-
Notifications
You must be signed in to change notification settings - Fork 24
/
test_cgvsphoto.py
executable file
·120 lines (90 loc) · 4.2 KB
/
test_cgvsphoto.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
"""
Copyright (c) 2019, National Institute of Informatics
All rights reserved.
Author: Huy H. Nguyen
-----------------------------------------------------
Script for testing Capsule-Forensics-v2 on CGvsPhoto database
"""
import sys
sys.setrecursionlimit(15000)
import os
import torch
import torch.backends.cudnn as cudnn
import numpy as np
from torch.autograd import Variable
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
from tqdm import tqdm
import argparse
from sklearn import metrics
from scipy.optimize import brentq
from scipy.interpolate import interp1d
from sklearn.metrics import roc_curve
import model_big
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', default ='databases/cgvsphoto_patches', help='path to dataset')
parser.add_argument('--test_set', default ='test', help='test set')
parser.add_argument('--workers', type=int, help='number of data loading workers', default=0)
parser.add_argument('--batchSize', type=int, default=32, help='input batch size')
parser.add_argument('--imageSize', type=int, default=100, help='the height / width of the input image to network')
parser.add_argument('--gpu_id', type=int, default=0, help='GPU ID')
parser.add_argument('--outf', default='checkpoints/cgvsphoto', help='folder to output model checkpoints')
parser.add_argument('--random', action='store_true', default=False, help='enable randomness for routing matrix')
parser.add_argument('--id', type=int, default=41, help='checkpoint ID')
opt = parser.parse_args()
print(opt)
if __name__ == '__main__':
text_writer = open(os.path.join(opt.outf, 'test.txt'), 'w')
transform_fwd = transforms.Compose([
# transforms.Resize((opt.imageSize, opt.imageSize)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
dataset_test = dset.ImageFolder(root=os.path.join(opt.dataset, opt.test_set), transform=transform_fwd)
assert dataset_test
dataloader_test = torch.utils.data.DataLoader(dataset_test, batch_size=opt.batchSize, shuffle=False, num_workers=int(opt.workers))
vgg_ext = model_big.VggExtractor()
capnet = model_big.CapsuleNet(2, opt.gpu_id)
capnet.load_state_dict(torch.load(os.path.join(opt.outf,'capsule_' + str(opt.id) + '.pt')))
capnet.eval()
if opt.gpu_id >= 0:
vgg_ext.cuda(opt.gpu_id)
capnet.cuda(opt.gpu_id)
##################################################################################
tol_label = np.array([], dtype=np.float)
tol_pred = np.array([], dtype=np.float)
tol_pred_prob = np.array([], dtype=np.float)
count = 0
loss_test = 0
for img_data, labels_data in tqdm(dataloader_test):
labels_data[labels_data > 1] = 1
img_label = labels_data.numpy().astype(np.float)
if opt.gpu_id >= 0:
img_data = img_data.cuda(opt.gpu_id)
labels_data = labels_data.cuda(opt.gpu_id)
input_v = Variable(img_data)
x = vgg_ext(input_v)
classes, class_ = capnet(x, random=opt.random)
output_dis = class_.data.cpu()
output_pred = np.zeros((output_dis.shape[0]), dtype=np.float)
for i in range(output_dis.shape[0]):
if output_dis[i,1] >= output_dis[i,0]:
output_pred[i] = 1.0
else:
output_pred[i] = 0.0
tol_label = np.concatenate((tol_label, img_label))
tol_pred = np.concatenate((tol_pred, output_pred))
pred_prob = torch.softmax(output_dis, dim=1)
tol_pred_prob = np.concatenate((tol_pred_prob, pred_prob[:,1].data.numpy()))
count += 1
acc_test = metrics.accuracy_score(tol_label, tol_pred)
loss_test /= count
fpr, tpr, thresholds = roc_curve(tol_label, tol_pred_prob, pos_label=1)
eer = brentq(lambda x : 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
# fnr = 1 - tpr
# hter = (fpr + fnr)/2
print('[Epoch %d] Test acc: %.2f EER: %.2f' % (opt.id, acc_test*100, eer*100))
text_writer.write('%d,%.2f,%.2f\n'% (opt.id, acc_test*100, eer*100))
text_writer.flush()
text_writer.close()