Ruff and Isort

Author: c-salomonsen

main.py

@@ -30,9 +30,7 @@ def main():
 
     device = args.device
 
-
     if "usps" in args.dataset.lower():
-
         transform = transforms.Compose(
             [
                 transforms.Resize((28, 28)),
@@ -47,7 +45,6 @@ def main():
         data_dir=args.datafolder,
         transform=transform,
         val_size=args.val_size,
-
     )
 
     train_metrics = MetricWrapper(
@@ -129,7 +126,6 @@ def main():
         project=args.run_name,
         tags=[args.modelname, args.dataset],
         config=args,
-
     )
     wandb.watch(model)
 

tests/test_models.py

@@ -1,7 +1,6 @@
 import pytest
 import torch
 
-
 from utils.models import ChristianModel, JanModel, MagnusModel, SolveigModel
 
 

utils/arg_parser.py

@@ -33,7 +33,6 @@ def get_args():
         help="Whether model should be saved or not.",
     )
 
-
     # Data/Model specific values
     parser.add_argument(
         "--modelname",
@@ -83,7 +82,6 @@ def get_args():
         "--macro_averaging",
         action="store_true",
         help="If the flag is included, the metrics will be calculated using macro averaging.",
-
     )
 
     # Training specific values

utils/dataloaders/svhn.py

@@ -1,6 +1,5 @@
 import os
 
-
 import h5py
 import numpy as np
 from PIL import Image
@@ -95,7 +94,6 @@ def __getitem__(self, index):
             img = Image.fromarray(h5f["images"][index])
 
         if self.nr_channels == 1:
-
             img = img.convert("L")
         if self.transforms is not None:
             img = self.transforms(img)

utils/dataloaders/uspsh5_7_9.py

@@ -1,10 +1,11 @@
+from pathlib import Path
+
 import h5py
 import numpy as np
 import torch
 from PIL import Image
 from torch.utils.data import Dataset
 from torchvision import transforms
-from pathlib import Path
 
 
 class USPSH5_Digit_7_9_Dataset(Dataset):
@@ -31,7 +32,7 @@ class USPSH5_Digit_7_9_Dataset(Dataset):
         A transform function to apply to the images.
     """
 
-    def __init__(self, data_path, train = False, transform=None):
+    def __init__(self, data_path, train=False, transform=None):
         super().__init__()
         """
         Initializes the USPS dataset by loading images and labels from the given `.h5` file.
@@ -108,7 +109,7 @@ def main():
     # Load the dataset
     dataset = USPSH5_Digit_7_9_Dataset(
         data_path="C:/Users/Solveig/OneDrive/Dokumente/UiT PhD/Courses/Git",
-        train = False,
+        train=False,
         transform=transform,
     )
     data_loader = torch.utils.data.DataLoader(dataset, batch_size=2, shuffle=True)

utils/metrics/F1.py

@@ -76,7 +76,9 @@ def _micro_F1(self):
         precision = tp / (tp + fp + 1e-8)  # Avoid division by zero
         recall = tp / (tp + fn + 1e-8)  # Avoid division by zero
 
-        f1 = 2 * precision * recall / (precision + recall + 1e-8)  # Avoid division by zero
+        f1 = (
+            2 * precision * recall / (precision + recall + 1e-8)
+        )  # Avoid division by zero
         return f1
 
     def _macro_F1(self):
@@ -91,10 +93,18 @@ def _macro_F1(self):
         torch.Tensor
             The macro-averaged F1 score.
         """
-        precision_per_class = self.tp / (self.tp + self.fp + 1e-8)  # Avoid division by zero
-        recall_per_class = self.tp / (self.tp + self.fn + 1e-8)  # Avoid division by zero
-        f1_per_class = 2 * precision_per_class * recall_per_class / (
-                    precision_per_class + recall_per_class + 1e-8)  # Avoid division by zero
+        precision_per_class = self.tp / (
+            self.tp + self.fp + 1e-8
+        )  # Avoid division by zero
+        recall_per_class = self.tp / (
+            self.tp + self.fn + 1e-8
+        )  # Avoid division by zero
+        f1_per_class = (
+            2
+            * precision_per_class
+            * recall_per_class
+            / (precision_per_class + recall_per_class + 1e-8)
+        )  # Avoid division by zero
 
         # Take the average of F1 scores across all classes
         f1_score = torch.mean(f1_per_class)
@@ -138,4 +148,3 @@ def forward(self, preds, target):
             f1_score = self._micro_F1()
 
         return f1_score
-