-
Notifications
You must be signed in to change notification settings - Fork 399
/
train.py
238 lines (188 loc) · 8.39 KB
/
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
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
import tensorflow as tf
import numpy as np
import model
import argparse
import pickle
from os.path import join
import h5py
from Utils import image_processing
import scipy.misc
import random
import json
import os
import shutil
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--z_dim', type=int, default=100,
help='Noise dimension')
parser.add_argument('--t_dim', type=int, default=256,
help='Text feature dimension')
parser.add_argument('--batch_size', type=int, default=64,
help='Batch Size')
parser.add_argument('--image_size', type=int, default=64,
help='Image Size a, a x a')
parser.add_argument('--gf_dim', type=int, default=64,
help='Number of conv in the first layer gen.')
parser.add_argument('--df_dim', type=int, default=64,
help='Number of conv in the first layer discr.')
parser.add_argument('--gfc_dim', type=int, default=1024,
help='Dimension of gen untis for for fully connected layer 1024')
parser.add_argument('--caption_vector_length', type=int, default=2400,
help='Caption Vector Length')
parser.add_argument('--data_dir', type=str, default="Data",
help='Data Directory')
parser.add_argument('--learning_rate', type=float, default=0.0002,
help='Learning Rate')
parser.add_argument('--beta1', type=float, default=0.5,
help='Momentum for Adam Update')
parser.add_argument('--epochs', type=int, default=600,
help='Max number of epochs')
parser.add_argument('--save_every', type=int, default=30,
help='Save Model/Samples every x iterations over batches')
parser.add_argument('--resume_model', type=str, default=None,
help='Pre-Trained Model Path, to resume from')
parser.add_argument('--data_set', type=str, default="flowers",
help='Dat set: MS-COCO, flowers')
args = parser.parse_args()
model_options = {
'z_dim' : args.z_dim,
't_dim' : args.t_dim,
'batch_size' : args.batch_size,
'image_size' : args.image_size,
'gf_dim' : args.gf_dim,
'df_dim' : args.df_dim,
'gfc_dim' : args.gfc_dim,
'caption_vector_length' : args.caption_vector_length
}
gan = model.GAN(model_options)
input_tensors, variables, loss, outputs, checks = gan.build_model()
d_optim = tf.train.AdamOptimizer(args.learning_rate, beta1 = args.beta1).minimize(loss['d_loss'], var_list=variables['d_vars'])
g_optim = tf.train.AdamOptimizer(args.learning_rate, beta1 = args.beta1).minimize(loss['g_loss'], var_list=variables['g_vars'])
sess = tf.InteractiveSession()
tf.initialize_all_variables().run()
saver = tf.train.Saver()
if args.resume_model:
saver.restore(sess, args.resume_model)
loaded_data = load_training_data(args.data_dir, args.data_set)
for i in range(args.epochs):
batch_no = 0
while batch_no*args.batch_size < loaded_data['data_length']:
real_images, wrong_images, caption_vectors, z_noise, image_files = get_training_batch(batch_no, args.batch_size,
args.image_size, args.z_dim, args.caption_vector_length, 'train', args.data_dir, args.data_set, loaded_data)
# DISCR UPDATE
check_ts = [ checks['d_loss1'] , checks['d_loss2'], checks['d_loss3']]
_, d_loss, gen, d1, d2, d3 = sess.run([d_optim, loss['d_loss'], outputs['generator']] + check_ts,
feed_dict = {
input_tensors['t_real_image'] : real_images,
input_tensors['t_wrong_image'] : wrong_images,
input_tensors['t_real_caption'] : caption_vectors,
input_tensors['t_z'] : z_noise,
})
print "d1", d1
print "d2", d2
print "d3", d3
print "D", d_loss
# GEN UPDATE
_, g_loss, gen = sess.run([g_optim, loss['g_loss'], outputs['generator']],
feed_dict = {
input_tensors['t_real_image'] : real_images,
input_tensors['t_wrong_image'] : wrong_images,
input_tensors['t_real_caption'] : caption_vectors,
input_tensors['t_z'] : z_noise,
})
# GEN UPDATE TWICE, to make sure d_loss does not go to 0
_, g_loss, gen = sess.run([g_optim, loss['g_loss'], outputs['generator']],
feed_dict = {
input_tensors['t_real_image'] : real_images,
input_tensors['t_wrong_image'] : wrong_images,
input_tensors['t_real_caption'] : caption_vectors,
input_tensors['t_z'] : z_noise,
})
print "LOSSES", d_loss, g_loss, batch_no, i, len(loaded_data['image_list'])/ args.batch_size
batch_no += 1
if (batch_no % args.save_every) == 0:
print "Saving Images, Model"
save_for_vis(args.data_dir, real_images, gen, image_files)
save_path = saver.save(sess, "Data/Models/latest_model_{}_temp.ckpt".format(args.data_set))
if i%5 == 0:
save_path = saver.save(sess, "Data/Models/model_after_{}_epoch_{}.ckpt".format(args.data_set, i))
def load_training_data(data_dir, data_set):
if data_set == 'flowers':
h = h5py.File(join(data_dir, 'flower_tv.hdf5'))
flower_captions = {}
for ds in h.iteritems():
flower_captions[ds[0]] = np.array(ds[1])
image_list = [key for key in flower_captions]
image_list.sort()
img_75 = int(len(image_list)*0.75)
training_image_list = image_list[0:img_75]
random.shuffle(training_image_list)
return {
'image_list' : training_image_list,
'captions' : flower_captions,
'data_length' : len(training_image_list)
}
else:
with open(join(data_dir, 'meta_train.pkl')) as f:
meta_data = pickle.load(f)
# No preloading for MS-COCO
return meta_data
def save_for_vis(data_dir, real_images, generated_images, image_files):
shutil.rmtree( join(data_dir, 'samples') )
os.makedirs( join(data_dir, 'samples') )
for i in range(0, real_images.shape[0]):
real_image_255 = np.zeros( (64,64,3), dtype=np.uint8)
real_images_255 = (real_images[i,:,:,:])
scipy.misc.imsave( join(data_dir, 'samples/{}_{}.jpg'.format(i, image_files[i].split('/')[-1] )) , real_images_255)
fake_image_255 = np.zeros( (64,64,3), dtype=np.uint8)
fake_images_255 = (generated_images[i,:,:,:])
scipy.misc.imsave(join(data_dir, 'samples/fake_image_{}.jpg'.format(i)), fake_images_255)
def get_training_batch(batch_no, batch_size, image_size, z_dim,
caption_vector_length, split, data_dir, data_set, loaded_data = None):
if data_set == 'mscoco':
with h5py.File( join(data_dir, 'tvs/'+split + '_tvs_' + str(batch_no))) as hf:
caption_vectors = np.array(hf.get('tv'))
caption_vectors = caption_vectors[:,0:caption_vector_length]
with h5py.File( join(data_dir, 'tvs/'+split + '_tv_image_id_' + str(batch_no))) as hf:
image_ids = np.array(hf.get('tv'))
real_images = np.zeros((batch_size, 64, 64, 3))
wrong_images = np.zeros((batch_size, 64, 64, 3))
image_files = []
for idx, image_id in enumerate(image_ids):
image_file = join(data_dir, '%s2014/COCO_%s2014_%.12d.jpg'%(split, split, image_id) )
image_array = image_processing.load_image_array(image_file, image_size)
real_images[idx,:,:,:] = image_array
image_files.append(image_file)
# TODO>> As of Now, wrong images are just shuffled real images.
first_image = real_images[0,:,:,:]
for i in range(0, batch_size):
if i < batch_size - 1:
wrong_images[i,:,:,:] = real_images[i+1,:,:,:]
else:
wrong_images[i,:,:,:] = first_image
z_noise = np.random.uniform(-1, 1, [batch_size, z_dim])
return real_images, wrong_images, caption_vectors, z_noise, image_files
if data_set == 'flowers':
real_images = np.zeros((batch_size, 64, 64, 3))
wrong_images = np.zeros((batch_size, 64, 64, 3))
captions = np.zeros((batch_size, caption_vector_length))
cnt = 0
image_files = []
for i in range(batch_no * batch_size, batch_no * batch_size + batch_size):
idx = i % len(loaded_data['image_list'])
image_file = join(data_dir, 'flowers/jpg/'+loaded_data['image_list'][idx])
image_array = image_processing.load_image_array(image_file, image_size)
real_images[cnt,:,:,:] = image_array
# Improve this selection of wrong image
wrong_image_id = random.randint(0,len(loaded_data['image_list'])-1)
wrong_image_file = join(data_dir, 'flowers/jpg/'+loaded_data['image_list'][wrong_image_id])
wrong_image_array = image_processing.load_image_array(wrong_image_file, image_size)
wrong_images[cnt, :,:,:] = wrong_image_array
random_caption = random.randint(0,4)
captions[cnt,:] = loaded_data['captions'][ loaded_data['image_list'][idx] ][ random_caption ][0:caption_vector_length]
image_files.append( image_file )
cnt += 1
z_noise = np.random.uniform(-1, 1, [batch_size, z_dim])
return real_images, wrong_images, captions, z_noise, image_files
if __name__ == '__main__':
main()