Spaces:
Runtime error
Runtime error
File size: 4,772 Bytes
a64b7d4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
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
|