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Running
on
Zero
File size: 3,300 Bytes
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import torch
import numpy as np
# --- Loss Weighting
class BaseLossWeight():
def weight(self, logSNR):
raise NotImplementedError("this method needs to be overridden")
def __call__(self, logSNR, *args, shift=1, clamp_range=None, **kwargs):
clamp_range = [-1e9, 1e9] if clamp_range is None else clamp_range
if shift != 1:
logSNR = logSNR.clone() + 2 * np.log(shift)
return self.weight(logSNR, *args, **kwargs).clamp(*clamp_range)
class ComposedLossWeight(BaseLossWeight):
def __init__(self, div, mul):
self.mul = [mul] if isinstance(mul, BaseLossWeight) else mul
self.div = [div] if isinstance(div, BaseLossWeight) else div
def weight(self, logSNR):
prod, div = 1, 1
for m in self.mul:
prod *= m.weight(logSNR)
for d in self.div:
div *= d.weight(logSNR)
return prod/div
class ConstantLossWeight(BaseLossWeight):
def __init__(self, v=1):
self.v = v
def weight(self, logSNR):
return torch.ones_like(logSNR) * self.v
class SNRLossWeight(BaseLossWeight):
def weight(self, logSNR):
return logSNR.exp()
class P2LossWeight(BaseLossWeight):
def __init__(self, k=1.0, gamma=1.0, s=1.0):
self.k, self.gamma, self.s = k, gamma, s
def weight(self, logSNR):
return (self.k + (logSNR * self.s).exp()) ** -self.gamma
class SNRPlusOneLossWeight(BaseLossWeight):
def weight(self, logSNR):
return logSNR.exp() + 1
class MinSNRLossWeight(BaseLossWeight):
def __init__(self, max_snr=5):
self.max_snr = max_snr
def weight(self, logSNR):
return logSNR.exp().clamp(max=self.max_snr)
class MinSNRPlusOneLossWeight(BaseLossWeight):
def __init__(self, max_snr=5):
self.max_snr = max_snr
def weight(self, logSNR):
return (logSNR.exp() + 1).clamp(max=self.max_snr)
class TruncatedSNRLossWeight(BaseLossWeight):
def __init__(self, min_snr=1):
self.min_snr = min_snr
def weight(self, logSNR):
return logSNR.exp().clamp(min=self.min_snr)
class SechLossWeight(BaseLossWeight):
def __init__(self, div=2):
self.div = div
def weight(self, logSNR):
return 1/(logSNR/self.div).cosh()
class DebiasedLossWeight(BaseLossWeight):
def weight(self, logSNR):
return 1/logSNR.exp().sqrt()
class SigmoidLossWeight(BaseLossWeight):
def __init__(self, s=1):
self.s = s
def weight(self, logSNR):
return (logSNR * self.s).sigmoid()
class AdaptiveLossWeight(BaseLossWeight):
def __init__(self, logsnr_range=[-10, 10], buckets=300, weight_range=[1e-7, 1e7]):
self.bucket_ranges = torch.linspace(logsnr_range[0], logsnr_range[1], buckets-1)
self.bucket_losses = torch.ones(buckets)
self.weight_range = weight_range
def weight(self, logSNR):
indices = torch.searchsorted(self.bucket_ranges.to(logSNR.device), logSNR)
return (1/self.bucket_losses.to(logSNR.device)[indices]).clamp(*self.weight_range)
def update_buckets(self, logSNR, loss, beta=0.99):
indices = torch.searchsorted(self.bucket_ranges.to(logSNR.device), logSNR).cpu()
self.bucket_losses[indices] = self.bucket_losses[indices]*beta + loss.detach().cpu() * (1-beta)
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