46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
|
import numpy as np
|
||
|
|
import torch
|
||
|
|
from torch import nn
|
||
|
|
from torch.nn import init
|
||
|
|
|
||
|
|
|
||
|
|
class SEAttention(nn.Module):
|
||
|
|
|
||
|
|
def __init__(self, channel=512, reduction=16):
|
||
|
|
super().__init__()
|
||
|
|
self.avg_pool = nn.AdaptiveAvgPool2d(1)
|
||
|
|
self.fc = nn.Sequential(
|
||
|
|
nn.Linear(channel, channel // reduction, bias=False),
|
||
|
|
nn.ReLU(inplace=True),
|
||
|
|
nn.Linear(channel // reduction, channel, bias=False),
|
||
|
|
nn.Sigmoid()
|
||
|
|
)
|
||
|
|
|
||
|
|
def init_weights(self):
|
||
|
|
for m in self.modules():
|
||
|
|
if isinstance(m, nn.Conv2d):
|
||
|
|
init.kaiming_normal_(m.weight, mode='fan_out')
|
||
|
|
if m.bias is not None:
|
||
|
|
init.constant_(m.bias, 0)
|
||
|
|
elif isinstance(m, nn.BatchNorm2d):
|
||
|
|
init.constant_(m.weight, 1)
|
||
|
|
init.constant_(m.bias, 0)
|
||
|
|
elif isinstance(m, nn.Linear):
|
||
|
|
init.normal_(m.weight, std=0.001)
|
||
|
|
if m.bias is not None:
|
||
|
|
init.constant_(m.bias, 0)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
b, c, _, _ = x.size()
|
||
|
|
y = self.avg_pool(x).view(b, c)
|
||
|
|
y = self.fc(y).view(b, c, 1, 1)
|
||
|
|
return x * y.expand_as(x)
|
||
|
|
|
||
|
|
|
||
|
|
# 输入 N C H W, 输出 N C H W
|
||
|
|
if __name__ == '__main__':
|
||
|
|
input = torch.randn(50, 512, 7, 7)
|
||
|
|
se = SEAttention(channel=512, reduction=8)
|
||
|
|
output = se(input)
|
||
|
|
print(output.shape)
|