From 448588ac2401e9b68d5fb3f660d38d00cf633aa1 Mon Sep 17 00:00:00 2001
From: zehui-lin <zehui-lin_t@outlook.com>
Date: Fri, 15 Jan 2021 11:29:57 +0800
Subject: Add Feature: restore

---
 torch_ema/ema.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

(limited to 'torch_ema')

diff --git a/torch_ema/ema.py b/torch_ema/ema.py
index 4e9dd99..20c62c4 100644
--- a/torch_ema/ema.py
+++ b/torch_ema/ema.py
@@ -54,6 +54,22 @@ class ExponentialMovingAverage:
           parameters: Iterable of `torch.nn.Parameter`; the parameters to be
             updated with the stored moving averages.
         """
+        self.collected_parameters = []
         for s_param, param in zip(self.shadow_params, parameters):
+            self.collected_parameters.append(param.clone())
             if param.requires_grad:
                 param.data.copy_(s_param.data)
+
+    def restore(self, parameters):
+        """
+        Restore the parameters given to the copy_to function. 
+        Usually used in validation. Want to validate the model with EMA parameters without affecting the original optimization process.
+
+        Args: 
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            updated with the stored original parameters.
+        """
+        for s_param, param in zip(self.collected_parameters, parameters):
+            if param.requires_grad:
+                param.data.copy_(s_param.data)
+
-- 
cgit v1.2.3


From 205c76e4789709094ab7bb658b02f9abb789a66a Mon Sep 17 00:00:00 2001
From: Zehui Lin <zehui-lin_t@outlook.com>
Date: Thu, 21 Jan 2021 09:52:29 +0800
Subject: store and restore

---
 torch_ema/ema.py | 22 ++++++++++++++++++----
 1 file changed, 18 insertions(+), 4 deletions(-)

(limited to 'torch_ema')

diff --git a/torch_ema/ema.py b/torch_ema/ema.py
index 20c62c4..b819a04 100644
--- a/torch_ema/ema.py
+++ b/torch_ema/ema.py
@@ -60,16 +60,30 @@ class ExponentialMovingAverage:
             if param.requires_grad:
                 param.data.copy_(s_param.data)
 
+    def store(self, parameters):
+        """
+        Save the current parameters for restore.
+
+        Args: 
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            updated with the stored moving averages.
+        """
+        self.collected_parameters = []
+        for param in parameters:
+            self.collected_parameters.append(param.clone())
+
     def restore(self, parameters):
         """
-        Restore the parameters given to the copy_to function. 
+        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.
 
         Args: 
           parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            updated with the stored original parameters.
+            updated with the stored moving averages.
         """
-        for s_param, param in zip(self.collected_parameters, parameters):
+        for c_param, param in zip(self.collected_parameters, parameters):
             if param.requires_grad:
-                param.data.copy_(s_param.data)
+                param.data.copy_(c_param.data)
 
-- 
cgit v1.2.3


From 0ed9f9ddc9b93bbcd6d3828803f20a23cc500f5e Mon Sep 17 00:00:00 2001
From: Zehui Lin <zehui-lin_t@outlook.com>
Date: Thu, 21 Jan 2021 09:55:41 +0800
Subject: fix comments

---
 torch_ema/ema.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'torch_ema')

diff --git a/torch_ema/ema.py b/torch_ema/ema.py
index b819a04..8f39bc5 100644
--- a/torch_ema/ema.py
+++ b/torch_ema/ema.py
@@ -66,7 +66,7 @@ class ExponentialMovingAverage:
 
         Args: 
           parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            updated with the stored moving averages.
+            temporary stored in.
         """
         self.collected_parameters = []
         for param in parameters:
@@ -81,7 +81,7 @@ class ExponentialMovingAverage:
 
         Args: 
           parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            updated with the stored moving averages.
+            updated with the stored parameters.
         """
         for c_param, param in zip(self.collected_parameters, parameters):
             if param.requires_grad:
-- 
cgit v1.2.3


From 359c155723f0b08f6e6c1784c7951e33d92b306d Mon Sep 17 00:00:00 2001
From: Zehui Lin <zehui-lin_t@outlook.com>
Date: Mon, 8 Feb 2021 10:09:36 +0800
Subject: minor

---
 torch_ema/ema.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

(limited to 'torch_ema')

diff --git a/torch_ema/ema.py b/torch_ema/ema.py
index 8f39bc5..50f9aea 100644
--- a/torch_ema/ema.py
+++ b/torch_ema/ema.py
@@ -20,6 +20,7 @@ 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()
@@ -54,9 +55,7 @@ class ExponentialMovingAverage:
           parameters: Iterable of `torch.nn.Parameter`; the parameters to be
             updated with the stored moving averages.
         """
-        self.collected_parameters = []
         for s_param, param in zip(self.shadow_params, parameters):
-            self.collected_parameters.append(param.clone())
             if param.requires_grad:
                 param.data.copy_(s_param.data)
 
-- 
cgit v1.2.3