注意力机制（通道注意机制、空间注意力机制、CBAM、SELayer）
参考链接: 注意力机制
参考链接: 深度学习卷积神经网络重要结构之通道注意力和空间注意力模块
参考链接: 用于卷积神经网络的注意力机制(Attention)----CBAM: Convolutional Block Attention Module
参考链接: moskomule/senet.pytorch
参考链接: Squeeze-and-Excitation Networks
参考链接: CBAM: Convolutional Block Attention Module
空间注意力机制:
代码实验展示:
import torch
from torch import nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) # 7,3 3,1
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avg_out = torch.mean(x, dim=1, keepdim=True)
max_out, _ = torch.max(x, dim=1, keepdim=True)
x = torch.cat([avg_out, max_out], dim=1)
x = self.conv1(x)
return self.sigmoid(x)
if __name__ == '__main__':
SA = SpatialAttention(7)
data_in = torch.randn(8,32,300,300)
data_out = SA(data_in)
print(data_in.shape) # torch.Size([8, 32, 300, 300])
print(data_out.shape) # torch.Size([8, 1, 300, 300])
控制台结果输出展示:
Windows PowerShell
版权所有 (C) Microsoft Corporation。保留所有权利。
尝试新的跨平台 PowerShell https://aka.ms/pscore6
加载个人及系统配置文件用了 1003 毫秒。
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\测试注意力机制CBAM> & 'D:\Anaconda3\python.exe' 'c:\Users\chenxuqi\.vscode\extensions\ms-python.python-2021.1.502429796\pythonFiles\lib\python\debugpy\launcher' '55088' '--' 'c:\Users\chenxuqi\Desktop\News4cxq\测试注意力机制CBAM\
空间注意力机制.py'
torch.Size([8, 32, 300, 300])
torch.Size([8, 1, 300, 300])
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\测试注意力机制CBAM> conda activate base
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\测试注意力机制CBAM>
通道注意力机制:
代码实验展示:
import torch
from torch import nn
class ChannelAttention(nn.Module):
def __init__(self, in_planes, ratio=16):
super(ChannelAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False)
self.relu1 = nn.ReLU()
self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
out = avg_out + max_out
return self.sigmoid(out)
if __name__ == '__main__':
CA = ChannelAttention(32)
data_in = torch.randn(8,32,300,300)
data_out = CA(data_in)
print(data_in.shape) # torch.Size([8, 32, 300, 300])
print(data_out.shape) # torch.Size([8, 32, 1, 1])
控制台结果输出展示:
Windows PowerShell
版权所有 (C) Microsoft Corporation。保留所有权利。
尝试新的跨平台 PowerShell https://aka.ms/pscore6
加载个人及系统配置文件用了 882 毫秒。
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\测试注意力机制CBAM> conda activate base
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\测试注意力机制CBAM> & 'D:\Anaconda3\python.exe' 'c:\Users\chenxuqi\.vscode\extensions\ms-python.python-2021.1.502429796\pythonFiles\lib\python\debugpy\launcher' '55339' '--' 'c:\Users\chenxuqi\Desktop\News4cxq\测试注意力机制CBAM\
通道注意力机制.py'
torch.Size([8, 32, 300, 300])
torch.Size([8, 32, 1, 1])
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\测试注意力机制CBAM>
CBAM注意力机制:
代码实验展示:
import torch
from torch import nn
class ChannelAttention(nn.Module):
def __init__(self, in_planes, ratio=16):
super(ChannelAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False)
self.relu1 = nn.ReLU()
self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
out = avg_out + max_out
return self.sigmoid(out)
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) # 7,3 3,1
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avg_out = torch.mean(x, dim=1, keepdim=True)
max_out, _ = torch.max(x, dim=1, keepdim=True)
x = torch.cat([avg_out, max_out], dim=1)
x = self.conv1(x)
return self.sigmoid(x)
class CBAM(nn.Module):
def __init__(self, in_planes, ratio=16, kernel_size=7):
super(CBAM, self).__init__()
self.ca = ChannelAttention(in_planes, ratio)
self.sa = SpatialAttention(kernel_size)
def forward(self, x):
out = x * self.ca(x)
result = out * self.sa(out)
return result
if __name__ == '__main__':
print('testing ChannelAttention'.center(100,'-'))
torch.manual_seed(seed=20200910)
CA = ChannelAttention(32)
data_in = torch.randn(8,32,300,300)
data_out = CA(data_in)
print(data_in.shape) # torch.Size([8, 32, 300, 300])
print(data_out.shape) # torch.Size([8, 32, 1, 1])
if __name__ == '__main__':
print('testing SpatialAttention'.center(100,'-'))
torch.manual_seed(seed=20200910)
SA = SpatialAttention(7)
data_in = torch.randn(8,32,300,300)
data_out = SA(data_in)
print(data_in.shape) # torch.Size([8, 32, 300, 300])
print(data_out.shape) # torch.Size([8, 1, 300, 300])
if __name__ == '__main__':
print('testing CBAM'.center(100,'-'))
torch.manual_seed(seed=20200910)
cbam = CBAM(32, 16, 7)
data_in = torch.randn(8,32,300,300)
data_out = cbam(data_in)
print(data_in.shape) # torch.Size([8, 32, 300, 300])
print(data_out.shape) # torch.Size([8, 1, 300, 300])
控制台结果输出展示:
Windows PowerShell
版权所有 (C) Microsoft Corporation。保留所有权利。
尝试新的跨平台 PowerShell https://aka.ms/pscore6
加载个人及系统配置文件用了 1029 毫秒。
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\测试注意力机制CBAM> & 'D:\Anaconda3\python.exe' 'c:\Users\chenxuqi\.vscode\extensions\ms-python.python-2021.1.502429796\pythonFiles\lib\python\debugpy\launcher' '55659' '--' 'c:\Users\chenxuqi\Desktop\News4cxq\测试注意力机制CBAM\cbam注意力机制.py'
--------------------------------------testing ChannelAttention--------------------------------------
torch.Size([8, 32, 300, 300])
torch.Size([8, 32, 1, 1])
--------------------------------------testing SpatialAttention--------------------------------------
torch.Size([8, 32, 300, 300])
torch.Size([8, 1, 300, 300])
--------------------------------------------testing CBAM--------------------------------------------
torch.Size([8, 32, 300, 300])
torch.Size([8, 32, 300, 300])
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\测试注意力机制CBAM> conda activate base
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\测试注意力机制CBAM>
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\测试注意力机制CBAM>
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\测试注意力机制CBAM>
SE注意力机制:
代码实验展示:
from torch import nn
import torch
class SELayer(nn.Module):
def __init__(self, channel, reduction=16):
super(SELayer, self).__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 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)
# return x * y
if __name__ == '__main__':
torch.manual_seed(seed=20200910)
data_in = torch.randn(8,32,300,300)
SE = SELayer(32)
data_out = SE(data_in)
print(data_in.shape) # torch.Size([8, 32, 300, 300])
print(data_out.shape) # torch.Size([8, 32, 300, 300])
控制台输出结果展示:
Windows PowerShell
版权所有 (C) Microsoft Corporation。保留所有权利。
尝试新的跨平台 PowerShell https://aka.ms/pscore6
加载个人及系统配置文件用了 979 毫秒。
(base) PS F:\Iris_SSD_small\senet.pytorch-master> & 'D:\Anaconda3\envs\pytorch_1.7.1_cu102\python.exe' 'c:\Users\chenxuqi\.vscode\extensions\ms-python.python-2021.1.502429796\pythonFiles\lib\python\debugpy\launcher' '54904' '--' 'f:\Iris_SSD_small\senet.pytorch-master\senet\se_module.py'
torch.Size([8, 32, 300, 300])
torch.Size([8, 32, 300, 300])
(base) PS F:\Iris_SSD_small\senet.pytorch-master> conda activate pytorch_1.7.1_cu102
(pytorch_1.7.1_cu102) PS F:\Iris_SSD_small\senet.pytorch-master>