3218a3987ef9f83d1ad5efd6f96d6f6c741c3cfa,python/eight_mile/pytorch/layers.py,,viterbi,#Any#Any#Any#Any#Any#Any#,776

Before Change

    // Return list of paths
    paths = []
    best_path = best_path.transpose(0, 1)
    for path, length in zip(best_path, lengths):
        paths.append(path[:length])
    return paths, path_score.squeeze(0)


class TaggerGreedyDecoder(nn.Module):

After Change

    best_path = torch.stack(best_path)
    // Mask out the extra tags (This might be pointless given that anything that
    // will use this as a dense tensor downstream will mask it itself?)
    seq_mask = sequence_mask(lengths).to(best_path.device).transpose(0, 1)
    best_path = best_path.masked_fill(seq_mask == 0, 0)
    return best_path, path_score


class TaggerGreedyDecoder(nn.Module):

Instances