| import json |
| import datasets |
|
|
| class CocoBaseEval(datasets.GeneratorBasedBuilder): |
| VERSION = datasets.Version("1.0.0") |
| |
| DESCRIPTION = "COCO 2014 validation set subset (1000 images) with captions and object annotations" |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=self.DESCRIPTION, |
| features=datasets.Features({ |
| "image_id": datasets.Value("int32"), |
| "image": datasets.Image(), |
| "input_prompt": datasets.Value("string"), |
| "gt_objects": datasets.Sequence(datasets.Value("string")), |
| "gt_captions": datasets.Sequence(datasets.Value("string")) |
| }) |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={"data_file": "data.jsonl"} |
| ) |
| ] |
|
|
| def _generate_examples(self, data_file): |
| import json |
| with open(data_file, "r") as f: |
| for idx, line in enumerate(f): |
| item = json.loads(line) |
| |
| yield idx, { |
| "image_id": item["image_id"], |
| "image": item["image"], |
| "input_prompt": item["input_prompt"], |
| "gt_objects": item["gt_objects"], |
| "gt_captions": item["gt_captions"] |
| } |
|
|
