Unit tests upgrade.

depthai_nodes/ml/messages/creators/clusters.py

@@ -25,7 +25,7 @@ def create_cluster_message(clusters: List[List[List[Union[float, int]]]]) -> Clu
         if not isinstance(cluster, list):
             raise TypeError(f"All clusters must be of type List, got {type(cluster)}")
         for point in cluster:
-            if isinstance(point, list):
+            if not isinstance(point, list):
                 raise TypeError(
                     f"All points in clusters must be of type List, got {type(point)}"
                 )

depthai_nodes/ml/parsers/utils/keypoints.py

@@ -60,23 +60,23 @@ def transform_to_keypoints(
         raise ValueError("Keypoints must be a numpy array.")
     if len(keypoints.shape) != 2:
         raise ValueError(
-            "Keypoints must be of shape (N, 2). Got shape {keypoints.shape}."
+            f"Keypoints must be of shape (N, 2). Got shape {keypoints.shape}."
         )
     if keypoints.shape[1] != 2 and keypoints.shape[1] != 3:
         raise ValueError(
-            "Keypoints must be of shape (N, 2) or (N, 3). Got shape {keypoints.shape}."
+            f"Keypoints must be of shape (N, 2) or (N, 3). Got shape {keypoints.shape}."
         )
 
     if confidences is not None:
         if not isinstance(confidences, np.ndarray):
             raise ValueError("Confidences must be a numpy array.")
         if len(confidences.shape) != 1:
             raise ValueError(
-                "Confidences must be of shape (N,). Got shape {confidences.shape}."
+                f"Confidences must be of shape (N,). Got shape {confidences.shape}."
             )
         if len(confidences) != len(keypoints):
             raise ValueError(
-                "Confidences should have same length as keypoints, got {len(confidences)} confidences and {len(keypoints)} keypoints."
+                f"Confidences should have same length as keypoints, got {len(confidences)} confidences and {len(keypoints)} keypoints."
             )
 
     dim = keypoints.shape[1]

tests/unittests/test_creators/test_classification_sequence.py

@@ -1,102 +1,93 @@
-import re
-
 import numpy as np
 import pytest
 
+from depthai_nodes.ml.messages import Classifications
 from depthai_nodes.ml.messages.creators import (
     create_classification_sequence_message,
 )
 
 
+def test_valid_input():
+    classes = ["cat", "dog", "bird"]
+    scores = [[0.7, 0.2, 0.1], [0.1, 0.8, 0.1], [0.2, 0.3, 0.5]]
+    message = create_classification_sequence_message(classes, scores)
+
+    assert isinstance(message, Classifications)
+    assert message.classes == ["cat", "dog", "bird"]
+    assert np.array_equal(message.scores, np.array([0.7, 0.8, 0.5], dtype=np.float32))
+
+
 def test_none_classes():
     with pytest.raises(ValueError):
         create_classification_sequence_message(None, [0.5, 0.2, 0.3])
 
 
-def test_1D_scores():
-    with pytest.raises(
-        ValueError, match=re.escape("Scores should be a 2D array, got (3,).")
-    ):
+def test_empty_scores():
+    with pytest.raises(ValueError):
+        create_classification_sequence_message(["cat", "dog", "bird"], [[]])
+
+
+def test_invalid_classes():
+    with pytest.raises(ValueError):
+        create_classification_sequence_message("not a list", [[0.7, 0.2, 0.1]])
+
+
+def test_1d_scores():
+    with pytest.raises(ValueError):
         create_classification_sequence_message(["cat", "dog", "bird"], [0.5, 0.2, 0.3])
 
 
-def test_empty_scores():
-    with pytest.raises(
-        ValueError,
-        match=re.escape(
-            "Number of classes and scores mismatch. Provided 3 class names and 0 scores."
-        ),
-    ):
-        create_classification_sequence_message(["cat", "dog", "bird"], [[]])
+def test_invalid_scores():
+    with pytest.raises(ValueError):
+        create_classification_sequence_message(["cat", "dog", "bird"], [0.7, 0.2, 0.1])
 
 
-def test_scores():
-    with pytest.raises(
-        ValueError, match=re.escape("Scores should be in the range [0, 1].")
-    ):
+def test_mismatched_lengths():
+    with pytest.raises(ValueError):
         create_classification_sequence_message(
-            ["cat", "dog", "bird"], [[0.5, -0.2, 1.3]]
+            ["cat", "dog", "bird"], [[0.7, 0.2], [0.1, 0.8]]
         )
 
 
-def test_probabilities():
-    with pytest.raises(
-        ValueError, match=re.escape("Each row of scores should sum to 1.")
-    ):
+def test_scores_out_of_range():
+    with pytest.raises(ValueError):
         create_classification_sequence_message(
-            ["cat", "dog", "bird"], [[0.5, 0.2, 0.3], [0.5, 0.2, 0.4]]
+            ["cat", "dog", "bird"], [[1.2, 0.2, 0.1]]
         )
 
 
-def test_non_list_ignored_indexes():
-    with pytest.raises(
-        ValueError,
-        match=re.escape("Ignored indexes should be a list, got <class 'float'>."),
-    ):
+def test_scores_not_sum_to_1():
+    with pytest.raises(ValueError):
         create_classification_sequence_message(
-            ["cat", "dog", "bird"], [[0.5, 0.2, 0.3]], ignored_indexes=1.0
+            ["cat", "dog", "bird"], [[0.7, 0.2, 0.2]]
         )
 
 
-def test_integer_ignored_indexes():
-    with pytest.raises(
-        ValueError, match=re.escape("Ignored indexes should be integers.")
-    ):
+def test_invalid_ignored_indexes():
+    with pytest.raises(ValueError):
         create_classification_sequence_message(
-            ["cat", "dog", "bird"], [[0.5, 0.2, 0.3]], ignored_indexes=[1.0]
+            ["cat", "dog", "bird"], [[0.7, 0.2, 0.1]], ignored_indexes="not a list"
         )
 
 
-def test_2D_list_integers():
-    with pytest.raises(
-        ValueError, match=re.escape("Ignored indexes should be integers.")
-    ):
+def test_ignored_indexes_out_of_range():
+    with pytest.raises(ValueError):
         create_classification_sequence_message(
-            ["cat", "dog", "bird"], [[0.5, 0.2, 0.3]], ignored_indexes=[[3]]
+            ["cat", "dog", "bird"], [[0.7, 0.2, 0.1]], ignored_indexes=[3]
         )
 
 
-def test_upper_limit_integers():
-    with pytest.raises(
-        ValueError,
-        match=re.escape(
-            "Ignored indexes should be integers in the range [0, num_classes -1]."
-        ),
-    ):
+def test_integer_ignored_indexes():
+    with pytest.raises(ValueError):
         create_classification_sequence_message(
-            ["cat", "dog", "bird"], [[0.5, 0.2, 0.3]], ignored_indexes=[3]
+            ["cat", "dog", "bird"], [[0.5, 0.2, 0.3]], ignored_indexes=[1.0]
         )
 
 
-def test_lower_limit_integers():
-    with pytest.raises(
-        ValueError,
-        match=re.escape(
-            "Ignored indexes should be integers in the range [0, num_classes -1]."
-        ),
-    ):
+def test_2D_list_integers():
+    with pytest.raises(ValueError):
         create_classification_sequence_message(
-            ["cat", "dog", "bird"], [[0.5, 0.2, 0.3]], ignored_indexes=[-1]
+            ["cat", "dog", "bird"], [[0.5, 0.2, 0.3]], ignored_indexes=[[3]]
         )
 
 
@@ -110,34 +101,6 @@ def test_remove_duplicates():
     assert np.all(res.scores == np.array([0.5], dtype=np.float32))
 
 
-def test_ignored_indexes():
-    res = create_classification_sequence_message(
-        ["cat", "dog", "bird"], [[0.5, 0.2, 0.3], [0.1, 0.6, 0.3]], ignored_indexes=[1]
-    )
-    assert res.classes == ["cat"]
-    assert np.all(res.scores == np.array([0.5], dtype=np.float32))
-
-
-def test_all_ignored_indexes():
-    res = create_classification_sequence_message(
-        ["cat", "dog", "bird"],
-        [[0.5, 0.2, 0.3], [0.1, 0.6, 0.3]],
-        ignored_indexes=[0, 1, 2],
-    )
-    assert len(res.classes) == 0
-    assert len(res.scores) == 0
-
-
-def test_two_ignored_indexes():
-    res = create_classification_sequence_message(
-        ["cat", "dog", "bird"],
-        [[0.5, 0.2, 0.3], [0.1, 0.6, 0.3]],
-        ignored_indexes=[0, 2],
-    )
-    assert res.classes == ["dog"]
-    assert np.all(res.scores == np.array([0.6], dtype=np.float32))
-
-
 def test_concatenate_chars_nospace():
     res = create_classification_sequence_message(
         ["c", "a", "t"],
@@ -229,7 +192,3 @@ def test_concatenate_mixed_words():
     )
     assert res.classes == ["Quick b fox jumps o"]
     assert np.allclose(res.scores, [0.5])
-
-
-if __name__ == "__main__":
-    pytest.main()