-
Notifications
You must be signed in to change notification settings - Fork 1
/
test_custom.py
76 lines (71 loc) · 2.76 KB
/
test_custom.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
import torch
import argparse
import utility
from model.mwcnn import Model
from torch.utils.data import DataLoader
# import h5py
from option import args
from data.data_provider import SingleLoader
from torchsummary import summary
from utils.metric import calculate_psnr,calculate_ssim
import os
import matplotlib.pyplot as plt
import numpy as np
import torchvision.transforms as transforms
from utils.training_util import load_checkpoint
import math
from PIL import Image
import glob
import time
# from torchsummary import summary
torch.set_num_threads(4)
torch.manual_seed(args.seed)
checkpoint = utility.checkpoint(args)
torch.manual_seed(0)
def test(args):
model = Model(args)
save_img = ''
# summary(model,[[3,128,128],[0]])
# exit()
checkpoint_dir = "checkpoint/"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
try:
checkpoint = load_checkpoint(checkpoint_dir, device == 'cuda', 'latest')
start_epoch = checkpoint['epoch']
global_step = checkpoint['global_iter']
state_dict = checkpoint['state_dict']
model.model.load_state_dict(state_dict)
print('=> loaded checkpoint (epoch {}, global_step {})'.format(start_epoch, global_step))
except:
print('=> no checkpoint file to be loaded.') # model.load_state_dict(state_dict)
exit(1)
model.eval()
trans = transforms.ToPILImage()
torch.manual_seed(0)
noisy_path = sorted(glob.glob(args.noise_dir+ "/*.png"))
clean_path = [ i.replace("noisy","clean") for i in noisy_path]
for i in range(len(noisy_path)):
noise = transforms.ToTensor()(Image.open(noisy_path[i]).convert('RGB')).unsqueeze(0)
noise = noise.to(device)
begin = time.time()
# print(feedData.size())
pred = model(noise,0)
pred = pred.detach().cpu()
gt = transforms.ToTensor()(Image.open(clean_path[i]).convert('RGB'))
gt = gt.unsqueeze(0)
psnr_t = calculate_psnr(pred, gt)
ssim_t = calculate_ssim(pred, gt)
print(i," UP : PSNR : ", str(psnr_t)," : SSIM : ", str(ssim_t))
if save_img != '':
if not os.path.exists(args.save_img):
os.makedirs(args.save_img)
plt.figure(figsize=(15, 15))
plt.imshow(np.array(trans(pred[0])))
plt.title("denoise KPN DGF "+args.model_type, fontsize=25)
image_name = noisy_path[i].split("/")[-1].split(".")[0]
plt.axis("off")
plt.suptitle(image_name+" UP : PSNR : "+ str(psnr_t)+" : SSIM : "+ str(ssim_t), fontsize=25)
plt.savefig( os.path.join(args.save_img,image_name + "_" + args.checkpoint + '.png'),pad_inches=0)
if __name__ == "__main__":
# args.noise_dir = '/home/dell/Downloads/FullTest/noisy'
test(args)