ultralytics 8.0.12 - Hydra removal (#506)

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>

ultralytics/yolo/v8/classify/predict.py

@@ -1,11 +1,10 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-import hydra
 import torch
 
 from ultralytics.yolo.engine.predictor import BasePredictor
 from ultralytics.yolo.engine.results import Results
-from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, is_git_directory
+from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, is_git_directory
 from ultralytics.yolo.utils.plotting import Annotator
 
 
@@ -64,8 +63,7 @@ class ClassificationPredictor(BasePredictor):
         return log_string
 
 
-@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
-def predict(cfg):
+def predict(cfg=DEFAULT_CFG):
     cfg.model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
     cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" if is_git_directory() \
         else "https://ultralytics.com/images/bus.jpg"

ultralytics/yolo/v8/classify/train.py

@@ -1,6 +1,5 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-import hydra
 import torch
 import torchvision
 
@@ -8,13 +7,13 @@ from ultralytics.nn.tasks import ClassificationModel, attempt_load_one_weight
 from ultralytics.yolo import v8
 from ultralytics.yolo.data import build_classification_dataloader
 from ultralytics.yolo.engine.trainer import BaseTrainer
-from ultralytics.yolo.utils import DEFAULT_CONFIG
+from ultralytics.yolo.utils import DEFAULT_CFG
 from ultralytics.yolo.utils.torch_utils import strip_optimizer
 
 
 class ClassificationTrainer(BaseTrainer):
 
-    def __init__(self, config=DEFAULT_CONFIG, overrides=None):
+    def __init__(self, config=DEFAULT_CFG, overrides=None):
         if overrides is None:
             overrides = {}
         overrides["task"] = "classify"
@@ -136,8 +135,7 @@ class ClassificationTrainer(BaseTrainer):
                 #     self.run_callbacks('on_fit_epoch_end')
 
 
-@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
-def train(cfg):
+def train(cfg=DEFAULT_CFG):
     cfg.model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
     cfg.data = cfg.data or "mnist160"  # or yolo.ClassificationDataset("mnist")
 
@@ -152,7 +150,7 @@ def train(cfg):
     # trainer.train()
     from ultralytics import YOLO
     model = YOLO(cfg.model)
-    model.train(**cfg)
+    model.train(**vars(cfg))
 
 
 if __name__ == "__main__":

ultralytics/yolo/v8/classify/val.py

@@ -1,10 +1,8 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-import hydra
-
 from ultralytics.yolo.data import build_classification_dataloader
 from ultralytics.yolo.engine.validator import BaseValidator
-from ultralytics.yolo.utils import DEFAULT_CONFIG
+from ultralytics.yolo.utils import DEFAULT_CFG
 from ultralytics.yolo.utils.metrics import ClassifyMetrics
 
 
@@ -46,8 +44,7 @@ class ClassificationValidator(BaseValidator):
         self.logger.info(pf % ("all", self.metrics.top1, self.metrics.top5))
 
 
-@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
-def val(cfg):
+def val(cfg=DEFAULT_CFG):
     cfg.model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
     cfg.data = cfg.data or "imagenette160"
     validator = ClassificationValidator(args=cfg)