forked from wchliao/anime_face
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DCGAN.py
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DCGAN.py
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import argparse
import numpy as np
import tensorflow as tf
from DataSet import DataSet
import utils
import os
import time
import csv
import skipthoughts
import scipy.misc
##### Global Constants #####
image_path = 'faces/'
tag_file = 'tags_clean.csv'
model_file = './model/DCGAN/model.ckpt'
sample_path = 'samples/'
############################
##### Parameters #####
batch_size = 64
image_size = 64
caption_vec_size = 4800
noise_dim = 100
channel_dim = 64
reduced_text_dim = 256
learning_rate = 0.0002
momentum = 0.5
max_epoch = 300
max_epoch = 10
######################
##### GPU Options #####
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1)
#######################
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--train', action='store_true', help='Run training')
parser.add_argument('--generate', action='store_true', help='Run testing')
parser.add_argument('-t', '--testing_file', help='Should give testing text file')
parser.add_argument('-n', '--generate_num', help='How many images to generate for each caption')
return parser.parse_args()
class DCGAN():
def __init__(self, batch_size, image_size, caption_vec_size,
noise_dim, channel_dim, reduced_text_dim, momentum):
self.batch_size = batch_size
self.image_size = image_size
self.caption_vec_size = caption_vec_size
self.noise_dim = noise_dim
self.channel_dim = channel_dim
self.reduced_text_dim = reduced_text_dim
self.g_bn0 = utils.batch_norm(momentum=momentum, name='g_bn0')
self.g_bn1 = utils.batch_norm(momentum=momentum, name='g_bn1')
self.g_bn2 = utils.batch_norm(momentum=momentum, name='g_bn2')
self.g_bn3 = utils.batch_norm(momentum=momentum, name='g_bn3')
self.d_bn1 = utils.batch_norm(momentum=momentum, name='d_bn1')
self.d_bn2 = utils.batch_norm(momentum=momentum, name='d_bn2')
self.d_bn3 = utils.batch_norm(momentum=momentum, name='d_bn3')
self.d_bn4 = utils.batch_norm(momentum=momentum, name='d_bn4')
return
def build_model(self):
real_image = tf.placeholder('float32', [self.batch_size,
self.image_size, self.image_size, 3], name = 'read_image')
wrong_image = tf.placeholder('float32', [self.batch_size,
self.image_size, self.image_size, 3], name = 'wrong_image')
caption = tf.placeholder('float32', [self.batch_size, self.caption_vec_size], name = 'caption')
noise = tf.placeholder('float32', [self.batch_size, self.noise_dim])
fake_image = self.generator(noise, caption)
d_real_image, d_real_image_logits = self.discriminator(real_image, caption)
d_wrong_image, d_wrong_image_logits = self.discriminator(wrong_image, caption, reuse=True)
d_fake_image, d_fake_image_logits = self.discriminator(fake_image, caption, reuse=True)
g_loss = tf.reduce_mean(tf.square(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_image_logits, labels=tf.ones_like(d_fake_image))))
d_loss1 = tf.reduce_mean(tf.square(d_real_image_logits - tf.ones_like(d_real_image)))
d_loss2 = tf.reduce_mean(tf.square(d_wrong_image_logits))
d_loss3 = tf.reduce_mean(tf.square(d_fake_image_logits))
d_loss = d_loss1 + d_loss2 + d_loss3
all_var = tf.trainable_variables()
d_vars = [var for var in all_var if 'd_' in var.name]
g_vars = [var for var in all_var if 'g_' in var.name]
inputs = {
'real_image': real_image,
'wrong_image': wrong_image,
'caption': caption,
'noise': noise
}
variables = {
'd_vars': d_vars,
'g_vars': g_vars
}
loss = {
'g_loss': g_loss,
'd_loss': d_loss,
}
return inputs, variables, loss
def build_generator(self):
caption = tf.placeholder('float32', [self.batch_size, self.caption_vec_size], name = 'caption')
noise = tf.placeholder('float32', [self.batch_size, self.noise_dim])
fake_image = self.sampler(noise, caption)
inputs = {
'caption': caption,
'noise': noise
}
return inputs, fake_image
def sampler(self, noise, caption):
tf.get_variable_scope().reuse_variables()
s = self.image_size
s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16)
reduce_caption = utils.lrelu(utils.linear(caption, self.reduced_text_dim, 'g_embedding'))
noise_concat = tf.concat([noise, reduce_caption], 1)
new_noise = utils.linear(noise_concat, self.channel_dim*8*s16*s16, 'g_h0_lin')
h0 = tf.reshape(new_noise, [-1, s16, s16, self.channel_dim*8])
h0 = tf.nn.relu(self.g_bn0(h0, is_training = False))
h1 = utils.deconv2d(h0, [self.batch_size, s8, s8, self.channel_dim*4], name='g_h1')
h1 = tf.nn.relu(self.g_bn1(h1, is_training = False))
h2 = utils.deconv2d(h1, [self.batch_size, s4, s4, self.channel_dim*2], name='g_h2')
h2 = tf.nn.relu(self.g_bn2(h2, is_training = False))
h3 = utils.deconv2d(h2, [self.batch_size, s2, s2, self.channel_dim], name='g_h3')
h3 = tf.nn.relu(self.g_bn3(h3, is_training = False))
h4 = utils.deconv2d(h3, [self.batch_size, s, s, 3], name='g_h4')
return (tf.tanh(h4)/2. + 0.5)
def generator(self, noise, caption):
s = self.image_size
s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16)
reduced_caption = utils.lrelu(utils.linear(caption, self.reduced_text_dim, 'g_embedding'))
noise_concat = tf.concat([noise, reduced_caption], 1)
new_noise = utils.linear(noise_concat, self.channel_dim*8*s16*s16, 'g_h0_lin')
h0 = tf.reshape(new_noise, [-1, s16, s16, self.channel_dim*8])
h0 = tf.nn.relu(self.g_bn0(h0))
h1 = utils.deconv2d(h0, [self.batch_size, s8, s8, self.channel_dim*4], name='g_h1')
h1 = tf.nn.relu(self.g_bn1(h1))
h2 = utils.deconv2d(h1, [self.batch_size, s4, s4, self.channel_dim*2], name='g_h2')
h2 = tf.nn.relu(self.g_bn2(h2))
h3 = utils.deconv2d(h2, [self.batch_size, s2, s2, self.channel_dim], name='g_h3')
h3 = tf.nn.relu(self.g_bn3(h3))
h4 = utils.deconv2d(h3, [self.batch_size, s, s, 3], name='g_h4')
return (tf.tanh(h4)/2. + 0.5)
def discriminator(self, image, caption, reuse=False):
if reuse:
tf.get_variable_scope().reuse_variables()
h0 = utils.lrelu(utils.conv2d(image, self.channel_dim, name='d_h0_conv'))
h1 = utils.lrelu(self.d_bn1(utils.conv2d(h0, self.channel_dim*2, name = 'd_h1_conv')))
h2 = utils.lrelu(self.d_bn2(utils.conv2d(h1, self.channel_dim*4, name = 'd_h2_conv')))
h3 = utils.lrelu(self.d_bn3(utils.conv2d(h2, self.channel_dim*8, name = 'd_h3_conv')))
reduced_caption = utils.lrelu(utils.linear(caption, self.reduced_text_dim, 'd_embedding'))
reduced_caption = tf.expand_dims(reduced_caption, 1)
reduced_caption = tf.expand_dims(reduced_caption, 2)
tiled_caption = tf.tile(reduced_caption, [1,4,4,1], name='tiled_embedding')
h3_concat = tf.concat([h3, tiled_caption], 3, name='h3_concat')
h3_new = utils.lrelu(self.d_bn4(utils.conv2d(h3_concat, self.channel_dim*8, 1,1,1,1, name='d_h3_conv_new')))
h4 = utils.linear(tf.reshape(h3_new, [self.batch_size, -1]), 1, 'd_h3_lin')
return tf.nn.sigmoid(h4), h4
def save_model(self, sess, model_file):
if not os.path.isdir(os.path.dirname(model_file)):
os.mkdir(os.path.dirname(model_file))
saver = tf.train.Saver()
saver.save(sess, model_file)
return
def restore_model(self, sess, model_file):
if os.path.isdir(os.path.dirname(model_file)):
saver = tf.train.Saver()
saver.restore(sess, model_file)
def train():
model = DCGAN(
batch_size = batch_size,
image_size = image_size,
caption_vec_size = caption_vec_size,
noise_dim = noise_dim,
channel_dim = channel_dim,
reduced_text_dim = reduced_text_dim,
momentum = momentum
)
inputs, variables, loss = model.build_model()
with tf.variable_scope(tf.get_variable_scope(), reuse=False):
d_optimizer = tf.train.AdamOptimizer(learning_rate,
beta1 = momentum).minimize(loss['d_loss'], var_list=variables['d_vars'])
g_optimizer = tf.train.AdamOptimizer(learning_rate,
beta1 = momentum).minimize(loss['g_loss'], var_list=variables['g_vars'])
data = DataSet(image_path, tag_file, image_size)
init = tf.global_variables_initializer()
# with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
with tf.Session() as sess:
sess.run(init)
model.restore_model(sess, model_file)
epoch = -1
start_time = time.time()
while epoch < max_epoch:
real_image, wrong_image, caption = data.next_batch(batch_size=batch_size)
noise = np.random.uniform(-1, 1, [batch_size, noise_dim])
sess.run(d_optimizer, feed_dict={
inputs['real_image']: real_image,
inputs['wrong_image']: wrong_image,
inputs['caption']: caption,
inputs['noise']: noise
})
sess.run(g_optimizer, feed_dict={
inputs['real_image']: real_image,
inputs['wrong_image']: wrong_image,
inputs['caption']: caption,
inputs['noise']: noise
})
sess.run(g_optimizer, feed_dict={
inputs['real_image']: real_image,
inputs['wrong_image']: wrong_image,
inputs['caption']: caption,
inputs['noise']: noise
})
if epoch != data.N_epoch:
epoch = data.N_epoch
model.save_model(sess, model_file)
used_time = time.time() - start_time
start_time = time.time()
d_loss, g_loss = sess.run([loss['d_loss'], loss['g_loss']], feed_dict={
inputs['real_image']: real_image,
inputs['wrong_image']: wrong_image,
inputs['caption']: caption,
inputs['noise']: noise
})
print(str(epoch) + '/' + str(max_epoch) + ' epoch: ' +
'd_loss = ' + str(d_loss) + ' ' +
'g_loss = ' + str(g_loss) + ' ' +
'time = ' + str(used_time) + ' secs')
return
def generate(testing_file, generate_num):
model = DCGAN(
batch_size = generate_num,
image_size = image_size,
caption_vec_size = caption_vec_size,
noise_dim = noise_dim,
channel_dim = channel_dim,
reduced_text_dim = reduced_text_dim,
momentum = momentum
)
_, _, _ = model.build_model()
inputs, image = model.build_generator()
captions = []
with open(testing_file, 'r') as f:
reader = csv.reader(f, delimiter=',')
for line in reader:
captions.append(line[1])
sent2vec = skipthoughts.load_model()
caption_vecs = skipthoughts.encode(sent2vec, captions)
generated_images = []
# with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
with tf.Session() as sess:
model.restore_model(sess, model_file)
for vec in caption_vecs:
noise = np.random.uniform(-1, 1, [generate_num, noise_dim])
caption = [vec] * generate_num
generated_images.append(sess.run(image, feed_dict={
inputs['caption']: caption,
inputs['noise']: noise
}))
if not os.path.isdir(sample_path):
os.mkdir(sample_path)
for i, images in enumerate(generated_images, start=1):
for j, image in enumerate(images, start=1):
scipy.misc.imsave(os.path.join(sample_path, 'sample_{}_{}.jpg'.format(i, j)), image)
return
if __name__ == '__main__':
args = parse_args()
if args.train:
train()
if args.generate:
generate(args.testing_file, int(args.generate_num))