Welcome to our Support Center
Updated

1D Data to Input Array by index

Description

This VI adds a new input entry (of type BOOL, SGL, INT, UINT, or STRING) to an existing array of input data clusters. It is used to progressively build a structured list of model inputs. Once constructed, the full array of input clusters can be passed to Multi-Input Execution VIs, which perform inference or training using all specified inputs in a single execution step. Type : polymorphic.

 

 

Input parameters

 

 1D Input Data : array, 1D array of data with any type : integers (signed/unsigned), floats, doubles, booleans, or strings.

 Data in : array, is an array of clusters, where each cluster represents a single model input. Each cluster contains metadata and raw data required to describe and pass an input tensor to the model.

 input_order : integer, defines the position of the input within the data array. It corresponds to the index assigned to the input when it is created (via the index parameter).
 Inputs Info : cluster

 inputs_data : array, contains the raw byte representation of the input tensor data, stored as a 1D flattened buffer.
 inputs_shapes : array, specifies the shape of the input tensor. Since the data is stored as a flattened 1D buffer, this shape is necessary to reconstruct the original dimensions.
 inputs string length : array, used when the tensor type is string. If the tensor has shape [5,3], this field contains 15 values, each representing the length of a corresponding string element. This ensures that the actual size of inputs_data is known despite variable string lengths.
 inputs_ranks : array, indicates the rank of the tensor, i.e. the number of dimensions (Scalar = 0, 1D = 1, 2D = 2, etc.).
 inputs_types : array, defines the ONNX tensor type as an enumerated value (e.g. FLOAT, INT64, STRING).

​

 input order : integer, defines the position of the input within the data array. It corresponds to the index assigned to the input when it is created (via the index parameter).

 

Output parameters

 

 Data out : array, is an array of clusters, where each cluster represents a single model input. Each cluster contains metadata and raw data required to describe and pass an input tensor to the model.

 input_order : integer, defines the position of the input within the data array. It corresponds to the index assigned to the input when it is created (via the index parameter).
 Inputs Info : cluster

 inputs_data : array, contains the raw byte representation of the input tensor data, stored as a 1D flattened buffer.
 inputs_shapes : array, specifies the shape of the input tensor. Since the data is stored as a flattened 1D buffer, this shape is necessary to reconstruct the original dimensions.
 inputs string length : array, used when the tensor type is string. If the tensor has shape [5,3], this field contains 15 values, each representing the length of a corresponding string element. This ensures that the actual size of inputs_data is known despite variable string lengths.
 inputs_ranks : array, indicates the rank of the tensor, i.e. the number of dimensions (Scalar = 0, 1D = 1, 2D = 2, etc.).
 inputs_types : array, defines the ONNX tensor type as an enumerated value (e.g. FLOAT, INT64, STRING).

​

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).

Table of Contents