| from datasets import load_dataset | |
| GQA_RAW_IMAGE_DATASET = None | |
| GQA_ID2IMAGE = None | |
| def gqa_doc_to_visual(doc): | |
| global GQA_RAW_IMAGE_DATASET | |
| global GQA_ID2IMAGE | |
| if GQA_RAW_IMAGE_DATASET is None: | |
| GQA_RAW_IMAGE_DATASET = load_dataset("lmms-lab/GQA", "testdev_balanced_images", split="testdev", token=True) | |
| GQA_ID2IMAGE = {} | |
| for row in GQA_RAW_IMAGE_DATASET: | |
| GQA_ID2IMAGE[row["id"]] = row["image"].convert("RGB") | |
| image = GQA_ID2IMAGE[doc["imageId"]] | |
| return [image] | |
| def gqa_doc_to_text(doc, lmms_eval_specific_kwargs): | |
| question = doc["question"] | |
| pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] | |
| post_prompt = lmms_eval_specific_kwargs["post_prompt"] | |
| return f"{pre_prompt}{question}{post_prompt}" | |