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* Moved and merged mo/ and extensions/ into openvino/tools/mo
* edited imports
* edited docs to use mo script from entry_point
* edited MO transformations list loading and setup.py
* changed full path -> 'mo' entry point in docs (leftovers)
* corrected package_BOM
* updated resolving --transformation_config in cli_parser.py
* pkgutil-style __init__.py, added summarize_graph into entry points
* updated DOCs for the new --transformations_config
* fix select
* updated install instructions, fixed setup.py for windows and python_version < 3.8
* fixed typo in requirements.txt
* resolved conflicts
* removed creating custom __init__.py from setup.py
* corrected folder with caffe proto
* corrected loading user defined extensions
* fix openvino.tools.mo import in serialize.py
* corrected layer tests for new namespace
* fix in get_testdata.py
* moved model-optimizer into tools/
* renamed import in POT
* corrected mo.yml
* correct CMakeLists.txt for the newest tools/mo
* corrected find_ie_version.py
* docs and openvino-dev setup.py update for the newest tools/mo
* miscellaneous leftovers and fixes
* corrected CI files, pybind11_add_module in CMakeLists.txt and use of tools/mo path instead of tools/model_optimizer
* add_subdirectory pybind11 for tools/mo
* POT path fix
* updated setupvars.sh setupvars.bat
* Revert "updated setupvars.sh setupvars.bat"
This reverts commit c011142340.
* removed model-optimizer env variables from setupvars
* updated CMakeLists.txt to pack MO properly with tests component
* corrected left imports, corrected loading requirements for layer tests
* mo doc typo correction
* minor corrections in docs; removed summarize_graph from entry_points
* get_started_windows.md, MonoDepth_how_to.md corrections, mo path corrections
2.8 KiB
Model Optimizer Developer Guide
Introduction
Model Optimizer is a cross-platform command-line tool that facilitates the transition between the training and deployment environment, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices.
Model Optimizer process assumes you have a network model trained using supported deep learning frameworks: Caffe*, TensorFlow*, Kaldi*, MXNet* or converted to the ONNX* format. Model Optimizer produces an Intermediate Representation (IR) of the network, which can be inferred with the Inference Engine.
Note
: Model Optimizer does not infer models. Model Optimizer is an offline tool that runs before the inference takes place.
The scheme below illustrates the typical workflow for deploying a trained deep learning model:
The IR is a pair of files describing the model:
-
.xml- Describes the network topology -
.bin- Contains the weights and biases binary data.
Below is a simple command running Model Optimizer to generate an IR for the input model:
mo --input_model INPUT_MODEL
To learn about all Model Optimizer parameters and conversion technics, see the Converting a Model to IR page.
Tip
: You can quick start with the Model Optimizer inside the OpenVINO™ [Deep Learning Workbench](@ref openvino_docs_get_started_get_started_dl_workbench) (DL Workbench). [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is the OpenVINO™ toolkit UI that enables you to import a model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for deployment on various Intel® platforms.
Videos
| Model Optimizer Concept. Duration: 3:56 |
Model Optimizer Basic Operation. Duration: 2:57. |
Choosing the Right Precision. Duration: 4:18. |