/
model.py
264 lines (225 loc) · 13.1 KB
/
model.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
from __future__ import division
import os
import time
from glob import glob
import tensorflow as tf
import numpy as np
from collections import namedtuple
from module import *
from utils import *
class cyclegan(object):
def __init__(self, sess, args):
self.sess = sess
self.batch_size = args.batch_size
self.image_size = args.fine_size
self.input_c_dim = args.input_nc
self.output_c_dim = args.output_nc
self.L1_lambda = args.L1_lambda
self.dataset_dir = args.dataset_dir
self.discriminator = discriminator
if args.use_resnet:
self.generator = generator_resnet
else:
self.generator = generator_unet
if args.use_lsgan:
self.criterionGAN = mae_criterion
else:
self.criterionGAN = sce_criterion
OPTIONS = namedtuple('OPTIONS', 'batch_size image_size \
gf_dim df_dim output_c_dim is_training')
self.options = OPTIONS._make((args.batch_size, args.fine_size,
args.ngf, args.ndf, args.output_nc,
args.phase == 'train'))
self._build_model()
self.saver = tf.train.Saver()
self.pool = ImagePool(args.max_size)
def _build_model(self):
self.real_data = tf.placeholder(tf.float32,
[None, self.image_size, self.image_size,
self.input_c_dim + self.output_c_dim],
name='real_A_and_B_images')
self.real_A = self.real_data[:, :, :, :self.input_c_dim]
self.real_B = self.real_data[:, :, :, self.input_c_dim:self.input_c_dim + self.output_c_dim]
self.fake_B = self.generator(self.real_A, self.options, False, name="generatorA2B")
self.fake_A_ = self.generator(self.fake_B, self.options, False, name="generatorB2A")
self.fake_A = self.generator(self.real_B, self.options, True, name="generatorB2A")
self.fake_B_ = self.generator(self.fake_A, self.options, True, name="generatorA2B")
self.DB_fake = self.discriminator(self.fake_B, self.options, reuse=False, name="discriminatorB")
self.DA_fake = self.discriminator(self.fake_A, self.options, reuse=False, name="discriminatorA")
self.g_loss_a2b = self.criterionGAN(self.DB_fake, tf.ones_like(self.DB_fake)) \
+ self.L1_lambda * abs_criterion(self.real_A, self.fake_A_) \
+ self.L1_lambda * abs_criterion(self.real_B, self.fake_B_)
self.g_loss_b2a = self.criterionGAN(self.DA_fake, tf.ones_like(self.DA_fake)) \
+ self.L1_lambda * abs_criterion(self.real_A, self.fake_A_) \
+ self.L1_lambda * abs_criterion(self.real_B, self.fake_B_)
self.g_loss = self.criterionGAN(self.DA_fake, tf.ones_like(self.DA_fake)) \
+ self.criterionGAN(self.DB_fake, tf.ones_like(self.DB_fake)) \
+ self.L1_lambda * abs_criterion(self.real_A, self.fake_A_) \
+ self.L1_lambda * abs_criterion(self.real_B, self.fake_B_)
self.fake_A_sample = tf.placeholder(tf.float32,
[None, self.image_size, self.image_size,
self.input_c_dim], name='fake_A_sample')
self.fake_B_sample = tf.placeholder(tf.float32,
[None, self.image_size, self.image_size,
self.output_c_dim], name='fake_B_sample')
self.DB_real = self.discriminator(self.real_B, self.options, reuse=True, name="discriminatorB")
self.DA_real = self.discriminator(self.real_A, self.options, reuse=True, name="discriminatorA")
self.DB_fake_sample = self.discriminator(self.fake_B_sample, self.options, reuse=True, name="discriminatorB")
self.DA_fake_sample = self.discriminator(self.fake_A_sample, self.options, reuse=True, name="discriminatorA")
self.db_loss_real = self.criterionGAN(self.DB_real, tf.ones_like(self.DB_real))
self.db_loss_fake = self.criterionGAN(self.DB_fake_sample, tf.zeros_like(self.DB_fake_sample))
self.db_loss = (self.db_loss_real + self.db_loss_fake) / 2
self.da_loss_real = self.criterionGAN(self.DA_real, tf.ones_like(self.DA_real))
self.da_loss_fake = self.criterionGAN(self.DA_fake_sample, tf.zeros_like(self.DA_fake_sample))
self.da_loss = (self.da_loss_real + self.da_loss_fake) / 2
self.d_loss = self.da_loss + self.db_loss
self.g_loss_a2b_sum = tf.summary.scalar("g_loss_a2b", self.g_loss_a2b)
self.g_loss_b2a_sum = tf.summary.scalar("g_loss_b2a", self.g_loss_b2a)
self.g_loss_sum = tf.summary.scalar("g_loss", self.g_loss)
self.g_sum = tf.summary.merge([self.g_loss_a2b_sum, self.g_loss_b2a_sum, self.g_loss_sum])
self.db_loss_sum = tf.summary.scalar("db_loss", self.db_loss)
self.da_loss_sum = tf.summary.scalar("da_loss", self.da_loss)
self.d_loss_sum = tf.summary.scalar("d_loss", self.d_loss)
self.db_loss_real_sum = tf.summary.scalar("db_loss_real", self.db_loss_real)
self.db_loss_fake_sum = tf.summary.scalar("db_loss_fake", self.db_loss_fake)
self.da_loss_real_sum = tf.summary.scalar("da_loss_real", self.da_loss_real)
self.da_loss_fake_sum = tf.summary.scalar("da_loss_fake", self.da_loss_fake)
self.d_sum = tf.summary.merge(
[self.da_loss_sum, self.da_loss_real_sum, self.da_loss_fake_sum,
self.db_loss_sum, self.db_loss_real_sum, self.db_loss_fake_sum,
self.d_loss_sum]
)
self.test_A = tf.placeholder(tf.float32,
[None, self.image_size, self.image_size,
self.input_c_dim], name='test_A')
self.test_B = tf.placeholder(tf.float32,
[None, self.image_size, self.image_size,
self.output_c_dim], name='test_B')
self.testB = self.generator(self.test_A, self.options, True, name="generatorA2B")
self.testA = self.generator(self.test_B, self.options, True, name="generatorB2A")
t_vars = tf.trainable_variables()
self.d_vars = [var for var in t_vars if 'discriminator' in var.name]
self.g_vars = [var for var in t_vars if 'generator' in var.name]
for var in t_vars: print(var.name)
def train(self, args):
"""Train cyclegan"""
self.lr = tf.placeholder(tf.float32, None, name='learning_rate')
self.d_optim = tf.train.AdamOptimizer(self.lr, beta1=args.beta1) \
.minimize(self.d_loss, var_list=self.d_vars)
self.g_optim = tf.train.AdamOptimizer(self.lr, beta1=args.beta1) \
.minimize(self.g_loss, var_list=self.g_vars)
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
self.writer = tf.summary.FileWriter("./logs", self.sess.graph)
counter = 1
start_time = time.time()
if args.continue_train:
if self.load(args.checkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
for epoch in range(args.epoch):
dataA = glob('./datasets/{}/*.*'.format(self.dataset_dir + '/trainA'))
dataB = glob('./datasets/{}/*.*'.format(self.dataset_dir + '/trainB'))
np.random.shuffle(dataA)
np.random.shuffle(dataB)
batch_idxs = min(min(len(dataA), len(dataB)), args.train_size) // self.batch_size
lr = args.lr if epoch < args.epoch_step else args.lr*(args.epoch-epoch)/(args.epoch-args.epoch_step)
for idx in range(0, batch_idxs):
batch_files = list(zip(dataA[idx * self.batch_size:(idx + 1) * self.batch_size],
dataB[idx * self.batch_size:(idx + 1) * self.batch_size]))
batch_images = [load_train_data(batch_file, args.load_size, args.fine_size) for batch_file in batch_files]
batch_images = np.array(batch_images).astype(np.float32)
# Update G network and record fake outputs
fake_A, fake_B, _, summary_str = self.sess.run(
[self.fake_A, self.fake_B, self.g_optim, self.g_sum],
feed_dict={self.real_data: batch_images, self.lr: lr})
self.writer.add_summary(summary_str, counter)
[fake_A, fake_B] = self.pool([fake_A, fake_B])
# Update D network
_, summary_str = self.sess.run(
[self.d_optim, self.d_sum],
feed_dict={self.real_data: batch_images,
self.fake_A_sample: fake_A,
self.fake_B_sample: fake_B,
self.lr: lr})
self.writer.add_summary(summary_str, counter)
counter += 1
print(("Epoch: [%2d] [%4d/%4d] time: %4.4f" % (
epoch, idx, batch_idxs, time.time() - start_time)))
if np.mod(counter, args.print_freq) == 1:
self.sample_model(args.sample_dir, epoch, idx)
if np.mod(counter, args.save_freq) == 2:
self.save(args.checkpoint_dir, counter)
def save(self, checkpoint_dir, step):
model_name = "cyclegan.model"
model_dir = "%s_%s" % (self.dataset_dir, self.image_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 checkpoint...")
model_dir = "%s_%s" % (self.dataset_dir, self.image_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))
return True
else:
return False
def sample_model(self, sample_dir, epoch, idx):
dataA = glob('./datasets/{}/*.*'.format(self.dataset_dir + '/testA'))
dataB = glob('./datasets/{}/*.*'.format(self.dataset_dir + '/testB'))
np.random.shuffle(dataA)
np.random.shuffle(dataB)
batch_files = list(zip(dataA[:self.batch_size], dataB[:self.batch_size]))
sample_images = [load_train_data(batch_file, is_testing=True) for batch_file in batch_files]
sample_images = np.array(sample_images).astype(np.float32)
fake_A, fake_B = self.sess.run(
[self.fake_A, self.fake_B],
feed_dict={self.real_data: sample_images}
)
save_images(fake_A, [self.batch_size, 1],
'./{}/A_{:02d}_{:04d}.jpg'.format(sample_dir, epoch, idx))
save_images(fake_B, [self.batch_size, 1],
'./{}/B_{:02d}_{:04d}.jpg'.format(sample_dir, epoch, idx))
def test(self, args):
"""Test cyclegan"""
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
if args.which_direction == 'AtoB':
sample_files = glob('./datasets/{}/*.*'.format(self.dataset_dir + '/testA'))
elif args.which_direction == 'BtoA':
sample_files = glob('./datasets/{}/*.*'.format(self.dataset_dir + '/testB'))
else:
raise Exception('--which_direction must be AtoB or BtoA')
if self.load(args.checkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
# write html for visual comparison
index_path = os.path.join(args.test_dir, '{0}_index.html'.format(args.which_direction))
index = open(index_path, "w")
index.write("<html><body><table><tr>")
index.write("<th>name</th><th>input</th><th>output</th></tr>")
out_var, in_var = (self.testB, self.test_A) if args.which_direction == 'AtoB' else (
self.testA, self.test_B)
for sample_file in sample_files:
print('Processing image: ' + sample_file)
sample_image = [load_test_data(sample_file, args.fine_size)]
sample_image = np.array(sample_image).astype(np.float32)
image_path = os.path.join(args.test_dir,
'{0}_{1}'.format(args.which_direction, os.path.basename(sample_file)))
fake_img = self.sess.run(out_var, feed_dict={in_var: sample_image})
save_images(fake_img, [1, 1], image_path)
index.write("<td>%s</td>" % os.path.basename(image_path))
index.write("<td><img src='%s'></td>" % (sample_file if os.path.isabs(sample_file) else (
'..' + os.path.sep + sample_file)))
index.write("<td><img src='%s'></td>" % (image_path if os.path.isabs(image_path) else (
'..' + os.path.sep + image_path)))
index.write("</tr>")
index.close()