fixed tests

luxonis · Sep 25, 2024 · 7f12681 · 7f12681
1 parent 0bdec98
commit 7f12681
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Showing 5 changed files with 10 additions and 9 deletions.
diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py
@@ -248,7 +248,7 @@ def CIFAR10_subset_generator():
 
 @pytest.fixture
 def config(train_overfit: bool) -> dict[str, Any]:
-    if train_overfit:
+    if train_overfit:  # pragma: no cover
         epochs = 100
     else:
         epochs = 1

diff --git a/tests/integration/test_detection.py b/tests/integration/test_detection.py
@@ -78,7 +78,7 @@ def train_and_test(
     model = LuxonisModel(config, opts)
     model.train()
     results = model.test(view="val")
-    if train_overfit:
+    if train_overfit:  # pragma: no cover
         for name, value in results.items():
             if "/map_50" in name or "/kpt_map_medium" in name:
                 assert value > 0.8, f"{name} = {value} (expected > 0.8)"

diff --git a/tests/integration/test_segmentation.py b/tests/integration/test_segmentation.py
@@ -117,7 +117,7 @@ def train_and_test(
     model = LuxonisModel(config, opts)
     model.train()
     results = model.test(view="val")
-    if train_overfit:
+    if train_overfit:  # pragma: no cover
         for name, value in results.items():
             if "metric" in name:
                 assert value > 0.8, f"{name} = {value} (expected > 0.8)"

diff --git a/tests/unittests/test_base_node.py b/tests/unittests/test_base_node.py
@@ -76,7 +76,7 @@ def test_invalid(packet: Packet[Tensor]):
         node.wrap({"inp": torch.rand(3, 224, 224)})
 
 
-def tets_in_sizes():
+def test_in_sizes():
     node = DummyNode(
         input_shapes=[{"features": [Size((3, 224, 224)) for _ in range(3)]}]
     )

diff --git a/tests/unittests/test_utils/test_boxutils.py b/tests/unittests/test_utils/test_boxutils.py
@@ -1,3 +1,5 @@
+from typing import Literal
+
 import pytest
 import torch
 
@@ -13,7 +15,10 @@
 
 
 def generate_random_bboxes(
-    n_bboxes: int, max_width: int, max_height: int, format: str = "xyxy"
+    n_bboxes: int,
+    max_width: int,
+    max_height: int,
+    format: Literal["xyxy", "xywh", "cxcywh"],
 ):
     x1y1 = torch.rand(n_bboxes, 2) * torch.tensor(
         [max_width - 1, max_height - 1]
@@ -33,10 +38,6 @@ def generate_random_bboxes(
     elif format == "cxcywh":
         cxcy = x1y1 + wh / 2
         bboxes = torch.cat((cxcy, wh), dim=1)
-    else:
-        raise ValueError(
-            "Unsupported format. Choose from 'xyxy', 'xywh', 'cxcywh'."
-        )
 
     return bboxes