Snpe

Public technical reference documentation from Graiphic. For current SOTA installation and toolkit guides, visit the documentation center.

Description

Onnx node for SNPE.

Documentation illustration (illustration unavailable in the archive).

Input parameters

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.
Documentation illustration (illustration unavailable in the archive). inputs (variadic) – T : array, list of tensors for SNPE DLC input.

Parameters : cluster,

DLC : string, payload of the SNPE DLC file.
notes : string, some notes for the model.
snpe_version : string, SNPE version used to convert the model.
target_device : string, target device like CPU, DSP, etc.
training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
lda coeff : float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value “1”.

name (optional) : string, name of the node.

Documentation illustration (illustration unavailable in the archive).

Output parameters

Documentation illustration (illustration unavailable in the archive). outputs (variadic) – T : object, one or more outputs, list of tensors for DLC output.

Type Constraints

T in (tensor(uint8), tensor(uint16), tensor(float)) : Constrain input and output types to uint8, uint16, float tensors.

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).