=0)
merged_proposals, _ = nms(aug_proposals, rpn_test_cfg.nms_thr)
scores = merged_proposals[:, 4]
_, order = scores.sort(0, descending=True)
num = min(rpn_test_cfg.max_num, merged_proposals.shape[0])
order = order[:num]
merged_proposals = merged_proposals[order, :]
return merged_proposals
示例6: get_bboxes_single
# 需要导入模块: from mmdet import ops [as 别名]
# 或者: from mmdet.ops import nms [as 别名]
def get_bboxes_single(self,
cls_scores,
bbox_preds,
mlvl_anchors,
img_shape,
scale_factor,
cfg,
rescale=False):
mlvl_proposals = []
for idx in range(len(cls_scores)):
rpn_cls_score = cls_scores[idx]
rpn_bbox_pred = bbox_preds[idx]
assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
anchors = mlvl_anchors[idx]
rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
if self.use_sigmoid_cls:
rpn_cls_score = rpn_cls_score.reshape(-1)
scores = rpn_cls_score.sigmoid()
else:
rpn_cls_score = rpn_cls_score.reshape(-1, 2)
scores = rpn_cls_score.softmax(dim=1)[:, 1]
rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4)
if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre:
_, topk_inds = scores.topk(cfg.nms_pre)
rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
anchors = anchors[topk_inds, :]
scores = scores[topk_inds]
proposals = delta2bbox(anchors, rpn_bbox_pred, self.target_means,
self.target_stds, img_shape)
if cfg.min_bbox_size > 0:
w = proposals[:, 2] - proposals[:, 0] + 1
h = proposals[:, 3] - proposals[:, 1] + 1
valid_inds = torch.nonzero((w >= cfg.min_bbox_size) &
(h >= cfg.min_bbox_size)).squeeze()
proposals = proposals[valid_inds, :]
scores = scores[valid_inds]
proposals = torch.cat([proposals, scores.unsqueeze(-1)], dim=-1)
proposals, _ = nms(proposals, cfg.nms_thr)
proposals = proposals[:cfg.nms_post, :]
mlvl_proposals.append(proposals)
proposals = torch.cat(mlvl_proposals, 0)
if cfg.nms_across_levels:
proposals, _ = nms(proposals, cfg.nms_thr)
proposals = proposals[:cfg.max_num, :]
else:
scores = proposals[:, 4]
num = min(cfg.max_num, proposals.shape[0])
_, topk_inds = scores.topk(num)
proposals = proposals[topk_inds, :]
return proposals