dbad8809bf
* Release mo dev guide refactoring (#3266) * Updated MO extension guide * Minor change and adding svg images * Added additional information about operation extractors. Fixed links and markdown issues * Added missing file with information about Caffe Python layers and image for MO transformations dependencies graph * Added section with common graph transformations attributes and diagram with anchor transformations. Added list of available front phase transformations * Added description of front-phase transformations except the scope-defined and points defined. Removed legacy document and examples for such transformations. * Added sections about node name pattern defined front phase transformations. Copy-pasted the old one for the points defined front transformation * Added description of the rest of front transformations and and all middle and back phase transformations * Refactored Legacy_Mode_for_Caffe_Custom_Layers and updated the Customize_Model_Optimizer with information about extractors order * Added TOC for the MO Dev guide document and updated SVG images with PNG ones * Fixed broken link. Removed redundant image * Fixed broken links * Added information about attributes 'run_not_recursively', 'force_clean_up' and 'force_shape_inference' of the transformation * Code review comments * Added a section about `Port`s * Extended Ports description with examples * Added information about Connections * Updated MO README.md and removed a lot of redundant and misleading information * Updates to the Customize_Model_Optimizer.md * More updates to the Customize_Model_Optimizer.md * Final updates for the Customize_Model_Optimizer.md * Fixed some broken links * More fixed links * Refactored Custom Layers Guide: removed legacy and incorrect text, added up-to-date. * Draft implementation of the Custom layer guide example for the MO part * Fixed broken links using #. Change layer->operation in extensibility documents * Updated Custom operation guide with IE part * Fixed broken links and minor updates to the Custom Operations Guide * Updating links * Layer->Operation * Moved FFTOp implementation to the template extension * Update the CMake for template_extension to build the FFT op conditionally * Fixed template extension compilation * Fixed CMake for template extension * Fixed broken snippet * Added mri_demo script and updated documentation * One more compilation error fix * Added missing header for a demo file * Added reference to OpenCV * Fixed unit test for the template extension * Fixed typos in the template extension * Fixed compilation of template extension for case when ONNX importer is disabled Co-authored-by: Alexander Zhogov <alexander.zhogov@intel.com>
82 lines
2.6 KiB
Markdown
82 lines
2.6 KiB
Markdown
## Prerequisites
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Model Optimizer requires:
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1. Python 3 or newer
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2. [Optional] Please read about use cases that require Caffe\* to be available on the machine in the documentation.
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## Installation instructions
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1. Go to the Model Optimizer folder:
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<pre>
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cd PATH_TO_INSTALL_DIR/deployment_tools/model_optimizer
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</pre>
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2. Create virtual environment and activate it. This option is strongly recommended as it creates a Python sandbox and
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dependencies for the Model Optimizer do not influence global Python configuration, installed libraries etc. At the
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same time, special flag ensures that system-wide Python libraries are also available in this sandbox. Skip this
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step only if you do want to install all Model Optimizer dependencies globally:
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* Create environment:
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<pre>
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virtualenv -p /usr/bin/python3.6 .env3 --system-site-packages
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</pre>
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* Activate it:
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<pre>
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. .env3/bin/activate
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</pre>
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3. Install dependencies. If you want to convert models only from particular framework, you should use one of
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available <code>requirements_\*.txt</code> files corresponding to the framework of choice. For example, for Caffe
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use <code>requirements_caffe.txt</code> and so on. When you decide to switch later to other frameworks, please
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install dependencies for them using the same mechanism:
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<pre>
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pip3 install -r requirements.txt
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</pre>
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Or you can use the installation scripts from the "install_prerequisites" directory.
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4. [OPTIONAL] If you use Windows OS, most probably you get python version of `protobuf` library. It is known to be rather slow,
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and you can use a boosted version of library by building the .egg file (Python package format) yourself,
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using instructions below (section 'How to boost Caffe model loading') for the target OS and Python, or install it
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with the pre-built .egg (it is built for Python 3.4, 3.5, 3.6, 3.7):
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<pre>
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python3 -m easy_install protobuf-3.6.1-py3.6-win-amd64.egg
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</pre>
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It overrides the protobuf python package installed by the previous command.
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Set environment variable to enable boost in protobuf performance:
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<pre>
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set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp
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</pre>
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## Setup development environment
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### How to run unit-tests
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1. Run tests with:
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<pre>
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python -m unittest discover -p "*_test.py" [-s PATH_TO_DIR]
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</pre>
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### How to capture unit-tests coverage
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1. Run tests with:
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<pre>
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coverage run -m unittest discover -p "*_test.py" [-s PATH_TO_DIR]
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</pre>
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2. Build html report:
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<pre>
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coverage html
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</pre>
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### How to run code linting
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1. Run the following command:
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<pre>
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pylint mo/ extensions/ mo.py
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</pre>
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