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43 lines
1.2 KiB
43 lines
1.2 KiB
import abc
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import torch
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import torch.nn as nn
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from pydantic import BaseModel
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from typing import Dict, Any, Optional
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from monai.metrics.cumulative_average import CumulativeAverage
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class BaseLoss(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: Optional[BaseModel] = 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):
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"""Resets the stored loss metrics."""
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