Function predict

eval/run_predict_minicpm.py:130–204

(args)

def predict(args):
    args.data_dir, args.split, data_subset = get_dataset_dir(args.data_name)
    print(f"Predicting on: {args.data_dir}/{args.split}")
    print(f"Data subset: {data_subset}")

    if multiprocessing.get_start_method(allow_none=True) != "spawn":
        multiprocessing.set_start_method("spawn", force=True)

    with ProcessPoolExecutor(max_workers=len(DEVICES),initializer=_init_llm,initargs=(args.model_path,)) as poolexec:
        tasks = []
        print("Moving model to devices")
        futures = [poolexec.submit(move_to, dev) for dev in DEVICES]
        for fut in futures: 
            print(fut.result())

        for dataset in data_subset:
            save_dir = os.path.join(args.output_dir, dataset)
            if not os.path.exists(save_dir):
                os.makedirs(save_dir)

            episode_dir = os.path.join(args.data_dir, args.split, dataset)
            output_file = os.path.join(save_dir, "predict.jsonl")

            # Get the list of all episodes files
            if os.path.exists(episode_dir):
                episodes_files = os.listdir(episode_dir)
            else:
                continue

            future = []
            all_tasks = []
            print("Loading episodes")
            with ThreadPoolExecutor(max_workers=16) as executor:
                for episodes_file in episodes_files:

                    episodes_path = os.path.join(episode_dir, episodes_file, f"{episodes_file}.json")
                    try:
                        with open(episodes_path, 'r', encoding='utf-8') as f:
                            episodes = json.load(f)
                    except Exception as e:
                        print(f"Failed to load {episodes_path}: {e}")
                        continue
                        # Skip this file on error

                    for episode in episodes:
                        episode["category"] = dataset
                        image_path = os.path.join(episode_dir, episodes_file, f"{episodes_file}_{episode['step_id']}.jpeg")
                        if not os.path.exists(image_path):
                            image_path = image_path.replace(".jpeg", ".png")
                            if not os.path.exists(image_path):
                                image_path = episode['image_path']
                        future.append(executor.submit(load_image, episode, image_path, args.data_name))

                for f in as_completed(future):
                    all_tasks.append(f.result())

            with open(output_file, "w", encoding="utf-8") as f_out:
                print("Predicting")