Format after conflicts resolved

Author: c-salomonsen

main.py

@@ -152,15 +152,16 @@ def main():
     )
     model.to(device)
 
-    trainloader = DataLoader(traindata,
-                             batch_size=args.batchsize,
-                             shuffle=True,
-                             pin_memory=True,
-                             drop_last=True)
-    valiloader = DataLoader(validata,
-                            batch_size=args.batchsize,
-                            shuffle=False,
-                            pin_memory=True)
+    trainloader = DataLoader(
+        traindata,
+        batch_size=args.batchsize,
+        shuffle=True,
+        pin_memory=True,
+        drop_last=True,
+    )
+    valiloader = DataLoader(
+        validata, batch_size=args.batchsize, shuffle=False, pin_memory=True
+    )
 
     criterion = nn.CrossEntropyLoss()
     optimizer = th.optim.Adam(model.parameters(), lr=args.learning_rate)
@@ -170,12 +171,10 @@ def main():
         print("Dry run completed")
         exit(0)
 
-    wandb.init(project='',
-               tags=[])
+    wandb.init(project="", tags=[])
     wandb.watch(model)
 
     for epoch in range(args.epoch):
-
         # Training loop start
         trainingloss = []
         model.train()
@@ -200,12 +199,14 @@ def main():
                 loss = criterion(y, pred)
                 evalloss.append(loss.item())
 
-        wandb.log({
-            'Epoch': epoch,
-            'Train loss': np.mean(trainingloss),
-            'Evaluation Loss': np.mean(evalloss)
-        })
+        wandb.log(
+            {
+                "Epoch": epoch,
+                "Train loss": np.mean(trainingloss),
+                "Evaluation Loss": np.mean(evalloss),
+            }
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

tests/test_metrics.py

@@ -1,4 +1,4 @@
-from utils.metrics import Recall, F1Score
+from utils.metrics import F1Score, Recall
 
 
 def test_recall():

utils/dataloaders/__init__.py

@@ -2,4 +2,4 @@
 
 from .mnist_0_3 import MNISTDataset0_3
 from .usps_0_6 import USPSDataset0_6
-from .uspsh5_7_9 import USPSH5_Digit_7_9_Dataset
+from .uspsh5_7_9 import USPSH5_Digit_7_9_Dataset

utils/load_data.py

@@ -1,6 +1,7 @@
 from torch.utils.data import Dataset
 
-from .dataloaders import MNISTDataset0_3, USPSDataset0_6, USPSH5_Digit_7_9_Dataset
+from .dataloaders import (MNISTDataset0_3, USPSDataset0_6,
+                          USPSH5_Digit_7_9_Dataset)
 
 
 def load_data(dataset: str, *args, **kwargs) -> Dataset:
@@ -10,6 +11,6 @@ def load_data(dataset: str, *args, **kwargs) -> Dataset:
         case "mnist_0-3":
             return MNISTDataset0_3(*args, **kwargs)
         case "usps_7-9":
-            return  USPSH5_Digit_7_9_Dataset(*args, **kwargs)
+            return USPSH5_Digit_7_9_Dataset(*args, **kwargs)
         case _:
             raise ValueError(f"Dataset: {dataset} not implemented.")

utils/metrics/F1.py

@@ -84,4 +84,3 @@ def compute(self):
         )
 
         return f1_score
-

utils/models/solveig_model.py

@@ -4,26 +4,26 @@
 
 class SolveigModel(nn.Module):
     """
-        A Convolutional Neural Network model for classification.
-
-         Args
-        ----
-        image_shape : tuple(int, int, int)
-            Shape of the input image (C, H, W).
-        num_classes : int
-            Number of classes in the dataset.
-
-        Attributes:
-        -----------
-        conv_block1 : nn.Sequential
-            First convolutional block containing a convolutional layer, ReLU activation, and max-pooling.
-        conv_block2 : nn.Sequential
-            Second convolutional block containing a convolutional layer and ReLU activation.
-        conv_block3 : nn.Sequential
-            Third convolutional block containing a convolutional layer and ReLU activation.
-        fc1 : nn.Linear
-            Fully connected layer that outputs the final classification scores.
-        """
+    A Convolutional Neural Network model for classification.
+
+     Args
+    ----
+    image_shape : tuple(int, int, int)
+        Shape of the input image (C, H, W).
+    num_classes : int
+        Number of classes in the dataset.
+
+    Attributes:
+    -----------
+    conv_block1 : nn.Sequential
+        First convolutional block containing a convolutional layer, ReLU activation, and max-pooling.
+    conv_block2 : nn.Sequential
+        Second convolutional block containing a convolutional layer and ReLU activation.
+    conv_block3 : nn.Sequential
+        Third convolutional block containing a convolutional layer and ReLU activation.
+    fc1 : nn.Linear
+        Fully connected layer that outputs the final classification scores.
+    """
 
     def __init__(self, image_shape, num_classes):
         super().__init__()
@@ -34,19 +34,19 @@ def __init__(self, image_shape, num_classes):
         self.conv_block1 = nn.Sequential(
             nn.Conv2d(in_channels=C, out_channels=25, kernel_size=3, padding=1),
             nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=2)
+            nn.MaxPool2d(kernel_size=2, stride=2),
         )
 
         # Define the second convolutional block (conv + relu)
         self.conv_block2 = nn.Sequential(
             nn.Conv2d(in_channels=25, out_channels=50, kernel_size=3, padding=1),
-            nn.ReLU()
+            nn.ReLU(),
         )
 
         # Define the third convolutional block (conv + relu)
         self.conv_block3 = nn.Sequential(
             nn.Conv2d(in_channels=50, out_channels=100, kernel_size=3, padding=1),
-            nn.ReLU()
+            nn.ReLU(),
         )
 
         self.fc1 = nn.Linear(100 * 8 * 8, num_classes)
@@ -64,8 +64,7 @@ def forward(self, x):
 
 
 if __name__ == "__main__":
-
-    x = torch.randn(1,3, 16, 16)
+    x = torch.randn(1, 3, 16, 16)
 
     model = SolveigModel(x.shape[1:], 3)