51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
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import torch.nn.functional as F
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def reduce_loss(loss, reduction):
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"""Reduce loss as specified.
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Args:
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loss (Tensor): Elementwise loss tensor.
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reduction (str): Options are "none", "mean" and "sum".
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Return:
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Tensor: Reduced loss tensor.
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"""
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reduction_enum = F._Reduction.get_enum(reduction)
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# none: 0, elementwise_mean:1, sum: 2
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if reduction_enum == 0:
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return loss
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elif reduction_enum == 1:
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return loss.mean()
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elif reduction_enum == 2:
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return loss.sum()
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def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
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"""Apply element-wise weight and reduce loss.
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Args:
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loss (Tensor): Element-wise loss.
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weight (Tensor): Element-wise weights.
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reduction (str): Same as built-in losses of PyTorch.
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avg_factor (float): Avarage factor when computing the mean of losses.
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Returns:
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Tensor: Processed loss values.
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"""
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# if weight is specified, apply element-wise weight
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if weight is not None:
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loss = loss * weight
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# if avg_factor is not specified, just reduce the loss
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if avg_factor is None:
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loss = reduce_loss(loss, reduction)
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else:
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# if reduction is mean, then average the loss by avg_factor
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if reduction == 'mean':
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loss = loss.sum() / avg_factor
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# if reduction is 'none', then do nothing, otherwise raise an error
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elif reduction != 'none':
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raise ValueError('avg_factor can not be used with reduction="sum"')
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return loss
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