How to convert the format of data from sequences to matrices when designing deep learning networks?

Hello,

After importing the network into DeepNetworkDesigner for analysis, I encountered the following problem: after being processed by selfattentionLayer, the data size format is 577 (S) x 577 (C) x 1 (B).

I want to convert it to a format similar to imageInputLayer, _ (S) x_ (S) x_ (C) x_ (B). How can I use MATLAB to implement it?

The code for the network is as follows:

patchSize = 16;
embeddingOutputSize = 768;
layer = patchEmbeddingLayer(patchSize,embeddingOutputSize)

net = dlnetwork;

inputSize = [384 384 3];

maxPosition = (inputSize(1)/patchSize)^2 + 1;

numHeads = 4;
numKeyChannels = 4*embeddingOutputSize;

numClasses = 1000;

layers = [ 
    imageInputLayer(inputSize)
    patchEmbeddingLayer(patchSize,embeddingOutputSize,Name="patch-emb")
    embeddingConcatenationLayer(Name="emb-cat")
    positionEmbeddingLayer(embeddingOutputSize,maxPosition,Name="pos-emb");
    additionLayer(2,Name="add")
    selfAttentionLayer(numHeads,numKeyChannels,AttentionMask="causal",OutputSize=maxPosition)
    fullyConnectedLayer(numClasses)
    softmaxLayer];
net = addLayers(net,layers);

net = connectLayers(net,"emb-cat","add/in2");

[Cris LaPierre]

Are you sure your data is 577 (S) x 577 (C) x 1 (B)?

Could it instead be 577 (S) x 577 (S) x 1 (C) or 577 (S) x 577 (S) x 1 (B)?

[Tian,HCong]

Thank you very much for your prompt reply.

The corresponding data is indeed 577 (S) x 577 (C) x 1 (B), as shown in the data graph for each layer.

[Cris LaPierre]

Can you share a sample data set? That will make it easier to help.

[Tian,HCong]

@Cris LaPierre

Thank you very much for your prompt reply.

This dataset comes from the folder in Matlab and is commonly used by me when testing simple deep learning networks.