caffe訓練後模型測試
阿新 • • 發佈:2018-11-11
# coding:utf-8 import sys import numpy as np sys.path.append('/home/hadoop/caffe/python') import caffe WEIGHTS_FILE = '/home/hadoop/桌面/eye_data3/eyes_lmdb/snapshot/solver_iter_20000.caffemodel' DEPLOY_FILE = '/home/hadoop/桌面/eye_data3/eyes_lmdb/deploy.prototxt' IMAGE_SIZE = (32, 32) caffe.set_mode_gpu() net = caffe.Net(DEPLOY_FILE, WEIGHTS_FILE, caffe.TEST) net.blobs['data'].reshape(1, 3, *IMAGE_SIZE) transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape}) transformer.set_transpose('data', (2,0,1)) mu = np.load("/home/hadoop/桌面/eye_data3/eyes_lmdb/mean.npy") mu = mu.mean(1).mean(1) transformer.set_mean('data', mu) transformer.set_raw_scale('data', 255) # transformer.set_raw_scale('data', 1) transformer.set_channel_swap('data', (2, 1, 0)) # swap channels from RGB to BGR image_list = "/home/hadoop/桌面/10.13眼睛閉合度/eye1_txt_connect.txt" img_path = "/home/hadoop/桌面/10.13眼睛閉合度/eyes1/" with open(image_list, 'r') as f: for line in f.readlines(): filename = line.split(" ")[0] # print img_path+filename image = caffe.io.load_image(img_path+filename) # print image.shape transformed_image = transformer.preprocess('data', image) net.blobs['data'].data[...] = transformed_image output = net.forward() score = output['fc'].argmax() # score = output['pred'] print('The predicted score for {} is {}'.format(filename, score))