67 lines
2.6 KiB
Python
67 lines
2.6 KiB
Python
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import paddle
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from paddle import nn
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import paddle.nn.functional as F
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class CELoss(nn.Layer):
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def __init__(self,
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smoothing=False,
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with_all=False,
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ignore_index=-1,
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**kwargs):
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super(CELoss, self).__init__()
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if ignore_index >= 0:
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self.loss_func = nn.CrossEntropyLoss(
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reduction='mean', ignore_index=ignore_index)
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else:
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self.loss_func = nn.CrossEntropyLoss(reduction='mean')
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self.smoothing = smoothing
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self.with_all = with_all
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def forward(self, pred, batch):
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if isinstance(pred, dict): # for ABINet
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loss = {}
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loss_sum = []
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for name, logits in pred.items():
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if isinstance(logits, list):
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logit_num = len(logits)
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all_tgt = paddle.concat([batch[1]] * logit_num, 0)
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all_logits = paddle.concat(logits, 0)
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flt_logtis = all_logits.reshape([-1, all_logits.shape[2]])
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flt_tgt = all_tgt.reshape([-1])
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else:
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flt_logtis = logits.reshape([-1, logits.shape[2]])
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flt_tgt = batch[1].reshape([-1])
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loss[name + '_loss'] = self.loss_func(flt_logtis, flt_tgt)
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loss_sum.append(loss[name + '_loss'])
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loss['loss'] = sum(loss_sum)
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return loss
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else:
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if self.with_all: # for ViTSTR
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tgt = batch[1]
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pred = pred.reshape([-1, pred.shape[2]])
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tgt = tgt.reshape([-1])
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loss = self.loss_func(pred, tgt)
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return {'loss': loss}
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else: # for NRTR
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max_len = batch[2].max()
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tgt = batch[1][:, 1:2 + max_len]
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pred = pred.reshape([-1, pred.shape[2]])
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tgt = tgt.reshape([-1])
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if self.smoothing:
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eps = 0.1
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n_class = pred.shape[1]
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one_hot = F.one_hot(tgt, pred.shape[1])
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one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (
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n_class - 1)
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log_prb = F.log_softmax(pred, axis=1)
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non_pad_mask = paddle.not_equal(
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tgt, paddle.zeros(
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tgt.shape, dtype=tgt.dtype))
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loss = -(one_hot * log_prb).sum(axis=1)
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loss = loss.masked_select(non_pad_mask).mean()
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else:
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loss = self.loss_func(pred, tgt)
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return {'loss': loss}
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