-
Notifications
You must be signed in to change notification settings - Fork 6
/
SRDefog_test.py
100 lines (79 loc) · 4.57 KB
/
SRDefog_test.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
import time, itertools
from dataset import ImageFolder
from torchvision import transforms
from torch.utils.data import DataLoader
from networks import *
from utils import *
from glob import glob
from PIL import Image
from cv2 import resize
class SRDefog(object) :
def __init__(self, args):
self.model_name = 'SRDefog'
self.result_dir = args.result_dir
self.dataset = args.dataset
self.datasetpath = args.datasetpath
self.batch_size = args.batch_size
self.ch = args.ch
self.n_res = args.n_res
self.img_size = args.img_size
self.img_h = args.img_h
self.img_w = args.img_w
self.img_ch = args.img_ch
self.device = args.device
self.im_suf_A = args.im_suf_A
print()
print("##### Information #####")
print("# dataset : ", self.dataset)
print("# datasetpath : ", self.datasetpath)
def build_model(self):
self.test_transform = transforms.Compose([
transforms.Resize((self.img_size, self.img_size)),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
self.testA = ImageFolder(os.path.join('dataset', self.datasetpath), self.test_transform)
self.testA_loader = DataLoader(self.testA, batch_size=1, shuffle=False)
self.genA2B = ResnetGenerator(input_nc=3, output_nc=3, ngf=self.ch, n_blocks=self.n_res, img_size=self.img_size, light=True).to(self.device)
self.genB2A = ResnetGenerator(input_nc=3, output_nc=3, ngf=self.ch, n_blocks=self.n_res, img_size=self.img_size, light=True).to(self.device)
self.disGA = Discriminator(input_nc=3, ndf=self.ch, n_layers=7).to(self.device)
self.disGB = Discriminator(input_nc=3, ndf=self.ch, n_layers=7).to(self.device)
self.disLA = Discriminator(input_nc=3, ndf=self.ch, n_layers=5).to(self.device)
self.disLB = Discriminator(input_nc=3, ndf=self.ch, n_layers=5).to(self.device)
def load(self, dir, step):
params = torch.load(os.path.join(dir, self.dataset + '_params_%07d.pt' % step))
self.genA2B.load_state_dict(params['genA2B'])
self.genB2A.load_state_dict(params['genB2A'])
self.disGA.load_state_dict(params['disGA'])
self.disGB.load_state_dict(params['disGB'])
self.disLA.load_state_dict(params['disLA'])
self.disLB.load_state_dict(params['disLB'])
def test(self):
print(os.path.join(self.result_dir, self.dataset, 'model', '*.pt'))
model_list = glob(os.path.join(self.result_dir, self.dataset, 'model', '*.pt'))
if not len(model_list) == 0:
model_list.sort()
print('model_list',model_list)
for i in range(-1,0,1):
iter = int(model_list[i].split('_')[-1].split('.')[0])
print('iter',iter)
self.load(os.path.join(self.result_dir, self.dataset, 'model'), iter)
print(" [*] Load SUCCESS")
self.genA2B.eval(), self.genB2A.eval()
path_fakeB=os.path.join(self.result_dir, self.dataset, str(iter)+'/'+'output')
if not os.path.exists(path_fakeB):
os.makedirs(path_fakeB)
self.gt_list = [os.path.splitext(f)[0] for f in os.listdir(os.path.join(self.datasetpath)) if f.endswith(self.im_suf_A)]
for n, img_name in enumerate(self.gt_list):
print('predicting: %d / %d' % (n + 1, len(self.gt_list)))
img = Image.open(os.path.join('dataset', self.datasetpath, img_name + self.im_suf_A)).convert('RGB')
img_width, img_height =img.size
real_A = (self.test_transform(img).unsqueeze(0)).to(self.device)
fake_A2B, _, _ = self.genA2B(real_A)
A_real = RGB2BGR(tensor2numpy(denorm(real_A[0])))
B_fake = RGB2BGR(tensor2numpy(denorm(fake_A2B[0])))
A_real = resize(A_real, (img_width, img_height))
B_fake = resize(B_fake, (img_width, img_height))
A2B = np.concatenate((A_real, B_fake), 1)
cv2.imwrite(os.path.join(path_fakeB, '%s_out.png' % img_name), B_fake * 255.0)
cv2.imwrite(os.path.join(path_fakeB, '%s_inout.png' % img_name), A2B * 255.0)