Modify docstring

ppcls/arch/gears/metabnneck.py

@@ -99,9 +99,9 @@ class MetaBNNeck(nn.Layer):
 
     def setup_opt(self, opt):
         """
-        enable_inside_update: enable inside updating for `gate` in MetaBIN
-        lr_gate: learning rate of `gate` during meta-train phase
-        bn_mode: control the running stats & updating of BN
+        Arg:
+            opt (dict): Optional setting to change the behavior of MetaBIN during training. 
+                It includes three settings which are `enable_inside_update`, `lr_gate` and `bn_mode`.
         """
         self.check_opt(opt)
         self.opt = copy.deepcopy(opt)

ppcls/optimizer/learning_rate.py

@@ -257,31 +257,31 @@ class Cyclic(LRBase):
     """Cyclic learning rate decay
     
     Args:
-        epochs (int): Total epoch(s)
-        step_each_epoch (int): Number of iterations within an epoch
+        epochs (int): Total epoch(s).
+        step_each_epoch (int): Number of iterations within an epoch.
         base_learning_rate (float): Initial learning rate, which is the lower boundary in the cycle. The paper recommends
             that set the base_learning_rate to 1/3 or 1/4 of max_learning_rate.
         max_learning_rate (float): Maximum learning rate in the cycle. It defines the cycle amplitude as above.
             Since there is some scaling operation during process of learning rate adjustment,
             max_learning_rate may not actually be reached.
-        warmup_epoch (int): Number of warmup epoch(s)
-        warmup_start_lr (float): Start learning rate within warmup
+        warmup_epoch (int): Number of warmup epoch(s).
+        warmup_start_lr (float): Start learning rate within warmup.
         step_size_up (int): Number of training steps, which is used to increase learning rate in a cycle.
             The step size of one cycle will be defined by step_size_up + step_size_down. According to the paper, step
             size should be set as at least 3 or 4 times steps in one epoch.
         step_size_down (int, optional): Number of training steps, which is used to decrease learning rate in a cycle.
-            If not specified, it's value will initialize to `` step_size_up `` . Default: None
+            If not specified, it's value will initialize to `` step_size_up `` . Default: None.
         mode (str, optional): One of 'triangular', 'triangular2' or 'exp_range'.
-            If scale_fn is specified, this argument will be ignored. Default: 'triangular'
-        exp_gamma (float): Constant in 'exp_range' scaling function: exp_gamma**iterations. Used only when mode = 'exp_range'. Default: 1.0
+            If scale_fn is specified, this argument will be ignored. Default: 'triangular'.
+        exp_gamma (float): Constant in 'exp_range' scaling function: exp_gamma**iterations. Used only when mode = 'exp_range'. Default: 1.0.
         scale_fn (function, optional): A custom scaling function, which is used to replace three build-in methods.
             It should only have one argument. For all x >= 0, 0 <= scale_fn(x) <= 1.
-            If specified, then 'mode' will be ignored. Default: None
+            If specified, then 'mode' will be ignored. Default: None.
         scale_mode (str, optional): One of 'cycle' or 'iterations'. Defines whether scale_fn is evaluated on cycle
-            number or cycle iterations (total iterations since start of training). Default: 'cycle'
+            number or cycle iterations (total iterations since start of training). Default: 'cycle'.
         last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
-        by_epoch (bool): Learning rate decays by epoch when by_epoch is True, else by iter
-        verbose: (bool, optional): If True, prints a message to stdout for each update. Defaults to False
+        by_epoch (bool): Learning rate decays by epoch when by_epoch is True, else by iter.
+        verbose: (bool, optional): If True, prints a message to stdout for each update. Defaults to False.
     """
 
     def __init__(self,