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You're reading from  Caffe2 Quick Start Guide

Product typeBook
Published inMay 2019
Reading LevelBeginner
PublisherPackt
ISBN-139781789137750
Edition1st Edition
Languages
C++
Tools
Caffe
Concepts
Deep Learning
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Author (1)
Ashwin Nanjappa
Ashwin Nanjappa
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Ashwin Nanjappa

Ashwin Nanjappa is a senior architect at NVIDIA, working in the TensorRT team on improving deep learning inference on GPU accelerators. He has a PhD from the National University of Singapore in developing GPU algorithms for the fundamental computational geometry problem of 3D Delaunay triangulation. As a post-doctoral research fellow at the BioInformatics Institute (Singapore), he developed GPU-accelerated machine learning algorithms for pose estimation using depth cameras. As an algorithms research engineer at Visenze (Singapore), he implemented computer vision algorithm pipelines in C++, developed a training framework built upon Caffe in Python, and trained deep learning models for some of the world's most popular online shopping portals.
Read more about Ashwin Nanjappa

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Exporting the Caffe2 model to ONNX

Caffe2 models can be easily exported to ONNX format using Python. This enables a vast number of other DL frameworks to use our Caffe2 models for training and inference. The frontend module provided by Caffe2-ONNX does all of the heavy lifting of the exporting. This module is located as the python/onnx/frontend.py file in the Caffe2 source code.

The ch5/export_to_onnx.py script provided along with this book's source code shows how to export an existing Caffe2 model to ONNX format. As an example, consider converting the Caffe2 model of AlexNet that we created in Chapter 4, Working with Caffe. We exported the operators and the weights of this network in Caffe2 to the files predict_net.pb and init_net.pb files respectively.

We can invoke the ONNX conversion script, as follows, to convert this Caffe2 model to an ONNX file named alexnet.onnx:

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Caffe2 Quick Start Guide
Published in: May 2019Publisher: PacktISBN-13: 9781789137750
© 2019 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Ashwin Nanjappa

Ashwin Nanjappa is a senior architect at NVIDIA, working in the TensorRT team on improving deep learning inference on GPU accelerators. He has a PhD from the National University of Singapore in developing GPU algorithms for the fundamental computational geometry problem of 3D Delaunay triangulation. As a post-doctoral research fellow at the BioInformatics Institute (Singapore), he developed GPU-accelerated machine learning algorithms for pose estimation using depth cameras. As an algorithms research engineer at Visenze (Singapore), he implemented computer vision algorithm pipelines in C++, developed a training framework built upon Caffe in Python, and trained deep learning models for some of the world's most popular online shopping portals.
Read more about Ashwin Nanjappa

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