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.

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

  • Common changes:
    • Implemented several optimization transformations to replace sub-graphs of operations with HSwish, Mish, Swish and SoftPlus operations.
    • Model Optimizer generates IR keeping shape-calculating sub-graphs by default. Previously, this behavior was triggered if the "--keep_shape_ops" command line parameter was provided. The key is ignored in this release and will be deleted in the next release. To trigger the legacy behavior to generate an IR for a fixed input shape (folding ShapeOf operations and shape-calculating sub-graphs to Constant), use the "--static_shape" command line parameter. Changing model input shape using the Inference Engine API in runtime may fail for such an IR.
    • Fixed Model Optimizer conversion issues resulted in non-reshapeable IR using the Inference Engine reshape API.
    • Enabled transformations to fix non-reshapeable patterns in the original networks:
      • Hardcoded Reshape
        • In Reshape(2D)->MatMul pattern
        • Reshape->Transpose->Reshape when the pattern can be fused to the ShuffleChannels or DepthToSpace operation
      • Hardcoded Interpolate
        • In Interpolate->Concat pattern
      • Added a dedicated requirements file for TensorFlow 2.X as well as the dedicated install prerequisites scripts.
      • Replaced the SparseToDense operation with ScatterNDUpdate-4.
  • ONNX*:
    • Enabled an ability to specify the model output tensor name using the "--output" command line parameter.
    • Added support for the following operations:
      • Acosh
      • Asinh
      • Atanh
      • DepthToSpace-11, 13
      • DequantizeLinear-10 (zero_point must be constant)
      • HardSigmoid-1,6
      • QuantizeLinear-10 (zero_point must be constant)
      • ReduceL1-11, 13
      • ReduceL2-11, 13
      • Resize-11, 13 (except mode="nearest" with 5D+ input, mode="tf_crop_and_resize", and attributes exclude_outside and extrapolation_value with non-zero values)
      • ScatterND-11, 13
      • SpaceToDepth-11, 13
  • TensorFlow*:
    • Added support for the following operations:
      • Acosh
      • Asinh
      • Atanh
      • CTCLoss
      • EuclideanNorm
      • ExtractImagePatches
      • FloorDiv
  • MXNet*:
    • Added support for the following operations:
      • Acosh
      • Asinh
      • Atanh
  • Kaldi*:
    • Fixed bug with ParallelComponent support. Now it is fully supported with no restrictions.

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

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