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# BitwiseAnd {#openvino_docs_ops_bitwise_BitwiseAnd_13} | ||
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@sphinxdirective | ||
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.. meta:: | ||
:description: Learn about BitwiseAnd-13 - an element-wise, bitwise AND operation, which can be performed on two required input tensors. | ||
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**Versioned name**: *BitwiseAnd-13* | ||
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**Category**: *Bitwise binary* | ||
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**Short description**: *BitwiseAnd* performs a bitwise logical AND operation with two given tensors element-wise, applying multi-directional broadcast rules. | ||
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**Detailed description**: Before performing the operation, input tensors *a* and *b* are broadcasted if their shapes are different and the ``auto_broadcast`` attribute is not ``none``. Broadcasting is performed according to the ``auto_broadcast`` value. | ||
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After broadcasting input tensors *a* and *b*, *BitwiseAnd* performs a bitwise logical AND operation for each corresponding element in the given tensors, based on the following algorithm. | ||
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For ``boolean`` type tensors, BitwiseAnd is equivalent to :doc:`LogicalAnd <openvino_docs_ops_logical_LogicalAnd_1>`. | ||
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If tensor is of ``any supported integer`` type, for each element of the tensor: | ||
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1. Convert values from input tensors to their binary representation according to the input tensor datatype. | ||
2. Perform a logical AND on each bit in the binary representation of values from *a* and *b*, where value ``0`` represents ``false`` and value ``1`` represents ``true``. | ||
3. Convert the results of AND in binary representation to the input datatype. | ||
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Example 1 - *BitwiseAnd* output for boolean tensor: | ||
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.. code-block:: py | ||
:force: | ||
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# For given boolean inputs: | ||
a = [True, False, False] | ||
b = [True, True, False] | ||
# Perform logical AND operation same as in LogicalAnd operator: | ||
output = [True, False, False] | ||
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Example 2 - *BitwiseAnd* output for uint8 tensor: | ||
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.. code-block:: py | ||
:force: | ||
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# For given uint8 inputs: | ||
a = [21, 120] | ||
b = [3, 37] | ||
# Create a binary representation of uint8: | ||
# binary a: [00010101, 01111000] | ||
# binary b: [00000011, 00100101] | ||
# Perform bitwise AND of corresponding elements in a and b: | ||
# [00000001, 00100000] | ||
# Convert binary values to uint8: | ||
output = [1, 32] | ||
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**Attributes**: | ||
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* *auto_broadcast* | ||
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* **Description**: specifies the rules used for auto-broadcasting of input tensors. | ||
* **Range of values**: | ||
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* *none* - no auto-broadcasting is allowed, all input shapes must match, | ||
* *numpy* - numpy broadcasting rules, description is available in :doc:`Broadcast Rules For Elementwise Operations <openvino_docs_ops_broadcast_rules>`, | ||
* *pdpd* - PaddlePaddle-style implicit broadcasting, description is available in :doc:`Broadcast Rules For Elementwise Operations <openvino_docs_ops_broadcast_rules>`. | ||
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* **Type**: string | ||
* **Default value**: "numpy" | ||
* **Required**: *no* | ||
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**Inputs** | ||
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* **1**: A tensor of type *T* and arbitrary shape. **Required.** | ||
* **2**: A tensor of type *T* and arbitrary shape. **Required.** | ||
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**Outputs** | ||
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* **1**: The result of element-wise *BitwiseAnd* operation. A tensor of type *T* and the same shape equal to the broadcasted shape of two inputs. | ||
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**Types** | ||
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* *T*: ``any supported integer or boolean type``. | ||
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**Examples** | ||
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*Example 1: no broadcast* | ||
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.. code-block:: xml | ||
:force: | ||
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<layer ... type="BitwiseAnd"> | ||
<input> | ||
<port id="0"> | ||
<dim>256</dim> | ||
<dim>56</dim> | ||
</port> | ||
<port id="1"> | ||
<dim>256</dim> | ||
<dim>56</dim> | ||
</port> | ||
</input> | ||
<output> | ||
<port id="2"> | ||
<dim>256</dim> | ||
<dim>56</dim> | ||
</port> | ||
</output> | ||
</layer> | ||
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*Example 2: numpy broadcast* | ||
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.. code-block:: xml | ||
:force: | ||
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<layer ... type="BitwiseAnd"> | ||
<input> | ||
<port id="0"> | ||
<dim>8</dim> | ||
<dim>1</dim> | ||
<dim>6</dim> | ||
<dim>1</dim> | ||
</port> | ||
<port id="1"> | ||
<dim>7</dim> | ||
<dim>1</dim> | ||
<dim>5</dim> | ||
</port> | ||
</input> | ||
<output> | ||
<port id="2"> | ||
<dim>8</dim> | ||
<dim>7</dim> | ||
<dim>6</dim> | ||
<dim>5</dim> | ||
</port> | ||
</output> | ||
</layer> | ||
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@endsphinxdirective |
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# BitwiseNot {#openvino_docs_ops_bitwise_BitwiseNot_13} | ||
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@sphinxdirective | ||
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.. meta:: | ||
:description: Learn about BitwiseNot-13 - an element-wise, bitwise negation operation, which can be performed on a single input tensor. | ||
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**Versioned name**: *BitwiseNot-13* | ||
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**Category**: *Bitwise unary* | ||
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**Short description**: *BitwiseNot* performs a bitwise logical negation operation with given tensor element-wise. | ||
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**Detailed description**: *BitwiseNot* performs a bitwise logical negation operation for each element in the given tensor, based on the following algorithm. | ||
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For ``boolean`` type tensors, BitwiseNot is equivalent to :doc:`LogicalNot <openvino_docs_ops_logical_LogicalNot_1>`. | ||
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If tensor is of ``any supported integer`` type, for each element of the tensor: | ||
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1. Convert the value from the input tensor to binary representation according to the input tensor datatype. | ||
2. Perform a logical negation on each bit in the binary representation, where value ``0`` represents ``false`` and value ``1`` represents ``true``. | ||
3. Convert back the binary representation to the input datatype. | ||
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Example 1 - *BitwiseNot* output for boolean tensor: | ||
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.. code-block:: py | ||
:force: | ||
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# For given boolean input: | ||
input = [True, False] | ||
# Perform logical negation operation same as in LogicalNot operator: | ||
output = [False, True] | ||
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Example 2 - *BitwiseNot* output for uint8 tensor: | ||
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.. code-block:: py | ||
:force: | ||
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# For given uint8 input: | ||
input = [1, 3] | ||
# Create a binary representation of uint8: | ||
# [00000001, 00000011] | ||
# Perform bitwise negation: | ||
# [11111110, 11111100] | ||
# Convert back binary values to uint8: | ||
output = [254, 252] | ||
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**Attributes**: *BitwiseNot* operation has no attributes. | ||
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**Inputs** | ||
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* **1**: A tensor of type *T* and arbitrary shape. **Required.** | ||
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**Outputs** | ||
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* **1**: The result of bitwise logical negation operation. A tensor of type *T* and the same shape as the input tensor. | ||
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**Types** | ||
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* *T*: ``any supported integer or boolean type``. | ||
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**Example** | ||
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.. code-block:: xml | ||
:force: | ||
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<layer ... type="BitwiseNot"> | ||
<input> | ||
<port id="0"> | ||
<dim>256</dim> | ||
<dim>56</dim> | ||
</port> | ||
</input> | ||
<output> | ||
<port id="1"> | ||
<dim>256</dim> | ||
<dim>56</dim> | ||
</port> | ||
</output> | ||
</layer> | ||
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@endsphinxdirective |
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@@ -0,0 +1,138 @@ | ||
# BitwiseOr {#openvino_docs_ops_bitwise_BitwiseOr_13} | ||
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@sphinxdirective | ||
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.. meta:: | ||
:description: Learn about BitwiseOr-13 - an element-wise, bitwise OR operation, which can be performed on two required input tensors. | ||
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**Versioned name**: *BitwiseOr-13* | ||
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**Category**: *Bitwise binary* | ||
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**Short description**: *BitwiseOr* performs a bitwise logical OR operation with two given tensors element-wise, applying multi-directional broadcast rules. | ||
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**Detailed description**: Before performing the operation, input tensors *a* and *b* are broadcasted if their shapes are different and the ``auto_broadcast`` attribute is not ``none``. Broadcasting is performed according to the ``auto_broadcast`` value. | ||
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||
After broadcasting input tensors *a* and *b*, *BitwiseOr* performs a bitwise logical OR operation for each corresponding element in the given tensors, based on the following algorithm. | ||
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For ``boolean`` type tensors, BitwiseOr is equivalent to :doc:`LogicalOr <openvino_docs_ops_logical_LogicalOr_1>`. | ||
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If tensor is of ``any supported integer`` type, for each element of the tensor: | ||
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||
1. Convert values from input tensors to their binary representation according to the input tensor datatype. | ||
2. Perform a logical OR on each bit in the binary representation of values from *a* and *b*, where value ``0`` represents ``false`` and value ``1`` represents ``true``. | ||
3. Convert the results of OR in binary representation to the input datatype. | ||
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Example 1 - *BitwiseOr* output for boolean tensor: | ||
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.. code-block:: py | ||
:force: | ||
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# For given boolean inputs: | ||
a = [True, False, False] | ||
b = [True, True, False] | ||
# Perform logical OR operation same as in LogicalOr operator: | ||
output = [True, True, False] | ||
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Example 2 - *BitwiseOr* output for uint8 tensor: | ||
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.. code-block:: py | ||
:force: | ||
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# For given uint8 inputs: | ||
a = [21, 120] | ||
b = [3, 37] | ||
# Create a binary representation of uint8: | ||
# binary a: [00010101, 01111000] | ||
# binary b: [00000011, 00100101] | ||
# Perform bitwise OR of corresponding elements in a and b: | ||
# [00010111, 01111101] | ||
# Convert binary values to uint8: | ||
output = [23, 125] | ||
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**Attributes**: | ||
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* *auto_broadcast* | ||
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||
* **Description**: specifies the rules used for auto-broadcasting of input tensors. | ||
* **Range of values**: | ||
|
||
* *none* - no auto-broadcasting is allowed, all input shapes must match, | ||
* *numpy* - numpy broadcasting rules, description is available in :doc:`Broadcast Rules For Elementwise Operations <openvino_docs_ops_broadcast_rules>`, | ||
* *pdpd* - PaddlePaddle-style implicit broadcasting, description is available in :doc:`Broadcast Rules For Elementwise Operations <openvino_docs_ops_broadcast_rules>`. | ||
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* **Type**: string | ||
* **Default value**: "numpy" | ||
* **Required**: *no* | ||
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**Inputs** | ||
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* **1**: A tensor of type *T* and arbitrary shape. **Required.** | ||
* **2**: A tensor of type *T* and arbitrary shape. **Required.** | ||
|
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**Outputs** | ||
|
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* **1**: The result of element-wise *BitwiseOr* operation. A tensor of type *T* and the same shape equal to the broadcasted shape of two inputs. | ||
|
||
**Types** | ||
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* *T*: ``any supported integer or boolean type``. | ||
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**Examples** | ||
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*Example 1: no broadcast* | ||
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.. code-block:: xml | ||
:force: | ||
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<layer ... type="BitwiseOr"> | ||
<input> | ||
<port id="0"> | ||
<dim>256</dim> | ||
<dim>56</dim> | ||
</port> | ||
<port id="1"> | ||
<dim>256</dim> | ||
<dim>56</dim> | ||
</port> | ||
</input> | ||
<output> | ||
<port id="2"> | ||
<dim>256</dim> | ||
<dim>56</dim> | ||
</port> | ||
</output> | ||
</layer> | ||
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*Example 2: numpy broadcast* | ||
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.. code-block:: xml | ||
:force: | ||
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<layer ... type="BitwiseOr"> | ||
<input> | ||
<port id="0"> | ||
<dim>8</dim> | ||
<dim>1</dim> | ||
<dim>6</dim> | ||
<dim>1</dim> | ||
</port> | ||
<port id="1"> | ||
<dim>7</dim> | ||
<dim>1</dim> | ||
<dim>5</dim> | ||
</port> | ||
</input> | ||
<output> | ||
<port id="2"> | ||
<dim>8</dim> | ||
<dim>7</dim> | ||
<dim>6</dim> | ||
<dim>5</dim> | ||
</port> | ||
</output> | ||
</layer> | ||
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@endsphinxdirective |
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