数据格式

Based on the provided code and data, let me explain how the trajectory data is preprocessed in the TrajectoryLoader class:

1. Data Loading and Label Processing:

• The data is loaded from a pickle file containing trajectory information
• Each trajectory consists of GPS coordinates that have been mapped to node IDs
• The class adds three special tokens:

  def bos_eos_pad(self):
      self.bos = self.label_num-3  # Beginning of sequence token
      self.eos = self.label_num-2  # End of sequence token
      self.pad = self.label_num-1  # Padding token
  

2. Batch Processing:

• For each trajectory in a batch, the following processing is done:

  # Original trajectory becomes source sequence
  src_batch.append(item)
  
  # Target sequence gets BOS token prepended and EOS token appended
  trg_batch.append([self.bos] + deepcopy(item) + [self.eos])
  

3. Length Handling and Padding:

• The code tracks original lengths:

  src_length.append(len(item))  # Source sequence length
  trg_length.append(len(trg_batch[-1]))  # Target sequence length (including BOS/EOS)
  

• Sequences in a batch are padded to the same length:

  # Pad source sequences
  for item in src_batch:
      item += [self.pad] * (max_length - len(item))
      
  # Pad target sequences (which include BOS/EOS)
  for item in trg_batch:
      item += [self.pad] * (max_length + 2 - len(item))
  

4. Final Data Structure:

• Source sequences: Original trajectory + padding
• Target sequences: [BOS] + trajectory + [EOS] + padding
• The loader keeps track of actual sequence lengths for both source and target

So to directly answer your questions:

• Yes, it inserts labels:
  • BOS (Beginning of Sequence) token at the start of target sequences
  • EOS (End of Sequence) token at the end of target sequences
  • PAD tokens to make all sequences in a batch the same length
• The special tokens are positioned at:
  • label_num-3: BOS token
  • label_num-2: EOS token
  • label_num-1: PAD token

This preprocessing enables the transformer model to:

• Know where sequences start (BOS)
• Know where sequences end (EOS)
• Handle variable-length sequences in batches (PAD)
• Generate proper autoregressive predictions during training and inference

confidence loss 只包含正常路径的计算

Let me analyze how the confidence KL loss is calculated in the TransformerTrajectoryModel and whether it includes the special tokens (BOS, EOS, PAD):

Looking at the confidence computation in TransformerTrajectoryModel:

def compute_confidence(self, h, src):
    # Encode and transform the input
    encoded = self.confidence_encoder(h)
    transformed = self.confidence_transformer(encoded)
    
    # Get distribution parameters
    mu = self.confidence_mu(transformed)
    logvar = self.confidence_logvar(transformed)
    sigma = torch.exp(0.5 * logvar)
    
    # Sample and decode
    q_z = torch.distributions.Normal(mu, sigma)
    z = q_z.rsample()
    decoded = self.confidence_decoder(z)
    logits = self.output_projection1(decoded)
    log_probs = torch.log_softmax(logits, dim=-1)
    
    # Reshape for loss calculation
    batch_size, seq_len = src.shape
    log_probs_flat = log_probs.reshape(batch_size * seq_len, -1)
    src_flat = src.reshape(-1)
    
    # Calculate NLL loss with padding mask
    nll_loss = self.nll(log_probs_flat, src_flat)
    nll_loss = nll_loss.reshape(batch_size, seq_len)
    
    # Important part: Create mask to ignore padding
    mask = (src != self.num_nodes-1).float()  # Ignore PAD tokens
    nll_loss = (nll_loss * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-8)

Key observations:

1. The confidence computation uses the source sequence (src), which does NOT include BOS or EOS tokens - it's the original trajectory with only padding

2. The masking explicitly handles PAD tokens:

mask = (src != self.num_nodes-1).float()  # Masks out PAD tokens only

3. The NLL loss is computed only on the actual trajectory nodes, as it uses the source sequence (src) as the target, not the target sequence that would contain BOS/EOS

4. The KL divergence is calculated on the entire latent distribution but is also scaled by the mask:

kl_div = torch.distributions.kl_divergence(q_z, prior)
kl_div = kl_div.mean(dim=-1)
kl_div = (kl_div * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-8)

So to directly answer:

• BOS: Not included in confidence loss computation
• EOS: Not included in confidence loss computation
• PAD: Explicitly masked out and not included in the loss

The confidence VAE only tries to reconstruct the actual trajectory points, ignoring all special tokens. This makes sense because the confidence measure should be about how well the model can reconstruct the actual movement patterns, not the artificial tokens we add for the transformer sequence processing.

Let me check the data loading and trajectory handling to determine if src includes the destination.

Looking at the provided sample data in the first document:

# From the example trajectory data structure:
'n_geo': [[104.07337, 30.68815], [104.07319, 30.68815], ..., [104.073, 30.65492]]

# In TrajectoryLoader.load_data:
def load_data(self, trajectory_path, node2id):
    dataset = pickle.load(open(trajectory_path, 'rb'))
    self.data = []
    for line in dataset.values():
        traj = line['n_geo']  # Takes the n_geo field directly
        item = []
        for node in traj:
            item.append(node2id[str(node)])
        self.data.append(item)

And looking at how src is created:

# In TrajectoryLoader.batch_preprocess:
src_batch = []
for item in cur_batch:
    src_length.append(len(item))
    src_batch.append(item)  # Direct use of the trajectory

From this code, we can see that:

1. src includes ALL points from the n_geo field of the trajectory data
2. Looking at the sample data, n_geo contains the complete trajectory including the destination point
3. The destination point [104.073, 30.65492] is included as the last point in the trajectory

Therefore, yes, src does include the destination point. It contains the complete trajectory from start to finish, before any special tokens (BOS/EOS) are added. The confidence computation would thus work on reconstructing the entire path including the destination.

The way it works is:

• src: [start_point, ..., intermediate_points, ..., destination_point] + padding
• trg: [BOS, start_point, ..., intermediate_points, ..., destination_point, EOS] + padding

The confidence VAE tries to reconstruct the entire source sequence, including both the starting point and the destination point.

vae loss 包含vae_kl + sd_loss + nll_loss

then in the rest, it seems to input the start_point and destination_point, it will get three loss, one is vae_kl + sd_loss + nll_loss. in this part, I am also wondering if BOS, EOS, and the padding participate into the calculation