openvino/docs/ops/convolution/GroupConvolutionBackpropData_1.md

GroupConvolutionBackpropData

Versioned name: GroupConvolutionBackpropData-1

Category: Convolution

Short description: Computes 1D, 2D or 3D GroupConvolutionBackpropData of input and kernel tensors.

Detailed description: Splits input and filters into multiple groups, computes ConvolutionBackpropData on them and concatenates the results. It is equivalent to GroupConvolution and Convolution relationship.

Attributes: The operation has the same attributes as a ConvolutionBackpropData. Number of groups is derived from the kernel shape.

• strides

  • Description: strides has the same definition as strides for a regular Convolution but applied in the backward way, for the output tensor.
  • Range of values: positive integers
  • Type: int[]
  • Default value: None
  • Required: yes

• pads_begin

  • Description: pads_begin has the same definition as pads_begin for a regular Convolution but applied in the backward way, for the output tensor. May be omitted, in which case pads are calculated automatically.
  • Range of values: non-negative integers
  • Type: int[]
  • Default value: None
  • Required: yes
  • Note: the attribute is ignored when auto_pad attribute is specified.

• pads_end

  • Description: pads_end has the same definition as pads_end for a regular Convolution but applied in the backward way, for the output tensor. May be omitted, in which case pads are calculated automatically.
  • Range of values: non-negative integers
  • Type: int[]
  • Default value: None
  • Required: yes
  • Note: the attribute is ignored when auto_pad attribute is specified.

• dilations

  • Description: dilations has the same definition as dilations for a regular Convolution but applied in the backward way, for the output tensor.
  • Range of values: positive integers
  • Type: int[]
  • Default value: None
  • Required: yes

• auto_pad

  • Description: auto_pad has the same definition as auto_pad for a regular Convolution but applied in the backward way, for the output tensor.
    • explicit - use explicit padding values from pads_begin and pads_end.
    • same_upper - the input is padded to match the output size. In case of odd padding value an extra padding is added at the end.
    • same_lower - the input is padded to match the output size. In case of odd padding value an extra padding is added at the beginning.
    • valid - do not use padding.
  • Type: string
  • Default value: explicit
  • Required: no
  • Note: pads_begin and pads_end attributes are ignored when auto_pad is specified.

• output_padding

  • Description: output_padding adds additional amount of paddings per each spatial axis in the output tensor. It unlocks more elements in the output allowing them to be computed. Elements are added at the higher coordinate indices for the spatial dimensions. Number of elements in output_padding list matches the number of spatial dimensions in input and output tensors.
  • Range of values: non-negative integer values
  • Type: int[]
  • Default value: all zeros
  • Required: no

Inputs:

• 1: Input tensor of type T1 and rank 3, 4 or 5. Layout is [N, GROUPS * C_IN, Z, Y, X] (number of batches, number of channels, spatial axes Z, Y, X). Required.

• 2: Kernel tensor of type T1 and rank 4, 5 or 6. Layout is [GROUPS, C_IN, C_OUT, X, Y, Z] (number of groups, number of input channels, number of output channels, spatial axes X, Y, Z). Required.

• 3: Output shape tensor of type T2 and rank 1. It specifies spatial shape of the output. Optional.

• Note Number of groups is derived from the shape of the kernel and not specified by any attribute.

• Note: Type of the convolution (1D, 2D or 3D) is derived from the rank of the input tensors and not specified by any attribute:

  • 1D convolution (input tensors rank 3) means that there is only one spatial axis X
  • 2D convolution (input tensors rank 4) means that there are two spatial axes Y, X
  • 3D convolution (input tensors rank 5) means that there are three spatial axes Z, Y, X

Outputs:

• 1: Output tensor of type T1 and rank 3, 4 or 5 (the same as input 1). Layout is [N, GROUPS * C_OUT, Z, Y, X] (number of batches, number of kernel output channels, spatial axes Z, Y, X).

Types:

• T1: any numeric type.
• T2: any integer type.

Example

1D GroupConvolutionBackpropData

<layer id="5" name="upsampling_node" type="GroupConvolutionBackpropData">
    <data dilations="1" pads_begin="1" pads_end="1" strides="2"/>
    <input>
        <port id="0">
            <dim>1</dim>
            <dim>20</dim>
            <dim>224</dim>
        </port>
        <port id="1">
            <dim>4</dim>
            <dim>5</dim>
            <dim>2</dim>
            <dim>3</dim>
        </port>
    </input>
    <output>
        <port id="0" precision="FP32">
            <dim>1</dim>
            <dim>8</dim>
            <dim>447</dim>
        </port>
    </output>
</layer>

2D GroupConvolutionBackpropData

<layer id="5" name="upsampling_node" type="GroupConvolutionBackpropData">
    <data dilations="1,1" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
    <input>
        <port id="0">
            <dim>1</dim>
            <dim>20</dim>
            <dim>224</dim>
            <dim>224</dim>
        </port>
        <port id="1">
            <dim>4</dim>
            <dim>5</dim>
            <dim>2</dim>
            <dim>3</dim>
            <dim>3</dim>
        </port>
    </input>
    <output>
        <port id="0" precision="FP32">
            <dim>1</dim>
            <dim>8</dim>
            <dim>447</dim>
            <dim>447</dim>
        </port>
    </output>
</layer>

3D GroupConvolutionBackpropData

<layer id="5" name="upsampling_node" type="GroupConvolutionBackpropData">
    <data dilations="1,1,1" pads_begin="1,1,1" pads_end="1,1,1" strides="2,2,2"/>
    <input>
        <port id="0">
            <dim>1</dim>
            <dim>20</dim>
            <dim>224</dim>
            <dim>224</dim>
            <dim>224</dim>
        </port>
        <port id="1">
            <dim>4</dim>
            <dim>5</dim>
            <dim>2</dim>
            <dim>3</dim>
            <dim>3</dim>
            <dim>3</dim>
        </port>
    </input>
    <output>
        <port id="0" precision="FP32">
            <dim>1</dim>
            <dim>8</dim>
            <dim>447</dim>
            <dim>447</dim>
            <dim>447</dim>
        </port>
    </output>
</layer>