ultralytics 8.0.12 - Hydra removal (#506)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Pronoy Mandal <lukex9442@gmail.com> Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
This commit is contained in:
Glenn Jocher
GitHub
@ -1,36 +1,221 @@
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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 omegaconf import DictConfig, OmegaConf
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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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from ultralytics.yolo.configs.hydra_patch import check_config_mismatch
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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/config.
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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-config
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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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def get_config(config: Union[str, Path, DictConfig], overrides: Union[str, Dict] = None):
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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_config(config: 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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config (str) or (Path) or (DictConfig): Configuration data in the form of a file name or a DictConfig object.
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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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config (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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OmegaConf.Namespace: Training arguments namespace.
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(SimpleNamespace): Training arguments namespace.
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"""
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if overrides is None:
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overrides = {}
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if isinstance(config, (str, Path)):
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config = OmegaConf.load(config)
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elif isinstance(config, Dict):
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config = OmegaConf.create(config)
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# override
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if isinstance(overrides, str):
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overrides = OmegaConf.load(overrides)
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elif isinstance(overrides, Dict):
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overrides = OmegaConf.create(overrides)
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config = cfg2dict(config)
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check_config_mismatch(dict(overrides).keys(), dict(config).keys())
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# Merge overrides
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if overrides:
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overrides = cfg2dict(overrides)
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check_config_mismatch(config, overrides)
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config = {**config, **overrides} # merge config and overrides dicts (prefer overrides)
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return OmegaConf.merge(config, overrides)
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# Return instance
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return SimpleNamespace(**config)
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def check_config_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=True):
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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 config and initializes it using the passed overrides.
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Then it calls the CLI function with the composed config
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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-config': 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(" ", "").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_config(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 config:\nyolo cfg={new_file} args...")
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if __name__ == '__main__':
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entrypoint()
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@ -1,68 +1,68 @@
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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" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case. Specify task to run.
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mode: "train" # choices=['train', 'val', 'predict'] # mode to run task in.
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task: "detect" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case. Specify task to run.
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mode: "train" # choices=['train', 'val', 'predict'] # mode to run task in.
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# Train settings -------------------------------------------------------------------------------------------------------
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model: null # i.e. yolov8n.pt, yolov8n.yaml. Path to model file
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data: null # i.e. coco128.yaml. Path to data file
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epochs: 100 # number of epochs to train for
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model: null # i.e. yolov8n.pt, yolov8n.yaml. Path to model file
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data: null # i.e. coco128.yaml. Path to data file
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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
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imgsz: 640 # size of input images
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save: True # save checkpoints
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cache: False # True/ram, disk or False. Use cache for data loading
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device: null # cuda device, i.e. 0 or 0,1,2,3 or cpu. Device to run on
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workers: 8 # number of worker threads for data loading
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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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batch: 16 # number of images per batch
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imgsz: 640 # size of input images
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save: True # save checkpoints
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cache: False # True/ram, disk or False. Use cache for data loading
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device: null # cuda device, i.e. 0 or 0,1,2,3 or cpu. Device to run on
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workers: 8 # number of worker threads for data loading
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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
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mask_ratio: 4 # mask downsample ratio
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overlap_mask: True # masks should overlap during training
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mask_ratio: 4 # mask downsample ratio
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# Classification
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dropout: 0.0 # use dropout regularization
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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 # show plots during training
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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 # show plots during training
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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 results
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augment: False # apply data augmentation to images
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agnostic_nms: False # class-agnostic NMS
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retina_masks: False # use retina masks for object detection
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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 results
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augment: False # apply data augmentation to images
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agnostic_nms: False # class-agnostic NMS
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retina_masks: False # use retina masks for object detection
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# Export settings ------------------------------------------------------------------------------------------------------
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format: torchscript # format to export to
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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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@ -100,12 +100,8 @@ 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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# Hydra configs --------------------------------------------------------------------------------------------------------
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cfg: null # for overriding defaults.yaml
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hydra:
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output_subdir: null # disable hydra directory creation
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run:
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dir: .
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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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@ -1,77 +0,0 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
|
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import sys
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from difflib import get_close_matches
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from textwrap import dedent
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import hydra
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from hydra.errors import ConfigCompositionException
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from omegaconf import OmegaConf, open_dict # noqa
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from omegaconf.errors import ConfigAttributeError, ConfigKeyError, OmegaConfBaseException # noqa
|
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from ultralytics.yolo.utils import LOGGER, colorstr
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|
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def override_config(overrides, cfg):
|
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override_keys = [override.key_or_group for override in overrides]
|
||||
check_config_mismatch(override_keys, cfg.keys())
|
||||
for override in overrides:
|
||||
if override.package is not None:
|
||||
raise ConfigCompositionException(f"Override {override.input_line} looks like a config group"
|
||||
f" override, but config group '{override.key_or_group}' does not exist.")
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key = override.key_or_group
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value = override.value()
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try:
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if override.is_delete():
|
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config_val = OmegaConf.select(cfg, key, throw_on_missing=False)
|
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if config_val is None:
|
||||
raise ConfigCompositionException(f"Could not delete from config. '{override.key_or_group}'"
|
||||
" does not exist.")
|
||||
elif value is not None and value != config_val:
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raise ConfigCompositionException("Could not delete from config. The value of"
|
||||
f" '{override.key_or_group}' is {config_val} and not"
|
||||
f" {value}.")
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||||
|
||||
last_dot = key.rfind(".")
|
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with open_dict(cfg):
|
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if last_dot == -1:
|
||||
del cfg[key]
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else:
|
||||
node = OmegaConf.select(cfg, key[:last_dot])
|
||||
del node[key[last_dot + 1:]]
|
||||
|
||||
elif override.is_add():
|
||||
if OmegaConf.select(cfg, key, throw_on_missing=False) is None or isinstance(value, (dict, list)):
|
||||
OmegaConf.update(cfg, key, value, merge=True, force_add=True)
|
||||
else:
|
||||
assert override.input_line is not None
|
||||
raise ConfigCompositionException(
|
||||
dedent(f"""\
|
||||
Could not append to config. An item is already at '{override.key_or_group}'.
|
||||
Either remove + prefix: '{override.input_line[1:]}'
|
||||
Or add a second + to add or override '{override.key_or_group}': '+{override.input_line}'
|
||||
"""))
|
||||
elif override.is_force_add():
|
||||
OmegaConf.update(cfg, key, value, merge=True, force_add=True)
|
||||
else:
|
||||
try:
|
||||
OmegaConf.update(cfg, key, value, merge=True)
|
||||
except (ConfigAttributeError, ConfigKeyError) as ex:
|
||||
raise ConfigCompositionException(f"Could not override '{override.key_or_group}'."
|
||||
f"\nTo append to your config use +{override.input_line}") from ex
|
||||
except OmegaConfBaseException as ex:
|
||||
raise ConfigCompositionException(f"Error merging override {override.input_line}").with_traceback(
|
||||
sys.exc_info()[2]) from ex
|
||||
|
||||
|
||||
def check_config_mismatch(overrides, cfg):
|
||||
mismatched = [option for option in overrides if option not in cfg and 'hydra.' not in option]
|
||||
|
||||
for option in mismatched:
|
||||
LOGGER.info(f"{colorstr(option)} is not a valid key. Similar keys: {get_close_matches(option, cfg, 3, 0.6)}")
|
||||
if mismatched:
|
||||
sys.exit()
|
||||
|
||||
|
||||
hydra._internal.config_loader_impl.ConfigLoaderImpl._apply_overrides_to_config = override_config
|
||||
Reference in New Issue
Block a user