model.py
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import tensorflow as tf
import tensorflow.contrib.slim as slim
class DTN(object):
"""Domain Transfer Network
"""
def __init__(self, mode='train', learning_rate=0.0003):
self.mode = mode
self.learning_rate = learning_rate
def content_extractor(self, images, reuse=False):
# images: (batch, 32, 32, 3) or (batch, 32, 32, 1)
if images.get_shape()[3] == 1:
# For mnist dataset, replicate the gray scale image 3 times.
images = tf.image.grayscale_to_rgb(images)
with tf.variable_scope('content_extractor', reuse=reuse):
with slim.arg_scope([slim.conv2d], padding='SAME', activation_fn=None,
stride=2, weights_initializer=tf.contrib.layers.xavier_initializer()):
with slim.arg_scope([slim.batch_norm], decay=0.95, center=True, scale=True,
activation_fn=tf.nn.relu, is_training=(self.mode=='train' or self.mode=='pretrain')):
net = slim.conv2d(images, 64, [3, 3], scope='conv1') # (batch_size, 16, 16, 64)
net = slim.batch_norm(net, scope='bn1')
net = slim.conv2d(net, 128, [3, 3], scope='conv2') # (batch_size, 8, 8, 128)
net = slim.batch_norm(net, scope='bn2')
net = slim.conv2d(net, 256, [3, 3], scope='conv3') # (batch_size, 4, 4, 256)
net = slim.batch_norm(net, scope='bn3')
net = slim.conv2d(net, 128, [4, 4], padding='VALID', scope='conv4') # (batch_size, 1, 1, 128)
net = slim.batch_norm(net, activation_fn=tf.nn.tanh, scope='bn4')
if self.mode == 'pretrain':
net = slim.conv2d(net, 10, [1, 1], padding='VALID', scope='out')
net = slim.flatten(net)
return net
def generator(self, inputs, reuse=False):
# inputs: (batch, 1, 1, 128)
with tf.variable_scope('generator', reuse=reuse):
with slim.arg_scope([slim.conv2d_transpose], padding='SAME', activation_fn=None,
stride=2, weights_initializer=tf.contrib.layers.xavier_initializer()):
with slim.arg_scope([slim.batch_norm], decay=0.95, center=True, scale=True,
activation_fn=tf.nn.relu, is_training=(self.mode=='train')):
net = slim.conv2d_transpose(inputs, 512, [4, 4], padding='VALID', scope='conv_transpose1') # (batch_size, 4, 4, 512)
net = slim.batch_norm(net, scope='bn1')
net = slim.conv2d_transpose(net, 256, [3, 3], scope='conv_transpose2') # (batch_size, 8, 8, 256)
net = slim.batch_norm(net, scope='bn2')
net = slim.conv2d_transpose(net, 128, [3, 3], scope='conv_transpose3') # (batch_size, 16, 16, 128)
net = slim.batch_norm(net, scope='bn3')
net = slim.conv2d_transpose(net, 1, [3, 3], activation_fn=tf.nn.tanh, scope='conv_transpose4') # (batch_size, 32, 32, 1)
return net
def discriminator(self, images, reuse=False):
# images: (batch, 32, 32, 1)
with tf.variable_scope('discriminator', reuse=reuse):
with slim.arg_scope([slim.conv2d], padding='SAME', activation_fn=None,
stride=2, weights_initializer=tf.contrib.layers.xavier_initializer()):
with slim.arg_scope([slim.batch_norm], decay=0.95, center=True, scale=True,
activation_fn=tf.nn.relu, is_training=(self.mode=='train')):
net = slim.conv2d(images, 128, [3, 3], activation_fn=tf.nn.relu, scope='conv1') # (batch_size, 16, 16, 128)
net = slim.batch_norm(net, scope='bn1')
net = slim.conv2d(net, 256, [3, 3], scope='conv2') # (batch_size, 8, 8, 256)
net = slim.batch_norm(net, scope='bn2')
net = slim.conv2d(net, 512, [3, 3], scope='conv3') # (batch_size, 4, 4, 512)
net = slim.batch_norm(net, scope='bn3')
net = slim.conv2d(net, 1, [4, 4], padding='VALID', scope='conv4') # (batch_size, 1, 1, 1)
net = slim.flatten(net)
return net
def build_model(self):
if self.mode == 'pretrain':
self.images = tf.placeholder(tf.float32, [None, 32, 32, 3], 'svhn_images')
self.labels = tf.placeholder(tf.int64, [None], 'svhn_labels')
# logits and accuracy
self.logits = self.content_extractor(self.images)
self.pred = tf.argmax(self.logits, 1)
self.correct_pred = tf.equal(self.pred, self.labels)
self.accuracy = tf.reduce_mean(tf.cast(self.correct_pred, tf.float32))
# loss and train op
self.loss = slim.losses.sparse_softmax_cross_entropy(self.logits, self.labels)
self.optimizer = tf.train.AdamOptimizer(self.learning_rate)
self.train_op = slim.learning.create_train_op(self.loss, self.optimizer)
# summary op
loss_summary = tf.summary.scalar('classification_loss', self.loss)
accuracy_summary = tf.summary.scalar('accuracy', self.accuracy)
self.summary_op = tf.summary.merge([loss_summary, accuracy_summary])
elif self.mode == 'eval':
self.images = tf.placeholder(tf.float32, [None, 32, 32, 3], 'svhn_images')
# source domain (svhn to mnist)
self.fx = self.content_extractor(self.images)
self.sampled_images = self.generator(self.fx)
elif self.mode == 'train':
self.src_images = tf.placeholder(tf.float32, [None, 32, 32, 3], 'svhn_images')
self.trg_images = tf.placeholder(tf.float32, [None, 32, 32, 1], 'mnist_images')
# source domain (svhn to mnist)
with tf.name_scope('model_for_source_domain'):
self.fx = self.content_extractor(self.src_images)
self.fake_images = self.generator(self.fx)
self.logits = self.discriminator(self.fake_images)
self.fgfx = self.content_extractor(self.fake_images, reuse=True)
# loss
self.d_loss_src = slim.losses.sigmoid_cross_entropy(self.logits, tf.zeros_like(self.logits))
self.g_loss_src = slim.losses.sigmoid_cross_entropy(self.logits, tf.ones_like(self.logits))
self.f_loss_src = tf.reduce_mean(tf.square(self.fx - self.fgfx)) * 15.0
# optimizer
self.d_optimizer_src = tf.train.AdamOptimizer(self.learning_rate)
self.g_optimizer_src = tf.train.AdamOptimizer(self.learning_rate)
self.f_optimizer_src = tf.train.AdamOptimizer(self.learning_rate)
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if 'discriminator' in var.name]
g_vars = [var for var in t_vars if 'generator' in var.name]
f_vars = [var for var in t_vars if 'content_extractor' in var.name]
# train op
with tf.name_scope('source_train_op'):
self.d_train_op_src = slim.learning.create_train_op(self.d_loss_src, self.d_optimizer_src, variables_to_train=d_vars)
self.g_train_op_src = slim.learning.create_train_op(self.g_loss_src, self.g_optimizer_src, variables_to_train=g_vars)
self.f_train_op_src = slim.learning.create_train_op(self.f_loss_src, self.f_optimizer_src, variables_to_train=f_vars)
# summary op
d_loss_src_summary = tf.summary.scalar('src_d_loss', self.d_loss_src)
g_loss_src_summary = tf.summary.scalar('src_g_loss', self.g_loss_src)
f_loss_src_summary = tf.summary.scalar('src_f_loss', self.f_loss_src)
origin_images_summary = tf.summary.image('src_origin_images', self.src_images)
sampled_images_summary = tf.summary.image('src_sampled_images', self.fake_images)
self.summary_op_src = tf.summary.merge([d_loss_src_summary, g_loss_src_summary,
f_loss_src_summary, origin_images_summary,
sampled_images_summary])
# target domain (mnist)
with tf.name_scope('model_for_target_domain'):
self.fx = self.content_extractor(self.trg_images, reuse=True)
self.reconst_images = self.generator(self.fx, reuse=True)
self.logits_fake = self.discriminator(self.reconst_images, reuse=True)
self.logits_real = self.discriminator(self.trg_images, reuse=True)
# loss
self.d_loss_fake_trg = slim.losses.sigmoid_cross_entropy(self.logits_fake, tf.zeros_like(self.logits_fake))
self.d_loss_real_trg = slim.losses.sigmoid_cross_entropy(self.logits_real, tf.ones_like(self.logits_real))
self.d_loss_trg = self.d_loss_fake_trg + self.d_loss_real_trg
self.g_loss_fake_trg = slim.losses.sigmoid_cross_entropy(self.logits_fake, tf.ones_like(self.logits_fake))
self.g_loss_const_trg = tf.reduce_mean(tf.square(self.trg_images - self.reconst_images)) * 15.0
self.g_loss_trg = self.g_loss_fake_trg + self.g_loss_const_trg
# optimizer
self.d_optimizer_trg = tf.train.AdamOptimizer(self.learning_rate)
self.g_optimizer_trg = tf.train.AdamOptimizer(self.learning_rate)
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if 'discriminator' in var.name]
g_vars = [var for var in t_vars if 'generator' in var.name]
f_vars = [var for var in t_vars if 'content_extractor' in var.name]
# train op
with tf.name_scope('target_train_op'):
self.d_train_op_trg = slim.learning.create_train_op(self.d_loss_trg, self.d_optimizer_trg, variables_to_train=d_vars)
self.g_train_op_trg = slim.learning.create_train_op(self.g_loss_trg, self.g_optimizer_trg, variables_to_train=g_vars)
# summary op
d_loss_fake_trg_summary = tf.summary.scalar('trg_d_loss_fake', self.d_loss_fake_trg)
d_loss_real_trg_summary = tf.summary.scalar('trg_d_loss_real', self.d_loss_real_trg)
d_loss_trg_summary = tf.summary.scalar('trg_d_loss', self.d_loss_trg)
g_loss_fake_trg_summary = tf.summary.scalar('trg_g_loss_fake', self.g_loss_fake_trg)
g_loss_const_trg_summary = tf.summary.scalar('trg_g_loss_const', self.g_loss_const_trg)
g_loss_trg_summary = tf.summary.scalar('trg_g_loss', self.g_loss_trg)
origin_images_summary = tf.summary.image('trg_origin_images', self.trg_images)
sampled_images_summary = tf.summary.image('trg_reconstructed_images', self.reconst_images)
self.summary_op_trg = tf.summary.merge([d_loss_trg_summary, g_loss_trg_summary,
d_loss_fake_trg_summary, d_loss_real_trg_summary,
g_loss_fake_trg_summary, g_loss_const_trg_summary,
origin_images_summary, sampled_images_summary])
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)