CBAM-2018学习笔记

名称：

Convolutional Block Attention Module (CBAM)

来源：

CBAM: Convolutional Block Attention Module

创新点：

fpg0umoj.4ze.png

贡献：

提出CBAM
验证了其有效性
改善提高了以往模型的性能

代码：

  
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  # 输入 N C H W,  输出 N C H Wif __name__ == '__main__':  block = CBAM(64)  input = torch.rand(3, 64, 32, 32)  output = block(input)  print(input.size(), output.size())