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ultralytics 8.0.48 Edge TPU fix and Metrics updates (#1171)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: majid nasiri <majnasai@gmail.com>
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Glenn Jocher 2023-02-27 21:34:22 -08:00 • committed by GitHub
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commit 74e4c94806
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23 changed files with 426 additions and 245 deletions
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35
ultralytics/yolo/utils/callbacks/hub.py
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@ -4,24 +4,33 @@ import json
from time import time
from ultralytics.hub.utils import PREFIX, traces
from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING
from ultralytics.yolo.utils import LOGGER
from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
def on_pretrain_routine_end(trainer):
session = not TESTS_RUNNING and 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 https://hub.ultralytics.com/models/{session.model_id} 🚀')
session.t = {'metrics': time(), 'ckpt': time()} # start timer on self.rate_limit
session.timers = {'metrics': time(), 'ckpt': time()} # start timer on session.rate_limit
def on_fit_epoch_end(trainer):
session = getattr(trainer, 'hub_session', None)
if session:
session.metrics_queue[trainer.epoch] = json.dumps(trainer.metrics) # json string
if time() - session.t['metrics'] > session.rate_limits['metrics']:
# Upload metrics after val end
all_plots = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics}
if trainer.epoch == 0:
model_info = {
'model/parameters': get_num_params(trainer.model),
'model/GFLOPs': round(get_flops(trainer.model), 3),
'model/speed(ms)': round(trainer.validator.speed['inference'], 3)}
all_plots = {**all_plots, **model_info}
session.metrics_queue[trainer.epoch] = json.dumps(all_plots)
if time() - session.timers['metrics'] > session.rate_limits['metrics']:
session.upload_metrics()
session.t['metrics'] = time() # reset timer
session.timers['metrics'] = time() # reset timer
session.metrics_queue = {} # reset queue
@ -30,21 +39,21 @@ def on_model_save(trainer):
if session:
# Upload checkpoints with rate limiting
is_best = trainer.best_fitness == trainer.fitness
if time() - session.t['ckpt'] > session.rate_limits['ckpt']:
if time() - session.timers['ckpt'] > session.rate_limits['ckpt']:
LOGGER.info(f'{PREFIX}Uploading checkpoint {session.model_id}')
session.upload_model(trainer.epoch, trainer.last, is_best)
session.t['ckpt'] = time() # reset timer
session.timers['ckpt'] = time() # reset timer
def on_train_end(trainer):
session = getattr(trainer, 'hub_session', None)
if session:
# Upload final model and metrics with exponential standoff
LOGGER.info(f'{PREFIX}Training completed successfully ✅\n'
f'{PREFIX}Uploading final {session.model_id}')
session.upload_model(trainer.epoch, trainer.best, map=trainer.metrics['metrics/mAP50-95(B)'], final=True)
session.shutdown() # stop heartbeats
LOGGER.info(f'{PREFIX}View model at https://hub.ultralytics.com/models/{session.model_id} 🚀')
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 https://hub.ultralytics.com/models/{session.model_id} 🚀')
def on_train_start(trainer):