# ngraph.opset6.interpolate¶

ngraph.opset6.interpolate(image: _pyngraph.Node, output_shape: Union[_pyngraph.Node, int, float, numpy.ndarray], attrs: dict, name: Optional[str] = None)_pyngraph.Node

Perform interpolation of independent slices in input tensor.

Parameters
• image – The node providing input tensor with data for interpolation.

• output_shape – 1D tensor describing output shape for spatial axes.

• attrs – The dictionary containing key, value pairs for attributes.

• name – Optional name for the output node.

Returns

Node representing interpolation operation.

Available attributes are:

• axes Specify spatial dimension indices where interpolation is applied.

Type: List of non-negative integer numbers. Required: yes.

• mode Specifies type of interpolation.

Range of values: one of {nearest, linear, cubic, area} Type: string Required: yes

• align_corners A flag that specifies whether to align corners or not. True means the

alignment is applied, False means the alignment isn’t applied. Range of values: True or False. Default: True. Required: no

• antialias A flag that specifies whether to perform anti-aliasing.
Range of values: False - do not perform anti-aliasing

True - perform anti-aliasing

Default value: False Required: no

• pads_begin Specify the number of pixels to add to the beginning of the image being

interpolated. A scalar that specifies padding for each spatial dimension. Range of values: list of non-negative integer numbers. Default value: 0 Required: no

• pads_end Specify the number of pixels to add to the beginning of the image being

interpolated. A scalar that specifies padding for each spatial dimension. Range of values: list of non-negative integer numbers. Default value: 0 Required: no

Example of attribute dictionary:

# just required ones
attrs = {
'axes': [2, 3],
'mode': 'cubic',
}

attrs = {
'axes': [2, 3],
'mode': 'cubic',
'antialias': True,
'pads_begin': [2, 2, 2],
}


Optional attributes which are absent from dictionary will be set with corresponding default.