Ruff + isort

Author: c-salomonsen

CollaborativeCoding/dataloaders/uspsh5_7_9.py

@@ -32,7 +32,9 @@ class USPSH5_Digit_7_9_Dataset(Dataset):
         A transform function to apply to the images.
     """
 
-    def __init__(self, data_path, sample_ids, train=False, transform=None,  nr_channels=1):
+    def __init__(
+        self, data_path, sample_ids, train=False, transform=None, nr_channels=1
+    ):
         super().__init__()
         """
         Initializes the USPS dataset by loading images and labels from the given `.h5` file.
@@ -112,7 +114,8 @@ def main():
     indices = np.array([7, 8, 9])
     # Load the dataset
     dataset = USPSH5_Digit_7_9_Dataset(
-        data_path="C:/Users/Solveig/OneDrive/Dokumente/UiT PhD/Courses/Git", sample_ids=indices,
+        data_path="C:/Users/Solveig/OneDrive/Dokumente/UiT PhD/Courses/Git",
+        sample_ids=indices,
         train=False,
         transform=transform,
     )

CollaborativeCoding/metrics/F1.py

@@ -159,11 +159,13 @@ def __returnmetric__(self):
             else:
                 self.y_true = torch.cat(self.y_true)
                 self.y_pred = torch.cat(self.y_pred)
-        return self._micro_F1(self.y_true, self.y_pred) if not self.macro_averaging else self._macro_F1(self.y_true, self.y_pred)
+        return (
+            self._micro_F1(self.y_true, self.y_pred)
+            if not self.macro_averaging
+            else self._macro_F1(self.y_true, self.y_pred)
+        )
 
     def __reset__(self):
         self.y_true = []
         self.y_pred = []
         return None
-
-

CollaborativeCoding/models/solveig_model.py

@@ -4,24 +4,24 @@
 
 def find_fc_input_shape(image_shape, model):
     """
-      Find the shape of the input to the fully connected layer after passing through the convolutional layers.
-
-      Code inspired by @Seilmast (https://github.com/SFI-Visual-Intelligence/Collaborative-Coding-Exam/issues/67#issuecomment-2651212254)
-
-      Args
-      ----
-      image_shape : tuple(int, int, int)
-          Shape of the input image (C, H, W), where C is the number of channels,
-          H is the height, and W is the width of the image.
-      model : nn.Module
-          The CNN model containing the convolutional layers, whose output size is used to
-          determine the number of input features for the fully connected layer.
-
-      Returns
-      -------
-      int
-          The number of elements in the input to the fully connected layer.
-      """
+    Find the shape of the input to the fully connected layer after passing through the convolutional layers.
+
+    Code inspired by @Seilmast (https://github.com/SFI-Visual-Intelligence/Collaborative-Coding-Exam/issues/67#issuecomment-2651212254)
+
+    Args
+    ----
+    image_shape : tuple(int, int, int)
+        Shape of the input image (C, H, W), where C is the number of channels,
+        H is the height, and W is the width of the image.
+    model : nn.Module
+        The CNN model containing the convolutional layers, whose output size is used to
+        determine the number of input features for the fully connected layer.
+
+    Returns
+    -------
+    int
+        The number of elements in the input to the fully connected layer.
+    """
 
     dummy_img = torch.randn(1, *image_shape)
     with torch.no_grad():

main.py

@@ -1,11 +1,11 @@
 import numpy as np
 import torch as th
 import torch.nn as nn
+import wandb
 from torch.utils.data import DataLoader
 from torchvision import transforms
 from tqdm import tqdm
 
-import wandb
 from CollaborativeCoding import (
     MetricWrapper,
     createfolders,
@@ -17,7 +17,6 @@
 # from wandb_api import WANDB_API
 
 
-
 def main():
     """
 

tests/test_metrics.py

@@ -91,8 +91,12 @@ def test_f1score():
     macro_f1_score = f1_macro.__returnmetric__()
 
     # Check if outputs are tensors
-    assert isinstance(micro_f1_score, torch.Tensor), "Micro F1 score should be a tensor."
-    assert isinstance(macro_f1_score, torch.Tensor), "Macro F1 score should be a tensor."
+    assert isinstance(micro_f1_score, torch.Tensor), (
+        "Micro F1 score should be a tensor."
+    )
+    assert isinstance(macro_f1_score, torch.Tensor), (
+        "Macro F1 score should be a tensor."
+    )
 
     # Check that F1 scores are between 0 and 1
     assert 0 <= micro_f1_score.item() <= 1, "Micro F1 score should be between 0 and 1."

tests/test_wrappers.py

@@ -65,9 +65,7 @@ def test_load_data(data_name, expected):
             assert len(dataset) > 0
             assert isinstance(dataset[0], tuple)
             assert isinstance(dataset[0][0], torch.Tensor)
-            assert isinstance(
-                dataset[0][1], int
-            )
+            assert isinstance(dataset[0][1], int)
 
 
 def test_load_metric():