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@ -54,7 +54,7 @@ class CTCLoss(nn.Layer):
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self.norm_by_total_logits_len = True
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else:
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raise ValueError(f"CTCLoss Grad Norm no support {grad_norm_type}")
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self.kwargs = {
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kwargs = {
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"norm_by_times": self.norm_by_times,
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"norm_by_batchsize": self.norm_by_batchsize,
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"norm_by_total_logits_len": self.norm_by_total_logits_len,
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@ -66,10 +66,9 @@ class CTCLoss(nn.Layer):
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except ValueError:
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# Some function, e.g. built-in function, are failed
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param = {}
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self._kwargs = {k: v for k, v in self.kwargs.items() if k in param}
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_notin = {k: v for k, v in self.kwargs.items() if k not in param}
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self._kwargs = {k: v for k, v in kwargs.items() if k in param}
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_notin = {k: v for k, v in kwargs.items() if k not in param}
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logger.info(f"{self.loss} kwargs:{self._kwargs}, not support: {_notin}")
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#self.loss_fn = partial(self.loss.forward, **_kwargs)
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def forward(self, logits, ys_pad, hlens, ys_lens):
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"""Compute CTC loss.
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@ -89,8 +88,7 @@ class CTCLoss(nn.Layer):
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# logits: (B, L, D) -> (L, B, D)
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logits = logits.transpose([1, 0, 2])
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ys_pad = ys_pad.astype(paddle.int32)
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#loss = self.loss_fn(logits, ys_pad, hlens, ys_lens)
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loss = self.loss(logits, ys_pad, hlens, ys_lens)
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loss = self.loss(logits, ys_pad, hlens, ys_lens, **self._kwargs)
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if self.batch_average:
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# Batch-size average
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loss = loss / B
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Hui Zhang
3 years ago
committed by
GitHub