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model.py
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model.py
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import os
import tensorflow as tf
from module import discriminator, generator_gatedcnn
from utils import l1_loss, l2_loss, cross_entropy_loss
from datetime import datetime
class CycleGAN(object):
def __init__(self, num_features, discriminator = discriminator, generator = generator_gatedcnn, mode = 'train', log_dir = './log'):
self.num_features = num_features
self.input_shape = [None, num_features, None] # [batch_size, num_features, num_frames]
self.discriminator = discriminator
self.generator = generator
self.mode = mode
self.build_model()
self.optimizer_initializer()
self.saver = tf.train.Saver()
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
if self.mode == 'train':
self.train_step = 0
now = datetime.now()
self.log_dir = os.path.join(log_dir, now.strftime('%Y%m%d-%H%M%S'))
self.writer = tf.summary.FileWriter(self.log_dir, tf.get_default_graph())
self.generator_summaries, self.discriminator_summaries = self.summary()
def build_model(self):
# Placeholders for real training samples
self.input_A_real = tf.placeholder(tf.float32, shape = self.input_shape, name = 'input_A_real')
self.input_B_real = tf.placeholder(tf.float32, shape = self.input_shape, name = 'input_B_real')
# Placeholders for fake generated samples
self.input_A_fake = tf.placeholder(tf.float32, shape = self.input_shape, name = 'input_A_fake')
self.input_B_fake = tf.placeholder(tf.float32, shape = self.input_shape, name = 'input_B_fake')
# Placeholder for test samples
self.input_A_test = tf.placeholder(tf.float32, shape = self.input_shape, name = 'input_A_test')
self.input_B_test = tf.placeholder(tf.float32, shape = self.input_shape, name = 'input_B_test')
self.generation_B = self.generator(inputs = self.input_A_real, reuse = False, scope_name = 'generator_A2B')
self.cycle_A = self.generator(inputs = self.generation_B, reuse = False, scope_name = 'generator_B2A')
self.generation_A = self.generator(inputs = self.input_B_real, reuse = True, scope_name = 'generator_B2A')
self.cycle_B = self.generator(inputs = self.generation_A, reuse = True, scope_name = 'generator_A2B')
self.generation_A_identity = self.generator(inputs = self.input_A_real, reuse = True, scope_name = 'generator_B2A')
self.generation_B_identity = self.generator(inputs = self.input_B_real, reuse = True, scope_name = 'generator_A2B')
self.discrimination_A_fake = self.discriminator(inputs = self.generation_A, reuse = False, scope_name = 'discriminator_A')
self.discrimination_B_fake = self.discriminator(inputs = self.generation_B, reuse = False, scope_name = 'discriminator_B')
# Cycle loss
self.cycle_loss = l1_loss(y = self.input_A_real, y_hat = self.cycle_A) + l1_loss(y = self.input_B_real, y_hat = self.cycle_B)
# Identity loss
self.identity_loss = l1_loss(y = self.input_A_real, y_hat = self.generation_A_identity) + l1_loss(y = self.input_B_real, y_hat = self.generation_B_identity)
# Place holder for lambda_cycle and lambda_identity
self.lambda_cycle = tf.placeholder(tf.float32, None, name = 'lambda_cycle')
self.lambda_identity = tf.placeholder(tf.float32, None, name = 'lambda_identity')
# Generator loss
# Generator wants to fool discriminator
self.generator_loss_A2B = l2_loss(y = tf.ones_like(self.discrimination_B_fake), y_hat = self.discrimination_B_fake)
self.generator_loss_B2A = l2_loss(y = tf.ones_like(self.discrimination_A_fake), y_hat = self.discrimination_A_fake)
# Merge the two generators and the cycle loss
self.generator_loss = self.generator_loss_A2B + self.generator_loss_B2A + self.lambda_cycle * self.cycle_loss + self.lambda_identity * self.identity_loss
# Discriminator loss
self.discrimination_input_A_real = self.discriminator(inputs = self.input_A_real, reuse = True, scope_name = 'discriminator_A')
self.discrimination_input_B_real = self.discriminator(inputs = self.input_B_real, reuse = True, scope_name = 'discriminator_B')
self.discrimination_input_A_fake = self.discriminator(inputs = self.input_A_fake, reuse = True, scope_name = 'discriminator_A')
self.discrimination_input_B_fake = self.discriminator(inputs = self.input_B_fake, reuse = True, scope_name = 'discriminator_B')
# Discriminator wants to classify real and fake correctly
self.discriminator_loss_input_A_real = l2_loss(y = tf.ones_like(self.discrimination_input_A_real), y_hat = self.discrimination_input_A_real)
self.discriminator_loss_input_A_fake = l2_loss(y = tf.zeros_like(self.discrimination_input_A_fake), y_hat = self.discrimination_input_A_fake)
self.discriminator_loss_A = (self.discriminator_loss_input_A_real + self.discriminator_loss_input_A_fake) / 2
self.discriminator_loss_input_B_real = l2_loss(y = tf.ones_like(self.discrimination_input_B_real), y_hat = self.discrimination_input_B_real)
self.discriminator_loss_input_B_fake = l2_loss(y = tf.zeros_like(self.discrimination_input_B_fake), y_hat = self.discrimination_input_B_fake)
self.discriminator_loss_B = (self.discriminator_loss_input_B_real + self.discriminator_loss_input_B_fake) / 2
# Merge the two discriminators into one
self.discriminator_loss = self.discriminator_loss_A + self.discriminator_loss_B
# Categorize variables because we have to optimize the two sets of the variables separately
trainable_variables = tf.trainable_variables()
self.discriminator_vars = [var for var in trainable_variables if 'discriminator' in var.name]
self.generator_vars = [var for var in trainable_variables if 'generator' in var.name]
#for var in t_vars: print(var.name)
# Reserved for test
self.generation_B_test = self.generator(inputs = self.input_A_test, reuse = True, scope_name = 'generator_A2B')
self.generation_A_test = self.generator(inputs = self.input_B_test, reuse = True, scope_name = 'generator_B2A')
def optimizer_initializer(self):
self.generator_learning_rate = tf.placeholder(tf.float32, None, name = 'generator_learning_rate')
self.discriminator_learning_rate = tf.placeholder(tf.float32, None, name = 'discriminator_learning_rate')
self.discriminator_optimizer = tf.train.AdamOptimizer(learning_rate = self.discriminator_learning_rate, beta1 = 0.5).minimize(self.discriminator_loss, var_list = self.discriminator_vars)
self.generator_optimizer = tf.train.AdamOptimizer(learning_rate = self.generator_learning_rate, beta1 = 0.5).minimize(self.generator_loss, var_list = self.generator_vars)
def train(self, input_A, input_B, lambda_cycle, lambda_identity, generator_learning_rate, discriminator_learning_rate):
generation_A, generation_B, generator_loss, _, generator_summaries = self.sess.run(
[self.generation_A, self.generation_B, self.generator_loss, self.generator_optimizer, self.generator_summaries], \
feed_dict = {self.lambda_cycle: lambda_cycle, self.lambda_identity: lambda_identity, self.input_A_real: input_A, self.input_B_real: input_B, self.generator_learning_rate: generator_learning_rate})
self.writer.add_summary(generator_summaries, self.train_step)
discriminator_loss, _, discriminator_summaries = self.sess.run([self.discriminator_loss, self.discriminator_optimizer, self.discriminator_summaries], \
feed_dict = {self.input_A_real: input_A, self.input_B_real: input_B, self.discriminator_learning_rate: discriminator_learning_rate, self.input_A_fake: generation_A, self.input_B_fake: generation_B})
self.writer.add_summary(discriminator_summaries, self.train_step)
self.train_step += 1
return generator_loss, discriminator_loss
def test(self, inputs, direction):
if direction == 'A2B':
generation = self.sess.run(self.generation_B_test, feed_dict = {self.input_A_test: inputs})
elif direction == 'B2A':
generation = self.sess.run(self.generation_A_test, feed_dict = {self.input_B_test: inputs})
else:
raise Exception('Conversion direction must be specified.')
return generation
def save(self, directory, filename):
if not os.path.exists(directory):
os.makedirs(directory)
self.saver.save(self.sess, os.path.join(directory, filename))
return os.path.join(directory, filename)
def load(self, filepath):
self.saver.restore(self.sess, filepath)
def summary(self):
with tf.name_scope('generator_summaries'):
cycle_loss_summary = tf.summary.scalar('cycle_loss', self.cycle_loss)
identity_loss_summary = tf.summary.scalar('identity_loss', self.identity_loss)
generator_loss_A2B_summary = tf.summary.scalar('generator_loss_A2B', self.generator_loss_A2B)
generator_loss_B2A_summary = tf.summary.scalar('generator_loss_B2A', self.generator_loss_B2A)
generator_loss_summary = tf.summary.scalar('generator_loss', self.generator_loss)
generator_summaries = tf.summary.merge([cycle_loss_summary, identity_loss_summary, generator_loss_A2B_summary, generator_loss_B2A_summary, generator_loss_summary])
with tf.name_scope('discriminator_summaries'):
discriminator_loss_A_summary = tf.summary.scalar('discriminator_loss_A', self.discriminator_loss_A)
discriminator_loss_B_summary = tf.summary.scalar('discriminator_loss_B', self.discriminator_loss_B)
discriminator_loss_summary = tf.summary.scalar('discriminator_loss', self.discriminator_loss)
discriminator_summaries = tf.summary.merge([discriminator_loss_A_summary, discriminator_loss_B_summary, discriminator_loss_summary])
return generator_summaries, discriminator_summaries
if __name__ == '__main__':
model = CycleGAN(num_features = 24)
print('Graph Compile Successeded.')