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Caffe2 Quick Start Guide

You're reading from Caffe2 Quick Start Guide Modular and scalable deep learning made easy

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781789137750

Length 136 pages

Edition 1st Edition

Languages

C++

Tools

Caffe

Concepts

Deep Learning

Author (1):

Ashwin Nanjappa

View More author details

Table of Contents (9) Chapters

Preface

1. Introduction and Installation

2. Composing Networks FREE CHAPTER

3. Training Networks

4. Working with Caffe

5. Working with Other Frameworks

6. Deploying Models to Accelerators for Inference

7. Caffe2 at the Edge and in the cloud

8. Other Books You May Enjoy

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Visualizing the ONNX model

When working with ONNX models, it can be useful to have a tool that can help in visualizing the network structure. ONNX ships with such a script called net_drawer.py. You can find this tool in the onnx/onnx/tools directory in the ONNX source repository. If you installed ONNX from its Python package, then you can find this script at /usr/local/lib/python2.7/dist-packages/onnx/tools/net_drawer.py.

This script can be applied to convert an ONNX file to a directed acyclic graph representation of the network in the GraphViz DOT format. For example, consider the ONNX file alexnet.onnx that we obtained in the earlier section on converting from the Caffe2 model to the ONNX model.

We can convert this AlexNet ONNX file to a DOT file using the following command:

$ python /usr/local/lib/python2.7/dist-packages/onnx/tools/net_drawer.py --input alexnet.onnx --output...

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Authors (1)

Ashwin Nanjappa

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.

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