kopia lustrzana https://github.com/lzzcd001/MeshDiffusion
98 wiersze
3.3 KiB
Python
98 wiersze
3.3 KiB
Python
# Modified from https://raw.githubusercontent.com/fadel/pytorch_ema/master/torch_ema/ema.py
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from __future__ import division
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from __future__ import unicode_literals
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import torch
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# Partially based on: https://github.com/tensorflow/tensorflow/blob/r1.13/tensorflow/python/training/moving_averages.py
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class ExponentialMovingAverage:
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"""
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Maintains (exponential) moving average of a set of parameters.
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"""
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def __init__(self, parameters, decay, use_num_updates=True):
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"""
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Args:
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parameters: Iterable of `torch.nn.Parameter`; usually the result of
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`model.parameters()`.
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decay: The exponential decay.
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use_num_updates: Whether to use number of updates when computing
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averages.
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"""
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if decay < 0.0 or decay > 1.0:
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raise ValueError('Decay must be between 0 and 1')
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self.decay = decay
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self.num_updates = 0 if use_num_updates else None
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self.shadow_params = [p.clone().detach()
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for p in parameters if p.requires_grad]
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self.collected_params = []
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def update(self, parameters):
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"""
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Update currently maintained parameters.
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Call this every time the parameters are updated, such as the result of
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the `optimizer.step()` call.
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Args:
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parameters: Iterable of `torch.nn.Parameter`; usually the same set of
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parameters used to initialize this object.
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"""
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decay = self.decay
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if self.num_updates is not None:
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self.num_updates += 1
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decay = min(decay, (1 + self.num_updates) / (10 + self.num_updates))
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one_minus_decay = 1.0 - decay
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with torch.no_grad():
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parameters = [p for p in parameters if p.requires_grad]
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for s_param, param in zip(self.shadow_params, parameters):
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s_param.sub_(one_minus_decay * (s_param - param))
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def copy_to(self, parameters):
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"""
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Copy current parameters into given collection of parameters.
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Args:
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parameters: Iterable of `torch.nn.Parameter`; the parameters to be
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updated with the stored moving averages.
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"""
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parameters = [p for p in parameters if p.requires_grad]
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for s_param, param in zip(self.shadow_params, parameters):
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if param.requires_grad:
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param.data.copy_(s_param.data)
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def store(self, parameters):
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"""
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Save the current parameters for restoring later.
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Args:
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parameters: Iterable of `torch.nn.Parameter`; the parameters to be
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temporarily stored.
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"""
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self.collected_params = [param.clone() for param in parameters]
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def restore(self, parameters):
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"""
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Restore the parameters stored with the `store` method.
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Useful to validate the model with EMA parameters without affecting the
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original optimization process. Store the parameters before the
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`copy_to` method. After validation (or model saving), use this to
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restore the former parameters.
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Args:
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parameters: Iterable of `torch.nn.Parameter`; the parameters to be
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updated with the stored parameters.
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"""
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for c_param, param in zip(self.collected_params, parameters):
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param.data.copy_(c_param.data)
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def state_dict(self):
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return dict(decay=self.decay, num_updates=self.num_updates,
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shadow_params=self.shadow_params)
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def load_state_dict(self, state_dict):
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self.decay = state_dict['decay']
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self.num_updates = state_dict['num_updates']
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self.shadow_params = state_dict['shadow_params'] |