-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmodel.py
214 lines (184 loc) · 7.35 KB
/
model.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
from torch import nn, load
# from torchvision.models import resnet18, resnet50
from torch.nn.functional import relu, avg_pool2d
# Class for VGG16 convolutional neural network architecture
class VGG16(nn.Module):
def __init__(self, num_classes: int):
super(VGG16, self).__init__()
# Based on VGG16 architecture
self.conv = nn.Sequential(
# Block 1
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(0.5),
# Block 2
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(0.5),
# Block 3
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(0.5),
# Block 4
nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(0.5),
# Block 5
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(0.5)
)
self.fc = nn.Sequential(
nn.Linear(512, 512),
nn.ReLU(inplace=True),
nn.Linear(512, 512),
nn.ReLU(inplace=True),
nn.Linear(512, num_classes),
)
self.flatten = nn.Flatten()
self.device = None
# Compute forward pass
def forward(self, x):
out = self.conv(x)
out = self.flatten(out)
out = self.fc(out)
return out
# # Class for ResNet18 from torchvision
# class ResNet18(nn.Module):
# def __init__(self, num_classes: int):
# super(ResNet18, self).__init__()
# self.model = resnet18(weights=None, num_classes=num_classes)
# # self.model.conv1 = nn.Conv2d(input_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
# # self.model.fc = nn.Linear(512, num_classes)
# self.device = None
# # Compute forward pass
# def forward(self, x):
# return self.model(x)
# Class for ResNet50 from torchvision
# class ResNet50(nn.Module):
# def __init__(self, num_classes: int):
# super(ResNet50, self).__init__()
# self.model = resnet50(weights=None, num_classes=num_classes)
# # self.model.conv1 = nn.Conv2d(input_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
# # self.model.fc = nn.Linear(2048, num_classes)
# self.device = None
# # Compute forward pass
# def forward(self, x):
# return self.model(x)
def conv3x3(in_planes, out_planes, stride=1):
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, in_planes, planes, stride=1, config={}):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(in_planes, planes, stride)
self.conv2 = conv3x3(planes, planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1,
stride=stride, bias=False),
)
self.IC1 = nn.Sequential(
nn.BatchNorm2d(planes),
nn.Dropout(p=config['dropout'])
)
self.IC2 = nn.Sequential(
nn.BatchNorm2d(planes),
nn.Dropout(p=config['dropout'])
)
def forward(self, x):
out = self.conv1(x)
out = relu(out)
out = self.IC1(out)
out += self.shortcut(x)
out = relu(out)
out = self.IC2(out)
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes, nf, config={}):
super(ResNet, self).__init__()
self.in_planes = nf
self.conv1 = conv3x3(3, nf * 1)
self.bn1 = nn.BatchNorm2d(nf * 1)
self.layer1 = self._make_layer(block, nf * 1, num_blocks[0], stride=1, config=config)
self.layer2 = self._make_layer(block, nf * 2, num_blocks[1], stride=2, config=config)
self.layer3 = self._make_layer(block, nf * 4, num_blocks[2], stride=2, config=config)
self.layer4 = self._make_layer(block, nf * 8, num_blocks[3], stride=2, config=config)
self.linear = nn.Linear(nf * 8 * block.expansion, num_classes)
def _make_layer(self, block, planes, num_blocks, stride, config):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride, config=config))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x, task_id=None):
bsz = x.size(0)
out = relu(self.bn1(self.conv1(x.view(bsz, 3, 32, 32))))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
if task_id is not None:
t = task_id
offset1 = int((t-1) * 5)
offset2 = int(t * 5)
if offset1 > 0:
out[:, :offset1].data.fill_(-10e10)
if offset2 < 100:
out[:, offset2:100].data.fill_(-10e10)
return out
def resnet18(num_classes=10, nf=20, config={'dropout': 0.5}):
net = ResNet(BasicBlock, [2, 2, 2, 2], num_classes, nf, config=config)
return net
def resnet50(num_classes=10, nf=20, config={'dropout': 0.5}):
net = ResNet(BasicBlock, [3, 4, 6, 3], num_classes, nf, config=config)
return net
# Main function that instantiates all models and prints their architectures
# to facilitate a quick view of avaliable models
if __name__ == "__main__":
print("VGG16 with 10 classes")
print(VGG16(10))
print("resnet18 with 10 classes")
print(resnet18(10))
print("resnet50 with 10 classes")
print(resnet50(10))