forked from lengstrom/fast-style-transfer
/
style.py
183 lines (154 loc) · 6.82 KB
/
style.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
from __future__ import print_function
import sys, os, pdb
sys.path.insert(0, 'src')
import numpy as np, scipy.misc
from optimize import optimize
from argparse import ArgumentParser
from utils import save_img, get_img, exists, list_files, log_time_usage
import evaluate
CONTENT_WEIGHT = 7.5e0
STYLE_WEIGHT = 1e2
TV_WEIGHT = 2e2
LEARNING_RATE = 1e-3
NUM_EPOCHS = 2
CHECKPOINT_DIR = 'checkpoints'
CHECKPOINT_ITERATIONS = 2000
VGG_PATH = 'data/imagenet-vgg-verydeep-19.mat'
TRAIN_PATH = 'data/train2014'
BATCH_SIZE = 4
DEVICE = '/gpu:0'
FRAC_GPU = 1
def build_parser():
parser = ArgumentParser()
parser.add_argument('--checkpoint-dir', type=str,
dest='checkpoint_dir', help='dir to save checkpoint in',
metavar='CHECKPOINT_DIR', required=True)
parser.add_argument('--style', type=str,
dest='style', help='style image path',
metavar='STYLE', required=True)
parser.add_argument('--train-path', type=str,
dest='train_path', help='path to training images folder',
metavar='TRAIN_PATH', default=TRAIN_PATH)
parser.add_argument('--test', type=str,
dest='test', help='test image path',
metavar='TEST', default=False)
parser.add_argument('--test-dir', type=str,
dest='test_dir', help='test image save dir',
metavar='TEST_DIR', default=False)
parser.add_argument('--slow', dest='slow', action='store_true',
help='gatys\' approach (for debugging, not supported)',
default=False)
parser.add_argument('--debug', dest='debug', action='store_true',
help='add more debugging logs',
default=False)
parser.add_argument('--epochs', type=int,
dest='epochs', help='num epochs',
metavar='EPOCHS', default=NUM_EPOCHS)
parser.add_argument('--batch-size', type=int,
dest='batch_size', help='batch size',
metavar='BATCH_SIZE', default=BATCH_SIZE)
parser.add_argument('--checkpoint-iterations', type=int,
dest='checkpoint_iterations', help='checkpoint frequency',
metavar='CHECKPOINT_ITERATIONS',
default=CHECKPOINT_ITERATIONS)
parser.add_argument('--vgg-path', type=str,
dest='vgg_path',
help='path to VGG19 network (default %(default)s)',
metavar='VGG_PATH', default=VGG_PATH)
parser.add_argument('--content-weight', type=float,
dest='content_weight',
help='content weight (default %(default)s)',
metavar='CONTENT_WEIGHT', default=CONTENT_WEIGHT)
parser.add_argument('--style-weight', type=float,
dest='style_weight',
help='style weight (default %(default)s)',
metavar='STYLE_WEIGHT', default=STYLE_WEIGHT)
parser.add_argument('--tv-weight', type=float,
dest='tv_weight',
help='total variation regularization weight (default %(default)s)',
metavar='TV_WEIGHT', default=TV_WEIGHT)
parser.add_argument('--learning-rate', type=float,
dest='learning_rate',
help='learning rate (default %(default)s)',
metavar='LEARNING_RATE', default=LEARNING_RATE)
parser.add_argument('--tensorboard-dir', type=str,
dest='tensorboard_dir',
help='TensorBoard log directory', default=False)
return parser
def check_opts(opts):
exists(opts.checkpoint_dir, "checkpoint dir not found!")
exists(opts.style, "style path not found!")
exists(opts.train_path, "train path not found!")
if opts.test or opts.test_dir:
exists(opts.test, "test img not found!")
exists(opts.test_dir, "test directory not found!")
exists(opts.vgg_path, "vgg network data not found!")
assert opts.epochs > 0
assert opts.batch_size > 0
assert opts.checkpoint_iterations > 0
assert os.path.exists(opts.vgg_path)
assert opts.content_weight >= 0
assert opts.style_weight >= 0
assert opts.tv_weight >= 0
assert opts.learning_rate >= 0
def _get_files(img_dir):
files = list_files(img_dir)
return [os.path.join(img_dir,x) for x in files]
def main():
print("Enter main")
parser = build_parser()
options = parser.parse_args()
check_opts(options)
print("Get image target")
style_target = get_img(options.style)
if not options.slow:
with log_time_usage("get images targets"):
content_targets = _get_files(options.train_path)
elif options.test:
content_targets = [options.test]
kwargs = {
"slow":options.slow,
"debug":options.debug,
"epochs":options.epochs,
"print_iterations":options.checkpoint_iterations,
"batch_size":options.batch_size,
"save_path":os.path.join(options.checkpoint_dir,'fns.ckpt'),
"learning_rate":options.learning_rate,
"tensorboard_dir":options.tensorboard_dir
}
if options.slow:
if options.epochs < 10:
kwargs['epochs'] = 1000
if options.learning_rate < 1:
kwargs['learning_rate'] = 1e1
args = [
content_targets,
style_target,
options.content_weight,
options.style_weight,
options.tv_weight,
options.vgg_path
]
print("Start training")
with log_time_usage("Training completed in"):
for preds, losses, i, epoch, time_info in optimize(*args, **kwargs):
style_loss, content_loss, tv_loss, loss = losses
print('Epoch %d, Iteration: %d, Loss: %s, AVG batch time: %.2f, total_time: %.2f, ETA (in h): %.2f' % (epoch, i, loss, *time_info))
to_print = (style_loss, content_loss, tv_loss)
print('style: %s, content:%s, tv: %s' % to_print)
if options.test:
assert options.test_dir != False
preds_path = '%s/%s_%s.png' % (options.test_dir,epoch,i)
if not options.slow:
ckpt_dir = os.path.dirname(options.checkpoint_dir)
evaluate.ffwd_to_img(options.test,preds_path,
options.checkpoint_dir)
else:
# TODO: img is not defined
# save_img(preds_path, img)
pass
ckpt_dir = options.checkpoint_dir
cmd_text = 'python evaluate.py --checkpoint %s ...' % ckpt_dir
print("Training complete. For evaluation:\n `%s`" % cmd_text)
if __name__ == '__main__':
main()