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discoGAN.py
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discoGAN.py
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import tensorflow as tf
import utils
import data
import os
"""
This object represents a discogan
machine learning model. It comes with
functions to train and restore weights
for a DiscoGAN
"""
class DiscoGAN(object):
def __init__(self,batch_size=10,im_size=64,channels=3,dtype=tf.float32,analytics=True):
self.analytics = analytics
self.batch_size = batch_size
self.x_a = tf.placeholder(dtype,[None,im_size,im_size,channels],name='xa')
self.x_b = tf.placeholder(dtype,[None,im_size,im_size,channels],name='xb')
#Generator Networks
self.g_ab = utils.generator(self.x_a,name="gen_AB",im_size=im_size)
self.g_ba = utils.generator(self.x_b,name="gen_BA",im_size=im_size)
#Secondary generator networks, reusing params of previous two
self.g_aba = utils.generator(self.g_ab,name="gen_BA",im_size=im_size,reuse=True)
self.g_bab = utils.generator(self.g_ba,name="gen_AB",im_size=im_size,reuse=True)
#Discriminator for input a
self.disc_a_real = utils.discriminator(self.x_a,name="disc_a",im_size=im_size)
self.disc_a_fake = utils.discriminator(self.g_ba,name="disc_a",im_size=im_size,reuse=True)
#Discriminator for input b
self.disc_b_real = utils.discriminator(self.x_b,name="disc_b")
self.disc_b_fake = utils.discriminator(self.g_ab,name="disc_b",reuse=True)
#Reconstruction loss for generators
self.l_const_a = tf.reduce_mean(utils.huber_loss(self.g_aba,self.x_a))
self.l_const_b = tf.reduce_mean(utils.huber_loss(self.g_bab,self.x_b))
#Generation loss for generators
self.l_gan_a = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.disc_a_fake,labels=tf.ones_like(self.disc_a_fake)))
self.l_gan_b = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.disc_b_fake,labels=tf.ones_like(self.disc_b_fake)))
#Real example loss for discriminators
self.l_disc_a_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.disc_a_real,labels=tf.ones_like(self.disc_a_real)))
self.l_disc_b_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.disc_b_real,labels=tf.ones_like(self.disc_b_real)))
#Fake example loss for discriminators
self.l_disc_a_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.disc_a_fake,labels=tf.zeros_like(self.disc_a_fake)))
self.l_disc_b_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.disc_b_fake,labels=tf.zeros_like(self.disc_b_fake)))
#Combined loss for individual discriminators
self.l_disc_a = self.l_disc_a_real + self.l_disc_a_fake
self.l_disc_b = self.l_disc_b_real + self.l_disc_b_fake
#Total discriminator loss
self.l_disc = self.l_disc_a + self.l_disc_b
#Combined loss for individual generators
self.l_ga = self.l_gan_a + self.l_const_b
self.l_gb = self.l_gan_b + self.l_const_a
#Total GAN loss
self.l_g = self.l_ga + self.l_gb
#Parameter Lists
self.disc_params = []
self.gen_params = []
for v in tf.trainable_variables():
if 'disc' in v.name:
self.disc_params.append(v)
if 'gen' in v.name:
self.gen_params.append(v)
if self.analytics:
self.init_analytics()
self.gen_a_dir = 'generator a->b'
self.gen_b_dir = 'generator b->a'
self.rec_a_dir = 'reconstruct a'
self.rec_b_dir = 'reconstruct b'
self.model_directory = "models"
if not os.path.exists(self.gen_a_dir):
os.makedirs(self.gen_a_dir)
if not os.path.exists(self.gen_b_dir):
os.makedirs(self.gen_b_dir)
if not os.path.exists(self.rec_b_dir):
os.makedirs(self.rec_b_dir)
if not os.path.exists(self.rec_a_dir):
os.makedirs(self.rec_a_dir)
self.sess = tf.Session()
self.saver = tf.train.Saver()
"""
Enable logging of analytics
for tensorboard
"""
def init_analytics(self):
#Scalars for all losses
tf.summary.scalar("loss_g", self.l_g)
tf.summary.scalar("loss_ga", self.l_ga)
tf.summary.scalar("loss_gb", self.l_gb)
tf.summary.scalar("loss_d", self.l_disc)
tf.summary.scalar("loss_d_a", self.l_disc_a)
tf.summary.scalar("loss_d_b", self.l_disc_b)
tf.summary.scalar("l_const_a",self.l_const_a)
tf.summary.scalar("l_const_b",self.l_const_b)
#Histograms for all vars
for v in tf.trainable_variables():
tf.summary.histogram(v.name,v)
self.merged_summary_op = tf.summary.merge_all()
"""
Train DiscoGAN
"""
def train(self,LR=2e-4,B1=0.5,B2=0.999,iterations=50000,sample_frequency=10,
sample_overlap=500,save_frequency=1000,domain_a="a",domain_b="b"):
self.trainer_D = tf.train.AdamOptimizer(LR,beta1=B1,beta2=B2).minimize(self.l_disc,var_list=self.disc_params)
self.trainer_G = tf.train.AdamOptimizer(LR,beta1=B1,beta2=B2).minimize(self.l_g,var_list=self.gen_params)
with self.sess as sess:
sess.run(tf.global_variables_initializer())
if self.analytics:
if not os.path.exists("logs"):
os.makedirs("logs")
self.summary_writer = tf.summary.FileWriter(os.getcwd()+'/logs',graph=sess.graph)
for i in range(iterations):
realA = data.get_batch(self.batch_size,domain_a)
realB = data.get_batch(self.batch_size,domain_b)
op_list = [self.trainer_D,self.l_disc,self.trainer_G,self.l_g,self.merged_summary_op]
_,dLoss,_,gLoss,summary_str = sess.run(op_list,feed_dict={self.x_a:realA,self.x_b:realB})
realA = data.get_batch(self.batch_size,domain_a)
realB = data.get_batch(self.batch_size,domain_b)
_,gLoss = sess.run([self.trainer_G,self.l_g],feed_dict={self.x_a:realA,self.x_b:realB})
if i%10 == 0:
self.summary_writer.add_summary(summary_str, i)
print("Generator Loss: " + str(gLoss) + "\tDiscriminator Loss: " + str(dLoss))
if i % sample_frequency == 0:
realA = data.get_batch(1,domain_a)
realB = data.get_batch(1,domain_b)
ops = [self.g_ba,self.g_ab,self.g_aba,self.g_bab]
out_a,out_b,out_ab,out_ba = sess.run(ops,feed_dict={self.x_a:realA,self.x_b:realB})
data.save(self.gen_a_dir+"/img"+str(i%sample_overlap)+'.png',out_a[0])
data.save(self.gen_b_dir+"/img"+str(i%sample_overlap)+'.png',out_b[0])
data.save(self.rec_a_dir+"/img"+str(i%sample_overlap)+'.png',out_ba[0])
data.save(self.rec_b_dir+"/img"+str(i%sample_overlap)+'.png',out_ab[0])
if i % save_frequency == 0:
if not os.path.exists(self.model_directory):
os.makedirs(self.model_directory)
self.saver.save(sess,self.model_directory+'/model-'+str(i)+'.ckpt')
print("Saved Model")
"""
Restore previously saved weights from
trained / in-progress model
"""
def restore():
try:
self.saver.restore(self.sess, tf.train.latest_checkpoint(self.model_directory))
except:
print("Previous weights not found")