回传数据解析，兼容v5和v10

ultralytics/utils/callbacks/hub.py

@@ -0,0 +1,108 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import json
+from time import time
+
+from ultralytics.hub.utils import HUB_WEB_ROOT, PREFIX, events
+from ultralytics.utils import LOGGER, SETTINGS
+
+
+def on_pretrain_routine_end(trainer):
+    """Logs info before starting timer for upload rate limit."""
+    session = getattr(trainer, "hub_session", None)
+    if session:
+        # Start timer for upload rate limit
+        session.timers = {
+            "metrics": time(),
+            "ckpt": time(),
+        }  # start timer on session.rate_limit
+
+
+def on_fit_epoch_end(trainer):
+    """Uploads training progress metrics at the end of each epoch."""
+    session = getattr(trainer, "hub_session", None)
+    if session:
+        # Upload metrics after val end
+        all_plots = {
+            **trainer.label_loss_items(trainer.tloss, prefix="train"),
+            **trainer.metrics,
+        }
+        if trainer.epoch == 0:
+            from ultralytics.utils.torch_utils import model_info_for_loggers
+
+            all_plots = {**all_plots, **model_info_for_loggers(trainer)}
+
+        session.metrics_queue[trainer.epoch] = json.dumps(all_plots)
+
+        # If any metrics fail to upload, add them to the queue to attempt uploading again.
+        if session.metrics_upload_failed_queue:
+            session.metrics_queue.update(session.metrics_upload_failed_queue)
+
+        if time() - session.timers["metrics"] > session.rate_limits["metrics"]:
+            session.upload_metrics()
+            session.timers["metrics"] = time()  # reset timer
+            session.metrics_queue = {}  # reset queue
+
+
+def on_model_save(trainer):
+    """Saves checkpoints to Ultralytics HUB with rate limiting."""
+    session = getattr(trainer, "hub_session", None)
+    if session:
+        # Upload checkpoints with rate limiting
+        is_best = trainer.best_fitness == trainer.fitness
+        if time() - session.timers["ckpt"] > session.rate_limits["ckpt"]:
+            LOGGER.info(f"{PREFIX}Uploading checkpoint {HUB_WEB_ROOT}/models/{session.model.id}")
+            session.upload_model(trainer.epoch, trainer.last, is_best)
+            session.timers["ckpt"] = time()  # reset timer
+
+
+def on_train_end(trainer):
+    """Upload final model and metrics to Ultralytics HUB at the end of training."""
+    session = getattr(trainer, "hub_session", None)
+    if session:
+        # Upload final model and metrics with exponential standoff
+        LOGGER.info(f"{PREFIX}Syncing final model...")
+        session.upload_model(
+            trainer.epoch,
+            trainer.best,
+            map=trainer.metrics.get("metrics/mAP50-95(B)", 0),
+            final=True,
+        )
+        session.alive = False  # stop heartbeats
+        LOGGER.info(f"{PREFIX}Done ✅\n" f"{PREFIX}View model at {session.model_url} 🚀")
+
+
+def on_train_start(trainer):
+    """Run events on train start."""
+    events(trainer.args)
+
+
+def on_val_start(validator):
+    """Runs events on validation start."""
+    events(validator.args)
+
+
+def on_predict_start(predictor):
+    """Run events on predict start."""
+    events(predictor.args)
+
+
+def on_export_start(exporter):
+    """Run events on export start."""
+    events(exporter.args)
+
+
+callbacks = (
+    {
+        "on_pretrain_routine_end": on_pretrain_routine_end,
+        "on_fit_epoch_end": on_fit_epoch_end,
+        "on_model_save": on_model_save,
+        "on_train_end": on_train_end,
+        "on_train_start": on_train_start,
+        "on_val_start": on_val_start,
+        "on_predict_start": on_predict_start,
+        "on_export_start": on_export_start,
+    }
+    if SETTINGS["hub"] is True
+    else {}
+)  # verify enabled