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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,
)
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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,
)
