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