/
detect.py
206 lines (183 loc) · 9.33 KB
/
detect.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
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
from __future__ import print_function
import argparse
import torch
import models
import os
import losses
import data_loader
import calculate_log as callog
from torchvision import transforms
import timm
from tqdm import tqdm
parser = argparse.ArgumentParser(description='OOD Detector')
parser.add_argument('-bs', '--batch-size', type=int, default=64, metavar='N', help='batch size for data loader')
parser.add_argument('--dataset', required=True, help='cifar10 | cifar100 | imagenet1k')
parser.add_argument('--dataroot', default='data', help='path to dataset')
parser.add_argument('--net_type', required=True, help='resnet | wideresnet')
parser.add_argument('--gpu', type=int, default=0, help='gpu index')
parser.add_argument('--loss', required=True, help='the loss used')
parser.add_argument('--dir', default="", type=str, help='Part of the dir to use')
parser.add_argument('-x', '--executions', default=1, type=int, metavar='N', help='Number of executions (default: 1)')
args = parser.parse_args()
torch.cuda.set_device(args.gpu)
def main():
print("\n\n\n\n\n")
print("#############################")
print("#############################")
print("######### DETECTION #########")
print("#############################")
print("#############################")
print(args)
dir_path = os.path.join("experiments", args.dir, "train_classify", "data~"+args.dataset+"+model~"+args.net_type+"+loss~"+str(args.loss))
file_path = os.path.join(dir_path, "results_odd.csv")
with open(file_path, "w") as results_file:
results_file.write(
"EXECUTION,MODEL,IN-DATA,OUT-DATA,LOSS,AD-HOC,SCORE,INFER-LEARN,INFER-TRANS,"
"TNR,AUROC,DTACC,AUIN,AUOUT,CPU_FALSE,CPU_TRUE,GPU_FALSE,GPU_TRUE,TEMPERATURE,MAGNITUDE\n")
args_outf = os.path.join("temp", "ood", args.loss, args.net_type + '+' + args.dataset)
if os.path.isdir(args_outf) == False:
os.makedirs(args_outf)
# define number of classes
if args.dataset == 'cifar10':
args.num_classes = 10
args.data_type = "image"
elif args.dataset == 'cifar100':
args.num_classes = 100
args.data_type = "image"
elif args.dataset == 'imagenet1k':
args.num_classes = 1000
args.data_type = "image"
if args.dataset == 'cifar10':
out_dist_list = ['cifar100', 'imagenet_resize', 'lsun_resize', 'svhn']
elif args.dataset == 'cifar100':
out_dist_list = ['cifar10', 'imagenet_resize', 'lsun_resize', 'svhn']
elif args.dataset == 'imagenet1k':
out_dist_list = ['imagenet-o']
if args.dataset == 'cifar10':
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.491, 0.482, 0.446), (0.247, 0.243, 0.261))])
elif args.dataset == 'cifar100':
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.507, 0.486, 0.440), (0.267, 0.256, 0.276))])
elif args.dataset == 'imagenet1k':
args.dataroot = '/mnt/ssd/imagenet1k'
args.input_size = 224
args.DEFAULT_CROP_RATIO = 0.875
in_transform = transforms.Compose([
transforms.Resize(int(args.input_size / args.DEFAULT_CROP_RATIO)),
transforms.CenterCrop(args.input_size),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])
for args.execution in range(1, args.executions + 1):
print("\nEXECUTION:", args.execution)
pre_trained_net = os.path.join(dir_path, "model" + str(args.execution) + ".pth")
if args.loss.split("_")[0] == "softmax":
loss_first_part = losses.SoftMaxLossFirstPart
scores = ["MPS"]
elif args.loss.split("_")[0] == "isomax":
loss_first_part = losses.IsoMaxLossFirstPart
scores = ["ES"]
elif args.loss.split("_")[0] == "isomaxplus":
loss_first_part = losses.IsoMaxPlusLossFirstPart
scores = ["MDS"]
elif args.loss.split("_")[0] == "dismax":
loss_first_part = losses.DisMaxLossFirstPart
scores = ["MMLES","MPS"]
# load networks
if args.net_type == 'resnet34':
model = models.ResNet34(num_c=args.num_classes, loss_first_part=loss_first_part)
elif args.net_type == 'densenetbc100':
model = models.DenseNet3(100, int(args.num_classes), loss_first_part=loss_first_part)
elif args.net_type == "wideresnet2810":
model = models.Wide_ResNet(depth=28, widen_factor=10, num_classes=args.num_classes, loss_first_part=loss_first_part)
elif args.net_type == "resnet18":
model = timm.create_model('resnet18', pretrained=False)
num_in_features = model.get_classifier().in_features
model.fc = loss_first_part(num_in_features, args.num_classes)
model.load_state_dict(torch.load(pre_trained_net, map_location="cuda:" + str(args.gpu)))
model.cuda()
print('load model: ' + args.net_type)
# load dataset
print('load target valid data: ', args.dataset)
_, test_loader = data_loader.getTargetDataSet(args, args.dataset, args.batch_size, in_transform, args.dataroot)
for score in scores:
print("###############################")
print("###############################")
print("SCORE:", score)
print("###############################")
print("###############################")
base_line_list = []
print("In-distribution")
get_scores(args, model, test_loader, args_outf, True, score)
out_count = 0
for out_dist in out_dist_list:
print('Out-distribution: ' + out_dist)
out_test_loader = data_loader.getNonTargetDataSet(args, out_dist, args.batch_size, in_transform, args.dataroot)
get_scores(args, model, out_test_loader, args_outf, False, score)
test_results = callog.metric(args_outf, ['PoT'])
base_line_list.append(test_results)
out_count += 1
# print the results
mtypes = ['TNR', 'AUROC', 'DTACC', 'AUIN', 'AUOUT']
print('Baseline method: train in_distribution: ' + args.dataset + '==========')
count_out = 0
for results in base_line_list:
print('out_distribution: '+ out_dist_list[count_out])
for mtype in mtypes:
print(' {mtype:6s}'.format(mtype=mtype), end='')
print('\n{val:6.2f}'.format(val=100.*results['PoT']['TNR']), end='')
print(' {val:6.2f}'.format(val=100.*results['PoT']['AUROC']), end='')
print(' {val:6.2f}'.format(val=100.*results['PoT']['DTACC']), end='')
print(' {val:6.2f}'.format(val=100.*results['PoT']['AUIN']), end='')
print(' {val:6.2f}\n'.format(val=100.*results['PoT']['AUOUT']), end='')
with open(file_path, "a") as results_file:
results_file.write("{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}\n".format(
str(args.execution), args.net_type, args.dataset, out_dist_list[count_out],
str(args.loss), "NATIVE", score, 'NO', False,
'{:.2f}'.format(100.*results['PoT']['TNR']),
'{:.2f}'.format(100.*results['PoT']['AUROC']),
'{:.2f}'.format(100.*results['PoT']['DTACC']),
'{:.2f}'.format(100.*results['PoT']['AUIN']),
'{:.2f}'.format(100.*results['PoT']['AUOUT']),
0, 0, 0, 0, 1, 0))
count_out += 1
def get_scores(args, model, test_loader, outf, out_flag, score_type=None):
print("===>>> get scores <<<===")
model.eval()
total = 0
if out_flag == True:
temp_file_name_val = '%s/confidence_PoV_In.txt'%(outf)
temp_file_name_test = '%s/confidence_PoT_In.txt'%(outf)
else:
temp_file_name_val = '%s/confidence_PoV_Out.txt'%(outf)
temp_file_name_test = '%s/confidence_PoT_Out.txt'%(outf)
g = open(temp_file_name_val, 'w')
f = open(temp_file_name_test, 'w')
for batch_index, batch_data in enumerate(tqdm(test_loader)):
data = batch_data[0]
batch_size = data.size(0)
total += batch_size
data = data.cuda()
with torch.no_grad():
logits = model(data)
probabilities = torch.nn.Softmax(dim=1)(logits)
if score_type == "MPS":
scores = probabilities.max(dim=1)[0]
elif score_type == "ES":
scores = (probabilities * torch.log(probabilities)).sum(dim=1)
elif score_type == "MDS":
scores = logits.max(dim=1)[0]
elif score_type == "MMLS":
scores = logits.max(dim=1)[0] + logits.mean(dim=1)
elif score_type == "MMLES":
scores = logits.max(dim=1)[0] + logits.mean(dim=1) + (probabilities * torch.log(probabilities)).sum(dim=1)
elif score_type == "MMLEPS":
scores = logits.max(dim=1)[0] + logits.mean(dim=1) + (probabilities * torch.log(probabilities)).sum(dim=1) + probabilities.max(dim=1)[0]
for i in range(batch_size):
f.write("{}\n".format(scores[i]))
f.close()
g.close()
if __name__ == '__main__':
main()