Pytorch Label Smoothing

import torch.nn as nn

class LabelSmoothingLoss(nn.Module):
    def __init__(self, classes, smoothing=0.0, dim=-1):
        super(LabelSmoothingLoss, self).__init__()
        self.confidence = 1.0 - smoothing
        self.smoothing = smoothing
        self.cls = classes
        self.dim = dim

    def forward(self, pred, target):
        pred = pred.log_softmax(dim=self.dim) # Cross Entropy 부분의 log softmax 미리 계산하기
        with torch.no_grad():
            # true_dist = pred.data.clone()
            true_dist = torch.zeros_like(pred) # 예측값과 동일한 크기의 영텐서 생성
            true_dist.fill_(self.smoothing / (self.cls - 1)) # alpha/(K-1)을 만들어 줌(alpha/K로 할 수도 있음)
            true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence) # (1-alpha)y + alpha/(K-1)
        return torch.mean(torch.sum(-true_dist * pred, dim=self.dim)) # Cross Entropy Loss 계산