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| # Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import copy | |
| import os | |
| import json | |
| from tqdm import tqdm | |
| import ipdb | |
| import random | |
| from torch.nn.utils.rnn import pad_sequence | |
| from dataclasses import dataclass, field | |
| from typing import Callable, Dict, Sequence | |
| import torch | |
| import torch.distributed as dist | |
| import transformers | |
| from torch.utils.data import Dataset | |
| from tqdm import tqdm | |
| class SupervisedDataset(Dataset): | |
| """Dataset for supervised fine-tuning.""" | |
| def __init__(self, data_path: str, image_root_path: str): | |
| super(SupervisedDataset, self).__init__() | |
| with open(data_path, 'r') as f: | |
| json_data = json.load(f) | |
| # for debug: | |
| #json_data = json_data[:100000] | |
| self.image_path_list, self.caption_list = [], [] | |
| for item in json_data: | |
| one_image_name, one_caption = item["image_name"], item["conversation"] | |
| # TODO: stage 2 dataset format is invalid | |
| if not one_image_name.endswith('.jpg'): | |
| one_image_name += '.jpg' | |
| one_image_path = image_root_path + '/{}'.format(one_image_name) | |
| self.image_path_list.append(one_image_path) | |
| self.caption_list.append(one_caption) | |
| print(f'[!] collect {len(self.image_path_list)} samples for training') | |
| def __len__(self): # number of instances | |
| return len(self.image_path_list) | |
| #def __getitem__(self, i) -> Dict[str, torch.Tensor]: # how to get item, 取一个样本 | |
| def __getitem__(self, i): | |
| return dict(image_paths=self.image_path_list[i], output_texts=self.caption_list[i]) | |
| def collate(self, instances): | |
| image_paths, output_texts = tuple([instance[key] for instance in instances] for key in ("image_paths", "output_texts")) | |
| return dict( | |
| image_paths=image_paths, | |
| output_texts=output_texts | |
| ) | |