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model.py
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from __future__ import division
import os
import time
from glob import glob
import tensorflow as tf
import numpy as np
from six.moves import xrange
from ops import *
from utils import *
class DCGAN(object):
def __init__(self, sess, image_size=108, is_crop=True,
batch_size=64, sample_size = 64, output_size=64,
y_dim=None, z_dim=100, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024, c_dim=3, dataset_name='default',
checkpoint_dir=None, sample_dir=None):
"""
Args:
sess: TensorFlow session
batch_size: The size of batch. Should be specified before training.
output_size: (optional) The resolution in pixels of the images. [64]
y_dim: (optional) Dimension of dim for y. [None]
z_dim: (optional) Dimension of dim for Z. [100]
gf_dim: (optional) Dimension of gen filters in first conv layer. [64]
df_dim: (optional) Dimension of discrim filters in first conv layer. [64]
gfc_dim: (optional) Dimension of gen units for for fully connected layer. [1024]
dfc_dim: (optional) Dimension of discrim units for fully connected layer. [1024]
c_dim: (optional) Dimension of image color. For grayscale input, set to 1. [3]
"""
self.sess = sess
self.is_crop = is_crop
self.is_grayscale = (c_dim == 1)
self.batch_size = batch_size
self.image_size = image_size
self.sample_size = sample_size
self.output_size = output_size
self.y_dim = y_dim
self.z_dim = z_dim
self.gf_dim = gf_dim
self.df_dim = df_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
self.c_dim = c_dim
# batch normalization : deals with poor initialization helps gradient flow
self.d_bn1 = batch_norm(name='d_bn1')
self.d_bn2 = batch_norm(name='d_bn2')
if not self.y_dim:
self.d_bn3 = batch_norm(name='d_bn3')
self.g_bn0 = batch_norm(name='g_bn0')
self.g_bn1 = batch_norm(name='g_bn1')
self.g_bn2 = batch_norm(name='g_bn2')
if not self.y_dim:
self.g_bn3 = batch_norm(name='g_bn3')
self.dataset_name = dataset_name
self.checkpoint_dir = checkpoint_dir
self.build_model()
def build_model(self):
if self.y_dim:
self.y= tf.placeholder(tf.float32, [self.batch_size, self.y_dim], name='y')
self.images = tf.placeholder(tf.float32, [self.batch_size] + [self.output_size, self.output_size, self.c_dim],
name='real_images')
self.sample_images= tf.placeholder(tf.float32, [self.sample_size] + [self.output_size, self.output_size, self.c_dim],
name='sample_images')
self.z = tf.placeholder(tf.float32, [None, self.z_dim],
name='z')
self.z_sum = histogram_summary("z", self.z)
if self.y_dim:
self.G = self.generator(self.z, self.y)
self.D, self.D_logits = self.discriminator(self.images, self.y, reuse=False)
self.sampler = self.sampler(self.z, self.y)
self.D_, self.D_logits_ = self.discriminator(self.G, self.y, reuse=True)
else:
self.G = self.generator(self.z)
self.D, self.D_logits = self.discriminator(self.images)
self.sampler = self.sampler(self.z)
self.D_, self.D_logits_ = self.discriminator(self.G, reuse=True)
self.d_sum = histogram_summary("d", self.D)
self.d__sum = histogram_summary("d_", self.D_)
self.G_sum = image_summary("G", self.G)
self.d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(self.D_logits, tf.ones_like(self.D)))
self.d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(self.D_logits_, tf.zeros_like(self.D_)))
self.g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(self.D_logits_, tf.ones_like(self.D_)))
self.d_loss_real_sum = scalar_summary("d_loss_real", self.d_loss_real)
self.d_loss_fake_sum = scalar_summary("d_loss_fake", self.d_loss_fake)
self.d_loss = self.d_loss_real + self.d_loss_fake
self.g_loss_sum = scalar_summary("g_loss", self.g_loss)
self.d_loss_sum = scalar_summary("d_loss", self.d_loss)
t_vars = tf.trainable_variables()
self.d_vars = [var for var in t_vars if 'd_' in var.name]
self.g_vars = [var for var in t_vars if 'g_' in var.name]
self.saver = tf.train.Saver()
def train(self, config):
"""Train DCGAN"""
if config.dataset == 'mnist':
data_X, data_y = self.load_mnist()
else:
data = glob(os.path.join("./data", config.dataset, "*.jpg"))
#np.random.shuffle(data)
d_optim = tf.train.AdamOptimizer(config.learning_rate, beta1=config.beta1) \
.minimize(self.d_loss, var_list=self.d_vars)
g_optim = tf.train.AdamOptimizer(config.learning_rate, beta1=config.beta1) \
.minimize(self.g_loss, var_list=self.g_vars)
try:
tf.initialize_all_variables().run()
except:
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
self.g_sum = merge_summary([self.z_sum, self.d__sum,
self.G_sum, self.d_loss_fake_sum, self.g_loss_sum])
self.d_sum = merge_summary([self.z_sum, self.d_sum, self.d_loss_real_sum, self.d_loss_sum])
self.writer = SummaryWriter("./logs", self.sess.graph)
sample_z = np.random.uniform(-1, 1, size=(self.sample_size , self.z_dim))
if config.dataset == 'mnist':
sample_images = data_X[0:self.sample_size]
sample_labels = data_y[0:self.sample_size]
else:
sample_files = data[0:self.sample_size]
sample = [get_image(sample_file, self.image_size, is_crop=self.is_crop, resize_w=self.output_size, is_grayscale = self.is_grayscale) for sample_file in sample_files]
if (self.is_grayscale):
sample_images = np.array(sample).astype(np.float32)[:, :, :, None]
else:
sample_images = np.array(sample).astype(np.float32)
counter = 1
start_time = time.time()
if self.load(self.checkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
for epoch in xrange(config.epoch):
if config.dataset == 'mnist':
batch_idxs = min(len(data_X), config.train_size) // config.batch_size
else:
data = glob(os.path.join("./data", config.dataset, "*.jpg"))
batch_idxs = min(len(data), config.train_size) // config.batch_size
for idx in xrange(0, batch_idxs):
if config.dataset == 'mnist':
batch_images = data_X[idx*config.batch_size:(idx+1)*config.batch_size]
batch_labels = data_y[idx*config.batch_size:(idx+1)*config.batch_size]
else:
batch_files = data[idx*config.batch_size:(idx+1)*config.batch_size]
batch = [get_image(batch_file, self.image_size, is_crop=self.is_crop, resize_w=self.output_size, is_grayscale = self.is_grayscale) for batch_file in batch_files]
if (self.is_grayscale):
batch_images = np.array(batch).astype(np.float32)[:, :, :, None]
else:
batch_images = np.array(batch).astype(np.float32)
batch_z = np.random.uniform(-1, 1, [config.batch_size, self.z_dim]) \
.astype(np.float32)
if config.dataset == 'mnist':
# Update D network
_, summary_str = self.sess.run([d_optim, self.d_sum],
feed_dict={ self.images: batch_images, self.z: batch_z, self.y:batch_labels })
self.writer.add_summary(summary_str, counter)
# Update G network
_, summary_str = self.sess.run([g_optim, self.g_sum],
feed_dict={ self.z: batch_z, self.y:batch_labels })
self.writer.add_summary(summary_str, counter)
# Run g_optim twice to make sure that d_loss does not go to zero (different from paper)
_, summary_str = self.sess.run([g_optim, self.g_sum],
feed_dict={ self.z: batch_z, self.y:batch_labels })
self.writer.add_summary(summary_str, counter)
errD_fake = self.d_loss_fake.eval({self.z: batch_z, self.y:batch_labels})
errD_real = self.d_loss_real.eval({self.images: batch_images, self.y:batch_labels})
errG = self.g_loss.eval({self.z: batch_z, self.y:batch_labels})
else:
# Update D network
_, summary_str = self.sess.run([d_optim, self.d_sum],
feed_dict={ self.images: batch_images, self.z: batch_z })
self.writer.add_summary(summary_str, counter)
# Update G network
_, summary_str = self.sess.run([g_optim, self.g_sum],
feed_dict={ self.z: batch_z })
self.writer.add_summary(summary_str, counter)
# Run g_optim twice to make sure that d_loss does not go to zero (different from paper)
_, summary_str = self.sess.run([g_optim, self.g_sum],
feed_dict={ self.z: batch_z })
self.writer.add_summary(summary_str, counter)
errD_fake = self.d_loss_fake.eval({self.z: batch_z})
errD_real = self.d_loss_real.eval({self.images: batch_images})
errG = self.g_loss.eval({self.z: batch_z})
counter += 1
print("Epoch: [%2d] [%4d/%4d] time: %4.4f, d_loss: %.8f, g_loss: %.8f" \
% (epoch, idx, batch_idxs,
time.time() - start_time, errD_fake+errD_real, errG))
if np.mod(counter, 100) == 1:
if config.dataset == 'mnist':
samples, d_loss, g_loss = self.sess.run(
[self.sampler, self.d_loss, self.g_loss],
feed_dict={self.z: sample_z, self.images: sample_images, self.y:sample_labels}
)
save_images(samples, [8, 8],
'./{}/train_{:02d}_{:04d}.png'.format(config.sample_dir, epoch, idx))
print("[Sample] d_loss: %.8f, g_loss: %.8f" % (d_loss, g_loss))
else:
try:
samples, d_loss, g_loss = self.sess.run(
[self.sampler, self.d_loss, self.g_loss],
feed_dict={self.z: sample_z, self.images: sample_images}
)
save_images(samples, [8, 8],
'./{}/train_{:02d}_{:04d}.png'.format(config.sample_dir, epoch, idx))
print("[Sample] d_loss: %.8f, g_loss: %.8f" % (d_loss, g_loss))
except:
print("one pic error!...")
if np.mod(counter, 500) == 2:
self.save(config.checkpoint_dir, counter)
def discriminator(self, image, y=None, reuse=False):
with tf.variable_scope("discriminator") as scope:
if reuse:
scope.reuse_variables()
if not self.y_dim:
h0 = lrelu(conv2d(image, self.df_dim, name='d_h0_conv'))
h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim*2, name='d_h1_conv')))
h2 = lrelu(self.d_bn2(conv2d(h1, self.df_dim*4, name='d_h2_conv')))
h3 = lrelu(self.d_bn3(conv2d(h2, self.df_dim*8, name='d_h3_conv')))
h4 = linear(tf.reshape(h3, [self.batch_size, -1]), 1, 'd_h3_lin')
return tf.nn.sigmoid(h4), h4
else:
yb = tf.reshape(y, [self.batch_size, 1, 1, self.y_dim])
x = conv_cond_concat(image, yb)
h0 = lrelu(conv2d(x, self.c_dim + self.y_dim, name='d_h0_conv'))
h0 = conv_cond_concat(h0, yb)
h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim + self.y_dim, name='d_h1_conv')))
h1 = tf.reshape(h1, [self.batch_size, -1])
h1 = tf.concat(1, [h1, y])
h2 = lrelu(self.d_bn2(linear(h1, self.dfc_dim, 'd_h2_lin')))
h2 = tf.concat(1, [h2, y])
h3 = linear(h2, 1, 'd_h3_lin')
return tf.nn.sigmoid(h3), h3
def generator(self, z, y=None):
with tf.variable_scope("generator") as scope:
if not self.y_dim:
s = self.output_size
s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16)
# project `z` and reshape
self.z_, self.h0_w, self.h0_b = linear(z, self.gf_dim*8*s16*s16, 'g_h0_lin', with_w=True)
self.h0 = tf.reshape(self.z_, [-1, s16, s16, self.gf_dim * 8])
h0 = tf.nn.relu(self.g_bn0(self.h0))
self.h1, self.h1_w, self.h1_b = deconv2d(h0,
[self.batch_size, s8, s8, self.gf_dim*4], name='g_h1', with_w=True)
h1 = tf.nn.relu(self.g_bn1(self.h1))
h2, self.h2_w, self.h2_b = deconv2d(h1,
[self.batch_size, s4, s4, self.gf_dim*2], name='g_h2', with_w=True)
h2 = tf.nn.relu(self.g_bn2(h2))
h3, self.h3_w, self.h3_b = deconv2d(h2,
[self.batch_size, s2, s2, self.gf_dim*1], name='g_h3', with_w=True)
h3 = tf.nn.relu(self.g_bn3(h3))
h4, self.h4_w, self.h4_b = deconv2d(h3,
[self.batch_size, s, s, self.c_dim], name='g_h4', with_w=True)
return tf.nn.tanh(h4)
else:
s = self.output_size
s2, s4 = int(s/2), int(s/4)
# yb = tf.expand_dims(tf.expand_dims(y, 1),2)
yb = tf.reshape(y, [self.batch_size, 1, 1, self.y_dim])
z = tf.concat(1, [z, y])
h0 = tf.nn.relu(self.g_bn0(linear(z, self.gfc_dim, 'g_h0_lin')))
h0 = tf.concat(1, [h0, y])
h1 = tf.nn.relu(self.g_bn1(linear(h0, self.gf_dim*2*s4*s4, 'g_h1_lin')))
h1 = tf.reshape(h1, [self.batch_size, s4, s4, self.gf_dim * 2])
h1 = conv_cond_concat(h1, yb)
h2 = tf.nn.relu(self.g_bn2(deconv2d(h1, [self.batch_size, s2, s2, self.gf_dim * 2], name='g_h2')))
h2 = conv_cond_concat(h2, yb)
return tf.nn.sigmoid(deconv2d(h2, [self.batch_size, s, s, self.c_dim], name='g_h3'))
def sampler(self, z, y=None):
with tf.variable_scope("generator") as scope:
scope.reuse_variables()
if not self.y_dim:
s = self.output_size
s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16)
# project `z` and reshape
h0 = tf.reshape(linear(z, self.gf_dim*8*s16*s16, 'g_h0_lin'),
[-1, s16, s16, self.gf_dim * 8])
h0 = tf.nn.relu(self.g_bn0(h0, train=False))
h1 = deconv2d(h0, [self.batch_size, s8, s8, self.gf_dim*4], name='g_h1')
h1 = tf.nn.relu(self.g_bn1(h1, train=False))
h2 = deconv2d(h1, [self.batch_size, s4, s4, self.gf_dim*2], name='g_h2')
h2 = tf.nn.relu(self.g_bn2(h2, train=False))
h3 = deconv2d(h2, [self.batch_size, s2, s2, self.gf_dim*1], name='g_h3')
h3 = tf.nn.relu(self.g_bn3(h3, train=False))
h4 = deconv2d(h3, [self.batch_size, s, s, self.c_dim], name='g_h4')
return tf.nn.tanh(h4)
else:
s = self.output_size
s2, s4 = int(s/2), int(s/4)
# yb = tf.reshape(y, [-1, 1, 1, self.y_dim])
yb = tf.reshape(y, [self.batch_size, 1, 1, self.y_dim])
z = tf.concat(1, [z, y])
h0 = tf.nn.relu(self.g_bn0(linear(z, self.gfc_dim, 'g_h0_lin')))
h0 = tf.concat(1, [h0, y])
h1 = tf.nn.relu(self.g_bn1(linear(h0, self.gf_dim*2*s4*s4, 'g_h1_lin'), train=False))
h1 = tf.reshape(h1, [self.batch_size, s4, s4, self.gf_dim * 2])
h1 = conv_cond_concat(h1, yb)
h2 = tf.nn.relu(self.g_bn2(deconv2d(h1, [self.batch_size, s2, s2, self.gf_dim * 2], name='g_h2'), train=False))
h2 = conv_cond_concat(h2, yb)
return tf.nn.sigmoid(deconv2d(h2, [self.batch_size, s, s, self.c_dim], name='g_h3'))
def load_mnist(self):
data_dir = os.path.join("./data", self.dataset_name)
fd = open(os.path.join(data_dir,'train-images-idx3-ubyte'))
loaded = np.fromfile(file=fd,dtype=np.uint8)
trX = loaded[16:].reshape((60000,28,28,1)).astype(np.float)
fd = open(os.path.join(data_dir,'train-labels-idx1-ubyte'))
loaded = np.fromfile(file=fd,dtype=np.uint8)
trY = loaded[8:].reshape((60000)).astype(np.float)
fd = open(os.path.join(data_dir,'t10k-images-idx3-ubyte'))
loaded = np.fromfile(file=fd,dtype=np.uint8)
teX = loaded[16:].reshape((10000,28,28,1)).astype(np.float)
fd = open(os.path.join(data_dir,'t10k-labels-idx1-ubyte'))
loaded = np.fromfile(file=fd,dtype=np.uint8)
teY = loaded[8:].reshape((10000)).astype(np.float)
trY = np.asarray(trY)
teY = np.asarray(teY)
X = np.concatenate((trX, teX), axis=0)
y = np.concatenate((trY, teY), axis=0)
seed = 547
np.random.seed(seed)
np.random.shuffle(X)
np.random.seed(seed)
np.random.shuffle(y)
y_vec = np.zeros((len(y), self.y_dim), dtype=np.float)
for i, label in enumerate(y):
y_vec[i,y[i]] = 1.0
return X/255.,y_vec
def save(self, checkpoint_dir, step):
model_name = "DCGAN.model"
model_dir = "%s_%s_%s" % (self.dataset_name, self.batch_size, self.output_size)
checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
self.saver.save(self.sess,
os.path.join(checkpoint_dir, model_name),
global_step=step)
def load(self, checkpoint_dir):
print(" [*] Reading checkpoints...")
model_dir = "%s_%s_%s" % (self.dataset_name, self.batch_size, self.output_size)
checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
print(" [*] Success to read {}".format(ckpt_name))
return True
else:
print(" [*] Failed to find a checkpoint")
return False