forked from ZhendongWang6/Uformer
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_in_any_resolution.py
117 lines (87 loc) · 5 KB
/
test_in_any_resolution.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
import numpy as np
import os,sys,math
import argparse
from tqdm import tqdm
from einops import rearrange, repeat
import torch.nn as nn
import torch
from torch.utils.data import DataLoader
import torch.nn.functional as F
from ptflops import get_model_complexity_info
sys.path.append('/home/wangzd/uformer/')
import scipy.io as sio
from utils.loader import get_validation_data
import utils
from model import UNet,Uformer,Uformer_Cross,Uformer_CatCross
from skimage import img_as_float32, img_as_ubyte
from skimage.metrics import peak_signal_noise_ratio as psnr_loss
from skimage.metrics import structural_similarity as ssim_loss
parser = argparse.ArgumentParser(description='RGB denoising evaluation on the validation set of SIDD')
parser.add_argument('--input_dir', default='../uformer/datasets/denoising/sidd/test/',
type=str, help='Directory of validation images')
parser.add_argument('--result_dir', default='./results/denoising/sidd/',
type=str, help='Directory for results')
parser.add_argument('--weights', default='./log/Uformer32_0806_1/models/model_best.pth',
type=str, help='Path to weights')
parser.add_argument('--gpus', default='0', type=str, help='CUDA_VISIBLE_DEVICES')
parser.add_argument('--arch', default='Uformer', type=str, help='arch')
parser.add_argument('--batch_size', default=1, type=int, help='Batch size for dataloader')
parser.add_argument('--save_images', action='store_true', help='Save denoised images in result directory')
parser.add_argument('--embed_dim', type=int, default=32, help='number of data loading workers')
parser.add_argument('--win_size', type=int, default=8, help='number of data loading workers')
parser.add_argument('--token_projection', type=str,default='linear', help='linear/conv token projection')
parser.add_argument('--token_mlp', type=str,default='leff', help='ffn/leff token mlp')
# args for vit
parser.add_argument('--vit_dim', type=int, default=256, help='vit hidden_dim')
parser.add_argument('--vit_depth', type=int, default=12, help='vit depth')
parser.add_argument('--vit_nheads', type=int, default=8, help='vit hidden_dim')
parser.add_argument('--vit_mlp_dim', type=int, default=512, help='vit mlp_dim')
parser.add_argument('--vit_patch_size', type=int, default=16, help='vit patch_size')
parser.add_argument('--global_skip', action='store_true', default=False, help='global skip connection')
parser.add_argument('--local_skip', action='store_true', default=False, help='local skip connection')
parser.add_argument('--vit_share', action='store_true', default=False, help='share vit module')
parser.add_argument('--train_ps', type=int, default=128, help='patch size of training sample')
args = parser.parse_args()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
utils.mkdir(args.result_dir)
test_dataset = get_validation_data(args.input_dir)
test_loader = DataLoader(dataset=test_dataset, batch_size=1, shuffle=False, num_workers=8, drop_last=False)
model_restoration= utils.get_arch(args)
model_restoration = torch.nn.DataParallel(model_restoration)
utils.load_checkpoint(model_restoration,args.weights)
print("===>Testing using weights: ", args.weights)
model_restoration.cuda()
model_restoration.eval()
def expand2square(timg,factor=16.0):
_, _, h, w = timg.size()
X = int(math.ceil(max(h,w)/float(factor))*factor)
img = torch.zeros(1,3,X,X).type_as(timg) # 3, h,w
mask = torch.zeros(1,1,X,X).type_as(timg)
# print(img.size(),mask.size())
# print((X - h)//2, (X - h)//2+h, (X - w)//2, (X - w)//2+w)
img[:,:, ((X - h)//2):((X - h)//2 + h),((X - w)//2):((X - w)//2 + w)] = timg
mask[:,:, ((X - h)//2):((X - h)//2 + h),((X - w)//2):((X - w)//2 + w)].fill_(1.0)
return img, mask
with torch.no_grad():
psnr_val_rgb = []
ssim_val_rgb = []
for ii, data_test in enumerate(tqdm(test_loader), 0):
## TEST THE EFFECT IN DIFFERENT SIZE
# xsize = (456,234)
# rgb_gt = F.interpolate(data_test[0],size=xsize).numpy().squeeze().transpose((1,2,0))
# rgb_noisy, mask = expand2square(F.interpolate(data_test[1].cuda(),size=xsize), factor=64)
rgb_gt = data_test[0].numpy().squeeze().transpose((1,2,0))
# The factor is calculated (window_size(8) * down_scale(2^4) in this case)
rgb_noisy, mask = expand2square(data_test[1].cuda(), factor=128)
filenames = data_test[2]
rgb_restored = model_restoration(rgb_noisy, 1 - mask)
rgb_restored = torch.masked_select(rgb_restored,mask.bool()).reshape(1,3,rgb_gt.shape[0],rgb_gt.shape[1])
rgb_restored = torch.clamp(rgb_restored,0,1).cpu().numpy().squeeze().transpose((1,2,0))
psnr_val_rgb.append(psnr_loss(rgb_restored, rgb_gt))
ssim_val_rgb.append(ssim_loss(rgb_restored, rgb_gt, multichannel=True))
if args.save_images:
utils.save_img(os.path.join(args.result_dir,filenames[0]), img_as_ubyte(rgb_restored))
psnr_val_rgb = sum(psnr_val_rgb)/len(test_dataset)
ssim_val_rgb = sum(ssim_val_rgb)/len(test_dataset)
print("PSNR: %f, SSIM: %f " %(psnr_val_rgb,ssim_val_rgb))