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