HongChuang/ultralytics-ascend
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Refactor Python code (#13448)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-06-09 02:32:17 +02:00 • committed by GitHub
parent 6a234f3639
commit 1b26838def
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22 changed files with 81 additions and 101 deletions
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30
ultralytics/utils/callbacks/mlflow.py
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@ -103,22 +103,22 @@ def on_fit_epoch_end(trainer):
def on_train_end(trainer):
"""Log model artifacts at the end of the training."""
if mlflow:
mlflow.log_artifact(str(trainer.best.parent)) # log save_dir/weights directory with best.pt and last.pt
for f in trainer.save_dir.glob("*"): # log all other files in save_dir
if f.suffix in {".png", ".jpg", ".csv", ".pt", ".yaml"}:
mlflow.log_artifact(str(f))
keep_run_active = os.environ.get("MLFLOW_KEEP_RUN_ACTIVE", "False").lower() == "true"
if keep_run_active:
LOGGER.info(f"{PREFIX}mlflow run still alive, remember to close it using mlflow.end_run()")
else:
mlflow.end_run()
LOGGER.debug(f"{PREFIX}mlflow run ended")
if not mlflow:
return
mlflow.log_artifact(str(trainer.best.parent)) # log save_dir/weights directory with best.pt and last.pt
for f in trainer.save_dir.glob("*"): # log all other files in save_dir
if f.suffix in {".png", ".jpg", ".csv", ".pt", ".yaml"}:
mlflow.log_artifact(str(f))
keep_run_active = os.environ.get("MLFLOW_KEEP_RUN_ACTIVE", "False").lower() == "true"
if keep_run_active:
LOGGER.info(f"{PREFIX}mlflow run still alive, remember to close it using mlflow.end_run()")
else:
mlflow.end_run()
LOGGER.debug(f"{PREFIX}mlflow run ended")
LOGGER.info(
f"{PREFIX}results logged to {mlflow.get_tracking_uri()}\n"
f"{PREFIX}disable with 'yolo settings mlflow=False'"
)
LOGGER.info(
f"{PREFIX}results logged to {mlflow.get_tracking_uri()}\n{PREFIX}disable with 'yolo settings mlflow=False'"
)
callbacks = (

2
ultralytics/utils/callbacks/wb.py
View file

@ -19,7 +19,7 @@ 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
This function crafts a custom metric visualization that mimics the behavior of the default wandb precision-recall
curve while allowing for enhanced customization. The visual metric is useful for monitoring model performance across
different classes.