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| from torch import nn | |
| class RobustScannerLoss(nn.Module): | |
| def __init__(self, **kwargs): | |
| super(RobustScannerLoss, self).__init__() | |
| ignore_index = kwargs.get('ignore_index', 38) | |
| self.loss_func = nn.CrossEntropyLoss(reduction='mean', | |
| ignore_index=ignore_index) | |
| def forward(self, pred, batch): | |
| pred = pred[:, :-1, :] | |
| label = batch[1][:, 1:].reshape([-1]) | |
| inputs = pred.reshape([-1, pred.shape[2]]) | |
| loss = self.loss_func(inputs, label) | |
| return {'loss': loss} | |