deeplearning/modulus/modulus-core/_modules/modulus/metrics/general/reduction.html

Source code for modulus.metrics.general.reduction

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from abc import ABC

import torch
from torch import Tensor


[docs]class WeightedStatistic(ABC): """A convenience class for computing weighted statistics of some input Parameters ---------- weights : Tensor Weight tensor """ def __init__(self, weights: Tensor): if not torch.all(weights > 0.0).item(): raise ValueError("Expected all weights to be positive.") self.weights = self._normalize(weights) def __call__(self, x: Tensor, dim: int): """ Convenience method to make sure weights have appropriate shapes. """ w = self.weights if w.ndim == 1: if x.shape[dim] != len(w): raise ValueError( "Expected inputs and weights to have the same size along the reduction dimension but have dimensions" + str(len(x[dim])) + " and " + str(len(w)) + "." ) if dim < 0: dim = x.ndim + dim for i in range(x.ndim): if i < dim: w = w.unsqueeze(0) elif i > dim: w = w.unsqueeze(-1) else: if not ((x.ndim == w.ndim) and (x.shape[dim] == w.shape[dim])): raise ValueError( "Expected inputs and weights to have compatible shapes." ) return w def _normalize(self, weights: Tensor) -> Tensor: """Normalize unnormalized weights, for convenience Parameters ---------- weights : Tensor Unnormalized weights Returns ------- Tensor Normalized weights """ return weights / torch.sum(weights)
[docs]class WeightedMean(WeightedStatistic): """ Compute weighted mean of some input. Parameters ---------- weights : Tensor Weight tensor """ def __init__(self, weights: Tensor): super().__init__(weights) def __call__(self, x: Tensor, dim: int, keepdims: bool = False) -> Tensor: """Compute weighted mean Parameters ---------- x : Tensor Input data dim : int Dimension to take aggregate keepdims : bool, optional Keep aggregated dimension, by default False Returns ------- Tensor Weighted mean """ w = super().__call__(x, dim) return torch.sum(x * w, dim=dim, keepdims=keepdims)
[docs]class WeightedVariance(WeightedStatistic): """ Compute weighted variance of some input. Parameters ---------- weights : Tensor Weight tensor """ def __init__(self, weights: Tensor): super().__init__(weights) self.wm = WeightedMean(self.weights) def __call__(self, x: Tensor, dim: int, keepdims: bool = False): """Compute weighted variance Parameters ---------- x : Tensor Input data dim : int Dimension to take aggregate keepdims : bool, optional Keep aggregated dimension, by default False Returns ------- Tensor Weighted variance """ w = super().__call__(x, dim) # Compute weighted mean wm = self.wm(x, dim, keepdims=True) # Computing scaling for standard deviation number_of_non_zero_weights = torch.sum(w > 0.0) scale = (number_of_non_zero_weights - 1.0) / number_of_non_zero_weights return torch.sum(w * (x - wm) ** 2, dim=dim, keepdims=keepdims) / scale
© Copyright 2023, NVIDIA Modulus Team. Last updated on Jan 25, 2024.