0e521a1e22
* added get_name * add support to supported_impl * remove debug code * review remarks * changed name to onnx_experimental * fixed test * revert onnx_experimental name * integrate reader and fe api * add unit tests * removed prototxt from model_validator * reader refactor * add supress * Update inference-engine/src/readers/onnx_reader/ie_onnx_reader.cpp Co-authored-by: Tomasz Dołbniak <tomasz.dolbniak@intel.com> * fix segfaults * removed onnx reader * handle istringstream * wstring support * removed saving path * styles applied * changed name to onnx experimental * Apply suggestions from code review Co-authored-by: Tatiana Savina <tatiana.savina@intel.com> * skip onnx_experimental frontend in mo.py * add support of wstring paths * fix wstring ctor of InputModelONNX * added NGRAPH_SUPPRESS * make one instance of manager * change onnx_experimental name to onnx * creation frontend manager refactor Co-authored-by: Tomasz Dołbniak <tomasz.dolbniak@intel.com> Co-authored-by: Tatiana Savina <tatiana.savina@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/deployment_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