def clone(self, first_order=None, allow_unused=None, allow_nograd=None):
"""
**Description**
Returns a `MAML`-wrapped copy of the module whose parameters and buffers
are `torch.clone`d from the original module.
This implies that back-propagating losses on the cloned module will
populate the buffers of the original module.
For more information, refer to learn2learn.clone_module().
**Arguments**
* **first_order** (bool, *optional*, default=None) - Whether the clone uses first-
or second-order updates. Defaults to self.first_order.
* **allow_unused** (bool, *optional*, default=None) - Whether to allow differentiation
of unused parameters. Defaults to self.allow_unused.
* **allow_nograd** (bool, *optional*, default=False) - Whether to allow adaptation with
parameters that have `requires_grad = False`. Defaults to self.allow_nograd.
"""
if first_order is None:
first_order = self.first_order
if allow_unused is None:
allow_unused = self.allow_unused
if allow_nograd is None:
allow_nograd = self.allow_nograd
return MAML(clone_module(self.module),
lr=self.lr,
first_order=first_order,
allow_unused=allow_unused,
allow_nograd=allow_nograd)
def clone(self):
"""
**Descritpion**
Akin to `MAML.clone()` but for MetaSGD: it includes a set of learnable fast-adaptation
learning rates.
"""
return MetaSGD(clone_module(self.module),
lrs=clone_parameters(self.lrs),
first_order=self.first_order)
def clone(self, first_order=None):
"""
**Description**
Returns a `MAML`-wrapped copy of the module whose parameters and buffers
are `torch.clone`d from the original module.
This implies that back-propagating losses on the cloned module will
populate the buffers of the original module.
For more information, refer to learn2learn.clone_module().
**Arguments**
* **first_order** (bool, *optional*, default=None) - Whether the clone uses first-
or second-order updates. Defaults to self.first_order.
"""
if first_order is None:
first_order = self.first_order
return MAML(clone_module(self.module),
lr=self.lr,
first_order=first_order)
def clone(self):
return MetaSGD(clone_module(self.module),
lrs=clone_parameters(self.lrs),
first_order=self.first_order)