Move tests to test directory

tests/test_createfolders.py

@@ -0,0 +1,49 @@
+from utils import createfolders
+
+
+def test_createfolders():
+    import argparse
+    from pathlib import Path
+    from tempfile import TemporaryDirectory
+
+    with TemporaryDirectory() as temp_dir:
+        temp_dir = Path(temp_dir)
+
+        parser = argparse.ArgumentParser()
+
+        # Structuture related values
+        parser.add_argument(
+            "--datafolder",
+            type=Path,
+            default=temp_dir / "Data",
+            help="Path to where data will be saved during training.",
+        )
+        parser.add_argument(
+            "--resultfolder",
+            type=Path,
+            default=temp_dir / "Results",
+            help="Path to where results will be saved during evaluation.",
+        )
+        parser.add_argument(
+            "--modelfolder",
+            type=Path,
+            default=temp_dir / "Experiments",
+            help="Path to where model weights will be saved at the end of training.",
+        )
+
+        args = parser.parse_args(
+            [
+                "--datafolder",
+                str(temp_dir / "Data"),
+                "--resultfolder",
+                str(temp_dir / "Results"),
+                "--modelfolder",
+                str(temp_dir / "Experiments"),
+            ]
+        )
+
+        createfolders(args.datafolder, args.resultfolder, args.modelfolder)
+
+        assert (temp_dir / "Data").exists()
+        assert (temp_dir / "Results").exists()
+        assert (temp_dir / "Experiments").exists()

tests/test_dataloaders.py

@@ -0,0 +1,15 @@
+from utils.dataloaders.usps_0_6 import USPSDataset0_6
+
+
+def test_uspsdataset0_6():
+    from pathlib import Path
+
+    import numpy as np
+
+    datapath = Path("data/USPS")
+
+    dataset = USPSDataset0_6(data_path=datapath, train=True)
+    assert len(dataset) == 5460
+    data, target = dataset[0]
+    assert data.shape == (1, 16, 16)
+    assert all(target == np.array([0, 0, 0, 0, 0, 0, 1]))

tests/test_metrics.py

@@ -0,0 +1,16 @@
+from utils.metrics import Recall
+
+
+def test_recall():
+    import torch
+
+    recall = Recall(7)
+
+    y_true = torch.tensor([0, 1, 2, 3, 4, 5, 6])
+    y_pred = torch.tensor([2, 1, 2, 1, 4, 5, 6])
+
+    recall_score = recall(y_true, y_pred)
+
+    assert recall_score.allclose(torch.tensor(0.7143), atol=1e-5), (
+        f"Recall Score: {recall_score.item()}"
+    )

tests/test_models.py

@@ -0,0 +1,19 @@
+import pytest
+import torch
+
+from utils.models import ChristianModel
+
+
+@pytest.mark.parametrize("in_channels, num_classes", [(1, 6), (3, 6)])
+def test_christian_model(in_channels, num_classes):
+    n, c, h, w = 5, in_channels, 16, 16
+
+    model = ChristianModel(c, num_classes)
+
+    x = torch.randn(n, c, h, w)
+    y = model(x)
+
+    assert y.shape == (n, num_classes), f"Shape: {y.shape}"
+    assert y.sum(dim=1).allclose(torch.ones(n), atol=1e-5), (
+        f"Softmax output should sum to 1, but got: {y.sum()}"
+    )

utils/createfolders.py

@@ -16,47 +16,3 @@ def createfolders(*dirs: Path) -> None:
 
     for dir in dirs:
         dir.mkdir(parents=True, exist_ok=True)
-
-
-def test_createfolders():
-    with TemporaryDirectory() as temp_dir:
-        temp_dir = Path(temp_dir)
-
-        parser = argparse.ArgumentParser()
-
-        # Structuture related values
-        parser.add_argument(
-            "--datafolder",
-            type=Path,
-            default=temp_dir / "Data",
-            help="Path to where data will be saved during training.",
-        )
-        parser.add_argument(
-            "--resultfolder",
-            type=Path,
-            default=temp_dir / "Results",
-            help="Path to where results will be saved during evaluation.",
-        )
-        parser.add_argument(
-            "--modelfolder",
-            type=Path,
-            default=temp_dir / "Experiments",
-            help="Path to where model weights will be saved at the end of training.",
-        )
-
-        args = parser.parse_args(
-            [
-                "--datafolder",
-                temp_dir / "Data",
-                "--resultfolder",
-                temp_dir / "Results",
-                "--modelfolder",
-                temp_dir / "Experiments",
-            ]
-        )
-
-        createfolders(args.datafolder, args.resultfolder, args.modelfolder)
-
-        assert (temp_dir / "Data").exists()
-        assert (temp_dir / "Results").exists()
-        assert (temp_dir / "Experiments").exists()

utils/dataloaders/usps_0_6.py

@@ -116,19 +116,3 @@ def __getitem__(self, idx):
             data = self.transform(data)
 
         return data, target
-
-
-def test_uspsdataset0_6():
-    import pytest
-
-    datapath = Path("data/USPS/usps.h5")
-
-    dataset = USPSDataset0_6(path=datapath, mode="train")
-    assert len(dataset) == 5460
-    data, target = dataset[0]
-    assert data.shape == (16, 16)
-    assert target == 6
-
-    # Test for an invalid mode
-    with pytest.raises(ValueError):
-        USPSDataset0_6(path=datapath, mode="inference")

utils/metrics/recall.py

@@ -40,23 +40,3 @@ def forward(self, y_true, y_pred):
         recall = true_positives / (true_positives + false_negatives)
 
         return recall
-
-
-def test_recall():
-    recall = Recall(7)
-
-    y_true = torch.tensor([0, 1, 2, 3, 4, 5, 6])
-    y_pred = torch.tensor([2, 1, 2, 1, 4, 5, 6])
-
-    recall_score = recall(y_true, y_pred)
-
-    assert recall_score.allclose(torch.tensor(0.7143), atol=1e-5), f"Recall Score: {recall_score.item()}"
-
-
-def test_one_hot_encode():
-    num_classes = 7
-
-    y_true = torch.tensor([0, 1, 2, 3, 4, 5, 6])
-    y_onehot = one_hot_encode(y_true, num_classes)
-
-    assert y_onehot.shape == (7, 7), f"Shape: {y_onehot.shape}"

utils/models/christian_model.py

@@ -1,4 +1,3 @@
-import pytest
 import torch
 import torch.nn as nn
 
@@ -49,6 +48,7 @@ class ChristianModel(nn.Module):
     CNN2 Output Shape: (5, 100, 4, 4)
     FC Output Shape: (5, num_classes)
     """
+
     def __init__(self, in_channels, num_classes):
         super().__init__()
 
@@ -69,21 +69,7 @@ def forward(self, x):
         return x
 
 
-@pytest.mark.parametrize("in_channels, num_classes", [(1, 6), (3, 6)])
-def test_christian_model(in_channels, num_classes):
-    n, c, h, w = 5, in_channels, 16, 16
-
-    model = ChristianModel(c, num_classes)
-
-    x = torch.randn(n, c, h, w)
-    y = model(x)
-
-    assert y.shape == (n, num_classes), f"Shape: {y.shape}"
-    assert y.sum(dim=1).allclose(torch.ones(n), atol=1e-5), f"Softmax output should sum to 1, but got: {y.sum()}"
-
-
 if __name__ == "__main__":
-
     model = ChristianModel(3, 7)
 
     x = torch.randn(3, 3, 16, 16)