openvino.runtime.opset1.group_convolution_backprop_data¶
- openvino.runtime.opset1.group_convolution_backprop_data(data: Union[openvino.pyopenvino.Node, int, float, numpy.ndarray], filters: Union[openvino.pyopenvino.Node, int, float, numpy.ndarray], strides: List[int], output_shape: Optional[Union[openvino.pyopenvino.Node, int, float, numpy.ndarray]] = None, pads_begin: Optional[List[int]] = None, pads_end: Optional[List[int]] = None, dilations: Optional[List[int]] = None, auto_pad: str = 'EXPLICIT', output_padding: Optional[List[int]] = None, name: Optional[str] = None) openvino.pyopenvino.Node¶
Perform Group Convolution operation on data from input node.
- Parameters
data – The node producing input data.
filters – The node producing filter data.
strides – The distance (in pixels) to slide the filter on the feature map over the axes.
output_shape – The node that specifies spatial shape of the output.
pads_begin – The number of pixels to add at the beginning along each axis.
pads_end – The number of pixels to add at the end along each axis.
dilations – The distance in width and height between elements (weights) in the filter.
auto_pad –
Describes how to perform padding. Possible values: EXPLICIT: Pad dimensions are explicity specified SAME_LOWER: Pad dimensions computed to match input shape
Ceil(num_dims/2) at the beginning and Floor(num_dims/2) at the end
- SAME_UPPER: Pad dimensions computed to match input shape
Floor(num_dims/2) at the beginning and Ceil(num_dims/2) at the end
VALID: No padding
output_padding – The additional amount of paddings added per each spatial axis in the output tensor.
name – Optional output node name.
- Returns
The new node performing a Group Convolution operation on tensor from input node.