Minor updates to docstrings and code organization.

* Docstrings using the same imperative verb form
* Use list comprehension when collecting params with store()

1 files changed, 11 insertions, 12 deletions

diff --git a/torch_ema/ema.py b/torch_ema/ema.py
index 50f9aea..447fd1e 100644
--- a/torch_ema/ema.py
+++ b/torch_ema/ema.py
@@ -20,11 +20,11 @@ class ExponentialMovingAverage:
         """
         if decay < 0.0 or decay > 1.0:
             raise ValueError('Decay must be between 0 and 1')
-        self.collected_parameters = []
         self.decay = decay
         self.num_updates = 0 if use_num_updates else None
         self.shadow_params = [p.clone().detach()
                               for p in parameters if p.requires_grad]
+        self.collected_params = []
 
     def update(self, parameters):
         """
@@ -49,7 +49,7 @@ class ExponentialMovingAverage:
 
     def copy_to(self, parameters):
         """
-        Copies current parameters into given collection of parameters.
+        Copy current parameters into given collection of parameters.
 
         Args: 
           parameters: Iterable of `torch.nn.Parameter`; the parameters to be
@@ -61,28 +61,27 @@ class ExponentialMovingAverage:
 
     def store(self, parameters):
         """
-        Save the current parameters for restore.
+        Save the current parameters for restoring later.
 
         Args: 
           parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            temporary stored in.
+            temporarily stored.
         """
-        self.collected_parameters = []
-        for param in parameters:
-            self.collected_parameters.append(param.clone())
+        self.collected_params = [param.clone() for param in parameters]
 
     def restore(self, parameters):
         """
-        Restore the parameters from the `store` function.
-        Usually used in validation. Want to validate the model with EMA parameters without affecting the original optimization process.
-        Store the parameters before the `copy_to` function.
-        After the validation(or model saving), restore the former parameters.
+        Restore the parameters stored with the `store` method.
+        Useful to validate the model with EMA parameters without affecting the
+        original optimization process. Store the parameters before the
+        `copy_to` method. After validation (or model saving), use this to
+        restore the former parameters.
 
         Args: 
           parameters: Iterable of `torch.nn.Parameter`; the parameters to be
             updated with the stored parameters.
         """
-        for c_param, param in zip(self.collected_parameters, parameters):
+        for c_param, param in zip(self.collected_params, parameters):
             if param.requires_grad:
                 param.data.copy_(c_param.data)