Fixed docstring in colossalai (#171)

colossalai/trainer/hooks/_metric_hook.py

@@ -124,6 +124,7 @@ class LossMetric(Metric):
         """
         return self.last_step_loss
 
+    @staticmethod
     def is_better(a, b):
         return a < b
 
@@ -133,7 +134,7 @@ class LearningRateMetric(Metric):
 
     :param epoch_only: Whether the metric only read for the full epoch
     :type epoch_only: bool
-    :param initial_lr: initial learning rate, defaults to 0.0
+    :param initial_lr: Initial learning rate, defaults to 0.0
     :type initial_lr: float, optional
     """
 
@@ -153,6 +154,7 @@ class LearningRateMetric(Metric):
     def get_accumulated_value(self):
         return self.lr
 
+    @staticmethod
     def is_better(a, b) -> bool:
         pass
 
@@ -163,8 +165,8 @@ class AccuracyMetric(Metric):
 
     :param epoch_only: Whether the metric only read for the full epoch
     :type epoch_only: bool
-    :param accuracy_func: accuracy function for the classification task
-    :type accuracy_func: typing.Callable
+    :param accuracy_func: Accuracy function for the classification task
+    :type accuracy_func: :class:`typing.Callable`
     """
 
     def __init__(self, epoch_only: bool, accuracy_func: Callable):
@@ -186,8 +188,8 @@ class AccuracyMetric(Metric):
         and labels. It expects the output has logits and labels.
 
         :param logits: The logits output of the model
-        :param targets: real labels of the dataset
-        :param batch_size: batch size of the task
+        :param targets: Real labels of the dataset
+        :param batch_size: Batch size of the task
         """
         if isinstance(logits, (list, tuple)):
             logits = logits[0]
@@ -211,6 +213,7 @@ class AccuracyMetric(Metric):
         self.accumulated_correct = all_reduce(self.accumulated_correct, ParallelMode.DATA)
         return (self.accumulated_correct / self.accumulated_sum).item()
 
+    @staticmethod
     def is_better(a, b) -> bool:
         return a > b
 
@@ -223,8 +226,6 @@ class MetricHook(BaseHook):
 
     :param priority: Priority in the printing, hooks with small priority will be printed in front
     :type priority: int
-    :param trainer: Trainer attached with current hook
-    :type trainer: Trainer
     """
 
     def __init__(
@@ -245,8 +246,6 @@ class LossHook(MetricHook):
 
     :param priority: Priority in the printing, hooks with small priority will be printed in front, defaults to 0
     :type priority: int, optional
-    :param trainer: Trainer attached with current hook
-    :type trainer: Trainer
     """
 
     def __init__(self, priority: int = 0):
@@ -288,8 +287,6 @@ class AccuracyHook(MetricHook):
     :type accuracy_func: typing.Callable
     :param priority: Priority in the printing, hooks with small priority will be printed in front, defaults to 0
     :type priority: int, optional
-    :param trainer: Trainer attached with current hook
-    :type trainer: Trainer
     """
 
     def __init__(self, accuracy_func: Callable, priority: int = 0):
@@ -319,8 +316,6 @@ class ThroughputMetric(Metric):
 
     :param epoch_only: epoch only
     :type epoch_only: bool
-    :param num_samples: number of samples
-    :param time: time
     """
     def __init__(self, epoch_only: bool):
         super().__init__(epoch_only=epoch_only)
@@ -353,6 +348,7 @@ class ThroughputMetric(Metric):
         self.accumulated_num_samples = all_reduce(self.accumulated_num_samples, ParallelMode.DATA)
         return (self.accumulated_num_samples / (self.accumulated_used_time + 1e-12)).item()
 
+    @staticmethod
     def is_better(a, b) -> bool:
         pass
 
@@ -363,8 +359,6 @@ class ThroughputHook(MetricHook):
 
     :param priority: priority of throughput hook, defaults to 10
     :type priority: int, optional
-    :param trainer: Trainer attached with current hook
-    :type trainer: Trainer
     """
     def __init__(self, priority: int = 10):
         super().__init__(priority)