行人轨迹预测的Transformer神经网络代码

行人轨迹预测通常涉及到序列建模技术，其中Transformer是一种强大的模型结构，它最初是由Google在自然语言处理任务上提出的，但也被广泛应用于计算机视觉领域。在行人轨迹预测中，Transformer可以用来捕捉行人位置随时间的变化规律。

以下是使用PyTorch库的一个简化版的Transformer模型示例，用于行人轨迹预测的大致框架：

import torch
import torch.nn as nn

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=500):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)
        
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-torch.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)

class TransformerBlock(nn.Module):
    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1):
        super().__init__()
        self.self_attention = nn.MultiHeadAttention(d_model, nhead, dropout=dropout)
        self.positional_encoding = PositionalEncoding(d_model)
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)

    def forward(self, x):
        x = self.self_attention(x, x, x)[0]
        x = self.dropout(x)
        x = self.linear2(self.dropout(F.relu(self.linear1(x))))
        return x + x

class TrajectoryPredictionTransformer(nn.Module):
    def __init__(self, input_dim, output_dim, num_layers=2, heads=8, dropout=0.1):
        super(TrajectoryPredictionTransformer, self).__init__()
        self.d_model = input_dim
        self.transformer_blocks = nn.Sequential(*[TransformerBlock(d_model, heads, dropout=dropout) for _ in range(num_layers)])
        self.fc = nn.Linear(d_model, output_dim)

    def forward(self, trajectory):
        trajectory = trajectory + self.positional_encoding(trajectory)
        trajectory = self.transformer_blocks(trajectory)
        trajectory = self.fc(trajectory[:, -1, :])
        return trajectory

# 使用示例
model = TrajectoryPredictionTransformer(input_dim, output_dim)
inputs = torch.randn(16, 50, input_dim)  # (batch_size, sequence_length, input_dim)
outputs = model(inputs)

注意这只是一个简化的版本，实际应用中你还需要预处理数据、损失函数、优化器以及训练循环等部分。此外，Transformer模型对计算资源需求较高，尤其是在长序列上。