How to implement spatial attention mechanism in Deep Network Designer

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Chuan Yan
Chuan Yan 2021-11-30
评论： shen hedong 2024-8-13
How to implement spatial attention mechanism in Deep Network Designer
spatial attention:
input = [256,256,64]
max_pool = max(input,[],3);
men_pool = mean(input,3);

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Aditya
Aditya 2024-4-17
To implement a spatial attention mechanism within a deep learning model using MATLAB's Deep Network Designer, you would typically follow a series of steps to first create the attention module separately, and then integrate it into your network. The spatial attention mechanism you're describing seems to follow a common pattern where both max pooling and mean pooling across the channels are used to highlight important spatial features.
Step 1: Define the Spatial Attention Layer
Since custom operations like spatial attention are not directly available in Deep Network Designer's layer catalog, you would typically define this as a custom layer in MATLAB code. However, for simplicity and to provide a conceptual understanding, I'll describe the process focusing on the operations involved.
For custom implementation, you would define a class inheriting from nnet.layer.Layer and implement the spatial attention mechanism inside its forward function.
Step 2: Implementing Pooling Operations
Max and mean pooling across the channels can be done using operations like:
% Assuming 'input' is the input tensor of size [256, 256, 64]
max_pool = max(input, [], 3); % Max pooling across channels
mean_pool = mean(input, 3); % Mean pooling across channels
Step 3: Combining Features and Applying Convolution
After pooling, you would concatenate these maps and apply a convolution. In code, this step might require a custom layer or function to handle the concatenation and convolution:
% Concatenating along the third dimension
combined_features = cat(3, max_pool, mean_pool);
% Applying a convolution to get a single channel output
% Note: You need to define 'convLayer' based on your network architecture
attention_map = convolution2dLayer([7, 7], 1, 'Padding', 'same').forward(combined_features);
Step 4: Applying the Attention Map
Finally, you apply the spatial attention map to the original input:
% Assuming 'attention_map' is resized or processed to match input dimensions if needed
modulated_input = input .* repmat(attention_map, [1, 1, 64]);
  1 个评论
shen hedong
shen hedong 2024-8-13
May I ask how to use MATLAB code to build an ECA module? The ECA module can refer to this paper: ECA Net: Efficient Channel Attention for Deep Convolutional Neural Networks.
Paper address: https://arxiv.org/abs/1910.03151
I found the following Python code about ECA: but I don't know how to implement "squeeze" and "transpose" in MATLAB.Please help me!
class ECA(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""
def __init__(self, c1,c2, k_size=3):
super(ECA, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
# feature descriptor on the global spatial information
y = self.avg_pool(x)
y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
# Multi-scale information fusion
y = self.sigmoid(y)
return x * y.expand_as(x)

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