211 lines
7.1 KiB
Markdown
211 lines
7.1 KiB
Markdown
# GroupConvolutionBackpropData {#openvino_docs_ops_convolution_GroupConvolutionBackpropData_1}
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@sphinxdirective
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.. meta::
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:description: Learn about GroupConvolutionBackpropData-1 - a 1D, 2D or 3D convolution operation, which
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can be performed on input and kernel tensors in OpenVINO.
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**Versioned name**: *GroupConvolutionBackpropData-1*
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**Category**: *Convolution*
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**Short description**: Computes 1D, 2D or 3D *GroupConvolutionBackpropData* of input and kernel tensors.
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**Detailed description**: Splits input and filters into multiple groups, computes *ConvolutionBackpropData*
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on them and concatenates the results. It is equivalent to GroupConvolution and Convolution relationship.
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**Attributes**: The operation has the same attributes as a *ConvolutionBackpropData*. Number of groups
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is derived from the kernel shape.
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* *strides*
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* **Description**: *strides* has the same definition as *strides* for a regular Convolution but applied in
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the backward way, for the output tensor.
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* **Range of values**: positive integers
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* **Type**: ``int[]``
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* **Required**: *yes*
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* *pads_begin*
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* **Description**: *pads_begin* has the same definition as *pads_begin* for a regular Convolution but applied in
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the backward way, for the output tensor. May be omitted, in which case pads are calculated automatically.
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* **Range of values**: non-negative integers
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* **Type**: ``int[]``
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* **Required**: *yes*
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* **Note**: the attribute is ignored when *auto_pad* attribute is specified.
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* *pads_end*
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* **Description**: *pads_end* has the same definition as *pads_end* for a regular Convolution but applied
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in the backward way, for the output tensor. May be omitted, in which case pads are calculated automatically.
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* **Range of values**: non-negative integers
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* **Type**: ``int[]``
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* **Required**: *yes*
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* **Note**: the attribute is ignored when *auto_pad* attribute is specified.
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* *dilations*
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* **Description**: *dilations* has the same definition as *dilations* for a regular Convolution but applied
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in the backward way, for the output tensor.
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* **Range of values**: positive integers
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* **Type**: ``int[]``
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* **Required**: *yes*
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* *auto_pad*
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* **Description**: *auto_pad* has the same definition as *auto_pad* for a regular Convolution but applied
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in the backward way, for the output tensor.
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* *explicit* - use explicit padding values from *pads_begin* and *pads_end*.
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* *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.
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* *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.
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* *valid* - do not use padding.
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* **Type**: ``string``
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* **Default value**: explicit
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* **Required**: *no*
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* **Note**: *pads_begin* and *pads_end* attributes are ignored when *auto_pad* is specified.
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* *output_padding*
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* **Description**: *output_padding* adds additional amount of paddings per each spatial axis in the output tensor.
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It unlocks more elements in the output allowing them to be computed. Elements are added at the higher coordinate
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indices for the spatial dimensions. Number of elements in *output_padding* list matches the number of spatial
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dimensions in input and output tensors.
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* **Range of values**: non-negative integer values
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* **Type**: ``int[]``
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* **Default value**: all zeros
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* **Required**: *no*
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**Inputs**:
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* **1**: Input tensor of type ``T1`` and rank 3, 4 or 5. Layout is ``[N, GROUPS * C_IN, Z, Y, X]``
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(number of batches, number of channels, spatial axes Z, Y, X). **Required.**
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* **2**: Kernel tensor of type ``T1`` and rank 4, 5 or 6. Layout is ``[GROUPS, C_IN, C_OUT, X, Y, Z]``
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(number of groups, number of input channels, number of output channels, spatial axes X, Y, Z). **Required.**
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* **3**: Output shape tensor of type ``T2`` and rank 1. It specifies spatial shape of the output. **Optional.**
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* **Note** Number of groups is derived from the shape of the kernel and not specified by any attribute.
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* **Note**: Type of the convolution (1D, 2D or 3D) is derived from the rank of the input tensors and not specified by any attribute:
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* 1D convolution (input tensors rank 3) means that there is only one spatial axis X
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* 2D convolution (input tensors rank 4) means that there are two spatial axes Y, X
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* 3D convolution (input tensors rank 5) means that there are three spatial axes Z, Y, X
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**Outputs**:
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* **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]``
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(number of batches, number of kernel output channels, spatial axes Z, Y, X).
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**Types**:
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* *T1*: any numeric type.
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* *T2*: any integer type.
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**Example**
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1D GroupConvolutionBackpropData
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.. code-block:: xml
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:force:
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<layer id="5" name="upsampling_node" type="GroupConvolutionBackpropData">
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<data dilations="1" pads_begin="1" pads_end="1" strides="2"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>20</dim>
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<dim>224</dim>
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</port>
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<port id="1">
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<dim>4</dim>
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<dim>5</dim>
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<dim>2</dim>
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<dim>3</dim>
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</port>
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</input>
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<output>
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<port id="0" precision="FP32">
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<dim>1</dim>
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<dim>8</dim>
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<dim>447</dim>
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</port>
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</output>
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</layer>
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2D GroupConvolutionBackpropData
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.. code-block:: xml
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:force:
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<layer id="5" name="upsampling_node" type="GroupConvolutionBackpropData">
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<data dilations="1,1" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>20</dim>
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<dim>224</dim>
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<dim>224</dim>
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</port>
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<port id="1">
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<dim>4</dim>
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<dim>5</dim>
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<dim>2</dim>
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<dim>3</dim>
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<dim>3</dim>
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</port>
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</input>
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<output>
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<port id="0" precision="FP32">
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<dim>1</dim>
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<dim>8</dim>
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<dim>447</dim>
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<dim>447</dim>
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</port>
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</output>
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</layer>
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3D GroupConvolutionBackpropData
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.. code-block:: xml
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:force:
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<layer id="5" name="upsampling_node" type="GroupConvolutionBackpropData">
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<data dilations="1,1,1" pads_begin="1,1,1" pads_end="1,1,1" strides="2,2,2"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>20</dim>
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<dim>224</dim>
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<dim>224</dim>
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<dim>224</dim>
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</port>
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<port id="1">
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<dim>4</dim>
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<dim>5</dim>
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<dim>2</dim>
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<dim>3</dim>
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<dim>3</dim>
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<dim>3</dim>
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</port>
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</input>
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<output>
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<port id="0" precision="FP32">
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<dim>1</dim>
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<dim>8</dim>
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<dim>447</dim>
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<dim>447</dim>
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<dim>447</dim>
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</port>
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</output>
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</layer>
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@endsphinxdirective
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