-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmodules.py
161 lines (139 loc) · 6.34 KB
/
modules.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
import torch
import torch.nn as nn
import torch.nn.functional as F
class GaussianNoise(nn.Module):
def __init__(self, stddev):
super().__init__()
self.stddev = stddev
def forward(self, din):
if self.stddev == 0.0: return din
if self.training:
return din + torch.randn(din.size(),device=din.device) * self.stddev
return din
def hidden_block(dim_hidden,dim_hidden2,noise_sd,kernel_size=3,padding=1):
return nn.Sequential(
nn.GroupNorm(1,dim_hidden),
GaussianNoise(stddev=noise_sd),
nn.Conv2d(dim_hidden,dim_hidden2,kernel_size=(kernel_size,kernel_size),padding=padding,stride=1,bias=False),
)
class ResidualBlock(nn.Module):
def __init__(self,dim_hidden,dim_hidden2,noise_sd,dropout=0.0,kernel_size=3,padding=1):
super().__init__()
self.dropout=dropout
self.block1=hidden_block(dim_hidden,dim_hidden2,noise_sd,kernel_size=kernel_size,padding=padding)
self.block2=hidden_block(dim_hidden2,dim_hidden2,noise_sd,kernel_size=kernel_size,padding=padding)
self.block3=hidden_block(dim_hidden2,dim_hidden2,noise_sd=0.0,kernel_size=kernel_size,padding=padding)
self.bottleneck=nn.Conv2d(dim_hidden,dim_hidden2,kernel_size=(1,1),padding=0)
def forward(self,x):
identity=x
out=F.leaky_relu(self.block1(x),0.01)
out=F.dropout2d(out,self.dropout)
out=F.leaky_relu(self.block2(out),0.01)
out = F.dropout2d(out, self.dropout)
out=self.block3(out)
out = out + self.bottleneck(identity)
out= F.leaky_relu(out,0.01)
return out
class MLPModel(nn.Module):
def __init__(self,dropout=0.01,hidden_dim=32,dim_out=1,normalization_groups=0,gaussian_sd=0,n_blocks=2,expansion=2):
super().__init__()
self.dropout=dropout
if normalization_groups==0:
self.input_norm=nn.Identity()
else:
self.input_norm=nn.GroupNorm(normalization_groups,12)
self.conv1=nn.Conv2d(12,hidden_dim,kernel_size=(1,1),stride=1,padding=0)
# self.bn1=nn.BatchNorm2d(hidden_dim)
block_sizes=[hidden_dim]
for i in range(n_blocks): block_sizes.append(block_sizes[i]*expansion)
self.blocks=nn.Sequential(*[ResidualBlock(block_sizes[l-1],block_sizes[l],gaussian_sd) for l in range(1,len(block_sizes))])
self.fc=nn.Linear(block_sizes[-1],dim_out)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="leaky_relu")
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self,x):
# x=x.sqrt()
x=self.input_norm(x)
x = F.dropout2d(x, self.dropout)
x=F.leaky_relu(self.conv1(x),0.01)
x=self.blocks(x)
x = F.adaptive_avg_pool2d(x,(1,1))
x = torch.flatten(x,1)
x=F.dropout(x,p=self.dropout)
x = self.fc(x)
return x
class MLPModel2(nn.Module):
def __init__(self,dropout=0.01,hidden_dim=32,dim_out=1,normalization_groups=0,gaussian_sd=0,n_blocks=2,expansion=2):
super().__init__()
self.dropout=dropout
if normalization_groups==0:
self.input_norm=nn.Identity()
else:
self.input_norm=nn.GroupNorm(normalization_groups,12)
self.conv1=nn.Conv2d(12,hidden_dim,kernel_size=(1,1),stride=1,padding=0)
# self.bn1=nn.BatchNorm2d(hidden_dim)
block_sizes=[hidden_dim]
for i in range(n_blocks): block_sizes.append(block_sizes[i]*expansion)
self.blocks=nn.Sequential(*[ResidualBlock(block_sizes[l-1],block_sizes[l],gaussian_sd) for l in range(1,len(block_sizes))])
# self.fc=nn.Linear(block_sizes[-1],dim_out)
self.conv_final=nn.Conv2d(block_sizes[-1],dim_out,kernel_size=(1,1),stride=1,padding=0)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="leaky_relu")
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self,x):
# x=x.sqrt()
x=self.input_norm(x)
x = F.dropout2d(x, self.dropout)
x=F.leaky_relu(self.conv1(x),0.01)
x=self.blocks(x)
x=self.conv_final(x)
x=torch.mean(x,dim=(2,3))
# x = F.adaptive_avg_pool2d(x,(1,1))
# x = torch.flatten(x,1)
# x=F.dropout(x,p=self.dropout)
# x = self.fc(x)
return x
class CNN(nn.Module):
def __init__(self,dropout=0.01,hidden_dim=32,dim_input=12,dim_out=1,normalization_groups=0,gaussian_sd=0,n_blocks=1,expansion=2,kernel_size=3):
super().__init__()
if kernel_size ==1:
padding=0
elif kernel_size == 3:
padding = 1
elif kernel_size == 5:
padding = 2
elif kernel_size ==7:
padding=3
else:
raise Exception(f"CNN not implemented for kernel_size{kernel_size}")
self.dropout=dropout
if normalization_groups==0:
self.input_norm=nn.Identity()
else:
self.input_norm=nn.GroupNorm(normalization_groups,12)
self.conv1=nn.Conv2d(dim_input,hidden_dim,kernel_size=(kernel_size,kernel_size),stride=1,padding=padding,bias=False)
block_sizes=[hidden_dim]
for i in range(n_blocks): block_sizes.append(block_sizes[i]*expansion)
self.blocks=nn.Sequential(*[ResidualBlock(block_sizes[l-1],block_sizes[l],gaussian_sd,kernel_size=kernel_size,padding=padding)
for l in range(1,len(block_sizes))])
self.conv_final=nn.Conv2d(block_sizes[-1],dim_out,kernel_size=(kernel_size,kernel_size),stride=1,padding=padding)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight, mean=0.0, std=0.01)
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self,x):
x=self.input_norm(x)
x = F.dropout2d(x, self.dropout)
x=F.leaky_relu(self.conv1(x),0.1)
x=self.blocks(x)
x=self.conv_final(x)
x=torch.mean(x,dim=(2,3))
return x