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3D Data to Input Array by name

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

 

ย 3D Input Data : array,ย 3D array of data with any type : integers (signed/unsigned), floats, doubles, booleans, or strings.

ย Data in :ย array,ย 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.

ย input_name :ย string,ย specifies the identifier of the input. It corresponds to the name given to the input during its creation (via the optionalย nameย parameter).
ย Inputs Info :ย cluster

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

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ย input name : string,ย specifies the identifier of the input. It corresponds to the name given to the input during its creation (via the optionalย nameย parameter).

 

Output parameters

 

ย Data out : array,ย 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.

ย input_name :ย string,ย specifies the identifier of the input. It corresponds to the name given to the input during its creation (via the optionalย nameย parameter).
ย Inputs Info :ย cluster

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

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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).

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