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from torch import nn | |
class PARSeqLoss(nn.Module): | |
def __init__(self, **kwargs): | |
super(PARSeqLoss, self).__init__() | |
def forward(self, predicts, batch): | |
# predicts = predicts['res'] | |
loss, _ = predicts | |
return {'loss': loss} | |