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.--disable_weights_compression
Model Optimizer command-line parameter to get an expanded version.Erf
operation into the GeLU
operation.Split
and Concat
operations to a single Interpolate
operation.--keep_shape_ops
.version="opset1"
: MVN
, ROIPooling
, ReorgYolo
. They became a part of new opset2
operation set and generated with version="opset2"
. Before this fix, the operations were generated with version="opset1"
by mistake, they were not a part of opset1
nGraph namespace; opset1
specification was fixed accordingly.MeanVarianceNormalization
if normalization is performed over spatial dimensions.BatchToSpaceND
, SpaceToBatchND
, Floor
.Reshape
with input shape values equal to -2, -3, and -4.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.
Typical Next Step: Introduction to Intel® Deep Learning Deployment Toolkit