Update README.md

README.md

@@ -42,6 +42,77 @@ Finally `train.json`, `val.json`, `test.json` store box, label, score and path i
   "labels": ["bird", "dirt field", "vulture", "land"], 
   "masks": ["masks/val_masks/ILSVRC2012_val_00000025_n01616318_00.png", "masks/val_masks/ILSVRC2012_val_00000025_n01616318_01.png", "masks/val_masks/ILSVRC2012_val_00000025_n01616318_02.png", "masks/val_masks/ILSVRC2012_val_00000025_n01616318_03.png"]
 }
+
+You can use this dataloader for your patch level labels. Patch size is a hyperparameter.
+
+```
+class PatchDataset(Dataset):
+    def __init__(self, dataset, patch_size=16, width=224, height=224):
+        """
+        dataset: A list of dictionaries, each dictionary corresponds to an image and its details
+        """
+        self.dataset = dataset
+        self.transform = transforms.Compose([
+            transforms.Resize((width, height)), # Resize the image
+            # 3 channels
+            # transforms.Grayscale(num_output_channels=3), # Convert the image to grayscale
+            transforms.ToTensor(), # Convert the image to a tensor
+        ])
+        self.patch_size = patch_size
+
+        self.width = width
+        self.height = height
+        
+    def __len__(self):
+        return len(self.dataset)
+    
+    def __getitem__(self, idx):
+        item = self.dataset[idx]
+        image = self.transform(item['image'])
+        masks = item['masks']
+        labels = item['labels']  # Assuming labels are aligned with masks
+        
+        # Calculate the size of the reduced mask
+        num_patches = self.width // self.patch_size
+        label_array = [[[] for _ in range(num_patches)] for _ in range(num_patches)]
+        
+        for mask, label in zip(masks, labels):
+            # Resize and reduce the mask
+            mask = mask.resize((self.width, self.height))
+            mask_array = np.array(mask) > 0
+            reduced_mask = self.reduce_mask(mask_array)
+            
+            # Populate the label array based on the reduced mask
+            for i in range(num_patches):
+                for j in range(num_patches):
+                    if reduced_mask[i, j]:
+                        label_array[i][j].append(label)
+        
+        # Convert label_array to a format suitable for tensor operations, if necessary
+        # For now, it's a list of lists of lists, which can be used directly in Python
+        
+        return image, label_array
+    
+
+    def reduce_mask(self, mask):
+        """
+        Reduce the mask size by dividing it into patches and checking if there's at least
+        one True value within each patch.
+        """
+        # Calculate new height and width
+        new_h = mask.shape[0] // self.patch_size
+        new_w = mask.shape[1] // self.patch_size
+        
+        reduced_mask = np.zeros((new_h, new_w), dtype=bool)
+        
+        for i in range(new_h):
+            for j in range(new_w):
+                patch = mask[i*self.patch_size:(i+1)*self.patch_size, j*self.patch_size:(j+1)*self.patch_size]
+                reduced_mask[i, j] = np.any(patch)  # Set to True if any value in the patch is True
+        
+        return reduced_mask
+```
+
 ```
 ### Citation