更新 detacttracking

detecttracking/ultralytics/utils/callbacks/wb.py

@@ -0,0 +1,63 @@
+# 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__')
+
+    _processed_plots = {}
+
+except (ImportError, AssertionError):
+    wb = None
+
+
+def _log_plots(plots, 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'])
+
+
+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 {}