ultralytics 8.0.18 new python callbacks and minor fixes (#580)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jeroen Rombouts <36196499+jarombouts@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/yolo/utils/torch_utils.py

@@ -307,18 +307,20 @@ def strip_optimizer(f='best.pt', s=''):
     LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")
 
 
-def guess_task_from_head(head):
-    task = None
-    if head.lower() in ["classify", "classifier", "cls", "fc"]:
-        task = "classify"
-    if head.lower() in ["detect"]:
-        task = "detect"
-    if head.lower() in ["segment"]:
-        task = "segment"
-
-    if not task:
-        raise SyntaxError("task or model not recognized! Please refer the docs at : ")  # TODO: add docs links
-
+def guess_task_from_model_yaml(model):
+    try:
+        cfg = model if isinstance(model, dict) else model.yaml  # model cfg dict
+        m = cfg["head"][-1][-2].lower()  # output module name
+        task = None
+        if m in ["classify", "classifier", "cls", "fc"]:
+            task = "classify"
+        if m in ["detect"]:
+            task = "detect"
+        if m in ["segment"]:
+            task = "segment"
+    except Exception as e:
+        raise SyntaxError('Unknown task. Define task explicitly, i.e. task=detect when running your command. '
+                          'Valid tasks are detect, segment, classify.') from e
     return task
 
 
@@ -374,14 +376,36 @@ def profile(input, ops, n=10, device=None):
 
 
 class EarlyStopping:
-    # early stopper
+    """
+    Early stopping class that stops training when a specified number of epochs have passed without improvement.
+    """
+
     def __init__(self, patience=30):
+        """
+        Initialize early stopping object
+
+        Args:
+            patience (int, optional): Number of epochs to wait after fitness stops improving before stopping. Default is 30.
+        """
         self.best_fitness = 0.0  # i.e. mAP
         self.best_epoch = 0
         self.patience = patience or float('inf')  # epochs to wait after fitness stops improving to stop
         self.possible_stop = False  # possible stop may occur next epoch
 
     def __call__(self, epoch, fitness):
+        """
+        Check whether to stop training
+
+        Args:
+            epoch (int): Current epoch of training
+            fitness (float): Fitness value of current epoch
+
+        Returns:
+            bool: True if training should stop, False otherwise
+        """
+        if fitness is None:  # check if fitness=None (happens when val=False)
+            return False
+
         if fitness >= self.best_fitness:  # >= 0 to allow for early zero-fitness stage of training
             self.best_epoch = epoch
             self.best_fitness = fitness