DC_GAN source code parsing

DCGAN generator network structure

There are four files in it, namely mian.py, ops.py, modle.py, utils.py.

1. main.py
   

 This file has 96 lines of code.

import os
import scipy.misc
import numpy as np

from model import DCGAN
from utils import pp, visualize, to_json

import tensorflow as tf

flags = tf.app.flags
flags.DEFINE_integer("epoch", 25, "Epoch to train [25]")
flags.DEFINE_float("learning_rate", 0.001, "Learning rate of for adam [0.0002]")
flags.DEFINE_float("beta1", 0.5, "Momentum term of adam [0.5]")
flags.DEFINE_float("train_size",np.inf, "The size of train images [np.inf]")
flags.DEFINE_integer("batch_size", 64, "The size of batch images [64]")
flags.DEFINE_integer("input_height", 64, "The size of image to use (will be center cropped). [108]")
flags.DEFINE_integer("input_width", 64, "The size of image to use (will be center cropped). If None, same value as input_height [None]")
flags.DEFINE_integer("output_height", 64, "The size of the output images to produce [64]")
flags.DEFINE_integer("output_width", 64, "The size of the output images to produce. If None, same value as output_height [None]")
flags.DEFINE_integer("c_dim", 3, "Dimension of image color. [3]")
flags.DEFINE_string("dataset", "celebA", "The name of dataset [celebA, mnist, lsun]")
flags.DEFINE_string("input_fname_pattern", "*.jpg", "Glob pattern of filename of input images [*]")
flags.DEFINE_string("checkpoint_dir", "checkpoint", "Directory name to save the checkpoints [checkpoint]")
flags.DEFINE_string("sample_dir", "samples", "Directory name to save the image samples [samples]")
flags.DEFINE_boolean("is_train", False, "True for training, False for testing [False]")
flags.DEFINE_boolean("is_crop", False, "True for training, False for testing [False]")
flags.DEFINE_boolean("visualize", False, "True for visualizing, False for nothing [False]")
FLAGS = flags.FLAGS

def main(_):
  pp.pprint(flags.FLAGS.__flags)

  if FLAGS.input_width is None:
    FLAGS.input_width = FLAGS.input_height
  if FLAGS.output_width is None:
    FLAGS.output_width = FLAGS.output_height

  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)

  #gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
  run_config = tf.ConfigProto()
  run_config.gpu_options.allow_growth=True

  with tf.Session(config=run_config) as sess:
    if FLAGS.dataset == 'mnist':
      dcgan = DCGAN(
          sess,
          input_width=FLAGS.input_width,
          input_height=FLAGS.input_height,
          output_width=FLAGS.output_width,
          output_height=FLAGS.output_height,
          batch_size=FLAGS.batch_size,
          y_dim=10,
          c_dim=1,
          dataset_name=FLAGS.dataset,
          input_fname_pattern=FLAGS.input_fname_pattern,
          is_crop=FLAGS.is_crop,
          checkpoint_dir=FLAGS.checkpoint_dir,
          sample_dir=FLAGS.sample_dir)
    else:
      dcgan = DCGAN(
          sess,
          input_width=FLAGS.input_width,
          input_height=FLAGS.input_height,
          output_width=FLAGS.output_width,
          output_height=FLAGS.output_height,
          batch_size=FLAGS.batch_size,
          c_dim=FLAGS.c_dim,
          dataset_name=FLAGS.dataset,
          input_fname_pattern=FLAGS.input_fname_pattern,
          is_crop=FLAGS.is_crop,
          checkpoint_dir=FLAGS.checkpoint_dir,
          sample_dir=FLAGS.sample_dir)

    if FLAGS.is_train:
      dcgan.train(FLAGS)
    else:
      if not dcgan.load(FLAGS.checkpoint_dir):
        raise Exception("[!] Train a model first, then run test mode")
      

    # to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
    #                 [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
    #                 [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
    #                 [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
    #                 [dcgan.h4_w, dcgan.h4_b, None])

    # Below is codes for visualization
    OPTION = 1
    visualize(sess, dcgan, FLAGS, OPTION)
# python main.py --dataset faces_ --is_train True
if __name__ == '__main__':
  tf.app.run()

This file invokes the model.py and utils.py

step0:  in carrying out the main.py before the first resolution for flags.TensorFlow uses the python-gflags(how to use python-gflags https://blog.csdn.net/chenglinhust/article/details/50428114) project at the bottom. Then it is encapsulated as tf.app.flags interface. We can initialize flags directly before the program runs, or set command-line parameters when the program runs to achieve the purpose of passing parameters.

Parameter setting

• epoch: The Number of Iterations is 25
• learning rate:  0.002
• batch_size:The number of images per iteration
• input_height: To specify the height of the input image
• input_width:To specify the wideth of the input image
• output_height: To specify the height of the output image
• output_width:To specify the wideth of the output image
• dataset: To specify data set to process

step1:First, the program prints the parameter data and then determines whether the output of the input image specifies its width.If the width of this output image is not specified, it is equal to the height of this image