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Python nms.nms_wrapper方法代码示例

本文整理汇总了Python中mmdet.ops.nms.nms_wrapper方法的典型用法代码示例。如果您正苦于以下问题:Python nms.nms_wrapper方法的具体用法?Python nms.nms_wrapper怎么用?Python nms.nms_wrapper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mmdet.ops.nms的用法示例。


在下文中一共展示了nms.nms_wrapper方法的4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: multiclass_nms

# 需要导入模块: from mmdet.ops import nms [as 别名]
# 或者: from mmdet.ops.nms import nms_wrapper [as 别名]
def multiclass_nms(multi_bboxes,
                   multi_scores,
                   score_thr,
                   nms_cfg,
                   max_num=-1,
                   score_factors=None):
    """NMS for multi-class bboxes.

    Args:
        multi_bboxes (Tensor): shape (n, #class*4) or (n, 4)
        multi_scores (Tensor): shape (n, #class)
        score_thr (float): bbox threshold, bboxes with scores lower than it
            will not be considered.
        nms_thr (float): NMS IoU threshold
        max_num (int): if there are more than max_num bboxes after NMS,
            only top max_num will be kept.
        score_factors (Tensor): The factors multiplied to scores before
            applying NMS

    Returns:
        tuple: (bboxes, labels), tensors of shape (k, 5) and (k, 1). Labels
            are 0-based.
    """
    num_classes = multi_scores.shape[1]
    bboxes, labels = [], []
    nms_cfg_ = nms_cfg.copy()
    nms_type = nms_cfg_.pop('type', 'nms')
    nms_op = getattr(nms_wrapper, nms_type)
    for i in range(1, num_classes):
        cls_inds = multi_scores[:, i] > score_thr
        if not cls_inds.any():
            continue
        # get bboxes and scores of this class
        if multi_bboxes.shape[1] == 4:
            _bboxes = multi_bboxes[cls_inds, :]
        else:
            _bboxes = multi_bboxes[cls_inds, i * 4:(i + 1) * 4]
        _scores = multi_scores[cls_inds, i]
        if score_factors is not None:
            _scores *= score_factors[cls_inds]
        cls_dets = torch.cat([_bboxes, _scores[:, None]], dim=1)
        cls_dets, _ = nms_op(cls_dets, **nms_cfg_)
        cls_labels = multi_bboxes.new_full(
            (cls_dets.shape[0], ), i - 1, dtype=torch.long)
        bboxes.append(cls_dets)
        labels.append(cls_labels)
    if bboxes:
        bboxes = torch.cat(bboxes)
        labels = torch.cat(labels)
        if bboxes.shape[0] > max_num:
            _, inds = bboxes[:, -1].sort(descending=True)
            inds = inds[:max_num]
            bboxes = bboxes[inds]
            labels = labels[inds]
    else:
        bboxes = multi_bboxes.new_zeros((0, 5))
        labels = multi_bboxes.new_zeros((0, ), dtype=torch.long)

    return bboxes, labels 
开发者ID:dingjiansw101,项目名称:AerialDetection,代码行数:61,代码来源:bbox_nms.py

示例2: multiclass_nms

# 需要导入模块: from mmdet.ops import nms [as 别名]
# 或者: from mmdet.ops.nms import nms_wrapper [as 别名]
def multiclass_nms(multi_bboxes,
                   multi_scores,
                   score_thr,
                   nms_cfg,
                   max_num=-1,
                   score_factors=None):
    """NMS for multi-class bboxes.

    Args:
        multi_bboxes (Tensor): shape (n, #class*4) or (n, 4)
        multi_scores (Tensor): shape (n, #class)
        score_thr (float): bbox threshold, bboxes with scores lower than it
            will not be considered.
        nms_thr (float): NMS IoU threshold
        max_num (int): if there are more than max_num bboxes after NMS,
            only top max_num will be kept.
        score_factors (Tensor): The factors multiplied to scores before
            applying NMS

    Returns:
        tuple: (bboxes, labels), tensors of shape (k, 5) and (k, 1). Labels
            are 0-based.
    """
    num_classes = multi_scores.shape[1]
    bboxes, labels = [], []
    nms_cfg_ = nms_cfg.copy()
    nms_type = nms_cfg_.pop('type', 'nms')
    nms_op = getattr(nms_wrapper, nms_type)
    for i in range(1, num_classes):
        cls_inds = multi_scores[:, i] > score_thr
        if not cls_inds.any():
            continue
        # get bboxes and scores of this class
        if multi_bboxes.shape[1] == 4:
            _bboxes = multi_bboxes[cls_inds, :]
        else:
            _bboxes = multi_bboxes[cls_inds, i * 4:(i + 1) * 4]
        _scores = multi_scores[cls_inds, i]
        if score_factors is not None:
            _scores *= score_factors[cls_inds]
        cls_dets = torch.cat([_bboxes, _scores[:, None]], dim=1)
        cls_dets, _ = nms_op(cls_dets, **nms_cfg_)
        cls_labels = multi_bboxes.new_full((cls_dets.shape[0], ),
                                           i - 1,
                                           dtype=torch.long)
        bboxes.append(cls_dets)
        labels.append(cls_labels)
    if bboxes:
        bboxes = torch.cat(bboxes)
        labels = torch.cat(labels)
        if bboxes.shape[0] > max_num:
            _, inds = bboxes[:, -1].sort(descending=True)
            inds = inds[:max_num]
            bboxes = bboxes[inds]
            labels = labels[inds]
    else:
        bboxes = multi_bboxes.new_zeros((0, 5))
        labels = multi_bboxes.new_zeros((0, ), dtype=torch.long)

    return bboxes, labels 
开发者ID:xieenze,项目名称:PolarMask,代码行数:62,代码来源:bbox_nms.py

示例3: multiclass_nms

# 需要导入模块: from mmdet.ops import nms [as 别名]
# 或者: from mmdet.ops.nms import nms_wrapper [as 别名]
def multiclass_nms(multi_bboxes,
                   multi_scores,
                   score_thr,
                   nms_cfg,
                   max_num=-1,
                   score_factors=None):
    """NMS for multi-class bboxes.

    Args:
        multi_bboxes (Tensor): shape (n, #class*4) or (n, 4)
        multi_scores (Tensor): shape (n, #class), where the 0th column
            contains scores of the background class, but this will be ignored.
        score_thr (float): bbox threshold, bboxes with scores lower than it
            will not be considered.
        nms_thr (float): NMS IoU threshold
        max_num (int): if there are more than max_num bboxes after NMS,
            only top max_num will be kept.
        score_factors (Tensor): The factors multiplied to scores before
            applying NMS

    Returns:
        tuple: (bboxes, labels), tensors of shape (k, 5) and (k, 1). Labels
            are 0-based.
    """
    num_classes = multi_scores.shape[1]
    bboxes, labels = [], []
    nms_cfg_ = nms_cfg.copy()
    nms_type = nms_cfg_.pop('type', 'nms')
    nms_op = getattr(nms_wrapper, nms_type)
    for i in range(1, num_classes):
        cls_inds = multi_scores[:, i] > score_thr
        if not cls_inds.any():
            continue
        # get bboxes and scores of this class
        if multi_bboxes.shape[1] == 4:
            _bboxes = multi_bboxes[cls_inds, :]
        else:
            _bboxes = multi_bboxes[cls_inds, i * 4:(i + 1) * 4]
        _scores = multi_scores[cls_inds, i]
        if score_factors is not None:
            _scores *= score_factors[cls_inds]
        cls_dets = torch.cat([_bboxes, _scores[:, None]], dim=1)
        cls_dets, _ = nms_op(cls_dets, **nms_cfg_)
        cls_labels = multi_bboxes.new_full((cls_dets.shape[0], ),
                                           i - 1,
                                           dtype=torch.long)
        bboxes.append(cls_dets)
        labels.append(cls_labels)
    if bboxes:
        bboxes = torch.cat(bboxes)
        labels = torch.cat(labels)
        if bboxes.shape[0] > max_num:
            _, inds = bboxes[:, -1].sort(descending=True)
            inds = inds[:max_num]
            bboxes = bboxes[inds]
            labels = labels[inds]
    else:
        bboxes = multi_bboxes.new_zeros((0, 5))
        labels = multi_bboxes.new_zeros((0, ), dtype=torch.long)

    return bboxes, labels 
开发者ID:zl1994,项目名称:IoU-Uniform-R-CNN,代码行数:63,代码来源:bbox_nms.py

示例4: multiclass_nms

# 需要导入模块: from mmdet.ops import nms [as 别名]
# 或者: from mmdet.ops.nms import nms_wrapper [as 别名]
def multiclass_nms(multi_bboxes, multi_scores, score_thr, nms_cfg, max_num=-1):
    """NMS for multi-class bboxes.

    Args:
        multi_bboxes (Tensor): shape (n, #class*4) or (n, 4)
        multi_scores (Tensor): shape (n, #class)
        score_thr (float): bbox threshold, bboxes with scores lower than it
            will not be considered.
        nms_thr (float): NMS IoU threshold
        max_num (int): if there are more than max_num bboxes after NMS,
            only top max_num will be kept.

    Returns:
        tuple: (bboxes, labels), tensors of shape (k, 5) and (k, 1). Labels
            are 0-based.
    """
    num_classes = multi_scores.shape[1]
    bboxes, labels = [], []
    nms_cfg_ = nms_cfg.copy()
    nms_type = nms_cfg_.pop('type', 'nms')
    nms_op = getattr(nms_wrapper, nms_type)
    for i in range(1, num_classes):
        cls_inds = multi_scores[:, i] > score_thr
        if not cls_inds.any():
            continue
        # get bboxes and scores of this class
        if multi_bboxes.shape[1] == 4:
            _bboxes = multi_bboxes[cls_inds, :]
        else:
            _bboxes = multi_bboxes[cls_inds, i * 4:(i + 1) * 4]
        _scores = multi_scores[cls_inds, i]
        cls_dets = torch.cat([_bboxes, _scores[:, None]], dim=1)
        cls_dets, _ = nms_op(cls_dets, **nms_cfg_)
        cls_labels = multi_bboxes.new_full(
            (cls_dets.shape[0], ), i - 1, dtype=torch.long)
        bboxes.append(cls_dets)
        labels.append(cls_labels)
    if bboxes:
        bboxes = torch.cat(bboxes)
        labels = torch.cat(labels)
        if bboxes.shape[0] > max_num:
            _, inds = bboxes[:, -1].sort(descending=True)
            inds = inds[:max_num]
            bboxes = bboxes[inds]
            labels = labels[inds]
    else:
        bboxes = multi_bboxes.new_zeros((0, 5))
        labels = multi_bboxes.new_zeros((0, ), dtype=torch.long)

    return bboxes, labels 
开发者ID:chanyn,项目名称:Reasoning-RCNN,代码行数:52,代码来源:bbox_nms.py


注:本文中的mmdet.ops.nms.nms_wrapper方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。