ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>

ultralytics/utils/callbacks/wb.py

@@ -5,10 +5,10 @@ from ultralytics.utils.torch_utils import model_info_for_loggers
 
 try:
     assert not TESTS_RUNNING  # do not log pytest
-    assert SETTINGS['wandb'] is True  # verify integration is enabled
+    assert SETTINGS["wandb"] is True  # verify integration is enabled
     import wandb as wb
 
-    assert hasattr(wb, '__version__')  # verify package is not directory
+    assert hasattr(wb, "__version__")  # verify package is not directory
 
     import numpy as np
     import pandas as pd
@@ -19,7 +19,7 @@ except (ImportError, AssertionError):
     wb = None
 
 
-def _custom_table(x, y, classes, title='Precision Recall Curve', x_title='Recall', y_title='Precision'):
+def _custom_table(x, y, classes, title="Precision Recall Curve", x_title="Recall", y_title="Precision"):
     """
     Create and log a custom metric visualization to wandb.plot.pr_curve.
 
@@ -37,24 +37,25 @@ 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)
-    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('wandb/area-under-curve/v0',
-                         wb.Table(dataframe=df),
-                         fields=fields,
-                         string_fields=string_fields)
+    df = pd.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(
+        "wandb/area-under-curve/v0", wb.Table(dataframe=df), fields=fields, string_fields=string_fields
+    )
 
 
-def _plot_curve(x,
-                y,
-                names=None,
-                id='precision-recall',
-                title='Precision Recall Curve',
-                x_title='Recall',
-                y_title='Precision',
-                num_x=100,
-                only_mean=False):
+def _plot_curve(
+    x,
+    y,
+    names=None,
+    id="precision-recall",
+    title="Precision Recall Curve",
+    x_title="Recall",
+    y_title="Precision",
+    num_x=100,
+    only_mean=False,
+):
     """
     Log a metric curve visualization.
 
@@ -88,7 +89,7 @@ def _plot_curve(x,
         table = wb.Table(data=list(zip(x_log, y_log)), columns=[x_title, y_title])
         wb.run.log({title: wb.plot.line(table, x_title, y_title, title=title)})
     else:
-        classes = ['mean'] * len(x_log)
+        classes = ["mean"] * len(x_log)
         for i, yi in enumerate(y):
             x_log.extend(x_new)  # add new x
             y_log.extend(np.interp(x_new, x, yi))  # interpolate y to new x
@@ -99,7 +100,7 @@ def _plot_curve(x,
 def _log_plots(plots, step):
     """Logs plots from the input dictionary if they haven't been logged already at the specified step."""
     for name, params in plots.items():
-        timestamp = params['timestamp']
+        timestamp = params["timestamp"]
         if _processed_plots.get(name) != timestamp:
             wb.run.log({name.stem: wb.Image(str(name))}, step=step)
             _processed_plots[name] = timestamp
@@ -107,7 +108,7 @@ def _log_plots(plots, step):
 
 def on_pretrain_routine_start(trainer):
     """Initiate and start project if module is present."""
-    wb.run or wb.init(project=trainer.args.project or 'YOLOv8', name=trainer.args.name, config=vars(trainer.args))
+    wb.run or wb.init(project=trainer.args.project or "YOLOv8", name=trainer.args.name, config=vars(trainer.args))
 
 
 def on_fit_epoch_end(trainer):
@@ -121,7 +122,7 @@ def on_fit_epoch_end(trainer):
 
 def on_train_epoch_end(trainer):
     """Log metrics and save images at the end of each training epoch."""
-    wb.run.log(trainer.label_loss_items(trainer.tloss, prefix='train'), step=trainer.epoch + 1)
+    wb.run.log(trainer.label_loss_items(trainer.tloss, prefix="train"), step=trainer.epoch + 1)
     wb.run.log(trainer.lr, step=trainer.epoch + 1)
     if trainer.epoch == 1:
         _log_plots(trainer.plots, step=trainer.epoch + 1)
@@ -131,17 +132,17 @@ def on_train_end(trainer):
     """Save the best model as an artifact at end of training."""
     _log_plots(trainer.validator.plots, step=trainer.epoch + 1)
     _log_plots(trainer.plots, step=trainer.epoch + 1)
-    art = wb.Artifact(type='model', name=f'run_{wb.run.id}_model')
+    art = wb.Artifact(type="model", name=f"run_{wb.run.id}_model")
     if trainer.best.exists():
         art.add_file(trainer.best)
-        wb.run.log_artifact(art, aliases=['best'])
+        wb.run.log_artifact(art, aliases=["best"])
     for curve_name, curve_values in zip(trainer.validator.metrics.curves, trainer.validator.metrics.curves_results):
         x, y, x_title, y_title = curve_values
         _plot_curve(
             x,
             y,
             names=list(trainer.validator.metrics.names.values()),
-            id=f'curves/{curve_name}',
+            id=f"curves/{curve_name}",
             title=curve_name,
             x_title=x_title,
             y_title=y_title,
@@ -149,8 +150,13 @@ def on_train_end(trainer):
     wb.run.finish()  # required or run continues on dashboard
 
 
-callbacks = {
-    'on_pretrain_routine_start': on_pretrain_routine_start,
-    'on_train_epoch_end': on_train_epoch_end,
-    'on_fit_epoch_end': on_fit_epoch_end,
-    'on_train_end': on_train_end} if wb else {}
+callbacks = (
+    {
+        "on_pretrain_routine_start": on_pretrain_routine_start,
+        "on_train_epoch_end": on_train_epoch_end,
+        "on_fit_epoch_end": on_fit_epoch_end,
+        "on_train_end": on_train_end,
+    }
+    if wb
+    else {}
+)