-
Notifications
You must be signed in to change notification settings - Fork 37
/
alnet.py
164 lines (135 loc) · 6.24 KB
/
alnet.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
import torch
from torch import nn
from torchvision.models import resnet
from typing import Optional, Callable
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels,
gate: Optional[Callable[..., nn.Module]] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None):
super().__init__()
if gate is None:
self.gate = nn.ReLU(inplace=True)
else:
self.gate = gate
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self.conv1 = resnet.conv3x3(in_channels, out_channels)
self.bn1 = norm_layer(out_channels)
self.conv2 = resnet.conv3x3(out_channels, out_channels)
self.bn2 = norm_layer(out_channels)
def forward(self, x):
x = self.gate(self.bn1(self.conv1(x))) # B x in_channels x H x W
x = self.gate(self.bn2(self.conv2(x))) # B x out_channels x H x W
return x
# copied from torchvision\models\resnet.py#27->BasicBlock
class ResBlock(nn.Module):
expansion: int = 1
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
groups: int = 1,
base_width: int = 64,
dilation: int = 1,
gate: Optional[Callable[..., nn.Module]] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None
) -> None:
super(ResBlock, self).__init__()
if gate is None:
self.gate = nn.ReLU(inplace=True)
else:
self.gate = gate
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError('ResBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in ResBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = resnet.conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.conv2 = resnet.conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x: torch.Tensor) -> torch.Tensor:
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.gate(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.gate(out)
return out
class ALNet(nn.Module):
def __init__(self, c1: int = 32, c2: int = 64, c3: int = 128, c4: int = 128, dim: int = 128,
single_head: bool = True,
):
super().__init__()
self.gate = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.pool4 = nn.MaxPool2d(kernel_size=4, stride=4)
self.block1 = ConvBlock(3, c1, self.gate, nn.BatchNorm2d)
self.block2 = ResBlock(inplanes=c1, planes=c2, stride=1,
downsample=nn.Conv2d(c1, c2, 1),
gate=self.gate,
norm_layer=nn.BatchNorm2d)
self.block3 = ResBlock(inplanes=c2, planes=c3, stride=1,
downsample=nn.Conv2d(c2, c3, 1),
gate=self.gate,
norm_layer=nn.BatchNorm2d)
self.block4 = ResBlock(inplanes=c3, planes=c4, stride=1,
downsample=nn.Conv2d(c3, c4, 1),
gate=self.gate,
norm_layer=nn.BatchNorm2d)
# ================================== feature aggregation
self.conv1 = resnet.conv1x1(c1, dim // 4)
self.conv2 = resnet.conv1x1(c2, dim // 4)
self.conv3 = resnet.conv1x1(c3, dim // 4)
self.conv4 = resnet.conv1x1(dim, dim // 4)
self.upsample2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.upsample4 = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=True)
self.upsample8 = nn.Upsample(scale_factor=8, mode='bilinear', align_corners=True)
self.upsample32 = nn.Upsample(scale_factor=32, mode='bilinear', align_corners=True)
# ================================== detector and descriptor head
self.single_head = single_head
if not self.single_head:
self.convhead1 = resnet.conv1x1(dim, dim)
self.convhead2 = resnet.conv1x1(dim, dim + 1)
def forward(self, image):
# ================================== feature encoder
x1 = self.block1(image) # B x c1 x H x W
x2 = self.pool2(x1)
x2 = self.block2(x2) # B x c2 x H/2 x W/2
x3 = self.pool4(x2)
x3 = self.block3(x3) # B x c3 x H/8 x W/8
x4 = self.pool4(x3)
x4 = self.block4(x4) # B x dim x H/32 x W/32
# ================================== feature aggregation
x1 = self.gate(self.conv1(x1)) # B x dim//4 x H x W
x2 = self.gate(self.conv2(x2)) # B x dim//4 x H//2 x W//2
x3 = self.gate(self.conv3(x3)) # B x dim//4 x H//8 x W//8
x4 = self.gate(self.conv4(x4)) # B x dim//4 x H//32 x W//32
x2_up = self.upsample2(x2) # B x dim//4 x H x W
x3_up = self.upsample8(x3) # B x dim//4 x H x W
x4_up = self.upsample32(x4) # B x dim//4 x H x W
x1234 = torch.cat([x1, x2_up, x3_up, x4_up], dim=1)
# ================================== detector and descriptor head
if not self.single_head:
x1234 = self.gate(self.convhead1(x1234))
x = self.convhead2(x1234) # B x dim+1 x H x W
descriptor_map = x[:, :-1, :, :]
scores_map = torch.sigmoid(x[:, -1, :, :]).unsqueeze(1)
return scores_map, descriptor_map
if __name__ == '__main__':
from thop import profile
net = ALNet(c1=16, c2=32, c3=64, c4=128, dim=128, single_head=True)
image = torch.randn(1, 3, 640, 480)
flops, params = profile(net, inputs=(image,), verbose=False)
print('{:<30} {:<8} GFLops'.format('Computational complexity: ', flops / 1e9))
print('{:<30} {:<8} KB'.format('Number of parameters: ', params / 1e3))