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import torch
import torch.nn as nn
from torch import Tensor
class MCRMSELoss(nn.Module):
def __init__(self):
super(MCRMSELoss, self).__init__()
self.mse = nn.MSELoss(reduction='none')
def forward(self, y_pred: Tensor, y_true: Tensor):
"""Calculate mean column-wise rmse on columns
:param y_pred: tensor of shape (bs, 6)
:param y_true: tensor of shape (bs, 6)
:return: tensor of shape 0 (scalar with grad)
"""
mse = self.mse(y_pred, y_true).mean(0) # column-wise mean
rmse = torch.sqrt(mse + 1e-7)
return rmse.mean()
def class_mcrmse(self, y_pred: Tensor, y_true: Tensor):
mse = self.mse(y_pred, y_true).mean(0) # column-wise mean
rmse = torch.sqrt(mse + 1e-7)
return rmse.squeeze()
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