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ultralytics 8.0.12 - Hydra removal (#506)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Pronoy Mandal <lukex9442@gmail.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
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Glenn Jocher 2023-01-21 00:45:19 +01:00 • committed by GitHub
parent 6eec39162a
commit c5fccc3fc4
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37 changed files with 395 additions and 469 deletions
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28
ultralytics/yolo/engine/trainer.py
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@ -15,8 +15,6 @@ import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
from omegaconf import OmegaConf # noqa
from omegaconf import open_dict
from torch.cuda import amp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim import lr_scheduler
@ -27,7 +25,7 @@ from ultralytics import __version__
from ultralytics.nn.tasks import attempt_load_one_weight
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.utils import (DEFAULT_CONFIG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr,
from ultralytics.yolo.utils import (DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr,
yaml_save)
from ultralytics.yolo.utils.autobatch import check_train_batch_size
from ultralytics.yolo.utils.checks import check_file, check_imgsz, print_args
@ -43,7 +41,7 @@ class BaseTrainer:
A base class for creating trainers.
Attributes:
args (OmegaConf): Configuration for the trainer.
args (SimpleNamespace): Configuration for the trainer.
check_resume (method): Method to check if training should be resumed from a saved checkpoint.
console (logging.Logger): Logger instance.
validator (BaseValidator): Validator instance.
@ -73,7 +71,7 @@ class BaseTrainer:
csv (Path): Path to results CSV file.
"""
def __init__(self, config=DEFAULT_CONFIG, overrides=None):
def __init__(self, config=DEFAULT_CFG_PATH, overrides=None):
"""
Initializes the BaseTrainer class.
@ -81,8 +79,6 @@ class BaseTrainer:
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
if overrides is None:
overrides = {}
self.args = get_config(config, overrides)
self.device = utils.torch_utils.select_device(self.args.device, self.args.batch)
self.check_resume()
@ -95,23 +91,23 @@ class BaseTrainer:
# Dirs
project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
name = self.args.name or f"{self.args.mode}"
self.save_dir = Path(
self.args.get(
"save_dir",
increment_path(Path(project) / name, exist_ok=self.args.exist_ok if RANK in {-1, 0} else True)))
if hasattr(self.args, 'save_dir'):
self.save_dir = Path(self.args.save_dir)
else:
self.save_dir = Path(
increment_path(Path(project) / name, exist_ok=self.args.exist_ok if RANK in {-1, 0} else True))
self.wdir = self.save_dir / 'weights' # weights dir
if RANK in {-1, 0}:
self.wdir.mkdir(parents=True, exist_ok=True) # make dir
with open_dict(self.args):
self.args.save_dir = str(self.save_dir)
yaml_save(self.save_dir / 'args.yaml', OmegaConf.to_container(self.args, resolve=True)) # save run args
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.batch_size = self.args.batch
self.epochs = self.args.epochs
self.start_epoch = 0
if RANK == -1:
print_args(dict(self.args))
print_args(vars(self.args))
# Device
self.amp = self.device.type != 'cpu'
@ -373,7 +369,7 @@ class BaseTrainer:
'ema': deepcopy(self.ema.ema).half(),
'updates': self.ema.updates,
'optimizer': self.optimizer.state_dict(),
'train_args': self.args,
'train_args': vars(self.args), # save as dict
'date': datetime.now().isoformat(),
'version': __version__}