main.py

from model import RECONNET
import tensorflow as tf

import pprint
import os

flags = tf.app.flags
flags.DEFINE_integer("epoch", 10, "Number of epoch [10]")
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", 33, "The size of label to produce [33]")
flags.DEFINE_float("learning_rate", 1e-4, "The learning rate of gradient descent algorithm [1e-4]")
flags.DEFINE_float("measurement_rate", 1e-1, "The measurement rate [1e-1]")
flags.DEFINE_integer("c_dim", 1, "Dimension of image color. [1]")
flags.DEFINE_integer("scale", 1, "The size of scale factor for preprocessing input image [1]")# ori 3
flags.DEFINE_integer("stride", 33, "The size of stride to apply input image [14]")#train14  test33
flags.DEFINE_string("checkpoint_dir", "checkpoint", "Name of checkpoint directory [checkpoint]")
flags.DEFINE_string("sample_dir", "sample", "Name of sample directory [restore]")
flags.DEFINE_boolean("is_train",False, "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:
        reconnet = RECONNET(sess,
                      image_size=FLAGS.image_size,
                      label_size=FLAGS.label_size,
                      batch_size=FLAGS.batch_size,
                      measurement_rate=FLAGS.measurement_rate,
                      c_dim=FLAGS.c_dim,
                      checkpoint_dir=FLAGS.checkpoint_dir,
                      sample_dir=FLAGS.sample_dir)

        reconnet.train(FLAGS)


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