ultralytics 8.0.14 Hydra removal fixes and cleanup (#542)
Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Kamlesh Kumar <patelkamleshpatel364@gmail.com>
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Glenn Jocher
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230
ultralytics/yolo/cfg/__init__.py
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230
ultralytics/yolo/cfg/__init__.py
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# Ultralytics YOLO 🚀, GPL-3.0 license
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import argparse
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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, Union
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from ultralytics import __version__, yolo
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from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, PREFIX, checks, colorstr, print_settings, yaml_load
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DIR = Path(__file__).parent
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CLI_HELP_MSG = \
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"""
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YOLOv8 CLI Usage examples:
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1. Install the ultralytics package:
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pip install ultralytics
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2. Train, Val, Predict and Export using 'yolo' commands:
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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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For a full list of available ARGS see https://docs.ultralytics.com/cfg.
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Train a detection model for 10 epochs with an initial learning_rate of 0.01
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
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Predict a YouTube video using a pretrained segmentation model at image size 320:
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yolo segment predict model=yolov8n-seg.pt source=https://youtu.be/Zgi9g1ksQHc imgsz=320
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Validate a pretrained detection model at batch-size 1 and image size 640:
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yolo detect val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
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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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3. 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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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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class UltralyticsCFG(SimpleNamespace):
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"""
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UltralyticsCFG iterable SimpleNamespace class to allow SimpleNamespace to be used with dict() and in for loops
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"""
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def __iter__(self):
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return iter(vars(self).items())
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def cfg2dict(cfg):
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"""
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Convert a configuration object to a dictionary.
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This function converts 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], 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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# Return instance
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return UltralyticsCFG(**cfg)
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def check_cfg_mismatch(base: Dict, custom: Dict):
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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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for option in mismatched:
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LOGGER.info(f"{colorstr(option)} is not a valid key. Similar keys: {get_close_matches(option, base, 3, 0.6)}")
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if mismatched:
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sys.exit()
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def entrypoint(debug=False):
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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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if debug:
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args = ['train', 'predict', 'model=yolov8n.pt'] # for testing
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else:
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if len(sys.argv) == 1: # no arguments passed
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LOGGER.info(CLI_HELP_MSG)
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return
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parser = argparse.ArgumentParser(description='YOLO parser')
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parser.add_argument('args', type=str, nargs='+', help='YOLO args')
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args = parser.parse_args().args
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args = re.sub(r'\s*=\s*', '=', ' '.join(args)).split(' ') # remove whitespaces around = sign
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tasks = 'detect', 'segment', 'classify'
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modes = 'train', 'val', 'predict', 'export'
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special_modes = {
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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': print_settings,
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'copy-cfg': copy_default_config}
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overrides = {} # basic overrides, i.e. imgsz=320
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defaults = yaml_load(DEFAULT_CFG_PATH)
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for a in args:
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if '=' in a:
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if a.startswith('cfg='): # custom.yaml passed
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custom_config = Path(a.split('=')[-1])
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LOGGER.info(f"{PREFIX}Overriding {DEFAULT_CFG_PATH} with {custom_config}")
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overrides = {k: v for k, v in yaml_load(custom_config).items() if k not in {'cfg'}}
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else:
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k, v = a.split('=')
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try:
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if k == 'device': # special DDP handling, i.e. device='0,1,2,3'
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v = v.replace('[', '').replace(']', '') # handle device=[0,1,2,3]
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v = v.replace(" ", "") # handle device=[0, 1, 2, 3]
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v = v.replace('\\', '') # handle device=\'0,1,2,3\'
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overrides[k] = v
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else:
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overrides[k] = eval(v) # convert strings to integers, floats, bools, etc.
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except (NameError, SyntaxError):
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overrides[k] = v
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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_modes:
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special_modes[a]()
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return
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elif a in defaults and defaults[a] is False:
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overrides[a] = True # auto-True for default False args, i.e. 'yolo show' sets show=True
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elif a in defaults:
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raise SyntaxError(f"'{a}' is a valid YOLO argument but is missing an '=' sign to set its value, "
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f"i.e. try '{a}={defaults[a]}'"
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f"\n{CLI_HELP_MSG}")
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else:
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raise SyntaxError(
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f"'{a}' is not a valid YOLO argument. For a full list of valid arguments see "
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f"https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/configs/default.yaml"
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f"\n{CLI_HELP_MSG}")
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cfg = get_cfg(defaults, overrides) # create CFG instance
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# Mapping from task to module
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module = {"detect": yolo.v8.detect, "segment": yolo.v8.segment, "classify": yolo.v8.classify}.get(cfg.task)
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if not module:
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raise SyntaxError(f"yolo task={cfg.task} is invalid. Valid tasks are: {', '.join(tasks)}\n{CLI_HELP_MSG}")
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# Mapping from mode to function
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func = {
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"train": module.train,
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"val": module.val,
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"predict": module.predict,
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"export": yolo.engine.exporter.export}.get(cfg.mode)
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if not func:
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raise SyntaxError(f"yolo mode={cfg.mode} is invalid. Valid modes are: {', '.join(modes)}\n{CLI_HELP_MSG}")
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func(cfg)
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# Special modes --------------------------------------------------------------------------------------------------------
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def copy_default_config():
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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"{PREFIX}{DEFAULT_CFG_PATH} copied to {new_file}\n"
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f"Usage for running YOLO with this new custom cfg:\nyolo cfg={new_file} args...")
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if __name__ == '__main__':
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entrypoint()
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108
ultralytics/yolo/cfg/default.yaml
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ultralytics/yolo/cfg/default.yaml
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# Default training settings and hyperparameters for medium-augmentation COCO training
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task: "detect" # inference task, i.e. detect, segment, classify
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mode: "train" # YOLO mode, i.e. train, val, predict, export
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# Train settings -------------------------------------------------------------------------------------------------------
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model: null # path to model file, i.e. yolov8n.pt, yolov8n.yaml
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data: null # path to data file, i.e. i.e. coco128.yaml
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epochs: 100 # number of epochs to train for
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patience: 50 # epochs to wait for no observable improvement for early stopping of training
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batch: 16 # number of images per batch (-1 for AutoBatch)
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imgsz: 640 # size of input images as integer or w,h
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save: True # save train checkpoints and predict results
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cache: False # True/ram, disk or False. Use cache for data loading
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device: null # device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
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workers: 8 # number of worker threads for data loading (per RANK if DDP)
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project: null # project name
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name: null # experiment name
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exist_ok: False # whether to overwrite existing experiment
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pretrained: False # whether to use a pretrained model
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optimizer: 'SGD' # optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
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verbose: False # whether to print verbose output
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seed: 0 # random seed for reproducibility
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deterministic: True # whether to enable deterministic mode
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single_cls: False # train multi-class data as single-class
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image_weights: False # use weighted image selection for training
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rect: False # support rectangular training
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cos_lr: False # use cosine learning rate scheduler
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close_mosaic: 10 # disable mosaic augmentation for final 10 epochs
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resume: False # resume training from last checkpoint
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# Segmentation
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overlap_mask: True # masks should overlap during training (segment train only)
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mask_ratio: 4 # mask downsample ratio (segment train only)
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# Classification
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dropout: 0.0 # use dropout regularization (classify train only)
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# Val/Test settings ----------------------------------------------------------------------------------------------------
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val: True # validate/test during training
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save_json: False # save results to JSON file
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save_hybrid: False # save hybrid version of labels (labels + additional predictions)
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conf: null # object confidence threshold for detection (default 0.25 predict, 0.001 val)
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iou: 0.7 # intersection over union (IoU) threshold for NMS
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max_det: 300 # maximum number of detections per image
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half: False # use half precision (FP16)
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dnn: False # use OpenCV DNN for ONNX inference
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plots: True # save plots during train/val
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# Prediction settings --------------------------------------------------------------------------------------------------
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source: null # source directory for images or videos
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show: False # show results if possible
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save_txt: False # save results as .txt file
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save_conf: False # save results with confidence scores
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save_crop: False # save cropped images with results
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hide_labels: False # hide labels
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hide_conf: False # hide confidence scores
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vid_stride: 1 # video frame-rate stride
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line_thickness: 3 # bounding box thickness (pixels)
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visualize: False # visualize model features
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augment: False # apply image augmentation to prediction sources
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agnostic_nms: False # class-agnostic NMS
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retina_masks: False # use high-resolution segmentation masks
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classes: null # filter results by class, i.e. class=0, or class=[0,2,3]
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# Export settings ------------------------------------------------------------------------------------------------------
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format: torchscript # format to export to
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keras: False # use Keras
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optimize: False # TorchScript: optimize for mobile
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int8: False # CoreML/TF INT8 quantization
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dynamic: False # ONNX/TF/TensorRT: dynamic axes
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simplify: False # ONNX: simplify model
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opset: 17 # ONNX: opset version
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workspace: 4 # TensorRT: workspace size (GB)
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nms: False # CoreML: add NMS
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# Hyperparameters ------------------------------------------------------------------------------------------------------
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lr0: 0.01 # initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
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lrf: 0.01 # final learning rate (lr0 * lrf)
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momentum: 0.937 # SGD momentum/Adam beta1
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weight_decay: 0.0005 # optimizer weight decay 5e-4
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warmup_epochs: 3.0 # warmup epochs (fractions ok)
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warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
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box: 7.5 # box loss gain
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cls: 0.5 # cls loss gain (scale with pixels)
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dfl: 1.5 # dfl loss gain
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fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
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label_smoothing: 0.0 # label smoothing (fraction)
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nbs: 64 # nominal batch size
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hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
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hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
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hsv_v: 0.4 # image HSV-Value augmentation (fraction)
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degrees: 0.0 # image rotation (+/- deg)
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translate: 0.1 # image translation (+/- fraction)
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scale: 0.5 # image scale (+/- gain)
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shear: 0.0 # image shear (+/- deg)
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perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
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flipud: 0.0 # image flip up-down (probability)
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fliplr: 0.5 # image flip left-right (probability)
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mosaic: 1.0 # image mosaic (probability)
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mixup: 0.0 # image mixup (probability)
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copy_paste: 0.0 # segment copy-paste (probability)
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# Custom config.yaml ---------------------------------------------------------------------------------------------------
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cfg: null # for overriding defaults.yaml
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# Debug, do not modify -------------------------------------------------------------------------------------------------
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v5loader: False # use legacy YOLOv5 dataloader
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