perplexity

Perplexity

Bases: Criterion

Source code in speechain/criterion/perplexity.py

class Perplexity(Criterion):
    """"""

    def __call__(
        self, logits: torch.Tensor, text: torch.Tensor, text_len: torch.Tensor
    ):
        """

        Args:
            logits:
            text:
            text_len:

        Returns:

        """
        # mask generation for the input text
        text_mask = make_mask_from_len(text_len - 1, return_3d=False)
        if text.is_cuda:
            text_mask = text_mask.cuda(text.device)

        # perplexity calculation
        log_prob = torch.log_softmax(logits, dim=-1)
        text_prob = log_prob.gather(-1, text[:, 1:].view(text.size(0), -1, 1)).squeeze(
            dim=-1
        )
        text_prob = text_prob.masked_fill(~text_mask, 0.0)
        text_ppl = torch.exp(
            torch.sum(text_prob, dim=-1) * (-1 / (text_len - 1))
        ).mean()

        return text_ppl

__call__(logits, text, text_len)

Parameters:

Name	Type	Description	Default
logits	Tensor		required
text	Tensor		required
text_len	Tensor		required

Returns:

Source code in speechain/criterion/perplexity.py

def __call__(
    self, logits: torch.Tensor, text: torch.Tensor, text_len: torch.Tensor
):
    """

    Args:
        logits:
        text:
        text_len:

    Returns:

    """
    # mask generation for the input text
    text_mask = make_mask_from_len(text_len - 1, return_3d=False)
    if text.is_cuda:
        text_mask = text_mask.cuda(text.device)

    # perplexity calculation
    log_prob = torch.log_softmax(logits, dim=-1)
    text_prob = log_prob.gather(-1, text[:, 1:].view(text.size(0), -1, 1)).squeeze(
        dim=-1
    )
    text_prob = text_prob.masked_fill(~text_mask, 0.0)
    text_ppl = torch.exp(
        torch.sum(text_prob, dim=-1) * (-1 / (text_len - 1))
    ).mean()

    return text_ppl