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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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Converting a Caffe model to Caffe2

To be able to use a Caffe model in Caffe2, we need to convert it from its Caffe formats to Caffe2 file formats. Caffe2 provides a script named python/caffe_translator.py that can be used for this purpose.

For example, we can convert our AlexNet files from Caffe to Caffe2 by invoking the script as follows:

$ python python/caffe_translator.py path_to_caffe/models/bvlc_alexnet/deploy.prototxt path_to_caffe/models/bvlc_alexnet/bvlc_alexnet.caffemodel --init_net init_net.pb --predict_net predict_net.pb

Running this script generates three files, predict_net.pb, predict_net.pbtxt, and init_net.pb, for AlexNet:

Figure 4.3: AlexNet network structure in Caffe2

Figure 4.3 shows the AlexNet network structure in Caffe2 after it was converted from the Caffe model. This graph visualization was generated using the Caffe2 net_drawer.py tool that utilizes GraphViz...

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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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