-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathtest.py
208 lines (184 loc) · 7.75 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
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
import torch
import torch.nn as nn
import config
import main
def Mytest(helper, epoch, model, is_poison=False, agent_name_key=""):
model.eval()
total_loss = 0
was_correct = 0 # means the data should be correct, but due to threshold, part of it was rejected
correct = 0
dataset_size = 0
if (
helper.params["type"] == config.TYPE_CIFAR
or helper.params["type"] == config.TYPE_MNIST
or helper.params["type"] == config.TYPE_FASHION_MNIST
):
data_iterator = helper.test_data
for batch_id, batch in enumerate(data_iterator):
data, targets = helper.get_batch(data_iterator, batch, evaluation=True)
dataset_size += len(data)
output = model(data)
total_loss += nn.functional.cross_entropy(
output, targets, reduction="sum"
).item() # sum up batch loss
pred = output.data.max(1)[1] # get the index of the max log-probability
probabilities = torch.nn.functional.softmax(output)
probabilities = torch.gather(probabilities, 1, pred.unsqueeze(1))
for idx, is_correct in enumerate(pred.eq(targets.data.view_as(pred)).cpu()):
if is_correct == True:
was_correct += 1
if probabilities[idx][0] >= helper.params["confidence_threshold"]:
correct += 1
acc = 100.0 * (float(correct) / float(dataset_size)) if dataset_size != 0 else 0
total_l = total_loss / dataset_size if dataset_size != 0 else 0
main.logger.info(
"___Test {} poisoned: {}, epoch: {}: , model {}, Average loss: {:.4f}, "
"Accuracy: {}/{} ({:.4f}%)".format(
model.name,
is_poison,
epoch,
agent_name_key,
total_l,
correct,
dataset_size,
acc,
)
)
model.train()
return (total_l, acc, was_correct, correct, dataset_size)
def Mytest_poison(helper, epoch, model, is_poison=False, agent_name_key=""):
model.eval()
total_loss = 0.0
was_correct = 0 # means the data should be correct, but due to threshold, part of it was rejected
correct = 0
dataset_size = 0
poison_data_count = 0
if (
helper.params["type"] == config.TYPE_CIFAR
or helper.params["type"] == config.TYPE_MNIST
or helper.params["type"] == config.TYPE_FASHION_MNIST
):
data_iterator = helper.test_data_poison
for batch_id, batch in enumerate(data_iterator):
data, targets, poison_num = helper.get_poison_batch(
batch, adversarial_index=-1, evaluation=True
)
poison_data_count += poison_num
dataset_size += len(data)
output = model(data)
total_loss += nn.functional.cross_entropy(
output, targets, reduction="sum"
).item() # sum up batch loss
pred = output.data.max(1)[1] # get the index of the max log-probability
probabilities = torch.nn.functional.softmax(output)
probabilities = torch.gather(probabilities, 1, pred.unsqueeze(1))
for idx, is_correct in enumerate(pred.eq(targets.data.view_as(pred)).cpu()):
if is_correct == True:
was_correct += 1
if probabilities[idx][0] >= helper.params["confidence_threshold"]:
correct += 1
acc = (
100.0 * (float(correct) / float(poison_data_count))
if poison_data_count != 0
else 0
)
total_l = total_loss / poison_data_count if poison_data_count != 0 else 0
main.logger.info(
"___Test {} poisoned: {}, epoch: {}: , model {}, Average loss: {:.4f}, "
"Accuracy: {}/{} ({:.4f}%)".format(
model.name,
is_poison,
epoch,
agent_name_key,
total_l,
correct,
poison_data_count,
acc,
)
)
model.train()
return total_l, acc, was_correct, correct, poison_data_count
def Mytest_poison_trigger(helper, model, adver_trigger_index):
model.eval()
total_loss = 0.0
was_correct = 0 # means the data should be correct, but due to threshold, part of it was rejected
correct = 0
dataset_size = 0
poison_data_count = 0
if (
helper.params["type"] == config.TYPE_CIFAR
or helper.params["type"] == config.TYPE_MNIST
or helper.params["type"] == config.TYPE_FASHION_MNIST
):
data_iterator = helper.test_data_poison
adv_index = adver_trigger_index
for batch_id, batch in enumerate(data_iterator):
data, targets, poison_num = helper.get_poison_batch(
batch, adversarial_index=adv_index, evaluation=True
)
poison_data_count += poison_num
dataset_size += len(data)
output = model(data)
total_loss += nn.functional.cross_entropy(
output, targets, reduction="sum"
).item() # sum up batch loss
pred = output.data.max(1)[1] # get the index of the max log-probability
probabilities = torch.nn.functional.softmax(output)
probabilities = torch.gather(probabilities, 1, pred.unsqueeze(1))
for idx, is_correct in enumerate(pred.eq(targets.data.view_as(pred)).cpu()):
if is_correct == True:
was_correct += 1
if probabilities[idx][0] >= helper.params["confidence_threshold"]:
correct += 1
acc = (
100.0 * (float(correct) / float(poison_data_count))
if poison_data_count != 0
else 0
)
total_l = total_loss / poison_data_count if poison_data_count != 0 else 0
model.train()
return total_l, acc, was_correct, correct, poison_data_count
def Mytest_poison_agent_trigger(helper, model, agent_name_key):
model.eval()
total_loss = 0.0
was_correct = 0 # means the data should be correct, but due to threshold, part of it was rejected
correct = 0
dataset_size = 0
poison_data_count = 0
if (
helper.params["type"] == config.TYPE_CIFAR
or helper.params["type"] == config.TYPE_MNIST
or helper.params["type"] == config.TYPE_FASHION_MNIST
):
data_iterator = helper.test_data_poison
adv_index = -1
for temp_index in range(0, len(helper.params["adversary_list"])):
if int(agent_name_key) == helper.params["adversary_list"][temp_index]:
adv_index = temp_index
break
for batch_id, batch in enumerate(data_iterator):
data, targets, poison_num = helper.get_poison_batch(
batch, adversarial_index=adv_index, evaluation=True
)
poison_data_count += poison_num
dataset_size += len(data)
output = model(data)
total_loss += nn.functional.cross_entropy(
output, targets, reduction="sum"
).item() # sum up batch loss
pred = output.data.max(1)[1] # get the index of the max log-probability
probabilities = torch.nn.functional.softmax(output)
probabilities = torch.gather(probabilities, 1, pred.unsqueeze(1))
for idx, is_correct in enumerate(pred.eq(targets.data.view_as(pred)).cpu()):
if is_correct == True:
was_correct += 1
if probabilities[idx][0] >= helper.params["confidence_threshold"]:
correct += 1
acc = (
100.0 * (float(correct) / float(poison_data_count))
if poison_data_count != 0
else 0
)
total_l = total_loss / poison_data_count if poison_data_count != 0 else 0
model.train()
return total_l, acc, was_correct, correct, poison_data_count