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* Release mo dev guide refactoring (#3266) * Updated MO extension guide * Minor change and adding svg images * Added additional information about operation extractors. Fixed links and markdown issues * Added missing file with information about Caffe Python layers and image for MO transformations dependencies graph * Added section with common graph transformations attributes and diagram with anchor transformations. Added list of available front phase transformations * Added description of front-phase transformations except the scope-defined and points defined. Removed legacy document and examples for such transformations. * Added sections about node name pattern defined front phase transformations. Copy-pasted the old one for the points defined front transformation * Added description of the rest of front transformations and and all middle and back phase transformations * Refactored Legacy_Mode_for_Caffe_Custom_Layers and updated the Customize_Model_Optimizer with information about extractors order * Added TOC for the MO Dev guide document and updated SVG images with PNG ones * Fixed broken link. Removed redundant image * Fixed broken links * Added information about attributes 'run_not_recursively', 'force_clean_up' and 'force_shape_inference' of the transformation * Code review comments * Added a section about `Port`s * Extended Ports description with examples * Added information about Connections * Updated MO README.md and removed a lot of redundant and misleading information * Updates to the Customize_Model_Optimizer.md * More updates to the Customize_Model_Optimizer.md * Final updates for the Customize_Model_Optimizer.md * Fixed some broken links * More fixed links * Refactored Custom Layers Guide: removed legacy and incorrect text, added up-to-date. * Draft implementation of the Custom layer guide example for the MO part * Fixed broken links using #. Change layer->operation in extensibility documents * Updated Custom operation guide with IE part * Fixed broken links and minor updates to the Custom Operations Guide * Updating links * Layer->Operation * Moved FFTOp implementation to the template extension * Update the CMake for template_extension to build the FFT op conditionally * Fixed template extension compilation * Fixed CMake for template extension * Fixed broken snippet * Added mri_demo script and updated documentation * One more compilation error fix * Added missing header for a demo file * Added reference to OpenCV * Fixed unit test for the template extension * Fixed typos in the template extension * Fixed compilation of template extension for case when ONNX importer is disabled Co-authored-by: Alexander Zhogov <alexander.zhogov@intel.com>
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Converting a Model to Intermediate Representation (IR)
Use the mo.py script from the <INSTALL_DIR>/deployment_tools/model_optimizer directory to run the Model Optimizer and convert the model to the Intermediate Representation (IR).
The simplest way to convert a model is to run mo.py with a path to the input model file:
python3 mo.py --input_model INPUT_MODEL
Note
: Some models require using additional arguments to specify conversion parameters, such as
--scale,--scale_values,--mean_values,--mean_file. To learn about when you need to use these parameters, refer to Converting a Model Using General Conversion Parameters.
The mo.py script is the universal entry point that can deduce the framework that has produced the input model by a standard extension of the model file:
.caffemodel- Caffe* models.pb- TensorFlow* models.params- MXNet* models.onnx- ONNX* models.nnet- Kaldi* models.
If the model files do not have standard extensions, you can use the --framework {tf,caffe,kaldi,onnx,mxnet} option to specify the framework type explicitly.
For example, the following commands are equivalent:
python3 mo.py --input_model /user/models/model.pb
python3 mo.py --framework tf --input_model /user/models/model.pb
To adjust the conversion process, you may use general parameters defined in the Converting a Model Using General Conversion Parameters and Framework-specific parameters for:
- Caffe,
- TensorFlow,
- MXNet,
- ONNX,
- Kaldi.