PyTorch CNN with gMLP for Feature Extraction

import torch
import torch.nn as nn
import torch.nn.functional as F
from gmlp import SpatialGatingUnit ,gMLPBlock,gMLP

class CNN(nn.Module):
    def __init__(self):  # 初始化方法
        super(CNN, self).__init__()  # 初始化方法
        # 第一层卷积层，输入为 (1,10000,12)
        self.conv1 = nn.Sequential(  # input shape (1,10000,12)
            nn.Conv2d(  # 二维卷积层
                in_channels=1,  # input height   # 输入通道数
                out_channels=5,  # n_filters     # 输出通道数（卷积核数量）
                kernel_size=(200, 3),  # filter size  # 卷积核大小
                stride=(50, 1),  # filter movement/step   # 卷积核移动步长
                padding=1,  # 填充大小
            ),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, padding=1),  # 最大池化层
        )
        # 第二层卷积层，输入为 (5,99,7)
        self.conv2 = nn.Sequential(  # input shape (5,99,7)
            nn.Conv2d(5, 10, (20, 2), (4, 1), 1),  # output shape   # 输出通道数为10，卷积核大小为(20,2)，步长为(4,1)，填充为1
            nn.ReLU(),  # activation
            nn.MaxPool2d(kernel_size=2),  # 最大池化层  # output shape (10,10,4)
        )

        self.gmlp = gMLP( d_model=40, d_ffn=80, seq_len=10, num_layers=6)
        # gmlp(data).shape  # 不需要在初始化时调用gMLP的forward方法


        self.linear = nn.Linear(in_features=40, out_features=10)
        self.flatten = nn.Flatten()
        self.out = nn.Linear(10 * 10 * 4, 6)  # 全连接层，输出6个类别

    # 定义一个名为CNN的类，继承自PyTorch的nn.Module

    def forward(self, x):  # 前向传播方法
        x = self.conv1(x)  # 通过第一个卷积层序列
        x = self.conv2(x)  # 通过第二个卷积层序列
        # x = x.view(x.size(0), -1)      # 将张量展平
        # 四维转三维
        N, C, H, W = x.shape
        x = x.view(N, C, H * W).permute(0, 2, 1)
        x = self.gmlp(x)
        x = self.flatten(x)#换顺序了 先展平 在输入线性层中
        x = self.linear(x)
        feature = x  # 提取特征
        output = self.out(x)  # 通过全连接层得到输出
        return feature, output  # 返回特征和输出

# 根据您提供的代码，我没有发现明显的错误。在卷积层后接gMLP是合理的操作，只要输入和输出的形状匹配即可。
# 请确保gMLP的输入形状与卷积层的输出形状匹配，并且在代码中正确调用gMLP的forward方法。

# 如果您遇到了错误，请提供具体的错误信息或描述问题的行为，以便我可以帮助您更好地解决问题。

Explanation:

The code defines a CNN model with two convolutional layers, followed by a gMLP block for feature extraction and a final linear layer for classification.

The input shape for the first convolutional layer is (1, 10000, 12).
The second convolutional layer takes the output from the first layer and processes it further.
The output from the second convolutional layer is reshaped and passed to the gMLP block.
The gMLP block is a multi-layer perceptron-based architecture that processes sequential data and extracts features.
Finally, the features extracted by the gMLP are flattened and passed through a linear layer to perform classification.

Important Considerations:

Shape Matching: Ensure the output shape of the convolutional layers matches the input shape expected by the gMLP block. You might need to adjust the kernel sizes, strides, and padding values in the convolutional layers to achieve this.
gMLP Configuration: The gMLP block requires parameters like d_model, d_ffn, seq_len, and num_layers. Choose these parameters based on the complexity of your data and the desired performance.
Error Handling: If you encounter any errors, carefully inspect the error messages and check for shape mismatches, incorrect layer configurations, or potential issues with the gMLP implementation.

This code provides a basic framework for using a CNN with a gMLP block. You can further customize the architecture and add more layers to improve accuracy and performance based on your specific task and dataset.

PyTorch CNN with gMLP for Feature Extraction