27c9ca749d
* [ONNX] Switched to ONNX 1.15.0 * Updated usage of ONNX serialization API * Updated xfailed lists * Updated xfailed lists * Added xfailed ML-operators * Added xfailed float8 cases * Fixing mo tests * Reverted legacy tests * Sets default opset version in layer tests * Skipped test which breaks moving on 1.15 * Updated xfailed list * Fixed typo and added more tests to skipped * Added xfailed tests * Reduced opset due to need to work on onnx 1.13 * Removed unnecessary xfails * Skipped misaligned tests * WA for old compiler build |
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|---|---|---|
| .. | ||
| automation | ||
| openvino | ||
| unit_tests | ||
| .coveragerc | ||
| .pylintdict | ||
| .pylintrc | ||
| CMakeLists.txt | ||
| README.md | ||
| requirements_caffe.txt | ||
| requirements_dev.txt | ||
| requirements_kaldi.txt | ||
| requirements_mxnet.txt | ||
| requirements_onnx.txt | ||
| requirements_tf.txt | ||
| requirements_tf2.txt | ||
| requirements.txt | ||
| setup.py | ||
Installation
Installing from PyPi
- Create a virtual environment and activate it, e.g.:
virtualenv -p /usr/bin/python3.7 .env3
source .env3/bin/activate
- Install openvino-dev package, it contains model conversion API:
pip install openvino-dev
This will download all requirements and will install MO in your current virtual environment. If you need only particular frameworks you can specify them manually as optional dependencies in square brackets. E.g. the command below will install dependencies to support ONNX* and TensorFlow2* models:
pip install openvino-dev[onnx,tensorflow2]
To enable support of MxNet* models run:
pip install openvino-dev[mxnet]
To enable support of all frameworks:
pip install openvino-dev[all]
By default, if no frameworks are specified, dependencies to support ONNX* and TensorFlow2* are installed.
Converting models
Setup development environment
If you want to contribute to model conversion API you will need to deploy developer environment. You can do that by following the steps below:
- Create virtual environment and activate it, e.g.:
virtualenv -p /usr/bin/python3.7 .env3
source .env3/bin/activate
- Clone the OpenVINO™ repository and change dir to model-optimizer
git clone https://github.com/openvinotoolkit/openvino
cd openvino/tools/mo/
- Install openvino-mo package for development:
pip install -e .
or run setup.py develop, result will be the same:
python setup.py develop
This will download all requirements and deploy model conversion API for development in your virtual environment: specifically will create *.egg-link into the current directory in your site-packages. As previously noted, you can also manually specify to support only selected frameworks :
pip install -e ".[onnx,tensorflow2]"
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 openvino/tools/mo/ mo.py