ultralytics 8.1.15 add Python 3.12 compatibility (#8210)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Johnny <johnnynuca14@gmail.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/utils/benchmarks.py

@@ -36,7 +36,7 @@ from ultralytics import YOLO
 from ultralytics.cfg import TASK2DATA, TASK2METRIC
 from ultralytics.engine.exporter import export_formats
 from ultralytics.utils import ASSETS, LINUX, LOGGER, MACOS, TQDM, WEIGHTS_DIR
-from ultralytics.utils.checks import check_requirements, check_yolo
+from ultralytics.utils.checks import IS_PYTHON_3_12, check_requirements, check_yolo
 from ultralytics.utils.files import file_size
 from ultralytics.utils.torch_utils import select_device
 
@@ -90,6 +90,8 @@ def benchmark(
                 assert model.task != "obb", "TensorFlow GraphDef not supported for OBB task"
             elif i in {5, 10}:  # CoreML and TF.js
                 assert MACOS or LINUX, "export only supported on macOS and Linux"
+            if i in {3, 5}:  # CoreML and OpenVINO
+                assert not IS_PYTHON_3_12, "CoreML and OpenVINO not supported on Python 3.12"
             if "cpu" in device.type:
                 assert cpu, "inference not supported on CPU"
             if "cuda" in device.type:
@@ -147,8 +149,7 @@ class ProfileModels:
     """
     ProfileModels class for profiling different models on ONNX and TensorRT.
 
-    This class profiles the performance of different models, provided their paths. The profiling includes parameters such as
-    model speed and FLOPs.
+    This class profiles the performance of different models, returning results such as model speed and FLOPs.
 
     Attributes:
         paths (list): Paths of the models to profile.
@@ -188,9 +189,9 @@ class ProfileModels:
             num_warmup_runs (int, optional): Number of warmup runs before the actual profiling starts. Default is 10.
             min_time (float, optional): Minimum time in seconds for profiling a model. Default is 60.
             imgsz (int, optional): Size of the image used during profiling. Default is 640.
-            half (bool, optional): Flag to indicate whether to use half-precision floating point for profiling. Default is True.
+            half (bool, optional): Flag to indicate whether to use half-precision floating point for profiling.
             trt (bool, optional): Flag to indicate whether to profile using TensorRT. Default is True.
-            device (torch.device, optional): Device used for profiling. If None, it is determined automatically. Default is None.
+            device (torch.device, optional): Device used for profiling. If None, it is determined automatically.
         """
         self.paths = paths
         self.num_timed_runs = num_timed_runs

ultralytics/utils/callbacks/wb.py

@@ -23,8 +23,9 @@ def _custom_table(x, y, classes, title="Precision Recall Curve", x_title="Recall
     """
     Create and log a custom metric visualization to wandb.plot.pr_curve.
 
-    This function crafts a custom metric visualization that mimics the behavior of wandb's default precision-recall curve
-    while allowing for enhanced customization. The visual metric is useful for monitoring model performance across different classes.
+    This function crafts a custom metric visualization that mimics the behavior of wandb's default precision-recall
+    curve while allowing for enhanced customization. The visual metric is useful for monitoring model performance across
+    different classes.
 
     Args:
         x (List): Values for the x-axis; expected to have length N.
@@ -64,8 +65,8 @@ def _plot_curve(
 
     Args:
         x (np.ndarray): Data points for the x-axis with length N.
-        y (np.ndarray): Corresponding data points for the y-axis with shape CxN, where C represents the number of classes.
-        names (list, optional): Names of the classes corresponding to the y-axis data; length C. Defaults to an empty list.
+        y (np.ndarray): Corresponding data points for the y-axis with shape CxN, where C is the number of classes.
+        names (list, optional): Names of the classes corresponding to the y-axis data; length C. Defaults to [].
         id (str, optional): Unique identifier for the logged data in wandb. Defaults to 'precision-recall'.
         title (str, optional): Title for the visualization plot. Defaults to 'Precision Recall Curve'.
         x_title (str, optional): Label for the x-axis. Defaults to 'Recall'.

ultralytics/utils/checks.py

@@ -9,7 +9,6 @@ import platform
 import re
 import shutil
 import subprocess
-import sys
 import time
 from importlib import metadata
 from pathlib import Path
@@ -46,6 +45,8 @@ from ultralytics.utils import (
     url2file,
 )
 
+PYTHON_VERSION = platform.python_version()
+
 
 def parse_requirements(file_path=ROOT.parent / "requirements.txt", package=""):
     """
@@ -329,7 +330,7 @@ def check_python(minimum: str = "3.8.0") -> bool:
     Returns:
         (bool): Whether the installed Python version meets the minimum constraints.
     """
-    return check_version(platform.python_version(), minimum, name="Python ", hard=True)
+    return check_version(PYTHON_VERSION, minimum, name="Python ", hard=True)
 
 
 @TryExcept()
@@ -580,7 +581,7 @@ def collect_system_info():
     LOGGER.info(
         f"\n{'OS':<20}{platform.platform()}\n"
         f"{'Environment':<20}{ENVIRONMENT}\n"
-        f"{'Python':<20}{sys.version.split()[0]}\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"{'RAM':<20}{ram_info:.2f} GB\n"
         f"{'CPU':<20}{get_cpu_info()}\n"
@@ -722,3 +723,7 @@ def cuda_is_available() -> bool:
         (bool): True if one or more NVIDIA GPUs are available, False otherwise.
     """
     return cuda_device_count() > 0
+
+
+# Define constants
+IS_PYTHON_3_12 = check_version(PYTHON_VERSION, "==3.12", name="Python ", hard=False)

ultralytics/utils/ops.py

@@ -546,7 +546,7 @@ def xywhr2xyxyxyxy(rboxes):
     be in degrees from 0 to 90.
 
     Args:
-        rboxes (numpy.ndarray | torch.Tensor): Input data in [cx, cy, w, h, rotation] format of shape (n, 5) or (b, n, 5).
+        rboxes (numpy.ndarray | torch.Tensor): Boxes in [cx, cy, w, h, rotation] format of shape (n, 5) or (b, n, 5).
 
     Returns:
         (numpy.ndarray | torch.Tensor): Converted corner points of shape (n, 4, 2) or (b, n, 4, 2).

ultralytics/utils/torch_utils.py

@@ -2,7 +2,6 @@
 
 import math
 import os
-import platform
 import random
 import time
 from contextlib import contextmanager
@@ -18,7 +17,7 @@ import torch.nn.functional as F
 import torchvision
 
 from ultralytics.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, __version__
-from ultralytics.utils.checks import check_version
+from ultralytics.utils.checks import PYTHON_VERSION, check_version
 
 try:
     import thop
@@ -103,7 +102,7 @@ def select_device(device="", batch=0, newline=False, verbose=True):
     if isinstance(device, torch.device):
         return device
 
-    s = f"Ultralytics YOLOv{__version__} 🚀 Python-{platform.python_version()} torch-{torch.__version__} "
+    s = f"Ultralytics YOLOv{__version__} 🚀 Python-{PYTHON_VERSION} torch-{torch.__version__} "
     device = str(device).lower()
     for remove in "cuda:", "none", "(", ")", "[", "]", "'", " ":
         device = device.replace(remove, "")  # to string, 'cuda:0' -> '0' and '(0, 1)' -> '0,1'