[Docs]: Add customization tutorial and address feedback (#155)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/yolo/engine/trainer.py

@@ -39,7 +39,7 @@ class BaseTrainer:
     """
     BaseTrainer
 
-    A base class for creating trainers.
+    > A base class for creating trainers.
 
     Attributes:
         args (OmegaConf): Configuration for the trainer.
@@ -74,7 +74,7 @@ class BaseTrainer:
 
     def __init__(self, config=DEFAULT_CONFIG, overrides=None):
         """
-        Initializes the BaseTrainer class.
+        > Initializes the BaseTrainer class.
 
         Args:
             config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
@@ -148,13 +148,13 @@ class BaseTrainer:
 
     def add_callback(self, event: str, callback):
         """
-        Appends the given callback. TODO: unused, consider removing
+        > Appends the given callback.
         """
         self.callbacks[event].append(callback)
 
     def set_callback(self, event: str, callback):
         """
-        Overrides the existing callbacks with the given callback.  TODO: unused, consider removing
+        > Overrides the existing callbacks with the given callback.
         """
         self.callbacks[event] = [callback]
 
@@ -185,7 +185,7 @@ class BaseTrainer:
 
     def _setup_train(self, rank, 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")
@@ -373,13 +373,13 @@ class BaseTrainer:
 
     def get_dataset(self, 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
@@ -405,15 +405,13 @@ class BaseTrainer:
 
     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.
-        # TODO: discuss validator class. Enforce that a validator metrics dict should contain
-        "fitness" metric.
+        > 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
@@ -423,9 +421,11 @@ class BaseTrainer:
 
     def log(self, text, rank=-1):
         """
-        Logs the given text to given ranks process if provided, otherwise logs to all ranks
-        :param text: text to log
-        :param rank: List[Int]
+        > Logs the given text to given ranks process if provided, otherwise logs to all ranks.
+
+        Args"
+            text (str): text to log
+            rank (List[Int]): process rank
 
         """
         if rank in {-1, 0}:
@@ -439,13 +439,13 @@ class BaseTrainer:
 
     def get_dataloader(self, dataset_path, batch_size=16, rank=0):
         """
-        Returns dataloader derived from torch.data.Dataloader
+        > Returns dataloader derived from torch.data.Dataloader.
         """
         raise NotImplementedError("get_dataloader function not implemented in trainer")
 
     def criterion(self, preds, batch):
         """
-        Returns loss and individual loss items as Tensor
+        > Returns loss and individual loss items as Tensor.
         """
         raise NotImplementedError("criterion function not implemented in trainer")
 
@@ -531,7 +531,7 @@ 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.
+        > Builds an optimizer with the specified parameters and parameter groups.
 
         Args:
             model (nn.Module): model to optimize