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ultralytics 8.0.18 new python callbacks and minor fixes (#580)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jeroen Rombouts <36196499+jarombouts@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Ayush Chaurasia 2023-01-23 23:01:29 +05:30 • committed by GitHub
parent e9ab157330
commit 936414c615
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24 changed files with 136 additions and 106 deletions
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17
ultralytics/yolo/engine/trainer.py
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@ -20,19 +20,18 @@ from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim import lr_scheduler
from tqdm import tqdm
import ultralytics.yolo.utils as utils
from ultralytics import __version__
from ultralytics.nn.tasks import attempt_load_one_weight
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
from ultralytics.yolo.utils import (DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr,
yaml_save)
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
from ultralytics.yolo.utils.files import get_latest_run, increment_path
from ultralytics.yolo.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, init_seeds, one_cycle,
strip_optimizer)
select_device, strip_optimizer)
class BaseTrainer:
@ -81,7 +80,7 @@ class BaseTrainer:
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
self.args = get_cfg(cfg, overrides)
self.device = utils.torch_utils.select_device(self.args.device, self.args.batch)
self.device = select_device(self.args.device, self.args.batch)
self.check_resume()
self.console = LOGGER
self.validator = None
@ -120,9 +119,11 @@ class BaseTrainer:
self.model = self.args.model
self.data = self.args.data
if self.data.endswith(".yaml"):
self.data = check_dataset_yaml(self.data)
self.data = check_det_dataset(self.data)
elif self.args.task == 'classify':
self.data = check_cls_dataset(self.data)
else:
self.data = check_dataset(self.data)
raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' not found ❌"))
self.trainset, self.testset = self.get_dataset(self.data)
self.ema = None
@ -140,7 +141,7 @@ class BaseTrainer:
self.plot_idx = [0, 1, 2]
# Callbacks
self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
self.callbacks = defaultdict(list, {k: v for k, v in callbacks.default_callbacks.items()}) # add callbacks
if RANK in {0, -1}:
callbacks.add_integration_callbacks(self)