ef45b5da8d
* Doc Migration from Gitlab (#1289) * doc migration * fix * Update FakeQuantize_1.md * Update performance_benchmarks.md * Updates graphs for FPGA * Update performance_benchmarks.md * Change DL Workbench structure (#1) * Changed DL Workbench structure * Fixed tags * fixes * Update ie_docs.xml * Update performance_benchmarks_faq.md * Fixes in DL Workbench layout * Fixes for CVS-31290 * [DL Workbench] Minor correction * Fix for CVS-30955 * Added nGraph deprecation notice as requested by Zoe * fix broken links in api doxy layouts * CVS-31131 fixes * Additional fixes * Fixed POT TOC * Update PAC_Configure.md PAC DCP 1.2.1 install guide. * Update inference_engine_intro.md * fix broken link * Update opset.md * fix * added opset4 to layout * added new opsets to layout, set labels for them * Update VisionAcceleratorFPGA_Configure.md Updated from 2020.3 to 2020.4 Co-authored-by: domi2000 <domi2000@users.noreply.github.com>
115 lines
4.4 KiB
Markdown
115 lines
4.4 KiB
Markdown
## Convolution<a name="Convolution"></a> {#openvino_docs_ops_convolution_Convolution_1}
|
|
|
|
**Versioned name**: *Convolution-1*
|
|
|
|
**Category**: Convolution
|
|
|
|
**Short description**: [Reference](http://caffe.berkeleyvision.org/tutorial/layers/convolution.html)
|
|
|
|
**Detailed description**: [Reference](http://cs231n.github.io/convolutional-networks/#conv)
|
|
|
|
|
|
* For the convolutional layer, the number of output features in each dimension is calculated using the formula:
|
|
\f[
|
|
n_{out} = \left ( \frac{n_{in} + 2p - k}{s} \right ) + 1
|
|
\f]
|
|
* The receptive field in each layer is calculated using the formulas:
|
|
* Jump in the output feature map:
|
|
\f[
|
|
j_{out} = j_{in} * s
|
|
\f]
|
|
* Size of the receptive field of output feature:
|
|
\f[
|
|
r_{out} = r_{in} + ( k - 1 ) * j_{in}
|
|
\f]
|
|
* Center position of the receptive field of the first output feature:
|
|
\f[
|
|
start_{out} = start_{in} + ( \frac{k - 1}{2} - p ) * j_{in}
|
|
\f]
|
|
* Output is calculated using the following formula:
|
|
\f[
|
|
out = \sum_{i = 0}^{n}w_{i}x_{i} + b
|
|
\f]
|
|
|
|
**Attributes**
|
|
|
|
* *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**: integer values starting from 0
|
|
* **Type**: int[]
|
|
* **Default value**: None
|
|
* **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**: integer values starting from 0
|
|
* **Type**: int[]
|
|
* **Default value**: None
|
|
* **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**: integer values starting from 0
|
|
* **Type**: int[]
|
|
* **Default value**: None
|
|
* **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**: integer value starting from 0
|
|
* **Type**: int[]
|
|
* **Default value**: None
|
|
* **Required**: *yes*
|
|
|
|
* *auto_pad*
|
|
|
|
* **Description**: *auto_pad* how the padding is calculated. Possible values:
|
|
* None (not specified): use explicit padding values.
|
|
* *same_upper (same_lower)* the input is padded to match the output size. In case of odd padding value an extra padding is added at the end (at the beginning).
|
|
* *valid* - do not use padding.
|
|
* **Type**: string
|
|
* **Default value**: None
|
|
* **Required**: *no*
|
|
* **Note**: *pads_begin* and *pads_end* attributes are ignored when *auto_pad* is specified.
|
|
|
|
**Inputs**:
|
|
|
|
* **1**: Input tensor of rank 3 or greater. Required.
|
|
* **2**: Convolution kernel tensor. Weights layout is OIYX (OIZYX for 3D convolution), which means that *X* is changing the fastest, then *Y*, then *Input*, then *Output*. The size of the kernel is derived from the shape of this input and not specified by any attribute. Required.
|
|
|
|
**Example**
|
|
|
|
```xml
|
|
<layer type="Convolution" ...>
|
|
<data dilations="1,1" pads_begin="2,2" pads_end="2,2" strides="1,1"/>
|
|
<input>
|
|
<port id="0">
|
|
<dim>1</dim>
|
|
<dim>3</dim>
|
|
<dim>224</dim>
|
|
<dim>224</dim>
|
|
</port>
|
|
<port id="1">
|
|
<dim>64</dim>
|
|
<dim>3</dim>
|
|
<dim>5</dim>
|
|
<dim>5</dim>
|
|
</port>
|
|
</input>
|
|
<output>
|
|
<port id="2" precision="FP32">
|
|
<dim>1</dim>
|
|
<dim>64</dim>
|
|
<dim>224</dim>
|
|
<dim>224</dim>
|
|
</port>
|
|
</output>
|
|
</layer>
|
|
```
|