Upload data/coco_karpathy_dataset.py

data/coco_karpathy_dataset.py

@@ -0,0 +1,126 @@
+import os
+import json
+
+from torch.utils.data import Dataset
+from torchvision.datasets.utils import download_url
+
+from PIL import Image
+
+from data.utils import pre_caption
+
+class coco_karpathy_train(Dataset):
+    def __init__(self, transform, image_root, ann_root, max_words=30, prompt=''):        
+        '''
+        image_root (string): Root directory of images (e.g. coco/images/)
+        ann_root (string): directory to store the annotation file
+        '''        
+        url = 'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json'
+        filename = 'coco_karpathy_train.json'
+
+        download_url(url,ann_root)
+        
+        self.annotation = json.load(open(os.path.join(ann_root,filename),'r'))
+        self.transform = transform
+        self.image_root = image_root
+        self.max_words = max_words      
+        self.prompt = prompt
+        
+        self.img_ids = {}  
+        n = 0
+        for ann in self.annotation:
+            img_id = ann['image_id']
+            if img_id not in self.img_ids.keys():
+                self.img_ids[img_id] = n
+                n += 1    
+        
+    def __len__(self):
+        return len(self.annotation)
+    
+    def __getitem__(self, index):    
+        
+        ann = self.annotation[index]
+        
+        image_path = os.path.join(self.image_root,ann['image'])        
+        image = Image.open(image_path).convert('RGB')   
+        image = self.transform(image)
+        
+        caption = self.prompt+pre_caption(ann['caption'], self.max_words) 
+
+        return image, caption, self.img_ids[ann['image_id']] 
+    
+    
+class coco_karpathy_caption_eval(Dataset):
+    def __init__(self, transform, image_root, ann_root, split):  
+        '''
+        image_root (string): Root directory of images (e.g. coco/images/)
+        ann_root (string): directory to store the annotation file
+        split (string): val or test
+        '''
+        urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json',
+                'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json'}
+        filenames = {'val':'coco_karpathy_val.json','test':'coco_karpathy_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]
+        
+        image_path = os.path.join(self.image_root,ann['image'])        
+        image = Image.open(image_path).convert('RGB')   
+        image = self.transform(image)          
+        
+        img_id = ann['image'].split('/')[-1].strip('.jpg').split('_')[-1]
+        
+        return image, int(img_id)   
+    
+    
+class coco_karpathy_retrieval_eval(Dataset):
+    def __init__(self, transform, image_root, ann_root, split, max_words=30):  
+        '''
+        image_root (string): Root directory of images (e.g. coco/images/)
+        ann_root (string): directory to store the annotation file
+        split (string): val or test
+        '''
+        urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json',
+                'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json'}
+        filenames = {'val':'coco_karpathy_val.json','test':'coco_karpathy_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
+        
+        self.text = []
+        self.image = []
+        self.txt2img = {}
+        self.img2txt = {}
+        
+        txt_id = 0
+        for img_id, ann in enumerate(self.annotation):
+            self.image.append(ann['image'])
+            self.img2txt[img_id] = []
+            for i, caption in enumerate(ann['caption']):
+                self.text.append(pre_caption(caption,max_words))
+                self.img2txt[img_id].append(txt_id)
+                self.txt2img[txt_id] = img_id
+                txt_id += 1
+                                    
+    def __len__(self):
+        return len(self.annotation)
+    
+    def __getitem__(self, index):    
+        
+        image_path = os.path.join(self.image_root, self.annotation[index]['image'])        
+        image = Image.open(image_path).convert('RGB')    
+        image = self.transform(image)  
+
+        return image, index