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train_lung-gan.py
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import os
import sys
import scipy.misc
import pprint
import numpy as np
import time
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import *
from glob import glob
from random import shuffle
from utils import *
from network import *
pp = pprint.PrettyPrinter()
"""
TensorLayer implementation of DCGAN to generate face image.
Usage : see README.md
"""
flags = tf.app.flags
flags.DEFINE_integer("epoch", 100, "Epoch to train [25]")
flags.DEFINE_float("learning_rate", 0.0002, "Learning rate of for adam [0.0002]")
flags.DEFINE_float("beta1", 0.5, "Momentum term of adam [0.5]")
flags.DEFINE_integer("train_size", sys.maxint, "The size of train images [np.inf]")
flags.DEFINE_integer("batch_size", 64, "The number of batch images [64]")
flags.DEFINE_integer("image_size", 256, "The size of image to use (will be center cropped) [108]")
flags.DEFINE_integer("output_size", 256, "The size of the output images to produce [64]")
flags.DEFINE_integer("sample_size", 64, "The number of sample images [64]")
flags.DEFINE_integer("c_dim", 3, "Dimension of image color. [3]")
flags.DEFINE_integer("sample_step", 500, "The interval of generating sample. [500]")
flags.DEFINE_integer("save_step", 50, "The interval of saveing checkpoints. [500]")
flags.DEFINE_string("dataset", "CT", "The name of dataset [celebA, mnist, lsun]")
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", True, "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 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)
z_dim = 100
with tf.device("/gpu:0"): # <-- if you have a GPU machine
z = tf.placeholder(tf.float32, [FLAGS.batch_size, z_dim], name='z_noise')
real_images = tf.placeholder(tf.float32, [FLAGS.batch_size, FLAGS.output_size, FLAGS.output_size, FLAGS.c_dim], name='real_images')
# z --> generator for training
net_g, g_logits = generator_simplified_api(z, is_train=True, reuse=False)
# generated fake images --> discriminator
net_d, d_logits, feature_fake = discriminator_simplified_api(net_g.outputs, is_train=True, reuse=False)
# real images --> discriminator
net_d2, d2_logits, feature_real = discriminator_simplified_api(real_images, is_train=True, reuse=True)
# sample_z --> generator for evaluation, set is_train to False
# so that BatchNormLayer behave differently
net_g2, g2_logits = generator_simplified_api(z, is_train=False, reuse=True)
#
net_d3, d3_logits, _ = discriminator_simplified_api(real_images, is_train=False, reuse=True)
# cost for updating discriminator and generator
# discriminator: real images are labelled as 1
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d2_logits, labels=tf.ones_like(d2_logits))) # real == 1
# discriminator: images from generator (fake) are labelled as 0
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits, labels=tf.zeros_like(d_logits))) # fake == 0
d_loss = d_loss_real + d_loss_fake
# generator: try to make the the fake images look real (1)
g_loss1 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits, labels=tf.ones_like(d_logits)))
g_loss2 = tf.reduce_mean(tf.nn.l2_loss(feature_real-feature_fake))/(FLAGS.image_size*FLAGS.image_size)
g_loss = g_loss1+g_loss2
#g_loss = tf.reduce_mean(tf.abs(feature_real-feature_fake))
# trainable parameters for updating discriminator and generator
g_vars = net_g.all_params # only updates the generator
d_vars = net_d.all_params # only updates the discriminator
net_g.print_params(False)
print("---------------")
net_d.print_params(False)
# optimizers for updating discriminator and generator
d_optim = tf.train.AdamOptimizer(FLAGS.learning_rate, beta1=FLAGS.beta1) \
.minimize(d_loss, var_list=d_vars)
g_optim = tf.train.AdamOptimizer(FLAGS.learning_rate, beta1=FLAGS.beta1) \
.minimize(g_loss, var_list=g_vars)
sess=tf.Session()
tl.ops.set_gpu_fraction(sess=sess, gpu_fraction=0.88)
sess.run(tf.initialize_all_variables())
# load checkpoints
print("[*] Loading checkpoints...")
model_dir = "%s_%s_%s" % (FLAGS.dataset, 64, FLAGS.output_size)
save_dir = os.path.join(FLAGS.checkpoint_dir, model_dir)
# load the latest checkpoints
#for num in xrange(70, 71):
net_g_name = os.path.join(save_dir, 'net_g.npz')
net_d_name = os.path.join(save_dir, 'net_d.npz')
print net_g_name, net_d_name
if not (os.path.exists(net_g_name) and os.path.exists(net_d_name)):
print("[!] Loading checkpoints failed!")
else:
net_g_loaded_params = tl.files.load_npz(name=net_g_name)
net_d_loaded_params = tl.files.load_npz(name=net_d_name)
tl.files.assign_params(sess, net_g_loaded_params, net_g)
tl.files.assign_params(sess, net_d_loaded_params, net_d)
print("[*] Loading checkpoints SUCCESS!")
# TODO: use minbatch to shuffle and iterate
data_files = glob(os.path.join("./data", FLAGS.dataset, "*.jpg"))
# TODO: shuffle sample_files each epoch
sample_seed = np.random.uniform(low=-1, high=1, size=(FLAGS.batch_size, z_dim)).astype(np.float32)
if FLAGS.is_train:
iter_counter = 0
for epoch in range(FLAGS.epoch):
#shuffle data
shuffle(data_files)
print("[*]Dataset shuffled!")
# update sample files based on shuffled data
sample_files = data_files[0:FLAGS.batch_size]
# sample = [get_image(sample_file, FLAGS.image_size, is_crop=FLAGS.is_crop, resize_w=FLAGS.output_size, is_grayscale = 0) for sample_file in sample_files]
sample = [transform_and_get_image(sample_file, FLAGS.image_size, FLAGS.image_size) for sample_file in sample_files]
# sample = []
# for sample_file in sample_files:
# sample_image = transform_and_get_image(sample_file, FLAGS.image_size, FLAGS.image_size)
# if len(sample_image) == 3:
# sample.append(sample_image)
sample_images = np.array(sample).astype(np.float32)
print sample_images.shape
print("[*]Sample images updated!")
# load image data
batch_idxs = min(len(data_files), FLAGS.train_size) // FLAGS.batch_size
for idx in xrange(batch_idxs):
batch_files = data_files[idx*FLAGS.batch_size:(idx+1)*FLAGS.batch_size]
# get real images
# batch = [get_image(batch_file, FLAGS.image_size, is_crop=FLAGS.is_crop, resize_w=FLAGS.output_size, is_grayscale = 0) for batch_file in batch_files]
batch = [transform_and_get_image(batch_file, FLAGS.image_size, FLAGS.image_size) for batch_file in batch_files]
# batch = []
# for batch_file in batch_files:
# batch_image = transform_and_get_image(batch_file, FLAGS.image_size, FLAGS.image_size)
# if len(batch_image) == 3:
# batch.append(batch_image)
batch_images = np.array(batch).astype(np.float32)
batch_z = np.random.uniform(low=-1, high=1, size=(FLAGS.batch_size, z_dim)).astype(np.float32)
start_time = time.time()
# updates the discriminator
errD, _ = sess.run([d_loss, d_optim], feed_dict={z: batch_z, real_images: batch_images })
# updates the generator, run generator twice to make sure that d_loss does not go to zero (difference from paper)
for _ in range(2):
errG, _ = sess.run([g_loss, g_optim], feed_dict={z: batch_z, real_images: batch_images})
print("Epoch: [%2d/%2d] [%4d/%4d] time: %4.4f, d_loss: %.8f, g_loss: %.8f" \
% (epoch, FLAGS.epoch, idx, batch_idxs,
time.time() - start_time, errD, errG))
sys.stdout.flush()
iter_counter += 1
if np.mod(epoch, 1) == 0:
# generate and visualize generated images
#img, errD, errG = sess.run([net_g2.outputs, d_loss, g_loss], feed_dict={z : sample_seed, real_images: sample_images})
img, errG = sess.run([net_g2.outputs, g_loss], feed_dict={z : sample_seed, real_images: sample_images})
D, D_, errD = sess.run([net_d3.all_layers, net_d3.outputs, d_loss_real], feed_dict={real_images: sample_images})
'''
img255 = (np.array(img) + 1) / 2 * 255
tl.visualize.images2d(images=img255, second=0, saveable=True,
name='./{}/train_{:02d}_{:04d}'.format(FLAGS.sample_dir, epoch, idx), dtype=None, fig_idx=2838)
'''
save_images(img, [8, 8],
'./{}/train_{:02d}.png'.format(FLAGS.sample_dir, epoch))
print("[Sample] d_loss: %.8f, g_loss: %.8f" % (errD, errG))
# for i in range(len(D)):
# print D[i].shape
#print D[-1], D_, sigmoid(D[-1]), sigmoid(D[-1])==D_
sys.stdout.flush()
if np.mod(epoch, 5) == 0:
print epoch
# save current network parameters
print("[*] Saving checkpoints...")
model_dir = "%s_%s_%s" % (FLAGS.dataset, FLAGS.batch_size, FLAGS.output_size)
save_dir = os.path.join(FLAGS.checkpoint_dir, model_dir)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# the latest version location
net_g_name = os.path.join(save_dir, str(epoch)+'net_g.npz')
net_d_name = os.path.join(save_dir, str(epoch)+'net_d.npz')
# this version is for future re-check and visualization analysis
# net_g_iter_name = os.path.join(save_dir, 'net_g_%d.npz' % iter_counter)
# net_d_iter_name = os.path.join(save_dir, 'net_d_%d.npz' % iter_counter)
tl.files.save_npz(net_g.all_params, name=net_g_name, sess=sess)
tl.files.save_npz(net_d.all_params, name=net_d_name, sess=sess)
# tl.files.save_npz(net_g.all_params, name=net_g_iter_name, sess=sess)
# tl.files.save_npz(net_d.all_params, name=net_d_iter_name, sess=sess)
print("[*] Saving checkpoints SUCCESS!")
if __name__ == '__main__':
tf.app.run()