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- import torch
- from torch import nn
- class LitEma(nn.Module):
- def __init__(self, model, decay=0.9999, use_num_updates=True):
- super().__init__()
- if decay < 0.0 or decay > 1.0:
- raise ValueError('Decay must be between 0 and 1')
- self.m_name2s_name = {}
- self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
- self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_updates
- else torch.tensor(-1, dtype=torch.int))
- for name, p in model.named_parameters():
- if p.requires_grad:
- # remove as '.'-character is not allowed in buffers
- s_name = name.replace('.', '')
- self.m_name2s_name.update({name: s_name})
- self.register_buffer(s_name, p.clone().detach().data)
- self.collected_params = []
- def reset_num_updates(self):
- del self.num_updates
- self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
- def forward(self, model):
- decay = self.decay
- if self.num_updates >= 0:
- self.num_updates += 1
- decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
- one_minus_decay = 1.0 - decay
- with torch.no_grad():
- m_param = dict(model.named_parameters())
- shadow_params = dict(self.named_buffers())
- for key in m_param:
- if m_param[key].requires_grad:
- sname = self.m_name2s_name[key]
- shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
- shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
- else:
- assert not key in self.m_name2s_name
- def copy_to(self, model):
- m_param = dict(model.named_parameters())
- shadow_params = dict(self.named_buffers())
- for key in m_param:
- if m_param[key].requires_grad:
- m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
- else:
- assert not key in self.m_name2s_name
- 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)
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