/
utils.py
320 lines (222 loc) · 13.1 KB
/
utils.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
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.autograd.gradcheck import zero_gradients
import torchvision
import torchvision.transforms as transforms
import torch_dct
import numpy as np
import copy
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
def train(model, trans, trainloader, testloader, epochs, opt, loss_fun, lr_schedule, save_train_dir):
# lr_schedule = lambda t: np.interp([t], [0, epochs * 2 // 5, epochs], [0, max_lr, 0])[0]
# loss_fun = nn.CrossEntropyLoss()
print('Starting training...')
print()
for epoch in range(epochs):
print('Epoch', epoch)
train_loss_sum = 0
train_acc_sum = 0
train_n = 0
model.train()
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(DEVICE), targets.to(DEVICE)
lr = lr_schedule(epoch + (batch_idx + 1) / len(trainloader))
opt.param_groups[0].update(lr=lr)
output = model(trans(inputs))
loss = loss_fun(output, targets)
opt.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 0.5)
opt.step()
train_loss_sum += loss.item() * targets.size(0)
train_acc_sum += (output.max(1)[1] == targets).sum().item()
train_n += targets.size(0)
if batch_idx % 100 == 0:
print('Batch idx: %d(%d)\tTrain Acc: %.3f%%\tTrain Loss: %.3f' %
(batch_idx, epoch, 100. * train_acc_sum / train_n, train_loss_sum / train_n))
print('\nTrain Summary\tEpoch: %d | Train Acc: %.3f%% | Train Loss: %.3f' %
(epoch, 100. * train_acc_sum / train_n, train_loss_sum / train_n))
test_acc, test_loss = test(model, trans, testloader)
print('Test Summary\tEpoch: %d | Test Acc: %.3f%% | Test Loss: %.3f\n' % (epoch, test_acc, test_loss))
try:
state_dict = model.module.state_dict()
except AttributeError:
state_dict = model.state_dict()
torch.save(state_dict, save_train_dir + 'model.t7')
return model
def test(model, trans, testloader):
loss_fun = nn.CrossEntropyLoss()
test_loss_sum = 0
test_acc_sum = 0
test_n = 0
model.eval()
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(DEVICE), targets.to(DEVICE)
output = model(trans(inputs))
loss = loss_fun(output, targets)
test_loss_sum += loss.item() * targets.size(0)
test_acc_sum += (output.max(1)[1] == targets).sum().item()
test_n += targets.size(0)
test_loss = (test_loss_sum / test_n)
test_acc = (100 * test_acc_sum / test_n)
return test_acc, test_loss
def subspace_deepfool(im, model, trans, num_classes=10, overshoot=0.02, max_iter=100, Sp=None, device=DEVICE):
image = copy.deepcopy(im)
input_shape = image.size()
f_image = model(trans(Variable(image, requires_grad=True))).view((-1,))
I = f_image.argsort(descending=True)
I = I[0:num_classes]
label_orig = I[0]
pert_image = copy.deepcopy(image)
r = torch.zeros(input_shape).to(device)
label_pert = label_orig
loop_i = 0
while label_pert == label_orig and loop_i < max_iter:
x = Variable(pert_image, requires_grad=True)
fs = model(trans(x))
pert = torch.Tensor([np.inf])[0].to(device)
w = torch.zeros(input_shape).to(device)
fs[0, I[0]].backward(retain_graph=True)
grad_orig = copy.deepcopy(x.grad.data)
for k in range(1, num_classes):
zero_gradients(x)
fs[0, I[k]].backward(retain_graph=True)
cur_grad = copy.deepcopy(x.grad.data)
w_k = cur_grad - grad_orig
f_k = (fs[0, I[k]] - fs[0, I[0]]).data
if Sp is None:
pert_k = torch.abs(f_k) / w_k.norm()
else:
pert_k = torch.abs(f_k) / torch.matmul(Sp.t(), w_k.view([-1, 1])).norm()
if pert_k < pert:
pert = pert_k + 0.
w = w_k + 0.
if Sp is not None:
w = torch.matmul(Sp, torch.matmul(Sp.t(), w.view([-1, 1]))).reshape(w.shape)
r_i = torch.clamp(pert, min=1e-4) * w / w.norm()
r = r + r_i
pert_image = pert_image + r_i
label_pert = torch.argmax(model(trans(Variable(image + (1 + overshoot) * r, requires_grad=False))).data).item()
loop_i += 1
return (1 + overshoot) * r, loop_i, label_orig, label_pert, image + (1 + overshoot) * r
def compute_margin_distribution(model, trans, dataloader, subspace_list, path, proc_fun=None):
margins = []
print('Measuring margin distribution...')
for s, Sp in enumerate(subspace_list):
Sp = Sp.to(DEVICE)
sp_margin = []
for inputs, targets in dataloader:
inputs, targets = inputs.to(DEVICE), targets.to(DEVICE)
if proc_fun:
inputs = proc_fun(inputs)
adv_perts = torch.zeros_like(inputs)
for n, im in enumerate(inputs):
adv_perts[n], _, _, _, _ = subspace_deepfool(im, model, trans, Sp=Sp)
sp_margin.append(adv_perts.cpu().view([-1, np.prod(inputs.shape[1:])]).norm(dim=[1]))
sp_margin = torch.cat(sp_margin)
margins.append(sp_margin.numpy())
print('Subspace %d:\tMedian margin: %5.5f' % (s, np.median(sp_margin)))
np.save(path, margins)
return np.array(margins)
def kron(a, b):
siz1 = torch.Size(torch.tensor(a.shape[-2:]) * torch.tensor(b.shape[-2:]))
res = a.unsqueeze(-1).unsqueeze(-3) * b.unsqueeze(-2).unsqueeze(-4)
siz0 = res.shape[:-4]
return res.reshape(siz0 + siz1)
def generate_subspace_list(subspace_dim, dim, subspace_step, channels):
subspace_list = []
idx_i = 0
idx_j = 0
while (idx_i + subspace_dim - 1 <= dim - 1) and (idx_j + subspace_dim - 1 <= dim - 1):
S = torch.zeros((subspace_dim, subspace_dim, dim, dim), dtype=torch.float32).to(DEVICE)
for i in range(subspace_dim):
for j in range(subspace_dim):
dirac = torch.zeros((dim, dim), dtype=torch.float32, device=DEVICE)
dirac[idx_i + i, idx_j + j] = 1.
S[i, j] = torch_dct.idct_2d(dirac, norm='ortho')
Sp = S.view(subspace_dim * subspace_dim, dim * dim)
if channels > 1:
Sp = kron(torch.eye(channels, dtype=torch.float32, device=DEVICE), Sp)
Sp = Sp.t()
Sp = Sp.to('cpu')
subspace_list.append(Sp)
idx_i += subspace_step
idx_j += subspace_step
return subspace_list
def get_dataset_loaders(dataset, dataset_dir, batch_size=128):
pin_memory = True if DEVICE == 'cuda' else False
if dataset == 'MNIST':
trainset = torchvision.datasets.MNIST(root=dataset_dir['train'], download=True, train=True, transform=torchvision.transforms.ToTensor())
testset = torchvision.datasets.MNIST(root=dataset_dir['val'], download=True, train=False, transform=torchvision.transforms.ToTensor())
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True,
num_workers=2, pin_memory=pin_memory)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False,
num_workers=2, pin_memory=pin_memory)
mean = torch.tensor([0.1307], device=DEVICE)[None, :, None, None]
std = torch.tensor([0.3081], device=DEVICE)[None, :, None, None]
elif dataset == 'CIFAR10':
trainset = torchvision.datasets.CIFAR10(root=dataset_dir['train'], download=True, train=True, transform=torchvision.transforms.ToTensor())
testset = torchvision.datasets.CIFAR10(root=dataset_dir['val'], download=True, train=False, transform=torchvision.transforms.ToTensor())
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True,
num_workers=2, pin_memory=pin_memory)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False,
num_workers=2, pin_memory=pin_memory)
mean = torch.tensor([0.4914, 0.4822, 0.4465], device=DEVICE)[None, :, None, None]
std = torch.tensor([0.247, 0.243, 0.261], device=DEVICE)[None, :, None, None]
elif dataset == 'ImageNet':
transform_train = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
])
transform_test = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor()
])
trainset = torchvision.datasets.ImageFolder(root=dataset_dir['train'], transform=transform_train)
testset = torchvision.datasets.ImageFolder(root=dataset_dir['val'], transform=transform_test)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True,
num_workers=4, pin_memory=True)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False,
num_workers=4, pin_memory=True)
mean = torch.as_tensor([0.485, 0.456, 0.406], dtype=torch.float, device=DEVICE)[None, :, None, None]
std = torch.as_tensor([0.229, 0.224, 0.225], dtype=torch.float, device=DEVICE)[None, :, None, None]
else:
raise NotImplementedError
return trainloader, testloader, trainset, testset, mean, std
def get_processed_dataset_loaders(proc_fun, dataset, dataset_dir, batch_size=128):
pin_memory = True if DEVICE == 'cuda' else False
if dataset == 'MNIST':
orig_trainset = torchvision.datasets.MNIST(root=dataset_dir['train'], download=True, train=True, transform=torchvision.transforms.ToTensor())
orig_testset = torchvision.datasets.MNIST(root=dataset_dir['val'], download=True, train=False, transform=torchvision.transforms.ToTensor())
# trainset = torch.utils.data.TensorDataset(proc_fun(torch.tensor(trainset.data).type(torch.float32).permute([-1, 1, 28, 28]) / 255.), torch.tensor(trainset.targets))
# testset = torch.utils.data.TensorDataset(proc_fun(torch.tensor(testset.data).type(torch.float32).permute([-1, 1, 28, 28]) / 255.), torch.tensor(testset.targets))
trainset = torch.utils.data.TensorDataset(proc_fun(orig_trainset.data.type(torch.float32).unsqueeze(1) / 255.), orig_trainset.targets)
testset = torch.utils.data.TensorDataset(proc_fun(orig_testset.data.type(torch.float32).unsqueeze(1) / 255.), orig_testset.targets)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True,
num_workers=2, pin_memory=pin_memory)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False,
num_workers=2, pin_memory=pin_memory)
mean = torch.tensor([0.1307], device=DEVICE)[None, :, None, None]
std = torch.tensor([0.3081], device=DEVICE)[None, :, None, None]
proc_mean = torch.as_tensor(trainset.tensors[0].mean(axis=(0, 2, 3)), dtype=torch.float, device=DEVICE)[None, :, None, None]
proc_std = torch.as_tensor(trainset.tensors[0].std(axis=(0, 2, 3)), dtype=torch.float, device=DEVICE)[None, :, None, None]
elif dataset == 'CIFAR10':
orig_trainset = torchvision.datasets.CIFAR10(root=dataset_dir['train'], download=True, train=True, transform=torchvision.transforms.ToTensor())
orig_testset = torchvision.datasets.CIFAR10(root=dataset_dir['val'], download=True, train=False, transform=torchvision.transforms.ToTensor())
trainset = torch.utils.data.TensorDataset(proc_fun(torch.tensor(orig_trainset.data).type(torch.float32).permute([0, 3, 1, 2]) / 255.), torch.tensor(orig_trainset.targets))
testset = torch.utils.data.TensorDataset(proc_fun(torch.tensor(orig_testset.data).type(torch.float32).permute([0, 3, 1, 2]) / 255.), torch.tensor(orig_testset.targets))
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True,
num_workers=2, pin_memory=pin_memory)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False,
num_workers=2, pin_memory=pin_memory)
mean = torch.tensor([0.4914, 0.4822, 0.4465], device=DEVICE)[None, :, None, None]
std = torch.tensor([0.247, 0.243, 0.261], device=DEVICE)[None, :, None, None]
proc_mean = torch.as_tensor(trainset.tensors[0].mean(axis=(0, 2, 3)), dtype=torch.float, device=DEVICE)[None, :, None, None]
proc_std = torch.as_tensor(trainset.tensors[0].std(axis=(0, 2, 3)), dtype=torch.float, device=DEVICE)[None, :, None, None]
else:
raise NotImplementedError
return trainloader, testloader, trainset, testset, mean, std, proc_mean, proc_std