SRCNN-图像超分辨的学习

 // 主函数

    
 from model import SRCNN	
    
 from utils import input_setup	
    
  
    
 	
    
 import numpy as np	
    
 import tensorflow as tf	
    
  
    
 	
    
 import pprint	
    
 import os	
    
  
    
 	
    
 flags = tf.app.flags	
    
 flags.DEFINE_integer("epoch", 15000, "Number of epoch [15000]")	
    
 flags.DEFINE_integer("batch_size", 128, "The size of batch images [128]")	
    
 flags.DEFINE_integer("image_size", 33, "The size of image to use [33]")	
    
 flags.DEFINE_integer("label_size", 21, "The size of label to produce [21]")	
    
 flags.DEFINE_float("learning_rate", 1e-4, "The learning rate of gradient descent algorithm [1e-4]")	
    
 flags.DEFINE_integer("c_dim", 1, "Dimension of image color. [1]")	
    
 flags.DEFINE_integer("scale", 3, "The size of scale factor for preprocessing input image [3]")	
    
 flags.DEFINE_integer("stride", 14, "The size of stride to apply input image [14]")	
    
 flags.DEFINE_string("checkpoint_dir", "checkpoint", "Name of checkpoint directory [checkpoint]")	
    
 flags.DEFINE_string("sample_dir", "sample", "Name of sample directory [sample]")	
    
 flags.DEFINE_boolean("is_train", True, "True for training, False for testing [True]")	
    
 FLAGS = flags.FLAGS	
    
  
    
 	
    
 pp = pprint.PrettyPrinter()	
    
  
    
 	
    
 def main(_):	
    
   pp.pprint(flags.FLAGS.__flags)	
    
  
    
 	
    
   if not os.path.exists(FLAGS.checkpoint_dir):	
    
     os.makedirs(FLAGS.checkpoint_dir)	
    
   if not os.path.exists(FLAGS.sample_dir):	
    
     os.makedirs(FLAGS.sample_dir)	
    
  
    
 	
    
   with tf.Session() as sess:	
    
     srcnn = SRCNN(sess, 	
    
               image_size=FLAGS.image_size, 	
    
               label_size=FLAGS.label_size, 	
    
               batch_size=FLAGS.batch_size,	
    
               c_dim=FLAGS.c_dim, 	
    
               checkpoint_dir=FLAGS.checkpoint_dir,	
    
               sample_dir=FLAGS.sample_dir)	
    
  
    
 	
    
     srcnn.train(FLAGS)	
    
  
    
 	
    
 if __name__ == '__main__':	
    
   tf.app.run()