* 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>
This commit is contained in:
Evgeny Lazarev
GitHub
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## Project structure
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Project structure:
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<pre>
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|-- root
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|-- extensions
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|-- front/caffe
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|-- CustomLayersMapping.xml.example - example of file for registering custom Caffe layers in 2017R3 public
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manner
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|-- mo
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|-- back - Back-End logic: contains IR emitting logic
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|-- front - Front-End logic: contains matching between Framework-specific layers and IR specific, calculation
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of output shapes for each registered layer
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|-- graph - Graph utilities to work with internal IR representation
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|-- middle - Graph transformations - optimizations of the model
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|-- pipeline - Sequence of steps required to create IR for each framework
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|-- utils - Utility functions
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|-- tf_call_ie_layer - Sources for TensorFlow fallback in Inference Engine during model inference
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|-- mo.py - Centralized entry point that can be used for any supported framework
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|-- mo_caffe.py - Entry point particularly for Caffe
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|-- mo_mxnet.py - Entry point particularly for MXNet
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|-- mo_tf.py - Entry point particularly for TensorFlow
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|-- ModelOptimizer - Entry point particularly for Caffe that contains same CLI as 2017R3 publicly released
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Model Optimizer
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</pre>
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## Prerequisites
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Model Optimizer requires:
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1. Python 3 or newer
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2. [Optional] Please read about use cases that require Caffe available on the machine (:doc:`caffe_dependency`).
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Please follow the steps described (:doc:`caffe_build`).
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2. [Optional] Please read about use cases that require Caffe\* to be available on the machine in the documentation.
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## Installation instructions
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1. Go to the Model Optimizer folder:
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<pre>
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cd PATH_TO_INSTALL_DIR/deployment_tools/model_optimizer/model_optimizer_tensorflow
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cd PATH_TO_INSTALL_DIR/deployment_tools/model_optimizer
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</pre>
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2. Create virtual environment and activate it. This option is strongly recommended as it creates a Python sandbox and
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dependencies for Model Optimizer do not influence global Python configuration, installed libraries etc. At the same
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time, special flag ensures that system-wide Python libraries are also available in this sandbox. Skip this
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dependencies for the Model Optimizer do not influence global Python configuration, installed libraries etc. At the
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same time, special flag ensures that system-wide Python libraries are also available in this sandbox. Skip this
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step only if you do want to install all Model Optimizer dependencies globally:
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* Create environment:
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@@ -54,110 +27,28 @@ Model Optimizer requires:
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. .env3/bin/activate
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</pre>
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3. Install dependencies. If you want to convert models only from particular framework, you should use one of
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available <code>requirements_*.txt</code> files corresponding to the framework of choice. For example, for Caffe use
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<code>requirements_caffe.txt</code> and so on. When you decide to switch later to other frameworks, please install dependencies
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for them using the same mechanism:
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available <code>requirements_\*.txt</code> files corresponding to the framework of choice. For example, for Caffe
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use <code>requirements_caffe.txt</code> and so on. When you decide to switch later to other frameworks, please
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install dependencies for them using the same mechanism:
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<pre>
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pip3 install -r requirements.txt
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</pre>
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pip3 install -r requirements.txt
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</pre>
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Or you can use the installation scripts from the "install_prerequisites" directory.
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4. [OPTIONAL] If you use Windows OS, most probably you get python version of `protobuf` library. It is known to be rather slow,
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and you can use a boosted version of library by building the .egg file (Python package format) yourself,
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using instructions below (section 'How to boost Caffe model loading') for the target OS and Python, or install it
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with the pre-built .egg (it is built for Python 3.4, 3.5, 3.6, 3.7):
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<pre>
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<pre>
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python3 -m easy_install protobuf-3.6.1-py3.6-win-amd64.egg
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</pre>
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</pre>
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It overrides the protobuf python package installed by the previous command.
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Set environment variable to enable boost in protobuf performance:
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<pre>
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<pre>
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set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp
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</pre>
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## Command-Line Interface (CLI)
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The following short examples are framework-dependent. Please read the complete help
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with --help option for details across all frameworks:
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<pre>
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python3 mo.py --help
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</pre>
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There are several scripts that convert a model:
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1. <code>mo.py</code> -- universal entry point that can convert a model from any supported framework
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2. <code>mo_caffe.py</code> -- dedicated script for Caffe models conversion
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3. <code>mo_mxnet.py</code> -- dedicated script for MXNet models conversion
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4. <code>mo_tf.py</code> -- dedicated script for TensorFlow models conversion
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5. <code>mo_onnx.py</code> -- dedicated script for ONNX models conversion
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6. <code>mo_kaldi.py</code> -- dedicated script for Kaldi models conversion
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<code>mo.py</code> can deduce original framework where input model was trained by an extension of
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the model file. Or <code>--framework</code> option can be used for this purpose if model files
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don't have standard extensions (<code>.pb</code> - for TensorFlow models, <code>.params</code> - for MXNet models,
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<code>.caffemodel</code> - for Caffe models). So, the following commands are equivalent::
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<pre>
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python3 mo.py --input_model /user/models/model.pb
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python3 mo.py --framework tf --input_model /user/models/model.pb
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</pre>
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The following examples illustrate the shortest command lines to convert a model per
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framework.
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### Convert TensorFlow model
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To convert a frozen TensorFlow model contained in binary file <code>model-file.pb</code>, run
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dedicated entry point <code>mo_tf.py</code>:
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python3 mo_tf.py --input_model model-file.pb
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### Convert Caffe model
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To convert a Caffe model contained in <code>model-file.prototxt</code> and <code>model-file.caffemodel</code> run
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dedicated entry point <code>mo_caffe.py</code>:
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<pre>
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python3 mo_caffe.py --input_model model-file.caffemodel
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</pre>
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### Convert MXNet model
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To Convert an MXNet model in <code>model-file-symbol.json</code> and <code>model-file-0000.params</code> run
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dedicated entry point <code>mo_mxnet.py</code>:
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<pre>
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python3 mo_mxnet.py --input_model model-file
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</pre>
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> **NOTE**: for TensorFlow* all Placeholder ops are represented as Input layers in the final IR.
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### Convert ONNX* model
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The Model Optimizer assumes that you have an ONNX model that was directly downloaded from a public repository or converted from any framework that supports exporting to the ONNX format.
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Use the mo_onnx.py script to simply convert a model with the path to the input model .onnx file:
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<pre>
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python3 mo_onnx.py --input_model model-file.onnx
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</pre>
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Input channels re-ordering, scaling, subtraction of mean values and other preprocessing features
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are not applied by default. To pass necessary values to Model Optimizer, please run <code>mo.py</code>
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(or <code>mo_tf.py</code>, <code>mo_caffe.py</code>, <code>mo_mxnet.py</code>) with <code>--help</code> and
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examine all available options.
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## Working with Inference Engine
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To the moment, Inference Engine is the only consumer of IR models that Model Optimizer produces.
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The whole workflow and more documentation on the structure of IR are documented in the Developer Guide
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of Inference Engine. Note that sections about running Model Optimizer refer to the old version
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of the tool and can not be applied to the current version of Model Optimizer.
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</pre>
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## Setup development environment
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@@ -185,14 +76,6 @@ of the tool and can not be applied to the current version of Model Optimizer.
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1. Run the following command:
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<pre>
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pylint mo/ mo.py
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pylint mo/ extensions/ mo.py
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</pre>
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### How to check requirements dependencies
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1. Run the following command:
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<pre>
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cat requirements_file | docker run -i --rm pyupio/safety safety check --stdin
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</pre>
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> **NOTE**: here <code>requirements_file</code> is one of the following: <code>requirements.txt</code>, <code>requirements_caffe.txt</code>, <code>requirements_tf.txt</code>, <code>requirements_tf2.txt</code>, <code>requirements_mxnet.txt</code>, <code>requirements_dev.txt</code>.
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