188 lines
6.6 KiB
Markdown
188 lines
6.6 KiB
Markdown
# GroupConvolution {#openvino_docs_ops_convolution_GroupConvolution_1}
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@sphinxdirective
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**Versioned name**: *GroupConvolution-1*
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**Category**: *Convolution*
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**Short description**: Computes 1D, 2D or 3D GroupConvolution of input and kernel tensors.
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**Detailed description**: Splits input into multiple groups, convolves them with group filters
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as in regular convolution and concatenates the results. More thorough explanation can be found in
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`ImageNet Classification with Deep Convolutional Neural Networks <https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf>`__
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**Attributes**: The operation has the same attributes as a regular _Convolution_. Number of groups is derived from the kernel shape.
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* *strides*
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* **Description**: *strides* is a distance (in pixels) to slide the filter on the feature map over the ``(z, y, x)``
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axes for 3D convolutions and ``(y, x)`` axes for 2D convolutions. For example, *strides* equal ``4,2,1`` means sliding
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the filter 4 pixel at a time over depth dimension, 2 over height dimension and 1 over width dimension.
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* **Range of values**: positive integer numbers
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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* is a number of pixels to add to the beginning along each axis. For example,
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*pads_begin* equal ``1,2`` means adding 1 pixel to the top of the input and 2 to the left of the input.
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* **Range of values**: positive integer numbers
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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* is a number of pixels to add to the ending along each axis. For example,
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*pads_end* equal ``1,2`` means adding 1 pixel to the bottom of the input and 2 to the right of the input.
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* **Range of values**: positive integer numbers
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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* denotes the distance in width and height between elements (weights) in the filter.
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For example, *dilation* equal ``1,1`` means that all the elements in the filter are neighbors,
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so it is the same as for the usual convolution. *dilation* equal ``2,2`` means that all the elements in the
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filter are matched not to adjacent elements in the input matrix, but to those that are adjacent with distance 1.
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* **Range of values**: positive integer numbers
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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* how the padding is calculated. Possible values:
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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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**Inputs**:
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* **1**: Input tensor of type *T* 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**: Convolution kernel tensor of type *T* and rank 4, 5 or 6. Layout is ``[GROUPS, C_OUT, C_IN, Z, Y, X]``
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(number of groups, number of output channels, number of input channels, spatial axes Z, Y, X),
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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 *T* and rank 3, 4 or 5. Layout is ``[N, GROUPS * C_OUT, Z, Y, X]``
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(number of batches, number of output channels, spatial axes Z, Y, X).
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**Types**:
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* *T*: any numeric type.
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**Example**:
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1D GroupConvolution
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.. code-block:: cpp
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<layer type="GroupConvolution" ...>
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<data dilations="1" pads_begin="2" pads_end="2" strides="1" auto_pad="explicit"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>12</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>1</dim>
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<dim>3</dim>
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<dim>5</dim>
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</port>
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</input>
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<output>
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<port id="2" precision="FP32">
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<dim>1</dim>
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<dim>4</dim>
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<dim>224</dim>
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</port>
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</output>
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2D GroupConvolution
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.. code-block:: cpp
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<layer type="GroupConvolution" ...>
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<data dilations="1,1" pads_begin="2,2" pads_end="2,2" strides="1,1" auto_pad="explicit"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>12</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>1</dim>
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<dim>3</dim>
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<dim>5</dim>
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<dim>5</dim>
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</port>
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</input>
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<output>
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<port id="2" precision="FP32">
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<dim>1</dim>
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<dim>4</dim>
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<dim>224</dim>
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<dim>224</dim>
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</port>
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</output>
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3D GroupConvolution
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.. code-block:: cpp
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<layer type="GroupConvolution" ...>
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<data dilations="1,1,1" pads_begin="2,2,2" pads_end="2,2,2" strides="1,1,1" auto_pad="explicit"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>12</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>1</dim>
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<dim>3</dim>
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<dim>5</dim>
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<dim>5</dim>
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<dim>5</dim>
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</port>
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</input>
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<output>
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<port id="2" precision="FP32">
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<dim>1</dim>
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<dim>4</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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</output>
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@endsphinxdirective
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