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@ -8,9 +8,9 @@ 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 import __version__ |
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from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, PREFIX, ROOT, |
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USER_CONFIG_DIR, IterableSimpleNamespace, colorstr, emojis, yaml_load, yaml_print) |
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USER_CONFIG_DIR, IterableSimpleNamespace, __version__, colorstr, emojis, yaml_load, |
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yaml_print) |
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from ultralytics.yolo.utils.checks import check_yolo |
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CLI_HELP_MSG = \ |
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@ -25,13 +25,13 @@ CLI_HELP_MSG = \ |
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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 detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=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 segment predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=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 detect val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=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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@ -56,7 +56,7 @@ CFG_FRACTION_KEYS = { |
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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'} |
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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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@ -131,7 +131,7 @@ def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG, override |
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return IterableSimpleNamespace(**cfg) |
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def check_cfg_mismatch(base: Dict, custom: Dict): |
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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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@ -143,12 +143,12 @@ def check_cfg_mismatch(base: Dict, custom: Dict): |
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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, 3, 0.6) |
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match_str = f"Similar arguments are {matches}." if matches else 'There are no similar arguments.' |
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LOGGER.warning(f"'{colorstr('red', 'bold', x)}' is not a valid YOLO argument. {match_str}") |
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LOGGER.warning(CLI_HELP_MSG) |
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sys.exit() |
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matches = get_close_matches(x, base) |
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match_str = f"Similar arguments are {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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@ -178,10 +178,6 @@ def merge_equals_args(args: List[str]) -> List[str]: |
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return new_args |
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def argument_error(arg): |
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return SyntaxError(f"'{arg}' is not a valid YOLO argument.\n{CLI_HELP_MSG}") |
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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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@ -212,6 +208,7 @@ def entrypoint(debug=''): |
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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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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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@ -236,7 +233,7 @@ def entrypoint(debug=''): |
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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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raise argument_error(a) from 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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@ -251,7 +248,7 @@ def entrypoint(debug=''): |
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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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raise argument_error(a) |
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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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Glenn Jocher
2 years ago
committed by
GitHub