main.py

from __future__ import print_function
import os
import argparse
from glob import glob
import numpy as np

from PIL import Image
import tensorflow as tf

from extra.model import lowlight_enhance
from extra.utils import *

parser = argparse.ArgumentParser(description='')

parser.add_argument('--use_gpu', dest='use_gpu', type=int, default=0, help='gpu flag, 1 for GPU and 0 for CPU')     #改gpu
parser.add_argument('--gpu_idx', dest='gpu_idx', default="0", help='GPU idx')
parser.add_argument('--gpu_mem', dest='gpu_mem', type=float, default=0.5, help="0 to 1, gpu memory usage")
parser.add_argument('--phase', dest='phase', default='train', help='train or test')                                 #改测试

parser.add_argument('--epoch', dest='epoch', type=int, default=100, help='number of total epoches')
parser.add_argument('--batch_size', dest='batch_size', type=int, default=16, help='number of samples in one batch')
parser.add_argument('--patch_size', dest='patch_size', type=int, default=48, help='patch size')
parser.add_argument('--start_lr', dest='start_lr', type=float, default=0.001, help='initial learning rate for adam')
parser.add_argument('--eval_every_epoch', dest='eval_every_epoch', default=20, help='evaluating and saving checkpoints every #  epoch')
parser.add_argument('--checkpoint_dir', dest='ckpt_dir', default='./checkpoint', help='directory for checkpoints')
parser.add_argument('--sample_dir', dest='sample_dir', default='./sample', help='directory for evaluating outputs')

parser.add_argument('--save_dir', dest='save_dir', default='./test_results', help='directory for testing outputs')
parser.add_argument('--test_dir', dest='test_dir', default='./data/test/low', help='directory for testing inputs')

args = parser.parse_args()

def lowlight_train(lowlight_enhance):
    if not os.path.exists(args.ckpt_dir):
        os.makedirs(args.ckpt_dir)
    if not os.path.exists(args.sample_dir):
        os.makedirs(args.sample_dir)

    lr = args.start_lr * np.ones([args.epoch])
    lr[20:50] = lr[0] / 10.0                                        #学习率衰减，20个epoch后学习率为初始的0.1
    lr[50:] = lr[20] / 10.0

    train_low_data = []
    train_high_data = []

    '''读入训练图片'''

    train_low_data_names = glob('./data/train/low/*.*')
    train_low_data_names.sort()
    train_high_data_names = glob('./data/train/high/*.*')
    train_high_data_names.sort()
    assert len(train_low_data_names) == len(train_high_data_names)
    print('[*] Number of training data: %d' % len(train_low_data_names))


    '''读入验证图片'''

    eval_low_data = []
    eval_high_data = []

    eval_low_data_name = glob('./data/eval/low/*.*')

    for idx in range(len(eval_low_data_name)):
        eval_low_im = load_images(eval_low_data_name[idx])
        eval_low_data.append(eval_low_im)

    lowlight_enhance.train(train_low_data_names, train_high_data_names, eval_low_data, batch_size=args.batch_size,
                           patch_size=args.patch_size, epoch=args.epoch, lr=lr, sample_dir=args.sample_dir,
                           ckpt_dir=os.path.join(args.ckpt_dir, 'Denoising'), eval_every_epoch=args.eval_every_epoch,
                           train_phase="Denoising")


def lowlight_test(lowlight_enhance):
    if args.test_dir == None:
        print("[!] please provide --test_dir")
        exit(0)

    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)

    test_low_data_name = glob(os.path.join(args.test_dir) + '/*.*')
    test_low_data = []
    test_high_data = []
    for idx in range(len(test_low_data_name)):
        test_low_im = load_images(test_low_data_name[idx])
        test_low_data.append(test_low_im)

    lowlight_enhance.test(test_low_data, test_high_data, test_low_data_name, save_dir=args.save_dir)


def main(_):
    if args.use_gpu:
        print("[*] GPU\n")
        os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_idx
        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_mem)
        with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
            if args.phase == 'train':
                model = lowlight_enhance(sess, is_training=True)
                lowlight_train(model)
            elif args.phase == 'test':
                model = lowlight_enhance(sess, is_training=False)
                lowlight_test(model)
            else:
                print('[!] Unknown phase')
                exit(0)
    else:
        print("[*] CPU\n")
        with tf.Session() as sess:
            if args.phase == 'train':
                model = lowlight_enhance(sess,is_training=True)
                lowlight_train(model)
            elif args.phase == 'test':
                model = lowlight_enhance(sess, is_training=False)
                lowlight_test(model)
            else:
                print('[!] Unknown phase')
                exit(0)

if __name__ == '__main__':
    tf.app.run()