Implementing custom loss from paper

Hi all :slight_smile:
Am trying to implement this paper: where they employ a detection head to estimate the objects normal distribution, and a regression head to estimate the offset vectors of the objects keypoints. Examplified here:

Have a hard time implementing the regression loss: l1_loss(offset(p) + p - ground_truth(K_n)) where K_n is the nth keypoint, offset(p) is the offset vectors to p which is the centroid.

I have the offset vectors and centroid p, however during the loss function there are possibly several p which need to be added to the offset vectors. So how would I go about this and avoid for-loops?

My current implementation now looks like this:

class Loss(nn.Module):
    def __init__(self):
        super(Loss, self).__init__()

    def forward(self, vector_preds, vector_targets, gauss_preds, gauss_targets):
        vector_targets = vector_targets.permute(0, 2, 3, 1)
        gauss_targets = gauss_targets.permute(0, 2, 3, 1)
        vector_preds = vector_preds.permute(0, 2, 3, 1)
        gauss_preds = gauss_preds.permute(0, 2, 3, 1)  

        E = 0.9
        gauss_mask = gauss_targets > E
        # only want to calculate offsets from centroids (gauss_target > 0.9)
        masked_vector_preds = gauss_mask * vector_preds
        masked_vector_targets = gauss_mask * vector_targets

        vector_loss = F.l1_loss(masked_vector_preds, masked_vector_targets)       

        gauss_loss = F.mse_loss(gauss_preds, gauss_targets)

        loss = vector_loss + gauss_loss
        return loss, vector_loss, gauss_loss

However, am unsure if i am not just minimizing the vectors now, therefore not taking into account the centroid(s) of the object. Or if, in this case, that even matters?
Hope you can help