-
Notifications
You must be signed in to change notification settings - Fork 18
/
MyTest.py
40 lines (35 loc) · 1.46 KB
/
MyTest.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
import os, argparse
import torch
import torch.nn.functional as F
import numpy as np
from scipy import misc
from lib.C2FNet import C2FNet
from utils.dataloader import test_dataset
parser = argparse.ArgumentParser()
parser.add_argument('--testsize', type=int, default=352, help='testing size')
parser.add_argument('--pth_path', type=str, default='./checkpoints/C2FNet40/C2FNet-39.pth')
for _data_name in ['CAMO','CHAMELEON','COD10K']: #'CAMO','CHAMELEON','COD10K'
data_path = './data/TestDataset/{}/'.format(_data_name)
save_path = './results/C2FNet40/{}/'.format(_data_name)
opt = parser.parse_args()
model = C2FNet()
#model = torch.nn.DataParallel(model)
#torch.backends.cudnn.benchmark = True
torch.cuda.set_device(0)
model.load_state_dict(torch.load(opt.pth_path))
model.cuda()
model.eval()
os.makedirs(save_path, exist_ok=True)
image_root = '{}/Imgs/'.format(data_path)
gt_root = '{}/GT/'.format(data_path)
test_loader = test_dataset(image_root, gt_root, opt.testsize)
for i in range(test_loader.size):
image, gt, name = test_loader.load_data()
gt = np.asarray(gt, np.float32)
gt /= (gt.max() + 1e-8)
image = image.cuda()
res = model(image)
res = F.upsample(res, size=gt.shape, mode='bilinear', align_corners=False)
res = res.sigmoid().data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
misc.imsave(save_path+name, res)