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openvino/model-optimizer/README.md
Evgeny Lazarev dbad8809bf MO dev guide refactoring (#3266) (#3595) 
* 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>
2021-01-14 16:28:53 +03:00

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Prerequisites

Model Optimizer requires:

  1. Python 3 or newer

  2. [Optional] Please read about use cases that require Caffe* to be available on the machine in the documentation.

Installation instructions

  1. Go to the Model Optimizer folder:
    cd PATH_TO_INSTALL_DIR/deployment_tools/model_optimizer

  1. Create virtual environment and activate it. This option is strongly recommended as it creates a Python sandbox and dependencies for the Model Optimizer do not influence global Python configuration, installed libraries etc. At the same time, special flag ensures that system-wide Python libraries are also available in this sandbox. Skip this step only if you do want to install all Model Optimizer dependencies globally:

    • Create environment: 
          virtualenv -p /usr/bin/python3.6 .env3 --system-site-packages
    • Activate it: 
        . .env3/bin/activate

  2. Install dependencies. If you want to convert models only from particular framework, you should use one of available requirements_*.txt files corresponding to the framework of choice. For example, for Caffe use requirements_caffe.txt and so on. When you decide to switch later to other frameworks, please install dependencies for them using the same mechanism:

    pip3 install -r requirements.txt

    Or you can use the installation scripts from the "install_prerequisites" directory.

  3. [OPTIONAL] If you use Windows OS, most probably you get python version of protobuf library. It is known to be rather slow, and you can use a boosted version of library by building the .egg file (Python package format) yourself, using instructions below (section 'How to boost Caffe model loading') for the target OS and Python, or install it with the pre-built .egg (it is built for Python 3.4, 3.5, 3.6, 3.7):

         python3 -m easy_install protobuf-3.6.1-py3.6-win-amd64.egg

    It overrides the protobuf python package installed by the previous command.

    Set environment variable to enable boost in protobuf performance:

         set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp

Setup development environment

How to run unit-tests

  1. Run tests with:
    python -m unittest discover -p "*_test.py" [-s PATH_TO_DIR]

How to capture unit-tests coverage

  1. Run tests with:
    coverage run -m unittest discover -p "*_test.py" [-s PATH_TO_DIR]
  1. Build html report:
    coverage html

How to run code linting

  1. Run the following command:
    pylint mo/ extensions/ mo.py