googlenet.py

import numpy as np
import matplotlib.pyplot as plt

# Make sure that caffe is on the python path:
caffe_root = '/home/joel/code/caffe/'  # this file is expected to be in {caffe_root}/examples
import sys
sys.path.insert(0, caffe_root + 'python')

import caffe

plt.rcParams['figure.figsize'] = (10, 10)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'

# GPU mode
caffe.set_device(0)
caffe.set_mode_gpu()
net = caffe.Net(caffe_root + 'models/bvlc_googlenet/deploy.prototxt',
                caffe_root + 'models/bvlc_googlenet/bvlc_googlenet.caffemodel',
                caffe.TEST)

# input preprocessing: 'data' is the name of the input blob == net.inputs[0]
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))
transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel
transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]
transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGBi


# set net to batch size of 50
net.blobs['data'].reshape(10,3,224,224)

#net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))
net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image('/home/joel/mushroom.png'))

out = net.forward()
print("Predicted class is #{}.".format(out['prob'].argmax()))

# load labels
imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'
try:
    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\t')
except:
    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\t')

# sort top k predictions from softmax output
top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]
print labels[top_k]