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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( |
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"num_updates", |
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torch.tensor(0, dtype=torch.int) |
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if use_num_upates |
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else torch.tensor(-1, dtype=torch.int), |
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) |
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for name, p in model.named_parameters(): |
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if p.requires_grad: |
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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_( |
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one_minus_decay * (shadow_params[sname] - m_param[key]) |
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) |
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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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