帮我用pytorch和TCN写一个三模态的时序模型代码

好的，我可以回答这个问题。以下是使用 PyTorch 和 TCN 编写三模态时序模型的代码示例：

import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset

class ThreeModalDataset(Dataset):
    def __init__(self, data):
        self.data = data
    
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        return self.data[idx]

class ThreeModalTCN(nn.Module):
    def __init__(self, input_size, output_size, num_channels, kernel_size, dropout):
        super(ThreeModalTCN, self).__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.num_channels = num_channels
        self.kernel_size = kernel_size
        self.dropout = dropout
        
        self.modal1_tcn = nn.Sequential(
            nn.Conv1d(input_size[], num_channels, kernel_size=kernel_size),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size=kernel_size),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size=kernel_size),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(),
            nn.Dropout(dropout)
        )
        
        self.modal2_tcn = nn.Sequential(
            nn.Conv1d(input_size[1], num_channels, kernel_size=kernel_size),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size=kernel_size),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size=kernel_size),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(),
            nn.Dropout(dropout)
        )
        
        self.modal3_tcn = nn.Sequential(
            nn.Conv1d(input_size[2], num_channels, kernel_size=kernel_size),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size=kernel_size),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size=kernel_size),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(),
            nn.Dropout(dropout)
        )
        
        self.fc = nn.Linear(num_channels*3, output_size)
        
    def forward(self, x):
        modal1_out = self.modal1_tcn(x[:, , :].unsqueeze(1)).squeeze(1)
        modal2_out = self.modal2_tcn(x[:, 1, :].unsqueeze(1)).squeeze(1)
        modal3_out = self.modal3_tcn(x[:, 2, :].unsqueeze(1)).squeeze(1)
        
        combined_out = torch.cat((modal1_out, modal2_out, modal3_out), dim=1)
        out = self.fc(combined_out)
        
        return out

# Example usage
input_size = [10, 20, 30] # number of features for each modality
output_size = 5 # number of classes
num_channels = 64 # number of channels in each TCN layer
kernel_size = 3 # size of the TCN kernel
dropout = .2 # dropout probability

model = ThreeModalTCN(input_size, output_size, num_channels, kernel_size, dropout)
data = torch.randn(100, 3, max(input_size)) # 100 samples, 3 modalities, variable length time series
dataset = ThreeModalDataset(data)
dataloader = DataLoader(dataset, batch_size=16)

for batch in dataloader:
    out = model(batch)
    print(out.shape) # should be [batch_size, output_size]

希望这个代码示例能够帮到你！