Spaces:
Sleeping
Sleeping
File size: 4,543 Bytes
d7e58f0 |
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 |
import torch
import torch.nn as nn
from .utils import weighted_loss
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth L1 loss.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction.
beta (float, optional): The threshold in the piecewise function.
Defaults to 1.0.
Returns:
torch.Tensor: Calculated loss
"""
assert beta > 0
assert pred.size() == target.size() and target.numel() > 0
diff = torch.abs(pred - target)
loss = torch.where(diff < beta, 0.5 * diff * diff / beta,
diff - 0.5 * beta)
return loss
@weighted_loss
def l1_loss(pred, target):
"""L1 loss.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction.
Returns:
torch.Tensor: Calculated loss
"""
assert pred.size() == target.size() and target.numel() > 0
loss = torch.abs(pred - target)
return loss
class SmoothL1Loss(nn.Module):
"""Smooth L1 loss.
Args:
beta (float, optional): The threshold in the piecewise function.
Defaults to 1.0.
reduction (str, optional): The method to reduce the loss.
Options are "none", "mean" and "sum". Defaults to "mean".
loss_weight (float, optional): The weight of loss.
"""
def __init__(self, beta=1.0, reduction='mean', loss_weight=1.0):
super(SmoothL1Loss, self).__init__()
self.beta = beta
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None,
**kwargs):
"""Forward function.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction.
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Defaults to None.
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (reduction_override
if reduction_override else self.reduction)
loss = self.loss_weight * smooth_l1_loss(pred,
target,
weight,
beta=self.beta,
reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss
class L1Loss(nn.Module):
"""L1 loss.
Args:
reduction (str, optional): The method to reduce the loss.
Options are "none", "mean" and "sum".
loss_weight (float, optional): The weight of loss.
"""
def __init__(self, reduction='mean', loss_weight=1.0):
super(L1Loss, self).__init__()
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None):
"""Forward function.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction.
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Defaults to None.
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (reduction_override
if reduction_override else self.reduction)
loss = self.loss_weight * l1_loss(
pred, target, weight, reduction=reduction, avg_factor=avg_factor)
return loss
|