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37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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from paddle import nn
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class SARLoss(nn.Layer):
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def __init__(self, **kwargs):
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super(SARLoss, self).__init__()
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ignore_index = kwargs.get("ignore_index", 92) # 6626
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self.loss_func = paddle.nn.loss.CrossEntropyLoss(
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reduction="mean", ignore_index=ignore_index
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)
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def forward(self, predicts, batch):
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predict = predicts[
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:, :-1, :
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] # ignore last index of outputs to be in same seq_len with targets
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label = batch[1].astype("int64")[
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:, 1:
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] # ignore first index of target in loss calculation
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batch_size, num_steps, num_classes = (
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predict.shape[0],
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predict.shape[1],
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predict.shape[2],
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)
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assert (
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len(label.shape) == len(list(predict.shape)) - 1
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), "The target's shape and inputs's shape is [N, d] and [N, num_steps]"
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inputs = paddle.reshape(predict, [-1, num_classes])
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targets = paddle.reshape(label, [-1])
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loss = self.loss_func(inputs, targets)
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return {"loss": loss}
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