Ivan Tikhonov
c1608628d4
* LowLatency 2.0: transformation and unit tests * low latency 2.0: unit tests * documentation and ngraph codestyle * update CNN Interface of LowLatency transformation * fix build on Windows * fix build on Windows * investigation of a failed build on Win OS * ngraph codestyle * fix build (werrors) * New unit tests, refactoring * update functional tests for Memory * update LowLatency functional tests * extend Memory tests to cover LowLatency v2 transformation * clean up, code style * fix unit tests * update and fix unit tests, add feature to apply LLTv2 after LLTv1 * update docs, refactoring * add several gna tests to skip config * fix python api tests * update python api, rename LowLatency_v2 to LowLatency2 * deprecate LowLatency v1 * Deprecate LowLatency v1 in IE * fix wrong merge, codestyle * resolve review comments * fix python test * update skip config * apply online review notes, fix unit tests * clean up, code style * fix docs * Use debug_messages instead of exceptions in llt v2 * fix unit tests * Resolve review remarks
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