ultralytics 8.0.136 refactor and simplify package (#3748)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/__init__.py

@@ -1,14 +1,13 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.135'
+__version__ = '8.0.136'
 
+from ultralytics.engine.model import YOLO
 from ultralytics.hub import start
-from ultralytics.vit.rtdetr import RTDETR
-from ultralytics.vit.sam import SAM
-from ultralytics.yolo.engine.model import YOLO
-from ultralytics.yolo.fastsam import FastSAM
-from ultralytics.yolo.nas import NAS
-from ultralytics.yolo.utils.checks import check_yolo as checks
-from ultralytics.yolo.utils.downloads import download
+from ultralytics.models import RTDETR, SAM
+from ultralytics.models.fastsam import FastSAM
+from ultralytics.models.nas import NAS
+from ultralytics.utils.checks import check_yolo as checks
+from ultralytics.utils.downloads import download
 
 __all__ = '__version__', 'YOLO', 'NAS', 'SAM', 'FastSAM', 'RTDETR', 'checks', 'download', 'start'  # allow simpler import

ultralytics/cfg/__init__.py

@@ -0,0 +1,421 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import contextlib
+import re
+import shutil
+import sys
+from difflib import get_close_matches
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Dict, List, Union
+
+from ultralytics.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, ROOT, USER_CONFIG_DIR,
+                               IterableSimpleNamespace, __version__, checks, colorstr, deprecation_warn, get_settings,
+                               yaml_load, yaml_print)
+
+# Define valid tasks and modes
+MODES = 'train', 'val', 'predict', 'export', 'track', 'benchmark'
+TASKS = 'detect', 'segment', 'classify', 'pose'
+TASK2DATA = {'detect': 'coco8.yaml', 'segment': 'coco8-seg.yaml', 'classify': 'imagenet100', 'pose': 'coco8-pose.yaml'}
+TASK2MODEL = {
+    'detect': 'yolov8n.pt',
+    'segment': 'yolov8n-seg.pt',
+    'classify': 'yolov8n-cls.pt',
+    'pose': 'yolov8n-pose.pt'}
+TASK2METRIC = {
+    'detect': 'metrics/mAP50-95(B)',
+    'segment': 'metrics/mAP50-95(M)',
+    'classify': 'metrics/accuracy_top1',
+    'pose': 'metrics/mAP50-95(P)'}
+
+
+CLI_HELP_MSG = \
+    f"""
+    Arguments received: {str(['yolo'] + sys.argv[1:])}. Ultralytics 'yolo' commands use the following syntax:
+
+        yolo TASK MODE ARGS
+
+        Where   TASK (optional) is one of {TASKS}
+                MODE (required) is one of {MODES}
+                ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
+                    See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'
+
+    1. Train a detection model for 10 epochs with an initial learning_rate of 0.01
+        yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
+
+    2. Predict a YouTube video using a pretrained segmentation model at image size 320:
+        yolo predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320
+
+    3. Val a pretrained detection model at batch-size 1 and image size 640:
+        yolo val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
+
+    4. Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required)
+        yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128
+
+    5. Run special commands:
+        yolo help
+        yolo checks
+        yolo version
+        yolo settings
+        yolo copy-cfg
+        yolo cfg
+
+    Docs: https://docs.ultralytics.com
+    Community: https://community.ultralytics.com
+    GitHub: https://github.com/ultralytics/ultralytics
+    """
+
+# Define keys for arg type checks
+CFG_FLOAT_KEYS = 'warmup_epochs', 'box', 'cls', 'dfl', 'degrees', 'shear'
+CFG_FRACTION_KEYS = ('dropout', 'iou', 'lr0', 'lrf', 'momentum', 'weight_decay', 'warmup_momentum', 'warmup_bias_lr',
+                     'label_smoothing', 'hsv_h', 'hsv_s', 'hsv_v', 'translate', 'scale', 'perspective', 'flipud',
+                     'fliplr', 'mosaic', 'mixup', 'copy_paste', 'conf', 'iou', 'fraction')  # fraction floats 0.0 - 1.0
+CFG_INT_KEYS = ('epochs', 'patience', 'batch', 'workers', 'seed', 'close_mosaic', 'mask_ratio', 'max_det', 'vid_stride',
+                'line_width', 'workspace', 'nbs', 'save_period')
+CFG_BOOL_KEYS = ('save', 'exist_ok', 'verbose', 'deterministic', 'single_cls', 'rect', 'cos_lr', 'overlap_mask', 'val',
+                 'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show', 'save_txt', 'save_conf', 'save_crop',
+                 'show_labels', 'show_conf', 'visualize', 'augment', 'agnostic_nms', 'retina_masks', 'boxes', 'keras',
+                 'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'profile')
+
+
+def cfg2dict(cfg):
+    """
+    Convert a configuration object to a dictionary, whether it is a file path, a string, or a SimpleNamespace object.
+
+    Args:
+        cfg (str | Path | SimpleNamespace): Configuration object to be converted to a dictionary.
+
+    Returns:
+        cfg (dict): Configuration object in dictionary format.
+    """
+    if isinstance(cfg, (str, Path)):
+        cfg = yaml_load(cfg)  # load dict
+    elif isinstance(cfg, SimpleNamespace):
+        cfg = vars(cfg)  # convert to dict
+    return cfg
+
+
+def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG_DICT, overrides: Dict = None):
+    """
+    Load and merge configuration data from a file or dictionary.
+
+    Args:
+        cfg (str | Path | Dict | SimpleNamespace): Configuration data.
+        overrides (str | Dict | optional): Overrides in the form of a file name or a dictionary. Default is None.
+
+    Returns:
+        (SimpleNamespace): Training arguments namespace.
+    """
+    cfg = cfg2dict(cfg)
+
+    # Merge overrides
+    if overrides:
+        overrides = cfg2dict(overrides)
+        check_cfg_mismatch(cfg, overrides)
+        cfg = {**cfg, **overrides}  # merge cfg and overrides dicts (prefer overrides)
+
+    # Special handling for numeric project/name
+    for k in 'project', 'name':
+        if k in cfg and isinstance(cfg[k], (int, float)):
+            cfg[k] = str(cfg[k])
+    if cfg.get('name') == 'model':  # assign model to 'name' arg
+        cfg['name'] = cfg.get('model', '').split('.')[0]
+        LOGGER.warning(f"WARNING ⚠️ 'name=model' automatically updated to 'name={cfg['name']}'.")
+
+    # Type and Value checks
+    for k, v in cfg.items():
+        if v is not None:  # None values may be from optional args
+            if k in CFG_FLOAT_KEYS and not isinstance(v, (int, float)):
+                raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
+                                f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')")
+            elif k in CFG_FRACTION_KEYS:
+                if not isinstance(v, (int, float)):
+                    raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
+                                    f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')")
+                if not (0.0 <= v <= 1.0):
+                    raise ValueError(f"'{k}={v}' is an invalid value. "
+                                     f"Valid '{k}' values are between 0.0 and 1.0.")
+            elif k in CFG_INT_KEYS and not isinstance(v, int):
+                raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
+                                f"'{k}' must be an int (i.e. '{k}=8')")
+            elif k in CFG_BOOL_KEYS and not isinstance(v, bool):
+                raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
+                                f"'{k}' must be a bool (i.e. '{k}=True' or '{k}=False')")
+
+    # Return instance
+    return IterableSimpleNamespace(**cfg)
+
+
+def _handle_deprecation(custom):
+    """
+    Hardcoded function to handle deprecated config keys
+    """
+
+    for key in custom.copy().keys():
+        if key == 'hide_labels':
+            deprecation_warn(key, 'show_labels')
+            custom['show_labels'] = custom.pop('hide_labels') == 'False'
+        if key == 'hide_conf':
+            deprecation_warn(key, 'show_conf')
+            custom['show_conf'] = custom.pop('hide_conf') == 'False'
+        if key == 'line_thickness':
+            deprecation_warn(key, 'line_width')
+            custom['line_width'] = custom.pop('line_thickness')
+
+    return custom
+
+
+def check_cfg_mismatch(base: Dict, custom: Dict, e=None):
+    """
+    This function checks for any mismatched keys between a custom configuration list and a base configuration list.
+    If any mismatched keys are found, the function prints out similar keys from the base list and exits the program.
+
+    Args:
+        custom (dict): a dictionary of custom configuration options
+        base (dict): a dictionary of base configuration options
+    """
+    custom = _handle_deprecation(custom)
+    base, custom = (set(x.keys()) for x in (base, custom))
+    mismatched = [x for x in custom if x not in base]
+    if mismatched:
+        string = ''
+        for x in mismatched:
+            matches = get_close_matches(x, base)  # key list
+            matches = [f'{k}={DEFAULT_CFG_DICT[k]}' if DEFAULT_CFG_DICT.get(k) is not None else k for k in matches]
+            match_str = f'Similar arguments are i.e. {matches}.' if matches else ''
+            string += f"'{colorstr('red', 'bold', x)}' is not a valid YOLO argument. {match_str}\n"
+        raise SyntaxError(string + CLI_HELP_MSG) from e
+
+
+def merge_equals_args(args: List[str]) -> List[str]:
+    """
+    Merges arguments around isolated '=' args in a list of strings.
+    The function considers cases where the first argument ends with '=' or the second starts with '=',
+    as well as when the middle one is an equals sign.
+
+    Args:
+        args (List[str]): A list of strings where each element is an argument.
+
+    Returns:
+        List[str]: A list of strings where the arguments around isolated '=' are merged.
+    """
+    new_args = []
+    for i, arg in enumerate(args):
+        if arg == '=' and 0 < i < len(args) - 1:  # merge ['arg', '=', 'val']
+            new_args[-1] += f'={args[i + 1]}'
+            del args[i + 1]
+        elif arg.endswith('=') and i < len(args) - 1 and '=' not in args[i + 1]:  # merge ['arg=', 'val']
+            new_args.append(f'{arg}{args[i + 1]}')
+            del args[i + 1]
+        elif arg.startswith('=') and i > 0:  # merge ['arg', '=val']
+            new_args[-1] += arg
+        else:
+            new_args.append(arg)
+    return new_args
+
+
+def handle_yolo_hub(args: List[str]) -> None:
+    """
+    Handle Ultralytics HUB command-line interface (CLI) commands.
+
+    This function processes Ultralytics HUB CLI commands such as login and logout.
+    It should be called when executing a script with arguments related to HUB authentication.
+
+    Args:
+        args (List[str]): A list of command line arguments
+
+    Example:
+        python my_script.py hub login your_api_key
+    """
+    from ultralytics import hub
+
+    if args[0] == 'login':
+        key = args[1] if len(args) > 1 else ''
+        # Log in to Ultralytics HUB using the provided API key
+        hub.login(key)
+    elif args[0] == 'logout':
+        # Log out from Ultralytics HUB
+        hub.logout()
+
+
+def handle_yolo_settings(args: List[str]) -> None:
+    """
+    Handle YOLO settings command-line interface (CLI) commands.
+
+    This function processes YOLO settings CLI commands such as reset.
+    It should be called when executing a script with arguments related to YOLO settings management.
+
+    Args:
+        args (List[str]): A list of command line arguments for YOLO settings management.
+
+    Example:
+        python my_script.py yolo settings reset
+    """
+    path = USER_CONFIG_DIR / 'settings.yaml'  # get SETTINGS YAML file path
+    if any(args) and args[0] == 'reset':
+        path.unlink()  # delete the settings file
+        get_settings()  # create new settings
+        LOGGER.info('Settings reset successfully')  # inform the user that settings have been reset
+    yaml_print(path)  # print the current settings
+
+
+def entrypoint(debug=''):
+    """
+    This function is the ultralytics package entrypoint, it's responsible for parsing the command line arguments passed
+    to the package.
+
+    This function allows for:
+    - passing mandatory YOLO args as a list of strings
+    - specifying the task to be performed, either 'detect', 'segment' or 'classify'
+    - specifying the mode, either 'train', 'val', 'test', or 'predict'
+    - running special modes like 'checks'
+    - passing overrides to the package's configuration
+
+    It uses the package's default cfg and initializes it using the passed overrides.
+    Then it calls the CLI function with the composed cfg
+    """
+    args = (debug.split(' ') if debug else sys.argv)[1:]
+    if not args:  # no arguments passed
+        LOGGER.info(CLI_HELP_MSG)
+        return
+
+    special = {
+        'help': lambda: LOGGER.info(CLI_HELP_MSG),
+        'checks': checks.check_yolo,
+        'version': lambda: LOGGER.info(__version__),
+        'settings': lambda: handle_yolo_settings(args[1:]),
+        'cfg': lambda: yaml_print(DEFAULT_CFG_PATH),
+        'hub': lambda: handle_yolo_hub(args[1:]),
+        'login': lambda: handle_yolo_hub(args),
+        'copy-cfg': copy_default_cfg}
+    full_args_dict = {**DEFAULT_CFG_DICT, **{k: None for k in TASKS}, **{k: None for k in MODES}, **special}
+
+    # Define common mis-uses of special commands, i.e. -h, -help, --help
+    special.update({k[0]: v for k, v in special.items()})  # singular
+    special.update({k[:-1]: v for k, v in special.items() if len(k) > 1 and k.endswith('s')})  # singular
+    special = {**special, **{f'-{k}': v for k, v in special.items()}, **{f'--{k}': v for k, v in special.items()}}
+
+    overrides = {}  # basic overrides, i.e. imgsz=320
+    for a in merge_equals_args(args):  # merge spaces around '=' sign
+        if a.startswith('--'):
+            LOGGER.warning(f"WARNING ⚠️ '{a}' does not require leading dashes '--', updating to '{a[2:]}'.")
+            a = a[2:]
+        if a.endswith(','):
+            LOGGER.warning(f"WARNING ⚠️ '{a}' does not require trailing comma ',', updating to '{a[:-1]}'.")
+            a = a[:-1]
+        if '=' in a:
+            try:
+                re.sub(r' *= *', '=', a)  # remove spaces around equals sign
+                k, v = a.split('=', 1)  # split on first '=' sign
+                assert v, f"missing '{k}' value"
+                if k == 'cfg':  # custom.yaml passed
+                    LOGGER.info(f'Overriding {DEFAULT_CFG_PATH} with {v}')
+                    overrides = {k: val for k, val in yaml_load(checks.check_yaml(v)).items() if k != 'cfg'}
+                else:
+                    if v.lower() == 'none':
+                        v = None
+                    elif v.lower() == 'true':
+                        v = True
+                    elif v.lower() == 'false':
+                        v = False
+                    else:
+                        with contextlib.suppress(Exception):
+                            v = eval(v)
+                    overrides[k] = v
+            except (NameError, SyntaxError, ValueError, AssertionError) as e:
+                check_cfg_mismatch(full_args_dict, {a: ''}, e)
+
+        elif a in TASKS:
+            overrides['task'] = a
+        elif a in MODES:
+            overrides['mode'] = a
+        elif a.lower() in special:
+            special[a.lower()]()
+            return
+        elif a in DEFAULT_CFG_DICT and isinstance(DEFAULT_CFG_DICT[a], bool):
+            overrides[a] = True  # auto-True for default bool args, i.e. 'yolo show' sets show=True
+        elif a in DEFAULT_CFG_DICT:
+            raise SyntaxError(f"'{colorstr('red', 'bold', a)}' is a valid YOLO argument but is missing an '=' sign "
+                              f"to set its value, i.e. try '{a}={DEFAULT_CFG_DICT[a]}'\n{CLI_HELP_MSG}")
+        else:
+            check_cfg_mismatch(full_args_dict, {a: ''})
+
+    # Check keys
+    check_cfg_mismatch(full_args_dict, overrides)
+
+    # Mode
+    mode = overrides.get('mode', None)
+    if mode is None:
+        mode = DEFAULT_CFG.mode or 'predict'
+        LOGGER.warning(f"WARNING ⚠️ 'mode' is missing. Valid modes are {MODES}. Using default 'mode={mode}'.")
+    elif mode not in MODES:
+        if mode not in ('checks', checks):
+            raise ValueError(f"Invalid 'mode={mode}'. Valid modes are {MODES}.\n{CLI_HELP_MSG}")
+        LOGGER.warning("WARNING ⚠️ 'yolo mode=checks' is deprecated. Use 'yolo checks' instead.")
+        checks.check_yolo()
+        return
+
+    # Task
+    task = overrides.pop('task', None)
+    if task:
+        if task not in TASKS:
+            raise ValueError(f"Invalid 'task={task}'. Valid tasks are {TASKS}.\n{CLI_HELP_MSG}")
+        if 'model' not in overrides:
+            overrides['model'] = TASK2MODEL[task]
+
+    # Model
+    model = overrides.pop('model', DEFAULT_CFG.model)
+    if model is None:
+        model = 'yolov8n.pt'
+        LOGGER.warning(f"WARNING ⚠️ 'model' is missing. Using default 'model={model}'.")
+    overrides['model'] = model
+    if 'rtdetr' in model.lower():  # guess architecture
+        from ultralytics import RTDETR
+        model = RTDETR(model)  # no task argument
+    elif 'sam' in model.lower():
+        from ultralytics import SAM
+        model = SAM(model)
+    else:
+        from ultralytics import YOLO
+        model = YOLO(model, task=task)
+    if isinstance(overrides.get('pretrained'), str):
+        model.load(overrides['pretrained'])
+
+    # Task Update
+    if task != model.task:
+        if task:
+            LOGGER.warning(f"WARNING ⚠️ conflicting 'task={task}' passed with 'task={model.task}' model. "
+                           f"Ignoring 'task={task}' and updating to 'task={model.task}' to match model.")
+        task = model.task
+
+    # Mode
+    if mode in ('predict', 'track') and 'source' not in overrides:
+        overrides['source'] = DEFAULT_CFG.source or ROOT / 'assets' if (ROOT / 'assets').exists() \
+            else 'https://ultralytics.com/images/bus.jpg'
+        LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using default 'source={overrides['source']}'.")
+    elif mode in ('train', 'val'):
+        if 'data' not in overrides:
+            overrides['data'] = TASK2DATA.get(task or DEFAULT_CFG.task, DEFAULT_CFG.data)
+            LOGGER.warning(f"WARNING ⚠️ 'data' is missing. Using default 'data={overrides['data']}'.")
+    elif mode == 'export':
+        if 'format' not in overrides:
+            overrides['format'] = DEFAULT_CFG.format or 'torchscript'
+            LOGGER.warning(f"WARNING ⚠️ 'format' is missing. Using default 'format={overrides['format']}'.")
+
+    # Run command in python
+    # getattr(model, mode)(**vars(get_cfg(overrides=overrides)))  # default args using default.yaml
+    getattr(model, mode)(**overrides)  # default args from model
+
+
+# Special modes --------------------------------------------------------------------------------------------------------
+def copy_default_cfg():
+    """Copy and create a new default configuration file with '_copy' appended to its name."""
+    new_file = Path.cwd() / DEFAULT_CFG_PATH.name.replace('.yaml', '_copy.yaml')
+    shutil.copy2(DEFAULT_CFG_PATH, new_file)
+    LOGGER.info(f'{DEFAULT_CFG_PATH} copied to {new_file}\n'
+                f"Example YOLO command with this new custom cfg:\n    yolo cfg='{new_file}' imgsz=320 batch=8")
+
+
+if __name__ == '__main__':
+    # Example Usage: entrypoint(debug='yolo predict model=yolov8n.pt')
+    entrypoint(debug='')

ultralytics/cfg/datasets/Argoverse.yaml

@@ -29,7 +29,7 @@ names:
 download: |
   import json
   from tqdm import tqdm
-  from ultralytics.yolo.utils.downloads import download
+  from ultralytics.utils.downloads import download
   from pathlib import Path
 
   def argoverse2yolo(set):

ultralytics/cfg/datasets/GlobalWheat2020.yaml

@@ -32,7 +32,7 @@ names:
 
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
-  from ultralytics.yolo.utils.downloads import download
+  from ultralytics.utils.downloads import download
   from pathlib import Path
 
   # Download

ultralytics/cfg/datasets/Objects365.yaml

@@ -386,9 +386,9 @@ names:
 download: |
   from tqdm import tqdm
 
-  from ultralytics.yolo.utils.checks import check_requirements
-  from ultralytics.yolo.utils.downloads import download
-  from ultralytics.yolo.utils.ops import xyxy2xywhn
+  from ultralytics.utils.checks import check_requirements
+  from ultralytics.utils.downloads import download
+  from ultralytics.utils.ops import xyxy2xywhn
 
   import numpy as np
   from pathlib import Path

ultralytics/cfg/datasets/SKU-110K.yaml

@@ -27,8 +27,8 @@ download: |
   import pandas as pd
   from tqdm import tqdm
 
-  from ultralytics.yolo.utils.downloads import download
-  from ultralytics.yolo.utils.ops import xyxy2xywh
+  from ultralytics.utils.downloads import download
+  from ultralytics.utils.ops import xyxy2xywh
 
   # Download
   dir = Path(yaml['path'])  # dataset root dir

ultralytics/cfg/datasets/VOC.yaml

@@ -48,7 +48,7 @@ download: |
   import xml.etree.ElementTree as ET
 
   from tqdm import tqdm
-  from ultralytics.yolo.utils.downloads import download
+  from ultralytics.utils.downloads import download
   from pathlib import Path
 
   def convert_label(path, lb_path, year, image_id):

ultralytics/cfg/datasets/VisDrone.yaml

@@ -32,7 +32,7 @@ download: |
   import os
   from pathlib import Path
 
-  from ultralytics.yolo.utils.downloads import download
+  from ultralytics.utils.downloads import download
 
   def visdrone2yolo(dir):
       from PIL import Image

ultralytics/cfg/datasets/coco-pose.yaml

@@ -23,7 +23,7 @@ names:
 
 # Download script/URL (optional)
 download: |
-  from ultralytics.yolo.utils.downloads import download
+  from ultralytics.utils.downloads import download
   from pathlib import Path
 
   # Download labels

ultralytics/cfg/datasets/coco.yaml

@@ -99,7 +99,7 @@ names:
 
 # Download script/URL (optional)
 download: |
-  from ultralytics.yolo.utils.downloads import download
+  from ultralytics.utils.downloads import download
   from pathlib import Path
 
   # Download labels

ultralytics/cfg/datasets/xView.yaml

@@ -87,8 +87,8 @@ download: |
   from PIL import Image
   from tqdm import tqdm
 
-  from ultralytics.yolo.data.utils import autosplit
-  from ultralytics.yolo.utils.ops import xyxy2xywhn
+  from ultralytics.data.utils import autosplit
+  from ultralytics.utils.ops import xyxy2xywhn
 
 
   def convert_labels(fname=Path('xView/xView_train.geojson')):

ultralytics/data/__init__.py

@@ -0,0 +1,8 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from .base import BaseDataset
+from .build import build_dataloader, build_yolo_dataset, load_inference_source
+from .dataset import ClassificationDataset, SemanticDataset, YOLODataset
+
+__all__ = ('BaseDataset', 'ClassificationDataset', 'SemanticDataset', 'YOLODataset', 'build_yolo_dataset',
+           'build_dataloader', 'load_inference_source')

ultralytics/data/augment.py

@@ -9,11 +9,12 @@ import numpy as np
 import torch
 import torchvision.transforms as T
 
-from ..utils import LOGGER, colorstr
-from ..utils.checks import check_version
-from ..utils.instance import Instances
-from ..utils.metrics import bbox_ioa
-from ..utils.ops import segment2box
+from ultralytics.utils import LOGGER, colorstr
+from ultralytics.utils.checks import check_version
+from ultralytics.utils.instance import Instances
+from ultralytics.utils.metrics import bbox_ioa
+from ultralytics.utils.ops import segment2box
+
 from .utils import polygons2masks, polygons2masks_overlap
 
 POSE_FLIPLR_INDEX = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]

ultralytics/data/base.py

@@ -15,7 +15,8 @@ import psutil
 from torch.utils.data import Dataset
 from tqdm import tqdm
 
-from ..utils import DEFAULT_CFG, LOCAL_RANK, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT
+from ultralytics.utils import DEFAULT_CFG, LOCAL_RANK, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT
+
 from .utils import HELP_URL, IMG_FORMATS
 
 

ultralytics/data/build.py

@@ -9,12 +9,12 @@ import torch
 from PIL import Image
 from torch.utils.data import dataloader, distributed
 
-from ultralytics.yolo.data.dataloaders.stream_loaders import (LOADERS, LoadImages, LoadPilAndNumpy, LoadScreenshots,
-                                                              LoadStreams, LoadTensor, SourceTypes, autocast_list)
-from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
-from ultralytics.yolo.utils.checks import check_file
+from ultralytics.data.loaders import (LOADERS, LoadImages, LoadPilAndNumpy, LoadScreenshots, LoadStreams, LoadTensor,
+                                      SourceTypes, autocast_list)
+from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS
+from ultralytics.utils import RANK, colorstr
+from ultralytics.utils.checks import check_file
 
-from ..utils import RANK, colorstr
 from .dataset import YOLODataset
 from .utils import PIN_MEMORY
 

ultralytics/data/converter.py

@@ -6,8 +6,8 @@ import cv2
 import numpy as np
 from tqdm import tqdm
 
-from ultralytics.yolo.utils.checks import check_requirements
-from ultralytics.yolo.utils.files import make_dirs
+from ultralytics.utils.checks import check_requirements
+from ultralytics.utils.files import make_dirs
 
 
 def coco91_to_coco80_class():

ultralytics/data/dataset.py

@@ -10,7 +10,8 @@ import torch
 import torchvision
 from tqdm import tqdm
 
-from ..utils import LOCAL_RANK, NUM_THREADS, TQDM_BAR_FORMAT, is_dir_writeable
+from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM_BAR_FORMAT, is_dir_writeable
+
 from .augment import Compose, Format, Instances, LetterBox, classify_albumentations, classify_transforms, v8_transforms
 from .base import BaseDataset
 from .utils import HELP_URL, LOGGER, get_hash, img2label_paths, verify_image_label

ultralytics/data/loaders.py

@@ -15,9 +15,9 @@ import requests
 import torch
 from PIL import Image
 
-from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
-from ultralytics.yolo.utils import LOGGER, ROOT, is_colab, is_kaggle, ops
-from ultralytics.yolo.utils.checks import check_requirements
+from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS
+from ultralytics.utils import LOGGER, ROOT, is_colab, is_kaggle, ops
+from ultralytics.utils.checks import check_requirements
 
 
 @dataclass
@@ -318,11 +318,10 @@ class LoadTensor:
         s = f'WARNING ⚠️ torch.Tensor inputs should be BCHW i.e. shape(1, 3, 640, 640) ' \
             f'divisible by stride {stride}. Input shape{tuple(im.shape)} is incompatible.'
         if len(im.shape) != 4:
-            if len(im.shape) == 3:
-                LOGGER.warning(s)
-                im = im.unsqueeze(0)
-            else:
+            if len(im.shape) != 3:
                 raise ValueError(s)
+            LOGGER.warning(s)
+            im = im.unsqueeze(0)
         if im.shape[2] % stride or im.shape[3] % stride:
             raise ValueError(s)
         if im.max() > 1.0:

ultralytics/data/scripts/download_weights.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # Download latest models from https://github.com/ultralytics/assets/releases
-# Example usage: bash ultralytics/yolo/data/scripts/download_weights.sh
+# Example usage: bash ultralytics/data/scripts/download_weights.sh
 # parent
 # └── weights
 #     ├── yolov8n.pt  ← downloads here
@@ -9,9 +9,9 @@
 #     └── ...
 
 python - <<EOF
-from ultralytics.yolo.utils.downloads import attempt_download_asset
+from ultralytics.utils.downloads import attempt_download_asset
 
-assets = [f'yolov8{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '-cls', '-seg')]
+assets = [f'yolov8{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '-cls', '-seg', '-pose')]
 for x in assets:
     attempt_download_asset(f'weights/{x}')
 

ultralytics/data/utils.py

@@ -18,11 +18,11 @@ from PIL import ExifTags, Image, ImageOps
 from tqdm import tqdm
 
 from ultralytics.nn.autobackend import check_class_names
-from ultralytics.yolo.utils import (DATASETS_DIR, LOGGER, NUM_THREADS, ROOT, SETTINGS_YAML, clean_url, colorstr, emojis,
-                                    yaml_load)
-from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii
-from ultralytics.yolo.utils.downloads import download, safe_download, unzip_file
-from ultralytics.yolo.utils.ops import segments2boxes
+from ultralytics.utils import (DATASETS_DIR, LOGGER, NUM_THREADS, ROOT, SETTINGS_YAML, clean_url, colorstr, emojis,
+                               yaml_load)
+from ultralytics.utils.checks import check_file, check_font, is_ascii
+from ultralytics.utils.downloads import download, safe_download, unzip_file
+from ultralytics.utils.ops import segments2boxes
 
 HELP_URL = 'See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data'
 IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm'  # image suffixes
@@ -296,7 +296,7 @@ def check_cls_dataset(dataset: str, split=''):
         LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
         t = time.time()
         if str(dataset) == 'imagenet':
-            subprocess.run(f"bash {ROOT / 'yolo/data/scripts/get_imagenet.sh'}", shell=True, check=True)
+            subprocess.run(f"bash {ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True)
         else:
             url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip'
             download(url, dir=data_dir.parent)
@@ -326,7 +326,7 @@ class HUBDatasetStats():
         autodownload (bool): Attempt to download dataset if not found locally. Default is False.
 
     Usage
-        from ultralytics.yolo.data.utils import HUBDatasetStats
+        from ultralytics.data.utils import HUBDatasetStats
         stats = HUBDatasetStats('/Users/glennjocher/Downloads/coco8.zip', task='detect')  # detect dataset
         stats = HUBDatasetStats('/Users/glennjocher/Downloads/coco8-seg.zip', task='segment')  # segment dataset
         stats = HUBDatasetStats('/Users/glennjocher/Downloads/coco8-pose.zip', task='pose')  # pose dataset
@@ -379,7 +379,7 @@ class HUBDatasetStats():
 
     def get_json(self, save=False, verbose=False):
         """Return dataset JSON for Ultralytics HUB."""
-        from ultralytics.yolo.data import YOLODataset  # ClassificationDataset
+        from ultralytics.data import YOLODataset  # ClassificationDataset
 
         def _round(labels):
             """Update labels to integer class and 4 decimal place floats."""
@@ -430,7 +430,7 @@ class HUBDatasetStats():
 
     def process_images(self):
         """Compress images for Ultralytics HUB."""
-        from ultralytics.yolo.data import YOLODataset  # ClassificationDataset
+        from ultralytics.data import YOLODataset  # ClassificationDataset
 
         for split in 'train', 'val', 'test':
             if self.data.get(split) is None:
@@ -457,7 +457,7 @@ def compress_one_image(f, f_new=None, max_dim=1920, quality=50):
 
     Usage:
         from pathlib import Path
-        from ultralytics.yolo.data.utils import compress_one_image
+        from ultralytics.data.utils import compress_one_image
         for f in Path('/Users/glennjocher/Downloads/dataset').rglob('*.jpg'):
             compress_one_image(f)
     """
@@ -485,7 +485,7 @@ def delete_dsstore(path):
         path (str, optional): The directory path where the ".DS_store" files should be deleted.
 
     Usage:
-        from ultralytics.yolo.data.utils import delete_dsstore
+        from ultralytics.data.utils import delete_dsstore
         delete_dsstore('/Users/glennjocher/Downloads/dataset')
 
     Note:
@@ -508,7 +508,7 @@ def zip_directory(dir, use_zipfile_library=True):
         use_zipfile_library (bool): Whether to use zipfile library or shutil for zipping.
 
     Usage:
-        from ultralytics.yolo.data.utils import zip_directory
+        from ultralytics.data.utils import zip_directory
         zip_directory('/Users/glennjocher/Downloads/playground')
 
         zip -r coco8-pose.zip coco8-pose

ultralytics/engine/exporter.py

@@ -60,17 +60,17 @@ from pathlib import Path
 
 import torch
 
+from ultralytics.cfg import get_cfg
 from ultralytics.nn.autobackend import check_class_names
 from ultralytics.nn.modules import C2f, Detect, RTDETRDecoder
 from ultralytics.nn.tasks import DetectionModel, SegmentationModel
-from ultralytics.yolo.cfg import get_cfg
-from ultralytics.yolo.utils import (ARM64, DEFAULT_CFG, LINUX, LOGGER, MACOS, ROOT, __version__, callbacks, colorstr,
-                                    get_default_args, yaml_save)
-from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version
-from ultralytics.yolo.utils.downloads import attempt_download_asset, get_github_assets
-from ultralytics.yolo.utils.files import file_size
-from ultralytics.yolo.utils.ops import Profile
-from ultralytics.yolo.utils.torch_utils import get_latest_opset, select_device, smart_inference_mode
+from ultralytics.utils import (ARM64, DEFAULT_CFG, LINUX, LOGGER, MACOS, ROOT, __version__, callbacks, colorstr,
+                               get_default_args, yaml_save)
+from ultralytics.utils.checks import check_imgsz, check_requirements, check_version
+from ultralytics.utils.downloads import attempt_download_asset, get_github_assets
+from ultralytics.utils.files import file_size
+from ultralytics.utils.ops import Profile
+from ultralytics.utils.torch_utils import get_latest_opset, select_device, smart_inference_mode
 
 
 def export_formats():

ultralytics/engine/model.py

@@ -4,29 +4,28 @@ import sys
 from pathlib import Path
 from typing import Union
 
-from ultralytics import yolo  # noqa
+from ultralytics.cfg import get_cfg
+from ultralytics.engine.exporter import Exporter
+from ultralytics.models import yolo  # noqa
 from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, PoseModel, SegmentationModel,
                                   attempt_load_one_weight, guess_model_task, nn, yaml_model_load)
-from ultralytics.yolo.cfg import get_cfg
-from ultralytics.yolo.engine.exporter import Exporter
-from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, ROOT, callbacks,
-                                    is_git_dir, yaml_load)
-from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_pip_update_available, check_yaml
-from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS
-from ultralytics.yolo.utils.torch_utils import smart_inference_mode
+from ultralytics.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, ROOT, callbacks,
+                               is_git_dir, yaml_load)
+from ultralytics.utils.checks import check_file, check_imgsz, check_pip_update_available, check_yaml
+from ultralytics.utils.downloads import GITHUB_ASSET_STEMS
+from ultralytics.utils.torch_utils import smart_inference_mode
 
 # Map head to model, trainer, validator, and predictor classes
 TASK_MAP = {
     'classify': [
-        ClassificationModel, yolo.v8.classify.ClassificationTrainer, yolo.v8.classify.ClassificationValidator,
-        yolo.v8.classify.ClassificationPredictor],
-    'detect': [
-        DetectionModel, yolo.v8.detect.DetectionTrainer, yolo.v8.detect.DetectionValidator,
-        yolo.v8.detect.DetectionPredictor],
+        ClassificationModel, yolo.classify.ClassificationTrainer, yolo.classify.ClassificationValidator,
+        yolo.classify.ClassificationPredictor],
+    'detect':
+    [DetectionModel, yolo.detect.DetectionTrainer, yolo.detect.DetectionValidator, yolo.detect.DetectionPredictor],
     'segment': [
-        SegmentationModel, yolo.v8.segment.SegmentationTrainer, yolo.v8.segment.SegmentationValidator,
-        yolo.v8.segment.SegmentationPredictor],
-    'pose': [PoseModel, yolo.v8.pose.PoseTrainer, yolo.v8.pose.PoseValidator, yolo.v8.pose.PosePredictor]}
+        SegmentationModel, yolo.segment.SegmentationTrainer, yolo.segment.SegmentationValidator,
+        yolo.segment.SegmentationPredictor],
+    'pose': [PoseModel, yolo.pose.PoseTrainer, yolo.pose.PoseValidator, yolo.pose.PosePredictor]}
 
 
 class YOLO:
@@ -63,11 +62,11 @@ class YOLO:
             Logs the model info.
         fuse() -> None:
             Fuses the model for faster inference.
-        predict(source=None, stream=False, **kwargs) -> List[ultralytics.yolo.engine.results.Results]:
+        predict(source=None, stream=False, **kwargs) -> List[ultralytics.engine.results.Results]:
             Performs prediction using the YOLO model.
 
     Returns:
-        list(ultralytics.yolo.engine.results.Results): The prediction results.
+        list(ultralytics.engine.results.Results): The prediction results.
     """
 
     def __init__(self, model: Union[str, Path] = 'yolov8n.pt', task=None) -> None:
@@ -230,7 +229,7 @@ class YOLO:
                        Check the 'configuration' section in the documentation for all available options.
 
         Returns:
-            (List[ultralytics.yolo.engine.results.Results]): The prediction results.
+            (List[ultralytics.engine.results.Results]): The prediction results.
         """
         if source is None:
             source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'
@@ -265,11 +264,11 @@ class YOLO:
             **kwargs (optional): Additional keyword arguments for the tracking process.
 
         Returns:
-            (List[ultralytics.yolo.engine.results.Results]): The tracking results.
+            (List[ultralytics.engine.results.Results]): The tracking results.
 
         """
         if not hasattr(self.predictor, 'trackers'):
-            from ultralytics.tracker import register_tracker
+            from ultralytics.trackers import register_tracker
             register_tracker(self, persist)
         # ByteTrack-based method needs low confidence predictions as input
         conf = kwargs.get('conf') or 0.1
@@ -315,7 +314,7 @@ class YOLO:
             **kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs
         """
         self._check_is_pytorch_model()
-        from ultralytics.yolo.utils.benchmarks import benchmark
+        from ultralytics.utils.benchmarks import benchmark
         overrides = self.model.args.copy()
         overrides.update(kwargs)
         overrides['mode'] = 'benchmark'
@@ -389,7 +388,7 @@ class YOLO:
 
     def tune(self, *args, **kwargs):
         """
-        Runs hyperparameter tuning using Ray Tune. See ultralytics.yolo.utils.tuner.run_ray_tune for Args.
+        Runs hyperparameter tuning using Ray Tune. See ultralytics.utils.tuner.run_ray_tune for Args.
 
         Returns:
             (dict): A dictionary containing the results of the hyperparameter search.
@@ -398,7 +397,7 @@ class YOLO:
             ModuleNotFoundError: If Ray Tune is not installed.
         """
         self._check_is_pytorch_model()
-        from ultralytics.yolo.utils.tuner import run_ray_tune
+        from ultralytics.utils.tuner import run_ray_tune
         return run_ray_tune(self, *args, **kwargs)
 
     @property

ultralytics/engine/predictor.py

@@ -34,14 +34,14 @@ import cv2
 import numpy as np
 import torch
 
+from ultralytics.cfg import get_cfg
+from ultralytics.data import load_inference_source
+from ultralytics.data.augment import LetterBox, classify_transforms
 from ultralytics.nn.autobackend import AutoBackend
-from ultralytics.yolo.cfg import get_cfg
-from ultralytics.yolo.data import load_inference_source
-from ultralytics.yolo.data.augment import LetterBox, classify_transforms
-from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, MACOS, SETTINGS, WINDOWS, callbacks, colorstr, ops
-from ultralytics.yolo.utils.checks import check_imgsz, check_imshow
-from ultralytics.yolo.utils.files import increment_path
-from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode
+from ultralytics.utils import DEFAULT_CFG, LOGGER, MACOS, SETTINGS, WINDOWS, callbacks, colorstr, ops
+from ultralytics.utils.checks import check_imgsz, check_imshow
+from ultralytics.utils.files import increment_path
+from ultralytics.utils.torch_utils import select_device, smart_inference_mode
 
 STREAM_WARNING = """
     WARNING ⚠️ stream/video/webcam/dir predict source will accumulate results in RAM unless `stream=True` is passed,

ultralytics/engine/results.py

@@ -12,9 +12,9 @@ from pathlib import Path
 import numpy as np
 import torch
 
-from ultralytics.yolo.data.augment import LetterBox
-from ultralytics.yolo.utils import LOGGER, SimpleClass, deprecation_warn, ops
-from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
+from ultralytics.data.augment import LetterBox
+from ultralytics.utils import LOGGER, SimpleClass, deprecation_warn, ops
+from ultralytics.utils.plotting import Annotator, colors, save_one_box
 
 
 class BaseTensor(SimpleClass):

ultralytics/engine/trainer.py

@@ -21,17 +21,17 @@ from torch.cuda import amp
 from torch.nn.parallel import DistributedDataParallel as DDP
 from tqdm import tqdm
 
+from ultralytics.cfg import get_cfg
+from ultralytics.data.utils import check_cls_dataset, check_det_dataset
 from ultralytics.nn.tasks import attempt_load_one_weight, attempt_load_weights
-from ultralytics.yolo.cfg import get_cfg
-from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
-from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, __version__, callbacks,
-                                    clean_url, colorstr, emojis, yaml_save)
-from ultralytics.yolo.utils.autobatch import check_train_batch_size
-from ultralytics.yolo.utils.checks import check_amp, check_file, check_imgsz, print_args
-from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
-from ultralytics.yolo.utils.files import get_latest_run, increment_path
-from ultralytics.yolo.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, init_seeds, one_cycle,
-                                                select_device, strip_optimizer)
+from ultralytics.utils import (DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, __version__, callbacks, clean_url,
+                               colorstr, emojis, yaml_save)
+from ultralytics.utils.autobatch import check_train_batch_size
+from ultralytics.utils.checks import check_amp, check_file, check_imgsz, print_args
+from ultralytics.utils.dist import ddp_cleanup, generate_ddp_command
+from ultralytics.utils.files import get_latest_run, increment_path
+from ultralytics.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, init_seeds, one_cycle, select_device,
+                                           strip_optimizer)
 
 
 class BaseTrainer:

ultralytics/engine/validator.py

@@ -25,14 +25,14 @@ from pathlib import Path
 import torch
 from tqdm import tqdm
 
+from ultralytics.cfg import get_cfg
+from ultralytics.data.utils import check_cls_dataset, check_det_dataset
 from ultralytics.nn.autobackend import AutoBackend
-from ultralytics.yolo.cfg import get_cfg
-from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
-from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr, emojis
-from ultralytics.yolo.utils.checks import check_imgsz
-from ultralytics.yolo.utils.files import increment_path
-from ultralytics.yolo.utils.ops import Profile
-from ultralytics.yolo.utils.torch_utils import de_parallel, select_device, smart_inference_mode
+from ultralytics.utils import DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr, emojis
+from ultralytics.utils.checks import check_imgsz
+from ultralytics.utils.files import increment_path
+from ultralytics.utils.ops import Profile
+from ultralytics.utils.torch_utils import de_parallel, select_device, smart_inference_mode
 
 
 class BaseValidator:

ultralytics/hub/__init__.py

@@ -2,10 +2,10 @@
 
 import requests
 
+from ultralytics.data.utils import HUBDatasetStats
 from ultralytics.hub.auth import Auth
 from ultralytics.hub.utils import PREFIX
-from ultralytics.yolo.data.utils import HUBDatasetStats
-from ultralytics.yolo.utils import LOGGER, SETTINGS, USER_CONFIG_DIR, yaml_save
+from ultralytics.utils import LOGGER, SETTINGS, USER_CONFIG_DIR, yaml_save
 
 
 def login(api_key=''):
@@ -65,7 +65,7 @@ def reset_model(model_id=''):
 
 def export_fmts_hub():
     """Returns a list of HUB-supported export formats."""
-    from ultralytics.yolo.engine.exporter import export_formats
+    from ultralytics.engine.exporter import export_formats
     return list(export_formats()['Argument'][1:]) + ['ultralytics_tflite', 'ultralytics_coreml']
 
 

ultralytics/hub/auth.py

@@ -3,7 +3,7 @@
 import requests
 
 from ultralytics.hub.utils import HUB_API_ROOT, PREFIX, request_with_credentials
-from ultralytics.yolo.utils import LOGGER, SETTINGS, emojis, is_colab, set_settings
+from ultralytics.utils import LOGGER, SETTINGS, emojis, is_colab, set_settings
 
 API_KEY_URL = 'https://hub.ultralytics.com/settings?tab=api+keys'
 

ultralytics/hub/session.py

@@ -7,8 +7,8 @@ from time import sleep
 import requests
 
 from ultralytics.hub.utils import HUB_API_ROOT, PREFIX, smart_request
-from ultralytics.yolo.utils import LOGGER, __version__, checks, emojis, is_colab, threaded
-from ultralytics.yolo.utils.errors import HUBModelError
+from ultralytics.utils import LOGGER, __version__, checks, emojis, is_colab, threaded
+from ultralytics.utils.errors import HUBModelError
 
 AGENT_NAME = f'python-{__version__}-colab' if is_colab() else f'python-{__version__}-local'
 

ultralytics/hub/utils.py

@@ -11,9 +11,8 @@ from pathlib import Path
 import requests
 from tqdm import tqdm
 
-from ultralytics.yolo.utils import (ENVIRONMENT, LOGGER, ONLINE, RANK, SETTINGS, TESTS_RUNNING, TQDM_BAR_FORMAT,
-                                    TryExcept, __version__, colorstr, get_git_origin_url, is_colab, is_git_dir,
-                                    is_pip_package)
+from ultralytics.utils import (ENVIRONMENT, LOGGER, ONLINE, RANK, SETTINGS, TESTS_RUNNING, TQDM_BAR_FORMAT, TryExcept,
+                               __version__, colorstr, get_git_origin_url, is_colab, is_git_dir, is_pip_package)
 
 PREFIX = colorstr('Ultralytics HUB: ')
 HELP_MSG = 'If this issue persists please visit https://github.com/ultralytics/hub/issues for assistance.'

ultralytics/models/__init__.py

@@ -1,5 +1,3 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
-
 from .rtdetr import RTDETR
 from .sam import SAM
 

ultralytics/models/fastsam/model.py

@@ -9,13 +9,13 @@ Usage - Predict:
     results = model.predict('ultralytics/assets/bus.jpg')
 """
 
-from ultralytics.yolo.cfg import get_cfg
-from ultralytics.yolo.engine.exporter import Exporter
-from ultralytics.yolo.engine.model import YOLO
-from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, ROOT, is_git_dir
-from ultralytics.yolo.utils.checks import check_imgsz
+from ultralytics.cfg import get_cfg
+from ultralytics.engine.exporter import Exporter
+from ultralytics.engine.model import YOLO
+from ultralytics.utils import DEFAULT_CFG, LOGGER, ROOT, is_git_dir
+from ultralytics.utils.checks import check_imgsz
+from ultralytics.utils.torch_utils import model_info, smart_inference_mode
 
-from ...yolo.utils.torch_utils import model_info, smart_inference_mode
 from .predict import FastSAMPredictor
 
 
@@ -41,7 +41,7 @@ class FastSAM(YOLO):
                        Check the 'configuration' section in the documentation for all available options.
 
         Returns:
-            (List[ultralytics.yolo.engine.results.Results]): The prediction results.
+            (List[ultralytics.engine.results.Results]): The prediction results.
         """
         if source is None:
             source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'

ultralytics/models/fastsam/predict.py

@@ -2,10 +2,10 @@
 
 import torch
 
-from ultralytics.yolo.engine.results import Results
-from ultralytics.yolo.fastsam.utils import bbox_iou
-from ultralytics.yolo.utils import DEFAULT_CFG, ops
-from ultralytics.yolo.v8.detect.predict import DetectionPredictor
+from ultralytics.engine.results import Results
+from ultralytics.models.fastsam.utils import bbox_iou
+from ultralytics.models.yolo.detect.predict import DetectionPredictor
+from ultralytics.utils import DEFAULT_CFG, ops
 
 
 class FastSAMPredictor(DetectionPredictor):

ultralytics/models/fastsam/prompt.py

@@ -22,7 +22,7 @@ class FastSAMPrompt:
         try:
             import clip  # for linear_assignment
         except ImportError:
-            from ultralytics.yolo.utils.checks import check_requirements
+            from ultralytics.utils.checks import check_requirements
             check_requirements('git+https://github.com/openai/CLIP.git')  # required before installing lap from source
             import clip
         self.clip = clip

ultralytics/models/fastsam/val.py

@@ -7,11 +7,11 @@ import numpy as np
 import torch
 import torch.nn.functional as F
 
-from ultralytics.yolo.utils import LOGGER, NUM_THREADS, ops
-from ultralytics.yolo.utils.checks import check_requirements
-from ultralytics.yolo.utils.metrics import SegmentMetrics, box_iou, mask_iou
-from ultralytics.yolo.utils.plotting import output_to_target, plot_images
-from ultralytics.yolo.v8.detect import DetectionValidator
+from ultralytics.models.yolo.detect import DetectionValidator
+from ultralytics.utils import LOGGER, NUM_THREADS, ops
+from ultralytics.utils.checks import check_requirements
+from ultralytics.utils.metrics import SegmentMetrics, box_iou, mask_iou
+from ultralytics.utils.plotting import output_to_target, plot_images
 
 
 class FastSAMValidator(DetectionValidator):

ultralytics/models/nas/model.py

@@ -13,12 +13,12 @@ from pathlib import Path
 
 import torch
 
-from ultralytics.yolo.cfg import get_cfg
-from ultralytics.yolo.engine.exporter import Exporter
-from ultralytics.yolo.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, ROOT, is_git_dir
-from ultralytics.yolo.utils.checks import check_imgsz
+from ultralytics.cfg import get_cfg
+from ultralytics.engine.exporter import Exporter
+from ultralytics.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, ROOT, is_git_dir
+from ultralytics.utils.checks import check_imgsz
+from ultralytics.utils.torch_utils import model_info, smart_inference_mode
 
-from ...yolo.utils.torch_utils import model_info, smart_inference_mode
 from .predict import NASPredictor
 from .val import NASValidator
 
@@ -65,7 +65,7 @@ class NAS:
                        Check the 'configuration' section in the documentation for all available options.
 
         Returns:
-            (List[ultralytics.yolo.engine.results.Results]): The prediction results.
+            (List[ultralytics.engine.results.Results]): The prediction results.
         """
         if source is None:
             source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'

ultralytics/models/nas/predict.py

@@ -2,10 +2,10 @@
 
 import torch
 
-from ultralytics.yolo.engine.predictor import BasePredictor
-from ultralytics.yolo.engine.results import Results
-from ultralytics.yolo.utils import ops
-from ultralytics.yolo.utils.ops import xyxy2xywh
+from ultralytics.engine.predictor import BasePredictor
+from ultralytics.engine.results import Results
+from ultralytics.utils import ops
+from ultralytics.utils.ops import xyxy2xywh
 
 
 class NASPredictor(BasePredictor):

ultralytics/models/nas/val.py

@@ -2,9 +2,9 @@
 
 import torch
 
-from ultralytics.yolo.utils import ops
-from ultralytics.yolo.utils.ops import xyxy2xywh
-from ultralytics.yolo.v8.detect import DetectionValidator
+from ultralytics.models.yolo.detect import DetectionValidator
+from ultralytics.utils import ops
+from ultralytics.utils.ops import xyxy2xywh
 
 __all__ = ['NASValidator']
 

ultralytics/models/rtdetr/model.py

@@ -7,12 +7,12 @@ from pathlib import Path
 
 import torch.nn as nn
 
+from ultralytics.cfg import get_cfg
+from ultralytics.engine.exporter import Exporter
 from ultralytics.nn.tasks import RTDETRDetectionModel, attempt_load_one_weight, yaml_model_load
-from ultralytics.yolo.cfg import get_cfg
-from ultralytics.yolo.engine.exporter import Exporter
-from ultralytics.yolo.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, RANK, ROOT, is_git_dir
-from ultralytics.yolo.utils.checks import check_imgsz
-from ultralytics.yolo.utils.torch_utils import model_info, smart_inference_mode
+from ultralytics.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, RANK, ROOT, is_git_dir
+from ultralytics.utils.checks import check_imgsz
+from ultralytics.utils.torch_utils import model_info, smart_inference_mode
 
 from .predict import RTDETRPredictor
 from .train import RTDETRTrainer
@@ -72,7 +72,7 @@ class RTDETR:
                        Check the 'configuration' section in the documentation for all available options.
 
         Returns:
-            (List[ultralytics.yolo.engine.results.Results]): The prediction results.
+            (List[ultralytics.engine.results.Results]): The prediction results.
         """
         if source is None:
             source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'

ultralytics/models/rtdetr/predict.py

@@ -2,10 +2,10 @@
 
 import torch
 
-from ultralytics.yolo.data.augment import LetterBox
-from ultralytics.yolo.engine.predictor import BasePredictor
-from ultralytics.yolo.engine.results import Results
-from ultralytics.yolo.utils import ops
+from ultralytics.data.augment import LetterBox
+from ultralytics.engine.predictor import BasePredictor
+from ultralytics.engine.results import Results
+from ultralytics.utils import ops
 
 
 class RTDETRPredictor(BasePredictor):

ultralytics/models/rtdetr/train.py

@@ -4,9 +4,9 @@ from copy import copy
 
 import torch
 
+from ultralytics.models.yolo.detect import DetectionTrainer
 from ultralytics.nn.tasks import RTDETRDetectionModel
-from ultralytics.yolo.utils import DEFAULT_CFG, RANK, colorstr
-from ultralytics.yolo.v8.detect import DetectionTrainer
+from ultralytics.utils import DEFAULT_CFG, RANK, colorstr
 
 from .val import RTDETRDataset, RTDETRValidator
 

ultralytics/models/rtdetr/val.py

@@ -6,10 +6,10 @@ import cv2
 import numpy as np
 import torch
 
-from ultralytics.yolo.data import YOLODataset
-from ultralytics.yolo.data.augment import Compose, Format, v8_transforms
-from ultralytics.yolo.utils import colorstr, ops
-from ultralytics.yolo.v8.detect import DetectionValidator
+from ultralytics.data import YOLODataset
+from ultralytics.data.augment import Compose, Format, v8_transforms
+from ultralytics.models.yolo.detect import DetectionValidator
+from ultralytics.utils import colorstr, ops
 
 __all__ = 'RTDETRValidator',  # tuple or list
 

ultralytics/models/sam/build.py

@@ -10,7 +10,8 @@ from functools import partial
 
 import torch
 
-from ...yolo.utils.downloads import attempt_download_asset
+from ultralytics.utils.downloads import attempt_download_asset
+
 from .modules.decoders import MaskDecoder
 from .modules.encoders import ImageEncoderViT, PromptEncoder
 from .modules.sam import Sam

ultralytics/models/sam/model.py

@@ -3,8 +3,8 @@
 SAM model interface
 """
 
-from ultralytics.yolo.cfg import get_cfg
-from ultralytics.yolo.utils.torch_utils import model_info
+from ultralytics.cfg import get_cfg
+from ultralytics.utils.torch_utils import model_info
 
 from .build import build_sam
 from .predict import Predictor

ultralytics/models/sam/modules/tiny_encoder.py

@@ -17,7 +17,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 import torch.utils.checkpoint as checkpoint
 
-from ultralytics.yolo.utils.instance import to_2tuple
+from ultralytics.utils.instance import to_2tuple
 
 
 class Conv2d_BN(torch.nn.Sequential):
@@ -50,7 +50,7 @@ class Conv2d_BN(torch.nn.Sequential):
 
 
 # NOTE: This module and timm package is needed only for training.
-# from ultralytics.yolo.utils.checks import check_requirements
+# from ultralytics.utils.checks import check_requirements
 # check_requirements('timm')
 # from timm.models.layers import DropPath as TimmDropPath
 # from timm.models.layers import trunc_normal_

ultralytics/models/sam/predict.py

@@ -5,11 +5,11 @@ import torch
 import torch.nn.functional as F
 import torchvision
 
-from ultralytics.yolo.data.augment import LetterBox
-from ultralytics.yolo.engine.predictor import BasePredictor
-from ultralytics.yolo.engine.results import Results
-from ultralytics.yolo.utils import DEFAULT_CFG, ops
-from ultralytics.yolo.utils.torch_utils import select_device
+from ultralytics.data.augment import LetterBox
+from ultralytics.engine.predictor import BasePredictor
+from ultralytics.engine.results import Results
+from ultralytics.utils import DEFAULT_CFG, ops
+from ultralytics.utils.torch_utils import select_device
 
 from .amg import (batch_iterator, batched_mask_to_box, build_all_layer_point_grids, calculate_stability_score,
                   generate_crop_boxes, is_box_near_crop_edge, remove_small_regions, uncrop_boxes_xyxy, uncrop_masks)

ultralytics/models/utils/loss.py

@@ -4,9 +4,10 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from ultralytics.vit.utils.ops import HungarianMatcher
-from ultralytics.yolo.utils.loss import FocalLoss, VarifocalLoss
-from ultralytics.yolo.utils.metrics import bbox_iou
+from ultralytics.utils.loss import FocalLoss, VarifocalLoss
+from ultralytics.utils.metrics import bbox_iou
+
+from .ops import HungarianMatcher
 
 
 class DETRLoss(nn.Module):

ultralytics/models/utils/ops.py

@@ -5,8 +5,8 @@ import torch.nn as nn
 import torch.nn.functional as F
 from scipy.optimize import linear_sum_assignment
 
-from ultralytics.yolo.utils.metrics import bbox_iou
-from ultralytics.yolo.utils.ops import xywh2xyxy, xyxy2xywh
+from ultralytics.utils.metrics import bbox_iou
+from ultralytics.utils.ops import xywh2xyxy, xyxy2xywh
 
 
 class HungarianMatcher(nn.Module):

ultralytics/models/yolo/__init__.py

@@ -0,0 +1,5 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from ultralytics.models.yolo import classify, detect, pose, segment
+
+__all__ = 'classify', 'segment', 'detect', 'pose'

ultralytics/models/yolo/classify/__init__.py

@@ -0,0 +1,7 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from ultralytics.models.yolo.classify.predict import ClassificationPredictor, predict
+from ultralytics.models.yolo.classify.train import ClassificationTrainer, train
+from ultralytics.models.yolo.classify.val import ClassificationValidator, val
+
+__all__ = 'ClassificationPredictor', 'predict', 'ClassificationTrainer', 'train', 'ClassificationValidator', 'val'

ultralytics/models/yolo/classify/predict.py

@@ -2,9 +2,9 @@
 
 import torch
 
-from ultralytics.yolo.engine.predictor import BasePredictor
-from ultralytics.yolo.engine.results import Results
-from ultralytics.yolo.utils import DEFAULT_CFG, ROOT
+from ultralytics.engine.predictor import BasePredictor
+from ultralytics.engine.results import Results
+from ultralytics.utils import DEFAULT_CFG, ROOT
 
 
 class ClassificationPredictor(BasePredictor):

ultralytics/models/yolo/classify/train.py

@@ -3,13 +3,13 @@
 import torch
 import torchvision
 
+from ultralytics.data import ClassificationDataset, build_dataloader
+from ultralytics.engine.trainer import BaseTrainer
+from ultralytics.models import yolo
 from ultralytics.nn.tasks import ClassificationModel, attempt_load_one_weight
-from ultralytics.yolo import v8
-from ultralytics.yolo.data import ClassificationDataset, build_dataloader
-from ultralytics.yolo.engine.trainer import BaseTrainer
-from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, colorstr
-from ultralytics.yolo.utils.plotting import plot_images, plot_results
-from ultralytics.yolo.utils.torch_utils import is_parallel, strip_optimizer, torch_distributed_zero_first
+from ultralytics.utils import DEFAULT_CFG, LOGGER, RANK, colorstr
+from ultralytics.utils.plotting import plot_images, plot_results
+from ultralytics.utils.torch_utils import is_parallel, strip_optimizer, torch_distributed_zero_first
 
 
 class ClassificationTrainer(BaseTrainer):
@@ -98,7 +98,7 @@ class ClassificationTrainer(BaseTrainer):
     def get_validator(self):
         """Returns an instance of ClassificationValidator for validation."""
         self.loss_names = ['loss']
-        return v8.classify.ClassificationValidator(self.test_loader, self.save_dir)
+        return yolo.classify.ClassificationValidator(self.test_loader, self.save_dir)
 
     def label_loss_items(self, loss_items=None, prefix='train'):
         """

ultralytics/models/yolo/classify/val.py

@@ -2,11 +2,11 @@
 
 import torch
 
-from ultralytics.yolo.data import ClassificationDataset, build_dataloader
-from ultralytics.yolo.engine.validator import BaseValidator
-from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER
-from ultralytics.yolo.utils.metrics import ClassifyMetrics, ConfusionMatrix
-from ultralytics.yolo.utils.plotting import plot_images
+from ultralytics.data import ClassificationDataset, build_dataloader
+from ultralytics.engine.validator import BaseValidator
+from ultralytics.utils import DEFAULT_CFG, LOGGER
+from ultralytics.utils.metrics import ClassifyMetrics, ConfusionMatrix
+from ultralytics.utils.plotting import plot_images
 
 
 class ClassificationValidator(BaseValidator):

ultralytics/models/yolo/detect/predict.py

@@ -2,9 +2,9 @@
 
 import torch
 
-from ultralytics.yolo.engine.predictor import BasePredictor
-from ultralytics.yolo.engine.results import Results
-from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops
+from ultralytics.engine.predictor import BasePredictor
+from ultralytics.engine.results import Results
+from ultralytics.utils import DEFAULT_CFG, ROOT, ops
 
 
 class DetectionPredictor(BasePredictor):

ultralytics/models/yolo/detect/train.py

@@ -3,13 +3,13 @@ from copy import copy
 
 import numpy as np
 
+from ultralytics.data import build_dataloader, build_yolo_dataset
+from ultralytics.engine.trainer import BaseTrainer
+from ultralytics.models import yolo
 from ultralytics.nn.tasks import DetectionModel
-from ultralytics.yolo import v8
-from ultralytics.yolo.data import build_dataloader, build_yolo_dataset
-from ultralytics.yolo.engine.trainer import BaseTrainer
-from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK
-from ultralytics.yolo.utils.plotting import plot_images, plot_labels, plot_results
-from ultralytics.yolo.utils.torch_utils import de_parallel, torch_distributed_zero_first
+from ultralytics.utils import DEFAULT_CFG, LOGGER, RANK
+from ultralytics.utils.plotting import plot_images, plot_labels, plot_results
+from ultralytics.utils.torch_utils import de_parallel, torch_distributed_zero_first
 
 
 # BaseTrainer python usage
@@ -64,7 +64,7 @@ class DetectionTrainer(BaseTrainer):
     def get_validator(self):
         """Returns a DetectionValidator for YOLO model validation."""
         self.loss_names = 'box_loss', 'cls_loss', 'dfl_loss'
-        return v8.detect.DetectionValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))
+        return yolo.detect.DetectionValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))
 
     def label_loss_items(self, loss_items=None, prefix='train'):
         """