def matrix_nms(bboxes,
               scores,
               score_threshold,
               post_threshold,
               nms_top_k,
               keep_top_k,
               use_gaussian=False,
               gaussian_sigma=2.,
               background_label=0,
               normalized=True,
               return_index=False,
               return_rois_num=True,
               name=None):
    """
    **Matrix NMS**
    This operator does matrix non maximum suppression (NMS).
    First selects a subset of candidate bounding boxes that have higher scores
    than score_threshold (if provided), then the top k candidate is selected if
    nms_top_k is larger than -1. Score of the remaining candidate are then
    decayed according to the Matrix NMS scheme.
    Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
    per image if keep_top_k is larger than -1.
    Args:
        bboxes (Tensor): A 3-D Tensor with shape [N, M, 4] represents the
                           predicted locations of M bounding bboxes,
                           N is the batch size. Each bounding box has four
                           coordinate values and the layout is
                           [xmin, ymin, xmax, ymax], when box size equals to 4.
                           The data type is float32 or float64.
        scores (Tensor): A 3-D Tensor with shape [N, C, M]
                           represents the predicted confidence predictions.
                           N is the batch size, C is the class number, M is
                           number of bounding boxes. For each category there
                           are total M scores which corresponding M bounding
                           boxes. Please note, M is equal to the 2nd dimension
                           of BBoxes. The data type is float32 or float64.
        score_threshold (float): Threshold to filter out bounding boxes with
                                 low confidence score.
        post_threshold (float): Threshold to filter out bounding boxes with
                                low confidence score AFTER decaying.
        nms_top_k (int): Maximum number of detections to be kept according to
                         the confidences after the filtering detections based
                         on score_threshold.
        keep_top_k (int): Number of total bboxes to be kept per image after NMS
                          step. -1 means keeping all bboxes after NMS step.
        use_gaussian (bool): Use Gaussian as the decay function. Default: False
        gaussian_sigma (float): Sigma for Gaussian decay function. Default: 2.0
        background_label (int): The index of background label, the background
                                label will be ignored. If set to -1, then all
                                categories will be considered. Default: 0
        normalized (bool): Whether detections are normalized. Default: True
        return_index(bool): Whether return selected index. Default: False
        return_rois_num(bool): whether return rois_num. Default: True
        name(str): Name of the matrix nms op. Default: None.
    Returns:
        A tuple with three Tensor: (Out, Index, RoisNum) if return_index is True,
        otherwise, a tuple with two Tensor (Out, RoisNum) is returned.
        Out (Tensor): A 2-D Tensor with shape [No, 6] containing the
             detection results.
             Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]
             (After version 1.3, when no boxes detected, the lod is changed
             from {0} to {1})
        Index (Tensor): A 2-D Tensor with shape [No, 1] containing the
            selected indices, which are absolute values cross batches.
        rois_num (Tensor): A 1-D Tensor with shape [N] containing 
            the number of detected boxes in each image.
    Examples:
        .. code-block:: python
            import paddle
            from ppdet.modeling import ops
            boxes = paddle.static.data(name='bboxes', shape=[None,81, 4],
                                      dtype='float32', lod_level=1)
            scores = paddle.static.data(name='scores', shape=[None,81],
                                      dtype='float32', lod_level=1)
            out = ops.matrix_nms(bboxes=boxes, scores=scores, background_label=0,
                                 score_threshold=0.5, post_threshold=0.1,
                                 nms_top_k=400, keep_top_k=200, normalized=False)
    """
    check_variable_and_dtype(bboxes, 'BBoxes', ['float32', 'float64'],
                             'matrix_nms')
    check_variable_and_dtype(scores, 'Scores', ['float32', 'float64'],
                             'matrix_nms')
    check_type(score_threshold, 'score_threshold', float, 'matrix_nms')
    check_type(post_threshold, 'post_threshold', float, 'matrix_nms')
    check_type(nms_top_k, 'nums_top_k', int, 'matrix_nms')
    check_type(keep_top_k, 'keep_top_k', int, 'matrix_nms')
    check_type(normalized, 'normalized', bool, 'matrix_nms')
    check_type(use_gaussian, 'use_gaussian', bool, 'matrix_nms')
    check_type(gaussian_sigma, 'gaussian_sigma', float, 'matrix_nms')
    check_type(background_label, 'background_label', int, 'matrix_nms')

    if in_dynamic_mode():
        attrs = ('background_label', background_label, 'score_threshold',
                 score_threshold, 'post_threshold', post_threshold, 'nms_top_k',
                 nms_top_k, 'gaussian_sigma', gaussian_sigma, 'use_gaussian',
                 use_gaussian, 'keep_top_k', keep_top_k, 'normalized',
                 normalized)
        out, index, rois_num = C_ops.matrix_nms(bboxes, scores, *attrs)
        if not return_index:
            index = None
        if not return_rois_num:
            rois_num = None
        return out, rois_num, index
    else:
        helper = LayerHelper('matrix_nms', **locals())
        output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
        index = helper.create_variable_for_type_inference(dtype='int32')
        outputs = {'Out': output, 'Index': index}
        if return_rois_num:
            rois_num = helper.create_variable_for_type_inference(dtype='int32')
            outputs['RoisNum'] = rois_num

        helper.append_op(
            type="matrix_nms",
            inputs={'BBoxes': bboxes,
                    'Scores': scores},
            attrs={
                'background_label': background_label,
                'score_threshold': score_threshold,
                'post_threshold': post_threshold,
                'nms_top_k': nms_top_k,
                'gaussian_sigma': gaussian_sigma,
                'use_gaussian': use_gaussian,
                'keep_top_k': keep_top_k,
                'normalized': normalized
            },
            outputs=outputs)
        output.stop_gradient = True

        if not return_index:
            index = None
        if not return_rois_num:
            rois_num = None
        return output, rois_num, index

    **Matrix NMS**
    This operator does matrix non maximum suppression (NMS).
    First selects a subset of candidate bounding boxes that have higher scores
    than score_threshold (if provided), then the top k candidate is selected if
    nms_top_k is larger than -1. Score of the remaining candidate are then
    decayed according to the Matrix NMS scheme.
    Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
    per image if keep_top_k is larger than -1.
    Args:
        bboxes (Tensor): A 3-D Tensor with shape [N, M, 4] represents the
                           predicted locations of M bounding bboxes,
                           N is the batch size. Each bounding box has four
                           coordinate values and the layout is
                           [xmin, ymin, xmax, ymax], when box size equals to 4.
                           The data type is float32 or float64.
        scores (Tensor): A 3-D Tensor with shape [N, C, M]
                           represents the predicted confidence predictions.
                           N is the batch size, C is the class number, M is
                           number of bounding boxes. For each category there
                           are total M scores which corresponding M bounding
                           boxes. Please note, M is equal to the 2nd dimension
                           of BBoxes. The data type is float32 or float64.
        score_threshold (float): Threshold to filter out bounding boxes with
                                 low confidence score.
        post_threshold (float): Threshold to filter out bounding boxes with
                                low confidence score AFTER decaying.
        nms_top_k (int): Maximum number of detections to be kept according to
                         the confidences after the filtering detections based
                         on score_threshold.
        keep_top_k (int): Number of total bboxes to be kept per image after NMS
                          step. -1 means keeping all bboxes after NMS step.
        use_gaussian (bool): Use Gaussian as the decay function. Default: False
        gaussian_sigma (float): Sigma for Gaussian decay function. Default: 2.0
        background_label (int): The index of background label, the background
                                label will be ignored. If set to -1, then all
                                categories will be considered. Default: 0
        normalized (bool): Whether detections are normalized. Default: True
        return_index(bool): Whether return selected index. Default: False
        return_rois_num(bool): whether return rois_num. Default: True
        name(str): Name of the matrix nms op. Default: None.
    Returns:
        A tuple with three Tensor: (Out, Index, RoisNum) if return_index is True,
        otherwise, a tuple with two Tensor (Out, RoisNum) is returned.
        Out (Tensor): A 2-D Tensor with shape [No, 6] containing the
             detection results.
             Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]
             (After version 1.3, when no boxes detected, the lod is changed
             from {0} to {1})
        Index (Tensor): A 2-D Tensor with shape [No, 1] containing the
            selected indices, which are absolute values cross batches.
        rois_num (Tensor): A 1-D Tensor with shape [N] containing 
            the number of detected boxes in each image.
    Examples:
        .. code-block:: python
            import paddle
            from ppdet.modeling import ops
            boxes = paddle.static.data(name='bboxes', shape=[None,81, 4],
                                      dtype='float32', lod_level=1)
            scores = paddle.static.data(name='scores', shape=[None,81],
                                      dtype='float32', lod_level=1)
            out = ops.matrix_nms(bboxes=boxes, scores=scores, background_label=0,
                                 score_threshold=0.5, post_threshold=0.1,
                                 nms_top_k=400, keep_top_k=200, normalized=False)
    
ppdet/modeling/ops.py