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34 lines
1.2 KiB
Python
34 lines
1.2 KiB
Python
8 months ago
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import paddle
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from paddle import nn
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import paddle.nn.functional as F
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class NRTRLoss(nn.Layer):
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def __init__(self, smoothing=True, ignore_index=0, **kwargs):
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super(NRTRLoss, self).__init__()
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if ignore_index >= 0 and not smoothing:
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self.loss_func = nn.CrossEntropyLoss(
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reduction="mean", ignore_index=ignore_index
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)
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self.smoothing = smoothing
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def forward(self, pred, batch):
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max_len = batch[2].max()
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tgt = batch[1][:, 1 : 2 + max_len]
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pred = pred.reshape([-1, pred.shape[2]])
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tgt = tgt.reshape([-1])
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if self.smoothing:
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eps = 0.1
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n_class = pred.shape[1]
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one_hot = F.one_hot(tgt, pred.shape[1])
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one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
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log_prb = F.log_softmax(pred, axis=1)
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non_pad_mask = paddle.not_equal(
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tgt, paddle.zeros(tgt.shape, dtype=tgt.dtype)
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)
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loss = -(one_hot * log_prb).sum(axis=1)
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loss = loss.masked_select(non_pad_mask).mean()
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else:
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loss = self.loss_func(pred, tgt)
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return {"loss": loss}
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