Add global `settings.yaml` in `USER_CONFIG_DIR` (#125)

docs/index.md

@@ -37,4 +37,23 @@ For more information about the history and development of YOLO, you can refer to
 
 - Redmon, J., & Farhadi, A. (2015). You only look once: Unified, real-time object detection. In Proceedings of the IEEE
   conference on computer vision and pattern recognition (pp. 779-788).
-- Redmon, J., & Farhadi, A. (2016). YOLO9000: Better, faster, stronger. In Proceedings
+- Redmon, J., & Farhadi, A. (2016). YOLO9000: Better, faster, stronger. In Proceedings
+
+### YOLOv8 by Ultralytics
+
+YOLOv8 is the latest version of the YOLO object detection and image segmentation model developed by
+Ultralytics. YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO
+versions and introduces new features and improvements to further boost performance and flexibility.
+
+One key feature of YOLOv8 is its extensibility. It is designed as a framework that supports all previous versions of
+YOLO, making it easy to switch between different versions and compare their performance. This makes YOLOv8 an ideal
+choice for users who want to take advantage of the latest YOLO technology while still being able to use their existing
+YOLO models.
+
+In addition to its extensibility, YOLOv8 includes a number of other innovations that make it an appealing choice for a
+wide range of object detection and image segmentation tasks. These include a new backbone network, a new anchor-free
+detection head, and a new loss function. YOLOv8 is also highly efficient and can be run on a variety of hardware
+platforms, from CPUs to GPUs.
+
+Overall, YOLOv8 is a powerful and flexible tool for object detection and image segmentation that offers the best of both
+worlds: the latest SOTA technology and the ability to use and compare all previous YOLO versions.

ultralytics/nn/autobackend.py

@@ -10,10 +10,9 @@ import torch
 import torch.nn as nn
 from PIL import Image
 
-from ultralytics.yolo.utils import LOGGER, ROOT
+from ultralytics.yolo.utils import LOGGER, ROOT, yaml_load
 from ultralytics.yolo.utils.checks import check_requirements, check_suffix, check_version
 from ultralytics.yolo.utils.downloads import attempt_download, is_url
-from ultralytics.yolo.utils.files import yaml_load
 from ultralytics.yolo.utils.ops import xywh2xyxy
 
 

ultralytics/nn/tasks.py

@@ -10,9 +10,8 @@ import torchvision
 from ultralytics.nn.modules import (C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, Classify,
                                     Concat, Conv, ConvTranspose, Detect, DWConv, DWConvTranspose2d, Ensemble, Focus,
                                     GhostBottleneck, GhostConv, Segment)
-from ultralytics.yolo.utils import LOGGER, colorstr
+from ultralytics.yolo.utils import LOGGER, colorstr, yaml_load
 from ultralytics.yolo.utils.checks import check_yaml
-from ultralytics.yolo.utils.files import yaml_load
 from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, initialize_weights, intersect_state_dicts,
                                                 make_divisible, model_info, scale_img, time_sync)
 

ultralytics/yolo/configs/__init__.py

@@ -6,13 +6,19 @@ from omegaconf import DictConfig, OmegaConf
 from ultralytics.yolo.configs.hydra_patch import check_config_mismatch
 
 
-def get_config(config: Union[str, DictConfig], overrides: Union[str, Dict] = {}):
+def get_config(config: Union[str, DictConfig], overrides: Union[str, Dict] = None):
     """
-    Accepts yaml file name or DictConfig containing experiment configuration.
-    Returns training args namespace
-    :param overrides: Overrides str or Dict
-    :param config: Optional file name or DictConfig object
+    Load and merge configuration data from a file or dictionary.
+
+    Args:
+        config (Union[str, DictConfig]): Configuration data in the form of a file name or a DictConfig object.
+        overrides (Union[str, Dict], optional): Overrides in the form of a file name or a dictionary. Default is None.
+
+    Returns:
+        OmegaConf.Namespace: Training arguments namespace.
     """
+    if overrides is None:
+        overrides = {}
     if isinstance(config, (str, Path)):
         config = OmegaConf.load(config)
     elif isinstance(config, Dict):

ultralytics/yolo/data/base.py

@@ -91,7 +91,7 @@ class BaseDataset(Dataset):
             # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib
             assert im_files, f"{self.prefix}No images found"
         except Exception as e:
-            raise Exception(f"{self.prefix}Error loading data from {img_path}: {e}\n{HELP_URL}")
+            raise FileNotFoundError(f"{self.prefix}Error loading data from {img_path}: {e}\n{HELP_URL}") from e
         return im_files
 
     def update_labels(self, include_class: Optional[list]):

ultralytics/yolo/data/dataloaders/v5loader.py

@@ -484,7 +484,7 @@ class LoadImagesAndLabels(Dataset):
             # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib
             assert self.im_files, f'{prefix}No images found'
         except Exception as e:
-            raise Exception(f'{prefix}Error loading data from {path}: {e}\n{HELP_URL}') from e
+            raise FileNotFoundError(f'{prefix}Error loading data from {path}: {e}\n{HELP_URL}') from e
 
         # Check cache
         self.label_files = img2label_paths(self.im_files)  # labels

ultralytics/yolo/data/utils.py

@@ -12,10 +12,10 @@ import numpy as np
 import torch
 from PIL import ExifTags, Image, ImageOps
 
-from ultralytics.yolo.utils import LOGGER, ROOT, colorstr
+from ultralytics.yolo.utils import LOGGER, ROOT, colorstr, yaml_load
 from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii
 from ultralytics.yolo.utils.downloads import download
-from ultralytics.yolo.utils.files import unzip_file, yaml_load
+from ultralytics.yolo.utils.files import unzip_file
 
 from ..utils.ops import segments2boxes
 

ultralytics/yolo/engine/exporter.py

@@ -70,9 +70,9 @@ from ultralytics.nn.tasks import ClassificationModel, DetectionModel, Segmentati
 from ultralytics.yolo.configs import get_config
 from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages
 from ultralytics.yolo.data.utils import check_dataset
-from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, colorstr, get_default_args
+from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, colorstr, get_default_args, yaml_save
 from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml
-from ultralytics.yolo.utils.files import file_size, increment_path, yaml_save
+from ultralytics.yolo.utils.files import file_size, increment_path
 from ultralytics.yolo.utils.ops import Profile
 from ultralytics.yolo.utils.torch_utils import guess_task_from_head, select_device, smart_inference_mode
 
@@ -198,7 +198,7 @@ class Exporter:
         self.im = im
         self.model = model
         self.file = file
-        self.output_shape = tuple(y.shape)
+        self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else (x.shape for x in y)
         self.metadata = {'stride': int(max(model.stride)), 'names': model.names}  # model metadata
         self.pretty_name = self.file.stem.replace('yolo', 'YOLO')
 

ultralytics/yolo/engine/model.py

@@ -4,9 +4,8 @@ from ultralytics import yolo  # noqa
 from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, attempt_load_weights
 from ultralytics.yolo.configs import get_config
 from ultralytics.yolo.engine.exporter import Exporter
-from ultralytics.yolo.utils import DEFAULT_CONFIG, HELP_MSG, LOGGER
+from ultralytics.yolo.utils import DEFAULT_CONFIG, HELP_MSG, LOGGER, yaml_load
 from ultralytics.yolo.utils.checks import check_imgsz, check_yaml
-from ultralytics.yolo.utils.files import yaml_load
 from ultralytics.yolo.utils.torch_utils import guess_task_from_head, smart_inference_mode
 
 # Map head to model, trainer, validator, and predictor classes

ultralytics/yolo/engine/trainer.py

@@ -25,10 +25,10 @@ import ultralytics.yolo.utils.callbacks as callbacks
 from ultralytics import __version__
 from ultralytics.yolo.configs import get_config
 from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
-from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, RANK, TQDM_BAR_FORMAT, colorstr
+from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, RANK, TQDM_BAR_FORMAT, colorstr, yaml_save
 from ultralytics.yolo.utils.checks import check_file, print_args
 from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
-from ultralytics.yolo.utils.files import get_latest_run, increment_path, yaml_save
+from ultralytics.yolo.utils.files import get_latest_run, increment_path
 from ultralytics.yolo.utils.torch_utils import ModelEMA, de_parallel, init_seeds, one_cycle, strip_optimizer
 
 

ultralytics/yolo/utils/__init__.py

@@ -10,6 +10,7 @@ from pathlib import Path
 
 import cv2
 import pandas as pd
+import yaml
 
 # Constants
 FILE = Path(__file__).resolve()
@@ -224,13 +225,6 @@ def set_logging(name=LOGGING_NAME, verbose=True):
                 "propagate": False,}}})
 
 
-set_logging(LOGGING_NAME)  # run before defining LOGGER
-LOGGER = logging.getLogger(LOGGING_NAME)  # define globally (used in train.py, val.py, detect.py, etc.)
-if platform.system() == 'Windows':
-    for fn in LOGGER.info, LOGGER.warning:
-        setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x)))  # emoji safe logging
-
-
 class TryExcept(contextlib.ContextDecorator):
     # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
     def __init__(self, msg=''):
@@ -253,3 +247,82 @@ def threaded(func):
         return thread
 
     return wrapper
+
+
+def get_settings(file=USER_CONFIG_DIR / 'settings.yaml'):
+    """
+    Function that loads a global settings YAML, or creates it and populates it with default values if it does not exist.
+
+    If the datasets or weights directories are set to None, the current working directory will be used.
+    The 'sync' setting determines whether analytics will be synced to help with YOLO development.
+    """
+    from ultralytics.yolo.utils.torch_utils import torch_distributed_zero_first
+
+    with torch_distributed_zero_first(RANK):
+        if not file.exists():
+            settings = {
+                'datasets_dir': None,  # default datasets directory. If None, current working directory is used.
+                'weights_dir': None,  # default weights directory. If None, current working directory is used.
+                'sync': True}  # sync analytics to help with YOLO development
+            yaml_save(file, settings)
+
+    return yaml_load(file)
+
+
+def yaml_save(file='data.yaml', data=None):
+    """
+    Save YAML data to a file.
+
+    Args:
+        file (str, optional): File name. Default is 'data.yaml'.
+        data (dict, optional): Data to save in YAML format. Default is None.
+
+    Returns:
+        None: Data is saved to the specified file.
+    """
+    file = Path(file)
+    if not file.parent.exists():
+        # Create parent directories if they don't exist
+        file.parent.mkdir(parents=True, exist_ok=True)
+
+    with open(file, 'w') as f:
+        # Dump data to file in YAML format, converting Path objects to strings
+        yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)
+
+
+def yaml_load(file='data.yaml'):
+    """
+    Load YAML data from a file.
+
+    Args:
+        file (str, optional): File name. Default is 'data.yaml'.
+
+    Returns:
+        dict: YAML data and file name.
+    """
+    with open(file, errors='ignore') as f:
+        # Add YAML filename to dict and return
+        return {**yaml.safe_load(f), 'yaml_file': file}
+
+
+# Run below code on utils init -----------------------------------------------------------------------------------------
+
+# Set logger
+set_logging(LOGGING_NAME)  # run before defining LOGGER
+LOGGER = logging.getLogger(LOGGING_NAME)  # define globally (used in train.py, val.py, detect.py, etc.)
+if platform.system() == 'Windows':
+    for fn in LOGGER.info, LOGGER.warning:
+        setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x)))  # emoji safe logging
+
+# Check first-install steps
+SETTINGS = get_settings()
+
+
+def set_settings(kwargs, file=USER_CONFIG_DIR / 'settings.yaml'):
+    """
+    Function that runs on a first-time ultralytics package installation to set up global settings and create necessary
+    directories.
+    """
+    SETTINGS.update(kwargs)
+
+    yaml_save(file, SETTINGS)

ultralytics/yolo/utils/files.py

@@ -6,8 +6,6 @@ from datetime import datetime
 from pathlib import Path
 from zipfile import ZipFile
 
-import yaml
-
 
 class WorkingDirectory(contextlib.ContextDecorator):
     # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager
@@ -57,18 +55,6 @@ def increment_path(path, exist_ok=False, sep='', mkdir=False):
     return path
 
 
-def yaml_save(file='data.yaml', data=None):
-    # Single-line safe yaml saving
-    with open(file, 'w') as f:
-        yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)
-
-
-def yaml_load(file='data.yaml'):
-    # Single-line safe yaml loading
-    with open(file, errors='ignore') as f:
-        return {**yaml.safe_load(f), 'yaml_file': file}  # add YAML filename to dict and return
-
-
 def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')):
     # Unzip a *.zip file to path/, excluding files containing strings in exclude list
     if path is None:

ultralytics/yolo/utils/instance.py

@@ -162,13 +162,15 @@ class Bboxes:
 
 class Instances:
 
-    def __init__(self, bboxes, segments=[], keypoints=None, bbox_format="xywh", normalized=True) -> None:
+    def __init__(self, bboxes, segments=None, keypoints=None, bbox_format="xywh", normalized=True) -> None:
         """
         Args:
             bboxes (ndarray): bboxes with shape [N, 4].
             segments (list | ndarray): segments.
             keypoints (ndarray): keypoints with shape [N, 17, 2].
         """
+        if segments is None:
+            segments = []
         self._bboxes = Bboxes(bboxes=bboxes, format=bbox_format)
         self.keypoints = keypoints
         self.normalized = normalized

ultralytics/yolo/v8/detect/val.py

@@ -8,9 +8,8 @@ import torch
 from ultralytics.yolo.data import build_dataloader
 from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
 from ultralytics.yolo.engine.validator import BaseValidator
-from ultralytics.yolo.utils import DEFAULT_CONFIG, colorstr, ops
+from ultralytics.yolo.utils import DEFAULT_CONFIG, colorstr, ops, yaml_load
 from ultralytics.yolo.utils.checks import check_file, check_requirements
-from ultralytics.yolo.utils.files import yaml_load
 from ultralytics.yolo.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
 from ultralytics.yolo.utils.plotting import output_to_target, plot_images
 from ultralytics.yolo.utils.torch_utils import de_parallel