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| # 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 * | |
| from PIL import ImageFile | |
| ImageFile.LOAD_TRUNCATED_IMAGES = True | |
| class Caption(Dataset): | |
| def __init__(self, config, train=True): | |
| self.data_path = config['data_path'] | |
| self.label_path = config['label_path'] | |
| self.experts = config['experts'] | |
| self.prefix = config['prefix'] | |
| self.dataset = config['dataset'] | |
| self.transform = Transform(resize_resolution=config['image_resolution'], scale_size=[0.5, 1.0], train=train) | |
| self.train = train | |
| if train: | |
| self.data_list = [] | |
| if self.dataset in ['coco', 'nocaps']: | |
| self.data_list += json.load(open(os.path.join(self.data_path, 'coco_karpathy_train.json'), 'r')) | |
| else: | |
| if self.dataset == 'coco': | |
| self.data_list = json.load(open(os.path.join(self.data_path, 'coco_karpathy_test.json'), 'r')) | |
| elif self.dataset == 'nocaps': | |
| self.data_list = json.load(open(os.path.join(self.data_path, 'nocaps_val.json'), 'r')) | |
| elif self.dataset == 'demo': | |
| data_folders = glob.glob(f'{self.data_path}/*/') | |
| self.data_list = [{'image': data} for f in data_folders for data in glob.glob(f + '*.jpg')] | |
| self.data_list += [{'image': data} for f in data_folders for data in glob.glob(f + '*.png')] | |
| self.data_list += [{'image': data} for f in data_folders for data in glob.glob(f + '*.jpeg')] | |
| def __len__(self): | |
| return len(self.data_list) | |
| def __getitem__(self, index): | |
| data = self.data_list[index] | |
| if self.dataset == 'coco': | |
| image, labels, labels_info = get_expert_labels(self.data_path, self.label_path, data['image'], 'vqav2', self.experts) | |
| elif self.dataset == 'nocaps': | |
| image, labels, labels_info = get_expert_labels(self.data_path, self.label_path, data['image'], 'nocaps', self.experts) | |
| elif self.dataset == 'demo': | |
| img_path_split = self.data_list[index]['image'].split('/') | |
| img_name = img_path_split[-2] + '/' + img_path_split[-1] | |
| image, labels, labels_info = get_expert_labels('', self.label_path, img_name, 'helpers', self.experts) | |
| experts = self.transform(image, labels) | |
| experts = post_label_process(experts, labels_info) | |
| if self.train: | |
| caption = pre_caption(self.prefix + ' ' + self.data_list[index]['caption'], max_words=30) | |
| return experts, caption | |
| else: | |
| return experts, index | |