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ultralytics 8.0.34 security updates and fixes (#924)

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Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Fabian Greavu <fabiangreavu@gmail.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-02-11 01:26:44 +04:00 • committed by GitHub
parent 254adfa652
commit d32b339373
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31 changed files with 134 additions and 120 deletions
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9
ultralytics/yolo/engine/trainer.py
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@ -20,12 +20,11 @@ from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim import lr_scheduler
from tqdm import tqdm
from ultralytics import __version__
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,
yaml_save)
from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, __version__, callbacks,
colorstr, emojis, yaml_save)
from ultralytics.yolo.utils.autobatch import check_train_batch_size
from ultralytics.yolo.utils.checks import check_file, check_imgsz, print_args
from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
@ -51,6 +50,7 @@ class BaseTrainer:
wdir (Path): Directory to save weights.
last (Path): Path to last checkpoint.
best (Path): Path to best checkpoint.
save_period (int): Save checkpoint every x epochs (disabled if < 1).
batch_size (int): Batch size for training.
epochs (int): Number of epochs to train for.
start_epoch (int): Starting epoch for training.
@ -101,6 +101,7 @@ class BaseTrainer:
self.args.save_dir = str(self.save_dir)
yaml_save(self.save_dir / 'args.yaml', vars(self.args)) # save run args
self.last, self.best = self.wdir / 'last.pt', self.wdir / 'best.pt' # checkpoint paths
self.save_period = self.args.save_period
self.batch_size = self.args.batch
self.epochs = self.args.epochs
@ -392,6 +393,8 @@ class BaseTrainer:
torch.save(ckpt, self.last)
if self.best_fitness == self.fitness:
torch.save(ckpt, self.best)
if (self.epoch > 0) and (self.save_period > 0) and (self.epoch % self.save_period == 0):
torch.save(ckpt, self.wdir / f'epoch{self.epoch}.pt')
del ckpt
def get_dataset(self, data):