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84 lines
2.6 KiB
84 lines
2.6 KiB
from .base import *
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from typing import Literal
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from pydantic import BaseModel, ConfigDict
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class MSELossParams(BaseModel):
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"""
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Class for MSE loss parameters, compatible with `nn.MSELoss`.
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"""
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model_config = ConfigDict(frozen=True)
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reduction: Literal["none", "mean", "sum"] = "mean"
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def asdict(self) -> Dict[str, Any]:
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"""
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Returns a dictionary of valid parameters for `nn.MSELoss`.
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Returns:
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Dict[str, Any]: Dictionary of parameters for `nn.MSELoss`.
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"""
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loss_kwargs = self.model_dump()
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return {k: v for k, v in loss_kwargs.items() if v is not None} # Remove None values
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class MSELoss(BaseLoss):
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"""
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Custom loss function wrapper for `nn.MSELoss` with tracking of loss metrics.
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"""
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def __init__(self, mse_params: Optional[MSELossParams] = None):
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"""
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Initializes the loss function with optional MSELoss parameters.
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Args:
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mse_params (Optional[MSELossParams]): Parameters for `nn.MSELoss` (default: None).
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"""
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super().__init__()
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_mse_params = mse_params.asdict() if mse_params is not None else {}
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# Initialize MSE loss with user-provided parameters or PyTorch defaults
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self.mse_loss = nn.MSELoss(**_mse_params)
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# Using CumulativeAverage from MONAI to track loss metrics
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self.loss_mse_metric = CumulativeAverage()
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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 values and predictions.
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Args:
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outputs (torch.Tensor): Model predictions of shape (batch_size, channels, H, W).
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target (torch.Tensor): Ground truth labels of shape (batch_size, channels, H, W).
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Returns:
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torch.Tensor: The total loss value.
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"""
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# Ensure target is on the same device as outputs
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assert (
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target.device == outputs.device
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), (
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"Target tensor must be moved to the same device as outputs "
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"before calling forward()."
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)
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loss = self.mse_loss(outputs, target)
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self.loss_mse_metric.append(loss.item())
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return loss
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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 average MSE loss.
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"""
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return {
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"loss": round(self.loss_mse_metric.aggregate().item(), 4),
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}
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def reset_metrics(self):
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"""Resets the stored loss metrics."""
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self.loss_mse_metric.reset()
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