Add linter tests

.github/workflows/pytest.yml

@@ -30,6 +30,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install .
+        pip install black
 
     - name: Run tests
       run: |

alignit/visualize.py

@@ -25,11 +25,7 @@ def get_data(index):
         outputs=[gr.Image(type="pil", label="Image"), gr.Text(label="Label")],
         title="Dataset Image Viewer",
         live=True,
-    ).launch(
-        share=cfg.share,
-        server_name=cfg.server_name,
-        server_port=cfg.server_port
-    )
+    ).launch(share=cfg.share, server_name=cfg.server_name, server_port=cfg.server_port)
 
 
 if __name__ == "__main__":

tests/test_alignnet.py

@@ -4,7 +4,12 @@
 
 
 def test_alignnet_forward_shapes_cpu():
-    model = AlignNet(backbone_name="resnet18", backbone_weights=None, use_vector_input=False, output_dim=7)
+    model = AlignNet(
+        backbone_name="resnet18",
+        backbone_weights=None,
+        use_vector_input=False,
+        output_dim=7,
+    )
     model.eval()
     x = torch.randn(2, 3, 3, 64, 64)  # B=2, N=3 views
     with torch.no_grad():
@@ -13,7 +18,12 @@ def test_alignnet_forward_shapes_cpu():
 
 
 def test_alignnet_with_vector_input():
-    model = AlignNet(backbone_name="resnet18", backbone_weights=None, use_vector_input=True, output_dim=7)
+    model = AlignNet(
+        backbone_name="resnet18",
+        backbone_weights=None,
+        use_vector_input=True,
+        output_dim=7,
+    )
     model.eval()
     x = torch.randn(1, 2, 3, 64, 64)
     vecs = [torch.randn(5)]
@@ -23,12 +33,18 @@ def test_alignnet_with_vector_input():
 
 
 def test_alignnet_performance():
-    model = AlignNet(backbone_name="efficientnet_b0", backbone_weights=None, use_vector_input=True, output_dim=7)
+    model = AlignNet(
+        backbone_name="efficientnet_b0",
+        backbone_weights=None,
+        use_vector_input=True,
+        output_dim=7,
+    )
     model.eval()
     x = torch.randn(1, 3, 3, 224, 224)  # B=1, N=3 views
     vecs = [torch.randn(5)]
     with torch.no_grad():
         import time
+
         start_time = time.time()
         for _ in range(10):
             y = model(x, vecs)

tests/test_dataset_loader.py

@@ -1,7 +1,3 @@
-import os
-
-import pytest
-
 from alignit.utils.dataset import load_dataset
 
 
@@ -19,6 +15,7 @@ def fake_load_from_disk(p):
 
     # Patch inside module
     import alignit.utils.dataset as ds
+
     ds.load_from_disk = fake_load_from_disk
 
     d = load_dataset(str(tmp_path))
@@ -35,6 +32,7 @@ def fake_hf_load_dataset(name):
         return Dummy(name)
 
     import alignit.utils.dataset as ds
+
     ds.hf_load_dataset = fake_hf_load_dataset
 
     d = load_dataset("my-dataset/name")

tests/test_lint.py

@@ -0,0 +1,18 @@
+import subprocess
+import sys
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+
+
+def run(cmd: list[str]):
+    result = subprocess.run(cmd, cwd=PROJECT_ROOT, capture_output=True, text=True)
+    if result.returncode != 0:
+        print(result.stdout)
+        print(result.stderr, file=sys.stderr)
+    return result.returncode == 0
+
+
+def test_black():
+    result = run([sys.executable, "-m", "black", "--check", "--diff", "--verbose", "."])
+    assert result, "Black formatting issues found"

tests/test_tfs.py

@@ -21,5 +21,5 @@ def test_get_pose_str_format():
     pose = np.eye(4)
     s = get_pose_str(pose)
     assert isinstance(s, str)
-    parts = s.split(',')
+    parts = s.split(",")
     assert len(parts) == 6