vis.py

#!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
import caffe

# Make sure that caffe is on the python path:
caffe_root = './'  # this file is expected to be in {caffe_root}/examples

caffe.set_mode_cpu()
net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',
		caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.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 RGB

# set net to batch size of 50
net.blobs['data'].reshape(50,3,227,227)

net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))
out = net.forward()
print("Predicted class is #{}.".format(out['prob'].argmax()))

plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))
plt.show()

# load labels
imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'
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]

# CPU mode
net.forward()  # call once for allocation

print [(k, v.data.shape) for k, v in net.blobs.items()]