Adding custom weights to training data in PyTorch
Is it possible to add custom weights to the training instances in PyTorch? More explicitly, I'd like to add a custom weight for every row in my dataset. By default, the weights are 1, which means every data is equally important for my model.

Loss functions support class weights not sample weights. For sample weights you can do something like below (commented inline):
import torch

x = torch.rand(8, 4)
# Ground truth
y = torch.randint(2, (8,))
# Weights per sample 
weights = torch.rand(8, 1) 

# Add weights as a columns, so that it will be passed trough
# dataloaders in case you want to use one
x = torch.cat((x, weights), dim=1)

model = torch.nn.Linear(4, 2)

loss_fn = torch.nn.CrossEntropyLoss(reduction='none')
def weighted_loss(y, y_hat, w):
  return (loss_fn(y, y_hat)*w).mean()

loss = weighted_loss(model(x[:, :-1]), y, x[:, -1])
print (loss)

