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318 lines
14 KiB
318 lines
14 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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import contextlib
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import re
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import shutil
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import sys
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from difflib import get_close_matches
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from pathlib import Path
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from types import SimpleNamespace
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from typing import Dict, List, Union
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from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, ROOT, USER_CONFIG_DIR,
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IterableSimpleNamespace, __version__, checks, colorstr, yaml_load, yaml_print)
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CLI_HELP_MSG = \
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f"""
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Arguments received: {str(['yolo'] + sys.argv[1:])}. Note that Ultralytics 'yolo' commands use the following syntax:
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yolo TASK MODE ARGS
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Where TASK (optional) is one of [detect, segment, classify]
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MODE (required) is one of [train, val, predict, export]
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ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
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See all ARGS at https://docs.ultralytics.com/cfg or with 'yolo cfg'
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1. Train a detection model for 10 epochs with an initial learning_rate of 0.01
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yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
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2. Predict a YouTube video using a pretrained segmentation model at image size 320:
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yolo predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320
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3. Val a pretrained detection model at batch-size 1 and image size 640:
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yolo val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
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4. Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required)
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yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128
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5. Run special commands:
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yolo help
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yolo checks
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yolo version
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yolo settings
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yolo copy-cfg
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yolo cfg
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Docs: https://docs.ultralytics.com/cli
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Community: https://community.ultralytics.com
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GitHub: https://github.com/ultralytics/ultralytics
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"""
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CFG_FLOAT_KEYS = {'warmup_epochs', 'box', 'cls', 'dfl', 'degrees', 'shear'}
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CFG_FRACTION_KEYS = {
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'dropout', 'iou', 'lr0', 'lrf', 'momentum', 'weight_decay', 'warmup_momentum', 'warmup_bias_lr', 'fl_gamma',
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'label_smoothing', 'hsv_h', 'hsv_s', 'hsv_v', 'translate', 'scale', 'perspective', 'flipud', 'fliplr', 'mosaic',
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'mixup', 'copy_paste', 'conf', 'iou'}
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CFG_INT_KEYS = {
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'epochs', 'patience', 'batch', 'workers', 'seed', 'close_mosaic', 'mask_ratio', 'max_det', 'vid_stride',
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'line_thickness', 'workspace', 'nbs', 'save_period'}
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CFG_BOOL_KEYS = {
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'save', 'exist_ok', 'pretrained', 'verbose', 'deterministic', 'single_cls', 'image_weights', 'rect', 'cos_lr',
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'overlap_mask', 'val', 'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show', 'save_txt', 'save_conf',
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'save_crop', 'hide_labels', 'hide_conf', 'visualize', 'augment', 'agnostic_nms', 'retina_masks', 'boxes', 'keras',
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'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader'}
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def cfg2dict(cfg):
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"""
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Convert a configuration object to a dictionary, whether it is a file path, a string, or a SimpleNamespace object.
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Inputs:
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cfg (str) or (Path) or (SimpleNamespace): Configuration object to be converted to a dictionary.
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Returns:
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cfg (dict): Configuration object in dictionary format.
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"""
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if isinstance(cfg, (str, Path)):
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cfg = yaml_load(cfg) # load dict
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elif isinstance(cfg, SimpleNamespace):
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cfg = vars(cfg) # convert to dict
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return cfg
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def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG_DICT, overrides: Dict = None):
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"""
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Load and merge configuration data from a file or dictionary.
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Args:
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cfg (str) or (Path) or (Dict) or (SimpleNamespace): Configuration data.
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overrides (str) or (Dict), optional: Overrides in the form of a file name or a dictionary. Default is None.
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Returns:
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(SimpleNamespace): Training arguments namespace.
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"""
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cfg = cfg2dict(cfg)
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# Merge overrides
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if overrides:
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overrides = cfg2dict(overrides)
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check_cfg_mismatch(cfg, overrides)
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cfg = {**cfg, **overrides} # merge cfg and overrides dicts (prefer overrides)
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# Special handling for numeric project/names
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for k in 'project', 'name':
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if k in cfg and isinstance(cfg[k], (int, float)):
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cfg[k] = str(cfg[k])
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# Type and Value checks
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for k, v in cfg.items():
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if v is not None: # None values may be from optional args
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if k in CFG_FLOAT_KEYS and not isinstance(v, (int, float)):
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raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
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f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')")
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elif k in CFG_FRACTION_KEYS:
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if not isinstance(v, (int, float)):
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raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
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f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')")
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if not (0.0 <= v <= 1.0):
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raise ValueError(f"'{k}={v}' is an invalid value. "
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f"Valid '{k}' values are between 0.0 and 1.0.")
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elif k in CFG_INT_KEYS and not isinstance(v, int):
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raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
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f"'{k}' must be an int (i.e. '{k}=8')")
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elif k in CFG_BOOL_KEYS and not isinstance(v, bool):
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raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
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f"'{k}' must be a bool (i.e. '{k}=True' or '{k}=False')")
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# Return instance
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return IterableSimpleNamespace(**cfg)
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def check_cfg_mismatch(base: Dict, custom: Dict, e=None):
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"""
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This function checks for any mismatched keys between a custom configuration list and a base configuration list.
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If any mismatched keys are found, the function prints out similar keys from the base list and exits the program.
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Inputs:
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- custom (Dict): a dictionary of custom configuration options
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- base (Dict): a dictionary of base configuration options
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"""
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base, custom = (set(x.keys()) for x in (base, custom))
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mismatched = [x for x in custom if x not in base]
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if mismatched:
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string = ''
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for x in mismatched:
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matches = get_close_matches(x, base) # key list
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matches = [f'{k}={DEFAULT_CFG_DICT[k]}' if DEFAULT_CFG_DICT.get(k) is not None else k for k in matches]
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match_str = f'Similar arguments are i.e. {matches}.' if matches else ''
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string += f"'{colorstr('red', 'bold', x)}' is not a valid YOLO argument. {match_str}\n"
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raise SyntaxError(string + CLI_HELP_MSG) from e
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def merge_equals_args(args: List[str]) -> List[str]:
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"""
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Merges arguments around isolated '=' args in a list of strings.
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The function considers cases where the first argument ends with '=' or the second starts with '=',
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as well as when the middle one is an equals sign.
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Args:
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args (List[str]): A list of strings where each element is an argument.
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Returns:
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List[str]: A list of strings where the arguments around isolated '=' are merged.
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"""
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new_args = []
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for i, arg in enumerate(args):
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if arg == '=' and 0 < i < len(args) - 1: # merge ['arg', '=', 'val']
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new_args[-1] += f'={args[i + 1]}'
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del args[i + 1]
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elif arg.endswith('=') and i < len(args) - 1 and '=' not in args[i + 1]: # merge ['arg=', 'val']
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new_args.append(f'{arg}{args[i + 1]}')
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del args[i + 1]
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elif arg.startswith('=') and i > 0: # merge ['arg', '=val']
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new_args[-1] += arg
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else:
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new_args.append(arg)
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return new_args
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def entrypoint(debug=''):
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"""
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This function is the ultralytics package entrypoint, it's responsible for parsing the command line arguments passed
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to the package.
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This function allows for:
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- passing mandatory YOLO args as a list of strings
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- specifying the task to be performed, either 'detect', 'segment' or 'classify'
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- specifying the mode, either 'train', 'val', 'test', or 'predict'
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- running special modes like 'checks'
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- passing overrides to the package's configuration
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It uses the package's default cfg and initializes it using the passed overrides.
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Then it calls the CLI function with the composed cfg
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"""
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args = (debug.split(' ') if debug else sys.argv)[1:]
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if not args: # no arguments passed
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LOGGER.info(CLI_HELP_MSG)
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return
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# Define tasks and modes
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tasks = 'detect', 'segment', 'classify'
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modes = 'train', 'val', 'predict', 'export', 'track'
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special = {
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'help': lambda: LOGGER.info(CLI_HELP_MSG),
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'checks': checks.check_yolo,
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'version': lambda: LOGGER.info(__version__),
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'settings': lambda: yaml_print(USER_CONFIG_DIR / 'settings.yaml'),
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'cfg': lambda: yaml_print(DEFAULT_CFG_PATH),
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'copy-cfg': copy_default_cfg}
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full_args_dict = {**DEFAULT_CFG_DICT, **{k: None for k in tasks}, **{k: None for k in modes}, **special}
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# Define common mis-uses of special commands, i.e. -h, -help, --help
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special.update({k[0]: v for k, v in special.items()}) # singular
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special.update({k[:-1]: v for k, v in special.items() if len(k) > 1 and k.endswith('s')}) # singular
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special = {**special, **{f'-{k}': v for k, v in special.items()}, **{f'--{k}': v for k, v in special.items()}}
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overrides = {} # basic overrides, i.e. imgsz=320
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for a in merge_equals_args(args): # merge spaces around '=' sign
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if a.startswith('--'):
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LOGGER.warning(f"WARNING ⚠️ '{a}' does not require leading dashes '--', updating to '{a[2:]}'.")
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a = a[2:]
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if '=' in a:
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try:
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re.sub(r' *= *', '=', a) # remove spaces around equals sign
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k, v = a.split('=', 1) # split on first '=' sign
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assert v, f"missing '{k}' value"
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if k == 'cfg': # custom.yaml passed
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LOGGER.info(f'Overriding {DEFAULT_CFG_PATH} with {v}')
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overrides = {k: val for k, val in yaml_load(v).items() if k != 'cfg'}
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else:
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if v.lower() == 'none':
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v = None
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elif v.lower() == 'true':
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v = True
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elif v.lower() == 'false':
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v = False
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else:
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with contextlib.suppress(Exception):
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v = eval(v)
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overrides[k] = v
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except (NameError, SyntaxError, ValueError, AssertionError) as e:
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check_cfg_mismatch(full_args_dict, {a: ''}, e)
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elif a in tasks:
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overrides['task'] = a
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elif a in modes:
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overrides['mode'] = a
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elif a in special:
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special[a]()
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return
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elif a in DEFAULT_CFG_DICT and isinstance(DEFAULT_CFG_DICT[a], bool):
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overrides[a] = True # auto-True for default bool args, i.e. 'yolo show' sets show=True
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elif a in DEFAULT_CFG_DICT:
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raise SyntaxError(f"'{colorstr('red', 'bold', a)}' is a valid YOLO argument but is missing an '=' sign "
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f"to set its value, i.e. try '{a}={DEFAULT_CFG_DICT[a]}'\n{CLI_HELP_MSG}")
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else:
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check_cfg_mismatch(full_args_dict, {a: ''})
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# Defaults
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task2model = dict(detect='yolov8n.pt', segment='yolov8n-seg.pt', classify='yolov8n-cls.pt')
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task2data = dict(detect='coco128.yaml', segment='coco128-seg.yaml', classify='mnist160')
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# Mode
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mode = overrides.get('mode', None)
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if mode is None:
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mode = DEFAULT_CFG.mode or 'predict'
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LOGGER.warning(f"WARNING ⚠️ 'mode' is missing. Valid modes are {modes}. Using default 'mode={mode}'.")
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elif mode not in modes:
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if mode not in ('checks', checks):
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raise ValueError(f"Invalid 'mode={mode}'. Valid modes are {modes}.\n{CLI_HELP_MSG}")
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LOGGER.warning("WARNING ⚠️ 'yolo mode=checks' is deprecated. Use 'yolo checks' instead.")
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checks.check_yolo()
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return
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# Model
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model = overrides.pop('model', DEFAULT_CFG.model)
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task = overrides.pop('task', None)
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if model is None:
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model = task2model.get(task, 'yolov8n.pt')
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LOGGER.warning(f"WARNING ⚠️ 'model' is missing. Using default 'model={model}'.")
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from ultralytics.yolo.engine.model import YOLO
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overrides['model'] = model
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model = YOLO(model)
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# Task
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if task and task != model.task:
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LOGGER.warning(f"WARNING ⚠️ 'task={task}' conflicts with {model.task} model {overrides['model']}. "
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f"Inheriting 'task={model.task}' from {overrides['model']} and ignoring 'task={task}'.")
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task = model.task
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overrides['task'] = task
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if mode in {'predict', 'track'} and 'source' not in overrides:
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overrides['source'] = DEFAULT_CFG.source or ROOT / 'assets' if (ROOT / 'assets').exists() \
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else 'https://ultralytics.com/images/bus.jpg'
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LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using default 'source={overrides['source']}'.")
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elif mode in ('train', 'val'):
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if 'data' not in overrides:
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overrides['data'] = task2data.get(task, DEFAULT_CFG.data)
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LOGGER.warning(f"WARNING ⚠️ 'data' is missing. Using {model.task} default 'data={overrides['data']}'.")
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elif mode == 'export':
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if 'format' not in overrides:
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overrides['format'] = DEFAULT_CFG.format or 'torchscript'
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LOGGER.warning(f"WARNING ⚠️ 'format' is missing. Using default 'format={overrides['format']}'.")
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# Run command in python
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# getattr(model, mode)(**vars(get_cfg(overrides=overrides))) # default args using default.yaml
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getattr(model, mode)(**overrides) # default args from model
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# Special modes --------------------------------------------------------------------------------------------------------
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def copy_default_cfg():
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new_file = Path.cwd() / DEFAULT_CFG_PATH.name.replace('.yaml', '_copy.yaml')
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shutil.copy2(DEFAULT_CFG_PATH, new_file)
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LOGGER.info(f'{DEFAULT_CFG_PATH} copied to {new_file}\n'
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f"Example YOLO command with this new custom cfg:\n yolo cfg='{new_file}' imgsz=320 batch=8")
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if __name__ == '__main__':
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# entrypoint(debug='yolo predict model=yolov8n.pt')
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entrypoint(debug='')
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