def main()

in eval_demo.py [0:0]

• 24 lines of code
• 8 McCabe index (conditional complexity)

def main():
    """
    Evaluates new view synthesis metrics of a simple depth-based image rendering
    (DBIR) model for multisequence/singlesequence tasks for several categories.

    The evaluation is conducted on the same data as in [1] and, hence, the results
    are directly comparable to the numbers reported in [1].

    References:
        [1] J. Reizenstein, R. Shapovalov, P. Henzler, L. Sbordone,
                P. Labatut, D. Novotny:
            Common Objects in 3D: Large-Scale Learning
                and Evaluation of Real-life 3D Category Reconstruction
    """

    task_results = {}
    for task in ("singlesequence", "multisequence"):
        task_results[task] = []
        for category in CO3D_CATEGORIES[: (20 if task == "singlesequence" else 10)]:
            for single_sequence_id in (0, 1) if task == "singlesequence" else (None,):
                category_result = evaluate_dbir_for_category(
                    category, task=task, single_sequence_id=single_sequence_id
                )
                print("")
                print(
                    f"Results for task={task}; category={category};"
                    + (
                        f" sequence={single_sequence_id}:"
                        if single_sequence_id is not None
                        else ":"
                    )
                )
                pretty_print_nvs_metrics(category_result)
                print("")

                task_results[task].append(category_result)
            _print_aggregate_results(task, task_results)

    for task in task_results:
        _print_aggregate_results(task, task_results)