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