Ultralytics Code Refactor https://ultralytics.com/actions (#14109)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
ff63a56a42
commit
691b5daccb
16 changed files with 124 additions and 113 deletions
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -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")
|
||||
|
|
@ -30,30 +30,30 @@ 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")
|
||||
|
|
@ -61,7 +61,7 @@ def test_rtdetr(task="detect", model="yolov8n-rtdetr.yaml", data="coco8.yaml"):
|
|||
|
||||
@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
|
||||
|
|
|
|||
|
|
@ -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,
|
||||
|
|
@ -51,7 +51,7 @@ 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
|
||||
|
||||
|
|
@ -59,7 +59,7 @@ def test_train():
|
|||
@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
|
||||
|
||||
|
|
|
|||
|
|
@ -12,12 +12,12 @@ from ultralytics.utils import ASSETS, DEFAULT_CFG, WEIGHTS_DIR
|
|||
|
||||
|
||||
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"
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from ultralytics.utils import ASSETS
|
|||
|
||||
@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
|
||||
|
|
|
|||
|
|
@ -21,13 +21,13 @@ from ultralytics.utils.torch_utils import TORCH_1_9, TORCH_1_13
|
|||
|
||||
|
||||
def test_export_torchscript():
|
||||
"""Test YOLO exports to TorchScript format."""
|
||||
"""Test YOLO model exporting to TorchScript format for compatibility and correctness."""
|
||||
file = YOLO(MODEL).export(format="torchscript", optimize=False, imgsz=32)
|
||||
YOLO(file)(SOURCE, imgsz=32) # exported model inference
|
||||
|
||||
|
||||
def test_export_onnx():
|
||||
"""Test YOLO exports to ONNX format."""
|
||||
"""Test YOLO model export to ONNX format with dynamic axes."""
|
||||
file = YOLO(MODEL).export(format="onnx", dynamic=True, imgsz=32)
|
||||
YOLO(file)(SOURCE, imgsz=32) # exported model inference
|
||||
|
||||
|
|
@ -35,7 +35,7 @@ def test_export_onnx():
|
|||
@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="OpenVINO not supported in Python 3.12")
|
||||
@pytest.mark.skipif(not TORCH_1_13, reason="OpenVINO requires torch>=1.13")
|
||||
def test_export_openvino():
|
||||
"""Test YOLO exports to OpenVINO format."""
|
||||
"""Test YOLO exports to OpenVINO format for model inference compatibility."""
|
||||
file = YOLO(MODEL).export(format="openvino", imgsz=32)
|
||||
YOLO(file)(SOURCE, imgsz=32) # exported model inference
|
||||
|
||||
|
|
@ -52,7 +52,7 @@ def test_export_openvino():
|
|||
],
|
||||
)
|
||||
def test_export_openvino_matrix(task, dynamic, int8, half, batch):
|
||||
"""Test YOLO exports to OpenVINO format."""
|
||||
"""Test YOLO model exports to OpenVINO under various configuration matrix conditions."""
|
||||
file = YOLO(TASK2MODEL[task]).export(
|
||||
format="openvino",
|
||||
imgsz=32,
|
||||
|
|
@ -76,7 +76,7 @@ def test_export_openvino_matrix(task, dynamic, int8, half, batch):
|
|||
"task, dynamic, int8, half, batch, simplify", product(TASKS, [True, False], [False], [False], [1, 2], [True, False])
|
||||
)
|
||||
def test_export_onnx_matrix(task, dynamic, int8, half, batch, simplify):
|
||||
"""Test YOLO exports to ONNX format."""
|
||||
"""Test YOLO exports to ONNX format with various configurations and parameters."""
|
||||
file = YOLO(TASK2MODEL[task]).export(
|
||||
format="onnx",
|
||||
imgsz=32,
|
||||
|
|
@ -93,7 +93,7 @@ def test_export_onnx_matrix(task, dynamic, int8, half, batch, simplify):
|
|||
@pytest.mark.slow
|
||||
@pytest.mark.parametrize("task, dynamic, int8, half, batch", product(TASKS, [False], [False], [False], [1, 2]))
|
||||
def test_export_torchscript_matrix(task, dynamic, int8, half, batch):
|
||||
"""Test YOLO exports to TorchScript format."""
|
||||
"""Tests YOLO model exports to TorchScript format under varied configurations."""
|
||||
file = YOLO(TASK2MODEL[task]).export(
|
||||
format="torchscript",
|
||||
imgsz=32,
|
||||
|
|
@ -119,7 +119,7 @@ def test_export_torchscript_matrix(task, dynamic, int8, half, batch):
|
|||
],
|
||||
)
|
||||
def test_export_coreml_matrix(task, dynamic, int8, half, batch):
|
||||
"""Test YOLO exports to CoreML format."""
|
||||
"""Test YOLO exports to CoreML format with various parameter configurations."""
|
||||
file = YOLO(TASK2MODEL[task]).export(
|
||||
format="coreml",
|
||||
imgsz=32,
|
||||
|
|
@ -144,7 +144,7 @@ def test_export_coreml_matrix(task, dynamic, int8, half, batch):
|
|||
],
|
||||
)
|
||||
def test_export_tflite_matrix(task, dynamic, int8, half, batch):
|
||||
"""Test YOLO exports to TFLite format."""
|
||||
"""Test YOLO exports to TFLite format considering various export configurations."""
|
||||
file = YOLO(TASK2MODEL[task]).export(
|
||||
format="tflite",
|
||||
imgsz=32,
|
||||
|
|
@ -162,7 +162,7 @@ def test_export_tflite_matrix(task, dynamic, int8, half, batch):
|
|||
@pytest.mark.skipif(IS_RASPBERRYPI, reason="CoreML not supported on Raspberry Pi")
|
||||
@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="CoreML not supported in Python 3.12")
|
||||
def test_export_coreml():
|
||||
"""Test YOLO exports to CoreML format."""
|
||||
"""Test YOLO exports to CoreML format, optimized for macOS only."""
|
||||
if MACOS:
|
||||
file = YOLO(MODEL).export(format="coreml", imgsz=32)
|
||||
YOLO(file)(SOURCE, imgsz=32) # model prediction only supported on macOS for nms=False models
|
||||
|
|
@ -173,11 +173,7 @@ def test_export_coreml():
|
|||
@pytest.mark.skipif(not checks.IS_PYTHON_MINIMUM_3_10, reason="TFLite export requires Python>=3.10")
|
||||
@pytest.mark.skipif(not LINUX, reason="Test disabled as TF suffers from install conflicts on Windows and macOS")
|
||||
def test_export_tflite():
|
||||
"""
|
||||
Test YOLO exports to TFLite format.
|
||||
|
||||
Note TF suffers from install conflicts on Windows and macOS.
|
||||
"""
|
||||
"""Test YOLO exports to TFLite format under specific OS and Python version conditions."""
|
||||
model = YOLO(MODEL)
|
||||
file = model.export(format="tflite", imgsz=32)
|
||||
YOLO(file)(SOURCE, imgsz=32)
|
||||
|
|
@ -186,11 +182,7 @@ def test_export_tflite():
|
|||
@pytest.mark.skipif(True, reason="Test disabled")
|
||||
@pytest.mark.skipif(not LINUX, reason="TF suffers from install conflicts on Windows and macOS")
|
||||
def test_export_pb():
|
||||
"""
|
||||
Test YOLO exports to *.pb format.
|
||||
|
||||
Note TF suffers from install conflicts on Windows and macOS.
|
||||
"""
|
||||
"""Test YOLO exports to TensorFlow's Protobuf (*.pb) format."""
|
||||
model = YOLO(MODEL)
|
||||
file = model.export(format="pb", imgsz=32)
|
||||
YOLO(file)(SOURCE, imgsz=32)
|
||||
|
|
@ -198,11 +190,7 @@ def test_export_pb():
|
|||
|
||||
@pytest.mark.skipif(True, reason="Test disabled as Paddle protobuf and ONNX protobuf requirementsk conflict.")
|
||||
def test_export_paddle():
|
||||
"""
|
||||
Test YOLO exports to Paddle format.
|
||||
|
||||
Note Paddle protobuf requirements conflicting with onnx protobuf requirements.
|
||||
"""
|
||||
"""Test YOLO exports to Paddle format, noting protobuf conflicts with ONNX."""
|
||||
YOLO(MODEL).export(format="paddle", imgsz=32)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ from ultralytics.utils.checks import check_requirements
|
|||
|
||||
@pytest.mark.skipif(not check_requirements("ray", install=False), reason="ray[tune] not installed")
|
||||
def test_model_ray_tune():
|
||||
"""Tune YOLO model with Ray optimization library."""
|
||||
"""Tune YOLO model using Ray for hyperparameter optimization."""
|
||||
YOLO("yolov8n-cls.yaml").tune(
|
||||
use_ray=True, data="imagenet10", grace_period=1, iterations=1, imgsz=32, epochs=1, plots=False, device="cpu"
|
||||
)
|
||||
|
|
@ -24,7 +24,7 @@ def test_model_ray_tune():
|
|||
|
||||
@pytest.mark.skipif(not check_requirements("mlflow", install=False), reason="mlflow not installed")
|
||||
def test_mlflow():
|
||||
"""Test training with MLflow tracking enabled."""
|
||||
"""Test training with MLflow tracking enabled (see https://mlflow.org/ for details)."""
|
||||
SETTINGS["mlflow"] = True
|
||||
YOLO("yolov8n-cls.yaml").train(data="imagenet10", imgsz=32, epochs=3, plots=False, device="cpu")
|
||||
|
||||
|
|
@ -32,9 +32,9 @@ def test_mlflow():
|
|||
@pytest.mark.skipif(True, reason="Test failing in scheduled CI https://github.com/ultralytics/ultralytics/pull/8868")
|
||||
@pytest.mark.skipif(not check_requirements("mlflow", install=False), reason="mlflow not installed")
|
||||
def test_mlflow_keep_run_active():
|
||||
"""Ensure MLflow run status matches MLFLOW_KEEP_RUN_ACTIVE environment variable settings."""
|
||||
import mlflow
|
||||
|
||||
"""Test training with MLflow tracking enabled."""
|
||||
SETTINGS["mlflow"] = True
|
||||
run_name = "Test Run"
|
||||
os.environ["MLFLOW_RUN"] = run_name
|
||||
|
|
@ -62,7 +62,11 @@ def test_mlflow_keep_run_active():
|
|||
|
||||
@pytest.mark.skipif(not check_requirements("tritonclient", install=False), reason="tritonclient[all] not installed")
|
||||
def test_triton():
|
||||
"""Test NVIDIA Triton Server functionalities."""
|
||||
"""
|
||||
Test NVIDIA Triton Server functionalities with YOLO model.
|
||||
|
||||
See https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver.
|
||||
"""
|
||||
check_requirements("tritonclient[all]")
|
||||
from tritonclient.http import InferenceServerClient # noqa
|
||||
|
||||
|
|
@ -114,7 +118,7 @@ def test_triton():
|
|||
|
||||
@pytest.mark.skipif(not check_requirements("pycocotools", install=False), reason="pycocotools not installed")
|
||||
def test_pycocotools():
|
||||
"""Validate model predictions using pycocotools."""
|
||||
"""Validate YOLO model predictions on COCO dataset using pycocotools."""
|
||||
from ultralytics.models.yolo.detect import DetectionValidator
|
||||
from ultralytics.models.yolo.pose import PoseValidator
|
||||
from ultralytics.models.yolo.segment import SegmentationValidator
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@ def test_model_forward():
|
|||
|
||||
|
||||
def test_model_methods():
|
||||
"""Test various methods and properties of the YOLO model."""
|
||||
"""Test various methods and properties of the YOLO model to ensure correct functionality."""
|
||||
model = YOLO(MODEL)
|
||||
|
||||
# Model methods
|
||||
|
|
@ -58,7 +58,7 @@ def test_model_methods():
|
|||
|
||||
|
||||
def test_model_profile():
|
||||
"""Test profiling of the YOLO model with 'profile=True' argument."""
|
||||
"""Test profiling of the YOLO model with `profile=True` to assess performance and resource usage."""
|
||||
from ultralytics.nn.tasks import DetectionModel
|
||||
|
||||
model = DetectionModel() # build model
|
||||
|
|
@ -68,7 +68,7 @@ def test_model_profile():
|
|||
|
||||
@pytest.mark.skipif(not IS_TMP_WRITEABLE, reason="directory is not writeable")
|
||||
def test_predict_txt():
|
||||
"""Test YOLO predictions with sources (file, dir, glob, recursive glob) specified in a text file."""
|
||||
"""Tests YOLO predictions with file, directory, and pattern sources listed in a text file."""
|
||||
txt_file = TMP / "sources.txt"
|
||||
with open(txt_file, "w") as f:
|
||||
for x in [ASSETS / "bus.jpg", ASSETS, ASSETS / "*", ASSETS / "**/*.jpg"]:
|
||||
|
|
@ -78,7 +78,7 @@ def test_predict_txt():
|
|||
|
||||
@pytest.mark.parametrize("model_name", MODELS)
|
||||
def test_predict_img(model_name):
|
||||
"""Test YOLO prediction on various types of image sources."""
|
||||
"""Test YOLO model predictions on various image input types and sources, including online images."""
|
||||
model = YOLO(WEIGHTS_DIR / model_name)
|
||||
im = cv2.imread(str(SOURCE)) # uint8 numpy array
|
||||
assert len(model(source=Image.open(SOURCE), save=True, verbose=True, imgsz=32)) == 1 # PIL
|
||||
|
|
@ -100,12 +100,12 @@ def test_predict_img(model_name):
|
|||
|
||||
@pytest.mark.parametrize("model", MODELS)
|
||||
def test_predict_visualize(model):
|
||||
"""Test model predict methods with 'visualize=True' arguments."""
|
||||
"""Test model prediction methods with 'visualize=True' to generate and display prediction visualizations."""
|
||||
YOLO(WEIGHTS_DIR / model)(SOURCE, imgsz=32, visualize=True)
|
||||
|
||||
|
||||
def test_predict_grey_and_4ch():
|
||||
"""Test YOLO prediction on SOURCE converted to greyscale and 4-channel images."""
|
||||
"""Test YOLO prediction on SOURCE converted to greyscale and 4-channel images with various filenames."""
|
||||
im = Image.open(SOURCE)
|
||||
directory = TMP / "im4"
|
||||
directory.mkdir(parents=True, exist_ok=True)
|
||||
|
|
@ -132,11 +132,7 @@ def test_predict_grey_and_4ch():
|
|||
@pytest.mark.slow
|
||||
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
|
||||
def test_youtube():
|
||||
"""
|
||||
Test YouTube inference.
|
||||
|
||||
Note: ConnectionError may occur during this test due to network instability or YouTube server availability.
|
||||
"""
|
||||
"""Test YOLO model on a YouTube video stream, handling potential network-related errors."""
|
||||
model = YOLO(MODEL)
|
||||
try:
|
||||
model.predict("https://youtu.be/G17sBkb38XQ", imgsz=96, save=True)
|
||||
|
|
@ -149,9 +145,9 @@ def test_youtube():
|
|||
@pytest.mark.skipif(not IS_TMP_WRITEABLE, reason="directory is not writeable")
|
||||
def test_track_stream():
|
||||
"""
|
||||
Test streaming tracking (short 10 frame video) with non-default ByteTrack tracker.
|
||||
Tests streaming tracking on a short 10 frame video using ByteTrack tracker and different GMC methods.
|
||||
|
||||
Note imgsz=160 required for tracking for higher confidence and better matches
|
||||
Note imgsz=160 required for tracking for higher confidence and better matches.
|
||||
"""
|
||||
video_url = "https://ultralytics.com/assets/decelera_portrait_min.mov"
|
||||
model = YOLO(MODEL)
|
||||
|
|
@ -175,21 +171,21 @@ def test_val():
|
|||
|
||||
|
||||
def test_train_scratch():
|
||||
"""Test training the YOLO model from scratch."""
|
||||
"""Test training the YOLO model from scratch using the provided configuration."""
|
||||
model = YOLO(CFG)
|
||||
model.train(data="coco8.yaml", epochs=2, imgsz=32, cache="disk", batch=-1, close_mosaic=1, name="model")
|
||||
model(SOURCE)
|
||||
|
||||
|
||||
def test_train_pretrained():
|
||||
"""Test training the YOLO model from a pre-trained state."""
|
||||
"""Test training of the YOLO model starting from a pre-trained checkpoint."""
|
||||
model = YOLO(WEIGHTS_DIR / "yolov8n-seg.pt")
|
||||
model.train(data="coco8-seg.yaml", epochs=1, imgsz=32, cache="ram", copy_paste=0.5, mixup=0.5, name=0)
|
||||
model(SOURCE)
|
||||
|
||||
|
||||
def test_all_model_yamls():
|
||||
"""Test YOLO model creation for all available YAML configurations."""
|
||||
"""Test YOLO model creation for all available YAML configurations in the `cfg/models` directory."""
|
||||
for m in (ROOT / "cfg" / "models").rglob("*.yaml"):
|
||||
if "rtdetr" in m.name:
|
||||
if TORCH_1_9: # torch<=1.8 issue - TypeError: __init__() got an unexpected keyword argument 'batch_first'
|
||||
|
|
@ -208,7 +204,7 @@ def test_workflow():
|
|||
|
||||
|
||||
def test_predict_callback_and_setup():
|
||||
"""Test callback functionality during YOLO prediction."""
|
||||
"""Test callback functionality during YOLO prediction setup and execution."""
|
||||
|
||||
def on_predict_batch_end(predictor):
|
||||
"""Callback function that handles operations at the end of a prediction batch."""
|
||||
|
|
@ -232,7 +228,7 @@ def test_predict_callback_and_setup():
|
|||
|
||||
@pytest.mark.parametrize("model", MODELS)
|
||||
def test_results(model):
|
||||
"""Test various result formats for the YOLO model."""
|
||||
"""Ensure YOLO model predictions can be processed and printed in various formats."""
|
||||
results = YOLO(WEIGHTS_DIR / model)([SOURCE, SOURCE], imgsz=160)
|
||||
for r in results:
|
||||
r = r.cpu().numpy()
|
||||
|
|
@ -247,7 +243,7 @@ def test_results(model):
|
|||
|
||||
|
||||
def test_labels_and_crops():
|
||||
"""Test output from prediction args for saving detection labels and crops."""
|
||||
"""Test output from prediction args for saving YOLO detection labels and crops; ensures accurate saving."""
|
||||
imgs = [SOURCE, ASSETS / "zidane.jpg"]
|
||||
results = YOLO(WEIGHTS_DIR / "yolov8n.pt")(imgs, imgsz=160, save_txt=True, save_crop=True)
|
||||
save_path = Path(results[0].save_dir)
|
||||
|
|
@ -270,7 +266,7 @@ def test_labels_and_crops():
|
|||
|
||||
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
|
||||
def test_data_utils():
|
||||
"""Test utility functions in ultralytics/data/utils.py."""
|
||||
"""Test utility functions in ultralytics/data/utils.py, including dataset stats and auto-splitting."""
|
||||
from ultralytics.data.utils import HUBDatasetStats, autosplit
|
||||
from ultralytics.utils.downloads import zip_directory
|
||||
|
||||
|
|
@ -290,7 +286,7 @@ def test_data_utils():
|
|||
|
||||
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
|
||||
def test_data_converter():
|
||||
"""Test dataset converters."""
|
||||
"""Test dataset conversion functions from COCO to YOLO format and class mappings."""
|
||||
from ultralytics.data.converter import coco80_to_coco91_class, convert_coco
|
||||
|
||||
file = "instances_val2017.json"
|
||||
|
|
@ -300,7 +296,7 @@ def test_data_converter():
|
|||
|
||||
|
||||
def test_data_annotator():
|
||||
"""Test automatic data annotation."""
|
||||
"""Automatically annotate data using specified detection and segmentation models."""
|
||||
from ultralytics.data.annotator import auto_annotate
|
||||
|
||||
auto_annotate(
|
||||
|
|
@ -323,7 +319,7 @@ def test_events():
|
|||
|
||||
|
||||
def test_cfg_init():
|
||||
"""Test configuration initialization utilities."""
|
||||
"""Test configuration initialization utilities from the 'ultralytics.cfg' module."""
|
||||
from ultralytics.cfg import check_dict_alignment, copy_default_cfg, smart_value
|
||||
|
||||
with contextlib.suppress(SyntaxError):
|
||||
|
|
@ -334,7 +330,7 @@ def test_cfg_init():
|
|||
|
||||
|
||||
def test_utils_init():
|
||||
"""Test initialization utilities."""
|
||||
"""Test initialization utilities in the Ultralytics library."""
|
||||
from ultralytics.utils import get_git_branch, get_git_origin_url, get_ubuntu_version, is_github_action_running
|
||||
|
||||
get_ubuntu_version()
|
||||
|
|
@ -344,7 +340,7 @@ def test_utils_init():
|
|||
|
||||
|
||||
def test_utils_checks():
|
||||
"""Test various utility checks."""
|
||||
"""Test various utility checks for filenames, git status, requirements, image sizes, and versions."""
|
||||
checks.check_yolov5u_filename("yolov5n.pt")
|
||||
checks.git_describe(ROOT)
|
||||
checks.check_requirements() # check requirements.txt
|
||||
|
|
@ -356,14 +352,14 @@ def test_utils_checks():
|
|||
|
||||
@pytest.mark.skipif(WINDOWS, reason="Windows profiling is extremely slow (cause unknown)")
|
||||
def test_utils_benchmarks():
|
||||
"""Test model benchmarking."""
|
||||
"""Benchmark model performance using 'ProfileModels' from 'ultralytics.utils.benchmarks'."""
|
||||
from ultralytics.utils.benchmarks import ProfileModels
|
||||
|
||||
ProfileModels(["yolov8n.yaml"], imgsz=32, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
|
||||
|
||||
|
||||
def test_utils_torchutils():
|
||||
"""Test Torch utility functions."""
|
||||
"""Test Torch utility functions including profiling and FLOP calculations."""
|
||||
from ultralytics.nn.modules.conv import Conv
|
||||
from ultralytics.utils.torch_utils import get_flops_with_torch_profiler, profile, time_sync
|
||||
|
||||
|
|
@ -378,14 +374,14 @@ def test_utils_torchutils():
|
|||
@pytest.mark.slow
|
||||
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
|
||||
def test_utils_downloads():
|
||||
"""Test file download utilities."""
|
||||
"""Test file download utilities from ultralytics.utils.downloads."""
|
||||
from ultralytics.utils.downloads import get_google_drive_file_info
|
||||
|
||||
get_google_drive_file_info("https://drive.google.com/file/d/1cqT-cJgANNrhIHCrEufUYhQ4RqiWG_lJ/view?usp=drive_link")
|
||||
|
||||
|
||||
def test_utils_ops():
|
||||
"""Test various operations utilities."""
|
||||
"""Test utility operations functions for coordinate transformation and normalization."""
|
||||
from ultralytics.utils.ops import (
|
||||
ltwh2xywh,
|
||||
ltwh2xyxy,
|
||||
|
|
@ -414,7 +410,7 @@ def test_utils_ops():
|
|||
|
||||
|
||||
def test_utils_files():
|
||||
"""Test file handling utilities."""
|
||||
"""Test file handling utilities including file age, date, and paths with spaces."""
|
||||
from ultralytics.utils.files import file_age, file_date, get_latest_run, spaces_in_path
|
||||
|
||||
file_age(SOURCE)
|
||||
|
|
@ -429,7 +425,7 @@ def test_utils_files():
|
|||
|
||||
@pytest.mark.slow
|
||||
def test_utils_patches_torch_save():
|
||||
"""Test torch_save backoff when _torch_save throws RuntimeError."""
|
||||
"""Test torch_save backoff when _torch_save raises RuntimeError to ensure robustness."""
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
from ultralytics.utils.patches import torch_save
|
||||
|
|
@ -444,7 +440,7 @@ def test_utils_patches_torch_save():
|
|||
|
||||
|
||||
def test_nn_modules_conv():
|
||||
"""Test Convolutional Neural Network modules."""
|
||||
"""Test Convolutional Neural Network modules including CBAM, Conv2, and ConvTranspose."""
|
||||
from ultralytics.nn.modules.conv import CBAM, Conv2, ConvTranspose, DWConvTranspose2d, Focus
|
||||
|
||||
c1, c2 = 8, 16 # input and output channels
|
||||
|
|
@ -463,7 +459,7 @@ def test_nn_modules_conv():
|
|||
|
||||
|
||||
def test_nn_modules_block():
|
||||
"""Test Neural Network block modules."""
|
||||
"""Test various blocks in neural network modules including C1, C3TR, BottleneckCSP, C3Ghost, and C3x."""
|
||||
from ultralytics.nn.modules.block import C1, C3TR, BottleneckCSP, C3Ghost, C3x
|
||||
|
||||
c1, c2 = 8, 16 # input and output channels
|
||||
|
|
@ -479,7 +475,7 @@ def test_nn_modules_block():
|
|||
|
||||
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
|
||||
def test_hub():
|
||||
"""Test Ultralytics HUB functionalities."""
|
||||
"""Test Ultralytics HUB functionalities (e.g. export formats, logout)."""
|
||||
from ultralytics.hub import export_fmts_hub, logout
|
||||
from ultralytics.hub.utils import smart_request
|
||||
|
||||
|
|
@ -490,7 +486,7 @@ def test_hub():
|
|||
|
||||
@pytest.fixture
|
||||
def image():
|
||||
"""Loads an image from a predefined source using OpenCV."""
|
||||
"""Load and return an image from a predefined source using OpenCV."""
|
||||
return cv2.imread(str(SOURCE))
|
||||
|
||||
|
||||
|
|
@ -504,7 +500,7 @@ def image():
|
|||
],
|
||||
)
|
||||
def test_classify_transforms_train(image, auto_augment, erasing, force_color_jitter):
|
||||
"""Tests classification transforms during training with various augmentation settings."""
|
||||
"""Tests classification transforms during training with various augmentations to ensure proper functionality."""
|
||||
from ultralytics.data.augment import classify_augmentations
|
||||
|
||||
transform = classify_augmentations(
|
||||
|
|
@ -533,7 +529,7 @@ def test_classify_transforms_train(image, auto_augment, erasing, force_color_jit
|
|||
@pytest.mark.slow
|
||||
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
|
||||
def test_model_tune():
|
||||
"""Tune YOLO model for performance."""
|
||||
"""Tune YOLO model for performance improvement."""
|
||||
YOLO("yolov8n-pose.pt").tune(data="coco8-pose.yaml", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu")
|
||||
YOLO("yolov8n-cls.pt").tune(data="imagenet10", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu")
|
||||
|
||||
|
|
@ -550,7 +546,7 @@ def test_model_embeddings():
|
|||
|
||||
@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="YOLOWorld with CLIP is not supported in Python 3.12")
|
||||
def test_yolo_world():
|
||||
"""Tests YOLO world models with different configurations, including classes, detection, and training scenarios."""
|
||||
"""Tests YOLO world models with CLIP support, including detection and training scenarios."""
|
||||
model = YOLO("yolov8s-world.pt") # no YOLOv8n-world model yet
|
||||
model.set_classes(["tree", "window"])
|
||||
model(SOURCE, conf=0.01)
|
||||
|
|
@ -581,7 +577,7 @@ def test_yolo_world():
|
|||
|
||||
|
||||
def test_yolov10():
|
||||
"""A simple test for yolov10 for now."""
|
||||
"""Test YOLOv10 model training, validation, and prediction steps with minimal configurations."""
|
||||
model = YOLO("yolov10n.yaml")
|
||||
# train/val/predict
|
||||
model.train(data="coco8.yaml", epochs=1, imgsz=32, close_mosaic=1, cache="disk")
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue