Spaces:
Sleeping
Sleeping
| # Copyright (c) 2023, NVIDIA Corporation & Affiliates. All rights reserved. | |
| # | |
| # This work is made available under the Nvidia Source Code License-NC. | |
| # To view a copy of this license, visit | |
| # https://github.com/NVlabs/prismer/blob/main/LICENSE | |
| import glob | |
| from torch.utils.data import Dataset | |
| from dataset.utils import * | |
| class Pretrain(Dataset): | |
| def __init__(self, config): | |
| self.cc12m_data_path = config['cc12m_data_path'] | |
| self.cc3m_data_path = config['cc3m_data_path'] | |
| self.coco_data_path = config['coco_data_path'] | |
| self.vg_data_path = config['vg_data_path'] | |
| self.label_path = config['label_path'] | |
| self.experts = config['experts'] | |
| self.data_list = [] | |
| if 'cc12m' in config['datasets']: | |
| data_folders = glob.glob(f'{self.cc12m_data_path}/cc12m/*/') | |
| self.data_list += [{'image': data} for f in data_folders for data in glob.glob(f + '*.jpg')] | |
| if 'cc3m_sgu' in config['datasets']: | |
| data_folders = glob.glob(f'{self.cc3m_data_path}/cc3m_sgu/*/') | |
| self.data_list += [{'image': data} for f in data_folders for data in glob.glob(f + '*.jpg')] | |
| if 'coco' in config['datasets']: | |
| self.data_list += json.load(open(os.path.join(self.coco_data_path, 'coco_karpathy_train.json'), 'r')) | |
| if 'vg' in config['datasets']: | |
| self.data_list += json.load(open(os.path.join(self.vg_data_path, 'vg_caption.json'), 'r')) | |
| self.transform = Transform(resize_resolution=config['image_resolution'], scale_size=[0.5, 1.5], train=True) | |
| def __len__(self): | |
| return len(self.data_list) | |
| def __getitem__(self, index): | |
| img_path = self.data_list[index]['image'] | |
| if 'cc12m' in img_path: | |
| img_path_split = img_path.split('/') | |
| img_name = img_path_split[-2] + '/' + img_path_split[-1] | |
| image, labels, labels_info = get_expert_labels(self.cc12m_data_path, self.label_path, img_name, 'cc12m', self.experts) | |
| caption_path = img_path.replace('.jpg', '.txt') | |
| with open(caption_path) as f: | |
| caption = f.readlines()[0] | |
| elif 'cc3m_sgu' in img_path: | |
| img_path_split = img_path.split('/') | |
| img_name = img_path_split[-2] + '/' + img_path_split[-1] | |
| image, labels, labels_info = get_expert_labels(self.cc3m_data_path, self.label_path, img_name, 'cc3m_sgu', self.experts) | |
| caption_path = img_path.replace('.jpg', '.txt') | |
| with open(caption_path) as f: | |
| caption = f.readlines()[0] | |
| elif 'train2014' in img_path or 'val2014' in img_path: | |
| image, labels, labels_info = get_expert_labels(self.coco_data_path, self.label_path, img_path, 'vqav2', self.experts) | |
| caption = self.data_list[index]['caption'] | |
| elif 'visual-genome' in img_path: | |
| img_path_split = img_path.split('/') | |
| img_name = img_path_split[-2] + '/' + img_path_split[-1] | |
| image, labels, labels_info = get_expert_labels(self.vg_data_path, self.label_path, img_name, 'vg', self.experts) | |
| caption = self.data_list[index]['caption'] | |
| experts = self.transform(image, labels) | |
| experts = post_label_process(experts, labels_info) | |
| caption = pre_caption(caption, max_words=30) | |
| return experts, caption | |