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Zero
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import functools
import torch
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are 'none', 'mean' and 'sum'.
Returns:
Tensor: Reduced loss tensor.
"""
reduction_enum = F._Reduction.get_enum(reduction)
# none: 0, elementwise_mean:1, sum: 2
if reduction_enum == 0:
return loss
elif reduction_enum == 1:
return loss.mean()
else:
return loss.sum()
def weight_reduce_loss(loss, weight=None, reduction='mean'):
"""Apply element-wise weight and reduce loss.
Args:
loss (Tensor): Element-wise loss.
weight (Tensor): Element-wise weights. Default: None.
reduction (str): Same as built-in losses of PyTorch. Options are
'none', 'mean' and 'sum'. Default: 'mean'.
Returns:
Tensor: Loss values.
"""
# if weight is specified, apply element-wise weight
if weight is not None:
assert weight.dim() == loss.dim()
assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
loss = loss * weight
# if weight is not specified or reduction is sum, just reduce the loss
if weight is None or reduction == 'sum':
loss = reduce_loss(loss, reduction)
# if reduction is mean, then compute mean over weight region
elif reduction == 'mean':
if weight.size(1) > 1:
weight = weight.sum()
else:
weight = weight.sum() * loss.size(1)
loss = loss.sum() / weight
return loss
def weighted_loss(loss_func):
"""Create a weighted version of a given loss function.
To use this decorator, the loss function must have the signature like
`loss_func(pred, target, **kwargs)`. The function only needs to compute
element-wise loss without any reduction. This decorator will add weight
and reduction arguments to the function. The decorated function will have
the signature like `loss_func(pred, target, weight=None, reduction='mean',
**kwargs)`.
:Example:
>>> import torch
>>> @weighted_loss
>>> def l1_loss(pred, target):
>>> return (pred - target).abs()
>>> pred = torch.Tensor([0, 2, 3])
>>> target = torch.Tensor([1, 1, 1])
>>> weight = torch.Tensor([1, 0, 1])
>>> l1_loss(pred, target)
tensor(1.3333)
>>> l1_loss(pred, target, weight)
tensor(1.5000)
>>> l1_loss(pred, target, reduction='none')
tensor([1., 1., 2.])
>>> l1_loss(pred, target, weight, reduction='sum')
tensor(3.)
"""
@functools.wraps(loss_func)
def wrapper(pred, target, weight=None, reduction='mean', **kwargs):
# get element-wise loss
loss = loss_func(pred, target, **kwargs)
loss = weight_reduce_loss(loss, weight, reduction)
return loss
return wrapper
def get_local_weights(residual, ksize):
"""Get local weights for generating the artifact map of LDL.
It is only called by the `get_refined_artifact_map` function.
Args:
residual (Tensor): Residual between predicted and ground truth images.
ksize (Int): size of the local window.
Returns:
Tensor: weight for each pixel to be discriminated as an artifact pixel
"""
pad = (ksize - 1) // 2
residual_pad = F.pad(residual, pad=[pad, pad, pad, pad], mode='reflect')
unfolded_residual = residual_pad.unfold(2, ksize, 1).unfold(3, ksize, 1)
pixel_level_weight = torch.var(unfolded_residual, dim=(-1, -2), unbiased=True, keepdim=True).squeeze(-1).squeeze(-1)
return pixel_level_weight
def get_refined_artifact_map(img_gt, img_output, img_ema, ksize):
"""Calculate the artifact map of LDL
(Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution. In CVPR 2022)
Args:
img_gt (Tensor): ground truth images.
img_output (Tensor): output images given by the optimizing model.
img_ema (Tensor): output images given by the ema model.
ksize (Int): size of the local window.
Returns:
overall_weight: weight for each pixel to be discriminated as an artifact pixel
(calculated based on both local and global observations).
"""
residual_ema = torch.sum(torch.abs(img_gt - img_ema), 1, keepdim=True)
residual_sr = torch.sum(torch.abs(img_gt - img_output), 1, keepdim=True)
patch_level_weight = torch.var(residual_sr.clone(), dim=(-1, -2, -3), keepdim=True)**(1 / 5)
pixel_level_weight = get_local_weights(residual_sr.clone(), ksize)
overall_weight = patch_level_weight * pixel_level_weight
overall_weight[residual_sr < residual_ema] = 0
return overall_weight
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