-
Notifications
You must be signed in to change notification settings - Fork 12
/
DHDN_color.py
156 lines (101 loc) · 4.35 KB
/
DHDN_color.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
import torch
import torch.nn as nn
class _DCR_block(nn.Module):
def __init__(self, channel_in):
super(_DCR_block, self).__init__()
self.conv_1 = nn.Conv2d(in_channels=channel_in, out_channels=int(channel_in/2.), kernel_size=3, stride=1, padding=1)
self.relu1 = nn.PReLU()
self.conv_2 = nn.Conv2d(in_channels=int(channel_in*3/2.), out_channels=int(channel_in/2.), kernel_size=3, stride=1, padding=1)
self.relu2 = nn.PReLU()
self.conv_3 = nn.Conv2d(in_channels=channel_in*2, out_channels=channel_in, kernel_size=3, stride=1, padding=1)
self.relu3 = nn.PReLU()
def forward(self, x):
residual = x
out = self.relu1(self.conv_1(x))
conc = torch.cat([x, out], 1)
out = self.relu2(self.conv_2(conc))
conc = torch.cat([conc, out], 1)
out = self.relu3(self.conv_3(conc))
out = torch.add(out, residual)
return out
class _down(nn.Module):
def __init__(self, channel_in):
super(_down, self).__init__()
self.relu = nn.PReLU()
self.maxpool = nn.MaxPool2d(2)
self.conv = nn.Conv2d(in_channels=channel_in, out_channels=2*channel_in, kernel_size=1, stride=1, padding=0)
def forward(self, x):
out = self.maxpool(x)
out = self.relu(self.conv(out))
return out
class _up(nn.Module):
def __init__(self, channel_in):
super(_up, self).__init__()
self.relu = nn.PReLU()
self.subpixel = nn.PixelShuffle(2)
self.conv = nn.Conv2d(in_channels=channel_in, out_channels=channel_in, kernel_size=1, stride=1, padding=0)
def forward(self, x):
out = self.relu(self.conv(x))
out = self.subpixel(out)
return out
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv_i = nn.Conv2d(in_channels=3, out_channels=128, kernel_size=1, stride=1, padding=0)
self.relu1 = nn.PReLU()
self.DCR_block11 = self.make_layer(_DCR_block, 128)
self.DCR_block12 = self.make_layer(_DCR_block, 128)
self.down1 = self.make_layer(_down, 128)
self.DCR_block21 = self.make_layer(_DCR_block, 256)
self.DCR_block22 = self.make_layer(_DCR_block, 256)
self.down2 = self.make_layer(_down, 256)
self.DCR_block31 = self.make_layer(_DCR_block, 512)
self.DCR_block32 = self.make_layer(_DCR_block, 512)
self.down3 = self.make_layer(_down, 512)
self.DCR_block41 = self.make_layer(_DCR_block, 1024)
self.DCR_block42 = self.make_layer(_DCR_block, 1024)
self.up3 = self.make_layer(_up, 2048)
self.DCR_block33 = self.make_layer(_DCR_block, 1024)
self.DCR_block34 = self.make_layer(_DCR_block, 1024)
self.up2 = self.make_layer(_up, 1024)
self.DCR_block23 = self.make_layer(_DCR_block, 512)
self.DCR_block24 = self.make_layer(_DCR_block, 512)
self.up1 = self.make_layer(_up, 512)
self.DCR_block13 = self.make_layer(_DCR_block, 256)
self.DCR_block14 = self.make_layer(_DCR_block, 256)
self.conv_f = nn.Conv2d(in_channels=256, out_channels=3, kernel_size=1, stride=1, padding=0)
self.relu2 = nn.PReLU()
def make_layer(self, block, channel_in):
layers = []
layers.append(block(channel_in))
return nn.Sequential(*layers)
def forward(self, x):
residual = x
out = self.relu1(self.conv_i(x))
out = self.DCR_block11(out)
conc1 = self.DCR_block12(out)
out = self.down1(conc1)
out = self.DCR_block21(out)
conc2 = self.DCR_block22(out)
out = self.down2(conc2)
out = self.DCR_block31(out)
conc3 = self.DCR_block32(out)
conc4 = self.down3(conc3)
out = self.DCR_block41(conc4)
out = self.DCR_block42(out)
out = torch.cat([conc4, out], 1)
out = self.up3(out)
out = torch.cat([conc3, out], 1)
out = self.DCR_block33(out)
out = self.DCR_block34(out)
out = self.up2(out)
out = torch.cat([conc2, out], 1)
out = self.DCR_block23(out)
out = self.DCR_block24(out)
out = self.up1(out)
out = torch.cat([conc1, out], 1)
out = self.DCR_block13(out)
out = self.DCR_block14(out)
out = self.relu2(self.conv_f(out))
out = torch.add(residual, out)
return out