first commit

MobileNetV3/models/ema.py

@@ -0,0 +1,85 @@
+import torch
+
+
+class ExponentialMovingAverage:
+    """
+    Maintains (exponential) moving average of a set of parameters.
+    """
+
+    def __init__(self, parameters, decay, use_num_updates=True):
+        """
+        Args:
+            parameters: Iterable of `torch.nn.Parameter`; usually the result of `model.parameters()`.
+            decay: The exponential decay.
+            use_num_updates: Whether to use number of updates when computing averages.
+        """
+        if decay < 0.0 or decay > 1.0:
+            raise ValueError('Decay must be between 0 and 1')
+        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):
+        """
+        Update currently maintained parameters.
+
+        Call this every time the parameters are updated, such as the result of the `optimizer.step()` call.
+
+        Args:
+            parameters: Iterable of `torch.nn.Parameter`; usually the same set of parameters used to
+                initialize this object.
+        """
+        decay = self.decay
+        if self.num_updates is not None:
+            self.num_updates += 1
+            decay = min(decay, (1 + self.num_updates) / (10 + self.num_updates))
+        one_minus_decay = 1.0 - decay
+        with torch.no_grad():
+            parameters = [p for p in parameters if p.requires_grad]
+            for s_param, param in zip(self.shadow_params, parameters):
+                s_param.sub_(one_minus_decay * (s_param - param))
+
+    def copy_to(self, parameters):
+        """
+        Copy current parameters into given collection of parameters.
+
+        Args:
+            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+                updated with the stored moving averages.
+        """
+        parameters = [p for p in parameters if p.requires_grad]
+        for s_param, param in zip(self.shadow_params, parameters):
+            if param.requires_grad:
+                param.data.copy_(s_param.data)
+
+    def store(self, parameters):
+        """
+        Save the current parameters for restoring later.
+
+        Args:
+            parameters: Iterable of `torch.nn.Parameter`; the parameters to be temporarily stored.
+        """
+        self.collected_params = [param.clone() for param in parameters]
+
+    def restore(self, 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_params, parameters):
+            param.data.copy_(c_param.data)
+
+    def state_dict(self):
+        return dict(decay=self.decay, num_updates=self.num_updates, shadow_params=self.shadow_params)
+
+    def load_state_dict(self, state_dict):
+        self.decay = state_dict['decay']
+        self.num_updates = state_dict['num_updates']
+        self.shadow_params = state_dict['shadow_params']