Function test

tools/test_net.py:143–218

Perform multi-view testing on the pretrained video model. Args: cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py

(cfg)

def test(cfg):
    """
    Perform multi-view testing on the pretrained video model.
    Args:
        cfg (CfgNode): configs. Details can be found in
            slowfast/config/defaults.py
    """
    # Set up environment.
    du.init_distributed_training(cfg)
    # Set random seed from configs.
    np.random.seed(cfg.RNG_SEED)
    torch.manual_seed(cfg.RNG_SEED)

    # Setup logging format.
    logging.setup_logging(cfg.OUTPUT_DIR)

    # Print config.
    logger.info("Test with config:")
    logger.info(cfg)

    # Build the video model and print model statistics.
    model = build_model(cfg)
    if du.is_master_proc() and cfg.LOG_MODEL_INFO:
        misc.log_model_info(model, cfg, use_train_input=False)

    cu.load_test_checkpoint(cfg, model)

    # Create video testing loaders.
    test_loader = loader.construct_loader(cfg, "test")
    logger.info("Testing model for {} iterations".format(len(test_loader)))
    logger.info(f"Add softmax after prediction: {cfg.TEST.ADD_SOFTMAX}")

    if cfg.DETECTION.ENABLE:
        assert cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE or cfg.NUM_GPUS == 0
        test_meter = AVAMeter(len(test_loader), cfg, mode="test")
    else:
        assert (
            test_loader.dataset.num_videos
            % (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS)
            == 0
        )
        # Create meters for multi-view testing.
        test_meter = TestMeter(
            test_loader.dataset.num_videos
            // (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS),
            cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS,
            cfg.MODEL.NUM_CLASSES,
            len(test_loader),
            cfg.DATA.MULTI_LABEL,
            cfg.DATA.ENSEMBLE_METHOD,
        )

    # Set up writer for logging to Tensorboard format.
    if cfg.TENSORBOARD.ENABLE and du.is_master_proc(
        cfg.NUM_GPUS * cfg.NUM_SHARDS
    ):
        writer = tb.TensorboardWriter(cfg)
    else: