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ultralytics 8.0.14 Hydra removal fixes and cleanup (#542)

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Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Kamlesh Kumar <patelkamleshpatel364@gmail.com>
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Glenn Jocher 2023-01-21 21:22:40 +01:00 • committed by GitHub
parent cc3be0e223
commit d9a0fba251
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30 changed files with 339 additions and 301 deletions
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10
ultralytics/yolo/engine/model.py
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@ -4,7 +4,7 @@ from pathlib import Path
from ultralytics import yolo # noqa
from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, attempt_load_one_weight
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.engine.exporter import Exporter
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, yaml_load
from ultralytics.yolo.utils.checks import check_yaml
@ -136,7 +136,7 @@ class YOLO:
self.predictor = self.PredictorClass(overrides=overrides)
self.predictor.setup_model(model=self.model)
else: # only update args if predictor is already setup
self.predictor.args = get_config(self.predictor.args, overrides)
self.predictor.args = get_cfg(self.predictor.args, overrides)
return self.predictor(source=source, stream=stream, verbose=verbose)
@smart_inference_mode()
@ -151,7 +151,7 @@ class YOLO:
overrides = self.overrides.copy()
overrides.update(kwargs)
overrides["mode"] = "val"
args = get_config(config=DEFAULT_CFG_PATH, overrides=overrides)
args = get_cfg(cfg=DEFAULT_CFG_PATH, overrides=overrides)
args.data = data or args.data
args.task = self.task
@ -169,7 +169,7 @@ class YOLO:
overrides = self.overrides.copy()
overrides.update(kwargs)
args = get_config(config=DEFAULT_CFG_PATH, overrides=overrides)
args = get_cfg(cfg=DEFAULT_CFG_PATH, overrides=overrides)
args.task = self.task
print(args)
@ -201,7 +201,7 @@ class YOLO:
self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
self.model = self.trainer.model
self.trainer.train()
# update model and configs after training
# update model and cfg after training
self.model, _ = attempt_load_one_weight(str(self.trainer.best))
self.overrides = self.model.args