Add docformatter to pre-commit (#5279)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>

ultralytics/engine/trainer.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 """
-Train a model on a dataset
+Train a model on a dataset.
 
 Usage:
     $ yolo mode=train model=yolov8n.pt data=coco128.yaml imgsz=640 epochs=100 batch=16
@@ -37,7 +37,7 @@ from ultralytics.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel,
 
 class BaseTrainer:
     """
-    BaseTrainer
+    BaseTrainer.
 
     A base class for creating trainers.
 
@@ -143,15 +143,11 @@ class BaseTrainer:
             callbacks.add_integration_callbacks(self)
 
     def add_callback(self, event: str, callback):
-        """
-        Appends the given callback.
-        """
+        """Appends the given callback."""
         self.callbacks[event].append(callback)
 
     def set_callback(self, event: str, callback):
-        """
-        Overrides the existing callbacks with the given callback.
-        """
+        """Overrides the existing callbacks with the given callback."""
         self.callbacks[event] = [callback]
 
     def run_callbacks(self, event: str):
@@ -207,9 +203,7 @@ class BaseTrainer:
             world_size=world_size)
 
     def _setup_train(self, world_size):
-        """
-        Builds dataloaders and optimizer on correct rank process.
-        """
+        """Builds dataloaders and optimizer on correct rank process."""
 
         # Model
         self.run_callbacks('on_pretrain_routine_start')
@@ -450,14 +444,14 @@ class BaseTrainer:
     @staticmethod
     def get_dataset(data):
         """
-        Get train, val path from data dict if it exists. Returns None if data format is not recognized.
+        Get train, val path from data dict if it exists.
+
+        Returns None if data format is not recognized.
         """
         return data['train'], data.get('val') or data.get('test')
 
     def setup_model(self):
-        """
-        load/create/download model for any task.
-        """
+        """Load/create/download model for any task."""
         if isinstance(self.model, torch.nn.Module):  # if model is loaded beforehand. No setup needed
             return
 
@@ -482,14 +476,14 @@ class BaseTrainer:
             self.ema.update(self.model)
 
     def preprocess_batch(self, batch):
-        """
-        Allows custom preprocessing model inputs and ground truths depending on task type.
-        """
+        """Allows custom preprocessing model inputs and ground truths depending on task type."""
         return batch
 
     def validate(self):
         """
-        Runs validation on test set using self.validator. The returned dict is expected to contain "fitness" key.
+        Runs validation on test set using self.validator.
+
+        The returned dict is expected to contain "fitness" key.
         """
         metrics = self.validator(self)
         fitness = metrics.pop('fitness', -self.loss.detach().cpu().numpy())  # use loss as fitness measure if not found
@@ -506,26 +500,20 @@ class BaseTrainer:
         raise NotImplementedError('get_validator function not implemented in trainer')
 
     def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode='train'):
-        """
-        Returns dataloader derived from torch.data.Dataloader.
-        """
+        """Returns dataloader derived from torch.data.Dataloader."""
         raise NotImplementedError('get_dataloader function not implemented in trainer')
 
     def build_dataset(self, img_path, mode='train', batch=None):
-        """Build dataset"""
+        """Build dataset."""
         raise NotImplementedError('build_dataset function not implemented in trainer')
 
     def label_loss_items(self, loss_items=None, prefix='train'):
-        """
-        Returns a loss dict with labelled training loss items tensor
-        """
+        """Returns a loss dict with labelled training loss items tensor."""
         # Not needed for classification but necessary for segmentation & detection
         return {'loss': loss_items} if loss_items is not None else ['loss']
 
     def set_model_attributes(self):
-        """
-        To set or update model parameters before training.
-        """
+        """To set or update model parameters before training."""
         self.model.names = self.data['names']
 
     def build_targets(self, preds, targets):
@@ -632,8 +620,8 @@ class BaseTrainer:
 
     def build_optimizer(self, model, name='auto', lr=0.001, momentum=0.9, decay=1e-5, iterations=1e5):
         """
-        Constructs an optimizer for the given model, based on the specified optimizer name, learning rate,
-        momentum, weight decay, and number of iterations.
+        Constructs an optimizer for the given model, based on the specified optimizer name, learning rate, momentum,
+        weight decay, and number of iterations.
 
         Args:
             model (torch.nn.Module): The model for which to build an optimizer.