def get_predictions()

in slowfast/visualization/ava_demo_precomputed_boxes.py [0:0]
48 lines of code
8 McCabe index (conditional complexity)

    def get_predictions(self):
        """
        Predict and append prediction results to each box in each keyframe in
        `self.pred_boxes` dictionary.
        """
        # Set random seed from configs.
        np.random.seed(self.cfg.RNG_SEED)
        torch.manual_seed(self.cfg.RNG_SEED)

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

        # Print config.
        logger.info("Run demo with config:")
        logger.info(self.cfg)
        assert (
            self.cfg.NUM_GPUS <= 1
        ), "Cannot run demo visualization on multiple GPUs."

        # Build the video model and print model statistics.
        model = build_model(self.cfg)
        model.eval()
        logger.info("Start loading model info")
        misc.log_model_info(model, self.cfg, use_train_input=False)
        logger.info("Start loading model weights")
        cu.load_test_checkpoint(self.cfg, model)
        logger.info("Finish loading model weights")
        logger.info("Start making predictions for precomputed boxes.")
        for keyframe_idx, boxes_and_labels in tqdm.tqdm(
            self.pred_boxes.items()
        ):
            inputs = self.get_input_clip(keyframe_idx)
            boxes = boxes_and_labels[0]
            boxes = torch.from_numpy(np.array(boxes)).float()

            box_transformed = scale_boxes(
                self.cfg.DATA.TEST_CROP_SIZE,
                boxes,
                self.display_height,
                self.display_width,
            )

            # Pad frame index for each box.
            box_inputs = torch.cat(
                [
                    torch.full((box_transformed.shape[0], 1), float(0)),
                    box_transformed,
                ],
                axis=1,
            )
            if self.cfg.NUM_GPUS:
                # Transfer the data to the current GPU device.
                if isinstance(inputs, (list,)):
                    for i in range(len(inputs)):
                        inputs[i] = inputs[i].cuda(non_blocking=True)
                else:
                    inputs = inputs.cuda(non_blocking=True)

                box_inputs = box_inputs.cuda()

            preds = model(inputs, box_inputs)

            preds = preds.detach()

            if self.cfg.NUM_GPUS:
                preds = preds.cpu()

            boxes_and_labels[1] = preds