forked from kozistr/Awesome-GANs
-
Notifications
You must be signed in to change notification settings - Fork 0
/
cogan_train.py
158 lines (123 loc) · 5.66 KB
/
cogan_train.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
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import tensorflow as tf
import numpy as np
import sys
import time
import cogan_model as cogan
sys.path.append('../')
import image_utils as iu
from datasets import MNISTDataSet as DataSet
results = {
'output': './gen_img/',
'model': './model/CoGAN-model.ckpt'
}
train_step = {
'batch_size': 128,
'global_step': 12501,
'logging_interval': 250,
}
def main():
start_time = time.time() # Clocking start
# MNIST Dataset load
mnist = DataSet(ds_path="D:\\DataSet/mnist/").data
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# CoGAN Model
model = cogan.CoGAN(s, batch_size=train_step['batch_size'])
# Initializing
s.run(tf.global_variables_initializer())
# Load model & Graph & Weights
saved_global_step = 0
ckpt = tf.train.get_checkpoint_state('./model/')
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
model.saver.restore(s, ckpt.model_checkpoint_path)
saved_global_step = int(ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
print("[+] global step : %d" % saved_global_step, " successfully loaded")
else:
print('[-] No checkpoint file found')
sample_x, _ = mnist.test.next_batch(model.sample_num)
sample_x = np.reshape(sample_x, newshape=[model.sample_num] + model.image_shape[1:])
sample_rot_x = np.rot90(sample_x[:], 1, axes=(-3, -2))
# Generated image save
iu.save_images(sample_x,
size=[model.sample_size, model.sample_size],
image_path='./gen_img/sample_1.png')
iu.save_images(sample_rot_x,
size=[model.sample_size, model.sample_size],
image_path='./gen_img/sample_2.png')
sample_y = np.zeros(shape=[model.sample_num, model.n_classes])
for i in range(10):
sample_y[10 * i:10 * (i + 1), i] = 1
for global_step in range(saved_global_step, train_step['global_step']):
batch_x, batch_y = mnist.train.next_batch(model.batch_size)
batch_rot_x = np.reshape(np.rot90(np.reshape(batch_x, model.image_shape), 1, axes=(-3, -2)),
(-1, model.n_input))
batch_z = np.random.uniform(-1., 1., [model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x_1: batch_x,
model.x_2: batch_rot_x, # batch_x
# model.y: batch_y,
model.z: batch_z,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.x_1: batch_x,
model.x_2: batch_rot_x,
# model.y: batch_y,
model.z: batch_z,
})
if global_step % train_step['logging_interval'] == 0:
summary = s.run(model.merged,
feed_dict={
model.x_1: batch_x,
model.x_2: batch_rot_x,
# model.y: batch_y,
model.z: batch_z,
})
# Print loss
print("[+] Step %08d => " % global_step,
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss))
# Training G model with sample image and noise
sample_z = np.random.uniform(-1., 1., [model.sample_num, model.z_dim]).astype(np.float32)
samples_1 = s.run(model.g_sample_1,
feed_dict={
# model.y: sample_y,
model.z: sample_z,
})
samples_2 = s.run(model.g_sample_2,
feed_dict={
# model.y: sample_y,
model.z: sample_z,
})
# Summary saver
model.writer.add_summary(summary, global_step)
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir_1 = results['output'] + 'train_1_{:08d}.png'.format(global_step)
sample_dir_2 = results['output'] + 'train_2_{:08d}.png'.format(global_step)
# Generated image save
iu.save_images(samples_1,
size=[sample_image_height, sample_image_width],
image_path=sample_dir_1)
iu.save_images(samples_2,
size=[sample_image_height, sample_image_width],
image_path=sample_dir_2)
# Model save
model.saver.save(s, results['model'], global_step)
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
if __name__ == '__main__':
main()