Upload vqa_dataset.py

BLIP/data/vqa_dataset.py

@@ -0,0 +1,88 @@
+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