def add_param_DEPRECATED(self, param, key=None, shape=None, length=None):
logging.warning("add_param method is DEPRECATED")
self._update_param_info_deprecated()
self.AddParameter(param)
if key is not None and self.net.input_record() is not None:
idx = self.net.input_record().field_blobs().index(key)
key = self.net.input_record().field_names()[idx]
shape = shape if shape is not None else self._infer_param_shape(param)
if not isinstance(param, core.BlobReference):
raise ValueError("Param %s must be a BlobReference!" % str(param))
self._param_info_deprecated.append(
parameter_info.ParameterInfo(
param_id=len(self._param_info_deprecated),
param=param,
shape=shape,
key=key,
length=length,
))
return self._param_info_deprecated[-1]
def AddGradientOperators(self, *args, **kwargs):
if self.gradient_ops_added:
raise RuntimeError("You cannot run AddGradientOperators twice.")
self.gradient_ops_added = True
self.grad_map = self.net.AddGradientOperators(*args, **kwargs)
self.param_to_grad = self.get_param_to_grad(self.params)
# Populate ParameterInfo for all parameters if missing
# and add gradient blob information. So optimizers can use it
for param, grad in self.param_to_grad.items():
param_info = self.get_param_info(param)
if param_info:
param_info.grad = grad
else:
self._parameters_info[param] = parameter_info.ParameterInfo(
param_id=None,
param=param,
grad=grad,
)
return self.grad_map
