mirror of
https://github.com/Sygil-Dev/sygil-webui.git
synced 2024-12-14 22:13:41 +03:00
78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
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import os
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import json
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import random
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from torch.utils.data import Dataset
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from torchvision.datasets.utils import download_url
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from PIL import Image
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from data.utils import pre_caption
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class nlvr_dataset(Dataset):
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def __init__(self, transform, image_root, ann_root, split):
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'''
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image_root (string): Root directory of images
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ann_root (string): directory to store the annotation file
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split (string): train, val or test
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'''
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urls = {'train':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_train.json',
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'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_dev.json',
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'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_test.json'}
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filenames = {'train':'nlvr_train.json','val':'nlvr_dev.json','test':'nlvr_test.json'}
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download_url(urls[split],ann_root)
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self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r'))
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self.transform = transform
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self.image_root = image_root
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def __len__(self):
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return len(self.annotation)
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def __getitem__(self, index):
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ann = self.annotation[index]
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image0_path = os.path.join(self.image_root,ann['images'][0])
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image0 = Image.open(image0_path).convert('RGB')
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image0 = self.transform(image0)
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image1_path = os.path.join(self.image_root,ann['images'][1])
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image1 = Image.open(image1_path).convert('RGB')
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image1 = self.transform(image1)
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sentence = pre_caption(ann['sentence'], 40)
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if ann['label']=='True':
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label = 1
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else:
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label = 0
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words = sentence.split(' ')
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if 'left' not in words and 'right' not in words:
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if random.random()<0.5:
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return image0, image1, sentence, label
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else:
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return image1, image0, sentence, label
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else:
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if random.random()<0.5:
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return image0, image1, sentence, label
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else:
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new_words = []
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for word in words:
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if word=='left':
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new_words.append('right')
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elif word=='right':
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new_words.append('left')
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else:
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new_words.append(word)
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sentence = ' '.join(new_words)
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return image1, image0, sentence, label
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