Docstring additions (#122)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/engine/trainer.py

@@ -33,9 +33,53 @@ from ultralytics.yolo.utils.torch_utils import ModelEMA, de_parallel, init_seeds
 
 
 class BaseTrainer:
+    """
+    BaseTrainer
 
-    def __init__(self, cfg=DEFAULT_CONFIG, overrides={}):
-        self.args = get_config(cfg, overrides)
+    A base class for creating trainers.
+
+    Attributes:
+        args (OmegaConf): 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.
+        model (nn.Module): Model instance.
+        callbacks (defaultdict): Dictionary of callbacks.
+        save_dir (Path): Directory to save results.
+        wdir (Path): Directory to save weights.
+        last (Path): Path to last checkpoint.
+        best (Path): Path to best checkpoint.
+        batch_size (int): Batch size for training.
+        epochs (int): Number of epochs to train for.
+        start_epoch (int): Starting epoch for training.
+        device (torch.device): Device to use for training.
+        amp (bool): Flag to enable AMP (Automatic Mixed Precision).
+        scaler (amp.GradScaler): Gradient scaler for AMP.
+        data (str): Path to data.
+        trainset (torch.utils.data.Dataset): Training dataset.
+        testset (torch.utils.data.Dataset): Testing dataset.
+        ema (nn.Module): EMA (Exponential Moving Average) of the model.
+        lf (nn.Module): Loss function.
+        scheduler (torch.optim.lr_scheduler._LRScheduler): Learning rate scheduler.
+        best_fitness (float): The best fitness value achieved.
+        fitness (float): Current fitness value.
+        loss (float): Current loss value.
+        tloss (float): Total loss value.
+        loss_names (list): List of loss names.
+        csv (Path): Path to results CSV file.
+    """
+
+    def __init__(self, config=DEFAULT_CONFIG, overrides=None):
+        """
+        Initializes the BaseTrainer class.
+
+        Args:
+            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.check_resume()
         init_seeds(self.args.seed + 1 + RANK, deterministic=self.args.deterministic)
 
@@ -464,6 +508,19 @@ class BaseTrainer:
 
     @staticmethod
     def build_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
+        """
+        Builds an optimizer with the specified parameters and parameter groups.
+
+        Args:
+            model (nn.Module): model to optimize
+            name (str): name of the optimizer to use
+            lr (float): learning rate
+            momentum (float): momentum
+            decay (float): weight decay
+
+        Returns:
+            torch.optim.Optimizer: the built optimizer
+        """
         g = [], [], []  # optimizer parameter groups
         bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k)  # normalization layers, i.e. BatchNorm2d()
         for v in model.modules():