Pavel Esir
22169a05b9
* fix ss * successfully converted * successfully run moved infer and normalizer unit-tests * successfully rewritten StridedSlice infer unittests * int64 array * Successfully converter crash-when-loading, xj_feauture and toy nets (cherry-picked maxpoolV4 and tf_broadcast_ext) * successfully moved PermuteAttrs to general mechanism * successfully converted xj_feauture and crash when loading with the new rewritten SS infer * fixed get_shape_from_slice and moved to common utils * fixed extending masks and some other * some refactoring * fixed extending masks in extractor, fixed licence year and some other code clearing * corrected a couple of unittests * fox permute for 5 rank slice and 4 rank inputs/ * WIP * Added comments * fixed StridedSlice in ProposalMutation.py * rechecked shape_infer unittests added some new cases * added shape_infer unit-tests after StridedSliceNormalizer pass and Permute unit-tests * corrected unittests * Applied review comments * general permutations for inputs implemented, corrected ellipsis unrolling when shrink_axis is at the beginning, some other corrections * removed code duplication in infer and normalizer, moved 'slices' attr normalizing to StridedSliceNormalizer.py * removed some code duplication and other minor improvements * Added tests * minor corrections * wider range of unittests added (froze the number) * review comments applied * enabled skipped unit-test * comment corrections * applied review comments: changed op -> type, added some asserts, corrected comments and other minor corrections * sorted inputs, updated Supported_Frameworks_Layers.md, some minor
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