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automation
automation
 
 
extensions
extensions
 
 
install_prerequisites
install_prerequisites
 
 
mo
mo
 
 
unit_tests
unit_tests
 
 
.coveragerc
.coveragerc
 
 
.pylintdict
.pylintdict
 
 
.pylintrc
.pylintrc
 
 
CMakeLists.txt
CMakeLists.txt
 
 
README.md
README.md
 
 
mo.py
mo.py
 
 
mo_caffe.py
mo_caffe.py
 
 
mo_kaldi.py
mo_kaldi.py
 
 
mo_mxnet.py
mo_mxnet.py
 
 
mo_onnx.py
mo_onnx.py
 
 
mo_paddle.py
mo_paddle.py
 
 
mo_tf.py
mo_tf.py
 
 
requirements.txt
requirements.txt
 
 
requirements_caffe.txt
requirements_caffe.txt
 
 
requirements_dev.txt
requirements_dev.txt
 
 
requirements_kaldi.txt
requirements_kaldi.txt
 
 
requirements_mxnet.txt
requirements_mxnet.txt
 
 
requirements_onnx.txt
requirements_onnx.txt
 
 
requirements_tf.txt
requirements_tf.txt
 
 
requirements_tf2.txt
requirements_tf2.txt
 
 
setup.py
setup.py
 
 

README.md

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/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