INNER CODE UNIT · Python

log_sum_exp

nv-tlabs/ATISS · scene_synthesis/losses/__init__.py:28

def log_sum_exp(x):
    """Numerically stable log_sum_exp implementation that prevents
    overflow.
    """
    # TF ordering
    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

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