-
Notifications
You must be signed in to change notification settings - Fork 0
/
models.py
268 lines (212 loc) · 8.52 KB
/
models.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
import torch.nn as nn
import torch.nn.functional as F
import math
import torch.utils.model_zoo as model_zoo
import torch
from torch.autograd import Function
from torchvision import models
from easydl import *
from torchvision import utils as vutils
__all__ = ['ResNet', 'resnet50', 'resnet101', 'TotalNet101', 'TotalNet50']
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
}
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.baselayer = [self.conv1, self.bn1, self.layer1, self.layer2, self.layer3, self.layer4]
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
return x
def resnet50(pretrained=False, **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['resnet50'])
model_dict = model.state_dict()
# for key, value in pretrained_dict.items():
# print(key)
for k, v in model_dict.items():
if not "fc.weight" in k and not "fc.bias" in k and not "num_batches_tracked" in k:
model_dict[k] = pretrained_dict[k]
model.load_state_dict(model_dict)
return model
def resnet101(pretrained=False, **kwargs):
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['resnet101'])
model_dict = model.state_dict()
for k, v in model_dict.items():
if not "fc.weight" in k and not "fc.bias" in k and not "num_batches_tracked" in k:
model_dict[k] = pretrained_dict[k]
model.load_state_dict(model_dict)
return model
class FClayers(nn.Module):
def __init__(self, in_dim=2048, out_dim=12, bottleneck=1000):
super(FClayers, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.drop = nn.Dropout(0.5)
self.fc1 = nn.Linear(in_dim, bottleneck)
self.bn1 = nn.BatchNorm1d(bottleneck)
self.fc3 = nn.Linear(bottleneck, out_dim)
def forward(self, x):
x = self.fc1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.drop(x)
x = self.fc3(x)
return x
class AdversarialNetwork(nn.Module):
"""
AdversarialNetwork with a gredient reverse layer.
its ``forward`` function calls gredient reverse layer first, then applies ``self.main`` module.
"""
def __init__(self, in_feature, bottleneck=1024):
super(AdversarialNetwork, self).__init__()
self.main = nn.Sequential(
nn.Linear(in_feature, bottleneck),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(bottleneck, bottleneck),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(bottleneck, 1),
nn.Sigmoid()
)
self.grl = GradientReverseModule(lambda step: aToBSheduler(step, 0.0, 1.0, gamma=10, max_iter=10000))
def forward(self, x):
x_ = self.grl(x)
y = self.main(x_)
return y
def l2_norm(input, dim):
norm = torch.norm(input, dim=dim, keepdim=True)
output = torch.div(input, norm)
return output
def normalize_perturbation(d):
output = l2_norm(l2_norm(d, dim=2), dim=3)
return output
def perturb_image(x, p, feature_extractor, classifier, radius=3.5):
eps = 1e-6 * normalize_perturbation(torch.randn(x.shape))
eps = Variable(eps, requires_grad=True)
# Predict on randomly perturbed image
eps_f = feature_extractor(x + eps.cuda())
eps_p = classifier(eps_f)
eps_p = F.softmax(eps_p)
loss = F.nll_loss(torch.log(eps_p + 1e-6), p)
loss.backward()
# Based on perturbed image, get direction of greatest error
eps_adv = eps.grad
# Use that direction as adversarial perturbation
eps_adv = normalize_perturbation(eps_adv)
x_adv = x + radius * eps_adv.cuda()
return eps_adv, x_adv
def vat_loss(x, p, feature_extractor, classifier):
eps_adv, x_adv = perturb_image(x, p, feature_extractor, classifier)
f_adv = feature_extractor(x_adv)
p_adv = classifier(f_adv)
p_adv = F.softmax(p_adv)
loss = torch.mean(F.nll_loss(torch.log(p_adv + 1e-6), p))
return eps_adv, x_adv, loss
def save_image_tensor(input_tensor: torch.Tensor, filename):
assert (len(input_tensor.shape) == 4 and input_tensor.shape[0] == 1)
input_tensor = input_tensor.clone().detach()
input_tensor = input_tensor.to(torch.device('cpu'))
# input_tensor = unnormalize(input_tensor)
vutils.save_image(input_tensor, filename)