ultralytics 8.1.43 40% faster ultralytics imports (#9547)

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

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 = []