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145 lines
5.2 KiB
145 lines
5.2 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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from paddle import nn
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from paddle.nn import functional as F
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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__all__ = ['CTCLoss', "LabelSmoothingLoss"]
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class CTCLoss(nn.Layer):
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def __init__(self, blank=0, reduction='sum', batch_average=False):
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super().__init__()
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# last token id as blank id
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self.loss = nn.CTCLoss(blank=blank, reduction=reduction)
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self.batch_average = batch_average
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def forward(self, logits, ys_pad, hlens, ys_lens):
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"""Compute CTC loss.
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Args:
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logits ([paddle.Tensor]): [B, Tmax, D]
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ys_pad ([paddle.Tensor]): [B, Tmax]
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hlens ([paddle.Tensor]): [B]
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ys_lens ([paddle.Tensor]): [B]
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Returns:
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[paddle.Tensor]: scalar. If reduction is 'none', then (N), where N = \text{batch size}.
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"""
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B = paddle.shape(logits)[0]
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# warp-ctc need logits, and do softmax on logits by itself
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# warp-ctc need activation with shape [T, B, V + 1]
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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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# (TODO:Hui Zhang) ctc loss does not support int64 labels
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ys_pad = ys_pad.astype(paddle.int32)
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loss = self.loss(
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logits, ys_pad, hlens, ys_lens, norm_by_times=self.batch_average)
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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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return loss
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class LabelSmoothingLoss(nn.Layer):
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"""Label-smoothing loss.
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In a standard CE loss, the label's data distribution is:
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[0,1,2] ->
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[
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[1.0, 0.0, 0.0],
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[0.0, 1.0, 0.0],
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[0.0, 0.0, 1.0],
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]
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In the smoothing version CE Loss,some probabilities
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are taken from the true label prob (1.0) and are divided
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among other labels.
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e.g.
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smoothing=0.1
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[0,1,2] ->
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[
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[0.9, 0.05, 0.05],
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[0.05, 0.9, 0.05],
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[0.05, 0.05, 0.9],
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]
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"""
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def __init__(self,
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size: int,
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padding_idx: int,
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smoothing: float,
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normalize_length: bool=False):
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"""Label-smoothing loss.
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Args:
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size (int): the number of class
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padding_idx (int): padding class id which will be ignored for loss
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smoothing (float): smoothing rate (0.0 means the conventional CE)
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normalize_length (bool):
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True, normalize loss by sequence length;
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False, normalize loss by batch size.
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Defaults to False.
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"""
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super().__init__()
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self.size = size
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self.padding_idx = padding_idx
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self.smoothing = smoothing
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self.confidence = 1.0 - smoothing
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self.normalize_length = normalize_length
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self.criterion = nn.KLDivLoss(reduction="none")
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def forward(self, x: paddle.Tensor, target: paddle.Tensor) -> paddle.Tensor:
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"""Compute loss between x and target.
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The model outputs and data labels tensors are flatten to
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(batch*seqlen, class) shape and a mask is applied to the
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padding part which should not be calculated for loss.
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Args:
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x (paddle.Tensor): prediction (batch, seqlen, class)
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target (paddle.Tensor):
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target signal masked with self.padding_id (batch, seqlen)
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Returns:
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loss (paddle.Tensor) : The KL loss, scalar float value
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"""
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B, T, D = paddle.shape(x)
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assert D == self.size
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x = x.reshape((-1, self.size))
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target = target.reshape([-1])
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# use zeros_like instead of torch.no_grad() for true_dist,
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# since no_grad() can not be exported by JIT
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true_dist = paddle.full_like(x, self.smoothing / (self.size - 1))
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ignore = target == self.padding_idx # (B,)
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# target = target * (1 - ignore) # avoid -1 index
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target = target.masked_fill(ignore, 0) # avoid -1 index
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# true_dist.scatter_(1, target.unsqueeze(1), self.confidence)
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target_mask = F.one_hot(target, self.size)
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true_dist *= (1 - target_mask)
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true_dist += target_mask * self.confidence
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kl = self.criterion(F.log_softmax(x, axis=1), true_dist)
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#TODO(Hui Zhang): sum not support bool type
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#total = len(target) - int(ignore.sum())
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total = len(target) - int(ignore.type_as(target).sum())
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denom = total if self.normalize_length else B
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#numer = (kl * (1 - ignore)).sum()
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numer = kl.masked_fill(ignore.unsqueeze(1), 0).sum()
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return numer / denom
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