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Source code for lumin.nn.losses.advanced_losses

from typing import Optional

import torch
from torch import nn, Tensor

from ...utils.misc import to_device

__all__ = ['WeightedFractionalMSE', 'WeightedBinnedHuber', 'WeightedFractionalBinnedHuber']


[docs]class WeightedFractionalMSE(nn.MSELoss): r''' Class for computing the Mean fractional Squared-Error loss (<Delta^2/true>) with optional weights per prediction. For compatability with using basic PyTorch losses, weights are passed during initialisation rather than when computing the loss. Arguments: weight: sample weights as PyTorch Tensor, to be used with data to be passed when computing the loss Examples:: >>> loss = WeightedFractionalMSE() >>> >>> loss = WeightedFractionalMSE(weights) ''' def __init__(self, weight:Optional[Tensor]=None): super().__init__(reduction='none') self.weights = weight
[docs] def forward(self, input:Tensor, target:Tensor) -> Tensor: r''' Evaluate loss for given predictions Arguments: input: prediction tensor target: target tensor Returns: (weighted) loss ''' if self.weights is not None: return torch.mean(self.weights*super().forward(input, target)/target) else: return torch.mean(super().forward(input, target)/target)
[docs]class WeightedBinnedHuber(nn.MSELoss): r''' Class for computing the Huberised Mean Squared-Error loss (<Delta^2>) with optional weights per prediction. Losses soft-clamped with Huber like term above adaptive percentile in bins of the target. The thresholds used to transition from MSE to MAE per bin are initialised using the first batch of data as the value of the specified percentile in each bin, subsequently, the thresholds evolve according to: T <- (1-mom)*T + mom*T_batch, where T_batch are the percentiles comuted on the current batch, and mom(emtum) lies between [0,1] For compatability with using basic PyTorch losses, weights are passed during initialisation rather than when computing the loss. Arguments: perc: quantile of data in each bin above which to use MAE rather than MSE bins: tensor of edges for the binning of the target data mom: momentum for the running average of the thresholds weight: sample weights as PyTorch Tensor, to be used with data to be passed when computing the loss Examples:: >>> loss = WeightedBinnedHuber(perc=0.68) >>> >>> loss = WeightedBinnedHuber(perc=0.68, weights=weights) ''' def __init__(self, perc:float, bins:Tensor, mom=0.1, weight:Optional[Tensor]=None): super().__init__(reduction='none') self.perc,self.bins,self.weights,self.mom = perc,bins,weight,mom self.kths = to_device(torch.zeros_like(self.bins[:-1])-1) def _compute_losses(self, input:Tensor, target:Tensor) -> Tensor: loss = super().forward(input, target) # MSE # MAE for i in range(len(self.bins)-1): m = (target >= self.bins[i])*(target < self.bins[i+1]) if m.sum() == 0: continue kth = loss[m].view(-1).kthvalue(1+round(self.perc*(loss[m].numel()-1))).values.detach() if self.kths[i] < 0: self.kths[i] = kth else: self.kths[i].lerp_(kth, self.mom) m = m*(loss > self.kths[i]) d = torch.sqrt(self.kths[i]) loss[m] = self.kths[i]+(2*d*((torch.abs(input[m]-target[m]))-d)) if self.weights is not None: loss = loss*self.weights return loss
[docs] def forward(self, input:Tensor, target:Tensor) -> Tensor: r''' Evaluate loss for given predictions Arguments: input: prediction tensor target: target tensor Returns: (weighted) loss ''' loss = self._compute_losses(input, target) return torch.mean(loss)
[docs]class WeightedFractionalBinnedHuber(WeightedBinnedHuber): r''' Class for computing the Huberised Mean fractional Squared-Error loss (<Delta^2/true>) with optional weights per prediction. Losses soft-clamped with Huber like term above adaptive percentile in bins of the target. The thresholds used to transition from MSE to MAE per bin are initialised using the first batch of data as the value of the specified percentile in each bin, subsequently, the thresholds evolve according to: T <- (1-mom)*T + mom*T_batch, where T_batch are the percentiles comuted on the current batch, and mom(emtum) lies between [0,1] For compatability with using basic PyTorch losses, weights are passed during initialisation rather than when computing the loss. Arguments: perc: quantile of data in each bin above which to use MAE rather than MSE bins: tensor of edges for the binning of the target data mom: momentum for the running average of the thresholds weight: sample weights as PyTorch Tensor, to be used with data to be passed when computing the loss '''
[docs] def forward(self, input:Tensor, target:Tensor) -> Tensor: r''' Evaluate loss for given predictions Arguments: input: prediction tensor target: target tensor Returns: (weighted) loss ''' loss = self._compute_losses(input, target) loss = loss/target return torch.mean(loss)
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