-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmulti_sensor_3d.py
165 lines (124 loc) · 5.78 KB
/
multi_sensor_3d.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
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
def conv3x3(in_chn, out_chn, stride=1):
return nn.Conv2d(in_chn, out_chn, kernel_size=3, stride=stride, padding=1, bias=False)
def conv1x1(in_chn, out_chn, stride=1):
return nn.Conv2d(in_chn, out_chn, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
def __init__(self, in_chn, dim_size, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(in_chn, dim_size, stride)
self.bn1 = nn.BatchNorm2d(dim_size)
self.conv2 = conv3x3(dim_size, dim_size * 1)
self.bn2 = nn.BatchNorm2d(dim_size)
self.activation = nn.ReLU(inplace=True)
self.downsample = None
if stride == 2:
layers = []
layers += [conv1x1(in_chn, dim_size, stride)]
layers += [nn.BatchNorm2d(dim_size)]
self.downsample = nn.Sequential(*layers)
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.activation(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(identity)
out += identity
out = self.activation(out)
return out
class ResNet18FPN(nn.Module):
def __init__(self):
super(ResNet18FPN, self).__init__()
self.in_chn = 64
self.conv1 = nn.Conv2d(3, self.in_chn, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(self.in_chn)
self.activation = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(64, 2, stride=1)
self.layer2 = self._make_layer(128, 2, stride=2)
self.layer3 = self._make_layer(256, 2, stride=2)
self.layer4 = self._make_layer(512, 2, stride=2)
self.toplayer = nn.Conv2d(512, 256, kernel_size=1, stride=1)
self.lateral3 = nn.Conv2d(256, 256, kernel_size=1, stride=1)
self.lateral2 = nn.Conv2d(128, 256, kernel_size=1, stride=1)
self.lateral1 = nn.Conv2d(64, 256, kernel_size=1, stride=1)
self.final3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.final2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.final1 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
def _make_layer(self, dim_size, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers += [BasicBlock(self.in_chn, dim_size, stride)]
self.in_chn = dim_size * 1
return nn.Sequential(*layers)
def _upsample_add(self, x, y):
return F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) + y
def forward(self, x):
c1 = self.activation(self.bn1(self.conv1(x)))
c1 = self.maxpool(c1)
c2 = self.layer1(c1)
c3 = self.layer2(c2)
c4 = self.layer3(c3)
c5 = self.layer4(c4)
p5 = self.toplayer(c5)
p4 = self._upsample_add(p5, self.lateral3(c4))
p3 = self._upsample_add(p4, self.lateral2(c3))
p2 = self._upsample_add(p3, self.lateral1(c2))
p4 = self.final3(p4)
p3 = self.final2(p3)
p2 = self.final1(p2)
return p2, p3, p4, p5
class BEVBackbone(nn.Module):
def __init__(self):
super(BEVBackbone, self).__init__()
self.in_chn = 32
self.conv1 = nn.Conv2d(3, self.in_chn, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(self.in_chn)
self.conv2 = nn.Conv2d(self.in_chn, self.in_chn, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(self.in_chn)
self.activation = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(64, 2, stride=2)
self.layer2 = self._make_layer(128, 2, stride=2)
self.layer3 = self._make_layer(192, 2, stride=2)
self.layer4 = self._make_layer(256, 2, stride=2)
self.toplayer = nn.Conv2d(256, 256, kernel_size=1, stride=1)
self.lateral3 = nn.Conv2d(192, 256, kernel_size=1, stride=1)
self.lateral2 = nn.Conv2d(128, 256, kernel_size=1, stride=1)
self.lateral1 = nn.Conv2d(64, 256, kernel_size=1, stride=1)
self.final3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.final2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.final1 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
def _make_layer(self, dim_size, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers += [BasicBlock(self.in_chn, dim_size, stride)]
self.in_chn = dim_size * 1
return nn.Sequential(*layers)
def _upsample_add(self, x, y):
return F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) + y
def forward(self, x):
c1 = self.activation(self.bn1(self.conv1(x)))
c1 = self.activation(self.bn2(self.conv2(c1)))
c2 = self.layer1(c1)
c3 = self.layer2(c2)
c4 = self.layer3(c3)
c5 = self.layer4(c4)
p5 = self.toplayer(c5)
p4 = self._upsample_add(p5, self.lateral3(c4))
p3 = self._upsample_add(p4, self.lateral2(c3))
p2 = self._upsample_add(p3, self.lateral1(c2))
p4 = self.final3(p4)
p3 = self.final2(p3)
p2 = self.final1(p2)
return p2, p3, p4, p5
class MultiSensor3D(nn.Module):
def __init__(self):
super(MultiSensor3D, self).__init__()
resnet18 = models.resnet18(pretrained=True)