test_mycnn.py 文件源码

def main(_):
    test_img_list = load_data(dir_test)
    mean_var = np.load('log/log_mycnn/mean_var_out.npz')

    x1 = tf.placeholder(tf.float32, [None, 128, 128, 2])  # data
    x2 = tf.placeholder(tf.float32, [None, 8])  # label
    x4 = tf.placeholder(tf.float32, [])  # dropout
    net = Mycnn(x1, x4, bn_in=mean_var.f.arr_0)
    fc2 = net.out

    loss = tf.reduce_sum(tf.square(tf.sub(fc2, x2))) / 2 / batch_size

    # gpu configuration
    tf_config = tf.ConfigProto()
    tf_config.gpu_options.allow_growth = True
    # gpu_opinions = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)

    saver = tf.train.Saver(max_to_keep=None)

    with tf.Session(config=tf_config) as sess:
        saver.restore(sess, dir_load)
        test_model = DataSet(test_img_list)
        loss_total = []
        for i in range(iter_max):
            x_batch_test, y_batch_test, h1_test, img1, img2 = test_model.next_batch()
            np.savetxt(((dir_save + '/h1_%d.txt') % i), h1_test)
            np.savetxt(((dir_save + '/label_%d.txt') % i), y_batch_test)
            cv2.imwrite(((dir_save + '/image_%d_1.jpg') % i), img1)
            cv2.imwrite(((dir_save + '/image_%d_2.jpg') % i), img2)

            pre, average_loss = sess.run([fc2, loss], feed_dict={x1: x_batch_test, x2: y_batch_test, x4: 1.0})

            np.savetxt(((dir_save + '/predict_%d.txt') % i), pre)
            loss_total.append(average_loss)

            print ('iter %05d, test loss = %.5f' % ((i+1), average_loss))

        np.savetxt((dir_save + '/loss.txt'), loss_total)