|
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: |
|
|
|
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) |
|
|
