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