mirror of
https://github.com/Sygil-Dev/sygil-webui.git
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88 lines
3.4 KiB
Python
88 lines
3.4 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 PIL import Image
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import torch
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from torch.utils.data import Dataset
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from data.utils import pre_question
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from torchvision.datasets.utils import download_url
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class vqa_dataset(Dataset):
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def __init__(self, transform, ann_root, vqa_root, vg_root, train_files=[], split="train"):
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self.split = split
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self.transform = transform
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self.vqa_root = vqa_root
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self.vg_root = vg_root
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if split=='train':
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urls = {'vqa_train':'https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_train.json',
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'vqa_val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_val.json',
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'vg_qa':'https://storage.googleapis.com/sfr-vision-language-research/datasets/vg_qa.json'}
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self.annotation = []
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for f in train_files:
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download_url(urls[f],ann_root)
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self.annotation += json.load(open(os.path.join(ann_root,'%s.json'%f),'r'))
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else:
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download_url('https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_test.json',ann_root)
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self.annotation = json.load(open(os.path.join(ann_root,'vqa_test.json'),'r'))
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download_url('https://storage.googleapis.com/sfr-vision-language-research/datasets/answer_list.json',ann_root)
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self.answer_list = json.load(open(os.path.join(ann_root,'answer_list.json'),'r'))
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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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if ann['dataset']=='vqa':
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image_path = os.path.join(self.vqa_root,ann['image'])
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elif ann['dataset']=='vg':
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image_path = os.path.join(self.vg_root,ann['image'])
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image = Image.open(image_path).convert('RGB')
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image = self.transform(image)
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if self.split == 'test':
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question = pre_question(ann['question'])
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question_id = ann['question_id']
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return image, question, question_id
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elif self.split=='train':
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question = pre_question(ann['question'])
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if ann['dataset']=='vqa':
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answer_weight = {}
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for answer in ann['answer']:
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if answer in answer_weight.keys():
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answer_weight[answer] += 1/len(ann['answer'])
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else:
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answer_weight[answer] = 1/len(ann['answer'])
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answers = list(answer_weight.keys())
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weights = list(answer_weight.values())
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elif ann['dataset']=='vg':
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answers = [ann['answer']]
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weights = [0.2]
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return image, question, answers, weights
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def vqa_collate_fn(batch):
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image_list, question_list, answer_list, weight_list, n = [], [], [], [], []
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for image, question, answer, weights in batch:
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image_list.append(image)
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question_list.append(question)
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weight_list += weights
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answer_list += answer
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n.append(len(answer))
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return torch.stack(image_list,dim=0), question_list, answer_list, torch.Tensor(weight_list), n
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