141 lines
4.0 KiB
Python
141 lines
4.0 KiB
Python
import torch
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import torch.nn as nn
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class mean_squared_error(nn.Module):
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"""
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Mean squared error / L1 loss
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"""
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def __init__(self, reduction="mean"):
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super().__init__()
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self.loss_fun = nn.MSELoss(reduction=reduction)
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self.higher_better = False
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def forward(self, prediction: torch.Tensor, target: torch.Tensor):
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if prediction.size() != target.size() or target.ndim < 3:
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raise TypeError(
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f"""Inputs must be of the same shape (batch_size,channels,samples)
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got {prediction.size()} and {target.size()} instead"""
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)
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return self.loss_fun(prediction, target)
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class mean_absolute_error(nn.Module):
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"""
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Mean absolute error / L2 loss
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"""
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def __init__(self, reduction="mean"):
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super().__init__()
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self.loss_fun = nn.L1Loss(reduction=reduction)
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self.higher_better = False
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def forward(self, prediction: torch.Tensor, target: torch.Tensor):
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if prediction.size() != target.size() or target.ndim < 3:
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raise TypeError(
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f"""Inputs must be of the same shape (batch_size,channels,samples)
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got {prediction.size()} and {target.size()} instead"""
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)
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return self.loss_fun(prediction, target)
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class Si_SDR(nn.Module):
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"""
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SI-SDR metric based on SDR – HALF-BAKED OR WELL DONE?(https://arxiv.org/pdf/1811.02508.pdf)
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"""
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def __init__(self, reduction: str = "mean"):
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super().__init__()
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if reduction in ["sum", "mean", None]:
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self.reduction = reduction
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else:
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raise TypeError(
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"Invalid reduction, valid options are sum, mean, None"
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)
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self.higher_better = False
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def forward(self, prediction: torch.Tensor, target: torch.Tensor):
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if prediction.size() != target.size() or target.ndim < 3:
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raise TypeError(
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f"""Inputs must be of the same shape (batch_size,channels,samples)
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got {prediction.size()} and {target.size()} instead"""
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)
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target_energy = torch.sum(target**2, keepdim=True, dim=-1)
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scaling_factor = (
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torch.sum(prediction * target, keepdim=True, dim=-1) / target_energy
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)
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target_projection = target * scaling_factor
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noise = prediction - target_projection
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ratio = torch.sum(target_projection**2, dim=-1) / torch.sum(
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noise**2, dim=-1
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)
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si_sdr = 10 * torch.log10(ratio).mean(dim=-1)
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if self.reduction == "sum":
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si_sdr = si_sdr.sum()
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elif self.reduction == "mean":
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si_sdr = si_sdr.mean()
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else:
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pass
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return si_sdr
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class Avergeloss(nn.Module):
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"""
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Combine multiple metics of same nature.
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for example, ["mea","mae"]
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parameters:
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losses : loss function names to be combined
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"""
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def __init__(self, losses):
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super().__init__()
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self.valid_losses = nn.ModuleList()
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direction = [
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getattr(LOSS_MAP[loss](), "higher_better") for loss in losses
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]
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if len(set(direction)) > 1:
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raise ValueError(
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"all cost functions should be of same nature, maximize or minimize!"
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)
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self.higher_better = direction[0]
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for loss in losses:
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loss = self.validate_loss(loss)
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self.valid_losses.append(loss())
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def validate_loss(self, loss: str):
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if loss not in LOSS_MAP.keys():
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raise ValueError(
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f"""Invalid loss function {loss}, available loss functions are
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{tuple([loss for loss in LOSS_MAP.keys()])}"""
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)
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else:
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return LOSS_MAP[loss]
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def forward(self, prediction: torch.Tensor, target: torch.Tensor):
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loss = 0.0
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for loss_fun in self.valid_losses:
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loss += loss_fun(prediction, target)
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return loss
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LOSS_MAP = {
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"mae": mean_absolute_error,
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"mse": mean_squared_error,
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"SI-SDR": Si_SDR,
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}
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