-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathnet.py
executable file
·142 lines (119 loc) · 5.39 KB
/
net.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
# Copyright 2018-2020 Stanislav Pidhorskyi
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import torch
from torch import nn
from torch.nn import functional as F
torch.manual_seed(112211)
class Generator(nn.Module):
def __init__(self, z_size, d=128, channels=1):
super(Generator, self).__init__()
self.z_size = z_size
self.deconv1_1 = nn.ConvTranspose2d(z_size, d*2, 4, 1, 0)
self.deconv1_1_bn = nn.BatchNorm2d(d*2)
self.deconv2 = nn.ConvTranspose2d(d*2, d*2, 4, 2, 1)
self.deconv2_bn = nn.BatchNorm2d(d*2)
self.deconv3 = nn.ConvTranspose2d(d*2, d, 4, 2, 1)
self.deconv3_bn = nn.BatchNorm2d(d)
self.deconv4 = nn.ConvTranspose2d(d, channels, 4, 2, 1)
def weight_init(self, mean, std):
for m in self._modules:
normal_init(self._modules[m], mean, std)
def forward(self, x):
try:
x = F.relu(self.deconv1_1_bn(self.deconv1_1(x.view(x.shape[0], self.z_size, 1, 1))))
except:
x = F.relu(self.deconv1_1_bn(self.deconv1_1(x.view(1, self.z_size, 1, 1)))) # if single flattened batch
x = F.relu(self.deconv2_bn(self.deconv2(x)))
x = F.relu(self.deconv3_bn(self.deconv3(x)))
x = torch.tanh(self.deconv4(x)) * 0.5 + 0.5
x = x.view(x.shape[0], -1)
return x
class Discriminator(nn.Module):
def __init__(self, d=128, channels=1, img_width=32, img_height=32):
super(Discriminator, self).__init__()
self.img_width = img_width
self.img_height = img_height
self.conv1_1 = nn.Conv2d(channels, d//2, 4, 2, 1)
self.conv2 = nn.Conv2d(d // 2, d*2, 4, 2, 1)
self.conv2_bn = nn.BatchNorm2d(d*2)
self.conv3 = nn.Conv2d(d*2, d*4, 4, 2, 1)
self.conv3_bn = nn.BatchNorm2d(d*4)
self.conv4 = nn.Conv2d(d * 4, 1, 4, 1, 0)
def weight_init(self, mean, std):
for m in self._modules:
normal_init(self._modules[m], mean, std)
def forward(self, input):
# x = F.leaky_relu(self.conv1_1(input), 0.2)
x = F.leaky_relu(self.conv1_1(input.view(input.shape[0], 1, self.img_width, self.img_height)), 0.2)
x = F.leaky_relu(self.conv2_bn(self.conv2(x)), 0.2)
x = F.leaky_relu(self.conv3_bn(self.conv3(x)), 0.2)
x = torch.sigmoid(self.conv4(x))
return x
class Encoder(nn.Module):
def __init__(self, z_size, d=128, channels=1, img_width=32, img_height=32):
super(Encoder, self).__init__()
self.channels = 1
self.img_width = img_width
self.img_height = img_height
self.conv1_1 = nn.Conv2d(channels, d, 4, 2, 1)
self.conv2 = nn.Conv2d(d, d*2, 4, 2, 1)
self.conv2_bn = nn.BatchNorm2d(d*2)
self.conv3 = nn.Conv2d(d*2, d*4, 4, 2, 1)
self.conv3_bn = nn.BatchNorm2d(d*4)
self.conv4 = nn.Conv2d(d * 4, z_size, 4, 1, 0)
def weight_init(self, mean, std):
for m in self._modules:
normal_init(self._modules[m], mean, std)
def forward(self, x):
try:
x = F.leaky_relu(self.conv1_1(x.view(x.shape[0], 1, self.img_width, self.img_height)), 0.2)
except:
x = F.leaky_relu(self.conv1_1(x.view(1, 1, self.img_width, self.img_height)), 0.2) # single flattened batch for testing
x = F.leaky_relu(self.conv2_bn(self.conv2(x)), 0.2)
x = F.leaky_relu(self.conv3_bn(self.conv3(x)), 0.2)
x = self.conv4(x).squeeze()
return x
class ZDiscriminator(nn.Module):
def __init__(self, z_size, batchSize, d=128):
super(ZDiscriminator, self).__init__()
self.linear1 = nn.Linear(z_size, d)
self.linear2 = nn.Linear(d, d)
self.linear3 = nn.Linear(d, 1)
def weight_init(self, mean, std):
for m in self._modules:
normal_init(self._modules[m], mean, std)
def forward(self, x):
x = F.leaky_relu((self.linear1(x)), 0.2)
x = F.leaky_relu((self.linear2(x)), 0.2)
x = torch.sigmoid(self.linear3(x))
return x
class ZDiscriminator_mergebatch(nn.Module):
def __init__(self, z_size, batchSize, d=128):
super(ZDiscriminator_mergebatch, self).__init__()
self.linear1 = nn.Linear(z_size, d)
self.linear2 = nn.Linear(d * batchSize, d)
self.linear3 = nn.Linear(d, 1)
def weight_init(self, mean, std):
for m in self._modules:
normal_init(self._modules[m], mean, std)
def forward(self, x):
x = F.leaky_relu((self.linear1(x)), 0.2).view(1, -1) # after the second layer all samples are concatenated
x = F.leaky_relu((self.linear2(x)), 0.2)
x = torch.sigmoid(self.linear3(x))
return x
def normal_init(m, mean, std):
if isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
m.weight.data.normal_(mean, std)
m.bias.data.zero_()