Interpolate#
Versioned name: Interpolate-11
Category: Image processing
Short description: Interpolate layer performs interpolation of independent slices of the input tensor by specified dimensions and attributes.
Attributes
mode
Description: specifies type of interpolation
Range of values: one of
nearest
,linear
,linear_onnx
,cubic
,bilinear_pillow
,bicubic_pillow
Type: string
Required: yes
Note: Only 2D, 3D, 4D, 5D tensors with
axes = {0, 1}
,axes = {0, 1, 2}
,axes = {2, 3}
,axes = {2, 3, 4}
respectively are supported for"mode" == "linear_onnx"
. In case ofbilinear_pillow
orbicubic_pillow
only the spatial dimensions (H, W) can be specified in theaxes
tensor, for example in case of NHWC layout the axes should containaxes = {1, 2}
.
shape_calculation_mode
Description: specifies how the data in the
scales_or_sizes
input should be interpreted when determining the operator’s output shape.Range of values: name of a shape calculation mode in string format: *
sizes
- the output shape is calculated asoutput_shape[axes[i]] = scales_or_sizes[i]
for alli in range(0, len(axes))
andoutput_shape[j] = input_shape[j] + pads_begin[j] + pads_end[j]
forj not in axes
,j in range(0, rank(image))
. *scales
- an output shape is calculated asoutput_shape[axes[i]] = floor(scales_or_sizes[i] * (input_shape[axes[i]] + pads_begin[axes[i]] + pads_end[axes[i]]))
for alli in range(0, len(axes))
andoutput_shape[j] = input_shape[j] + pads_begin[j] + pads_end[j]
forj not in axes
,j in range(0, rank(image))
Type: string
Required: yes
coordinate_transformation_mode
Description: specifies how to transform the coordinate in the resized tensor to the coordinate in the original tensor
Range of values: name of the transformation mode in string format (here
scale[x]
isoutput_shape[x] / input_shape[x]
andx_resized
is a coordinate in axisx
, for any axisx
from the inputaxes
): *half_pixel
- the coordinate in the original tensor axisx
is calculated as((x_resized + 0.5) / scale[x]) - 0.5
. *pytorch_half_pixel
- the coordinate in the original tensor axisx
is calculated by(x_resized + 0.5) / scale[x] - 0.5 if output_shape[x] > 1 else 0.0
. *asymmetric
- the coordinate in the original tensor axisx
is calculated according to the formulax_resized / scale[x]
. *tf_half_pixel_for_nn
- the coordinate in the original tensor axisx
is(x_resized + 0.5) / scale[x]
. *align_corners
- the coordinate in the original tensor axisx
is calculated as0 if output_shape[x] == 1 else x_resized * (input_shape[x] - 1) / (output_shape[x] - 1)
.Type: string
Default value:
half_pixel
Required: no
Note: When the selected interpolation mode is
BILINEAR_PILLOW
orBICUBIC_PILLOW
this attribute is ignored.
nearest_mode
Description: specifies the rounding mode when
mode == nearest
and is used only whenmode == nearest
.Range of values: name of the rounding mode in string format: *
round_prefer_floor
- this mode is known as round half down. *round_prefer_ceil
- it is round half up mode. *floor
- this mode computes the largest integer value not greater than the rounded value. *ceil
- this mode computes the smallest integer value not less than the rounded value. *simple
- this mode behaves asceil
mode whenInterpolate
is downsample, and as dropping the fractional part otherwise.Type: string
Default value:
round_prefer_floor
Required: no
antialias
Description: antialias is a flag that specifies whether to perform anti-aliasing.
Range of values: * false - do not perform anti-aliasing * true - perform anti-aliasing
Type: boolean
Default value: false
Required: no
Note: When the selected interpolation mode is
BILINEAR_PILLOW
orBICUBIC_PILLOW
this attribute is ignored. Pillow-kind of antialiasing is applied in those modes.
pads_begin
Description: pads_begin specifies the number of pixels to add to the beginning of the image being interpolated. This addition of pixels is done before the interpolation calculation.
Range of values: list of non-negative integer numbers
Type:
int[]
Default value:
[0]
Required: no
pads_end
Description: pads_end specifies the number of pixels to add to the end of the image being interpolated. This addition of pixels is done before the interpolation calculation.
Range of values: list of non-negative integer numbers
Type:
int[]
Default value:
[0]
Required: no
cube_coeff
Description: cube_coeff specifies the parameter a for cubic interpolation (see, e.g. article. cube_coeff is used only when
mode == cubic
ormode == bicubic_pillow
.Range of values: floating-point number
Type: any of supported floating-point type
Default value:
-0.75
(applicable formode == cubic
). The value compatible withBICUBIC_PILLOW
needs to be manually set to-0.5
Required: no
Inputs
1:
image
- tensor of type T with data for interpolation. Required.2:
scales_or_sizes
- 1D tensor containing the data used to calculate the spatial output shape. The number of elements must match the number of values in theaxes
input tensor, the order needs to match as well. The type of this input tensor is either T_SCALES or T_SIZES depending on the value of theshape_calculation_mode
attribute. Required.3:
axes
- 1D tensor of type T_AXES specifying dimension indices where interpolation is applied, andaxes
is any unordered list of indices of different dimensions of input tensor, e.g.[0, 4]
,[4, 0]
,[4, 2, 1]
,[1, 2, 3]
. These indices should be non-negative integers from0
torank(image) - 1
inclusively. Input tensor’s dimensions not specified in theaxes
tensor are not modified by the operator. The order of elements inaxes
attribute matters and is mapped directly to the elements in the 2nd inputscales_or_sizes
. Optional with default value[0,1,...,rank(image) - 1]
. If theaxes
input is not provided the number of elements in thescales_or_sizes
tensor needs to match the number of automatically generated axes.
Outputs
1: Resulting interpolated tensor with elements of the same type as input
image
tensor. The shape of the output matches inputimage
shape except spatial dimensions mentioned inaxes
attribute. For other dimensions shape matches sizes fromsizes
in order specified inaxes
.
Types
T: any supported numeric type.
T_SIZES: any supported integer type.
T_SCALES: any supported floating-point type.
T_AXES: any supported integer type.
Example
<layer ... type="Interpolate" ...>
<data shape_calculation_mode="scales" pads_begin="0" pads_end="0" mode="bicubic_pillow"/>
<input>
<port id="0">
<dim>1</dim>
<dim>2</dim>
<dim>48</dim>
<dim>80</dim>
</port>
<port id="1">
<dim>2</dim> <!--The values in this input are [24, 160] -->
</port>
<port id="2">
<dim>2</dim> <!--The values in this input are [0.5, 2.0] -->
</port>
<port id="3">
<dim>2</dim> <!--The values in this input are [2, 3] (axes). -->
</port>
</input>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>2</dim>
<dim>24</dim>
<dim>160</dim>
</port>
</output>
</layer>