5efaf25f5499c7065f1c37bfd4288159714406d8,src/syft/lib/torch/uppercase_tensor.py,TorchTensorWrapper,_data_object2proto,#TorchTensorWrapper#,64

Before Change

        proto = TensorProto()

        dtype = TORCH_DTYPE_STR[tensor.dtype]
        if tensor.is_quantized:
            proto.is_quantized = True
            proto.scale = tensor.q_scale()
            proto.zero_point = tensor.q_zero_point()
            data = tensor.flatten().int_repr().tolist()
        else:
            data = tensor.flatten().tolist()

        proto.dtype = dtype
        proto.shape.extend(tensor.size())
        getattr(proto, "contents_" + dtype).extend(data)

After Change

    def _data_object2proto(self) -> TensorProto:
        proto = TensorProto()
        proto.tensor.CopyFrom(protobuf_tensor_serializer(self.value))
        if self.value.grad is not None:
            proto.grad.CopyFrom(protobuf_tensor_serializer(self.value.grad))

        return proto

    @staticmethod
    def _data_proto2object(proto: TensorProto) -> th.Tensor:

Instances