Azzera filtri
Azzera filtri

How to implement spatial attention mechanism in Deep Network Designer

30 visualizzazioni (ultimi 30 giorni)
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);
  1 Commento
Chuan Yan
Chuan Yan il 1 Dic 2021
average-poolingand max-poolingoperations along the channel axis respectively in Deep Network Designeri

Accedi per commentare.

Risposte (1)

Aditya
Aditya il 17 Apr 2024
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]);

Prodotti


Release

R2021b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by