CBAM注意力机制matlab代码

CBAM注意力机制的Matlab代码如下：

function [out, att] = CBAM(input, channel_reduction_ratio, spatial_reduction_ratio)
% input: input tensor of shape [batch_size, height, width, num_channels]
% channel_reduction_ratio: ratio of channel reduction in the channel attention module
% spatial_reduction_ratio: ratio of spatial reduction in the spatial attention module

% Channel Attention Module
avg_pool = averagePooling2d(input, [size(input,1), size(input,2)]);
max_pool = maxPooling2d(input, [size(input,1), size(input,2)]);
channel_avg = fullyConnected(avg_pool, round(size(input,4)/channel_reduction_ratio));
channel_max = fullyConnected(max_pool, round(size(input,4)/channel_reduction_ratio));
channel_att = sigmoid(channel_avg + channel_max);
channel_att = reshape(channel_att, [size(input,1), size(input,2), size(input,4)]);
out = input .* channel_att;

% Spatial Attention Module
spatial_avg = averagePooling2d(out, [size(out,1)/spatial_reduction_ratio, size(out,2)/spatial_reduction_ratio]);
spatial_max = maxPooling2d(out, [size(out,1)/spatial_reduction_ratio, size(out,2)/spatial_reduction_ratio]);
spatial_att = sigmoid(conv2(spatial_avg + spatial_max, ones([3,3]), 'same'));
att = spatial_att;
out = out .* repmat(spatial_att, [1,1,size(out,4)]);
end

其中，`averagePooling2d`和`maxPooling2d`为2D平均池化和最大池化函数，`fullyConnected`为全连接层函数，`conv2`为2D卷积函数，`sigmoid`为sigmoid函数。具体实现根据不同框架可能会有所不同，但思路一致。