-
Notifications
You must be signed in to change notification settings - Fork 33
/
test_on_folder.py
167 lines (143 loc) · 7.23 KB
/
test_on_folder.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
"""
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from __future__ import print_function
from utils import get_config, get_data_loader_folder, pytorch03_to_pytorch04
from trainer_council import Council_Trainer
from torch import nn
from scipy.stats import entropy
import torch.nn.functional as F
import argparse
from torch.autograd import Variable
from data import ImageFolder
import numpy as np
import torchvision.utils as vutils
try:
from itertools import izip as zip
except ImportError: # will be 3.x series
pass
import torch
import os
from tqdm import tqdm
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='configs/edges2handbags_folder', help='Path to the config file.')
parser.add_argument('--input_folder', type=str, help="input image folder")
parser.add_argument('--output_folder', type=str, help="output image folder")
parser.add_argument('--output_path', type=str, default='outputs', help="outputs path")
parser.add_argument('--checkpoint', type=str, help="checkpoint of autoencoders")
parser.add_argument('--a2b', type=int, default=1, help="1 for a2b 0 for b2a")
parser.add_argument('--seed', type=int, default=1, help="random seed")
parser.add_argument('--num_style',type=int, default=10, help="number of styles to sample")
parser.add_argument('--output_only', action='store_true', help="whether only save the output images or also save the input images")
parser.add_argument('--num_of_images_to_test', type=int, default=10000, help="number of images to sample")
data_name = 'out'
opts = parser.parse_args()
torch.manual_seed(opts.seed)
torch.cuda.manual_seed(opts.seed)
# Load experiment setting
config = get_config(opts.config)
input_dim = config['input_dim_a'] if opts.a2b else config['input_dim_b']
council_size = config['council']['council_size']
# Setup model and data loader
image_names = ImageFolder(opts.input_folder, transform=None, return_paths=True)
if not 'new_size_a' in config.keys():
config['new_size_a'] = config['new_size']
is_data_A = opts.a2b
data_loader = get_data_loader_folder(opts.input_folder, 1, False,\
new_size=config['new_size_a'] if 'new_size_a' in config.keys() else config['new_size'],\
crop=False, config=config, is_data_A=is_data_A)
style_dim = config['gen']['style_dim']
trainer = Council_Trainer(config)
only_one = False
if 'gen_' in opts.checkpoint[-21:]:
state_dict = torch.load(opts.checkpoint)
try:
if opts.a2b:
trainer.gen_a2b_s[0].load_state_dict(state_dict['a2b'])
else:
trainer.gen_b2a_s[0].load_state_dict(state_dict['b2a'])
except:
print('opts.a2b should be set to ' + str(not opts.a2b) + ' , Or config file could be wrong')
opts.a2b = not opts.a2b
if opts.a2b:
trainer.gen_a2b_s[0].load_state_dict(state_dict['a2b'])
else:
trainer.gen_b2a_s[0].load_state_dict(state_dict['b2a'])
council_size = 1
only_one = True
else:
for i in range(council_size):
try:
if opts.a2b:
tmp_checkpoint = opts.checkpoint[:-8] + 'a2b_gen_' + str(i) + '_' + opts.checkpoint[-8:] + '.pt'
state_dict = torch.load(tmp_checkpoint, map_location=trainer.gen_a2b_s[i].cuda_device)
trainer.gen_a2b_s[i].load_state_dict(state_dict['a2b'])
else:
tmp_checkpoint = opts.checkpoint[:-8] + 'b2a_gen_' + str(i) + '_' + opts.checkpoint[-8:] + '.pt'
state_dict = torch.load(tmp_checkpoint, map_location=trainer.gen_b2a_s[i].cuda_device)
trainer.gen_b2a_s[i].load_state_dict(state_dict['b2a'])
except:
print('opts.a2b should be set to ' + str(not opts.a2b) + ' , Or config file could be wrong')
opts.a2b = not opts.a2b
if opts.a2b:
tmp_checkpoint = opts.checkpoint[:-8] + 'a2b_gen_' + str(i) + '_' + opts.checkpoint[-8:] + '.pt'
state_dict = torch.load(tmp_checkpoint, map_location=trainer.gen_a2b_s[i].cuda_device)
trainer.gen_a2b_s[i].load_state_dict(state_dict['a2b'])
else:
tmp_checkpoint = opts.checkpoint[:-8] + 'b2a_gen_' + str(i) + '_' + opts.checkpoint[-8:] + '.pt'
state_dict = torch.load(tmp_checkpoint, map_location=trainer.gen_b2a_s[i].cuda_device)
trainer.gen_b2a_s[i].load_state_dict(state_dict['b2a'])
trainer.cuda()
trainer.eval()
encode_s = []
decode_s = []
if opts.a2b:
for i in range(council_size):
encode_s.append(trainer.gen_a2b_s[i].encode) # encode function
decode_s.append(trainer.gen_a2b_s[i].decode) # decode function
else:
for i in range(council_size):
encode_s.append(trainer.gen_b2a_s[i].encode) # encode function
decode_s.append(trainer.gen_b2a_s[i].decode) # decode function
# creat testing images
num_of_images_to_test = opts.num_of_images_to_test
seed = 1
curr_image_num = -1
for i, (images, names) in tqdm(enumerate(zip(data_loader, image_names)), total=num_of_images_to_test):
if curr_image_num == num_of_images_to_test:
break
curr_image_num += 1
k = np.random.randint(council_size)
style_fixed = Variable(torch.randn(opts.num_style, style_dim, 1, 1).cuda(), volatile=True)
print(names[1])
images = Variable(images.cuda(), volatile=True)
content, _ = encode_s[k](images)
seed += 1
torch.random.manual_seed(seed)
style = Variable(torch.randn(opts.num_style, style_dim, 1, 1).cuda(), volatile=True)
for j in range(opts.num_style):
s = style[j].unsqueeze(0)
outputs = decode_s[k](content, s, images)
basename = os.path.basename(names[1])
output_folder = os.path.join(opts.output_path, 'test_res')
if only_one:
path = os.path.join(output_folder, opts.checkpoint[-11:-3] + "_%02d" % j, data_name + '_out_' + str(curr_image_num) + '_' + str(j) + '.jpg')
path_all_in_one = os.path.join(output_folder, opts.checkpoint[-11:-3] + '_all_in_1', data_name + '_out_' + str(curr_image_num) + '_' + str(j) + '.jpg')
else:
path = os.path.join(output_folder, opts.checkpoint[-8:] + "_%02d" % j, data_name + '_out_' + str(curr_image_num) + '_' + str(j) + '.jpg')
path_all_in_one = os.path.join(output_folder, opts.checkpoint[-8:] + '_all_in_1', data_name + '_out_' + str(curr_image_num) + '_' + str(j) + '.jpg')
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
vutils.save_image(outputs.data, path, padding=0, normalize=True)
do_all_in_one = True
if do_all_in_one:
if not os.path.exists(os.path.dirname(path_all_in_one)):
os.makedirs(os.path.dirname(path_all_in_one))
vutils.save_image(outputs.data, path_all_in_one, padding=0, normalize=True)
if not opts.output_only:
# also save input images
output_folder = os.path.join(output_folder, 'input')
if not os.path.exists(output_folder):
os.makedirs(output_folder)
vutils.save_image(images.data, os.path.join(output_folder, 'input{:03d}.jpg'.format(i)), padding=0, normalize=True)