ultralytics 8.1.44 add IS_RASPBERRYPI and constants refactor (#9827)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

tests/test_engine.py

@@ -2,6 +2,7 @@
 
 import sys
 from unittest import mock
+
 from ultralytics import YOLO
 from ultralytics.cfg import get_cfg
 from ultralytics.engine.exporter import Exporter
@@ -52,7 +53,7 @@ def test_detect():
     pred.add_callback("on_predict_start", test_func)
     assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
     # Confirm there is no issue with sys.argv being empty.
-    with mock.patch.object(sys, 'argv', []):
+    with mock.patch.object(sys, "argv", []):
         result = pred(source=ASSETS, model=f"{MODEL}.pt")
         assert len(result), "predictor test failed"
 

tests/test_integrations.py

@@ -34,6 +34,7 @@ def test_mlflow():
 @pytest.mark.skipif(not check_requirements("mlflow", install=False), reason="mlflow not installed")
 def test_mlflow_keep_run_active():
     import os
+
     import mlflow
 
     """Test training with MLflow tracking enabled."""

tests/test_python.py

@@ -514,7 +514,8 @@ def test_utils_files():
 @pytest.mark.slow
 def test_utils_patches_torch_save():
     """Test torch_save backoff when _torch_save throws RuntimeError."""
-    from unittest.mock import patch, MagicMock
+    from unittest.mock import MagicMock, patch
+
     from ultralytics.utils.patches import torch_save
 
     mock = MagicMock(side_effect=RuntimeError)
@@ -651,9 +652,8 @@ def test_yolo_world():
     from ultralytics.models.yolo.world.train_world import WorldTrainerFromScratch
 
     model = YOLO("yolov8s-worldv2.yaml")  # no YOLOv8n-world model yet
-    data = dict(train=dict(yolo_data=["coco8.yaml"]), val=dict(yolo_data=["coco8.yaml"]))
     model.train(
-        data=data,
+        data={"train": {"yolo_data": ["coco8.yaml"]}, "val": {"yolo_data": ["coco8.yaml"]}},
         epochs=2,
         imgsz=32,
         cache="disk",

ultralytics/__init__.py

@@ -1,15 +1,16 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.1.43"
+__version__ = "8.1.44"
 
 from ultralytics.data.explorer.explorer import Explorer
 from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld
 from ultralytics.models.fastsam import FastSAM
 from ultralytics.models.nas import NAS
-from ultralytics.utils import ASSETS, SETTINGS as settings
+from ultralytics.utils import ASSETS, SETTINGS
 from ultralytics.utils.checks import check_yolo as checks
 from ultralytics.utils.downloads import download
 
+settings = SETTINGS
 __all__ = (
     "__version__",
     "ASSETS",

ultralytics/cfg/models/v9/yolov9c-seg.yaml

@@ -35,4 +35,4 @@ head:
   - [[-1, 9], 1, Concat, [1]]  # cat head P5
   - [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]]  # 21 (P5/32-large)
 
-  - [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)
+  - [[15, 18, 21], 1, Segment, [nc, 32, 256]]  # Segment(P3, P4, P5)

ultralytics/cfg/models/v9/yolov9c.yaml

@@ -35,4 +35,4 @@ head:
   - [[-1, 9], 1, Concat, [1]]  # cat head P5
   - [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]]  # 21 (P5/32-large)
 
-  - [[15, 18, 21], 1, Detect, [nc]]  # DDetect(P3, P4, P5)
+  - [[15, 18, 21], 1, Detect, [nc]]  # Detect(P3, P4, P5)

ultralytics/cfg/models/v9/yolov9e-seg.yaml

@@ -58,5 +58,4 @@ head:
   - [[-1, 29], 1, Concat, [1]]  # cat head P5
   - [-1, 1, RepNCSPELAN4, [512, 1024, 512, 2]]  # 41 (P5/32-large)
 
-   # segment
   - [[35, 38, 41], 1, Segment, [nc, 32, 256]]  # Segment (P3, P4, P5)

ultralytics/cfg/models/v9/yolov9e.yaml

@@ -58,5 +58,4 @@ head:
   - [[-1, 29], 1, Concat, [1]]  # cat head P5
   - [-1, 1, RepNCSPELAN4, [512, 1024, 512, 2]]  # 41 (P5/32-large)
 
-   # detect
   - [[35, 38, 41], 1, Detect, [nc]]  # Detect(P3, P4, P5)

ultralytics/data/__init__.py

@@ -1,19 +1,14 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
 from .base import BaseDataset
-from .build import (
+from .build import build_dataloader, build_grounding, build_yolo_dataset, load_inference_source
-    build_dataloader,
-    build_yolo_dataset,
-    build_grounding,
-    load_inference_source,
-)
 from .dataset import (
     ClassificationDataset,
+    GroundingDataset,
     SemanticDataset,
+    YOLOConcatDataset,
     YOLODataset,
     YOLOMultiModalDataset,
-    GroundingDataset,
-    YOLOConcatDataset,
 )
 
 __all__ = (

ultralytics/data/augment.py

@@ -20,7 +20,7 @@ from .utils import polygons2masks, polygons2masks_overlap
 
 DEFAULT_MEAN = (0.0, 0.0, 0.0)
 DEFAULT_STD = (1.0, 1.0, 1.0)
-DEFAULT_CROP_FTACTION = 1.0
+DEFAULT_CROP_FRACTION = 1.0
 
 
 # TODO: we might need a BaseTransform to make all these augments be compatible with both classification and semantic
@@ -1134,7 +1134,7 @@ def classify_transforms(
     mean=DEFAULT_MEAN,
     std=DEFAULT_STD,
     interpolation=Image.BILINEAR,
-    crop_fraction: float = DEFAULT_CROP_FTACTION,
+    crop_fraction: float = DEFAULT_CROP_FRACTION,
 ):
     """
     Classification transforms for evaluation/inference. Inspired by timm/data/transforms_factory.py.

ultralytics/data/base.py

@@ -15,7 +15,7 @@ import psutil
 from torch.utils.data import Dataset
 
 from ultralytics.utils import DEFAULT_CFG, LOCAL_RANK, LOGGER, NUM_THREADS, TQDM
-from .utils import HELP_URL, FORMATS_HELP_MSG, IMG_FORMATS
+from .utils import FORMATS_HELP_MSG, HELP_URL, IMG_FORMATS
 
 
 class BaseDataset(Dataset):

ultralytics/data/build.py

@@ -22,7 +22,7 @@ from ultralytics.data.loaders import (
 from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS
 from ultralytics.utils import RANK, colorstr
 from ultralytics.utils.checks import check_file
-from .dataset import YOLODataset, YOLOMultiModalDataset, GroundingDataset
+from .dataset import GroundingDataset, YOLODataset, YOLOMultiModalDataset
 from .utils import PIN_MEMORY
 
 

ultralytics/data/converter.py

@@ -519,12 +519,13 @@ def yolo_bbox2segment(im_dir, save_dir=None, sam_model="sam_b.pt"):
                 ├─ ..
                 └─ NNN.txt
     """
-    from ultralytics.data import YOLODataset
-    from ultralytics.utils.ops import xywh2xyxy
-    from ultralytics.utils import LOGGER
-    from ultralytics import SAM
     from tqdm import tqdm
 
+    from ultralytics import SAM
+    from ultralytics.data import YOLODataset
+    from ultralytics.utils import LOGGER
+    from ultralytics.utils.ops import xywh2xyxy
+
     # NOTE: add placeholder to pass class index check
     dataset = YOLODataset(im_dir, data=dict(names=list(range(1000))))
     if len(dataset.labels[0]["segments"]) > 0:  # if it's segment data

ultralytics/data/dataset.py

@@ -30,10 +30,10 @@ from .utils import (
     LOGGER,
     get_hash,
     img2label_paths,
-    verify_image,
-    verify_image_label,
     load_dataset_cache_file,
     save_dataset_cache_file,
+    verify_image,
+    verify_image_label,
 )
 
 # Ultralytics dataset *.cache version, >= 1.0.0 for YOLOv8

ultralytics/data/explorer/explorer.py

@@ -15,7 +15,7 @@ from ultralytics.data.augment import Format
 from ultralytics.data.dataset import YOLODataset
 from ultralytics.data.utils import check_det_dataset
 from ultralytics.models.yolo.model import YOLO
-from ultralytics.utils import LOGGER, IterableSimpleNamespace, checks, USER_CONFIG_DIR
+from ultralytics.utils import LOGGER, USER_CONFIG_DIR, IterableSimpleNamespace, checks
 from .utils import get_sim_index_schema, get_table_schema, plot_query_result, prompt_sql_query, sanitize_batch
 
 
@@ -203,7 +203,8 @@ class Explorer:
         table = self.table.to_arrow()  # noqa NOTE: Don't comment this. This line is used by DuckDB
         if not query.startswith("SELECT") and not query.startswith("WHERE"):
             raise ValueError(
-                f"Query must start with SELECT or WHERE. You can either pass the entire query or just the WHERE clause. found {query}"
+                f"Query must start with SELECT or WHERE. You can either pass the entire query or just the WHERE "
+                f"clause. found {query}"
             )
         if query.startswith("WHERE"):
             query = f"SELECT * FROM 'table' {query}"
@@ -318,13 +319,13 @@ class Explorer:
 
         Args:
             max_dist (float): maximum L2 distance between the embeddings to consider. Defaults to 0.2.
-            top_k (float): Percentage of the closest data points to consider when counting. Used to apply limit when running
+            top_k (float): Percentage of the closest data points to consider when counting. Used to apply limit.
                            vector search. Defaults: None.
             force (bool): Whether to overwrite the existing similarity index or not. Defaults to True.
 
         Returns:
-            (pandas.DataFrame): A dataframe containing the similarity index. Each row corresponds to an image, and columns
+            (pandas.DataFrame): A dataframe containing the similarity index. Each row corresponds to an image,
-                                include indices of similar images and their respective distances.
+                and columns include indices of similar images and their respective distances.
 
         Example:
             ```python

ultralytics/data/loaders.py

@@ -15,8 +15,8 @@ import requests
 import torch
 from PIL import Image
 
-from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS, FORMATS_HELP_MSG
+from ultralytics.data.utils import FORMATS_HELP_MSG, IMG_FORMATS, VID_FORMATS
-from ultralytics.utils import LOGGER, is_colab, is_kaggle, ops
+from ultralytics.utils import IS_COLAB, IS_KAGGLE, LOGGER, ops
 from ultralytics.utils.checks import check_requirements
 
 
@@ -87,7 +87,7 @@ class LoadStreams:
                 # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4'
                 s = get_best_youtube_url(s)
             s = eval(s) if s.isnumeric() else s  # i.e. s = '0' local webcam
-            if s == 0 and (is_colab() or is_kaggle()):
+            if s == 0 and (IS_COLAB or IS_KAGGLE):
                 raise NotImplementedError(
                     "'source=0' webcam not supported in Colab and Kaggle notebooks. "
                     "Try running 'source=0' in a local environment."

ultralytics/data/utils.py

@@ -27,9 +27,9 @@ from ultralytics.utils import (
     clean_url,
     colorstr,
     emojis,
+    is_dir_writeable,
     yaml_load,
     yaml_save,
-    is_dir_writeable,
 )
 from ultralytics.utils.checks import check_file, check_font, is_ascii
 from ultralytics.utils.downloads import download, safe_download, unzip_file

ultralytics/engine/model.py

@@ -321,9 +321,10 @@ class Model(nn.Module):
             AssertionError: If the model is not a PyTorch model.
         """
         self._check_is_pytorch_model()
-        from ultralytics import __version__
         from datetime import datetime
 
+        from ultralytics import __version__
+
         updates = {
             "date": datetime.now().isoformat(),
             "version": __version__,

ultralytics/engine/trainer.py

@@ -464,6 +464,7 @@ class BaseTrainer:
     def save_model(self):
         """Save model training checkpoints with additional metadata."""
         import io
+
         import pandas as pd  # scope for faster 'import ultralytics'
 
         # Serialize ckpt to a byte buffer once (faster than repeated torch.save() calls)

ultralytics/hub/auth.py

@@ -3,7 +3,7 @@
 import requests
 
 from ultralytics.hub.utils import HUB_API_ROOT, HUB_WEB_ROOT, PREFIX, request_with_credentials
-from ultralytics.utils import LOGGER, SETTINGS, emojis, is_colab
+from ultralytics.utils import IS_COLAB, LOGGER, SETTINGS, emojis
 
 API_KEY_URL = f"{HUB_WEB_ROOT}/settings?tab=api+keys"
 
@@ -50,7 +50,7 @@ class Auth:
                 # Attempt to authenticate with the provided API key
                 success = self.authenticate()
         # If the API key is not provided and the environment is a Google Colab notebook
-        elif is_colab():
+        elif IS_COLAB:
             # Attempt to authenticate using browser cookies
             success = self.auth_with_cookies()
         else:
@@ -109,7 +109,7 @@ class Auth:
         Returns:
             (bool): True if authentication is successful, False otherwise.
         """
-        if not is_colab():
+        if not IS_COLAB:
             return False  # Currently only works with Colab
         try:
             authn = request_with_credentials(f"{HUB_API_ROOT}/v1/auth/auto")

ultralytics/hub/session.py

@@ -7,11 +7,11 @@ from pathlib import Path
 
 import requests
 
-from ultralytics.hub.utils import HUB_WEB_ROOT, HELP_MSG, PREFIX, TQDM
+from ultralytics.hub.utils import HELP_MSG, HUB_WEB_ROOT, PREFIX, TQDM
-from ultralytics.utils import LOGGER, SETTINGS, __version__, checks, emojis, is_colab
+from ultralytics.utils import IS_COLAB, LOGGER, SETTINGS, __version__, checks, emojis
 from ultralytics.utils.errors import HUBModelError
 
-AGENT_NAME = f"python-{__version__}-colab" if is_colab() else f"python-{__version__}-local"
+AGENT_NAME = f"python-{__version__}-colab" if IS_COLAB else f"python-{__version__}-local"
 
 
 class HUBTrainingSession:

ultralytics/hub/utils.py

@@ -12,6 +12,9 @@ import requests
 from ultralytics.utils import (
     ARGV,
     ENVIRONMENT,
+    IS_COLAB,
+    IS_GIT_DIR,
+    IS_PIP_PACKAGE,
     LOGGER,
     ONLINE,
     RANK,
@@ -22,9 +25,6 @@ from ultralytics.utils import (
     __version__,
     colorstr,
     get_git_origin_url,
-    is_colab,
-    is_git_dir,
-    is_pip_package,
 )
 from ultralytics.utils.downloads import GITHUB_ASSETS_NAMES
 
@@ -48,7 +48,7 @@ def request_with_credentials(url: str) -> any:
     Raises:
         OSError: If the function is not run in a Google Colab environment.
     """
-    if not is_colab():
+    if not IS_COLAB:
         raise OSError("request_with_credentials() must run in a Colab environment")
     from google.colab import output  # noqa
     from IPython import display  # noqa
@@ -189,7 +189,7 @@ class Events:
         self.t = 0.0  # rate limit timer (seconds)
         self.metadata = {
             "cli": Path(ARGV[0]).name == "yolo",
-            "install": "git" if is_git_dir() else "pip" if is_pip_package() else "other",
+            "install": "git" if IS_GIT_DIR else "pip" if IS_PIP_PACKAGE else "other",
             "python": ".".join(platform.python_version_tuple()[:2]),  # i.e. 3.10
             "version": __version__,
             "env": ENVIRONMENT,
@@ -201,7 +201,7 @@ class Events:
             and RANK in {-1, 0}
             and not TESTS_RUNNING
             and ONLINE
-            and (is_pip_package() or get_git_origin_url() == "https://github.com/ultralytics/ultralytics.git")
+            and (IS_PIP_PACKAGE or get_git_origin_url() == "https://github.com/ultralytics/ultralytics.git")
         )
 
     def __call__(self, cfg):

ultralytics/models/rtdetr/val.py

@@ -125,7 +125,7 @@ class RTDETRValidator(DetectionValidator):
             bbox = ops.xywh2xyxy(bbox)  # target boxes
             bbox[..., [0, 2]] *= ori_shape[1]  # native-space pred
             bbox[..., [1, 3]] *= ori_shape[0]  # native-space pred
-        return dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
+        return {"cls": cls, "bbox": bbox, "ori_shape": ori_shape, "imgsz": imgsz, "ratio_pad": ratio_pad}
 
     def _prepare_pred(self, pred, pbatch):
         """Prepares and returns a batch with transformed bounding boxes and class labels."""

ultralytics/models/sam/modules/tiny_encoder.py

@@ -584,9 +584,9 @@ class TinyViT(nn.Module):
             img_size (int, optional): The input image size. Defaults to 224.
             in_chans (int, optional): Number of input channels. Defaults to 3.
             num_classes (int, optional): Number of classification classes. Defaults to 1000.
-            embed_dims (List[int], optional): List of embedding dimensions for each layer. Defaults to [96, 192, 384, 768].
+            embed_dims (List[int], optional): List of embedding dimensions per layer. Defaults to [96, 192, 384, 768].
             depths (List[int], optional): List of depths for each layer. Defaults to [2, 2, 6, 2].
-            num_heads (List[int], optional): List of number of attention heads for each layer. Defaults to [3, 6, 12, 24].
+            num_heads (List[int], optional): List of number of attention heads per layer. Defaults to [3, 6, 12, 24].
             window_sizes (List[int], optional): List of window sizes for each layer. Defaults to [7, 7, 14, 7].
             mlp_ratio (float, optional): Ratio of MLP hidden dimension to embedding dimension. Defaults to 4.
             drop_rate (float, optional): Dropout rate. Defaults to 0.

ultralytics/models/sam/modules/transformer.py

@@ -222,7 +222,7 @@ class Attention(nn.Module):
             downsample_rate (int, optional): The factor by which the internal dimensions are downsampled. Defaults to 1.
 
         Raises:
-            AssertionError: If 'num_heads' does not evenly divide the internal dimension (embedding_dim / downsample_rate).
+            AssertionError: If 'num_heads' does not evenly divide the internal dim (embedding_dim / downsample_rate).
         """
         super().__init__()
         self.embedding_dim = embedding_dim

ultralytics/models/sam/predict.py

@@ -127,10 +127,10 @@ class Predictor(BasePredictor):
         Args:
             im (torch.Tensor): The preprocessed input image in tensor format, with shape (N, C, H, W).
             bboxes (np.ndarray | List, optional): Bounding boxes with shape (N, 4), in XYXY format.
-            points (np.ndarray | List, optional): Points indicating object locations with shape (N, 2), in pixel coordinates.
+            points (np.ndarray | List, optional): Points indicating object locations with shape (N, 2), in pixels.
-            labels (np.ndarray | List, optional): Labels for point prompts, shape (N, ). 1 for foreground and 0 for background.
+            labels (np.ndarray | List, optional): Labels for point prompts, shape (N, ). 1 = foreground, 0 = background.
-            masks (np.ndarray, optional): Low-resolution masks from previous predictions. Shape should be (N, H, W). For SAM, H=W=256.
+            masks (np.ndarray, optional): Low-resolution masks from previous predictions shape (N,H,W). For SAM H=W=256.
-            multimask_output (bool, optional): Flag to return multiple masks. Helpful for ambiguous prompts. Defaults to False.
+            multimask_output (bool, optional): Flag to return multiple masks. Helpful for ambiguous prompts.
 
         Returns:
             (tuple): Contains the following three elements.
@@ -156,10 +156,10 @@ class Predictor(BasePredictor):
         Args:
             im (torch.Tensor): The preprocessed input image in tensor format, with shape (N, C, H, W).
             bboxes (np.ndarray | List, optional): Bounding boxes with shape (N, 4), in XYXY format.
-            points (np.ndarray | List, optional): Points indicating object locations with shape (N, 2), in pixel coordinates.
+            points (np.ndarray | List, optional): Points indicating object locations with shape (N, 2), in pixels.
-            labels (np.ndarray | List, optional): Labels for point prompts, shape (N, ). 1 for foreground and 0 for background.
+            labels (np.ndarray | List, optional): Labels for point prompts, shape (N, ). 1 = foreground, 0 = background.
-            masks (np.ndarray, optional): Low-resolution masks from previous predictions. Shape should be (N, H, W). For SAM, H=W=256.
+            masks (np.ndarray, optional): Low-resolution masks from previous predictions shape (N,H,W). For SAM H=W=256.
-            multimask_output (bool, optional): Flag to return multiple masks. Helpful for ambiguous prompts. Defaults to False.
+            multimask_output (bool, optional): Flag to return multiple masks. Helpful for ambiguous prompts.
 
         Returns:
             (tuple): Contains the following three elements.
@@ -230,7 +230,7 @@ class Predictor(BasePredictor):
             im (torch.Tensor): Input tensor representing the preprocessed image with dimensions (N, C, H, W).
             crop_n_layers (int): Specifies the number of layers for additional mask predictions on image crops.
                                  Each layer produces 2**i_layer number of image crops.
-            crop_overlap_ratio (float): Determines the extent of overlap between crops. Scaled down in subsequent layers.
+            crop_overlap_ratio (float): Determines the overlap between crops. Scaled down in subsequent layers.
             crop_downscale_factor (int): Scaling factor for the number of sampled points-per-side in each layer.
             point_grids (list[np.ndarray], optional): Custom grids for point sampling normalized to [0,1].
                                                       Used in the nth crop layer.
@@ -240,7 +240,7 @@ class Predictor(BasePredictor):
             conf_thres (float): Confidence threshold [0,1] for filtering based on the model's mask quality prediction.
             stability_score_thresh (float): Stability threshold [0,1] for mask filtering based on mask stability.
             stability_score_offset (float): Offset value for calculating stability score.
-            crop_nms_thresh (float): IoU cutoff for Non-Maximum Suppression (NMS) to remove duplicate masks between crops.
+            crop_nms_thresh (float): IoU cutoff for NMS to remove duplicate masks between crops.
 
         Returns:
             (tuple): A tuple containing segmented masks, confidence scores, and bounding boxes.
@@ -351,8 +351,8 @@ class Predictor(BasePredictor):
         """
         Post-processes SAM's inference outputs to generate object detection masks and bounding boxes.
 
-        The method scales masks and boxes to the original image size and applies a threshold to the mask predictions. The
+        The method scales masks and boxes to the original image size and applies a threshold to the mask predictions.
-        SAM model uses advanced architecture and promptable segmentation tasks to achieve real-time performance.
+        The SAM model uses advanced architecture and promptable segmentation tasks to achieve real-time performance.
 
         Args:
             preds (tuple): The output from SAM model inference, containing masks, scores, and optional bounding boxes.

ultralytics/models/yolo/detect/val.py

@@ -106,7 +106,7 @@ class DetectionValidator(BaseValidator):
         if len(cls):
             bbox = ops.xywh2xyxy(bbox) * torch.tensor(imgsz, device=self.device)[[1, 0, 1, 0]]  # target boxes
             ops.scale_boxes(imgsz, bbox, ori_shape, ratio_pad=ratio_pad)  # native-space labels
-        return dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
+        return {"cls": cls, "bbox": bbox, "ori_shape": ori_shape, "imgsz": imgsz, "ratio_pad": ratio_pad}
 
     def _prepare_pred(self, pred, pbatch):
         """Prepares a batch of images and annotations for validation."""

ultralytics/models/yolo/model.py

@@ -5,7 +5,7 @@ from pathlib import Path
 from ultralytics.engine.model import Model
 from ultralytics.models import yolo
 from ultralytics.nn.tasks import ClassificationModel, DetectionModel, OBBModel, PoseModel, SegmentationModel, WorldModel
-from ultralytics.utils import yaml_load, ROOT
+from ultralytics.utils import ROOT, yaml_load
 
 
 class YOLO(Model):

ultralytics/models/yolo/obb/val.py

@@ -78,7 +78,7 @@ class OBBValidator(DetectionValidator):
         if len(cls):
             bbox[..., :4].mul_(torch.tensor(imgsz, device=self.device)[[1, 0, 1, 0]])  # target boxes
             ops.scale_boxes(imgsz, bbox, ori_shape, ratio_pad=ratio_pad, xywh=True)  # native-space labels
-        return dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
+        return {"cls": cls, "bbox": bbox, "ori_shape": ori_shape, "imgsz": imgsz, "ratio_pad": ratio_pad}
 
     def _prepare_pred(self, pred, pbatch):
         """Prepares and returns a batch for OBB validation with scaled and padded bounding boxes."""

ultralytics/models/yolo/world/train_world.py

@@ -1,8 +1,8 @@
-from ultralytics.data import build_yolo_dataset, build_grounding, YOLOConcatDataset
+from ultralytics.data import YOLOConcatDataset, build_grounding, build_yolo_dataset
 from ultralytics.data.utils import check_det_dataset
 from ultralytics.models.yolo.world import WorldTrainer
-from ultralytics.utils.torch_utils import de_parallel
 from ultralytics.utils import DEFAULT_CFG
+from ultralytics.utils.torch_utils import de_parallel
 
 
 class WorldTrainerFromScratch(WorldTrainer):

ultralytics/nn/modules/__init__.py

@@ -24,27 +24,27 @@ from .block import (
     C3TR,
     DFL,
     SPP,
+    SPPELAN,
     SPPF,
+    ADown,
+    BNContrastiveHead,
     Bottleneck,
     BottleneckCSP,
     C2f,
     C2fAttn,
-    ImagePoolingAttn,
     C3Ghost,
     C3x,
+    CBFuse,
+    CBLinear,
+    ContrastiveHead,
     GhostBottleneck,
     HGBlock,
     HGStem,
+    ImagePoolingAttn,
     Proto,
     RepC3,
-    ResNetLayer,
-    ContrastiveHead,
-    BNContrastiveHead,
     RepNCSPELAN4,
-    ADown,
+    ResNetLayer,
-    SPPELAN,
-    CBFuse,
-    CBLinear,
     Silence,
 )
 from .conv import (

ultralytics/nn/modules/head.py

@@ -8,7 +8,7 @@ import torch.nn as nn
 from torch.nn.init import constant_, xavier_uniform_
 
 from ultralytics.utils.tal import TORCH_1_10, dist2bbox, dist2rbox, make_anchors
-from .block import DFL, Proto, ContrastiveHead, BNContrastiveHead
+from .block import DFL, BNContrastiveHead, ContrastiveHead, Proto
 from .conv import Conv
 from .transformer import MLP, DeformableTransformerDecoder, DeformableTransformerDecoderLayer
 from .utils import bias_init_with_prob, linear_init

ultralytics/nn/tasks.py

@@ -15,14 +15,17 @@ from ultralytics.nn.modules import (
     C3TR,
     OBB,
     SPP,
+    SPPELAN,
     SPPF,
+    ADown,
     Bottleneck,
     BottleneckCSP,
     C2f,
     C2fAttn,
-    ImagePoolingAttn,
     C3Ghost,
     C3x,
+    CBFuse,
+    CBLinear,
     Classify,
     Concat,
     Conv,
@@ -36,19 +39,16 @@ from ultralytics.nn.modules import (
     GhostConv,
     HGBlock,
     HGStem,
+    ImagePoolingAttn,
     Pose,
     RepC3,
     RepConv,
+    RepNCSPELAN4,
     ResNetLayer,
     RTDETRDecoder,
     Segment,
-    WorldDetect,
-    RepNCSPELAN4,
-    ADown,
-    SPPELAN,
-    CBFuse,
-    CBLinear,
     Silence,
+    WorldDetect,
 )
 from ultralytics.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, emojis, yaml_load
 from ultralytics.utils.checks import check_requirements, check_suffix, check_yaml

ultralytics/solutions/object_counter.py

@@ -1,6 +1,7 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
 from collections import defaultdict
+
 import cv2
 
 from ultralytics.utils.checks import check_imshow, check_requirements

ultralytics/trackers/byte_tracker.py

@@ -5,8 +5,8 @@ import numpy as np
 from .basetrack import BaseTrack, TrackState
 from .utils import matching
 from .utils.kalman_filter import KalmanFilterXYAH
-from ..utils.ops import xywh2ltwh
 from ..utils import LOGGER
+from ..utils.ops import xywh2ltwh
 
 
 class STrack(BaseTrack):

ultralytics/trackers/utils/kalman_filter.py

@@ -39,8 +39,8 @@ class KalmanFilterXYAH:
                 and height h.
 
         Returns:
-            (tuple[ndarray, ndarray]): Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional) of
+            (tuple[ndarray, ndarray]): Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional)
-                the new track. Unobserved velocities are initialized to 0 mean.
+                of the new track. Unobserved velocities are initialized to 0 mean.
         """
         mean_pos = measurement
         mean_vel = np.zeros_like(mean_pos)
@@ -235,8 +235,8 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
             measurement (ndarray): Bounding box coordinates (x, y, w, h) with center position (x, y), width, and height.
 
         Returns:
-            (tuple[ndarray, ndarray]): Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional) of
+            (tuple[ndarray, ndarray]): Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional)
-                the new track. Unobserved velocities are initialized to 0 mean.
+                of the new track. Unobserved velocities are initialized to 0 mean.
         """
         mean_pos = measurement
         mean_vel = np.zeros_like(mean_pos)

ultralytics/trackers/utils/matching.py

@@ -4,7 +4,7 @@ import numpy as np
 import scipy
 from scipy.spatial.distance import cdist
 
-from ultralytics.utils.metrics import bbox_ioa, batch_probiou
+from ultralytics.utils.metrics import batch_probiou, bbox_ioa
 
 try:
     import lap  # for linear_assignment

ultralytics/utils/__init__.py

@@ -495,9 +495,6 @@ def is_online() -> bool:
     return False
 
 
-ONLINE = is_online()
-
-
 def is_pip_package(filepath: str = __name__) -> bool:
     """
     Determines if the file at the given filepath is part of a pip package.
@@ -550,17 +547,6 @@ def is_github_action_running() -> bool:
     return "GITHUB_ACTIONS" in os.environ and "GITHUB_WORKFLOW" in os.environ and "RUNNER_OS" in os.environ
 
 
-def is_git_dir():
-    """
-    Determines whether the current file is part of a git repository. If the current file is not part of a git
-    repository, returns None.
-
-    Returns:
-        (bool): True if current file is part of a git repository.
-    """
-    return get_git_dir() is not None
-
-
 def get_git_dir():
     """
     Determines whether the current file is part of a git repository and if so, returns the repository root directory. If
@@ -574,6 +560,17 @@ def get_git_dir():
             return d
 
 
+def is_git_dir():
+    """
+    Determines whether the current file is part of a git repository. If the current file is not part of a git
+    repository, returns None.
+
+    Returns:
+        (bool): True if current file is part of a git repository.
+    """
+    return GIT_DIR is not None
+
+
 def get_git_origin_url():
     """
     Retrieves the origin URL of a git repository.
@@ -581,7 +578,7 @@ def get_git_origin_url():
     Returns:
         (str | None): The origin URL of the git repository or None if not git directory.
     """
-    if is_git_dir():
+    if IS_GIT_DIR:
         with contextlib.suppress(subprocess.CalledProcessError):
             origin = subprocess.check_output(["git", "config", "--get", "remote.origin.url"])
             return origin.decode().strip()
@@ -594,7 +591,7 @@ def get_git_branch():
     Returns:
         (str | None): The current git branch name or None if not a git directory.
     """
-    if is_git_dir():
+    if IS_GIT_DIR:
         with contextlib.suppress(subprocess.CalledProcessError):
             origin = subprocess.check_output(["git", "rev-parse", "--abbrev-ref", "HEAD"])
             return origin.decode().strip()
@@ -660,6 +657,16 @@ def get_user_config_dir(sub_dir="Ultralytics"):
     return path
 
 
+# Define constants (required below)
+ONLINE = is_online()
+IS_COLAB = is_colab()
+IS_DOCKER = is_docker()
+IS_JUPYTER = is_jupyter()
+IS_KAGGLE = is_kaggle()
+IS_PIP_PACKAGE = is_pip_package()
+IS_RASPBERRYPI = is_raspberrypi()
+GIT_DIR = get_git_dir()
+IS_GIT_DIR = is_git_dir()
 USER_CONFIG_DIR = Path(os.getenv("YOLO_CONFIG_DIR") or get_user_config_dir())  # Ultralytics settings dir
 SETTINGS_YAML = USER_CONFIG_DIR / "settings.yaml"
 
@@ -886,7 +893,7 @@ def set_sentry():
         event["tags"] = {
             "sys_argv": ARGV[0],
             "sys_argv_name": Path(ARGV[0]).name,
-            "install": "git" if is_git_dir() else "pip" if is_pip_package() else "other",
+            "install": "git" if IS_GIT_DIR else "pip" if IS_PIP_PACKAGE else "other",
             "os": ENVIRONMENT,
         }
         return event
@@ -897,8 +904,8 @@ def set_sentry():
         and Path(ARGV[0]).name == "yolo"
         and not TESTS_RUNNING
         and ONLINE
-        and is_pip_package()
+        and IS_PIP_PACKAGE
-        and not is_git_dir()
+        and not IS_GIT_DIR
     ):
         # If sentry_sdk package is not installed then return and do not use Sentry
         try:
@@ -937,9 +944,8 @@ class SettingsManager(dict):
         from ultralytics.utils.checks import check_version
         from ultralytics.utils.torch_utils import torch_distributed_zero_first
 
-        git_dir = get_git_dir()
+        root = GIT_DIR or Path()
-        root = git_dir or Path()
+        datasets_root = (root.parent if GIT_DIR and is_dir_writeable(root.parent) else root).resolve()
-        datasets_root = (root.parent if git_dir and is_dir_writeable(root.parent) else root).resolve()
 
         self.file = Path(file)
         self.version = version
@@ -1043,13 +1049,13 @@ WEIGHTS_DIR = Path(SETTINGS["weights_dir"])  # global weights directory
 RUNS_DIR = Path(SETTINGS["runs_dir"])  # global runs directory
 ENVIRONMENT = (
     "Colab"
-    if is_colab()
+    if IS_COLAB
     else "Kaggle"
-    if is_kaggle()
+    if IS_KAGGLE
     else "Jupyter"
-    if is_jupyter()
+    if IS_JUPYTER
     else "Docker"
-    if is_docker()
+    if IS_DOCKER
     else platform.system()
 )
 TESTS_RUNNING = is_pytest_running() or is_github_action_running()

ultralytics/utils/callbacks/hub.py

@@ -12,10 +12,7 @@ def on_pretrain_routine_end(trainer):
     session = getattr(trainer, "hub_session", None)
     if session:
         # Start timer for upload rate limit
-        session.timers = {
+        session.timers = {"metrics": time(), "ckpt": time()}  # start timer on session.rate_limit
-            "metrics": time(),
-            "ckpt": time(),
-        }  # start timer on session.rate_limit
 
 
 def on_fit_epoch_end(trainer):

ultralytics/utils/callbacks/mlflow.py

@@ -58,7 +58,7 @@ def on_pretrain_routine_end(trainer):
         MLFLOW_TRACKING_URI: The URI for MLflow tracking. If not set, defaults to 'runs/mlflow'.
         MLFLOW_EXPERIMENT_NAME: The name of the MLflow experiment. If not set, defaults to trainer.args.project.
         MLFLOW_RUN: The name of the MLflow run. If not set, defaults to trainer.args.name.
-        MLFLOW_KEEP_RUN_ACTIVE: Boolean indicating whether to keep the MLflow run active after the end of the training phase.
+        MLFLOW_KEEP_RUN_ACTIVE: Boolean indicating whether to keep the MLflow run active after the end of training.
     """
     global mlflow
 

ultralytics/utils/callbacks/tensorboard.py

@@ -15,6 +15,7 @@ try:
     # Imports below only required if TensorBoard enabled
     import warnings
     from copy import deepcopy
+
     from ultralytics.utils.torch_utils import de_parallel, torch
 
 except (ImportError, AssertionError, TypeError, AttributeError):

ultralytics/utils/checks.py

@@ -22,10 +22,15 @@ import torch
 from ultralytics.utils import (
     ASSETS,
     AUTOINSTALL,
+    IS_COLAB,
+    IS_DOCKER,
+    IS_JUPYTER,
+    IS_KAGGLE,
+    IS_PIP_PACKAGE,
     LINUX,
     LOGGER,
-    PYTHON_VERSION,
     ONLINE,
+    PYTHON_VERSION,
     ROOT,
     TORCHVISION_VERSION,
     USER_CONFIG_DIR,
@@ -37,12 +42,7 @@ from ultralytics.utils import (
     colorstr,
     downloads,
     emojis,
-    is_colab,
-    is_docker,
     is_github_action_running,
-    is_jupyter,
-    is_kaggle,
-    is_pip_package,
     url2file,
 )
 
@@ -277,7 +277,7 @@ def check_pip_update_available():
     Returns:
         (bool): True if an update is available, False otherwise.
     """
-    if ONLINE and is_pip_package():
+    if ONLINE and IS_PIP_PACKAGE:
         with contextlib.suppress(Exception):
             from ultralytics import __version__
 
@@ -528,7 +528,7 @@ def check_imshow(warn=False):
     """Check if environment supports image displays."""
     try:
         if LINUX:
-            assert "DISPLAY" in os.environ and not is_docker() and not is_colab() and not is_kaggle()
+            assert "DISPLAY" in os.environ and not IS_DOCKER and not IS_COLAB and not IS_KAGGLE
         cv2.imshow("test", np.zeros((8, 8, 3), dtype=np.uint8))  # show a small 8-pixel image
         cv2.waitKey(1)
         cv2.destroyAllWindows()
@@ -546,10 +546,10 @@ def check_yolo(verbose=True, device=""):
 
     from ultralytics.utils.torch_utils import select_device
 
-    if is_jupyter():
+    if IS_JUPYTER:
         if check_requirements("wandb", install=False):
             os.system("pip uninstall -y wandb")  # uninstall wandb: unwanted account creation prompt with infinite hang
-        if is_colab():
+        if IS_COLAB:
             shutil.rmtree("sample_data", ignore_errors=True)  # remove colab /sample_data directory
 
     if verbose:
@@ -574,7 +574,7 @@ def collect_system_info():
 
     import psutil
 
-    from ultralytics.utils import ENVIRONMENT, is_git_dir
+    from ultralytics.utils import ENVIRONMENT, IS_GIT_DIR
     from ultralytics.utils.torch_utils import get_cpu_info
 
     ram_info = psutil.virtual_memory().total / (1024**3)  # Convert bytes to GB
@@ -583,7 +583,7 @@ def collect_system_info():
         f"\n{'OS':<20}{platform.platform()}\n"
         f"{'Environment':<20}{ENVIRONMENT}\n"
         f"{'Python':<20}{PYTHON_VERSION}\n"
-        f"{'Install':<20}{'git' if is_git_dir() else 'pip' if is_pip_package() else 'other'}\n"
+        f"{'Install':<20}{'git' if IS_GIT_DIR else 'pip' if IS_PIP_PACKAGE else 'other'}\n"
         f"{'RAM':<20}{ram_info:.2f} GB\n"
         f"{'CPU':<20}{get_cpu_info()}\n"
         f"{'CUDA':<20}{torch.version.cuda if torch and torch.cuda.is_available() else None}\n"

ultralytics/utils/torch_utils.py

@@ -21,8 +21,8 @@ from ultralytics.utils import (
     LOGGER,
     PYTHON_VERSION,
     TORCHVISION_VERSION,
-    colorstr,
     __version__,
+    colorstr,
 )
 from ultralytics.utils.checks import check_version