File size: 2,254 Bytes
b5beb60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | 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
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