这段代码什么意思# 得出注意力分数 weight = torch.nn.Softmax(dim=2)(weight) if self.hard != 0: # hard mode weight = torch.where(weight > self.hard, weight, torch.full_like(weight, 0)) if mean: weight = weight.mean(dim=1) weight = weight.unsqueeze(1) weight = weight.repeat(1, hidden_dim, 1) weight = weight.transpose(2, 1) # 将注意力分数作用在输入值上 features_attention = weight * features

这段代码的意思是，首先通过 softmax 函数将注意力分数进行归一化，然后根据设定的 hard 参数进行二值化，从而获得一个更加稀疏的注意力权重矩阵。接着，如果 mean 参数为 True，则对注意力权重矩阵进行平均池化，并将结果复制成与输入特征相同的维度，最后将注意力权重矩阵作用在输入特征上，得到加权后的特征向量。

相关问题

equalized focal loss代码

### Equalized Focal Loss Code Implementation

Equalized Focal Loss aims to address class imbalance and hard example mining more effectively by adjusting the standard Focal Loss formulation. The following Python code demonstrates an implementation of equalized focal loss based on existing research advancements[^1]:

import torch
import torch.nn as nn
import torch.nn.functional as F


class EqualizedFocalLoss(nn.Module):
    def __init__(self, alpha=0.25, gamma=2.0, num_classes=80, reduction='mean'):
        super(EqualizedFocalLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.num_classes = num_classes
        self.reduction = reduction
        
    def forward(self, logits, targets):
        # Calculate probabilities from logits
        probas = F.softmax(logits, dim=-1)

        # Create one-hot encoding for target classes
        y_onehot = F.one_hot(targets, num_classes=self.num_classes).float()

        # Compute weights inversely proportional to frequency
        freq_weights = 1 / ((y_onehot.sum(dim=0) + 1e-6) ** 0.5)
        
        # Normalize frequencies so they sum up to number of samples
        norm_freqs = freq_weights * (targets.shape[0] / freq_weights.sum())
        
        # Apply normalization factor per sample
        eq_factor = norm_freqs.gather(1, targets.unsqueeze(-1)).squeeze()
        
        # Standard FL component
        ce_loss = F.cross_entropy(logits, targets, reduction="none")
        p_t = probas.gather(1, targets.unsqueeze(-1)).squeeze() + 1e-9
        fl_modulating_factor = (1 - p_t)**self.gamma
        balanced_fl_weight = self.alpha * y_onehot + (1-self.alpha)*(1-y_onehot)
        
        # Combine all components into final EFL formula
        ef_loss = eq_factor * balanced_fl_weight.gather(
            1, targets.unsqueeze(-1)).squeeze() * \
                  fl_modulating_factor * ce_loss
            
        if self.reduction == 'mean':
            return ef_loss.mean()
        elif self.reduction == 'sum':
            return ef_loss.sum()
        else:
            return ef_loss

This implementation introduces a new term `eq_factor` which adjusts each training instance's contribution according to its rarity within the dataset.