PyTorch CNN with gMLP: A Comprehensive Guide

This article delves into integrating a gMLP layer after convolutional layers in a PyTorch model. While combining these layers is valid, there's a common error that needs addressing. In the provided code, after the gMLP layer, the output feature tensor needs to be flattened before being fed into the linear layer. This is crucial to ensure the feature tensor's shape aligns with the linear layer's input requirements.

The incorrect code snippet is:

x = self.linear(x)

The corrected code snippet, incorporating the necessary flattening, is:

x = self.flatten(x)
x = self.linear(x)

This modification guarantees that the feature tensor's shape matches the linear layer's input, preventing errors and ensuring smooth model execution.