Ultralytics Code Refactor https://ultralytics.com/actions (ultralytic…

…s#14109)

Signed-off-by: Glenn Jocher <[email protected]>
Co-authored-by: UltralyticsAssistant <[email protected]>

examples/YOLOv8-Action-Recognition/action_recognition.py

@@ -69,7 +69,7 @@ def available_model_names() -> List[str]:
         """
         return list(TorchVisionVideoClassifier.model_name_to_model_and_weights.keys())
 
-    def preprocess_crops_for_video_cls(self, crops: List[np.ndarray], input_size: list = [224, 224]) -> torch.Tensor:
+    def preprocess_crops_for_video_cls(self, crops: List[np.ndarray], input_size: list = None) -> torch.Tensor:
         """
         Preprocess a list of crops for video classification.
 
@@ -80,6 +80,8 @@ def preprocess_crops_for_video_cls(self, crops: List[np.ndarray], input_size: li
         Returns:
             torch.Tensor: Preprocessed crops as a tensor with dimensions (1, T, C, H, W).
         """
+        if input_size is None:
+            input_size = [224, 224]
         from torchvision.transforms import v2
 
         transform = v2.Compose(
@@ -156,7 +158,7 @@ def __init__(
             model = model.half()
         self.model = model.eval()
 
-    def preprocess_crops_for_video_cls(self, crops: List[np.ndarray], input_size: list = [224, 224]) -> torch.Tensor:
+    def preprocess_crops_for_video_cls(self, crops: List[np.ndarray], input_size: list = None) -> torch.Tensor:
         """
         Preprocess a list of crops for video classification.
 
@@ -167,6 +169,8 @@ def preprocess_crops_for_video_cls(self, crops: List[np.ndarray], input_size: li
         Returns:
             torch.Tensor: Preprocessed crops as a tensor (1, T, C, H, W).
         """
+        if input_size is None:
+            input_size = [224, 224]
         from torchvision.transforms import v2
 
         transform = v2.Compose(
@@ -266,15 +270,7 @@ def run(
     video_cls_overlap_ratio: float = 0.25,
     fp16: bool = False,
     video_classifier_model: str = "microsoft/xclip-base-patch32",
-    labels: List[str] = [
-        "walking",
-        "running",
-        "brushing teeth",
-        "looking into phone",
-        "weight lifting",
-        "cooking",
-        "sitting",
-    ],
+    labels: List[str] = None,
 ) -> None:
     """
     Run action recognition on a video source using YOLO for object detection and a video classifier.
@@ -295,6 +291,16 @@ def run(
     Returns:
         None</edit>
     """
+    if labels is None:
+        labels = [
+            "walking",
+            "running",
+            "brushing teeth",
+            "looking into phone",
+            "weight lifting",
+            "cooking",
+            "sitting",
+        ]
     # Initialize models and device
     device = select_device(device)
     yolo_model = YOLO(weights).to(device)
@@ -312,9 +318,7 @@ def run(
     # Initialize video capture
     if source.startswith("http") and urlparse(source).hostname in {"www.youtube.com", "youtube.com", "youtu.be"}:
         source = get_best_youtube_url(source)
-    elif source.endswith(".mp4"):
-        pass
-    else:
+    elif not source.endswith(".mp4"):
         raise ValueError("Invalid source. Supported sources are YouTube URLs and MP4 files.")
     cap = cv2.VideoCapture(source)
 

examples/YOLOv8-OpenCV-int8-tflite-Python/main.py

@@ -18,6 +18,7 @@ class LetterBox:
     def __init__(
         self, new_shape=(img_width, img_height), auto=False, scaleFill=False, scaleup=True, center=True, stride=32
     ):
+        """Initializes LetterBox with parameters for reshaping and transforming image while maintaining aspect ratio."""
         self.new_shape = new_shape
         self.auto = auto
         self.scaleFill = scaleFill

tests/conftest.py

@@ -11,18 +11,24 @@ def pytest_addoption(parser):
     Add custom command-line options to pytest.
 
     Args:
-        parser (pytest.config.Parser): The pytest parser object.
+        parser (pytest.config.Parser): The pytest parser object for adding custom command-line options.
+
+    Returns:
+        (None)
     """
     parser.addoption("--slow", action="store_true", default=False, help="Run slow tests")
 
 
 def pytest_collection_modifyitems(config, items):
     """
-    Modify the list of test items to remove tests marked as slow if the --slow option is not provided.
+    Modify the list of test items to exclude tests marked as slow if the --slow option is not specified.
 
     Args:
-        config (pytest.config.Config): The pytest config object.
-        items (list): List of test items to be executed.
+        config (pytest.config.Config): The pytest configuration object that provides access to command-line options.
+        items (list): The list of collected pytest item objects to be modified based on the presence of --slow option.
+
+    Returns:
+        (None) The function modifies the 'items' list in place, and does not return a value.
     """
     if not config.getoption("--slow"):
         # Remove the item entirely from the list of test items if it's marked as 'slow'
@@ -38,6 +44,9 @@ def pytest_sessionstart(session):
 
     Args:
         session (pytest.Session): The pytest session object.
+
+    Returns:
+        (None)
     """
     from ultralytics.utils.torch_utils import init_seeds
 
@@ -54,9 +63,12 @@ def pytest_terminal_summary(terminalreporter, exitstatus, config):
     and directories used during testing.
 
     Args:
-        terminalreporter (pytest.terminal.TerminalReporter): The terminal reporter object.
+        terminalreporter (pytest.terminal.TerminalReporter): The terminal reporter object used for terminal output.
         exitstatus (int): The exit status of the test run.
         config (pytest.config.Config): The pytest config object.
+
+    Returns:
+        (None)
     """
     from ultralytics.utils import WEIGHTS_DIR
 

tests/test_cli.py

@@ -20,7 +20,7 @@ def run(cmd):
 
 
 def test_special_modes():
-    """Test various special command modes of YOLO."""
+    """Test various special command-line modes for YOLO functionality."""
     run("yolo help")
     run("yolo checks")
     run("yolo version")
@@ -30,38 +30,38 @@ def test_special_modes():
 
 @pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA)
 def test_train(task, model, data):
-    """Test YOLO training for a given task, model, and data."""
+    """Test YOLO training for different tasks, models, and datasets."""
     run(f"yolo train {task} model={model} data={data} imgsz=32 epochs=1 cache=disk")
 
 
 @pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA)
 def test_val(task, model, data):
-    """Test YOLO validation for a given task, model, and data."""
+    """Test YOLO validation process for specified task, model, and data using a shell command."""
     run(f"yolo val {task} model={model} data={data} imgsz=32 save_txt save_json")
 
 
 @pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA)
 def test_predict(task, model, data):
-    """Test YOLO prediction on sample assets for a given task and model."""
+    """Test YOLO prediction on provided sample assets for specified task and model."""
     run(f"yolo predict model={model} source={ASSETS} imgsz=32 save save_crop save_txt")
 
 
 @pytest.mark.parametrize("model", MODELS)
 def test_export(model):
-    """Test exporting a YOLO model to different formats."""
+    """Test exporting a YOLO model to TorchScript format."""
     run(f"yolo export model={model} format=torchscript imgsz=32")
 
 
 def test_rtdetr(task="detect", model="yolov8n-rtdetr.yaml", data="coco8.yaml"):
-    """Test the RTDETR functionality with the Ultralytics framework."""
+    """Test the RTDETR functionality within Ultralytics for detection tasks using specified model and data."""
     # Warning: must use imgsz=640 (note also add coma, spaces, fraction=0.25 args to test single-image training)
     run(f"yolo train {task} model={model} data={data} --imgsz= 160 epochs =1, cache = disk fraction=0.25")
     run(f"yolo predict {task} model={model} source={ASSETS / 'bus.jpg'} imgsz=160 save save_crop save_txt")
 
 
 @pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="MobileSAM with CLIP is not supported in Python 3.12")
 def test_fastsam(task="segment", model=WEIGHTS_DIR / "FastSAM-s.pt", data="coco8-seg.yaml"):
-    """Test FastSAM segmentation functionality within Ultralytics."""
+    """Test FastSAM model for segmenting objects in images using various prompts within Ultralytics."""
     source = ASSETS / "bus.jpg"
 
     run(f"yolo segment val {task} model={model} data={data} imgsz=32")
@@ -99,7 +99,7 @@ def test_fastsam(task="segment", model=WEIGHTS_DIR / "FastSAM-s.pt", data="coco8
 
 
 def test_mobilesam():
-    """Test MobileSAM segmentation functionality using Ultralytics."""
+    """Test MobileSAM segmentation with point prompts using Ultralytics."""
     from ultralytics import SAM
 
     # Load the model

tests/test_cuda.py

@@ -32,7 +32,7 @@ def test_checks():
     ],
 )
 def test_export_engine_matrix(task, dynamic, int8, half, batch):
-    """Test YOLO exports to TensorRT format."""
+    """Test YOLO model export to TensorRT format for various configurations and run inference."""
     file = YOLO(TASK2MODEL[task]).export(
         format="engine",
         imgsz=32,
@@ -51,15 +51,15 @@ def test_export_engine_matrix(task, dynamic, int8, half, batch):
 
 @pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
 def test_train():
-    """Test model training on a minimal dataset."""
+    """Test model training on a minimal dataset using available CUDA devices."""
     device = 0 if CUDA_DEVICE_COUNT == 1 else [0, 1]
     YOLO(MODEL).train(data="coco8.yaml", imgsz=64, epochs=1, device=device)  # requires imgsz>=64
 
 
 @pytest.mark.slow
 @pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
 def test_predict_multiple_devices():
-    """Validate model prediction on multiple devices."""
+    """Validate model prediction consistency across CPU and CUDA devices."""
     model = YOLO("yolov8n.pt")
     model = model.cpu()
     assert str(model.device) == "cpu"
@@ -84,7 +84,7 @@ def test_predict_multiple_devices():
 
 @pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
 def test_autobatch():
-    """Check batch size for YOLO model using autobatch."""
+    """Check optimal batch size for YOLO model training using autobatch utility."""
     from ultralytics.utils.autobatch import check_train_batch_size
 
     check_train_batch_size(YOLO(MODEL).model.cuda(), imgsz=128, amp=True)
@@ -103,7 +103,7 @@ def test_utils_benchmarks():
 
 @pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
 def test_predict_sam():
-    """Test SAM model prediction with various prompts."""
+    """Test SAM model predictions using different prompts, including bounding boxes and point annotations."""
     from ultralytics import SAM
     from ultralytics.models.sam import Predictor as SAMPredictor
 

tests/test_engine.py

@@ -12,12 +12,12 @@
 
 
 def test_func(*args):  # noqa
-    """Test function callback."""
+    """Test function callback for evaluating YOLO model performance metrics."""
     print("callback test passed")
 
 
 def test_export():
-    """Test model exporting functionality."""
+    """Tests the model exporting function by adding a callback and asserting its execution."""
     exporter = Exporter()
     exporter.add_callback("on_export_start", test_func)
     assert test_func in exporter.callbacks["on_export_start"], "callback test failed"
@@ -26,7 +26,7 @@ def test_export():
 
 
 def test_detect():
-    """Test object detection functionality."""
+    """Test YOLO object detection training, validation, and prediction functionality."""
     overrides = {"data": "coco8.yaml", "model": "yolov8n.yaml", "imgsz": 32, "epochs": 1, "save": False}
     cfg = get_cfg(DEFAULT_CFG)
     cfg.data = "coco8.yaml"
@@ -65,7 +65,7 @@ def test_detect():
 
 
 def test_segment():
-    """Test image segmentation functionality."""
+    """Tests image segmentation training, validation, and prediction pipelines using YOLO models."""
     overrides = {"data": "coco8-seg.yaml", "model": "yolov8n-seg.yaml", "imgsz": 32, "epochs": 1, "save": False}
     cfg = get_cfg(DEFAULT_CFG)
     cfg.data = "coco8-seg.yaml"
@@ -104,7 +104,7 @@ def test_segment():
 
 
 def test_classify():
-    """Test image classification functionality."""
+    """Test image classification including training, validation, and prediction phases."""
     overrides = {"data": "imagenet10", "model": "yolov8n-cls.yaml", "imgsz": 32, "epochs": 1, "save": False}
     cfg = get_cfg(DEFAULT_CFG)
     cfg.data = "imagenet10"

tests/test_explorer.py

@@ -9,7 +9,7 @@
 
 @pytest.mark.slow
 def test_similarity():
-    """Test similarity calculations and SQL queries for correctness and response length."""
+    """Test the correctness and response length of similarity calculations and SQL queries in the Explorer."""
     exp = Explorer(data="coco8.yaml")
     exp.create_embeddings_table()
     similar = exp.get_similar(idx=1)
@@ -26,7 +26,7 @@ def test_similarity():
 
 @pytest.mark.slow
 def test_det():
-    """Test detection functionalities and ensure the embedding table has bounding boxes."""
+    """Test detection functionalities and verify embedding table includes bounding boxes."""
     exp = Explorer(data="coco8.yaml", model="yolov8n.pt")
     exp.create_embeddings_table(force=True)
     assert len(exp.table.head()["bboxes"]) > 0
@@ -39,7 +39,7 @@ def test_det():
 
 @pytest.mark.slow
 def test_seg():
-    """Test segmentation functionalities and verify the embedding table includes masks."""
+    """Test segmentation functionalities and ensure the embedding table includes segmentation masks."""
     exp = Explorer(data="coco8-seg.yaml", model="yolov8n-seg.pt")
     exp.create_embeddings_table(force=True)
     assert len(exp.table.head()["masks"]) > 0
@@ -51,7 +51,7 @@ def test_seg():
 
 @pytest.mark.slow
 def test_pose():
-    """Test pose estimation functionalities and check the embedding table for keypoints."""
+    """Test pose estimation functionality and verify the embedding table includes keypoints."""
     exp = Explorer(data="coco8-pose.yaml", model="yolov8n-pose.pt")
     exp.create_embeddings_table(force=True)
     assert len(exp.table.head()["keypoints"]) > 0