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| #-*- encoding:utf-8 -*- | |
| import torch | |
| from torch import nn | |
| from pytorch_lightning.callbacks import Callback | |
| 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 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) | |
| class EMACallback(Callback): | |
| def __init__(self, decay=0.9999): | |
| self.decay = decay | |
| self.shadow_params = {} | |
| def on_train_start(self, trainer, pl_module): | |
| # initialize shadow parameters for original models | |
| total_ema_cnt = 0 | |
| for name, param in pl_module.named_parameters(): | |
| if name not in self.shadow_params: | |
| self.shadow_params[name] = param.data.clone() | |
| else: # already in dict, maybe load from checkpoint | |
| pass | |
| print('will calc ema for param: %s' % name) | |
| total_ema_cnt += 1 | |
| print('total_ema_cnt=%d' % total_ema_cnt) | |
| def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx): | |
| # Update the shadow params at the end of each epoch | |
| for name, param in pl_module.named_parameters(): | |
| assert name in self.shadow_params | |
| new_average = (1.0 - self.decay) * param.data + self.decay * self.shadow_params[name] | |
| self.shadow_params[name] = new_average.clone() | |
| def on_save_checkpoint(self, trainer, pl_module, checkpoint): | |
| # Save EMA parameters in the checkpoint | |
| checkpoint['ema_params'] = self.shadow_params | |
| def on_load_checkpoint(self, trainer, pl_module, checkpoint): | |
| # Restore EMA parameters from the checkpoint | |
| if 'ema_params' in checkpoint: | |
| self.shadow_params = checkpoint.get('ema_params', {}) | |
| for k in self.shadow_params: | |
| self.shadow_params[k] = self.shadow_params[k].cuda() | |
| print('load shadow params from checkpoint, cnt=%d' % len(self.shadow_params)) | |
| else: | |
| print('ema_params is not in checkpoint') |