def grad(self):
# TODO(hvy): Cache constructed `torch.Tensor`.
if self._param.grad is not None:
return cpm.astensor(self._param.grad)
else:
return None
def step(self, closure=None):
for param_group in self._base_optimizer.param_groups:
for param in param_group['params']:
assert isinstance(param, ChainerParameter)
param.grad.copy_(cpm.astensor(param._param.grad))
self._base_optimizer.step(closure)
def __new__(cls, param):
return super().__new__(cls, cpm.astensor(param.array))
def __new__(cls, variable):
assert isinstance(variable, chainer.Variable)
obj = cpm.astensor(variable.array)
obj.__class__ = cls
return obj