99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
import functools
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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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def weighted_loss(loss_func):
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"""Create a weighted version of a given loss function.
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To use this decorator, the loss function must have the signature like
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`loss_func(pred, target, **kwargs)`. The function only needs to compute
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element-wise loss without any reduction. This decorator will add weight
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and reduction arguments to the function. The decorated function will have
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the signature like `loss_func(pred, target, weight=None, reduction='mean',
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avg_factor=None, **kwargs)`.
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:Example:
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>>> import torch
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>>> @weighted_loss
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>>> def l1_loss(pred, target):
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>>> return (pred - target).abs()
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>>> pred = torch.Tensor([0, 2, 3])
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>>> target = torch.Tensor([1, 1, 1])
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>>> weight = torch.Tensor([1, 0, 1])
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>>> l1_loss(pred, target)
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tensor(1.3333)
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>>> l1_loss(pred, target, weight)
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tensor(1.)
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>>> l1_loss(pred, target, reduction='none')
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tensor([1., 1., 2.])
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>>> l1_loss(pred, target, weight, avg_factor=2)
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tensor(1.5000)
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"""
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@functools.wraps(loss_func)
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def wrapper(pred,
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target,
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weight=None,
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reduction='mean',
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avg_factor=None,
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**kwargs):
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# get element-wise loss
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loss = loss_func(pred, target, **kwargs)
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loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
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return loss
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return wrapper
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