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Publishing 2019 R3 content

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Alexey Suhov
2019-10-04 19:26:43 +03:00
parent ba6e22b1b5
commit 0923303e02
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model-optimizer/README.md
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@@ -4,17 +4,15 @@ Project structure:
<pre>
|-- root
|-- extensions
|-- front/ - graph transformations during front phase
|-- middle/ - graph transformations during middle phase (after partial inference)
|-- end/ - graph transformations during back phase (before IR generation)
|-- ops/ - Model Optimizer operation classes
|-- front/caffe
|-- CustomLayersMapping.xml.example - example of file for registering custom Caffe layers in 2017R3 public
manner
|-- mo
|-- back - Back-End logic: contains IR emitting logic
|-- front - Front-End logic: contains matching between Framework-specific layers and IR specific,
calculation of output shapes for each registered layer
|-- front - Front-End logic: contains matching between Framework-specific layers and IR specific, calculation
of output shapes for each registered layer
|-- graph - Graph utilities to work with internal IR representation
|-- middle - Graph transformations - optimizations of the model
|-- ops - Model Optimizer operation classes
|-- pipeline - Sequence of steps required to create IR for each framework
|-- utils - Utility functions
|-- tf_call_ie_layer - Sources for TensorFlow fallback in Inference Engine during model inference
@@ -22,18 +20,25 @@ Project structure:
|-- mo_caffe.py - Entry point particularly for Caffe
|-- mo_mxnet.py - Entry point particularly for MXNet
|-- mo_tf.py - Entry point particularly for TensorFlow
|-- ModelOptimizer - Entry point particularly for Caffe that contains same CLI as 2017R3 publicly released
Model Optimizer
</pre>
## Prerequisites
Model Optimizer requires:
1. Python 3.4 or newer
1. Python 3 or newer
2. [Optional] Please read about use cases that require Caffe available on the machine (:doc:`caffe_dependency`).
Please follow the steps described (:doc:`caffe_build`).
## Installation instructions
1. Go to the Model Optimizer folder
1. Go to the Model Optimizer folder:
<pre>
cd PATH_TO_INSTALL_DIR/deployment_tools/model_optimizer/model_optimizer_tensorflow
</pre>
2. Create virtual environment and activate it. This option is strongly recommended as it creates a Python sandbox and
dependencies for Model Optimizer do not influence global Python configuration, installed libraries etc. At the same
@@ -41,9 +46,13 @@ Model Optimizer requires:
step only if you do want to install all Model Optimizer dependencies globally:
* Create environment:
<pre>virtualenv -p /usr/bin/python3.6 .env3 --system-site-packages</pre>
<pre>
virtualenv -p /usr/bin/python3.6 .env3 --system-site-packages
</pre>
* Activate it:
<pre>. .env3/bin/activate</pre>
<pre>
. .env3/bin/activate
</pre>
3. Install dependencies. If you want to convert models only from particular framework, you should use one of
available <code>requirements_*.txt</code> files corresponding to the framework of choice. For example, for Caffe use
<code>requirements_caffe.txt</code> and so on. When you decide to switch later to other frameworks, please install dependencies