def evaluate()

in evaluate.py [0:0]

• 58 lines of code
• 5 McCabe index (conditional complexity)

def evaluate(experiment_directory, checkpoint, data_dir, split_filename):

    with open(split_filename, "r") as f:
        split = json.load(f)

    chamfer_results = []

    for dataset in split:
        for class_name in split[dataset]:
            for instance_name in split[dataset][class_name]:
                logging.debug(
                    "evaluating " + os.path.join(dataset, class_name, instance_name)
                )

                reconstructed_mesh_filename = ws.get_reconstructed_mesh_filename(
                    experiment_directory, checkpoint, dataset, class_name, instance_name
                )

                logging.debug(
                    'reconstructed mesh is "' + reconstructed_mesh_filename + '"'
                )

                ground_truth_samples_filename = os.path.join(
                    data_dir,
                    "SurfaceSamples",
                    dataset,
                    class_name,
                    instance_name + ".ply",
                )

                logging.debug(
                    "ground truth samples are " + ground_truth_samples_filename
                )

                normalization_params_filename = os.path.join(
                    data_dir,
                    "NormalizationParameters",
                    dataset,
                    class_name,
                    instance_name + ".npz",
                )

                logging.debug(
                    "normalization params are " + ground_truth_samples_filename
                )

                ground_truth_points = trimesh.load(ground_truth_samples_filename)
                reconstruction = trimesh.load(reconstructed_mesh_filename)

                normalization_params = np.load(normalization_params_filename)

                chamfer_dist = deep_sdf.metrics.chamfer.compute_trimesh_chamfer(
                    ground_truth_points,
                    reconstruction,
                    normalization_params["offset"],
                    normalization_params["scale"],
                )

                logging.debug("chamfer distance: " + str(chamfer_dist))

                chamfer_results.append(
                    (os.path.join(dataset, class_name, instance_name), chamfer_dist)
                )

    with open(
        os.path.join(
            ws.get_evaluation_dir(experiment_directory, checkpoint, True), "chamfer.csv"
        ),
        "w",
    ) as f:
        f.write("shape, chamfer_dist\n")
        for result in chamfer_results:
            f.write("{}, {}\n".format(result[0], result[1]))