-
Notifications
You must be signed in to change notification settings - Fork 0
/
model.py
172 lines (140 loc) · 6.06 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
import torch
from torch import nn
class VanillaBlock(nn.Module):
def __init__(self, layer_dims, dropout_rate):
super(VanillaBlock, self).__init__()
layers = []
for in_dim, out_dim in zip(layer_dims[:-2], layer_dims[1:-1]):
layers.append(nn.Linear(in_dim, out_dim))
layers.append(nn.LeakyReLU(0.2))
if dropout_rate != 0: layers.append(nn.Dropout1d(dropout_rate))
layers.append(nn.Linear(layer_dims[-2], layer_dims[-1]))
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
class BaseGenerator(nn.Module):
def __init__(self, layer_dims):
super(BaseGenerator, self).__init__()
self.layers = VanillaBlock(layer_dims, 0)
def forward(self, x):
return torch.tanh(self.layers(x))
class BaseDiscriminator(nn.Module):
def __init__(self, layer_dims, dropout_rate):
super(BaseDiscriminator, self).__init__()
self.layers = VanillaBlock(layer_dims, dropout_rate)
def _initialize_parameter(self):
for layer in self.modules():
if not isinstance(layer, nn.Linear): continue
nn.init.normal_(layer.weight, 0, 0.02)
break
def forward(self, x):
return torch.sigmoid(self.layers(x).squeeze())
class DCGenerator(nn.Module):
def __init__(self, latent_dim, out_channel, channels):
super().__init__()
layers = []
channels= [*channels, out_channel]
layers.append(nn.ConvTranspose2d(latent_dim, channels[0], 4, 1, 0, bias=False))
for idx in range(1, len(channels)):
layers.append(nn.ConvTranspose2d(channels[idx - 1], channels[idx], 4, 2, 1, bias=False))
if idx != len(channels) - 1:
layers.append(nn.BatchNorm2d(channels[idx], momentum=0.8))
layers.append(nn.ReLU())
layers.append(nn.Tanh())
self.layer = nn.Sequential(*layers)
self._initialize()
def _initialize(self):
for m in self.modules():
if isinstance(m, nn.ConvTranspose2d):
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif isinstance(m, nn.BatchNorm2d):
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
def forward(self, x):
x = x.unsqueeze(-1).unsqueeze(-1)
return self.layer(x)
class DCDiscriminator(nn.Module):
def __init__(self, in_channel, channels, dropout_rate):
super().__init__()
layers = []
channels = [in_channel, *channels]
for idx in range(1, len(channels)):
if idx != 1: layers.append(nn.Dropout2d(dropout_rate))
layers.append(nn.Conv2d(channels[idx - 1], channels[idx], 4, 2, 1, bias=False))
if idx != 1: layers.append(nn.BatchNorm2d(channels[idx]))
layers.append(nn.LeakyReLU(0.2))
layers.append(nn.Conv2d(channels[-1], 1, 4, 1, 0, bias=False))
layers.append(nn.Sigmoid())
self.layer = nn.Sequential(*layers)
self._initialize()
def _initialize(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif isinstance(m, nn.BatchNorm2d):
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
def forward(self, x):
return self.layer(x).squeeze()
class Unet(nn.Module):
def __init__(self, channels, dropout_rate, batchnorm_remove_cnt, dropout_apply_cnt):
super().__init__()
encoder_layer = [
nn.Conv2d(channels[0], channels[1], 4, 2, 1, bias=False)
]
if batchnorm_remove_cnt < 1 : encoder_layer.append(nn.BatchNorm2d(channels[1]))
self.encoder = nn.Sequential(
*encoder_layer,
nn.LeakyReLU(0.2)
)
self.submodule = Unet(channels[1:], dropout_rate,
batchnorm_remove_cnt -1, dropout_apply_cnt - 1) if len(channels) > 2 else None
in_channel = channels[1] * 2 if len(channels) > 2 else channels[1]
decoder_layer = [
nn.ConvTranspose2d(in_channel, channels[0], 4, 2, 1, bias=False),
nn.BatchNorm2d(channels[0])
]
if dropout_apply_cnt > 0: decoder_layer.append(nn.Dropout2d(dropout_rate))
self.decoder = nn.Sequential(
*decoder_layer,
nn.ReLU()
)
self._initialize()
def _initialize(self):
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
nn.init.normal_(m.weight, 0.0, 0.02)
def forward(self, x):
x = self.encoder(x)
if self.submodule is not None:
sub = self.submodule(x)
x = torch.concat([x, sub], axis=1)
return self.decoder(x)
class P2PGenerator(nn.Module):
def __init__(self, in_channel, channels, dropout_rate, batchnorm_remove_cnt, dropout_apply_cnt):
super().__init__()
channels = [in_channel, *channels]
self.conv_layers = Unet(channels, dropout_rate, batchnorm_remove_cnt, dropout_apply_cnt)
self.activation = nn.Tanh()
def forward(self, x):
x = self.conv_layers(x)
return self.activation(x)
class P2PDiscriminator(nn.Module):
def __init__(self, in_channel, channels):
super().__init__()
layers = []
channels = [in_channel * 2, *channels][:4]
for idx in range(1, len(channels)):
layers.append(nn.Conv2d(channels[idx - 1], channels[idx], 4, 2, 1, bias=False))
if idx != 1: layers.append(nn.BatchNorm2d(channels[idx]))
layers.append(nn.LeakyReLU(0.2))
layers.append(nn.Conv2d(channels[-1], 1, 3, 1, 0, bias=False))
layers.append(nn.Sigmoid())
self.layer = nn.Sequential(*layers)
self._initialize()
def _initialize(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight, 0.0, 0.02)
def forward(self, x):
return self.layer(x)