def __assert_method_preserve(self, method, *args, **kwargs):
# this method assert if method does not change the float tensor parameters
# of the object (i.e. it preserves the state of the object)
all_params0, names0 = _get_tensors(self)
all_params0 = [p.clone() for p in all_params0]
method(*args, **kwargs)
all_params1, names1 = _get_tensors(self)
# now assert if all_params0 == all_params1
clsname = method.__self__.__class__.__name__
methodname = method.__name__
msg = "The method %s.%s does not preserve the object's float tensors: \n" % (
clsname, methodname)
if len(all_params0) != len(all_params1):
msg += "The number of parameters changed:\n"
msg += "* number of object's parameters before: %d\n" % len(
all_params0)
msg += "* number of object's parameters after : %d\n" % len(
all_params1)
raise GetSetParamsError(msg)
for pname, p0, p1 in zip(names0, all_params0, all_params1):
if p0.shape != p1.shape:
msg += "The shape of %s changed\n" % pname
msg += "* (before) %s.shape: %s\n" % (pname, p0.shape)
msg += "* (after ) %s.shape: %s\n" % (pname, p1.shape)
raise GetSetParamsError(msg)
if not torch.allclose(p0, p1):
msg += "The value of %s changed\n" % pname
msg += "* (before) %s: %s\n" % (pname, p0)
msg += "* (after ) %s: %s\n" % (pname, p1)
raise GetSetParamsError(msg)
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 __assert_match_getsetparams(self, methodname):
# this function assert if get & set params functions correspond to the
# same parameters in the same order
# count the number of parameters in getparams and setparams
params0 = self.getparams(methodname)
len_setparams0 = self.setparams(methodname, *params0)
if len_setparams0 != len(params0):
raise GetSetParamsError("The number of parameters returned by getparams and set by setparams do not match \n"\
"(getparams: %d, setparams: %d)" % (len(params0), len_setparams0))
# check if the params are assigned correctly in the correct order
params1 = self.getparams(methodname)
names1 = self.getparamnames(methodname)
for i, p0, p1 in zip(range(len(params0)), params0, params1):
if id(p0) != id(p1):
msg = "The parameter %s in getparams and setparams does not match" % names1[
i]
raise GetSetParamsError(msg)
def __assert_method_preserve(self, method, *args, **kwargs):
# this method assert if method does not change the float tensor parameters
# of the object (i.e. it preserves the state of the object)
all_params0, names0 = _get_tensors(self)
all_params0 = [p.clone() for p in all_params0]
method(*args, **kwargs)
all_params1, names1 = _get_tensors(self)
# now assert if all_params0 == all_params1
clsname = method.__self__.__class__.__name__
methodname = method.__name__
msg = "The method %s.%s does not preserve the object's float tensors" % (
clsname, methodname)
if len(all_params0) != len(all_params1):
raise GetSetParamsError(msg)
for p0, p1 in zip(all_params0, all_params1):
if p0.shape != p1.shape:
raise GetSetParamsError(msg)
if not torch.allclose(p0, p1):
raise GetSetParamsError(msg)