test.py

import caffe
import numpy as np

transformer = caffe.io.Transformer({'data':net.blobs['data'].data.shape})

transformer.set_transpose('data', (2,0,1))
transformer.set_mean('data', mu)
transformer.set_raw_scale('data', 255)
transformer.set_channel_swap('data', (2,1,0))
	
PROTOTXT = 'test.pt'
CAFFEMODEL = 'alexnet_iter_10000.caffemodel'

caffe.set_mode_gpu()
caffe.set_device(0)

net = caffe.Net(PROTOTXT, CAFFEMODEL, caffe.TEST)

mu = np.load('mean.npy')
mu = mu.mean(1).mean(1)

while True:
	line = raw_input()
	
	if line == -1:
		break
	
	filename, y = line.split(' ')
	
	image = caffe.io.load_image(filename)
	transformed_image = transformer.preprocess('data', image)

	net.blobs['data'].data[...] = transformed_image
	net.forward()

	output = net.blobs['prob'].data[0, :].copy()
	predict_index = output.argmax()
	score = output.max()

	print filename, y, predict_index, score