-
Notifications
You must be signed in to change notification settings - Fork 3
/
detect.py
129 lines (117 loc) · 6.06 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
from __future__ import print_function
import argparse
import torch
import models
import os
import robust_deep_learning as rdl
import data_loader
from torchvision import transforms
import timm
parser = argparse.ArgumentParser(description='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 | tinyimagenet')
parser.add_argument('--dataroot', default='data', help='path to dataset')
parser.add_argument('--model', required=True, help='resnet | wideresnet')
parser.add_argument('--gpu', type=int, default=None, 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()
print("\n\n\n\n\n\n\n\n")
print("#############################")
print("#############################")
print("######### DETECTION #########")
print("#############################")
print("#############################")
print(args)
dir_path = os.path.join("experiments", args.dir, "data~"+args.dataset+"+model~"+args.model+"+loss~"+str(args.loss))
file_path = os.path.join(dir_path, "results_ood.csv")
with open(file_path, "w") as results_file:
results_file.write("EXECUTION,MODEL,IN-DATA,OUT-DATA,LOSS,SCORE,TPR,AUROC,AUPRIN,AUPROUT\n")
if args.dataset == 'cifar10':
args.num_classes = 10
out_dist_list = ['cifar100', 'imagenet_resize', 'lsun_resize', 'svhn']
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.491, 0.482, 0.446), (0.247, 0.243, 0.261))])
elif args.dataset == 'cifar100':
args.num_classes = 100
out_dist_list = ['cifar10', 'imagenet_resize', 'lsun_resize', 'svhn']
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.num_classes = 1000
out_dist_list = ['imagenet-o']
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("\n\n\n\nEXECUTION:", args.execution)
pre_trained_net = os.path.join(dir_path, "model" + str(args.execution) + ".pth")
if args.loss.split("_")[0] == "softmax":
args.loss_first_part = rdl.SoftMaxLossFirstPart
scores = ["MPS"]
elif args.loss.split("_")[0] == "isomax":
args.loss_first_part = rdl.IsoMaxLossFirstPart
scores = ["ES"]
elif args.loss.split("_")[0] == "isomaxplus":
args.loss_first_part = rdl.IsoMaxPlusLossFirstPart
scores = ["MDS"]
elif args.loss.split("_")[0] == "dismax":
args.loss_first_part = rdl.DisMaxLossFirstPart
scores = ["MMLES","MPS"]
# load networks
if args.model == 'resnet34':
model = models.ResNet34(num_c=args.num_classes)
model.classifier = args.loss_first_part(model.classifier.in_features, model.classifier.out_features)
elif args.model == 'densenetbc100':
model = models.DenseNet3(100, int(args.num_classes))
model.classifier = args.loss_first_part(model.classifier.in_features, model.classifier.out_features)
elif args.model == "wideresnet2810":
model = models.Wide_ResNet(depth=28, widen_factor=10, num_classes=args.num_classes)
model.classifier = args.loss_first_part(model.classifier.in_features, model.classifier.out_features)
elif args.model == "resnet18":
model = timm.create_model('resnet18', pretrained=False)
#num_in_features = model.get_classifier().in_features
#model.fc = args.loss_first_part(num_in_features, args.num_classes)
model.fc = args.loss_first_part(model.get_classifier().in_features, args.num_classes)
if args.gpu is not None:
model.load_state_dict(torch.load(pre_trained_net, map_location="cuda:" + str(args.gpu)))
else:
model.load_state_dict(torch.load(pre_trained_net, map_location="cuda"))
model.cuda(args.gpu)
model.eval()
print('load model: ' + args.model)
# 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("\n\n")
print("###############################")
print("###############################")
print("SCORE:", score)
print("###############################")
print("###############################")
base_line_list = []
print("In-distribution")
results = rdl.get_outputs_labels_and_metrics(model, test_loader, gpu=args.gpu)
in_data_scores = rdl.get_scores(results["outputs"], score)
for out_dist in out_dist_list:
print('\nOut-distribution: ' + out_dist)
out_test_loader = data_loader.getNonTargetDataSet(args, out_dist, args.batch_size, in_transform, args.dataroot)
results = rdl.get_outputs_labels_and_metrics(model, out_test_loader, gpu=args.gpu)
out_data_scores = rdl.get_scores(results["outputs"], score)
results = rdl.get_ood_metrics_from_scores(in_data_scores, out_data_scores, fpr=0.05)
with open(file_path, "a") as results_file:
results_file.write("{},{},{},{},{},{},{},{},{},{}\n".format(
str(args.execution), args.model, args.dataset, out_dist,str(args.loss), score,
'{:.2f}'.format(100.*results['tpr_at_given_fpr']),
'{:.2f}'.format(100.*results['auroc']),
'{:.2f}'.format(100.*results['auprin']),
'{:.2f}'.format(100.*results['auprout'])))