ultralytics 8.0.81 single-line docstring updates (#2061)

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

ultralytics/yolo/engine/trainer.py

@@ -159,6 +159,7 @@ class BaseTrainer:
         self.callbacks[event] = [callback]
 
     def run_callbacks(self, event: str):
+        """Run all existing callbacks associated with a particular event."""
         for callback in self.callbacks.get(event, []):
             callback(self)
 
@@ -190,6 +191,7 @@ class BaseTrainer:
             self._do_train(world_size)
 
     def _setup_ddp(self, world_size):
+        """Initializes and sets the DistributedDataParallel parameters for training."""
         torch.cuda.set_device(RANK)
         self.device = torch.device('cuda', RANK)
         LOGGER.info(f'DDP settings: RANK {RANK}, WORLD_SIZE {world_size}, DEVICE {self.device}')
@@ -259,6 +261,7 @@ class BaseTrainer:
         self.run_callbacks('on_pretrain_routine_end')
 
     def _do_train(self, world_size=1):
+        """Train completed, evaluate and plot if specified by arguments."""
         if world_size > 1:
             self._setup_ddp(world_size)
 
@@ -392,6 +395,7 @@ class BaseTrainer:
         self.run_callbacks('teardown')
 
     def save_model(self):
+        """Save model checkpoints based on various conditions."""
         ckpt = {
             'epoch': self.epoch,
             'best_fitness': self.best_fitness,
@@ -436,6 +440,7 @@ class BaseTrainer:
         return ckpt
 
     def optimizer_step(self):
+        """Perform a single step of the training optimizer with gradient clipping and EMA update."""
         self.scaler.unscale_(self.optimizer)  # unscale gradients
         torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=10.0)  # clip gradients
         self.scaler.step(self.optimizer)
@@ -461,9 +466,11 @@ class BaseTrainer:
         return metrics, fitness
 
     def get_model(self, cfg=None, weights=None, verbose=True):
+        """Get model and raise NotImplementedError for loading cfg files."""
         raise NotImplementedError("This task trainer doesn't support loading cfg files")
 
     def get_validator(self):
+        """Returns a NotImplementedError when the get_validator function is called."""
         raise NotImplementedError('get_validator function not implemented in trainer')
 
     def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode='train'):
@@ -492,19 +499,24 @@ class BaseTrainer:
         self.model.names = self.data['names']
 
     def build_targets(self, preds, targets):
+        """Builds target tensors for training YOLO model."""
         pass
 
     def progress_string(self):
+        """Returns a string describing training progress."""
         return ''
 
     # TODO: may need to put these following functions into callback
     def plot_training_samples(self, batch, ni):
+        """Plots training samples during YOLOv5 training."""
         pass
 
     def plot_training_labels(self):
+        """Plots training labels for YOLO model."""
         pass
 
     def save_metrics(self, metrics):
+        """Saves training metrics to a CSV file."""
         keys, vals = list(metrics.keys()), list(metrics.values())
         n = len(metrics) + 1  # number of cols
         s = '' if self.csv.exists() else (('%23s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n')  # header
@@ -512,9 +524,11 @@ class BaseTrainer:
             f.write(s + ('%23.5g,' * n % tuple([self.epoch] + vals)).rstrip(',') + '\n')
 
     def plot_metrics(self):
+        """Plot and display metrics visually."""
         pass
 
     def final_eval(self):
+        """Performs final evaluation and validation for object detection YOLO model."""
         for f in self.last, self.best:
             if f.exists():
                 strip_optimizer(f)  # strip optimizers
@@ -525,6 +539,7 @@ class BaseTrainer:
                     self.run_callbacks('on_fit_epoch_end')
 
     def check_resume(self):
+        """Check if resume checkpoint exists and update arguments accordingly."""
         resume = self.args.resume
         if resume:
             try:
@@ -539,6 +554,7 @@ class BaseTrainer:
         self.resume = resume
 
     def resume_training(self, ckpt):
+        """Resume YOLO training from given epoch and best fitness."""
         if ckpt is None:
             return
         best_fitness = 0.0