4547818fb1
* Refactored code, updated comments and documentation related to TF OD API models pre-processing. * Improved MO messages related to pre-processor block removal during conversion of the TD OD API models. Remove mean/scale if padding is used and mean/scale is applied before resize * Updated TF OD API transformation and documentation for SSD models * Updated comments and documentation for the ObjectDetectionAPIMaskRCNNSigmoidReplacement transformation * Updated comments and documentation for the ObjectDetectionAPIMaskRCNNROIPoolingSecondReplacement transformation * Updated comments and documentation for the ObjectDetectionAPIPSROIPoolingReplacement transformation * Updated comments and documentation for the ObjectDetectionAPIProposalReplacement transformation * Updated comments and documentation for the ObjectDetectionAPIDetectionOutputReplacement transformation * Minor code style fixes * Fixed unit tests for ObjectDetectionAPIPreprocessor2Replacement transformation * Improved unit test for pipeline.config parser. Fixed very long bug with incorrect test data for the PipelineConfig parser class * Code style fixes * Get rid of "coordinates_swap_method" parameter in the JSON configuration file for TF OD API models * Code style fixes and minor refactoring * Simplied code related to swapping Proposal coordinates * Removed incorrectly removed code * Fixed code review comments about the code comments
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