ff09708034d6914e2e110212ebf182ee802c2d8a,nussl/ml/train/loss.py,DeepClusteringLoss,forward,#DeepClusteringLoss#Any#Any#Any#,80

Before Change

            batch_size, -1).sum(dim=-1)
        yTy = ((assignments.transpose(2, 1) @ assignments) ** 2).reshape(
            batch_size, -1).sum(dim=-1)
        loss = (vTv - 2 * vTy + yTy) / (vTv + yTy).detach()
        return loss.mean()

class PermutationInvariantLoss(nn.Module):
    

After Change

        assignments = assignments.view(batch_size, -1, num_sources)
        assignments = nn.functional.normalize(assignments, dim=-1, p=2)

        norm = (((weights.reshape(batch_size, -1)) ** 2).sum(dim=1) ** 2)+ 1e-8

        assignments = weights * assignments
        embedding = weights * embedding

Instances