evaluate.py 文件源码

def classify(net, in_im, net_name, im_list, gt_labels):
    config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
    imgs = open(im_list).readlines()
    gt_labels = open(gt_labels).readlines()
    fool_rate = 0
    top_1 = 0
    with tf.Session(config=config) as sess:
        sess.run(tf.global_variables_initializer())
        for i,name in enumerate(imgs):
            if net_name == 'caffenet':
                im = img_preprocess(name.strip(), size=227)
            else:
                im = img_preprocess(name.strip())
            softmax_scores = sess.run(net['prob'], feed_dict={in_im: im})
            if i!=0 and i%1000 == 0:
                print 'iter: {:5d}\ttop-1: {:04.2f}\tfooling-rate: {:04.2f}'.format(i, (top_1/float(i))*100, (fool_rate)/float(i)*100)
            if np.argmax(softmax_scores[0]) == int(gt_labels[i].strip()):
                top_1 += 1
            if np.argmax(softmax_scores[0]) != np.argmax(softmax_scores[1]):
                fool_rate += 1
    print 'Top-1 Accuracy = {:.2f}'.format(top_1/500.0)
    print 'Fooling Rate = {:.2f}'.format(fool_rate/500.0)