-
Notifications
You must be signed in to change notification settings - Fork 5
/
mobilenet_cbam.py
51 lines (46 loc) · 1.58 KB
/
mobilenet_cbam.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
import torch
import torch.nn as nn
import torch.nn.functional as F
from attention_module import CBAM
class MobileNetCBAM(nn.Module):
def __init__(self, classes = 100):
super(MobileNetCBAM, self).__init__()
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU(inplace=True)
)
def conv_dw(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
nn.BatchNorm2d(inp),
nn.ReLU(inplace=True),
nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
CBAM(oup),
nn.ReLU(inplace=True),
)
self.model = nn.Sequential(
conv_bn( 3, 32, 1),
conv_dw( 32, 64, 1),
conv_dw( 64, 128, 1),
conv_dw(128, 128, 1),
conv_dw(128, 256, 2),
conv_dw(256, 256, 1),
conv_dw(256, 512, 2),
conv_dw(512, 512, 1),
conv_dw(512, 512, 1),
conv_dw(512, 512, 1),
conv_dw(512, 512, 1),
conv_dw(512, 512, 1),
conv_dw(512, 1024, 2),
conv_dw(1024, 1024, 1),
nn.AvgPool2d(4),
)
self.fc = nn.Linear(1024, classes)
def forward(self, x):
x = self.model(x)
x = x.view(-1, 1024)
x = self.fc(x)
return x