Book Image

Caffe2 Quick Start Guide

By : Ashwin Nanjappa
Book Image

Caffe2 Quick Start Guide

By: Ashwin Nanjappa

Overview of this book

Caffe2 is a popular deep learning library used for fast and scalable training, and inference of deep learning models on different platforms. This book introduces you to the Caffe2 framework and demonstrates how you can leverage its power to build, train, and deploy efficient neural network models at scale. The Caffe 2 Quick Start Guide will help you in installing Caffe2, composing networks using its operators, training models, and deploying models to different architectures. The book will also guide you on how to import models from Caffe and other frameworks using the ONNX interchange format. You will then cover deep learning accelerators such as CPU and GPU and learn how to deploy Caffe2 models for inference on accelerators using inference engines. Finally, you'll understand how to deploy Caffe2 to a diverse set of hardware, using containers on the cloud and resource-constrained hardware such as Raspberry Pi. By the end of this book, you will not only be able to compose and train popular neural network models with Caffe2, but also deploy them on accelerators, to the cloud and on resource-constrained platforms such as mobile and embedded hardware.
Table of Contents (9 chapters)

Using the ONNX model in Caffe2

In the previous section, we converted a Caffe2 model to ONNX format so that it could be used with other DL frameworks. In this section, we will learn how to use an ONNX model exported from other DL frameworks into Caffe2 for inference.

The backend module provided in the Caffe2 ONNX package enables this import of the ONNX model to Caffe2. This can be seen in the backend.py file in the python/onnx directory in the Caffe2 source code.

The ch5/run_onnx_model.py script provided along with this book's source code demonstrates how to load an ONNX model to Caffe2, and run an inference on an input image using that model.

The script first imports the Python modules necessary to work with the images (PIL.Image), Caffe2, and ONNX (caffe2.python.onnx.backend) as follows:

# Std
import PIL.Image
import json
import sys

# Ext
import numpy as np
from caffe2...