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| import torch | |
| from torch import nn | |
| class LitEma(nn.Module): | |
| def __init__(self, model, decay=0.9999, use_num_upates=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_upates 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) | |