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QLinearConcat

Description

Concatenate a list of tensors into a single tensor.All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.

 

Input parameters

 

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.

 Graphs in : cluster, ONNX model architecture.

Y_scale (heterogeneous) – TF : object, Y’s scale.
Y_zero_point (heterogeneous) – T8 : object, Y’s zero point.
inputs (variadic, heterogeneous) – TV : array, list of tensors/scale/zero_point for concatenation.

 Parameters : cluster,

axis : integer, which axis to concat on.
Default value “0”.
 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.

Output parameters

 

 Y (heterogeneous) – T8 : object, concatenated tensor.

Type Constraints

T8 in (tensor(uint8), tensor(int8)) : Constrain input and output types to 8 bit signed and unsigned tensors.

TF in (tensor(float)) : Constrain scale types to any float tensor type.

TV in (tensor(uint8), tensor(int8), tensor(float)) : Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).

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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