def _unstrip_tensor(tensor: onnx.TensorProto) -> None:
meta_dict = {}
meta_dict_idx = 0
for i, external_data in enumerate(tensor.external_data):
if external_data.key != "location":
continue
try:
external_data_dict = json.loads(external_data.value)
if external_data_dict.get("type", "") == "stripped":
meta_dict = external_data_dict
meta_dict_idx = i
break
except ValueError:
continue
if not meta_dict:
return None
ave = meta_dict.get("average", None)
var = meta_dict.get("variance", None)
if ave is None or var is None:
return None
np_dtype = onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[tensor.data_type]
dummy_array = numpy.random.normal(ave, math.sqrt(var),
tensor.dims).astype(np_dtype)
dummy_tensor = onnx.numpy_helper.from_array(dummy_array)
tensor.data_location = onnx.TensorProto.DEFAULT
tensor.raw_data = dummy_tensor.raw_data
del tensor.external_data[meta_dict_idx]
def _strip_raw_data(tensor: onnx.TensorProto) -> onnx.TensorProto:
arr = onnx.numpy_helper.to_array(tensor)
meta_dict = {}
meta_dict['type'] = "stripped"
meta_dict['average'] = float(arr.mean()) # type: ignore[assignment]
meta_dict['variance'] = float(arr.var()) # type: ignore[assignment]
if not tensor.HasField("raw_data"):
tensor.raw_data = onnx.numpy_helper.from_array(arr,
tensor.name).raw_data
onnx.external_data_helper.set_external_data(tensor,
location=json.dumps(meta_dict),
length=arr.nbytes)
tensor.data_location = onnx.TensorProto.EXTERNAL
tensor.ClearField('raw_data')
tensor.ClearField('float_data')
return tensor
