1 files changed, 41 insertions, 1 deletions
diff --git a/torch_ema/ema.py b/torch_ema/ema.py
index 2e8eb6f..6c0415f 100644
--- a/torch_ema/ema.py
+++ b/torch_ema/ema.py
@@ -3,6 +3,7 @@ from __future__ import unicode_literals
 
 from typing import Iterable, Optional
 import weakref
+import copy
 
 import torch
 
@@ -128,7 +129,6 @@ class ExponentialMovingAverage:
                                  for param in parameters
                                  if param.requires_grad]
 
-
     def restore(
         self,
         parameters: Optional[Iterable[torch.nn.Parameter]] = None
@@ -150,3 +150,43 @@ class ExponentialMovingAverage:
         for c_param, param in zip(self.collected_params, parameters):
             if param.requires_grad:
                 param.data.copy_(c_param.data)
+
+    def state_dict(self) -> dict:
+        r"""Returns the state of the ExponentialMovingAverage as a dict."""
+        # Following PyTorch conventions, references to tensors are returned:
+        # "returns a reference to the state and not its copy!" -
+        # https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict
+        return {
+            "decay": self.decay,
+            "num_updates": self.num_updates,
+            "shadow_params": self.shadow_params,
+            "collected_params": self.collected_params
+        }
+
+    def load_state_dict(self, state_dict: dict) -> None:
+        r"""Loads the ExponentialMovingAverage state.
+
+        Args:
+            state_dict (dict): EMA state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        # deepcopy, to be consistent with module API
+        state_dict = copy.deepcopy(state_dict)
+        self.decay = state_dict["decay"]
+        if self.decay < 0.0 or self.decay > 1.0:
+            raise ValueError('Decay must be between 0 and 1')
+        self.num_updates = state_dict["num_updates"]
+        assert self.num_updates is None or isinstance(self.num_updates, int), \
+            "Invalid num_updates"
+        self.shadow_params = state_dict["shadow_params"]
+        assert isinstance(self.shadow_params, list), \
+            "shadow_params must be a list"
+        assert all(
+            isinstance(p, torch.Tensor) for p in self.shadow_params
+        ), "shadow_params must all be Tensors"
+        self.collected_params = state_dict["collected_params"]
+        assert isinstance(self.collected_params, list), \
+            "collected_params must be a list"
+        assert all(
+            isinstance(p, torch.Tensor) for p in self.collected_params
+        ), "collected_params must all be Tensors"