| import importlib | |
| from mimetypes import guess_type | |
| def lazy_import(module_name, class_name): | |
| """Import the module lazily.""" | |
| def importer(): | |
| module = importlib.import_module(module_name) | |
| return getattr(module, class_name) | |
| return importer | |
| def is_video_file(file_path): | |
| mime_type, _ = guess_type(file_path) | |
| if not mime_type: | |
| return False | |
| return mime_type.startswith("video") | |
| def prepare_megabench_data(dataset_name, dataset_subset_name): | |
| """ | |
| Prepare the MEGA-Bench dataset for evaluation. | |
| Return: | |
| subset_dataset: The organized data of the specified subset | |
| all_dataset: The organized data of all tasks, used for evaluation | |
| """ | |
| from datasets import load_dataset | |
| if "single_image" in dataset_subset_name: | |
| core_data = load_dataset(dataset_name, "core_single_image") | |
| open_data = load_dataset(dataset_name, "open_single_image") | |
| else: | |
| core_data = load_dataset(dataset_name, "core") | |
| open_data = load_dataset(dataset_name, "open") | |
| core_test_samples = list(core_data["test"]) | |
| organized_core_dataset = organize_hf_dataset(core_test_samples) | |
| open_test_samples = list(open_data["test"]) | |
| organized_open_dataset = organize_hf_dataset(open_test_samples) | |
| subset_dataset = organized_core_dataset if "core" in dataset_subset_name else organized_open_dataset | |
| all_dataset = organized_core_dataset + organized_open_dataset | |
| return subset_dataset, all_dataset | |
| def organize_hf_dataset(dataset): | |
| """ | |
| Organize the dataset with task-based manner | |
| Return: | |
| organized_dataset: list, each item is a dict, with the following keys: | |
| - task_name: str | |
| - task_query_samples: list of dicts, each dict contains the sample information | |
| """ | |
| task_dict = {} | |
| for sample in dataset: | |
| task_name = sample["task_name"] | |
| if task_name not in task_dict: | |
| task_dict[task_name] = [] | |
| task_dict[task_name].append(sample) | |
| organized_dataset = [] | |
| for task_name, samples in task_dict.items(): | |
| organized_dataset.append({ | |
| "task_name": task_name, | |
| "task_samples": samples | |
| }) | |
| return organized_dataset | |