Deep learning pytorch perplexity calculation method

The following is the code I wrote to calculate the perplexity PPL
According to the definition of perplexity: (The definition is other forms of definition, not book definitions, this is what is commonly used in experiments form)
Source explanation:https://stackoverflow.com/questions/61988776/how-to-calculate-perplexity-for-a-language- model-using-pytorch

$PPL={It\; is}^{cross\_entropy}$
其中$cross\_entropy$ It is because of the demand that the intersection is lost exp()
Note: F.cross_entropy factor reduction required mean Ready to use Mean

from torch import Tensor
import numpy as np
import torch.nn.functional as F


def perplexity(outputs: Tensor, targets: Tensor, config=None):
    """
    计算语言模型困惑度
    :param outputs: [batch_size,seq_len,vocab_size]
    :param targets: [batch_size,seq_len]
    :param config:  配置文件 default:None
    :return: 困惑度数值
    """
    ce = F.cross_entropy(outputs.view(-1, outputs.size(-1)), targets.view(-1),
                         ignore_index=config.data.pad_id if config is not None else None)

    return torch.exp(ce)