transformer轨迹预测代码实例
时间: 2023-07-28 19:09:03 浏览: 150
以下是一个使用Transformer模型进行轨迹预测的代码实例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
# 定义Transformer模型
class TransformerModel(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim, num_layers, num_heads):
super(TransformerModel, self).__init__()
self.transformer = nn.Transformer(
d_model=input_dim,
nhead=num_heads,
num_encoder_layers=num_layers,
num_decoder_layers=num_layers,
dim_feedforward=hidden_dim,
)
self.fc = nn.Linear(input_dim, output_dim)
def forward(self, src, tgt):
# src: 输入序列,形状为[seq_len, batch_size, input_dim]
# tgt: 目标序列,形状为[seq_len, batch_size, input_dim]
output = self.transformer(src, tgt)
output = self.fc(output)
return output
# 定义自定义数据集类
class TrajectoryDataset(Dataset):
def __init__(self, data):
self.data = data
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]
# 构建训练数据
train_data = [...] # 输入训练数据,形状为[seq_len, batch_size, input_dim]
target_data = [...] # 目标训练数据,形状为[seq_len, batch_size, output_dim]
dataset = TrajectoryDataset(list(zip(train_data, target_data)))
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
# 定义模型参数
input_dim = ...
output_dim = ...
hidden_dim = ...
num_layers = ...
num_heads = ...
learning_rate = ...
# 初始化模型和优化器
model = TransformerModel(input_dim, output_dim, hidden_dim, num_layers, num_heads)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# 训练模型
model.train()
for epoch in range(num_epochs):
for batch in dataloader:
src, tgt = batch
optimizer.zero_grad()
output = model(src, tgt[:-1]) # 去掉目标序列的最后一个元素作为输入
loss = nn.MSELoss()(output, tgt[1:]) # 目标序列的下一个元素作为目标进行计算损失
loss.backward()
optimizer.step()
# 使用模型进行预测
model.eval()
test_input = [...] # 输入测试数据,形状为[seq_len, batch_size, input_dim]
with torch.no_grad():
predicted_output = model(test_input, tgt=None) # 预测输出,形状为[seq_len, batch_size, output_dim]
```
请注意,上述代码仅提供了一个基本的Transformer模型框架以及示例数据,你需要根据自己的具体任务和数据进行相应的修改和适配。