the use of GPU in the Tensorflow framework will only affect speed and why it will also affect the running results.

when you close GPU, the result will be blank. Using GPU is normal

https://pan.baidu.com/s/1mfA7.

Code as above, can be run directly

the main code is as follows:

-sharp -*- coding: utf-8 -*-
import numpy as np
from scipy.misc import imread, imsave
import argparse
import time

import os

-sharp os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  -sharp  -1  CPU GPU
import tensorflow as tf


def run(session):
    args = argparse.Namespace()
    args.input = "./7.jpg"
    args.output = "./7_666.jpg"
    args.model = "./models/wave.model"
    args.arch = "./models/model.meta"
    args.image = imread(args.input, mode="RGB").astype(np.float32)
    args.image = np.expand_dims(args.image, axis=0)

    -sharp 
    saver = tf.train.import_meta_graph(args.arch, clear_devices=True)
    saver.restore(session, args.model)
    inputs = tf.get_collection("inputs")[0]
    output = tf.get_collection("output")[0]
    result = output.eval({inputs: args.image})
    result = np.clip(result, 0.0, 255.0).astype(np.uint8)
    result = np.squeeze(result, 0)
    imsave(args.output, result)


def main():
    session = tf.Session()
    with session.as_default():
        run(session)


if __name__ == "__main__":
    time_a = time.time()
    main()
    time_b = time.time()
    print("run_time", time_b - time_a)