-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathgenerator.py
162 lines (132 loc) · 6.73 KB
/
generator.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
"""
generator network
Input: torch (1, 3, 256, 256)
upsampling: 放大图像
downsampling: 缩小图像，降低图像质量
downsampling first, then upsampling
"""
import torch
import torch.nn as nn
class Block(nn.Module):
def __init__(self, in_channels, out_channels, down=True, act="relu", use_dropout=False):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1, bias=False, padding_mode="reflect")
if down
else nn.ConvTranspose2d(in_channels, out_channels, 4, 2, 1, bias=False),
nn.InstanceNorm2d(out_channels, affine=True),
nn.ReLU() if act == "relu" else nn.LeakyReLU(0.2),
)
self.use_dropout = use_dropout
self.dropout = nn.Dropout(0.5)
def forward(self, x):
x = self.conv(x)
return self.dropout(x) if self.use_dropout else x
# input: torch.rand([1, 3, 256, 256])
class Deblur_G(nn.Module):
def __init__(self, in_channels=3, features=64):
super(Deblur_G, self).__init__()
# 7 downsampling blocks
# first downsampling block does not have Instance Norm
self.initial_down = nn.Sequential(
nn.Conv2d(in_channels, features, kernel_size=4, stride=2, padding=1, padding_mode='reflect'),
nn.LeakyReLU(0.2),
)
self.down1 = Block(features, features * 2, down=True, act="leaky", use_dropout=False) # 64
self.down2 = Block(features * 2, features * 4, down=True, act="leaky", use_dropout=False) # 32
self.down3 = Block(features * 4, features * 8, down=True, act="leaky", use_dropout=False) # 16
self.down4 = Block(features * 8, features * 8, down=True, act="leaky", use_dropout=False) # 8
self.down5 = Block(features * 8, features * 8, down=True, act="leaky", use_dropout=False) # 4
self.down6 = Block(features * 8, features * 8, down=True, act="leaky", use_dropout=False) # 2
self.bottleneck = nn.Sequential(
nn.Conv2d(features * 8, features * 8, 4, 2, 1, padding_mode='reflect'),
nn.ReLU(), # 1
)
self.up1 = Block(features * 8, features * 8, down=False, act='relu', use_dropout=True)
self.up2 = Block(features * 8 * 2, features * 8, down=False, act='relu', use_dropout=True)
self.up3 = Block(features * 8 * 2, features * 8, down=False, act='relu', use_dropout=True)
self.up4 = Block(features * 8 * 2, features * 8, down=False, act='relu', use_dropout=False)
self.up5 = Block(features * 8 * 2, features * 4, down=False, act='relu', use_dropout=False)
self.up6 = Block(features * 4 * 2, features * 2, down=False, act='relu', use_dropout=False)
self.up7 = Block(features * 2 * 2, features, down=False, act='relu', use_dropout=False)
self.final_up = nn.Sequential(
nn.ConvTranspose2d(features * 2, in_channels, kernel_size=4, stride=2, padding=1),
nn.Tanh(),
)
# Implement U-net with skip connections
def forward(self, x):
d1 = self.initial_down(x) # [1, 64, 128, 128]
d2 = self.down1(d1) # [1, 128, 64, 64]
d3 = self.down2(d2) # [1, 256, 32, 32]
d4 = self.down3(d3) # [1, 512, 16, 16]
d5 = self.down4(d4) # [1, 512, 8, 8]
d6 = self.down5(d5) # [1, 512, 4, 4]
d7 = self.down6(d6) # [1, 512, 2, 2]
bottleneck = self.bottleneck(d7) # [1, 512, 1, 1]
up1 = self.up1(bottleneck) # [1, 512, 2, 2]
# 按照维数1拼接
up2 = self.up2(torch.cat([up1, d7], 1)) # [1, 512, 4, 4]
up3 = self.up3(torch.cat([up2, d6], 1))
up4 = self.up4(torch.cat([up3, d5], 1))
up5 = self.up5(torch.cat([up4, d4], 1))
up6 = self.up6(torch.cat([up5, d3], 1))
up7 = self.up7(torch.cat([up6, d2], 1)) # [1, 64, 128, 128]
return self.final_up(torch.cat([up7, d1], 1)) # 1, 3, 256, 256]
# return up7
# generator = Deblur_G()
# input = torch.FloatTensor(1, 3, 256, 256)
# output = generator(input)
# print(output.shape)
nBottleneck = 4000
'''
generator
input: [batch, 3, 256, 256] the whole picture with partially missing
output: [batch, 3, 128, 128] the missing part
'''
class Inpainting_G(nn.Module):
def __init__(self):
super(Inpainting_G, self).__init__()
self.generate = nn.Sequential(
# input : 256 * 256 [batch, 3, 256, 256]
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=4, stride=2, padding=1, bias=False),
nn.LeakyReLU(0.2, inplace=True), # output: [batch, 64, 128, 128]
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.2, inplace=True), # output: [batch, 64, 64, 64]
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2, inplace=True), # output: [batch, 128, 32, 32]
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2, inplace=True), # output: [batch, 256, 16, 16]
nn.Conv2d(in_channels=256, out_channels=512, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2, inplace=True), # output: [batch, 512, 8, 8]
nn.Conv2d(in_channels=512, out_channels=nBottleneck, kernel_size=4, bias=False),
nn.BatchNorm2d(nBottleneck),
nn.LeakyReLU(0.2, inplace=True), # output: nBottleneck * 1 * 1
nn.ConvTranspose2d(in_channels=nBottleneck, out_channels=512, kernel_size=4, stride=1, padding=0,
bias=False),
nn.BatchNorm2d(512),
nn.ReLU(True),
nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(True),
nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(True),
nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(True),
nn.ConvTranspose2d(in_channels=64, out_channels=3, kernel_size=4, stride=2, padding=1, bias=False),
nn.Tanh()
)
def forward(self, input):
output = self.generate(input)
return output
# input = torch.FloatTensor(16, 3, 256, 256)
# print("input missing image size:" + str(input.shape))
# # netG = _netG(3)
# gen = generator()
# output = gen(input)
# print("output middle size: " + output.shape)