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40 lines
1.1 KiB
40 lines
1.1 KiB
import abc
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import torch
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from pydantic import BaseModel
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class BaseLoss(torch.nn.Module, abc.ABC):
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"""Custom loss function combining BCEWithLogitsLoss and MSE losses for cell recognition and distinction."""
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def __init__(self, params: BaseModel | None = None) -> None:
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super().__init__()
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@abc.abstractmethod
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def forward(self, outputs: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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"""
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Computes the loss between true labels and prediction outputs.
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Args:
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outputs (torch.Tensor): Model predictions.
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target (torch.Tensor): Ground truth.
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Returns:
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torch.Tensor: The total loss value.
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"""
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@abc.abstractmethod
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def get_loss_metrics(self) -> dict[str, float]:
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"""
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Retrieves the tracked loss metrics.
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Returns:
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dict(str, float): A dictionary containing the loss name and average loss value.
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"""
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@abc.abstractmethod
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def reset_metrics(self) -> None:
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"""Resets the stored loss metrics."""
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