-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathmodel.py
174 lines (139 loc) · 6.28 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
173
174
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from pytorch_revgrad import RevGrad
import random
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class DoubleConv(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
"""Downscaling with maxpool then double conv"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_channels, out_channels)
)
def forward(self, x):
return self.maxpool_conv(x)
class Up(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels, bilinear=True):
super().__init__()
# if bilinear, use the normal convolutions to reduce the number of channels
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
else:
self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x1, x2):
x1 = self.up(x1)
# input is CHW
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
# if you have padding issues, see
# https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
# https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
x = torch.cat([x2, x1], dim=1)
return self.conv(x)
class OutConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutConv, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
return self.conv(x)
class UNet(nn.Module):
def __init__(self, n_channels, n_classes, bilinear=True, domain=False, proto=False, projection=True):
super(UNet, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.inc = DoubleConv(n_channels, 64)
self.down1 = Down(64, 128)
self.down2 = Down(128, 256)
self.down3 = Down(256, 512)
factor = 2 if bilinear else 1
self.down4 = Down(512, 1024 // factor)
self.up1 = Up(1024, 512 // factor, bilinear)
self.up2 = Up(512, 256 // factor, bilinear)
self.up3 = Up(256, 128 // factor, bilinear)
self.up4 = Up(128, 64, bilinear)
self.outc = OutConv(64, n_classes)
self.domain_classifier = nn.Sequential(
RevGrad(),
OutConv(64, 1)
)
if projection:
self.proto_projection = nn.Sequential(
OutConv(64, 64),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True)
)
else:
self.proto_projection = nn.Sequential(
nn.Identity()
)
self.proto_pool = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
nn.Flatten(),
)
self.domain = domain
self.proto = proto
def forward_one(self, x, x_map=None):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
logits = self.outc(x)
domain_pred = None
proto_pred_pos = None
proto_pred_neg = None
if self.domain:
domain_pred = self.domain_classifier(x)
if self.proto:
# Generate Mask
if x_map == None:
logits_map_pos = torch.round(torch.sigmoid(logits))
logits_map_neg = 1.-logits_map_pos
else:
logits_map_pos = x_map
logits_map_neg = 1.-logits_map_pos
x_map_pos = self.proto_pool(self.proto_projection(x)*logits_map_pos)
x_map_neg = self.proto_pool(self.proto_projection(x)*logits_map_neg)
normalizing_factor = np.prod(np.array(logits_map_pos.shape[-2:]))
proto_pred_pos = x_map_pos*((normalizing_factor)/(logits_map_pos.sum(list(range(1, logits_map_pos.ndim))).reshape((logits_map_pos.size(0), 1))+1e-6))
proto_pred_neg = x_map_neg*((normalizing_factor)/(logits_map_neg.sum(list(range(1, logits_map_pos.ndim))).reshape((logits_map_pos.size(0), 1))+1e-6))
return logits, domain_pred, proto_pred_pos, proto_pred_neg
def forward(self, x_label, x_unlabel=None, x_label_map=None, x_unlabel_map=None, validation=False):
if validation:
logit_label, domain_label, proto_label_pos, proto_label_neg = self.forward_one(x_label)
return logit_label, domain_label, proto_label_pos, proto_label_neg
if x_unlabel == None:
return self.forward_one(x_label)
logit_label, domain_label, proto_label_pos, proto_label_neg = self.forward_one(x_label, x_label_map)
logit_unlabel, domain_unlabel, proto_unlabel_pos, proto_unlabel_neg = self.forward_one(x_unlabel, x_unlabel_map)
return logit_label, domain_label, proto_label_pos, proto_label_neg, \
logit_unlabel, domain_unlabel, proto_unlabel_pos, proto_unlabel_neg