Function eval_LP_Fscore

evaluation/patch_utils.py:100–122

compute LP-F-score over two set of patches. Args: gen_patches (torch.Tensor): patches from generated shape ref_patches (torch.Tensor): patches from reference shape threshold (float, optional): F-score threshold. Defaults to 0.95. Returns: average max F-score

(gen_patches: torch.Tensor, ref_patches: torch.Tensor, threshold=0.95)

def eval_LP_Fscore(gen_patches: torch.Tensor, ref_patches: torch.Tensor, threshold=0.95):
    """compute LP-F-score over two set of patches.

    Args:
        gen_patches (torch.Tensor): patches from generated shape
        ref_patches (torch.Tensor): patches from reference shape
        threshold (float, optional): F-score threshold. Defaults to 0.95.

    Returns:
        average max F-score, LP-F-score
    """
    values = []
    for i in range(gen_patches.shape[0]):
        true_positives = torch.logical_and(ref_patches, gen_patches[i:i+1]).sum(dim=(1, 2, 3))
        precision = true_positives / gen_patches[i:i+1].sum()
        recall = true_positives / ref_patches.sum(dim=(1, 2, 3))
        Fscores = 2 * precision * recall / (precision + recall + 1e-8)
        Fscore = torch.max(Fscores)
        values.append(Fscore)
    values = torch.stack(values)
    avg_fscore = torch.mean(values).item()
    percent = torch.sum((values > threshold).int()).item() * 1.0 / len(values)
    return avg_fscore, percent


def eval_LP_given_paths(data_paths, ref_path, patch_size=11, stride=5, patch_num=1000, device="cpu"):