CBAM实现(pytorch)

import torch import torch.nn as nn import torchvision class ChannelAttentionModule(nn.Module): def __init__(self, channel, ratio=16): super(ChannelAttentionModule, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.shared_MLP = nn.Sequential( nn.Conv2d(channel, channel // ratio, 1, bias=False), nn.ReLU(), nn.Conv2d(channel // ratio, channel, 1, bias=False) ) self.sigmoid = nn.Sigmoid() def forward(self, x): avgout = self.shared_MLP(self.avg_pool(x)) print(avgout.shape) maxout = self.shared_MLP(self.max_pool(x)) return self.sigmoid(avgout + maxout) class SpatialAttentionModule(nn.Module): def __init__(self): super(SpatialAttentionModule, self).__init__() self.conv2d = nn.Conv2d(in_channels=2, out_channels=1, kernel_size=7, stride=1, padding=3) self.sigmoid = nn.Sigmoid() def forward(self, x): avgout = torch.mean(x, dim=1, keepdim=True) maxout, _ = torch.max(x, dim=1, keepdim=True) out = torch.cat([avgout, maxout], dim=1) out = self.sigmoid(self.conv2d(out)) return out class CBAM(nn.Module): def __init__(self, channel): super(CBAM, self).__init__() self.channel_attention = ChannelAttentionModule(channel) self.spatial_attention = SpatialAttentionModule() def forward(self, x): out = self.channel_attention(x) * x print('outchannels:{}'.format(out.shape)) out = self.spatial_attention(out) * out return out class ResBlock_CBAM(nn.Module): def __init__(self,in_places, places, stride=1,downsampling=False, expansion = 4): super(ResBlock_CBAM,self).__init__() self.expansion = expansion self.downsampling = downsampling self.bottleneck = nn.Sequential( nn.Conv2d(in_channels=in_places,out_channels=places,kernel_size=1,stride=1, bias=False), nn.BatchNorm2d(places), nn.ReLU(inplace=True), nn.Conv2d(in_channels=places, out_channels=places, kernel_size=3, stride=stride, padding=1, bias=False), nn.BatchNorm2d(places), nn.ReLU(inplace=True), nn.Conv2d(in_channels=places, out_channels=places*self.expansion, kernel_size=1, stride=1, bias=False), nn.BatchNorm2d(places*self.expansion), ) self.cbam = CBAM(channel=places*self.expansion) if self.downsampling: self.downsample = nn.Sequential( nn.Conv2d(in_channels=in_places, out_channels=places*self.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(places*self.expansion) ) self.relu = nn.ReLU(inplace=True) def forward(self, x): residual = x out = self.bottleneck(x) print(x.shape) out = self.cbam(out) if self.downsampling: residual = self.downsample(x) out += residual out = self.relu(out) return out model = ResBlock_CBAM(in_places=16, places=4) print(model) input = torch.randn(1, 16, 64, 64) out = model(input) print(out.shape)