ruffisorted :'(

Author: hzavadil98

utils/arg_parser.py

@@ -68,20 +68,15 @@ def get_args():
         nargs="+",
         help="Which metric to use for evaluation",
     )
-    
-    parser.add_argument(
-        '--imagesize',
-        type=int,
-        default=28,
-        help='Imagesize'
-    )
-    
+
+    parser.add_argument("--imagesize", type=int, default=28, help="Imagesize")
+
     parser.add_argument(
-        '--nr_channels',
+        "--nr_channels",
         type=int,
         default=1,
-        choices=[1,3],
-        help='Number of image channels'
+        choices=[1, 3],
+        help="Number of image channels",
     )
 
     # Training specific values

utils/dataloaders/__init__.py

@@ -8,6 +8,6 @@
 
 from .download import Downloader
 from .mnist_0_3 import MNISTDataset0_3
+from .svhn import SVHNDataset
 from .usps_0_6 import USPSDataset0_6
 from .uspsh5_7_9 import USPSH5_Digit_7_9_Dataset
-from .svhn import SVHNDataset

utils/dataloaders/svhn.py

@@ -1,4 +1,5 @@
 import os
+
 import numpy as np
 from scipy.io import loadmat
 from torch.utils.data import Dataset
@@ -7,13 +8,13 @@
 
 class SVHNDataset(Dataset):
     def __init__(
-        self, 
-        data_path: str, 
+        self,
+        data_path: str,
         train: bool,
-        transform=None, 
-        download:bool=True, 
-        nr_channels=3
-        ):
+        transform=None,
+        download: bool = True,
+        nr_channels=3,
+    ):
         """
         Initializes the SVHNDataset object.
         Args:
@@ -26,8 +27,8 @@ def __init__(
         """
         super().__init__()
         # assert split == "train" or split == "test"
-        self.split = 'train' if train else 'test'
-    
+        self.split = "train" if train else "test"
+
         if download:
             self._download_data(data_path)
 
@@ -37,7 +38,7 @@ def __init__(
         self.images = data["X"].transpose(3, 1, 0, 2)
         self.labels = data["y"].flatten()
         self.labels[self.labels == 10] = 0
-        
+
         self.nr_channels = nr_channels
         self.transforms = transform
 
@@ -49,7 +50,7 @@ def _download_data(self, path: str):
             split (str): The dataset split to download, either 'train' or 'test'.
         """
         print(f"Downloading SVHN data into {path}")
-        
+
         SVHN(path, split=self.split, download=True)
 
     def __len__(self):
@@ -72,7 +73,7 @@ def __getitem__(self, index):
 
         if self.nr_channels == 1:
             img = np.mean(img, axis=2, keepdims=True)
-        
+
         if self.transforms is not None:
             img = self.transforms(img)
 

utils/load_data.py

@@ -4,9 +4,9 @@
 from .dataloaders import (
     Downloader,
     MNISTDataset0_3,
+    SVHNDataset,
     USPSDataset0_6,
     USPSH5_Digit_7_9_Dataset,
-    SVHNDataset,
 )
 
 

utils/models/magnus_model.py

@@ -22,18 +22,18 @@ def __init__(self, image_shape: int, num_classes: int, imagechannels: int):
         self.image_shape = image_shape
         self.imagechannels = imagechannels
 
-        self.layer1 = nn.Sequential(*([
-                        nn.Linear(self.imagechannels * self.imagesize * self.imagesize, 133),
-                        nn.ReLU(),
-                    ]))
-        self.layer2 = nn.Sequential(*([
-                        nn.Linear(133, 133), 
-                        nn.ReLU()
-                    ]))
-        self.layer3 = nn.Sequential(*([
-                        nn.Linear(133, num_classes), 
-                        nn.ReLU()
-                    ]))
+        self.layer1 = nn.Sequential(
+            *(
+                [
+                    nn.Linear(
+                        self.imagechannels * self.imagesize * self.imagesize, 133
+                    ),
+                    nn.ReLU(),
+                ]
+            )
+        )
+        self.layer2 = nn.Sequential(*([nn.Linear(133, 133), nn.ReLU()]))
+        self.layer3 = nn.Sequential(*([nn.Linear(133, num_classes), nn.ReLU()]))
 
     def forward(self, x):
         """