Upload nlvr_dataset.py

BLIP/data/nlvr_dataset.py

@@ -0,0 +1,78 @@
+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
+            
+            
+