| """ |
| copied from https://github.com/CompVis/latent-diffusion/blob/main/ldm/modules/ema.py#L5 |
| """ |
|
|
| from contextlib import contextmanager |
| import torch |
| from torch import nn |
|
|
|
|
| @contextmanager |
| def ema_scope(use_ema, model_ema, model, context=None): |
| if use_ema: |
| model_ema.store(model.parameters()) |
| model_ema.copy_to(model) |
| if context is not None: |
| print(f"{context}: Switched to EMA weights") |
| try: |
| yield None |
| finally: |
| if use_ema: |
| model_ema.restore(model.parameters()) |
| if context is not None: |
| print(f"{context}: Restored training weights") |
|
|
| 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: |
| |
| 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) |
|
|
|
|
| def save_litema(ema_model, filepath): |
| torch.save({ |
| "ema_buffers": ema_model.state_dict(), |
| "ema_name_map": ema_model.m_name2s_name, |
| }, filepath) |
|
|
|
|
|
|
| def load_litema(ema_model, filepath, strict=True, map_location='cpu'): |
| ckpt = torch.load(filepath, map_location=map_location) |
| msg = ema_model.load_state_dict(ckpt["ema_buffers"], strict=strict) |
| ema_model.m_name2s_name = ckpt["ema_name_map"] |
| return msg |
|
|
|
|
| def load_ema_into_model(model, filepath, decay=0.9999): |
| ema = LitEma(model, decay=decay) |
| load_litema(ema, filepath) |
| ema.copy_to(model) |
| return model |
|
|
