102 lines
3.2 KiB
Python
102 lines
3.2 KiB
Python
# ------------------------------------------------------------------------
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# Copyright (c) 2022 megvii-model. All Rights Reserved.
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# ------------------------------------------------------------------------
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# Modified from BasicSR (https://github.com/xinntao/BasicSR)
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# Copyright 2018-2020 BasicSR Authors
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# ------------------------------------------------------------------------
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import functools
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from torch.nn import 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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Returns:
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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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else:
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return loss.sum()
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def weight_reduce_loss(loss, weight=None, reduction='mean'):
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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. Default: None.
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reduction (str): Same as built-in losses of PyTorch. Options are
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'none', 'mean' and 'sum'. Default: 'mean'.
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Returns:
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Tensor: 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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assert weight.dim() == loss.dim()
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assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
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loss = loss * weight
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# if weight is not specified or reduction is sum, just reduce the loss
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if weight is None or reduction == 'sum':
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loss = reduce_loss(loss, reduction)
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# if reduction is mean, then compute mean over weight region
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elif reduction == 'mean':
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if weight.size(1) > 1:
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weight = weight.sum()
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else:
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weight = weight.sum() * loss.size(1)
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loss = loss.sum() / weight
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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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**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(1.5000)
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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, reduction='sum')
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tensor(3.)
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"""
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@functools.wraps(loss_func)
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def wrapper(pred, target, weight=None, reduction='mean', **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)
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return loss
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return wrapper
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