0df7dab345
* OV new package structure * Fixes * More fixes * Fixed code style in ngraph tests * Fixes * Paths to setupvars inside demo scripts * Fixed demo_security_barrier_camera.sh * Added setupvars.sh to old location as well * Fixed path * Fixed MO install path in .co * Fixed install of public headers * Fixed frontends installation * Updated DM config files * Keep opencv in the root * Improvements * Fixes for demo scripts * Added path to TBB * Fix for MO unit-tests * Fixed tests on Windows * Reverted arch * Removed arch * Reverted arch back: second attemp * System type * Fix for Windows * Resolve merge conflicts * Fixed path * Path for Windows * Added debug for Windows * Added requirements_dev.txt to install * Fixed wheel's setup.py * Fixed lin build * Fixes after merge * Fix 2 * Fixes * Frontends path * Fixed deployment manager * Fixed Windows * Added cldnn unit tests installation * Install samples * Fix samples * Fix path for samples * Proper path * Try to fix MO hardcodes * samples binary location * MO print * Added install for libopencv_c_wrapper.so * Added library destination * Fixed install rule for samples * Updated demo scripts readme.md * Samples * Keep source permissions for Python samples * Fixed python * Updated path to fast run scripts * Fixed C samples tests * Removed debug output * Small fixes * Try to unify prefix
2.6 KiB
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