--- a
+++ b/PathBLIP/dataset.py
@@ -0,0 +1,80 @@
+from torch.utils.data import Dataset
+import pandas as pd
+import os
+from PIL import Image
+from torchvision import transforms
+from collections import defaultdict
+import torch
+
+class ImageTextContrastiveCollator:
+    def __init__(self):
+        return
+    def __call__(self, batch):
+        inputs = defaultdict(list)
+        for data in batch:
+            inputs['image'].append(data['image'])
+            inputs['text_input'].append(data['text_input'])
+            inputs['text_output'].append(data['text_output'])
+            
+
+        # inputs['image'] = torch.stack(inputs['image'])
+
+        return inputs
+
+class Quiltdataset(Dataset):
+    def __init__(self):
+        # self.df = pd.read_csv(csv_path)
+        self.df = pd.read_csv('../BLIP/LAVIS-main/quilt.csv')
+        self.df = self.df.dropna(axis=0, subset=['pathology'])[400000:]
+        normalize = transforms.Normalize(
+            (0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)
+        )
+
+        self.transform = transforms.Compose(
+            [
+                transforms.RandomResizedCrop(224, scale=(0.2, 1.0)),
+                transforms.RandomHorizontalFlip(),
+                transforms.ToTensor(),
+                normalize,
+            ]
+        )
+        
+    def __len__(self):
+        return len(self.df)
+    def __getitem__(self, index):
+        caption = self.df.iloc[index]['caption']
+        if type(caption) == float:
+            caption = "This is a image about the pathology."
+        img_path = self.df.iloc[index]['image_path']
+        # img_path = os.path.join("../", img_path)
+        # image = Image.open(img_path).convert('RGB')
+        # image = self.transform(image)
+        # caption = self.text_processor(caption)
+        # img = self.transform(img)
+        
+        caption = caption.split()
+        prefix = caption[:int(len(caption) * 0.2)]
+        subfix = caption[int(len(caption) * 0.2):]
+        prefix = " ".join(prefix)
+        subfix = " ".join(subfix)
+        return {
+            "image": img_path,
+            "text_input": prefix,
+            "text_output": subfix,
+        }
+        # return {
+        #     "image": img_path,
+        #     "text_input": caption,
+        #     "text_output": caption,
+        # }
+         
+        
+        
+if __name__ == '__main__':
+    test = Quiltdataset()
+    print(test.__len__())
+    print(test.__getitem__(0))
+    print(test.__getitem__(1))
+    
+    
+    
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