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84 lines
2.8 KiB
84 lines
2.8 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 logging
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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 paddle.nn import initializer as I
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logger = logging.getLogger(__name__)
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__all__ = ['CTCLoss']
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# TODO(Hui Zhang): remove this hack, when `norm_by_times=True` is added
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def ctc_loss(logits,
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labels,
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input_lengths,
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label_lengths,
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blank=0,
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reduction='mean',
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norm_by_times=True):
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#logger.info("my ctc loss with norm by times")
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## https://github.com/PaddlePaddle/Paddle/blob/f5ca2db2cc/paddle/fluid/operators/warpctc_op.h#L403
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loss_out = paddle.fluid.layers.warpctc(logits, labels, blank, norm_by_times,
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input_lengths, label_lengths)
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loss_out = paddle.fluid.layers.squeeze(loss_out, [-1])
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logger.info(f"warpctc loss: {loss_out}/{loss_out.shape} ")
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assert reduction in ['mean', 'sum', 'none']
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if reduction == 'mean':
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loss_out = paddle.mean(loss_out / label_lengths)
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elif reduction == 'sum':
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loss_out = paddle.sum(loss_out)
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logger.info(f"ctc loss: {loss_out}")
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return loss_out
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# TODO(Hui Zhang): remove this hack
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F.ctc_loss = ctc_loss
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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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loss = self.loss(logits, ys_pad, hlens, ys_lens)
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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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