-
Notifications
You must be signed in to change notification settings - Fork 1
/
stylize.py
185 lines (169 loc) · 7.7 KB
/
stylize.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
import os
import sys
import torch as ch
from datetime import datetime
from parsing import stylize_parser
from torch.utils.data import DataLoader
from utils.core_utils import( stdout_logger, InputDenormalize, InputScaling,
InputDescaling)
from utils.st_utils import st_universal, st_transforms, st_custom_ds
from models.vgg import vgg19Comp, vgg19Comp_config
from models.alexnet import( AlexNetEncDec, AlexNetEncDec_config, AlexNetComp,
AlexNetComp_config)
# Parse arguments
parser= stylize_parser()
args = parser.parse_args()
# Custom arguments
args.stylize_layers= tuple( args.stylize_layers)
args.exp_name= datetime.now().strftime( "%Y_%m_%d_%H_%M_%S")
args.out_folder= os.path.join( args.out_dir, args.exp_name, "output") # Output folder
if not os.path.exists( args.out_folder): os.makedirs( args.out_folder) # create output folder
# Dataset parameters
if args.dataset== "imagenet":
args.num_classes= 1000
args.mean= ch.tensor([0.485, 0.456, 0.406])
args.std= ch.tensor([0.229, 0.224, 0.225])
else: raise ValueError( "Undefined dataset. Check 'dataset' input argument.")
if args.stdout_logger:
# Set stdout
args.stdout_str= os.path.join( args.stdout_dir, args.exp_name + '.txt')
sys.stdout= stdout_logger( stdout_str= args.stdout_str)
# Content and style data
content_transform, _= st_transforms( mode= args.transform_test,
init_dim= args.content_transform_init_dim,
final_dim= args.content_transform_final_dim)
style_transform, norm_flag= st_transforms( mode= args.transform_test,
init_dim= args.style_transform_init_dim,
final_dim= args.style_transform_final_dim)
content_data= st_custom_ds( im_path= args.content_images,
seg_path= args.content_labels,
preprocess= content_transform)
style_data= st_custom_ds( im_path= args.style_images,
seg_path= args.style_labels,
preprocess= style_transform)
content_loader= DataLoader( content_data,
batch_size= args.batch_size,
shuffle= False,
num_workers= args.workers)
style_loader= DataLoader( style_data,
batch_size= args.batch_size,
shuffle= False,
num_workers= args.workers)
# Multi-stage model
if args.arch== "alexnet":
# Conv5 autoencoder
model_conv5= AlexNetEncDec( num_classes= args.num_classes,
mean= args.mean,
std= args.std,
output_layer= "conv5",
upsample_mode= args.conv5_upsample_mode,
spectral_init= args.spectral_init)
# Load checkpoints and set layers
AlexNetEncDec_config( classifier= model_conv5.classifier,
generator= model_conv5.generator,
load_classifier= args.load_classifier,
load_generator= args.load_conv5_generator,
output_layer= "conv5")
# Conv2 autoencoder
model_conv2= AlexNetEncDec( num_classes= args.num_classes,
mean= args.mean,
std= args.std,
output_layer= "conv2",
upsample_mode= args.conv2_upsample_mode,
spectral_init= args.spectral_init)
# Load checkpoints and set layers
AlexNetEncDec_config( classifier= model_conv2.classifier,
generator= model_conv2.generator,
load_classifier= args.load_classifier,
load_generator= args.load_conv2_generator,
output_layer= "conv2")
# Conv1 autoencoder
model_conv1= AlexNetEncDec( num_classes= args.num_classes,
mean= args.mean,
std= args.std,
output_layer= "conv1",
upsample_mode= args.conv1_upsample_mode,
spectral_init= args.spectral_init)
# Load checkpoints and set layers
AlexNetEncDec_config( classifier= model_conv1.classifier,
generator= model_conv1.generator,
load_classifier= args.load_classifier,
load_generator= args.load_conv1_generator,
output_layer= "conv1")
# Replace normalizer by scaling.
if norm_flag:
model_conv5.normalize= InputScaling()
model_conv2.normalize= InputScaling()
model_conv1.normalize= InputScaling()
if args.arch=="VGG16":
model_conv4.normalize= InputScaling()
model_conv3.normalize= InputScaling()
denormalize= InputDescaling()
else: denormalize= InputDenormalize( new_mean= args.mean,
new_std= args.std)
else: raise ValueError( "Wrong model, check arch input argument.")
# Set comparator
if args.load_comparator:
if args.comparator_arch== "alexnet":
comparator= AlexNetComp( num_classes= args.num_classes,
mean= args.mean,
std= args.std,
output_layer= "conv5")
AlexNetComp_config( comparator= comparator.classifier,
load_comparator= args.load_comparator,
output_layer= "conv5")
elif args.comparator_arch== "vgg19":
comparator= vgg19Comp( num_classes= args.num_classes,
mean= args.mean,
std= args.std)
vgg19Comp_config( comparator= comparator.classifier,
load_comparator= args.load_comparator)
else: raise ValueError( "Undefined comparator architecture. Check 'comparator_arch' argument.")
else: comparator= None
# Pass model to device
device= ch.device( 'cuda' if ch.cuda.is_available() else 'cpu')
if args.load_conv1_generator:
model_conv1= model_conv1.to( device)
model_conv1.eval()
else: model_conv1= None
if args.load_conv2_generator:
model_conv2= model_conv2.to( device)
model_conv2.eval()
else: model_conv2= None
if args.load_conv3_generator:
model_conv3= model_conv3.to( device)
model_conv3.eval()
else: model_conv3= None
if args.load_conv4_generator:
model_conv4= model_conv4.to( device)
model_conv4.eval()
else: model_conv4= None
if args.load_conv5_generator:
model_conv5= model_conv5.to( device)
model_conv5.eval()
else: model_conv5= None
if denormalize: denormalize= denormalize.to( device)
if comparator:
comparator= comparator.to( device)
comparator.eval()
# Stylize
st_universal( args= args,
model_conv5= model_conv5,
model_conv4= model_conv4,
model_conv3= model_conv3,
model_conv2= model_conv2,
model_conv1= model_conv1,
denormalize= denormalize,
comparator= comparator,
comparator_arch= args.comparator_arch,
compare_layers= args.compare_layers,
content_loader= content_loader,
style_loader= style_loader,
out_folder= args.out_folder,
infer_export= args.infer_export,
device= device,
compute_gram= args.compute_gram,
compute_ssim= args.compute_ssim,
input_pad= args.pad_input,
reduction= args.reduction)
print( "Output folder: ", args.out_folder)