Ultralytics Code Refactor https://ultralytics.com/actions (#14109)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher
GitHub
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commit
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16 changed files with 124 additions and 113 deletions
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@ -38,7 +38,7 @@ def test_model_forward():
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def test_model_methods():
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"""Test various methods and properties of the YOLO model."""
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"""Test various methods and properties of the YOLO model to ensure correct functionality."""
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model = YOLO(MODEL)
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# Model methods
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@ -58,7 +58,7 @@ def test_model_methods():
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def test_model_profile():
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"""Test profiling of the YOLO model with 'profile=True' argument."""
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"""Test profiling of the YOLO model with `profile=True` to assess performance and resource usage."""
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from ultralytics.nn.tasks import DetectionModel
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model = DetectionModel() # build model
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@ -68,7 +68,7 @@ def test_model_profile():
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@pytest.mark.skipif(not IS_TMP_WRITEABLE, reason="directory is not writeable")
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def test_predict_txt():
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"""Test YOLO predictions with sources (file, dir, glob, recursive glob) specified in a text file."""
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"""Tests YOLO predictions with file, directory, and pattern sources listed in a text file."""
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txt_file = TMP / "sources.txt"
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with open(txt_file, "w") as f:
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for x in [ASSETS / "bus.jpg", ASSETS, ASSETS / "*", ASSETS / "**/*.jpg"]:
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@ -78,7 +78,7 @@ def test_predict_txt():
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@pytest.mark.parametrize("model_name", MODELS)
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def test_predict_img(model_name):
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"""Test YOLO prediction on various types of image sources."""
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"""Test YOLO model predictions on various image input types and sources, including online images."""
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model = YOLO(WEIGHTS_DIR / model_name)
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im = cv2.imread(str(SOURCE)) # uint8 numpy array
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assert len(model(source=Image.open(SOURCE), save=True, verbose=True, imgsz=32)) == 1 # PIL
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@ -100,12 +100,12 @@ def test_predict_img(model_name):
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@pytest.mark.parametrize("model", MODELS)
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def test_predict_visualize(model):
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"""Test model predict methods with 'visualize=True' arguments."""
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"""Test model prediction methods with 'visualize=True' to generate and display prediction visualizations."""
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YOLO(WEIGHTS_DIR / model)(SOURCE, imgsz=32, visualize=True)
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def test_predict_grey_and_4ch():
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"""Test YOLO prediction on SOURCE converted to greyscale and 4-channel images."""
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"""Test YOLO prediction on SOURCE converted to greyscale and 4-channel images with various filenames."""
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im = Image.open(SOURCE)
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directory = TMP / "im4"
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directory.mkdir(parents=True, exist_ok=True)
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@ -132,11 +132,7 @@ def test_predict_grey_and_4ch():
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@pytest.mark.slow
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@pytest.mark.skipif(not ONLINE, reason="environment is offline")
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def test_youtube():
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"""
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Test YouTube inference.
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Note: ConnectionError may occur during this test due to network instability or YouTube server availability.
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"""
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"""Test YOLO model on a YouTube video stream, handling potential network-related errors."""
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model = YOLO(MODEL)
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try:
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model.predict("https://youtu.be/G17sBkb38XQ", imgsz=96, save=True)
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@ -149,9 +145,9 @@ def test_youtube():
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@pytest.mark.skipif(not IS_TMP_WRITEABLE, reason="directory is not writeable")
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def test_track_stream():
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"""
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Test streaming tracking (short 10 frame video) with non-default ByteTrack tracker.
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Tests streaming tracking on a short 10 frame video using ByteTrack tracker and different GMC methods.
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Note imgsz=160 required for tracking for higher confidence and better matches
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Note imgsz=160 required for tracking for higher confidence and better matches.
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"""
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video_url = "https://ultralytics.com/assets/decelera_portrait_min.mov"
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model = YOLO(MODEL)
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@ -175,21 +171,21 @@ def test_val():
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def test_train_scratch():
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"""Test training the YOLO model from scratch."""
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"""Test training the YOLO model from scratch using the provided configuration."""
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model = YOLO(CFG)
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model.train(data="coco8.yaml", epochs=2, imgsz=32, cache="disk", batch=-1, close_mosaic=1, name="model")
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model(SOURCE)
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def test_train_pretrained():
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"""Test training the YOLO model from a pre-trained state."""
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"""Test training of the YOLO model starting from a pre-trained checkpoint."""
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model = YOLO(WEIGHTS_DIR / "yolov8n-seg.pt")
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model.train(data="coco8-seg.yaml", epochs=1, imgsz=32, cache="ram", copy_paste=0.5, mixup=0.5, name=0)
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model(SOURCE)
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def test_all_model_yamls():
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"""Test YOLO model creation for all available YAML configurations."""
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"""Test YOLO model creation for all available YAML configurations in the `cfg/models` directory."""
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for m in (ROOT / "cfg" / "models").rglob("*.yaml"):
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if "rtdetr" in m.name:
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if TORCH_1_9: # torch<=1.8 issue - TypeError: __init__() got an unexpected keyword argument 'batch_first'
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@ -208,7 +204,7 @@ def test_workflow():
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def test_predict_callback_and_setup():
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"""Test callback functionality during YOLO prediction."""
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"""Test callback functionality during YOLO prediction setup and execution."""
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def on_predict_batch_end(predictor):
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"""Callback function that handles operations at the end of a prediction batch."""
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@ -232,7 +228,7 @@ def test_predict_callback_and_setup():
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@pytest.mark.parametrize("model", MODELS)
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def test_results(model):
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"""Test various result formats for the YOLO model."""
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"""Ensure YOLO model predictions can be processed and printed in various formats."""
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results = YOLO(WEIGHTS_DIR / model)([SOURCE, SOURCE], imgsz=160)
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for r in results:
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r = r.cpu().numpy()
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@ -247,7 +243,7 @@ def test_results(model):
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def test_labels_and_crops():
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"""Test output from prediction args for saving detection labels and crops."""
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"""Test output from prediction args for saving YOLO detection labels and crops; ensures accurate saving."""
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imgs = [SOURCE, ASSETS / "zidane.jpg"]
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results = YOLO(WEIGHTS_DIR / "yolov8n.pt")(imgs, imgsz=160, save_txt=True, save_crop=True)
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save_path = Path(results[0].save_dir)
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@ -270,7 +266,7 @@ def test_labels_and_crops():
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@pytest.mark.skipif(not ONLINE, reason="environment is offline")
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def test_data_utils():
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"""Test utility functions in ultralytics/data/utils.py."""
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"""Test utility functions in ultralytics/data/utils.py, including dataset stats and auto-splitting."""
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from ultralytics.data.utils import HUBDatasetStats, autosplit
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from ultralytics.utils.downloads import zip_directory
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@ -290,7 +286,7 @@ def test_data_utils():
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@pytest.mark.skipif(not ONLINE, reason="environment is offline")
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def test_data_converter():
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"""Test dataset converters."""
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"""Test dataset conversion functions from COCO to YOLO format and class mappings."""
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from ultralytics.data.converter import coco80_to_coco91_class, convert_coco
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file = "instances_val2017.json"
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@ -300,7 +296,7 @@ def test_data_converter():
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def test_data_annotator():
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"""Test automatic data annotation."""
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"""Automatically annotate data using specified detection and segmentation models."""
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from ultralytics.data.annotator import auto_annotate
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auto_annotate(
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@ -323,7 +319,7 @@ def test_events():
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def test_cfg_init():
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"""Test configuration initialization utilities."""
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"""Test configuration initialization utilities from the 'ultralytics.cfg' module."""
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from ultralytics.cfg import check_dict_alignment, copy_default_cfg, smart_value
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with contextlib.suppress(SyntaxError):
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@ -334,7 +330,7 @@ def test_cfg_init():
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def test_utils_init():
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"""Test initialization utilities."""
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"""Test initialization utilities in the Ultralytics library."""
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from ultralytics.utils import get_git_branch, get_git_origin_url, get_ubuntu_version, is_github_action_running
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get_ubuntu_version()
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@ -344,7 +340,7 @@ def test_utils_init():
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def test_utils_checks():
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"""Test various utility checks."""
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"""Test various utility checks for filenames, git status, requirements, image sizes, and versions."""
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checks.check_yolov5u_filename("yolov5n.pt")
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checks.git_describe(ROOT)
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checks.check_requirements() # check requirements.txt
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@ -356,14 +352,14 @@ def test_utils_checks():
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@pytest.mark.skipif(WINDOWS, reason="Windows profiling is extremely slow (cause unknown)")
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def test_utils_benchmarks():
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"""Test model benchmarking."""
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"""Benchmark model performance using 'ProfileModels' from 'ultralytics.utils.benchmarks'."""
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from ultralytics.utils.benchmarks import ProfileModels
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ProfileModels(["yolov8n.yaml"], imgsz=32, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
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def test_utils_torchutils():
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"""Test Torch utility functions."""
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"""Test Torch utility functions including profiling and FLOP calculations."""
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from ultralytics.nn.modules.conv import Conv
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from ultralytics.utils.torch_utils import get_flops_with_torch_profiler, profile, time_sync
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@ -378,14 +374,14 @@ def test_utils_torchutils():
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@pytest.mark.slow
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@pytest.mark.skipif(not ONLINE, reason="environment is offline")
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def test_utils_downloads():
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"""Test file download utilities."""
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"""Test file download utilities from ultralytics.utils.downloads."""
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from ultralytics.utils.downloads import get_google_drive_file_info
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get_google_drive_file_info("https://drive.google.com/file/d/1cqT-cJgANNrhIHCrEufUYhQ4RqiWG_lJ/view?usp=drive_link")
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def test_utils_ops():
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"""Test various operations utilities."""
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"""Test utility operations functions for coordinate transformation and normalization."""
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from ultralytics.utils.ops import (
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ltwh2xywh,
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ltwh2xyxy,
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@ -414,7 +410,7 @@ def test_utils_ops():
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def test_utils_files():
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"""Test file handling utilities."""
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"""Test file handling utilities including file age, date, and paths with spaces."""
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from ultralytics.utils.files import file_age, file_date, get_latest_run, spaces_in_path
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file_age(SOURCE)
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@ -429,7 +425,7 @@ def test_utils_files():
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@pytest.mark.slow
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def test_utils_patches_torch_save():
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"""Test torch_save backoff when _torch_save throws RuntimeError."""
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"""Test torch_save backoff when _torch_save raises RuntimeError to ensure robustness."""
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from unittest.mock import MagicMock, patch
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from ultralytics.utils.patches import torch_save
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@ -444,7 +440,7 @@ def test_utils_patches_torch_save():
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def test_nn_modules_conv():
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"""Test Convolutional Neural Network modules."""
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"""Test Convolutional Neural Network modules including CBAM, Conv2, and ConvTranspose."""
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from ultralytics.nn.modules.conv import CBAM, Conv2, ConvTranspose, DWConvTranspose2d, Focus
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c1, c2 = 8, 16 # input and output channels
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@ -463,7 +459,7 @@ def test_nn_modules_conv():
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def test_nn_modules_block():
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"""Test Neural Network block modules."""
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"""Test various blocks in neural network modules including C1, C3TR, BottleneckCSP, C3Ghost, and C3x."""
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from ultralytics.nn.modules.block import C1, C3TR, BottleneckCSP, C3Ghost, C3x
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c1, c2 = 8, 16 # input and output channels
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@ -479,7 +475,7 @@ def test_nn_modules_block():
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@pytest.mark.skipif(not ONLINE, reason="environment is offline")
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def test_hub():
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"""Test Ultralytics HUB functionalities."""
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"""Test Ultralytics HUB functionalities (e.g. export formats, logout)."""
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from ultralytics.hub import export_fmts_hub, logout
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from ultralytics.hub.utils import smart_request
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@ -490,7 +486,7 @@ def test_hub():
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@pytest.fixture
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def image():
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"""Loads an image from a predefined source using OpenCV."""
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"""Load and return an image from a predefined source using OpenCV."""
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return cv2.imread(str(SOURCE))
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@ -504,7 +500,7 @@ def image():
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],
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)
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def test_classify_transforms_train(image, auto_augment, erasing, force_color_jitter):
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"""Tests classification transforms during training with various augmentation settings."""
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"""Tests classification transforms during training with various augmentations to ensure proper functionality."""
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from ultralytics.data.augment import classify_augmentations
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transform = classify_augmentations(
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@ -533,7 +529,7 @@ def test_classify_transforms_train(image, auto_augment, erasing, force_color_jit
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@pytest.mark.slow
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@pytest.mark.skipif(not ONLINE, reason="environment is offline")
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def test_model_tune():
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"""Tune YOLO model for performance."""
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"""Tune YOLO model for performance improvement."""
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YOLO("yolov8n-pose.pt").tune(data="coco8-pose.yaml", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu")
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YOLO("yolov8n-cls.pt").tune(data="imagenet10", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu")
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@ -550,7 +546,7 @@ def test_model_embeddings():
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@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="YOLOWorld with CLIP is not supported in Python 3.12")
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def test_yolo_world():
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"""Tests YOLO world models with different configurations, including classes, detection, and training scenarios."""
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"""Tests YOLO world models with CLIP support, including detection and training scenarios."""
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model = YOLO("yolov8s-world.pt") # no YOLOv8n-world model yet
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model.set_classes(["tree", "window"])
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model(SOURCE, conf=0.01)
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@ -581,7 +577,7 @@ def test_yolo_world():
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def test_yolov10():
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"""A simple test for yolov10 for now."""
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"""Test YOLOv10 model training, validation, and prediction steps with minimal configurations."""
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model = YOLO("yolov10n.yaml")
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# train/val/predict
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model.train(data="coco8.yaml", epochs=1, imgsz=32, close_mosaic=1, cache="disk")
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