4e6eeea6ff
* move frontend folder to pyopenvino * rename includes and add compile options * include frontend to pyopenvino * move __init__.py * move tests * remove mock from tests_compatibility * rename import module * Fix code style cpp * refactor a few lines * update few lines in mo * Add pyopenvino to dependencies * Fix mock * update docstring * Fix mo test * remove module local * fix code style * update comment * fix return type * update docs * fix code style * fix building * fix code style * try to move MO to use new api * Export more enum names from nrgaph * [Python API] quick fix of packaging * update tests * fix setup.py * small fix * small fixes according to comments * skip mo frontend tests * update mo to new imports * try to fix win wheel * fix win wheel * fix code style Co-authored-by: Anastasia Kuporosova <anastasia.kuporosova@intel.com> Co-authored-by: y <ilya.lavrenov@intel.com>
Prerequisites
Model Optimizer requires:
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Python 3 or newer
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[Optional] Please read about use cases that require Caffe* to be available on the machine in the documentation.
Installation instructions
- Go to the Model Optimizer folder:
cd PATH_TO_INSTALL_DIR/tools/model_optimizer
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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
- Create environment:
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Install dependencies. If you want to convert models only from particular framework, you should use one of available
requirements_*.txtfiles corresponding to the framework of choice. For example, for Caffe userequirements_caffe.txtand 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.
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[OPTIONAL] If you use Windows OS, most probably you get python version of
protobuflibrary. 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
- Run tests with:
python -m unittest discover -p "*_test.py" [-s PATH_TO_DIR]
How to capture unit-tests coverage
- Run tests with:
coverage run -m unittest discover -p "*_test.py" [-s PATH_TO_DIR]
- Build html report:
coverage html
How to run code linting
- Run the following command:
pylint mo/ extensions/ mo.py