回传数据解析，兼容v5和v10

ultralytics/utils/callbacks/wb.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from ultralytics.utils import SETTINGS, TESTS_RUNNING
+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
+    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):
+    wb = None
+
+
+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.
+
+    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.
+        y (List): Corresponding values for the y-axis; also expected to have length N.
+        classes (List): Labels identifying the class of each point; length N.
+        title (str, optional): Title for the plot; defaults to 'Precision Recall Curve'.
+        x_title (str, optional): Label for the x-axis; defaults to 'Recall'.
+        y_title (str, optional): Label for the y-axis; defaults to 'Precision'.
+
+    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
+    )
+
+
+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.
+
+    This function generates a metric curve based on input data and logs the visualization to wandb.
+    The curve can represent aggregated data (mean) or individual class data, depending on the 'only_mean' flag.
+
+    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 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'.
+        y_title (str, optional): Label for the y-axis. Defaults to 'Precision'.
+        num_x (int, optional): Number of interpolated data points for visualization. Defaults to 100.
+        only_mean (bool, optional): Flag to indicate if only the mean curve should be plotted. Defaults to True.
+
+    Note:
+        The function leverages the '_custom_table' function to generate the actual visualization.
+    """
+    # Create new x
+    if names is None:
+        names = []
+    x_new = np.linspace(x[0], x[-1], num_x).round(5)
+
+    # Create arrays for logging
+    x_log = x_new.tolist()
+    y_log = np.interp(x_new, x, np.mean(y, axis=0)).round(3).tolist()
+
+    if only_mean:
+        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)
+        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
+            classes.extend([names[i]] * len(x_new))  # add class names
+        wb.log({id: _custom_table(x_log, y_log, classes, title, x_title, y_title)}, commit=False)
+
+
+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"]
+        if _processed_plots.get(name) != timestamp:
+            wb.run.log({name.stem: wb.Image(str(name))}, step=step)
+            _processed_plots[name] = timestamp
+
+
+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))
+
+
+def on_fit_epoch_end(trainer):
+    """Logs training metrics and model information at the end of an epoch."""
+    wb.run.log(trainer.metrics, step=trainer.epoch + 1)
+    _log_plots(trainer.plots, step=trainer.epoch + 1)
+    _log_plots(trainer.validator.plots, step=trainer.epoch + 1)
+    if trainer.epoch == 0:
+        wb.run.log(model_info_for_loggers(trainer), step=trainer.epoch + 1)
+
+
+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.lr, step=trainer.epoch + 1)
+    if trainer.epoch == 1:
+        _log_plots(trainer.plots, step=trainer.epoch + 1)
+
+
+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")
+    if trainer.best.exists():
+        art.add_file(trainer.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}",
+            title=curve_name,
+            x_title=x_title,
+            y_title=y_title,
+        )
+    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 {}
+)