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import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
class SigmoidFocalLoss(nn.Module):
''' ... '''
def __init__(
self,
alpha: float = -1,
gamma: float = 2.0,
reduction: str = 'mean',
):
''' ... '''
super().__init__()
self.alpha = alpha
self.gamma = gamma
self.reduction = reduction
def forward(self, input, target):
''' ... '''
p = torch.sigmoid(input)
ce_loss = F.binary_cross_entropy_with_logits(input, target, reduction='none')
p_t = p * target + (1 - p) * (1 - target)
loss = ce_loss * ((1 - p_t) ** self.gamma)
if self.alpha >= 0:
alpha_t = self.alpha * target + (1 - self.alpha) * (1 - target)
loss = alpha_t * loss
if self.reduction == 'mean':
loss = loss.mean()
elif self.reduction == 'sum':
loss = loss.sum()
return loss
class SigmoidFocalLossBeta(nn.Module):
''' ... '''
def __init__(
self,
beta: float = 0.9999,
gamma: float = 2.0,
num_per_cls = (1, 1),
reduction: str = 'mean',
):
''' ... '''
super().__init__()
eps = sys.float_info.epsilon
self.gamma = gamma
self.reduction = reduction
# weights to balance loss
self.weight_neg = ((1 - beta) / (1 - beta ** num_per_cls[0] + eps))
self.weight_pos = ((1 - beta) / (1 - beta ** num_per_cls[1] + eps))
# weight_neg = (1 - beta) / (1 - beta ** num_per_cls[0])
# weight_pos = (1 - beta) / (1 - beta ** num_per_cls[1])
# self.weight_neg = weight_neg / (weight_neg + weight_pos)
# self.weight_pos = weight_pos / (weight_neg + weight_pos)
def forward(self, input, target):
''' ... '''
p = torch.sigmoid(input)
p_t = p * target + (1 - p) * (1 - target)
ce_loss = F.binary_cross_entropy_with_logits(input, target, reduction='none')
loss = ce_loss * ((1 - p_t) ** self.gamma)
alpha_t = self.weight_pos * target + self.weight_neg * (1 - target)
loss = alpha_t * loss
if self.reduction == 'mean':
loss = loss.mean()
elif self.reduction == 'sum':
loss = loss.sum()
return loss
class AsymmetricLoss(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, alpha=0.5, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=True):
super(AsymmetricLoss, self).__init__()
self.alpha = alpha
self.gamma_neg = gamma_neg
self.gamma_pos = gamma_pos
self.clip = clip
self.disable_torch_grad_focal_loss = disable_torch_grad_focal_loss
self.eps = eps
def forward(self, x, y):
""""
Parameters
----------
x: input logits
y: targets (multi-label binarized vector)
"""
# Calculating Probabilities
x_sigmoid = torch.sigmoid(x)
xs_pos = x_sigmoid
xs_neg = 1 - x_sigmoid
# Asymmetric Clipping
if self.clip is not None and self.clip > 0:
xs_neg = (xs_neg + self.clip).clamp(max=1)
# Basic CE calculation
los_pos = y * torch.log(xs_pos.clamp(min=self.eps))
los_neg = (1 - y) * torch.log(xs_neg.clamp(min=self.eps))
loss = self.alpha*los_pos + (1-self.alpha)*los_neg
# Asymmetric Focusing
if self.gamma_neg > 0 or self.gamma_pos > 0:
if self.disable_torch_grad_focal_loss:
torch.set_grad_enabled(False)
pt0 = xs_pos * y
pt1 = xs_neg * (1 - y) # pt = p if t > 0 else 1-p
pt = pt0 + pt1
one_sided_gamma = self.gamma_pos * y + self.gamma_neg * (1 - y)
one_sided_w = torch.pow(1 - pt, one_sided_gamma)
if self.disable_torch_grad_focal_loss:
torch.set_grad_enabled(True)
loss *= one_sided_w
return -loss#.sum()
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