8349849e1c
* Use FindPython3.cmake
* Fixed compilation on macOS 14 with new core development tools
* Try to use Python3_SOABI instead of PYTHON_MODULE_EXTENSION
* Use Development.Module
* Keep specifying only Python3_EXECUTABLE
* Print PYTHON_MODULE_EXTENSION
* Added check for minimal cmake version for python API
* Returned Python3_INCLUDE_DIR for cross-compilation case
* Try to allow cmake older than 3.18
* Use build python interpreter to check cython dependency
* revert changes in .ci/openvino-onnx/Dockerfile
* removed unused code
* Fixed issue with variables scope
* Experiment: remove include dirs
* Corrected docs
* Use pybind11 function to set extension
* Revert "Experiment: remove include dirs"
This reverts commit 6f7f90211c.
* Refactor ConvolutionBackpropDataLayerTest, ConvolutionLayerTest, DeformableConvolutionLayerTest (#19810)
* Refactor ConvolutionBackpropDataLayerTest
* Refactor ConvolutionLayerTest
* Refactor DeformableConvolutionLayerTest
* Apply comments
* Apply comments
* Fix
* Updated minimum cmake version for Windows
* Simplified check
* Removed useless message status
* Use puiblic option
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Co-authored-by: Oleg Pipikin <oleg.pipikin@intel.com>
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