Finished the SVHN dataset

Author: Seilmast

test.py

@@ -0,0 +1,4 @@
+from torch.utils.data import Dataset
+from torchvision.datasets import SVHN 
+
+SVHN('data/', download=True)

utils/dataloaders/svhn.py

@@ -1,12 +1,68 @@
+import os 
+from scipy.io import loadmat
+import torch as th
+from torchvision import transforms
 from torch.utils.data import Dataset
+from torchvision.datasets import SVHN 
 
-
-class SVHN(Dataset):
-    def __init__(self):
+class SVHNDataset(Dataset):
+    def __init__(self,
+                 datapath: str,
+                 transforms=None,
+                 download_data=True,
+                 split='train'):
+        """
+        Initializes the SVHNDataset object.
+        Args:
+            datapath (str): Path to where the data lies. If download_data is set to True, this is where the data will be downloaded.
+            transforms: Torch composite of transformations which are to be applied to the dataset images.
+            download_data (bool): If True, downloads the dataset to the specified datapath.
+            split (str): The dataset split to use, either 'train' or 'test'.
+        Raises:
+            AssertionError: If the split is not 'train' or 'test'.
+        """
         super().__init__()
-
+        assert split == 'train' or split == 'test'
+        
+        if download_data:
+            self._download_data(datapath, split)
+        
+        data = loadmat(os.path.join(datapath, f'{split}_32x32.mat'))
+        
+        # Images on the form N x H x W x C
+        self.images = data['X'].transpose(3, 1, 0, 2)
+        self.labels = data['y'].flatten()
+        self.labels[self.labels == 10] = 0
+        
+        self.transforms = transforms
+    def _download_data(self, path: str, split: str):
+        """
+        Downloads the SVHN dataset.
+        Args:
+            path (str): The directory where the dataset will be downloaded.
+            split (str): The dataset split to download, either 'train' or 'test'.
+        """
+        print(f'Downloading SVHN data into {path}')
+        SVHN(path, split=split, download=True) 
+    
     def __len__(self):
-        return
-
+        """
+        Returns the number of samples in the dataset.
+        Returns:
+            int: The number of samples.
+        """
+        return len(self.labels)
     def __getitem__(self, index):
-        return
+        """
+        Retrieves the image and label at the specified index.
+        Args:
+            index (int): The index of the sample to retrieve.
+        Returns:
+            tuple: A tuple containing the image and its corresponding label.
+        """
+        img, lab = self.images[index], self.labels[index]
+        
+        if self.transforms is not None:
+            img = self.transforms(img)
+        
+        return img, lab