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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,189 @@ | ||
| import sys | ||
| import torch | ||
| from .loss_interface import AbstractSegmentationMultiClassLoss, AbstractLossFunction | ||
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| class MulticlassDiceLoss(AbstractSegmentationMultiClassLoss): | ||
| """ | ||
| This class computes the Dice loss between two tensors. | ||
| """ | ||
|
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| def _single_class_loss_calculator( | ||
| self, prediction: torch.Tensor, target: torch.Tensor | ||
| ) -> torch.Tensor: | ||
| """ | ||
| Compute Dice score for a single class. | ||
| Args: | ||
| prediction (torch.Tensor): Network's predicted segmentation mask | ||
| target (torch.Tensor): Target segmentation mask | ||
| Returns: | ||
| torch.Tensor: The computed dice score. | ||
| """ | ||
| predicted_flat = prediction.flatten() | ||
| label_flat = target.flatten() | ||
| intersection = (predicted_flat * label_flat).sum() | ||
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| dice_score = (2.0 * intersection + sys.float_info.min) / ( | ||
| predicted_flat.sum() + label_flat.sum() + sys.float_info.min | ||
| ) | ||
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| return dice_score | ||
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| class MulticlassDiceLogLoss(MulticlassDiceLoss): | ||
| def _optional_loss_operations(self, loss): | ||
| return -torch.log( | ||
| loss + torch.finfo(torch.float32).eps | ||
| ) # epsilon for numerical stability | ||
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| class MulticlassMCCLoss(AbstractSegmentationMultiClassLoss): | ||
| """ | ||
| This class computes the Matthews Correlation Coefficient (MCC) loss between two tensors. | ||
| """ | ||
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| def _single_class_loss_calculator( | ||
| self, prediction: torch.Tensor, target: torch.Tensor | ||
| ) -> torch.Tensor: | ||
| """ | ||
| Compute MCC score for a single class. | ||
| Args: | ||
| prediction (torch.Tensor): Network's predicted segmentation mask | ||
| target (torch.Tensor): Target segmentation mask | ||
| Returns: | ||
| torch.Tensor: The computed MCC score. | ||
| """ | ||
| tp = torch.sum(torch.mul(prediction, target)) | ||
| tn = torch.sum(torch.mul((1 - prediction), (1 - target))) | ||
| fp = torch.sum(torch.mul(prediction, (1 - target))) | ||
| fn = torch.sum(torch.mul((1 - prediction), target)) | ||
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| numerator = torch.mul(tp, tn) - torch.mul(fp, fn) | ||
| # Adding epsilon to the denominator to avoid divide-by-zero errors. | ||
| denominator = ( | ||
| torch.sqrt( | ||
| torch.add(tp, 1, fp) | ||
| * torch.add(tp, 1, fn) | ||
| * torch.add(tn, 1, fp) | ||
| * torch.add(tn, 1, fn) | ||
| ) | ||
| + torch.finfo(torch.float32).eps | ||
| ) | ||
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| return torch.div(numerator.sum(), denominator.sum()) | ||
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| class MulticlassMCLLogLoss(MulticlassMCCLoss): | ||
| def _optional_loss_operations(self, loss): | ||
| return -torch.log( | ||
| loss + torch.finfo(torch.float32).eps | ||
| ) # epsilon for numerical stability | ||
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| class MulticlassTverskyLoss(AbstractSegmentationMultiClassLoss): | ||
| """ | ||
| This class computes the Tversky loss between two tensors. | ||
| """ | ||
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| def __init__(self, params: dict): | ||
| super().__init__(params) | ||
| self.alpha = params.get("alpha", 0.5) | ||
| self.beta = params.get("beta", 0.5) | ||
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| def _single_class_loss_calculator( | ||
| self, prediction: torch.Tensor, target: torch.Tensor | ||
| ) -> torch.Tensor: | ||
| """ | ||
| Compute Tversky score for a single class. | ||
| Args: | ||
| prediction (torch.Tensor): Network's predicted segmentation mask | ||
| target (torch.Tensor): Target segmentation mask | ||
| Returns: | ||
| torch.Tensor: The computed Tversky score. | ||
| """ | ||
| predicted_flat = prediction.contiguous().view(-1) | ||
| target_flat = target.contiguous().view(-1) | ||
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| true_positives = (predicted_flat * target_flat).sum() | ||
| false_positives = ((1 - target_flat) * predicted_flat).sum() | ||
| false_negatives = (target_flat * (1 - predicted_flat)).sum() | ||
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| numerator = true_positives | ||
| denominator = ( | ||
| true_positives + self.alpha * false_positives + self.beta * false_negatives | ||
| ) | ||
| loss = (numerator + sys.float_info.min) / (denominator + sys.float_info.min) | ||
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| return loss | ||
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| class MulticlassFocalLoss(AbstractSegmentationMultiClassLoss): | ||
| """ | ||
| This class computes the Focal loss between two tensors. | ||
| """ | ||
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| def __init__(self, params: dict): | ||
| super().__init__(params) | ||
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| self.ce_loss_helper = torch.nn.CrossEntropyLoss(reduction="none") | ||
| loss_params = params["loss_function"] | ||
| self.alpha = 1.0 | ||
| self.gamma = 2.0 | ||
| self.output_aggregation = "sum" | ||
| if isinstance(loss_params, dict): | ||
| self.alpha = loss_params.get("alpha", self.alpha) | ||
| self.gamma = loss_params.get("gamma", self.gamma) | ||
| self.output_aggregation = loss_params.get( | ||
| "size_average", | ||
| self.output_aggregation, # naming mismatch of key due to keeping API consistent with config format | ||
| ) | ||
| assert self.output_aggregation in [ | ||
| "sum", | ||
| "mean", | ||
| ], f"Invalid output aggregation method defined for Foal Loss: {self.output_aggregation}. Valid options are ['sum', 'mean']" | ||
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| def _single_class_loss_calculator( | ||
| self, prediction: torch.Tensor, target: torch.Tensor | ||
| ) -> torch.Tensor: | ||
| """ | ||
| Compute focal loss for a single class. It is based on the following formulas: | ||
| FocalLoss(p_t) = -alpha_t * (1 - p_t)^gamma * log(p_t) | ||
| CrossEntropy(pred, target) = -log(pred) if target = 1 else -log(1 - pred) | ||
| CrossEntropy(p_t) = CrossEntropy(pred, target) = -log(p_t) | ||
| p_t = p if target = 1 else 1 - p | ||
| """ | ||
| ce_loss = self.ce_loss_helper(prediction, target) | ||
| p_t = torch.exp(-ce_loss) | ||
| loss = -self.alpha * (1 - p_t) ** self.gamma * ce_loss | ||
| return loss.sum() if self.output_aggregation == "sum" else loss.mean() | ||
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| def _compute_single_class_loss( | ||
| self, prediction: torch.Tensor, target: torch.Tensor, class_idx: int | ||
| ) -> torch.Tensor: | ||
| """Compute loss for a single class.""" | ||
| loss_value = self._single_class_loss_calculator( | ||
| prediction[:, class_idx, ...], target[:, class_idx, ...] | ||
| ) | ||
| return loss_value # no need to subtract from 1 in this case, hence the override | ||
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| class KullbackLeiblerDivergence(AbstractLossFunction): | ||
| def forward(self, mu: torch.Tensor, logvar: torch.Tensor) -> torch.Tensor: | ||
| """ | ||
| Calculates the Kullback-Leibler divergence between two Gaussian distributions. | ||
| Args: | ||
| mu (torch.Tensor): The mean of the first Gaussian distribution. | ||
| logvar (torch.Tensor): The logarithm of the variance of the first Gaussian distribution. | ||
| Returns: | ||
| torch.Tensor: The computed Kullback-Leibler divergence | ||
| """ | ||
| loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=-1) | ||
| return loss.mean() |