def setparams(self, methodname: str, *params) -> int:
# Set the input parameters to the object's parameters to make a copy of
# the operations.
# *params is an excessive list of the parameters to be set and the
# method will return the number of parameters it sets.
paramnames = self.getparamnames(methodname)
for name, val in zip(paramnames, params):
set_attr(self, name, val)
return len(params)
def setparams(self, methodname: str, *params) -> int:
# Set the input parameters to the object's parameters to make a copy of
# the operations.
# *params is an excessive list of the parameters to be set and the
# method will return the number of parameters it sets.
paramnames = self.cached_getparamnames(methodname)
for name, val in zip(paramnames, params):
try:
set_attr(self, name, val)
except TypeError as e: # failed because val should be param
del_attr(self, name)
set_attr(self, name, val)
return len(params)
def _useobjparams(self, objparams):
nnmodule = self.obj
names = self.names
try:
# substitute the state dictionary of the module with the new tensor
# save the current state
state_tensors = [get_attr(nnmodule, name) for name in names]
# substitute the state with the given tensor
for (name, param) in zip(names, objparams):
del_attr(nnmodule, name) # delete require in case the param is not a torch.nn.Parameter
set_attr(nnmodule, name, param)
yield nnmodule
except Exception as exc:
raise exc
finally:
# restore back the saved tensors
for (name, param) in zip(names, state_tensors):
set_attr(nnmodule, name, param)
def _set_all_obj_params(self, objparams: List):
for (name, param) in zip(self.names, objparams):
del_attr(
self.obj, name
) # delete required in case the param is not a torch.nn.Parameter
set_attr(self.obj, name, param)