HongChuang/ultralytics-ascend
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Start export implementation (#110)

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Glenn Jocher 2022-12-29 14:17:14 +01:00 • committed by GitHub
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16
ultralytics/yolo/engine/trainer.py
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@ -23,16 +23,14 @@ from tqdm import tqdm
import ultralytics.yolo.utils as utils
import ultralytics.yolo.utils.callbacks as callbacks
from ultralytics import __version__
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.utils import LOGGER, RANK, ROOT, TQDM_BAR_FORMAT, colorstr
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, RANK, TQDM_BAR_FORMAT, colorstr
from ultralytics.yolo.utils.checks import check_file, print_args
from ultralytics.yolo.utils.configs import get_config
from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
from ultralytics.yolo.utils.files import get_latest_run, increment_path, save_yaml
from ultralytics.yolo.utils.files import get_latest_run, increment_path, yaml_save
from ultralytics.yolo.utils.torch_utils import ModelEMA, de_parallel, init_seeds, one_cycle, strip_optimizer
DEFAULT_CONFIG = ROOT / "yolo/utils/configs/default.yaml"
class BaseTrainer:
@ -53,8 +51,7 @@ class BaseTrainer:
self.wdir = self.save_dir / 'weights' # weights dir
if RANK in {-1, 0}:
self.wdir.mkdir(parents=True, exist_ok=True) # make dir
# Save run settings
save_yaml(self.save_dir / 'args.yaml', OmegaConf.to_container(self.args, resolve=True))
yaml_save(self.save_dir / 'args.yaml', OmegaConf.to_container(self.args, resolve=True)) # save run args
self.last, self.best = self.wdir / 'last.pt', self.wdir / 'best.pt' # checkpoint paths
self.batch_size = self.args.batch_size
@ -452,8 +449,9 @@ class BaseTrainer:
self.ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) # EMA
self.ema.updates = ckpt['updates']
if self.args.resume:
assert start_epoch > 0, 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}'"
assert start_epoch > 0, \
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')
if self.epochs < start_epoch: