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Fixed duplicated words (#10827)

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Ilya Churaev 2022-03-09 11:06:12 +03:00 committed by GitHub
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2
docs/OV_Runtime_UG/model_representation.md
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@ -64,7 +64,7 @@ The `ov::Op` class represents any abstract operation in the model representation
Operation set (opset) is a collection of operations that can be used to construct a model. The `ov::OpSet` class provides a functionality to work with operation sets. Operation set (opset) is a collection of operations that can be used to construct a model. The `ov::OpSet` class provides a functionality to work with operation sets.
For each operation set, OpenVINO™ Runtime provides a separate namespace, for example `opset8`. For each operation set, OpenVINO™ Runtime provides a separate namespace, for example `opset8`.
Each OpenVINO™ Release release introduces new operations and add these operations to a new operation set. New operation sets help to introduce a new version of operations that change behavior of previous operations. Using operation sets allows you to avoid changes in your application if new operations have been introduced. Each OpenVINO™ Release introduces new operations and add these operations to a new operation set. New operation sets help to introduce a new version of operations that change behavior of previous operations. Using operation sets allows you to avoid changes in your application if new operations have been introduced.
For a complete list of operation sets supported in OpenVINO™ toolkit, see [Available Operations Sets](../ops/opset.md). For a complete list of operation sets supported in OpenVINO™ toolkit, see [Available Operations Sets](../ops/opset.md).
To add support of custom operations, see the [Add Custom OpenVINO Operations](../Extensibility_UG/Intro.md) document. To add support of custom operations, see the [Add Custom OpenVINO Operations](../Extensibility_UG/Intro.md) document.

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src/bindings/c/docs/api_overview.md
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@ -352,7 +352,7 @@ This strcut represents an Inference Engine entity and allows you to manipulate w
- Parameters: - Parameters:
- `core` -A pointer to `ie_core_t` instance. - `core` -A pointer to `ie_core_t` instance.
- `device_name` - Name of the the registered plugin. - `device_name` - Name of the registered plugin.
- `version_result` - Dictionary mapping a plugin name . - `version_result` - Dictionary mapping a plugin name .
- Return value: Status of the operation: OK(0) for success. - Return value: Status of the operation: OK(0) for success.

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tools/pot/README_dev.md
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@ -33,7 +33,7 @@ Post-Training Optimization Tool includes standalone command-line tool and Python
3) Setup Model Optimizer. 3) Setup Model Optimizer.
You can setup Model Optimizer that needs for POT purposed with the two ways: You can setup Model Optimizer that needs for POT purposed with the two ways:
1. Install Model Optimizer with pip using "python setup.py install" at the mo folder (`<openvino_path>/tools/mo/setup.py`) 1. Install Model Optimizer with pip using "python setup.py install" at the mo folder (`<openvino_path>/tools/mo/setup.py`)
2. Setup Model Optimizer for Python using PYTHONPATH environment variable. Add the the following `<openvino_path>/tools/mo` into PYTHONPATH. 2. Setup Model Optimizer for Python using PYTHONPATH environment variable. Add the following `<openvino_path>/tools/mo` into PYTHONPATH.
4) Install requirements for accuracy checker: 4) Install requirements for accuracy checker:
- From POT root: `cd ./thirdparty/open_model_zoo/tools/accuracy_checker` - From POT root: `cd ./thirdparty/open_model_zoo/tools/accuracy_checker`
- Call setup script: `python3 setup.py install` - Call setup script: `python3 setup.py install`

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tools/pot/docs/LowPrecisionOptimizationGuide.md
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@ -32,7 +32,7 @@ generic component translating the model to real integer representation and HW-sp
## Model Optimization Workflow ## Model Optimization Workflow
We propose a common workflow which aligns with what other DL frameworks have. It contains two main components: post-training quantization and Quantization-Aware Training (QAT). We propose a common workflow which aligns with what other DL frameworks have. It contains two main components: post-training quantization and Quantization-Aware Training (QAT).
The first component is the the easiest way to get optimized models where the latter one can be considered as an alternative or an addition when the first does not give The first component is the easiest way to get optimized models where the latter one can be considered as an alternative or an addition when the first does not give
accurate results. accurate results.
The diagram below shows the optimization flow for the new model with OpenVINO and relative tools. The diagram below shows the optimization flow for the new model with OpenVINO and relative tools.