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98 lines
3.2 KiB
98 lines
3.2 KiB
import torch
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import torch.nn as nn
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class AsymmetricLossMultiLabel(nn.Module):
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def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=False):
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super(AsymmetricLossMultiLabel, self).__init__()
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self.gamma_neg = gamma_neg
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self.gamma_pos = gamma_pos
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self.clip = clip
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self.disable_torch_grad_focal_loss = disable_torch_grad_focal_loss
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self.eps = eps
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def forward(self, x, y):
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""""
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Parameters
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----------
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x: input logits
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y: targets (multi-label binarized vector)
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"""
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# Calculating Probabilities
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x_sigmoid = torch.sigmoid(x)
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xs_pos = x_sigmoid
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xs_neg = 1 - x_sigmoid
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# Asymmetric Clipping
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if self.clip is not None and self.clip > 0:
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xs_neg = (xs_neg + self.clip).clamp(max=1)
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# Basic CE calculation
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los_pos = y * torch.log(xs_pos.clamp(min=self.eps))
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los_neg = (1 - y) * torch.log(xs_neg.clamp(min=self.eps))
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loss = los_pos + los_neg
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# Asymmetric Focusing
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if self.gamma_neg > 0 or self.gamma_pos > 0:
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if self.disable_torch_grad_focal_loss:
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torch._C.set_grad_enabled(False)
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pt0 = xs_pos * y
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pt1 = xs_neg * (1 - y) # pt = p if t > 0 else 1-p
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pt = pt0 + pt1
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one_sided_gamma = self.gamma_pos * y + self.gamma_neg * (1 - y)
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one_sided_w = torch.pow(1 - pt, one_sided_gamma)
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if self.disable_torch_grad_focal_loss:
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torch._C.set_grad_enabled(True)
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loss *= one_sided_w
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return -loss.sum()
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class AsymmetricLossSingleLabel(nn.Module):
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def __init__(self, gamma_pos=1, gamma_neg=4, eps: float = 0.1, reduction='mean'):
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super(AsymmetricLossSingleLabel, self).__init__()
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self.eps = eps
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self.logsoftmax = nn.LogSoftmax(dim=-1)
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self.targets_classes = [] # prevent gpu repeated memory allocation
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self.gamma_pos = gamma_pos
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self.gamma_neg = gamma_neg
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self.reduction = reduction
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def forward(self, inputs, target, reduction=None):
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""""
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Parameters
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----------
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x: input logits
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y: targets (1-hot vector)
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"""
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num_classes = inputs.size()[-1]
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log_preds = self.logsoftmax(inputs)
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self.targets_classes = torch.zeros_like(inputs).scatter_(1, target.long().unsqueeze(1), 1)
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# ASL weights
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targets = self.targets_classes
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anti_targets = 1 - targets
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xs_pos = torch.exp(log_preds)
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xs_neg = 1 - xs_pos
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xs_pos = xs_pos * targets
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xs_neg = xs_neg * anti_targets
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asymmetric_w = torch.pow(1 - xs_pos - xs_neg,
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self.gamma_pos * targets + self.gamma_neg * anti_targets)
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log_preds = log_preds * asymmetric_w
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if self.eps > 0: # label smoothing
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self.targets_classes.mul_(1 - self.eps).add_(self.eps / num_classes)
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# loss calculation
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loss = - self.targets_classes.mul(log_preds)
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loss = loss.sum(dim=-1)
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if self.reduction == 'mean':
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loss = loss.mean()
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return loss
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