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DOCS shift to rst - Opsets B (#17169)

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docs/ops/convolution/BinaryConvolution_1.md
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# BinaryConvolution {#openvino_docs_ops_convolution_BinaryConvolution_1}
@sphinxdirective
**Versioned name**: *BinaryConvolution-1*
**Category**: *Convolution*
**Short description**: Computes 2D convolution of binary input and binary kernel tensors.
**Detailed description**: The operation behaves as regular *Convolution* but uses specialized algorithm for computations on binary data. More thorough explanation can be found in [Understanding Binary Neural Networks](https://sushscience.wordpress.com/2017/10/01/understanding-binary-neural-networks/) and [Bitwise Neural Networks](https://saige.sice.indiana.edu/wp-content/uploads/icml2015_mkim.pdf).
**Detailed description**: The operation behaves as regular *Convolution* but uses specialized algorithm for computations on binary data. More thorough explanation can be found in `Understanding Binary Neural Networks <https://sushscience.wordpress.com/2017/10/01/understanding-binary-neural-networks/>`__ and `Bitwise Neural Networks <https://saige.sice.indiana.edu/wp-content/uploads/icml2015_mkim.pdf>`__.
Computation algorithm for mode *xnor-popcount*:
- `X = XNOR(input_patch, filter)`, where XNOR is bitwise operation on two bit streams
- `P = popcount(X)`, where popcount is the number of `1` bits in the `X` bit stream
- `Output = 2 * P - B`, where `B` is the total number of bits in the `P` bit stream
- ``X = XNOR(input_patch, filter)``, where XNOR is bitwise operation on two bit streams
- ``P = popcount(X)``, where popcount is the number of ``1`` bits in the ``X`` bit stream
- ``Output = 2 \* P - B``, where ``B`` is the total number of bits in the ``P`` bit stream
**Attributes**:
* *strides*
* **Description**: *strides* is a distance (in pixels) to slide the filter on the feature map over the `(y, x)` axes for 2D convolutions. For example, *strides* equal `2,1` means sliding the filter 2 pixel at a time over height dimension and 1 over width dimension.
* **Description**: *strides* is a distance (in pixels) to slide the filter on the feature map over the ``(y, x)`` axes for 2D convolutions. For example, *strides* equal ``2,1`` means sliding the filter 2 pixel at a time over height dimension and 1 over width dimension.
* **Range of values**: integer values starting from 0
* **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.
* **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[]
* **Required**: *yes*
@@ -33,7 +36,7 @@ Computation algorithm for mode *xnor-popcount*:
* *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.
* **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[]
* **Required**: *yes*
@@ -41,30 +44,32 @@ Computation algorithm for mode *xnor-popcount*:
* *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.
* **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[]
* **Required**: *yes*
* *mode*
* **Description**: *mode* defines how input tensor `0/1` values and weights `0/1` are interpreted as real numbers and how the result is computed.
* **Description**: *mode* defines how input tensor ``0/1`` values and weights ``0/1`` are interpreted as real numbers and how the result is computed.
* **Range of values**:
* *xnor-popcount*
* **Type**: `string`
* **Type**: ``string``
* **Required**: *yes*
* **Note**: value `0` in inputs is interpreted as `-1`, value `1` as `1`
* **Note**: value ``0`` in inputs is interpreted as ``-1``, value ``1`` as ``1``
* *pad_value*
* **Description**: *pad_value* is a floating-point value used to fill pad area.
* **Range of values**: a floating-point number
* **Type**: `float`
* **Type**: ``float``
* **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.
@@ -76,47 +81,51 @@ Computation algorithm for mode *xnor-popcount*:
**Inputs**:
* **1**: Input tensor of type *T1* and rank 4. Layout is `[N, C_IN, Y, X]` (number of batches, number of channels, spatial axes Y, X). **Required.**
* **2**: Kernel tensor of type *T2* and rank 4. Layout is `[C_OUT, C_IN, Y, X]` (number of output channels, number of input channels, spatial axes Y, X). **Required.**
* **1**: Input tensor of type *T1* and rank 4. Layout is ``[N, C_IN, Y, X]`` (number of batches, number of channels, spatial axes Y, X). **Required.**
* **2**: Kernel tensor of type *T2* and rank 4. Layout is ``[C_OUT, C_IN, Y, X]`` (number of output channels, number of input channels, spatial axes Y, X). **Required.**
* **Note**: Interpretation of tensor values is defined by *mode* attribute.
**Outputs**:
* **1**: Output tensor of type *T3* and rank 4. Layout is `[N, C_OUT, Y, X]` (number of batches, number of kernel output channels, spatial axes Y, X).
* **1**: Output tensor of type *T3* and rank 4. Layout is ``[N, C_OUT, Y, X]`` (number of batches, number of kernel output channels, spatial axes Y, X).
**Types**:
* *T1*: any numeric type with values `0` or `1`.
* *T2*: `u1` type with binary values `0` or `1`.
* *T1*: any numeric type with values ``0`` or ``1``.
* *T2*: ``u1`` type with binary values ``0`` or ``1``.
* *T3*: *T1* type with full range of values.
**Example**:
2D Convolution
```xml
<layer type="BinaryConvolution" ...>
<data dilations="1,1" pads_begin="2,2" pads_end="2,2" strides="1,1" mode="xnor-popcount" pad_value="0" auto_pad="explicit"/>
<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>
```
.. code-block:: cpp
<layer type="BinaryConvolution" ...>
<data dilations="1,1" pads_begin="2,2" pads_end="2,2" strides="1,1" mode="xnor-popcount" pad_value="0" auto_pad="explicit"/>
<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>
@endsphinxdirective