Function visualize

tools/visualization.py:249–346

Perform layer weights and activations visualization on the model. Args: cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py

(cfg)

def visualize(cfg):
    """
    Perform layer weights and activations visualization on the model.
    Args:
        cfg (CfgNode): configs. Details can be found in
            slowfast/config/defaults.py
    """
    if cfg.TENSORBOARD.ENABLE and (
        cfg.TENSORBOARD.MODEL_VIS.ENABLE
        or cfg.TENSORBOARD.WRONG_PRED_VIS.ENABLE
    ):
        # 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("Model Visualization with config:")
        logger.info(cfg)

        # Build the video model and print model statistics.
        model = build_model(cfg)
        model.eval()
        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.
        vis_loader = loader.construct_loader(cfg, "test")

        if cfg.DETECTION.ENABLE:
            assert cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE or cfg.NUM_GPUS == 0

        # Set up writer for logging to Tensorboard format.
        if du.is_master_proc(cfg.NUM_GPUS * cfg.NUM_SHARDS):
            writer = tb.TensorboardWriter(cfg)
        else:
            writer = None
        if cfg.TENSORBOARD.PREDICTIONS_PATH != "":
            assert not cfg.DETECTION.ENABLE, "Detection is not supported."
            logger.info(
                "Visualizing class-level performance from saved results..."
            )
            if writer is not None:
                with PathManager.open(
                    cfg.TENSORBOARD.PREDICTIONS_PATH, "rb"
                ) as f:
                    preds, labels = pickle.load(f, encoding="latin1")

                writer.plot_eval(preds, labels)

        if cfg.TENSORBOARD.MODEL_VIS.ENABLE:
            if cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.ENABLE: