30 lines
1.1 KiB
Python
30 lines
1.1 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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def forward(self, predicts, batch):
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predict = predicts[:, :
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-1, :] # ignore last index of outputs to be in same seq_len with targets
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label = batch[1].astype(
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"int64")[:, 1:] # ignore first index of target in loss calculation
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batch_size, num_steps, num_classes = predict.shape[0], predict.shape[
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1], predict.shape[2]
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assert 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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