# https://github.com/Alibaba-MIIL/ASL/blob/main/src/loss_functions/losses.py # MIT License # Copyright (c) 2020 Alibaba-MIIL # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import torch import torch.nn as nn class AsymmetricLoss(nn.Module): def __init__( self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=True, ): super(AsymmetricLoss, self).__init__() 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 = los_pos + 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() class AsymmetricLossOptimized(nn.Module): """Notice - optimized version, minimizes memory allocation and gpu uploading, favors inplace operations""" def __init__( self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=False, ): super(AsymmetricLossOptimized, self).__init__() 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 # prevent memory allocation and gpu uploading every iteration, and encourages inplace operations self.targets = self.anti_targets = self.xs_pos = self.xs_neg = ( self.asymmetric_w ) = self.loss = None def forward(self, x, y): """ " Parameters ---------- x: input logits y: targets (multi-label binarized vector) """ self.targets = y self.anti_targets = 1 - y # Calculating Probabilities self.xs_pos = torch.sigmoid(x) self.xs_neg = 1.0 - self.xs_pos # Asymmetric Clipping if self.clip is not None and self.clip > 0: self.xs_neg.add_(self.clip).clamp_(max=1) # Basic CE calculation self.loss = self.targets * torch.log(self.xs_pos.clamp(min=self.eps)) self.loss.add_(self.anti_targets * torch.log(self.xs_neg.clamp(min=self.eps))) # Asymmetric Focusing if self.gamma_neg > 0 or self.gamma_pos > 0: if self.disable_torch_grad_focal_loss: torch.set_grad_enabled(False) self.xs_pos = self.xs_pos * self.targets self.xs_neg = self.xs_neg * self.anti_targets self.asymmetric_w = torch.pow( 1 - self.xs_pos - self.xs_neg, self.gamma_pos * self.targets + self.gamma_neg * self.anti_targets, ) if self.disable_torch_grad_focal_loss: torch.set_grad_enabled(True) self.loss *= self.asymmetric_w return -self.loss.sum() class ASLSingleLabel(nn.Module): """ This loss is intended for single-label classification problems """ def __init__(self, gamma_pos=0, gamma_neg=4, eps: float = 0.1, reduction="mean"): super(ASLSingleLabel, self).__init__() self.eps = eps self.logsoftmax = nn.LogSoftmax(dim=-1) self.targets_classes = [] self.gamma_pos = gamma_pos self.gamma_neg = gamma_neg self.reduction = reduction def forward(self, inputs, target): """ "input" dimensions: - (batch_size,number_classes) "target" dimensions: - (batch_size) """ num_classes = inputs.size()[-1] log_preds = self.logsoftmax(inputs) self.targets_classes = torch.zeros_like(inputs).scatter_( 1, target.long().unsqueeze(1), 1 ) # ASL weights targets = self.targets_classes anti_targets = 1 - targets xs_pos = torch.exp(log_preds) xs_neg = 1 - xs_pos xs_pos = xs_pos * targets xs_neg = xs_neg * anti_targets asymmetric_w = torch.pow( 1 - xs_pos - xs_neg, self.gamma_pos * targets + self.gamma_neg * anti_targets, ) log_preds = log_preds * asymmetric_w if self.eps > 0: # label smoothing self.targets_classes = self.targets_classes.mul(1 - self.eps).add( self.eps / num_classes ) # loss calculation loss = -self.targets_classes.mul(log_preds) loss = loss.sum(dim=-1) if self.reduction == "mean": loss = loss.mean() return loss