(
        x, rois, roi_indices, outsize,
        spatial_scale, group_size, sampling_ratio=None
)
return PSROIMaxAlign2D(
        outsize, spatial_scale,
        group_size, sampling_ratio)(x, rois, roi_indices)
Position Sensitive Region of Interest (ROI) Max align function.

    This function computes position sensitive max value of input spatial patch
    with the given region of interests. Each ROI is splitted into
    :math:`(group\_size, group\_size)` regions, and position sensitive values
    in each region is computed.

    Args:
        x (~chainer.Variable): Input variable. The shape is expected to be
            4 dimentional: (n: batch, c: channel, h, height, w: width).
        rois (array): Input roi. The shape is expected to
            be :math:`(R, 4)`, and each datum is set as below:
            (y_min, x_min, y_max, x_max). The dtype is :obj:`numpy.float32`.
        roi_indices (array): Input roi indices. The shape is expected to
            be :math:`(R, )`. The dtype is :obj:`numpy.int32`.
        outsize ((int, int, int) or (int, int) or int): Expected output size
            after pooled: (channel, height, width) or (height, width)
            or outsize. ``outsize=o`` and ``outsize=(o, o)`` are equivalent.
            Channel parameter is used to assert the input shape.
        spatial_scale (float): Scale of the roi is resized.
        group_size (int): Position sensitive group size.
        sampling_ratio ((int, int) or int): Sampling step for the alignment.
            It must be an integer over :math:`1` or :obj:`None`, and the value
            is automatically decided when :obj:`None` is passed.  Use of
            different ratio in height and width axis is also supported by
            passing tuple of int as ``(sampling_ratio_h, sampling_ratio_w)``.
            ``sampling_ratio=s`` and ``sampling_ratio=(s, s)`` are equivalent.

    Returns:
        ~chainer.Variable: Output variable.

    See the original paper proposing PSROIPooling:
    `R-FCN <https://arxiv.org/abs/1605.06409>`_.
    See the original paper proposing ROIAlign:
    `Mask R-CNN <https://arxiv.org/abs/1703.06870>`_.
Position Sensitive Region of Interest (ROI) Max align function.
Position Sensitive Region of Interest ( ROI ) Max align function .
def ps_roi_max_align_2d(
        x, rois, roi_indices, outsize,
        spatial_scale, group_size, sampling_ratio=None
):
    """Position Sensitive Region of Interest (ROI) Max align function.

    This function computes position sensitive max value of input spatial patch
    with the given region of interests. Each ROI is splitted into
    :math:`(group\_size, group\_size)` regions, and position sensitive values
    in each region is computed.

    Args:
        x (~chainer.Variable): Input variable. The shape is expected to be
            4 dimentional: (n: batch, c: channel, h, height, w: width).
        rois (array): Input roi. The shape is expected to
            be :math:`(R, 4)`, and each datum is set as below:
            (y_min, x_min, y_max, x_max). The dtype is :obj:`numpy.float32`.
        roi_indices (array): Input roi indices. The shape is expected to
            be :math:`(R, )`. The dtype is :obj:`numpy.int32`.
        outsize ((int, int, int) or (int, int) or int): Expected output size
            after pooled: (channel, height, width) or (height, width)
            or outsize. ``outsize=o`` and ``outsize=(o, o)`` are equivalent.
            Channel parameter is used to assert the input shape.
        spatial_scale (float): Scale of the roi is resized.
        group_size (int): Position sensitive group size.
        sampling_ratio ((int, int) or int): Sampling step for the alignment.
            It must be an integer over :math:`1` or :obj:`None`, and the value
            is automatically decided when :obj:`None` is passed.  Use of
            different ratio in height and width axis is also supported by
            passing tuple of int as ``(sampling_ratio_h, sampling_ratio_w)``.
            ``sampling_ratio=s`` and ``sampling_ratio=(s, s)`` are equivalent.

    Returns:
        ~chainer.Variable: Output variable.

    See the original paper proposing PSROIPooling:
    `R-FCN <https://arxiv.org/abs/1605.06409>`_.
    See the original paper proposing ROIAlign:
    `Mask R-CNN <https://arxiv.org/abs/1703.06870>`_.

    """
    return PSROIMaxAlign2D(
        outsize, spatial_scale,
        group_size, sampling_ratio)(x, rois, roi_indices)
def ps_roi_max_align_2d ( x , rois , roi_indices , outsize , spatial_scale , group_size , sampling_ratio = None ) : return PSROIMaxAlign2D ( outsize , spatial_scale , group_size , sampling_ratio ) ( x , rois , roi_indices )