openvino/docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md

Model Optimizer Developer Guide

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 a supported deep learning framework. The scheme below illustrates the typical workflow for deploying a trained deep learning model:

Model Optimizer produces an Intermediate Representation (IR) of the network, which can be read, loaded, and inferred with the Inference Engine. The Inference Engine API offers a unified API across a number of supported Intel® platforms. The Intermediate Representation is a pair of files describing the model:

• .xml - Describes the network topology

• .bin - Contains the weights and biases binary data.

TIP: You also can work with the Model Optimizer inside the OpenVINO™ [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench). [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is a platform built upon OpenVINO™ and provides a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare performance of deep learning models on various Intel® architecture configurations. In the DL Workbench, you can use most of OpenVINO™ toolkit components.
Proceed to an [easy installation from Docker](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub) to get started.

What's New in the Model Optimizer in this Release?

• Common changes:
  • Updated requirements for the numpy component to avoid compatibility issues with TensorFlow 1.x.
  • Improved reshape-ability of models with eltwise and CTCGreedyDecoder operations
• ONNX*:
  • Added support for the following operations:
    • Loop-11, 13
    • Round-11
    • GatherND-11, 12, 13
• TensorFlow*:
  • Added support for the TensorFlow Object Detection API models with pre-processing block when mean/scale values are applied prior to resizing of the image. Previously only the case when mean/scale values are applied after the resize was supported.
  • Aligned FakeQuantized limits adjustment with TensorFlow approach
  • Added support for the following operations:
    • GatherND
    • Round
    • NonMaxSuppression
    • LogSoftmax
    • FakeQuantWithMinMaxVarsPerChannel
• MXNet*:
  • Added support for the following operations:
    • GatherND
    • Round
• Kaldi*:
  • Added support for the following operations:
    • TdnnComponent

NOTE: Intel® System Studio is an all-in-one, cross-platform tool suite, purpose-built to simplify system bring-up and improve system and IoT device application performance on Intel® platforms. If you are using the Intel® Distribution of OpenVINO™ with Intel® System Studio, go to Get Started with Intel® System Studio.

Table of Content

• Preparing and Optimizing your Trained Model with Model Optimizer

  • Configuring Model Optimizer
  • Converting a Model to Intermediate Representation (IR)
    • Converting a Model Using General Conversion Parameters
    • Converting Your Caffe* Model
    • Converting Your TensorFlow* Model
      • Converting BERT from TensorFlow
      • Converting GNMT from TensorFlow
      • Converting YOLO from DarkNet to TensorFlow and then to IR
      • Converting Wide and Deep Models from TensorFlow
      • Converting FaceNet from TensorFlow
      • Converting DeepSpeech from TensorFlow
      • Converting Language Model on One Billion Word Benchmark from TensorFlow
      • Converting Neural Collaborative Filtering Model from TensorFlow*
      • Converting TensorFlow* Object Detection API Models
      • Converting TensorFlow*-Slim Image Classification Model Library Models
      • Converting CRNN Model from TensorFlow*
    • Converting Your MXNet* Model
      • Converting a Style Transfer Model from MXNet
    • Converting Your Kaldi* Model
    • Converting Your ONNX* Model
      • Converting Mask-RCNN ONNX* Model
      • Converting DLRM ONNX* Model
    • Model Optimizations Techniques
    • Cutting parts of the model
    • Sub-graph Replacement in Model Optimizer
    • Supported Framework Layers
    • Intermediate Representation and Operation Sets
    • Operations Specification
    • Intermediate Representation suitable for INT8 inference
  • Model Optimizer Extensibility
    • Extending Model Optimizer with New Primitives
    • Extending Model Optimizer with Caffe Python Layers
    • Extending Model Optimizer with Custom MXNet* Operations
    • Legacy Mode for Caffe* Custom Layers
  • Model Optimizer Frequently Asked Questions

• Known Issues

Typical Next Step: Preparing and Optimizing your Trained Model with Model Optimizer