mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
77 lines
2.9 KiB
Python
77 lines
2.9 KiB
Python
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import torch
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from torch import nn
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class LitEma(nn.Module):
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def __init__(self, model, decay=0.9999, use_num_upates=True):
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super().__init__()
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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.m_name2s_name = {}
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self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
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self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
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else torch.tensor(-1,dtype=torch.int))
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for name, p in model.named_parameters():
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if p.requires_grad:
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#remove as '.'-character is not allowed in buffers
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s_name = name.replace('.','')
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self.m_name2s_name.update({name:s_name})
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self.register_buffer(s_name,p.clone().detach().data)
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self.collected_params = []
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def forward(self,model):
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decay = self.decay
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if self.num_updates >= 0:
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self.num_updates += 1
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decay = min(self.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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m_param = dict(model.named_parameters())
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shadow_params = dict(self.named_buffers())
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for key in m_param:
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if m_param[key].requires_grad:
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sname = self.m_name2s_name[key]
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shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
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shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
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else:
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assert not key in self.m_name2s_name
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def copy_to(self, model):
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m_param = dict(model.named_parameters())
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shadow_params = dict(self.named_buffers())
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for key in m_param:
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if m_param[key].requires_grad:
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m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
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else:
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assert not key in self.m_name2s_name
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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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