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authorSamuel Fadel <samuelfadel@gmail.com>2019-04-16 15:33:58 -0300
committerSamuel Fadel <samuelfadel@gmail.com>2019-04-16 15:33:58 -0300
commit09cfcf97e0e938a93867c7d445f1c9b4dcfea023 (patch)
tree0e048bd3affce4cc6b9d4e301729ed3a5c769731 /torch_ema
Initial commit.
Diffstat (limited to 'torch_ema')
-rw-r--r--torch_ema/__init__.py1
-rw-r--r--torch_ema/ema.py59
2 files changed, 60 insertions, 0 deletions
diff --git a/torch_ema/__init__.py b/torch_ema/__init__.py
new file mode 100644
index 0000000..9732013
--- /dev/null
+++ b/torch_ema/__init__.py
@@ -0,0 +1 @@
+from .ema import *
diff --git a/torch_ema/ema.py b/torch_ema/ema.py
new file mode 100644
index 0000000..32ed7ca
--- /dev/null
+++ b/torch_ema/ema.py
@@ -0,0 +1,59 @@
+from __future__ import division
+from __future__ import unicode_literals
+
+import torch
+
+
+# Partially based on: https://github.com/tensorflow/tensorflow/blob/r1.13/tensorflow/python/training/moving_averages.py
+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]
+
+ 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():
+ for s_param, param in zip(self.shadow_params, parameters):
+ if param.requires_grad:
+ s_param.sub_(one_minus_decay * (s_param - param))
+
+ def copy_to(self, parameters):
+ """
+ Copies current parameters into given collection of parameters.
+
+ Args:
+ parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+ updated with the stored moving averages.
+ """
+ for s_param, param in zip(self.shadow_params, parameters):
+ if param.requires_grad:
+ param.data.copy_(s_param.data)