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| """ | |
| Copyright (c) 2022, salesforce.com, inc. | |
| All rights reserved. | |
| SPDX-License-Identifier: BSD-3-Clause | |
| For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause | |
| """ | |
| import torch | |
| from PIL import Image | |
| from minigpt4.datasets.datasets.vqa_datasets import VQADataset, VQAEvalDataset | |
| from collections import OrderedDict | |
| # class textVQADataset(VQADataset): | |
| # def __init__(self, vis_processor, text_processor, vis_root, ann_paths): | |
| # super().__init__(vis_processor, text_processor, vis_root, ann_paths) | |
| # def collater(self, samples): | |
| # image_list, question_list, answer_list, weight_list = [], [], [], [] | |
| # num_answers = [] | |
| # for sample in samples: | |
| # image_list.append(sample["image"]) | |
| # question_list.append(sample["text_input"]) | |
| # weight_list.extend(sample["weights"]) | |
| # answers = sample["answers"] | |
| # answer_list.extend(answers) | |
| # num_answers.append(len(answers)) | |
| # return { | |
| # "image": torch.stack(image_list, dim=0), | |
| # "text_input": question_list, | |
| # "answer": answer_list, | |
| # "weight": torch.Tensor(weight_list), | |
| # "n_answers": torch.LongTensor(num_answers), | |
| # } | |
| from minigpt4.datasets.datasets.vqa_datasets import VQADataset, VQAEvalDataset | |
| class textVQAEvalDataset(VQADataset): | |
| def __init__(self, vis_processor, text_processor, vis_root=None, ann_paths=None): | |
| # super().__init__(vis_processor, text_processor, vis_root, ann_paths) | |
| from datasets import load_dataset | |
| self.annotation = load_dataset("textvqa", split="validation") | |
| def __getitem__(self, index): | |
| ann = self.annotation[index] | |
| image = ann["image"].convert("RGB") | |
| image = self.vis_processor(image) | |
| question = self.text_processor(ann["question"]) | |
| instruction = random.choice(self.instruction_pool).format(question) | |
| instruction = "<Img><ImageHere></Img> {} ".format(instruction) | |
| print("instruction", instruction) | |
| answers = ann["answers"] | |
| if "unk" in answers: | |
| print(answers) | |
| return { | |
| "image": image, | |
| "text_input": question, | |
| "answer": answers, | |
| # 'image_path': image_path, | |
| "instruction_input": instruction, | |
| "question_id": ann["question_id"], | |
| "instance_id": ann["instance_id"], | |
| } | |
| dataset = textVQAEvalDataset(vis_processor, text_processor) | |
| dataloader = torch.utils.data.DataLoader(dataset, batch_size=1) |