def _unfreeze_and_add_param_group(module: torch.nn.Module,
optimizer: Optimizer,
lr: Optional[float] = None,
train_bn: bool = True):
"""Unfreezes a module and adds its parameters to an optimizer."""
_make_trainable(module)
params_lr = optimizer.param_groups[0]['lr'] if lr is None else float(lr)
optimizer.add_param_group({
'params':
filter_params(module=module, train_bn=train_bn),
'lr':
params_lr / 10.,
})
def unfreeze(module: Sequential, optimizer: Optimizer, unfreeze_from: int,
unfreeze_to: int, **kwargs):
"""
Unfreeze a model from ind
:param module:
:param optimizer
:param unfreeze_from:
:param unfreeze_to:
:return:
"""
for ind in range(len(module)):
submodule = module._modules[str(ind)]
if ind < unfreeze_from:
for param in submodule.parameters():
param.requires_grad = False
elif ind < unfreeze_to:
for param in submodule.parameters():
param.requires_grad = True
optimizer.add_param_group({
'params': submodule.parameters(),
**kwargs
})