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