Interpolate#
Versioned name: Interpolate-1
Category: Image processing
Short description: Interpolate layer performs interpolation of independent slices in input tensor by specified dimensions and attributes.
Attributes
axes
Description:
axes
specify spatial dimension indices where interpolation is applied. Other dimensions are treated as batch dimensions. The order of elements inaxes
attribute matters and mapped directly to elements with the same indices in the 2nd inputtarget_spatial_shape
.Range of values: list of non-negative integer numbers
Type:
int[]
Required: yes
mode
Description: specifies type of interpolation
Range of values: one of
nearest
,linear
,cubic
,area
Type: string
Required: yes
align_corners
Description: align_corners is a flag that specifies whether to align corners or not. 1 means the alignment is applied, 0 means the alignment isn’t applied.
Range of values: true or false
Type:
boolean
Default value: true
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
pads_begin
Description: pads_beg specify the number of pixels to add to the beginning of the image being interpolated. This is a scalar that specifies padding for each spatial dimension.
Range of values: list of non-negative integer numbers
Type:
int
Default value: 0
Required: no
pads_end
Description: pads_end specify the number of pixels to add to the beginning of the image being interpolated. This is a scalar that specifies padding for each spatial dimension.
Range of values: list of non-negative integer numbers
Type:
int
Default value: 0
Required: no
Inputs
1:
data
- Input tensor with data for interpolation. Type of elements is any supported floating-point type. Required.2:
target_spatial_shape
- 1D tensor describing output shape for spatial axes. Number of elements matches the number of indices in axes attribute, the order matches as well. Required.
Outputs
1: Resulting interpolated tensor with elements of the same type as input
data
tensor. The shape of the output matches inputdata
shape except spatial dimensions mentioned inaxes
attribute. For other dimensions shape matches sizes fromtarget_spatial_shape
in order specified inaxes
.
Example
<layer ... type="Interpolate" ...>
<data axes="2,3" align_corners="0" pads_begin="0" pads_end="0" mode="linear"/>
<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 [50, 60] -->
</port>
</input>
<output>
<port id="0">
<dim>1</dim>
<dim>2</dim>
<dim>50</dim>
<dim>60</dim>
</port>
</output>
</layer>