Snapdragon Neural Processing Engine SDK Reference Guide
Preparing a model with UDO

This section talks about the steps required to convert a framework model with user defined operations(UDO).

# Converting a network model with UDO into DLC

snpe-<framework>-to-dlc tools support UDO functionality by accepting configuration file(s) with the option --udo_config_paths. For input UDO config file specifications, see Defining a UDO.

Currently UDO functionality is supported on TensorFlow and ONNX models. It is also supported on models trained with Caffe2 that are expressed in ONNX formatting. Currently it is not supported for models trained with Caffe.

Note: Any modifications in the UDO configuration file should be followed up with re-generation of DLCs to reflect the changes.

Converting Tensorflow model with UDO to DLC

The following syntax showcases the way TensorFlow models can be converted using UDO:

snpe-tensorflow-to-dlc -i <input-tensorflow-model>
-d <input-name> <input-dim>
--out_node <output-node-name>
--udo_config_paths <input-model.json>
-o <output-model.dlc>


where the option --udo_config_paths allows users to specify the UDO configuration file to be used in the conversion.

See snpe-tensorflow-to-dlc and TensorFlow Model Conversion for further details.

Converting ONNX model with UDO to DLC

The following syntax showcases the way ONNX models can be converted using UDO:

snpe-onnx-to-dlc -i <input-onnx-model>
--udo_config_paths <input-model.json>
-o <output-model.dlc>


where the option --udo_config_paths allows users to specify the UDO configuration file to be used in the conversion.

# Quantizing a DLC with UDO

Additionally, users may want to quantize converted models having UDOs to run on fixed-point runtimes. SNPE SDK provides the tool snpe-dlc-quantize for this purpose. This is an offline tool that can be run on the host x86 platform. Since it runs inferences with a representative data-set in order to determine quantization ranges for all layers in the network including UDOs, users will need to provide a UDO package containing CPU reference implementation to the tool. Refer to Creating a UDO Package and Compiling a UDO package for further instructions on creating such a package for the x86 platform.

The following syntax showcases the way DLCs with UDOs can be quantized with snpe-dlc-quantize:

snpe-dlc-quantize --input_dlc <model.dlc>
--input_list <input-list.txt>
--udo_package_path <udo-registration-lib>
--output_dlc <quantized-model.dlc>


where the option --udo_package_path allows users to specify the absolute path to the UDO registration library.