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ImageNet names, classify inference, resume fixes (#712)

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Glenn Jocher 2023-01-30 22:34:28 +01:00 • committed by GitHub
parent aecd17d455
commit 522f1937ed
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16 changed files with 1121 additions and 115 deletions
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21
ultralytics/yolo/engine/trainer.py
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@ -21,7 +21,7 @@ from torch.optim import lr_scheduler
from tqdm import tqdm
from ultralytics import __version__
from ultralytics.nn.tasks import attempt_load_one_weight
from ultralytics.nn.tasks import attempt_load_one_weight, attempt_load_weights
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr, emojis,
@ -515,14 +515,15 @@ class BaseTrainer:
def check_resume(self):
resume = self.args.resume
if resume:
last = Path(check_file(resume) if isinstance(resume, (str, Path)) else get_latest_run())
args_yaml = last.parent.parent / 'args.yaml' # train options yaml
assert args_yaml.is_file(), \
FileNotFoundError(f'Resume checkpoint {last} not found. '
'Please pass a valid checkpoint to resume from, i.e. yolo resume=path/to/last.pt')
args = get_cfg(args_yaml) # replace
args.model, resume = str(last), True # reinstate
self.args = args
try:
last = Path(
check_file(resume) if isinstance(resume, (str,
Path)) and Path(resume).exists() else get_latest_run())
self.args = get_cfg(attempt_load_weights(last).args)
self.args.model, resume = str(last), True # reinstate
except Exception as e:
raise FileNotFoundError("Resume checkpoint not found. Please pass a valid checkpoint to resume from, "
"i.e. 'yolo train resume model=path/to/last.pt'") from e
self.resume = resume
def resume_training(self, ckpt):
@ -541,7 +542,7 @@ class BaseTrainer:
f'{self.args.model} training to {self.epochs} epochs is finished, nothing to resume.\n' \
f"Start a new training without --resume, i.e. 'yolo task=... mode=train model={self.args.model}'"
LOGGER.info(
f'Resuming training from {self.args.model} from epoch {start_epoch} to {self.epochs} total epochs')
f'Resuming training from {self.args.model} from epoch {start_epoch + 1} to {self.epochs} total epochs')
if self.epochs < start_epoch:
LOGGER.info(
f"{self.model} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {self.epochs} more epochs.")