# Introduction to caffe

Date 7 novembre 2016 Catégories Developpement par VulgaireDev Edit on Github

# Introduction

One the projects I am currently working on is about machine learning, more precisely about neural network. The aim is to train a neural network to detect faces on an image. To do so, we used caffe, a well-known framework for Convolutional Neural Network (CNN). In short, a CNN is a neural network processing on images. The main problem with caffe is its documentation, so I'd like to explain how to use it, as simply as I can.

# Tutorial

## Install caffe

The installation of caffe can be quite difficult. Some people use a Virtual Machine, I prefer using docker: it is lighter, and I had some problems with VM and caffe. Of course, you need docker installed https://docs.docker.com/engine/installation/

git clone https://github.com/BVLC/caffe.git


Then, we build the docker image (it will take some time):

cd caffe/docker
docker build -t caffe:cpu standalone/cpu


This step installed all needed dependencies to make caffe work.

Now, we will launch the docker container, with a shell access. Moreover, we mount a volume (/datas into the docker container) of datas from our computer to be able to access it from docker.

docker run -ti -v /path/to/folder/conf_and_images_for_caffe/:/datas caffe:cpu bash


Important: When you are in the docker container, caffe is installed in /opt/caffe/

## Convert your training images to the right format

To create a CNN, you need to give it datas so that it can learn. You need to have many images, divided in two categories here: faces and non-faces. Caffe needs a specific format to be trained : LMDB (default) or LevelDB. To convert you images to LMDB, you can use a tool included in caffe :

cd /datas
/opt/caffe/build/tools/convert_imageset --shuffle --gray train_images/ posneg.txt train_lmdb


train_images is the folder when you have your training images. posneg.txt is a file with a line for each image, containing the path to the image relative to train_images, and the corresponding class (face or non-face), it looks like this :

Image000605.pgm 1
Image000607.pgm 1
Image000608.pgm 0


Finally, train_lmdb is the output, used in the next steps to train the CNN.

## Train the CNN

To train the CNN, you will need to specify two files :

1. a config file : number of iterations, the learning rate ...
2. a file describing the architecture of the CNN (number of layers etc).

Take a look here. The solver.prototxt is the config file, and the train_val.prototxt is the description of the architecture of the CNN. As you can see, the solver.prototxt contains the train_val.prototxt.

Last thing before training your CNN, you need to specify the path to your train_lmdb (that you generated with convert_imageset) in the train_val.protoxt. Open it, find the TRAIN layer, and replace the source field, in data_param, by the path to your train_lmdb.

To train the CNN, you will do :

caffe train -solver solver.prototxt


This will generate two files. A .caffemodel, ready for deployment, and a .solverstate, used to keep doing modifications to the CNN later. To deploy, I recommand making a deploy.protoxt file, like here. It is the same as train_val.prototxt, without a data layer instead of TRAIN and TEST layers, and an ending layer specifying that we want a classification as the output:

layer {
name: "prob"
type: "Softmax"
bottom: "fc8"
top: "prob"
}


The bottom field is the name of the last layer of your CNN.

## Deploy to production with python

Now that we have a working CNN, we want to use it with pycaffe, the python interface for caffe. It is not very well documented, but it works.

The basic usage you have of a CNN is to use it with an image to determine the class of this image, that's what we are going to do. Create a cnn.py file into the volume you share with docker, and add a code like this :


import caffe
import numpy as np
from PIL import Image

#we choose the mode (cpu or gpu)
caffe.set_mode_cpu()

#we use our CNN. the first argument is the architecture of our CNN, the second one are the weights
net = caffe.Net('/datas/deploy.prototxt', '/datas/facenet_iter_200000.caffemodel', caffe.TEST)

#we create a transformer that resize the shape of our datas to give it to the CNN
#here my image is read as width*height*colorchannel (1 here because I work in black and white)
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))  # (36,36,1) -> (1,36,36), seen as (1,1,36,36) to the network

#we load our image, and we preprocess it
image_transformed = transformer.preprocess('data', im)

#we give it as the data to our CNN
net.blobs['data'].data[...] = image_transformed

#we make the CNN work
out = net.forward()

#we get the class
print out['prob']

# result [[ 0.01239852  0.98760146]] -> the more likely class is the second one (face), so this image is a face.

#we have to change the train_val.prototxt to deploy.prototxt. Then, we remove the train and test label, and put this one
#layer {
#  name: "data"
#  type: "Input"
# top: "data"
# top: "label"
# input_param { shape: {dim: 1 dim: 1 dim: 36 dim: 36 } }
#}
# and at the end we specify that we want a classification as the output
# layer {
#  name: "prob"
#  type: "Softmax"
#  bottom: "fc8"
#  top: "prob"
#}


I think it is commented enough to understand.

I hope this tutorial helped!