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"""Losses for granular models."""
from typing import Any, Dict
import torch
from torch import nn
[docs]class MSLELossNegMix9(nn.Module):
"""MSLE loss negative mix 9."""
[docs] def __init__(self) -> None:
"""Initialize the loss."""
super().__init__()
self.mse = nn.MSELoss(reduction="sum")
[docs] def forward(self, prediction: torch.Tensor, target: torch.Tensor) -> Any:
"""Forward pass in the loss.
Args:
prediction: predictions.
target: groundtruth.
Returns:
loss value.
"""
pred2 = prediction.clone()
true2 = target.clone()
pred2[pred2 < 0] = 0
pred2 = pred2 + 1e-6
true2 = true2 + 1e-6
pred3 = prediction.clone()
true3 = target.clone()
pred3[target < 0.0001] = 0
true3[target < 0.0001] = 0
pred4 = prediction.clone()
true4 = target.clone()
pred4[pred4 > 0] = 0
true4[true4 < 2] = 0
l4_ = self.mse(pred4, true4)
l1_ = self.mse(pred3 / (0.001 + true3), true3 / (0.001 + true3))
l2_ = self.mse(prediction, target)
l3_ = self.mse(torch.log(pred2), torch.log(true2))
l0_ = torch.abs(l1_ * 0.1 + l2_ * 1.0 + l3_ * 1.0e-5 + l4_ * 10.0)
return l0_
[docs]class MSLELossNegMix91(nn.Module):
"""MSLE loss negative mix 91."""
[docs] def __init__(self):
"""Initialize the loss."""
super().__init__()
self.mse = nn.MSELoss(reduction="sum")
self.mae = nn.L1Loss(reduction="sum")
[docs] def forward(self, prediction: torch.Tensor, target: torch.Tensor) -> Any:
"""Forward pass in the loss.
Args:
prediction: predictions.
target: groundtruth.
Returns:
loss value.
"""
pred2 = prediction.clone()
true2 = target.clone()
pred2[pred2 < 0] = 0
pred2 = pred2 + 1e-6
true2 = true2 + 1e-6
l1_ = self.mae(prediction, target)
l2_ = self.mse(prediction, target)
l3_ = self.mse(torch.log(pred2), torch.log(true2))
l0_ = torch.abs(l1_ * 0.3 + l2_ * 1.0 + l3_ * 1.0e-5)
return l0_
[docs]class MseWithNans(nn.Module):
"""MSE with NaNs handling."""
[docs] def __init__(self):
"""Initialize the loss."""
super().__init__()
[docs] def forward(self, prediction: torch.Tensor, target: torch.Tensor) -> Any:
"""Forward pass in the loss.
Args:
prediction: predictions.
target: groundtruth.
Returns:
loss value.
"""
mask = torch.isnan(target)
out = (prediction[~mask] - target[~mask]) ** 2
loss = out.mean()
return loss
[docs]class MaeWithNans(nn.Module):
"""MAE with NaNs handling."""
[docs] def __init__(self):
"""Initialize the loss."""
super().__init__()
[docs] def forward(self, prediction: torch.Tensor, target: torch.Tensor) -> Any:
"""Forward pass in the loss.
Args:
prediction: predictions.
target: groundtruth.
Returns:
loss value.
"""
mask = torch.isnan(target)
if sum(mask) == len(target):
return torch.tensor(0).type_as(prediction)
out = abs((prediction[~mask] - target[~mask]))
loss = sum(out) / len(prediction[~mask])
return loss
[docs]class MSLELossNegMix92(nn.Module):
"""MSLE loss negative mix 92."""
[docs] def __init__(self):
"""Initialize the loss."""
super().__init__()
self.mae = nn.L1Loss(reduction="mean")
[docs] def forward(self, prediction: torch.Tensor, target: torch.Tensor) -> Any:
"""Forward pass in the loss.
Args:
prediction: predictions.
target: groundtruth.
Returns:
loss value.
"""
l1_ = self.mae(prediction, target)
mask = target.ge(0.001)
l2_ = self.mae(prediction[mask], target[mask])
l0_ = torch.abs(l1_ + l2_ * 10.0)
return l0_
[docs]class MSLELossNegMix99(nn.Module):
"""MSLE loss negative mix 99."""
[docs] def __init__(self):
"""Initialize the loss."""
super().__init__()
self.mse = nn.MSELoss(reduction="sum")
[docs] def forward(self, prediction: torch.Tensor, target: torch.Tensor) -> Any:
"""Forward pass in the loss.
Args:
prediction: predictions.
target: groundtruth.
Returns:
loss value.
"""
mask = torch.isnan(target)
out = (prediction[~mask] - target[~mask]) ** 2
loss = out.mean()
return loss
LOSS_FACTORY: Dict[str, nn.Module] = {
"mse": nn.MSELoss(),
"mse-sum": nn.MSELoss(reduction="sum"),
"mse-mean": nn.MSELoss(reduction="mean"),
"mse-with-nans": MseWithNans(),
"msewithnans": MseWithNans(),
"mae": nn.L1Loss(),
"mae-sum": nn.L1Loss(reduction="sum"),
"mae-mean": nn.L1Loss(reduction="mean"),
"mae-with-nans": MaeWithNans(),
"maewithnans": MaeWithNans(),
"bce": nn.BCELoss(),
"bce-with-logits": nn.BCEWithLogitsLoss(),
"bcewl": nn.BCEWithLogitsLoss(),
"loss9": MSLELossNegMix9(),
"l9": MSLELossNegMix9(),
"msle-neg-mix-9": MSLELossNegMix9(),
"loss91": MSLELossNegMix91(),
"l91": MSLELossNegMix91(),
"msle-neg-mix-91": MSLELossNegMix91(),
"loss92": MSLELossNegMix92(),
"l92": MSLELossNegMix92(),
"msle-neg-mix-92": MSLELossNegMix92(),
"loss99": MSLELossNegMix99(),
"l99": MSLELossNegMix99(),
"msle-neg-mix-99": MSLELossNegMix99(),
"crossentropyloss": nn.CrossEntropyLoss(),
"ce": nn.CrossEntropyLoss(),
}