-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathtest.py
182 lines (141 loc) · 6.68 KB
/
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
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
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
from __future__ import print_function, absolute_import
import argparse
import torch
import os
from math import log10
import cv2
import numpy as np
# torch.backends.cudnn.benchmark = True
import datasets as datasets
import src.models as models
from options import Options
import torch.nn.functional as F
import pytorch_ssim
from evaluation import compute_IoU, FScore, AverageMeter, compute_RMSE, normPRED
from skimage.measure import compare_ssim as ssim
import time
def is_dic(x):
return type(x) == type([])
def tensor2np(x, isMask=False):
if isMask:
if x.shape[1] == 1:
x = x.repeat(1,3,1,1)
x = ((x.cpu().detach()))*255
else:
x = x.cpu().detach()
mean = 0
std = 1
x = (x * std + mean)*255
return x.numpy().transpose(0,2,3,1).astype(np.uint8)
def save_output(inputs, preds, save_dir, img_fn, extra_infos=None, verbose=False, alpha=0.5):
outs = []
image, bg_gt,mask_gt = inputs['I'], inputs['bg'], inputs['mask']
image = cv2.cvtColor(tensor2np(image)[0], cv2.COLOR_RGB2BGR)
# fg_gt = cv2.cvtColor(tensor2np(fg_gt)[0], cv2.COLOR_RGB2BGR)
bg_gt = cv2.cvtColor(tensor2np(bg_gt)[0], cv2.COLOR_RGB2BGR)
mask_gt = tensor2np(mask_gt, isMask=True)[0]
bg_pred,mask_preds = preds['bg'], preds['mask']
# fg_pred = cv2.cvtColor(tensor2np(fg_pred)[0], cv2.COLOR_RGB2BGR)
bg_pred = cv2.cvtColor(tensor2np(bg_pred)[0], cv2.COLOR_RGB2BGR)
mask_preds = [tensor2np(m, isMask=True)[0] for m in mask_preds]
main_mask = mask_preds[-2]
mask_pred = mask_preds[0]
outs = [image, bg_gt, bg_pred, mask_gt, mask_pred] #, main_mask]
outimg = np.concatenate(outs, axis=1)
if verbose==True:
# print("show")
cv2.imshow("out",outimg)
cv2.waitKey(0)
else:
psnr = extra_infos['psnr']
rmsew = extra_infos['rmsew']
f1 = extra_infos['f1']
img_fn = os.path.split(img_fn)[-1]
out_fn = os.path.join(save_dir, "{}_psnr_{:.2f}_rmsew_{:.2f}_f1_{:.4f}{}".format(os.path.splitext(img_fn)[0],psnr,rmsew, f1, os.path.splitext(img_fn)[1]))
cv2.imwrite(out_fn, outimg)
def main(args):
args.dataset = args.dataset.lower()
if args.dataset == 'clwd':
dataset_func = datasets.CLWDDataset
elif args.dataset == 'lvw':
dataset_func = datasets.LVWDataset
elif args.dataset == 'logo':
dataset_func = datasets.LOGODataset
val_loader = torch.utils.data.DataLoader(dataset_func('val',args),batch_size=args.test_batch, shuffle=False,
num_workers=args.workers, pin_memory=True)
data_loaders = (None,val_loader)
Machine = models.__dict__[args.models](datasets=data_loaders, args=args)
model = Machine
model.model.eval()
print("==> testing VM model ")
rmses = AverageMeter()
rmsews = AverageMeter()
ssimesx = AverageMeter()
psnresx = AverageMeter()
maskIoU = AverageMeter()
maskF1 = AverageMeter()
prime_maskIoU = AverageMeter()
prime_maskF1 = AverageMeter()
processTime = AverageMeter()
prediction_dir = os.path.join(args.checkpoint,'rst')
if not os.path.exists(prediction_dir): os.makedirs(prediction_dir)
save_flag = False
with torch.no_grad():
for i, batches in enumerate(model.val_loader):
inputs = batches['image'].to(model.device)
target = batches['target'].to(model.device)
mask =batches['mask'].to(model.device)
img_path = batches['img_path']
# select the outputs by the giving arch
start_time = time.time()
outputs = model.model(model.norm(inputs))
process_time = time.time() - start_time
processTime.update((process_time*1000), inputs.size(0))
imoutput,immask_all,_,_ = outputs
imoutput = imoutput[0] if is_dic(imoutput) else imoutput
immask = immask_all[0]
imfinal =imoutput*immask + model.norm(inputs)*(1-immask)
psnrx = 10 * log10(1 / F.mse_loss(imfinal,target).item())
final_np = (imfinal.detach().cpu().numpy()[0].transpose(1,2,0)*255).astype(np.uint8)
target_np = (target.detach().cpu().numpy()[0].transpose(1,2,0)*255).astype(np.uint8)
# ssimx = ssim(final_np, target_np, multichannel=True)
ssimx = pytorch_ssim.ssim(imfinal, target)
rmsex = compute_RMSE(imfinal, target, mask, is_w=False)
rmsewx = compute_RMSE(imfinal, target, mask, is_w=True)
rmses.update(rmsex, inputs.size(0))
rmsews.update(rmsewx, inputs.size(0))
psnresx.update(psnrx, inputs.size(0))
ssimesx.update(ssimx, inputs.size(0))
# main_mask = immask_all[1::2]
# comp_mask = immask_all[2::2]
out_mask = immask_all[0]
comp_mask = immask_all[0]
comp_sets = []
prime_mask_pred = torch.where(out_mask > 0.5, torch.ones_like(out_mask), torch.zeros_like(out_mask)).to(out_mask.device)
mask_pred = torch.where(comp_mask > 0.5, torch.ones_like(out_mask), torch.zeros_like(out_mask)).to(out_mask.device)
iou = compute_IoU(prime_mask_pred, mask)
prime_maskIoU.update(iou)
f1 = FScore(prime_mask_pred, mask).item()
prime_maskF1.update(f1, inputs.size(0))
iou = compute_IoU(mask_pred, mask)
maskIoU.update(iou)
f1 = FScore(mask_pred, mask).item()
maskF1.update(f1, inputs.size(0))
if save_flag:
save_output(
inputs={'I':inputs, 'bg':target, 'mask':mask},
preds={'bg':imfinal, 'mask':immask_all},
save_dir=prediction_dir,
img_fn=img_path[0],
extra_infos={"psnr":psnrx, "rmsew":rmsewx, "f1":f1},
verbose=False
)
if i % 100 == 0:
print("Batch[%d/%d]| PSNR:%.4f | SSIM:%.4f | RMSE:%.4f | RMSEw:%.4f | primeIoU:%.4f, primeF1:%.4f | maskIoU:%.4f | maskF1:%.4f | time:%.2f"
%(i,len(model.val_loader),psnresx.avg,ssimesx.avg, rmses.avg, rmsews.avg, prime_maskIoU.avg, prime_maskF1.avg, maskIoU.avg, maskF1.avg, processTime.avg))
print("Total:\nPSNR:%.4f | SSIM:%.4f | RMSE:%.4f | RMSEw:%.4f | primeIoU:%.4f, primeF1:%.4f | maskIoU:%.4f | maskF1:%.4f | time:%.2f"
%(psnresx.avg,ssimesx.avg, rmses.avg, rmsews.avg, prime_maskIoU.avg, prime_maskF1.avg, maskIoU.avg, maskF1.avg, processTime.avg))
print("DONE.\n")
if __name__ == '__main__':
parser=Options().init(argparse.ArgumentParser(description='WaterMark Removal'))
main(parser.parse_args())