/
test_cgvsphoto_full.py
executable file
·174 lines (126 loc) · 5.9 KB
/
test_cgvsphoto_full.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
"""
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
import math
import gc
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', default ='databases/cgvsphoto_full', help='path to dataset')
parser.add_argument('--test_set', default ='test', help='path to test dataset')
parser.add_argument('--workers', type=int, help='number of data loading workers', default=0)
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 images and model checkpoints')
parser.add_argument('--random_sample', type=int, default=0, help='number of random sample to test')
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_full.txt'), 'w')
transform_fwd = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
# folder dataset
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=1, shuffle=False, num_workers=int(opt.workers))
def extract_subimages(image, subimage_size=100):
#print(image.shape)
subimages = []
width = image.shape[3]
height = image.shape[2]
current_height = 0
while current_height + subimage_size <= height:
current_width = 0
while current_width + subimage_size <= width:
sub = image[:,:,current_height:current_height+subimage_size, current_width:current_width+subimage_size]
subimages.append(sub)
current_width += subimage_size
current_height += subimage_size
return subimages
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)
##################################################################################
predict_lst = np.array([], dtype=np.float)
#prob_lst = np.array([], dtype=np.float)
labels_lst = np.array([], dtype=np.float)
for img_data, labels_data in dataloader_test:
img_label = labels_data.numpy().astype(np.float)
subimages = extract_subimages(img_data, opt.imageSize)
prob = np.array([[0.0, 0.0]])
n_sub_imgs = len(subimages)
if (opt.random_sample > 0):
if n_sub_imgs > opt.random_sample:
np.random.shuffle(subimages)
n_sub_imgs = opt.random_sample
img_tmp = torch.FloatTensor([]).view(0, 3, opt.imageSize, opt.imageSize)
for i in range(n_sub_imgs):
img_tmp = torch.cat((img_tmp, subimages[i]), dim=0)
if opt.gpu_id >= 0:
img_tmp = img_tmp.cuda(opt.gpu_id)
input_v = Variable(img_tmp, requires_grad = False)
x = vgg_ext(input_v)
classes, class_ = capnet(x, random=opt.random)
output_pred = class_.data.cpu().numpy()
else:
batchSize = 10
steps = int(math.ceil(n_sub_imgs*1.0/batchSize))
output_pred = np.array([], dtype=np.float).reshape(0,2)
for i in range(steps):
img_tmp = torch.FloatTensor([]).view(0, 3, opt.imageSize, opt.imageSize)
end = (i + 1)*batchSize
if end > n_sub_imgs:
end = n_sub_imgs - i*batchSize
else:
end = batchSize
for j in range(end):
img_tmp = torch.cat((img_tmp, subimages[i*batchSize + j]), dim=0)
if opt.gpu_id >= 0:
img_tmp = img_tmp.cuda(opt.gpu_id)
input_v = Variable(img_tmp, requires_grad = False)
x = vgg_ext(input_v)
classes, class_ = capnet(x, random=opt.random)
output_p = class_.data.cpu().numpy()
output_pred = np.concatenate((output_pred, output_p), axis=0)
output_pred = output_pred.mean(0)
if output_pred[1] >= output_pred[0]:
pred = 1.0
else:
pred = 0.0
#print('%d - %d' %(pred, img_label))
text_writer.write('%d,%.2f\n' % (img_label, output_pred[1]))
predict_lst = np.concatenate((predict_lst, np.array([pred])), axis=0)
#prob_lst = np.concatenate((prob_lst, output_pred[1]), axis=0)
labels_lst = np.concatenate((labels_lst, img_label), axis=0)
acc = metrics.accuracy_score(labels_lst, predict_lst)
print(len(predict_lst))
print('%d\t%.4f' % (opt.id, acc))
text_writer.flush()
text_writer.close()