ultralytics 8.1.43 40% faster ultralytics imports (#9547)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/utils/__init__.py

@@ -1,6 +1,7 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
 import contextlib
+import importlib.metadata
 import inspect
 import logging.config
 import os
@@ -42,6 +43,8 @@ TQDM_BAR_FORMAT = "{l_bar}{bar:10}{r_bar}" if VERBOSE else None  # tqdm bar form
 LOGGING_NAME = "ultralytics"
 MACOS, LINUX, WINDOWS = (platform.system() == x for x in ["Darwin", "Linux", "Windows"])  # environment booleans
 ARM64 = platform.machine() in {"arm64", "aarch64"}  # ARM64 booleans
+PYTHON_VERSION = platform.python_version()
+TORCHVISION_VERSION = importlib.metadata.version("torchvision")  # faster than importing torchvision
 HELP_MSG = """
     Usage examples for running YOLOv8:
 
@@ -476,7 +479,7 @@ def is_online() -> bool:
 
     for host in "1.1.1.1", "8.8.8.8", "223.5.5.5":  # Cloudflare, Google, AliDNS:
         try:
-            test_connection = socket.create_connection(address=(host, 53), timeout=2)
+            test_connection = socket.create_connection(address=(host, 80), timeout=2)
         except (socket.timeout, socket.gaierror, OSError):
             continue
         else:

ultralytics/utils/benchmarks.py

@@ -69,8 +69,7 @@ def benchmark(
         benchmark(model='yolov8n.pt', imgsz=640)
         ```
     """
-
-    import pandas as pd
+    import pandas as pd  # scope for faster 'import ultralytics'
 
     pd.options.display.max_columns = 10
     pd.options.display.width = 120

ultralytics/utils/callbacks/clearml.py

@@ -7,8 +7,6 @@ try:
     assert SETTINGS["clearml"] is True  # verify integration is enabled
     import clearml
     from clearml import Task
-    from clearml.binding.frameworks.pytorch_bind import PatchPyTorchModelIO
-    from clearml.binding.matplotlib_bind import PatchedMatplotlib
 
     assert hasattr(clearml, "__version__")  # verify package is not directory
 
@@ -61,8 +59,11 @@ def on_pretrain_routine_start(trainer):
     """Runs at start of pretraining routine; initializes and connects/ logs task to ClearML."""
     try:
         if task := Task.current_task():
-            # Make sure the automatic pytorch and matplotlib bindings are disabled!
+            # WARNING: make sure the automatic pytorch and matplotlib bindings are disabled!
             # We are logging these plots and model files manually in the integration
+            from clearml.binding.frameworks.pytorch_bind import PatchPyTorchModelIO
+            from clearml.binding.matplotlib_bind import PatchedMatplotlib
+
             PatchPyTorchModelIO.update_current_task(None)
             PatchedMatplotlib.update_current_task(None)
         else:

ultralytics/utils/callbacks/wb.py

@@ -9,10 +9,6 @@ try:
     import wandb as wb
 
     assert hasattr(wb, "__version__")  # verify package is not directory
-
-    import numpy as np
-    import pandas as pd
-
     _processed_plots = {}
 
 except (ImportError, AssertionError):
@@ -38,7 +34,9 @@ def _custom_table(x, y, classes, title="Precision Recall Curve", x_title="Recall
     Returns:
         (wandb.Object): A wandb object suitable for logging, showcasing the crafted metric visualization.
     """
-    df = pd.DataFrame({"class": classes, "y": y, "x": x}).round(3)
+    import pandas  # scope for faster 'import ultralytics'
+
+    df = pandas.DataFrame({"class": classes, "y": y, "x": x}).round(3)
     fields = {"x": "x", "y": "y", "class": "class"}
     string_fields = {"title": title, "x-axis-title": x_title, "y-axis-title": y_title}
     return wb.plot_table(
@@ -77,6 +75,8 @@ def _plot_curve(
     Note:
         The function leverages the '_custom_table' function to generate the actual visualization.
     """
+    import numpy as np
+
     # Create new x
     if names is None:
         names = []

ultralytics/utils/checks.py

@@ -18,15 +18,16 @@ import cv2
 import numpy as np
 import requests
 import torch
-from matplotlib import font_manager
 
 from ultralytics.utils import (
     ASSETS,
     AUTOINSTALL,
     LINUX,
     LOGGER,
+    PYTHON_VERSION,
     ONLINE,
     ROOT,
+    TORCHVISION_VERSION,
     USER_CONFIG_DIR,
     Retry,
     SimpleNamespace,
@@ -41,13 +42,10 @@ from ultralytics.utils import (
     is_github_action_running,
     is_jupyter,
     is_kaggle,
-    is_online,
     is_pip_package,
     url2file,
 )
 
-PYTHON_VERSION = platform.python_version()
-
 
 def parse_requirements(file_path=ROOT.parent / "requirements.txt", package=""):
     """
@@ -304,9 +302,10 @@ def check_font(font="Arial.ttf"):
     Returns:
         file (Path): Resolved font file path.
     """
-    name = Path(font).name
+    from matplotlib import font_manager
 
     # Check USER_CONFIG_DIR
+    name = Path(font).name
     file = USER_CONFIG_DIR / name
     if file.exists():
         return file
@@ -390,7 +389,7 @@ def check_requirements(requirements=ROOT.parent / "requirements.txt", exclude=()
             LOGGER.info(f"{prefix} Ultralytics requirement{'s' * (n > 1)} {pkgs} not found, attempting AutoUpdate...")
             try:
                 t = time.time()
-                assert is_online(), "AutoUpdate skipped (offline)"
+                assert ONLINE, "AutoUpdate skipped (offline)"
                 with Retry(times=2, delay=1):  # run up to 2 times with 1-second retry delay
                     LOGGER.info(subprocess.check_output(f"pip install --no-cache {s} {cmds}", shell=True).decode())
                 dt = time.time() - t
@@ -419,14 +418,12 @@ def check_torchvision():
     Torchvision versions.
     """
 
-    import torchvision
-
     # Compatibility table
     compatibility_table = {"2.0": ["0.15"], "1.13": ["0.14"], "1.12": ["0.13"]}
 
     # Extract only the major and minor versions
     v_torch = ".".join(torch.__version__.split("+")[0].split(".")[:2])
-    v_torchvision = ".".join(torchvision.__version__.split("+")[0].split(".")[:2])
+    v_torchvision = ".".join(TORCHVISION_VERSION.split("+")[0].split(".")[:2])
 
     if v_torch in compatibility_table:
         compatible_versions = compatibility_table[v_torch]

ultralytics/utils/metrics.py

@@ -395,19 +395,19 @@ class ConfusionMatrix:
             names (tuple): Names of classes, used as labels on the plot.
             on_plot (func): An optional callback to pass plots path and data when they are rendered.
         """
-        import seaborn as sn
+        import seaborn  # scope for faster 'import ultralytics'
 
         array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1)  # normalize columns
         array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)
 
         fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)
         nc, nn = self.nc, len(names)  # number of classes, names
-        sn.set_theme(font_scale=1.0 if nc < 50 else 0.8)  # for label size
+        seaborn.set_theme(font_scale=1.0 if nc < 50 else 0.8)  # for label size
         labels = (0 < nn < 99) and (nn == nc)  # apply names to ticklabels
         ticklabels = (list(names) + ["background"]) if labels else "auto"
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")  # suppress empty matrix RuntimeWarning: All-NaN slice encountered
-            sn.heatmap(
+            seaborn.heatmap(
                 array,
                 ax=ax,
                 annot=nc < 30,

ultralytics/utils/ops.py

@@ -9,7 +9,6 @@ import cv2
 import numpy as np
 import torch
 import torch.nn.functional as F
-import torchvision
 
 from ultralytics.utils import LOGGER
 from ultralytics.utils.metrics import batch_probiou
@@ -206,6 +205,7 @@ def non_max_suppression(
             shape (num_boxes, 6 + num_masks) containing the kept boxes, with columns
             (x1, y1, x2, y2, confidence, class, mask1, mask2, ...).
     """
+    import torchvision  # scope for faster 'import ultralytics'
 
     # Checks
     assert 0 <= conf_thres <= 1, f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0"

ultralytics/utils/plotting.py

@@ -671,8 +671,8 @@ class Annotator:
 @plt_settings()
 def plot_labels(boxes, cls, names=(), save_dir=Path(""), on_plot=None):
     """Plot training labels including class histograms and box statistics."""
-    import pandas as pd
-    import seaborn as sn
+    import pandas  # scope for faster 'import ultralytics'
+    import seaborn  # scope for faster 'import ultralytics'
 
     # Filter matplotlib>=3.7.2 warning and Seaborn use_inf and is_categorical FutureWarnings
     warnings.filterwarnings("ignore", category=UserWarning, message="The figure layout has changed to tight")
@@ -682,10 +682,10 @@ def plot_labels(boxes, cls, names=(), save_dir=Path(""), on_plot=None):
     LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
     nc = int(cls.max() + 1)  # number of classes
     boxes = boxes[:1000000]  # limit to 1M boxes
-    x = pd.DataFrame(boxes, columns=["x", "y", "width", "height"])
+    x = pandas.DataFrame(boxes, columns=["x", "y", "width", "height"])
 
     # Seaborn correlogram
-    sn.pairplot(x, corner=True, diag_kind="auto", kind="hist", diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
+    seaborn.pairplot(x, corner=True, diag_kind="auto", kind="hist", diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
     plt.savefig(save_dir / "labels_correlogram.jpg", dpi=200)
     plt.close()
 
@@ -700,8 +700,8 @@ def plot_labels(boxes, cls, names=(), save_dir=Path(""), on_plot=None):
         ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10)
     else:
         ax[0].set_xlabel("classes")
-    sn.histplot(x, x="x", y="y", ax=ax[2], bins=50, pmax=0.9)
-    sn.histplot(x, x="width", y="height", ax=ax[3], bins=50, pmax=0.9)
+    seaborn.histplot(x, x="x", y="y", ax=ax[2], bins=50, pmax=0.9)
+    seaborn.histplot(x, x="width", y="height", ax=ax[3], bins=50, pmax=0.9)
 
     # Rectangles
     boxes[:, 0:2] = 0.5  # center
@@ -933,7 +933,7 @@ def plot_results(file="path/to/results.csv", dir="", segment=False, pose=False,
         plot_results('path/to/results.csv', segment=True)
         ```
     """
-    import pandas as pd
+    import pandas as pd  # scope for faster 'import ultralytics'
     from scipy.ndimage import gaussian_filter1d
 
     save_dir = Path(file).parent if file else Path(dir)
@@ -1019,7 +1019,7 @@ def plot_tune_results(csv_file="tune_results.csv"):
         >>> plot_tune_results('path/to/tune_results.csv')
     """
 
-    import pandas as pd
+    import pandas as pd  # scope for faster 'import ultralytics'
     from scipy.ndimage import gaussian_filter1d
 
     # Scatter plots for each hyperparameter

ultralytics/utils/torch_utils.py

@@ -14,10 +14,17 @@ import torch
 import torch.distributed as dist
 import torch.nn as nn
 import torch.nn.functional as F
-import torchvision
 
-from ultralytics.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, __version__
-from ultralytics.utils.checks import PYTHON_VERSION, check_version
+from ultralytics.utils import (
+    DEFAULT_CFG_DICT,
+    DEFAULT_CFG_KEYS,
+    LOGGER,
+    PYTHON_VERSION,
+    TORCHVISION_VERSION,
+    colorstr,
+    __version__,
+)
+from ultralytics.utils.checks import check_version
 
 try:
     import thop
@@ -28,9 +35,9 @@ except ImportError:
 TORCH_1_9 = check_version(torch.__version__, "1.9.0")
 TORCH_1_13 = check_version(torch.__version__, "1.13.0")
 TORCH_2_0 = check_version(torch.__version__, "2.0.0")
-TORCHVISION_0_10 = check_version(torchvision.__version__, "0.10.0")
-TORCHVISION_0_11 = check_version(torchvision.__version__, "0.11.0")
-TORCHVISION_0_13 = check_version(torchvision.__version__, "0.13.0")
+TORCHVISION_0_10 = check_version(TORCHVISION_VERSION, "0.10.0")
+TORCHVISION_0_11 = check_version(TORCHVISION_VERSION, "0.11.0")
+TORCHVISION_0_13 = check_version(TORCHVISION_VERSION, "0.13.0")
 
 
 @contextmanager