def __assert_get_correct_params(self, method, *args, **kwargs):
# this function perform checks if the getparams on the method returns
# the correct tensors
methodname = method.__name__
clsname = method.__self__.__class__.__name__
# get all tensor parameters in the object
all_params, all_names = _get_tensors(self)
def _get_tensor_name(param):
for i in range(len(all_params)):
if id(all_params[i]) == id(param):
return all_names[i]
return None
# get the parameter tensors used in the operation and the tensors specified by the developer
oper_names, oper_params = self.__list_operating_params(
method, *args, **kwargs)
user_names = self.getparamnames(method.__name__)
user_params = [get_attr(self, name) for name in user_names]
user_params_id = [id(p) for p in user_params]
oper_params_id = [id(p) for p in oper_params]
user_params_id_set = set(user_params_id)
oper_params_id_set = set(oper_params_id)
# check if the userparams contains non-tensor
for i in range(len(user_params)):
param = user_params[i]
if (not isinstance(param, torch.Tensor)) or \
(isinstance(param, torch.Tensor) and param.dtype not in torch_float_type):
msg = "Parameter %s is a non-floating point tensor" % user_names[
i]
raise GetSetParamsError(msg)
# check if there are missing parameters (present in operating params, but not in the user params)
missing_names = []
for i in range(len(oper_names)):
if oper_params_id[i] not in user_params_id_set:
# if oper_names[i] not in user_names:
missing_names.append(oper_names[i])
# if there are missing parameters, give a warning (because the program
# can still run correctly, e.g. missing parameters are parameters that
# are never set to require grad)
if len(missing_names) > 0:
msg = "getparams for %s.%s does not include: %s" % (
clsname, methodname, ", ".join(missing_names))
warnings.warn(msg, stacklevel=3)
# check if there are excessive parameters (present in the user params, but not in the operating params)
excess_names = []
for i in range(len(user_names)):
if user_params_id[i] not in oper_params_id_set:
# if user_names[i] not in oper_names:
excess_names.append(user_names[i])
# if there are excess parameters, give warnings
if len(excess_names) > 0:
msg = "getparams for %s.%s has excess parameters: %s" % \
(clsname, methodname, ", ".join(excess_names))
warnings.warn(msg, stacklevel=3)
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 getparams(self, methodname: str) -> Sequence[torch.Tensor]:
# Returns a list of tensor parameters used in the object's operations
paramnames = self.getparamnames(methodname)
return [get_attr(self, name) for name in paramnames]