39 lines
862 B
Python
39 lines
862 B
Python
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import numpy as np
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import torch
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import torch.nn.functional as F
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def cal_loss(pred, gold, smoothing=True):
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''' Calculate cross entropy loss, apply label smoothing if needed. '''
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gold = gold.contiguous().view(-1)
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if smoothing:
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eps = 0.2
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n_class = pred.size(1)
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one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1)
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one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
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log_prb = F.log_softmax(pred, dim=1)
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loss = -(one_hot * log_prb).sum(dim=1).mean()
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else:
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loss = F.cross_entropy(pred, gold, reduction='mean')
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return loss
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class IOStream():
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def __init__(self, path):
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self.f = open(path, 'a')
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def cprint(self, text):
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print(text)
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self.f.write(text+'\n')
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self.f.flush()
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def close(self):
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self.f.close()
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