def main_detection_evaluation()

in engine/eval_detection.py [0:0]

• 46 lines of code
• 18 McCabe index (conditional complexity)

def main_detection_evaluation(**kwargs):
    opts = get_detection_eval_arguments()

    dataset_name = getattr(opts, "dataset.name", "imagenet")
    if dataset_name.find("coco") > -1:
        # replace model specific datasets (e.g., coco_ssd) with general COCO dataset
        setattr(opts, "dataset.name", "coco")

    # device set-up
    opts = device_setup(opts)

    node_rank = getattr(opts, "ddp.rank", 0)
    if node_rank < 0:
        logger.error('--rank should be >=0. Got {}'.format(node_rank))

    is_master_node = is_master(opts)

    # create the directory for saving results
    save_dir = getattr(opts, "common.results_loc", "results")
    run_label = getattr(opts, "common.run_label", "run_1")
    exp_dir = '{}/{}'.format(save_dir, run_label)
    setattr(opts, "common.exp_loc", exp_dir)
    logger.log("Results (if any) will be stored here: {}".format(exp_dir))

    create_directories(dir_path=exp_dir, is_master_node=is_master_node)

    num_gpus = getattr(opts, "dev.num_gpus", 1)
    if num_gpus < 2:
        cls_norm_type = getattr(opts, "model.normalization.name", "batch_norm_2d")
        if cls_norm_type.find("sync") > -1:
            # replace sync_batch_norm with standard batch norm on PU
            setattr(opts, "model.normalization.name", cls_norm_type.replace("sync_", ""))
            setattr(opts, "model.classification.normalization.name", cls_norm_type.replace("sync_", ""))

    # we disable the DDP setting for evaluation tasks
    setattr(opts, "ddp.use_distributed", False)

    # No of data workers = no of CPUs (if not specified or -1)
    n_cpus = multiprocessing.cpu_count()
    dataset_workers = getattr(opts, "dataset.workers", -1)

    if dataset_workers == -1:
        setattr(opts, "dataset.workers", n_cpus)

    # We are not performing any operation like resizing and cropping on images
    # Because image dimensions are different, we process 1 sample at a time.
    setattr(opts, "dataset.train_batch_size0", 1)
    setattr(opts, "dataset.val_batch_size0", 1)
    setattr(opts, "dev.device_id", None)

    eval_mode = getattr(opts, "evaluation.detection.mode", None)

    if eval_mode == "single_image":
        num_classes = getattr(opts, "model.detection.n_classes", 81)
        assert num_classes is not None

        # test a single image
        img_f_name = getattr(opts, "evaluation.detection.path", None)
        predict_image(opts, img_f_name, **kwargs)
    elif eval_mode == "image_folder":
        num_seg_classes = getattr(opts, "model.detection.n_classes", 81)
        assert num_seg_classes is not None

        # test all images in a folder
        predict_images_in_folder(opts=opts, **kwargs)
    elif eval_mode == "validation_set":
        # evaluate and compute stats for labeled image dataset
        # This is useful for generating results for validation set and compute quantitative results
        predict_labeled_dataset(opts=opts, **kwargs)
    else:
        logger.error(
            "Supported modes are single_image, image_folder, and validation_set. Got: {}".format(eval_mode)
        )