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| import os | |
| import json | |
| import random | |
| from torch.utils.data import Dataset | |
| from torchvision.datasets.utils import download_url | |
| from PIL import Image | |
| from data.utils import pre_caption | |
| class nlvr_dataset(Dataset): | |
| def __init__(self, transform, image_root, ann_root, split): | |
| ''' | |
| image_root (string): Root directory of images | |
| ann_root (string): directory to store the annotation file | |
| split (string): train, val or test | |
| ''' | |
| urls = {'train':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_train.json', | |
| 'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_dev.json', | |
| 'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_test.json'} | |
| filenames = {'train':'nlvr_train.json','val':'nlvr_dev.json','test':'nlvr_test.json'} | |
| download_url(urls[split],ann_root) | |
| self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r')) | |
| self.transform = transform | |
| self.image_root = image_root | |
| def __len__(self): | |
| return len(self.annotation) | |
| def __getitem__(self, index): | |
| ann = self.annotation[index] | |
| image0_path = os.path.join(self.image_root,ann['images'][0]) | |
| image0 = Image.open(image0_path).convert('RGB') | |
| image0 = self.transform(image0) | |
| image1_path = os.path.join(self.image_root,ann['images'][1]) | |
| image1 = Image.open(image1_path).convert('RGB') | |
| image1 = self.transform(image1) | |
| sentence = pre_caption(ann['sentence'], 40) | |
| if ann['label']=='True': | |
| label = 1 | |
| else: | |
| label = 0 | |
| words = sentence.split(' ') | |
| if 'left' not in words and 'right' not in words: | |
| if random.random()<0.5: | |
| return image0, image1, sentence, label | |
| else: | |
| return image1, image0, sentence, label | |
| else: | |
| if random.random()<0.5: | |
| return image0, image1, sentence, label | |
| else: | |
| new_words = [] | |
| for word in words: | |
| if word=='left': | |
| new_words.append('right') | |
| elif word=='right': | |
| new_words.append('left') | |
| else: | |
| new_words.append(word) | |
| sentence = ' '.join(new_words) | |
| return image1, image0, sentence, label | |