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"""TVM operator upsampling compute."""
from tvm import topi
from tvm import te
from ..utils import simplify



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def upsampling(
    data,
    scale_h,
    scale_w,
    layout="NCHW",
    method="nearest_neighbor",
    align_corners=False,
    output_shape=None,
):
    """Perform upsampling on the data.
       Nearest neighbor and bilinear upsampling are supported.

    Parameters
    ----------
    inputs : tvm.te.Tensor
        inputs is a 4-D tensor with shape
        [batch, channel, in_height, in_width]
        or  [batch, in_height, in_width, channel]

    scale_h : float
        Scaling factor for height

    scale_w : float
        Scaling factor for width

    layout : string, optional
        either "NCHW" or "NHWC"

    method : {"bilinear", "nearest_neighbor", "bicubic"}
        Method to be used for upsampling.

    output_shape: tvm.tir.container.Array, optional
        Shape to return. If left None will be inferred
        (If shape is determined dynamically, pass out_dtype.shape as output_shape)

    Returns
    -------
    output : tvm.te.Tensor
        4-D with shape [batch, channel, in_height*scale_h, in_width*scale_w]
        or [batch, in_height*scale, in_width*scale, channel]
    """
    base_layout = layout[0:4]
    if base_layout == "NCHW":
        if not output_shape:  # static case
            scaled_h = data.shape[2] * scale_h
            scaled_w = data.shape[3] * scale_w
            reshape_size = (
                simplify(topi.cast(te.round(scaled_h), data.shape[2].dtype)),
                simplify(topi.cast(te.round(scaled_w), data.shape[3].dtype)),
            )
        else:  # dynamic case -- we don't need to scale; already done in shape func
            reshape_size = (
                simplify(topi.cast(te.round(output_shape[2]), output_shape[2].dtype)),
                simplify(topi.cast(te.round(output_shape[3]), output_shape[3].dtype)),
            )
    elif layout == "NHWC":
        if not output_shape:  # static case
            scaled_h = data.shape[1] * scale_h
            scaled_w = data.shape[2] * scale_w
            reshape_size = (
                simplify(topi.cast(te.round(scaled_h), data.shape[1].dtype)),
                simplify(topi.cast(te.round(scaled_w), data.shape[2].dtype)),
            )
        else:  # dynamic case
            reshape_size = (
                simplify(topi.cast(te.round(output_shape[1]), output_shape[1].dtype)),
                simplify(topi.cast(te.round(output_shape[2]), output_shape[2].dtype)),
            )

    else:
        raise ValueError(f"not support this layout {layout} yet")
    coord_trans = "align_corners" if align_corners else "asymmetric"
    if method[0:2] == "bi":
        method = method[2:]
    return topi.image.resize2d(
        data,
        [0.0] * 4,
        reshape_size,
        layout=layout,
        method=method,
        coordinate_transformation_mode=coord_trans,
        output_shape=output_shape,
    )




[文档]
def upsampling3d(
    data,
    scale_d,
    scale_h,
    scale_w,
    layout="NCDHW",
    method="nearest_neighbor",
    coordinate_transformation_mode="half_pixel",
    output_shape=None,
):
    """Perform upsampling on the data.
       Nearest neighbor and bilinear upsampling are supported.

    Parameters
    ----------
    inputs : tvm.te.Tensor
        inputs is a 5-D tensor with shape
        [batch, channel, in_depth, in_height, in_width]
        or  [batch, in_depth, in_height, in_width, channel]

    scale_d : float
        Scaling factor for depth

    scale_h : float
        Scaling factor for height

    scale_w : float
        Scaling factor for width

    layout : string, optional
        either "NCDHW" or "NDHWC"

    method : {"trilinear", "nearest_neighbor"}
        Method to be used for upsampling.

    coordinate_transformation_mode: string, optional
        Describes how to transform the coordinate in the resized tensor
        to the coordinate in the original tensor.
        Refer to the ONNX Resize operator specification for details.
        Available options are "half_pixel", "align_corners" and "asymmetric".

    output_shape: tvm.tir.container.Array, optional
        Shape to return. If left None will be inferred
        (If shape is determined dynamically, pass out_dtype.shape as output_shape)

    Returns
    -------
    output : tvm.te.Tensor
        5-D with shape [batch, channel, in_depth*scale, in_height*scale, in_width*scale]
        or [batch, in_depth*scale, in_height*scale, in_width*scale, channel]
    """
    base_layout = layout[0:5]
    if base_layout == "NCDHW":
        if not output_shape:  # static case
            scaled_d = data.shape[2] * scale_d
            scaled_h = data.shape[3] * scale_h
            scaled_w = data.shape[4] * scale_w
            resize_shape = (
                simplify(topi.cast(te.round(scaled_d), data.shape[2].dtype)),
                simplify(topi.cast(te.round(scaled_h), data.shape[3].dtype)),
                simplify(topi.cast(te.round(scaled_w), data.shape[4].dtype)),
            )
        else:  # dynamic case -- don't need to scale; already done in shape func
            resize_shape = (
                simplify(topi.cast(te.round(output_shape[2]), data.shape[2].dtype)),
                simplify(topi.cast(te.round(output_shape[3]), data.shape[3].dtype)),
                simplify(topi.cast(te.round(output_shape[4]), data.shape[4].dtype)),
            )
    elif layout == "NDHWC":
        if not output_shape:  # static case
            scaled_d = data.shape[1] * scale_d
            scaled_h = data.shape[2] * scale_h
            scaled_w = data.shape[3] * scale_w
            resize_shape = (
                simplify(topi.cast(te.round(scaled_d), data.shape[1].dtype)),
                simplify(topi.cast(te.round(scaled_h), data.shape[2].dtype)),
                simplify(topi.cast(te.round(scaled_w), data.shape[3].dtype)),
            )
        else:  # dynamic case
            resize_shape = (
                simplify(topi.cast(te.round(output_shape[1]), data.shape[1].dtype)),
                simplify(topi.cast(te.round(output_shape[2]), data.shape[2].dtype)),
                simplify(topi.cast(te.round(output_shape[3]), data.shape[3].dtype)),
            )
    else:
        raise ValueError(f"not support this layout {layout} yet")
    if method[0:3] == "tri":
        method = method[3:]
    return topi.image.resize3d(
        data,
        [0.0] * 6,
        resize_shape,
        layout=layout,
        method=method,
        coordinate_transformation_mode=coordinate_transformation_mode,
    )