INNER CODE UNIT · Python

axis

nv-tlabs/ATISS · scene_synthesis/losses/__init__.py:33

    axis = len(x.size()) - 1
    m, _ = torch.max(x, dim=axis)
    m2, _ = torch.max(x, dim=axis, keepdim=True)
    return m + torch.log(torch.sum(torch.exp(x - m2), dim=axis))


def dmll(pred, target, log_scale_min=-7.0, num_classes=256):
    """Discretized mixture of logistic distributions loss
    Note that it is assumed that input is scaled to [-1, 1].

    Code adapted
    from https://github.com/idiap/linear-transformer-experiments/blob/0a540938ec95e1ec5b159ceabe0463d748ba626c/image-generation/utils.py#L31

    Arguments
    ----------
        pred (Tensor): Predicted output (B x L x T)
        target (Tensor): Target (B x L x 1).
        log_scale_min (float): Log scale minimum value

View source record →