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add yolo v10 and modify pipeline

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王庆刚
2025-03-28 13:19:54 +08:00
parent 183299c06b
commit 798c596acc
471 changed files with 19109 additions and 7342 deletions
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71
ultralytics/utils/callbacks/hub.py
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@ -9,51 +9,67 @@ 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)
session = getattr(trainer, "hub_session", None)
if session:
# Start timer for upload rate limit
LOGGER.info(f'{PREFIX}View model at {HUB_WEB_ROOT}/models/{session.model_id} 🚀')
session.timers = {'metrics': time(), 'ckpt': time()} # start timer on session.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)
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}
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 time() - session.timers['metrics'] > session.rate_limits['metrics']:
# 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.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)
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}')
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
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)
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)
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 {HUB_WEB_ROOT}/models/{session.model_id} 🚀')
LOGGER.info(f"{PREFIX}Done ✅\n" f"{PREFIX}View model at {session.model_url} 🚀")
def on_train_start(trainer):
@ -76,12 +92,17 @@ def on_export_start(exporter):
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
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