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[]
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[]
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[]
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[]
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>