loss计算的bug

评估。原来采用的是sum的方式，现在改成均值

This is a very insightful observation. Looking at the evaluate.py code, specifically in the drop_head_tail function:

def drop_head_tail(output, delta):
    new_output = []
    for length, nll, mask in output:
        nll = np.array(nll[:int(length)+1])
        mask = np.array(mask[:int(length)]+[0])
        nll = nll - mask*delta
        new_output.append(sum(nll))  # Here's the key part
    return new_output

You're correct - the code is using sum(nll) rather than taking the mean, which could potentially bias the anomaly detection towards flagging longer trajectories as anomalous simply because they accumulate more negative log-likelihood values over their length.

This could be problematic because:

1. Longer trajectories will naturally accumulate larger total NLL values
2. A long normal trajectory might end up with a higher anomaly score than a shorter but actually anomalous trajectory
3. The comparison between trajectories of different lengths isn't normalized

To fix this issue, you could modify the code to use the mean instead of the sum. Here's how you could modify it:

def drop_head_tail(output, delta):
    new_output = []
    for length, nll, mask in output:
        nll = np.array(nll[:int(length)+1])
        mask = np.array(mask[:int(length)]+[0])
        nll = nll - mask*delta
        new_output.append(nll.mean())  # Changed from sum to mean
    return new_output

This modification would:

1. Normalize the anomaly scores by trajectory length
2. Make scores more comparable between trajectories of different lengths
3. Prevent the bias towards flagging longer trajectories as anomalous

This is particularly important because in real-world scenarios, anomalous trajectories could be of any length, and we don't want the detection to be biased by trajectory length alone.

训练对比

Let me break down the key differences between these two loss function implementations:

First Version:

def loss_fn(self, p_x, target, mask):
    p_x = p_x * mask.float()  # Simple multiplication with mask
    nll = -p_x[torch.arange(target.size(0)).to(target.device), target]  # Direct indexing
    return nll

Second Version:

def loss_fn(self, p_x, target, mask):
    mask = mask.float()
    # 1. Log-sum-exp trick
    max_val, _ = p_x.max(dim=-1, keepdim=True)
    log_sum_exp = max_val + torch.log(torch.exp(p_x - max_val).masked_fill(mask == 0, 0).sum(dim=-1, keepdim=True) + 1e-10)
    
    # 2. Proper normalization
    log_probs = p_x - log_sum_exp
    
    # 3. Special handling for padding
    log_probs[:, self.label_num-1] = 0
    
    target_log_probs = log_probs[torch.arange(target.size(0)).to(target.device), target]
    return -target_log_probs

Key differences:

1. Numerical Stability:

   • First version assumes p_x is already properly normalized (log probabilities)
   • Second version implements the log-sum-exp trick to prevent numerical overflow/underflow

2. Normalization:

   • First version just masks values
   • Second version properly normalizes the probabilities using log_sum_exp

3. Padding Handling:

   • First version doesn't handle padding specifically
   • Second version explicitly sets padding token probability to 1 (log prob = 0)

4. Assumptions:

   • First version assumes input is already in log space and normalized
   • Second version works with raw logits and handles normalization itself

The second version is more robust because:

1. It prevents numerical instability
2. It ensures proper probability normalization
3. It handles padding tokens correctly
4. It doesn't assume pre-normalized inputs

The first version might be causing your inf values because it doesn't handle numerical stability or proper normalization.