ultralytics/cfg/__init__.py

# Ultralytics YOLO 🚀, AGPL-3.0 license

import contextlib
import shutil
import subprocess
import sys
from pathlib import Path
from types import SimpleNamespace
from typing import Dict, List, Union

from ultralytics.utils import (
    ASSETS,
    DEFAULT_CFG,
    DEFAULT_CFG_DICT,
    DEFAULT_CFG_PATH,
    LOGGER,
    RANK,
    ROOT,
    RUNS_DIR,
    SETTINGS,
    SETTINGS_YAML,
    TESTS_RUNNING,
    IterableSimpleNamespace,
    __version__,
    checks,
    colorstr,
    deprecation_warn,
    yaml_load,
    yaml_print,
)

# Define valid tasks and modes
MODES = {"train", "val", "predict", "export", "track", "benchmark"}
TASKS = {"detect", "segment", "classify", "pose", "obb"}
TASK2DATA = {
    "detect": "coco8.yaml",
    "segment": "coco8-seg.yaml",
    "classify": "imagenet10",
    "pose": "coco8-pose.yaml",
    "obb": "dota8.yaml",
}
TASK2MODEL = {
    "detect": "yolov8n.pt",
    "segment": "yolov8n-seg.pt",
    "classify": "yolov8n-cls.pt",
    "pose": "yolov8n-pose.pt",
    "obb": "yolov8n-obb.pt",
}
TASK2METRIC = {
    "detect": "metrics/mAP50-95(B)",
    "segment": "metrics/mAP50-95(M)",
    "classify": "metrics/accuracy_top1",
    "pose": "metrics/mAP50-95(P)",
    "obb": "metrics/mAP50-95(B)",
}
MODELS = {TASK2MODEL[task] for task in TASKS}

ARGV = sys.argv or ["", ""]  # sometimes sys.argv = []
CLI_HELP_MSG = f"""
    Arguments received: {str(['yolo'] + ARGV[1:])}. Ultralytics 'yolo' commands use the following syntax:

        yolo TASK MODE ARGS

        Where   TASK (optional) is one of {TASKS}
                MODE (required) is one of {MODES}
                ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
                    See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'

    1. Train a detection model for 10 epochs with an initial learning_rate of 0.01
        yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01

    2. Predict a YouTube video using a pretrained segmentation model at image size 320:
        yolo predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320

    3. Val a pretrained detection model at batch-size 1 and image size 640:
        yolo val model=yolov8n.pt data=coco8.yaml batch=1 imgsz=640

    4. Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required)
        yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128

    6. Explore your datasets using semantic search and SQL with a simple GUI powered by Ultralytics Explorer API
        yolo explorer

    5. Run special commands:
        yolo help
        yolo checks
        yolo version
        yolo settings
        yolo copy-cfg
        yolo cfg

    Docs: https://docs.ultralytics.com
    Community: https://community.ultralytics.com
    GitHub: https://github.com/ultralytics/ultralytics
    """

# Define keys for arg type checks
CFG_FLOAT_KEYS = {  # integer or float arguments, i.e. x=2 and x=2.0
    "warmup_epochs",
    "box",
    "cls",
    "dfl",
    "degrees",
    "shear",
    "time",
    "workspace",
    "batch",
}
CFG_FRACTION_KEYS = {  # fractional float arguments with 0.0<=values<=1.0
    "dropout",
    "lr0",
    "lrf",
    "momentum",
    "weight_decay",
    "warmup_momentum",
    "warmup_bias_lr",
    "label_smoothing",
    "hsv_h",
    "hsv_s",
    "hsv_v",
    "translate",
    "scale",
    "perspective",
    "flipud",
    "fliplr",
    "bgr",
    "mosaic",
    "mixup",
    "copy_paste",
    "conf",
    "iou",
    "fraction",
}
CFG_INT_KEYS = {  # integer-only arguments
    "epochs",
    "patience",
    "workers",
    "seed",
    "close_mosaic",
    "mask_ratio",
    "max_det",
    "vid_stride",
    "line_width",
    "nbs",
    "save_period",
}
CFG_BOOL_KEYS = {  # boolean-only arguments
    "save",
    "exist_ok",
    "verbose",
    "deterministic",
    "single_cls",
    "rect",
    "cos_lr",
    "overlap_mask",
    "val",
    "save_json",
    "save_hybrid",
    "half",
    "dnn",
    "plots",
    "show",
    "save_txt",
    "save_conf",
    "save_crop",
    "save_frames",
    "show_labels",
    "show_conf",
    "visualize",
    "augment",
    "agnostic_nms",
    "retina_masks",
    "show_boxes",
    "keras",
    "optimize",
    "int8",
    "dynamic",
    "simplify",
    "nms",
    "profile",
    "multi_scale",
}


def cfg2dict(cfg):
    """
    Convert a configuration object to a dictionary, whether it is a file path, a string, or a SimpleNamespace object.

    Args:
        cfg (str | Path | dict | SimpleNamespace): Configuration object to be converted to a dictionary.

    Returns:
        cfg (dict): Configuration object in dictionary format.
    """
    if isinstance(cfg, (str, Path)):
        cfg = yaml_load(cfg)  # load dict
    elif isinstance(cfg, SimpleNamespace):
        cfg = vars(cfg)  # convert to dict
    return cfg


def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG_DICT, overrides: Dict = None):
    """
    Load and merge configuration data from a file or dictionary.

    Args:
        cfg (str | Path | Dict | SimpleNamespace): Configuration data.
        overrides (str | Dict | optional): Overrides in the form of a file name or a dictionary. Default is None.

    Returns:
        (SimpleNamespace): Training arguments namespace.
    """
    cfg = cfg2dict(cfg)

    # Merge overrides
    if overrides:
        overrides = cfg2dict(overrides)
        if "save_dir" not in cfg:
            overrides.pop("save_dir", None)  # special override keys to ignore
        check_dict_alignment(cfg, overrides)
        cfg = {**cfg, **overrides}  # merge cfg and overrides dicts (prefer overrides)

    # Special handling for numeric project/name
    for k in "project", "name":
        if k in cfg and isinstance(cfg[k], (int, float)):
            cfg[k] = str(cfg[k])
    if cfg.get("name") == "model":  # assign model to 'name' arg
        cfg["name"] = cfg.get("model", "").split(".")[0]
        LOGGER.warning(f"WARNING ⚠️ 'name=model' automatically updated to 'name={cfg['name']}'.")

    # Type and Value checks
    check_cfg(cfg)

    # Return instance
    return IterableSimpleNamespace(**cfg)


def check_cfg(cfg, hard=True):
    """Check Ultralytics configuration argument types and values."""
    for k, v in cfg.items():
        if v is not None:  # None values may be from optional args
            if k in CFG_FLOAT_KEYS and not isinstance(v, (int, float)):
                if hard:
                    raise TypeError(
                        f"'{k}={v}' is of invalid type {type(v).__name__}. "
                        f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')"
                    )
                cfg[k] = float(v)
            elif k in CFG_FRACTION_KEYS:
                if not isinstance(v, (int, float)):
                    if hard:
                        raise TypeError(
                            f"'{k}={v}' is of invalid type {type(v).__name__}. "
                            f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')"
                        )
                    cfg[k] = v = float(v)
                if not (0.0 <= v <= 1.0):
                    raise ValueError(f"'{k}={v}' is an invalid value. " f"Valid '{k}' values are between 0.0 and 1.0.")
            elif k in CFG_INT_KEYS and not isinstance(v, int):
                if hard:
                    raise TypeError(
                        f"'{k}={v}' is of invalid type {type(v).__name__}. " f"'{k}' must be an int (i.e. '{k}=8')"
                    )
                cfg[k] = int(v)
            elif k in CFG_BOOL_KEYS and not isinstance(v, bool):
                if hard:
                    raise TypeError(
                        f"'{k}={v}' is of invalid type {type(v).__name__}. "
                        f"'{k}' must be a bool (i.e. '{k}=True' or '{k}=False')"
                    )
                cfg[k] = bool(v)


def get_save_dir(args, name=None):
    """Return save_dir as created from train/val/predict arguments."""

    if getattr(args, "save_dir", None):
        save_dir = args.save_dir
    else:
        from ultralytics.utils.files import increment_path

        project = args.project or (ROOT.parent / "tests/tmp/runs" if TESTS_RUNNING else RUNS_DIR) / args.task
        name = name or args.name or f"{args.mode}"
        save_dir = increment_path(Path(project) / name, exist_ok=args.exist_ok if RANK in {-1, 0} else True)

    return Path(save_dir)


def _handle_deprecation(custom):
    """Hardcoded function to handle deprecated config keys."""

    for key in custom.copy().keys():
        if key == "boxes":
            deprecation_warn(key, "show_boxes")
            custom["show_boxes"] = custom.pop("boxes")
        if key == "hide_labels":
            deprecation_warn(key, "show_labels")
            custom["show_labels"] = custom.pop("hide_labels") == "False"
        if key == "hide_conf":
            deprecation_warn(key, "show_conf")
            custom["show_conf"] = custom.pop("hide_conf") == "False"
        if key == "line_thickness":
            deprecation_warn(key, "line_width")
            custom["line_width"] = custom.pop("line_thickness")

    return custom


def check_dict_alignment(base: Dict, custom: Dict, e=None):
    """
    This function checks for any mismatched keys between a custom configuration list and a base configuration list. If
    any mismatched keys are found, the function prints out similar keys from the base list and exits the program.

    Args:
        custom (dict): a dictionary of custom configuration options
        base (dict): a dictionary of base configuration options
        e (Error, optional): An optional error that is passed by the calling function.
    """
    custom = _handle_deprecation(custom)
    base_keys, custom_keys = (set(x.keys()) for x in (base, custom))
    mismatched = [k for k in custom_keys if k not in base_keys]
    if mismatched:
        from difflib import get_close_matches

        string = ""
        for x in mismatched:
            matches = get_close_matches(x, base_keys)  # key list
            matches = [f"{k}={base[k]}" if base.get(k) is not None else k for k in matches]
            match_str = f"Similar arguments are i.e. {matches}." if matches else ""
            string += f"'{colorstr('red', 'bold', x)}' is not a valid YOLO argument. {match_str}\n"
        raise SyntaxError(string + CLI_HELP_MSG) from e


def merge_equals_args(args: List[str]) -> List[str]:
    """
    Merges arguments around isolated '=' args in a list of strings. The function considers cases where the first
    argument ends with '=' or the second starts with '=', as well as when the middle one is an equals sign.

    Args:
        args (List[str]): A list of strings where each element is an argument.

    Returns:
        (List[str]): A list of strings where the arguments around isolated '=' are merged.
    """
    new_args = []
    for i, arg in enumerate(args):
        if arg == "=" and 0 < i < len(args) - 1:  # merge ['arg', '=', 'val']
            new_args[-1] += f"={args[i + 1]}"
            del args[i + 1]
        elif arg.endswith("=") and i < len(args) - 1 and "=" not in args[i + 1]:  # merge ['arg=', 'val']
            new_args.append(f"{arg}{args[i + 1]}")
            del args[i + 1]
        elif arg.startswith("=") and i > 0:  # merge ['arg', '=val']
            new_args[-1] += arg
        else:
            new_args.append(arg)
    return new_args


def handle_yolo_hub(args: List[str]) -> None:
    """
    Handle Ultralytics HUB command-line interface (CLI) commands.

    This function processes Ultralytics HUB CLI commands such as login and logout.
    It should be called when executing a script with arguments related to HUB authentication.

    Args:
        args (List[str]): A list of command line arguments

    Example:
        ```bash
        python my_script.py hub login your_api_key
        ```
    """
    from ultralytics import hub

    if args[0] == "login":
        key = args[1] if len(args) > 1 else ""
        # Log in to Ultralytics HUB using the provided API key
        hub.login(key)
    elif args[0] == "logout":
        # Log out from Ultralytics HUB
        hub.logout()


def handle_yolo_settings(args: List[str]) -> None:
    """
    Handle YOLO settings command-line interface (CLI) commands.

    This function processes YOLO settings CLI commands such as reset.
    It should be called when executing a script with arguments related to YOLO settings management.

    Args:
        args (List[str]): A list of command line arguments for YOLO settings management.

    Example:
        ```bash
        python my_script.py yolo settings reset
        ```
    """
    url = "https://docs.ultralytics.com/quickstart/#ultralytics-settings"  # help URL
    try:
        if any(args):
            if args[0] == "reset":
                SETTINGS_YAML.unlink()  # delete the settings file
                SETTINGS.reset()  # create new settings
                LOGGER.info("Settings reset successfully")  # inform the user that settings have been reset
            else:  # save a new setting
                new = dict(parse_key_value_pair(a) for a in args)
                check_dict_alignment(SETTINGS, new)
                SETTINGS.update(new)

        LOGGER.info(f"💡 Learn about settings at {url}")
        yaml_print(SETTINGS_YAML)  # print the current settings
    except Exception as e:
        LOGGER.warning(f"WARNING ⚠️ settings error: '{e}'. Please see {url} for help.")


def handle_explorer():
    """Open the Ultralytics Explorer GUI."""
    checks.check_requirements("streamlit")
    LOGGER.info("💡 Loading Explorer dashboard...")
    subprocess.run(["streamlit", "run", ROOT / "data/explorer/gui/dash.py", "--server.maxMessageSize", "2048"])


def parse_key_value_pair(pair):
    """Parse one 'key=value' pair and return key and value."""
    k, v = pair.split("=", 1)  # split on first '=' sign
    k, v = k.strip(), v.strip()  # remove spaces
    assert v, f"missing '{k}' value"
    return k, smart_value(v)


def smart_value(v):
    """Convert a string to an underlying type such as int, float, bool, etc."""
    v_lower = v.lower()
    if v_lower == "none":
        return None
    elif v_lower == "true":
        return True
    elif v_lower == "false":
        return False
    else:
        with contextlib.suppress(Exception):
            return eval(v)
        return v


def entrypoint(debug=""):
    """
    This function is the ultralytics package entrypoint, it's responsible for parsing the command line arguments passed
    to the package.

    This function allows for:
    - passing mandatory YOLO args as a list of strings
    - specifying the task to be performed, either 'detect', 'segment' or 'classify'
    - specifying the mode, either 'train', 'val', 'test', or 'predict'
    - running special modes like 'checks'
    - passing overrides to the package's configuration

    It uses the package's default cfg and initializes it using the passed overrides.
    Then it calls the CLI function with the composed cfg
    """
    args = (debug.split(" ") if debug else ARGV)[1:]
    if not args:  # no arguments passed
        LOGGER.info(CLI_HELP_MSG)
        return

    special = {
        "help": lambda: LOGGER.info(CLI_HELP_MSG),
        "checks": checks.collect_system_info,
        "version": lambda: LOGGER.info(__version__),
        "settings": lambda: handle_yolo_settings(args[1:]),
        "cfg": lambda: yaml_print(DEFAULT_CFG_PATH),
        "hub": lambda: handle_yolo_hub(args[1:]),
        "login": lambda: handle_yolo_hub(args),
        "copy-cfg": copy_default_cfg,
        "explorer": lambda: handle_explorer(),
    }
    full_args_dict = {**DEFAULT_CFG_DICT, **{k: None for k in TASKS}, **{k: None for k in MODES}, **special}

    # Define common misuses of special commands, i.e. -h, -help, --help
    special.update({k[0]: v for k, v in special.items()})  # singular
    special.update({k[:-1]: v for k, v in special.items() if len(k) > 1 and k.endswith("s")})  # singular
    special = {**special, **{f"-{k}": v for k, v in special.items()}, **{f"--{k}": v for k, v in special.items()}}

    overrides = {}  # basic overrides, i.e. imgsz=320
    for a in merge_equals_args(args):  # merge spaces around '=' sign
        if a.startswith("--"):
            LOGGER.warning(f"WARNING ⚠️ argument '{a}' does not require leading dashes '--', updating to '{a[2:]}'.")
            a = a[2:]
        if a.endswith(","):
            LOGGER.warning(f"WARNING ⚠️ argument '{a}' does not require trailing comma ',', updating to '{a[:-1]}'.")
            a = a[:-1]
        if "=" in a:
            try:
                k, v = parse_key_value_pair(a)
                if k == "cfg" and v is not None:  # custom.yaml passed
                    LOGGER.info(f"Overriding {DEFAULT_CFG_PATH} with {v}")
                    overrides = {k: val for k, val in yaml_load(checks.check_yaml(v)).items() if k != "cfg"}
                else:
                    overrides[k] = v
            except (NameError, SyntaxError, ValueError, AssertionError) as e:
                check_dict_alignment(full_args_dict, {a: ""}, e)

        elif a in TASKS:
            overrides["task"] = a
        elif a in MODES:
            overrides["mode"] = a
        elif a.lower() in special:
            special[a.lower()]()
            return
        elif a in DEFAULT_CFG_DICT and isinstance(DEFAULT_CFG_DICT[a], bool):
            overrides[a] = True  # auto-True for default bool args, i.e. 'yolo show' sets show=True
        elif a in DEFAULT_CFG_DICT:
            raise SyntaxError(
                f"'{colorstr('red', 'bold', a)}' is a valid YOLO argument but is missing an '=' sign "
                f"to set its value, i.e. try '{a}={DEFAULT_CFG_DICT[a]}'\n{CLI_HELP_MSG}"
            )
        else:
            check_dict_alignment(full_args_dict, {a: ""})

    # Check keys
    check_dict_alignment(full_args_dict, overrides)

    # Mode
    mode = overrides.get("mode")
    if mode is None:
        mode = DEFAULT_CFG.mode or "predict"
        LOGGER.warning(f"WARNING ⚠️ 'mode' argument is missing. Valid modes are {MODES}. Using default 'mode={mode}'.")
    elif mode not in MODES:
        raise ValueError(f"Invalid 'mode={mode}'. Valid modes are {MODES}.\n{CLI_HELP_MSG}")

    # Task
    task = overrides.pop("task", None)
    if task:
        if task not in TASKS:
            raise ValueError(f"Invalid 'task={task}'. Valid tasks are {TASKS}.\n{CLI_HELP_MSG}")
        if "model" not in overrides:
            overrides["model"] = TASK2MODEL[task]

    # Model
    model = overrides.pop("model", DEFAULT_CFG.model)
    if model is None:
        model = "yolov8n.pt"
        LOGGER.warning(f"WARNING ⚠️ 'model' argument is missing. Using default 'model={model}'.")
    overrides["model"] = model
    stem = Path(model).stem.lower()
    if "rtdetr" in stem:  # guess architecture
        from ultralytics import RTDETR

        model = RTDETR(model)  # no task argument
    elif "fastsam" in stem:
        from ultralytics import FastSAM

        model = FastSAM(model)
    elif "sam" in stem:
        from ultralytics import SAM

        model = SAM(model)
    else:
        from ultralytics import YOLO

        model = YOLO(model, task=task)
    if isinstance(overrides.get("pretrained"), str):
        model.load(overrides["pretrained"])

    # Task Update
    if task != model.task:
        if task:
            LOGGER.warning(
                f"WARNING ⚠️ conflicting 'task={task}' passed with 'task={model.task}' model. "
                f"Ignoring 'task={task}' and updating to 'task={model.task}' to match model."
            )
        task = model.task

    # Mode
    if mode in {"predict", "track"} and "source" not in overrides:
        overrides["source"] = DEFAULT_CFG.source or ASSETS
        LOGGER.warning(f"WARNING ⚠️ 'source' argument is missing. Using default 'source={overrides['source']}'.")
    elif mode in {"train", "val"}:
        if "data" not in overrides and "resume" not in overrides:
            overrides["data"] = DEFAULT_CFG.data or TASK2DATA.get(task or DEFAULT_CFG.task, DEFAULT_CFG.data)
            LOGGER.warning(f"WARNING ⚠️ 'data' argument is missing. Using default 'data={overrides['data']}'.")
    elif mode == "export":
        if "format" not in overrides:
            overrides["format"] = DEFAULT_CFG.format or "torchscript"
            LOGGER.warning(f"WARNING ⚠️ 'format' argument is missing. Using default 'format={overrides['format']}'.")

    # Run command in python
    getattr(model, mode)(**overrides)  # default args from model

    # Show help
    LOGGER.info(f"💡 Learn more at https://docs.ultralytics.com/modes/{mode}")


# Special modes --------------------------------------------------------------------------------------------------------
def copy_default_cfg():
    """Copy and create a new default configuration file with '_copy' appended to its name."""
    new_file = Path.cwd() / DEFAULT_CFG_PATH.name.replace(".yaml", "_copy.yaml")
    shutil.copy2(DEFAULT_CFG_PATH, new_file)
    LOGGER.info(
        f"{DEFAULT_CFG_PATH} copied to {new_file}\n"
        f"Example YOLO command with this new custom cfg:\n    yolo cfg='{new_file}' imgsz=320 batch=8"
    )


if __name__ == "__main__":
    # Example: entrypoint(debug='yolo predict model=yolov8n.pt')
    entrypoint(debug="")