deeplearning/modulus/modulus-core-v040/_modules/modulus/utils/graphcast/loss.html

Source code for modulus.utils.graphcast.loss

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import math

import torch
import torch.nn as nn
from torch.autograd.function import once_differentiable


[docs]class CellAreaWeightedLossFunction(nn.Module): """Loss function with cell area weighting. Parameters ---------- area : torch.Tensor Cell area with shape [H, W]. """ def __init__(self, area): super().__init__() self.area = area
[docs] def forward(self, invar, outvar): """ Implicit forward function which computes the loss given a prediction and the corresponding targets. Parameters ---------- invar : torch.Tensor prediction of shape [T, C, H, W]. outvar : torch.Tensor target values of shape [T, C, H, W]. """ loss = (invar - outvar) ** 2 loss = loss.mean(dim=(0, 1)) loss = torch.mul(loss, self.area) loss = loss.mean() return loss
[docs]class CustomCellAreaWeightedLossAutogradFunction(torch.autograd.Function): """Autograd fuunction for custom loss with cell area weighting."""
[docs] @staticmethod def forward(ctx, invar: torch.Tensor, outvar: torch.Tensor, area: torch.Tensor): """Forward of custom loss function with cell area weighting.""" diff = invar - outvar # T x C x H x W loss = diff**2 loss = loss.mean(dim=(0, 1)) loss = torch.mul(loss, area) loss = loss.mean() loss_grad = diff * (2.0 / (math.prod(invar.shape))) loss_grad *= area.unsqueeze(0).unsqueeze(0) ctx.save_for_backward(loss_grad) return loss
[docs] @staticmethod @once_differentiable def backward(ctx, grad_loss: torch.Tensor): """Backward method of custom loss function with cell area weighting.""" # grad_loss should be 1, multiply nevertheless # to avoid issues with cases where this isn't the case (grad_invar,) = ctx.saved_tensors return grad_invar * grad_loss, None, None
[docs]class CustomCellAreaWeightedLossFunction(CellAreaWeightedLossFunction): """Custom loss function with cell area weighting. Parameters ---------- area : torch.Tensor Cell area with shape [H, W]. """ def __init__(self, area: torch.Tensor): super().__init__(area)
[docs] def forward(self, invar: torch.Tensor, outvar: torch.Tensor) -> torch.Tensor: """ Implicit forward function which computes the loss given a prediction and the corresponding targets. Parameters ---------- invar : torch.Tensor prediction of shape [T, C, H, W]. outvar : torch.Tensor target values of shape [T, C, H, W]. """ return CustomCellAreaWeightedLossAutogradFunction.apply( invar, outvar, self.area )
© Copyright 2023, NVIDIA Modulus Team. Last updated on Jan 25, 2024.