-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathceleba_models.py
258 lines (200 loc) · 7.66 KB
/
celeba_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
from torch import nn
from torch.nn import init
import torch
import numpy as np
from models import BaseVAE
def fc_block(in_features, out_features, use_bn=True, use_relu=True):
moduels = [nn.Linear(in_features, out_features)]
if use_bn:
moduels.append(nn.BatchNorm1d(out_features))
if use_relu:
moduels.append(nn.ReLU(True))
return nn.ModuleList(moduels)
def conv_block(in_channels, out_channels, kernel_size,
stride=1, padding=0,
use_bn=True, use_relu=True):
modules = [nn.Conv2d(in_channels, out_channels,
kernel_size, stride, padding)]
if use_bn:
modules.append(nn.BatchNorm2d(out_channels))
if use_relu:
modules.append(nn.ReLU(True))
return nn.ModuleList(modules)
def deconv_block(in_channels, out_channels, kernel_size,
stride=1, padding=0, output_padding=0,
use_bn=True, use_relu=True):
moduels = [nn.ConvTranspose2d(
in_channels, out_channels, kernel_size, stride, padding, output_padding)]
if use_bn:
moduels.append(nn.BatchNorm2d(out_channels))
if use_relu:
moduels.append(nn.ReLU(True))
return nn.ModuleList(moduels)
def kaiming_init(m):
if isinstance(m, (nn.Linear, nn.Conv2d)):
init.kaiming_normal_(m.weight)
if m.bias is not None:
m.bias.data.fill_(0)
elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
m.weight.data.fill_(1)
if m.bias is not None:
m.bias.data.fill_(0)
class CelebaVAE(BaseVAE):
def __init__(self, latent_dim, input_dim):
super().__init__(latent_dim, input_dim)
self.latent_dim = latent_dim
self.input_dim = input_dim
hidden_dims = [3, 32, 32, 64, 64]
modules = []
for i, _ in enumerate(hidden_dims[:-1]):
modules.extend(
conv_block(in_channels=hidden_dims[i], out_channels=hidden_dims[i+1],
kernel_size=4, stride=2, padding=1, use_bn=False)
)
modules.extend(
conv_block(in_channels=hidden_dims[-1], out_channels=256,
kernel_size=4, stride=1, padding=0, use_bn=False)
)
self.cnn_encoder = nn.Sequential(*modules)
self.shape, self.flatten_shape = self.get_shape()
self.fc_encoder = nn.Linear(
in_features=self.flatten_shape, out_features=self.latent_dim*2)
self.fc_decoder = nn.Linear(
in_features=self.latent_dim, out_features=self.flatten_shape)
hidden_dims.reverse()
modules = []
modules.extend(
deconv_block(in_channels=256, out_channels=hidden_dims[0],
kernel_size=4, stride=1, padding=0, use_bn=False)
)
for i, _ in enumerate(hidden_dims[:-2]):
modules.extend(
deconv_block(in_channels=hidden_dims[i], out_channels=hidden_dims[i+1],
kernel_size=4, stride=2, padding=1, use_bn=False)
)
modules.append(
nn.ConvTranspose2d(in_channels=hidden_dims[-2], out_channels=hidden_dims[-1],
kernel_size=4, stride=2, padding=1)
)
self.cnn_decoder = nn.Sequential(*modules)
self.apply(kaiming_init)
def get_shape(self):
x = torch.zeros(1, *self.input_dim)
x = self.cnn_encoder(x)
return x.shape[1:], torch.prod(torch.tensor(x.shape)).item()
def encode(self, x):
x = self.cnn_encoder(x)
x = torch.flatten(x, start_dim=1)
x = self.fc_encoder(x)
mu, log_var = torch.chunk(x, chunks=2, dim=-1)
z = self.sampling(mu, log_var)
return z, mu, log_var
def decode(self, z):
x = self.fc_decoder(z)
x = torch.reshape(x, (x.shape[0], *self.shape))
x = self.cnn_decoder(x)
x = torch.sigmoid_(x)
return x
class EncoderCelebA(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.input_dim = input_dim
filters = [3, 64, 64, 128, 128, 256, 256]
modules = []
for i, _ in enumerate(filters[:-1]):
modules.extend(
conv_block(in_channels=filters[i], out_channels=filters[i+1],
kernel_size=4, stride=2, padding=1)
)
self.cnn_encoder = nn.Sequential(*modules)
self.shape, self.flatten_shape = self.get_shape()
self.fc_encoder = nn.Sequential(
*fc_block(in_features=self.flatten_shape, out_features=1024),
)
self.fc_decoder = nn.Sequential(
*fc_block(in_features=1024, out_features=self.flatten_shape),
)
modules = []
filters.reverse()
for i, _ in enumerate(filters[:-1]):
modules.extend(
deconv_block(in_channels=filters[i], out_channels=filters[i+1],
kernel_size=4, stride=2, padding=1)
)
self.cnn_decoder = nn.Sequential(*modules)
@torch.no_grad()
def get_shape(self):
self.eval()
x = torch.zeros(1, *self.input_dim)
x = self.cnn_encoder(x)
return tuple(x.shape[1:]), np.prod(x.shape)
def forward(self, x):
return self.encode(x)
def encode(self, x):
x = self.cnn_encoder(x).flatten(1)
return self.fc_encoder(x)
def decode(self, x):
x = self.fc_decoder(x)
x = x.reshape(-1, *self.shape)
return self.cnn_decoder(x)
class ClassifierCelebA(nn.Module):
def __init__(self, input_dim, num_class=2):
super().__init__()
self.input_dim = input_dim
self.classifier = nn.Linear(input_dim, num_class)
def forward(self, x):
return self.classifier(x).squeeze()
def predict(self, x):
logits = self.forward(x)
return logits.argmax(1)
class AutoEncoderCelebA(nn.Module):
def __init__(self, vae: BaseVAE, latent_encoder: EncoderCelebA, freeze: bool = True):
super().__init__()
self.vae = vae
self.encoder = latent_encoder
if freeze:
self._freeze_vae()
def forward(self, x):
return self.encode(x)
def encode(self, x):
x = self.vae.decode(x)
x = self.encoder.encode(x)
x = torch.flatten(x, start_dim=1)
return x
def decode(self, x):
x = self.encoder.decode(x)
z, _, _ = self.vae.encode(x)
return z
def _freeze_vae(self):
for param in self.vae.parameters():
param.requires_grad_(False)
class AttributeClassifierCelebA(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.input_dim = input_dim
filters = [3, 64, 64, 128, 128, 256, 256]
modules = []
for i, _ in enumerate(filters[:-1]):
modules.extend(
conv_block(in_channels=filters[i], out_channels=filters[i+1],
kernel_size=4, stride=2, padding=1)
)
self.cnn = nn.Sequential(*modules)
self.shape, self.flatten_shape = self.get_shape()
self.classifier = nn.Sequential(
*fc_block(in_features=self.flatten_shape, out_features=1024),
nn.Linear(in_features=1024, out_features=2)
)
@torch.no_grad()
def get_shape(self):
self.eval()
x = torch.zeros(1, *self.input_dim)
x = self.cnn(x)
return tuple(x.shape[1:]), np.prod(x.shape)
def forward(self, x):
x = self.cnn(x).flatten(1)
x = self.classifier(x)
return x
def predict(self, x):
logits = self.forward(x)
return logits.argmax(1)