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Permute3D

Description

Setup and add the permute 3D layer into the model during the definition graph step. Type : polymorphic.

 

Input parameters

 

Graph in : model architecture.

 parameters : layer parameters.

 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”.

 in/out param :

 input_shape : integer array, shape (not including the batch axis). NB : To be used only if it is the first layer of the model.
 output_behavior : enum, setup if the layer is an output layer.
Default “Not Output”​​.

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

 

Output parameters

 

Graph out : model architecture.

Dimension

Input shape

3D tensor with shape (batch_size, channels, width).

 

Output shape

3D tensor with shape (batch_size, width, channels).

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 HAIBAL library to run it).

Permute3D layer

1 – Generate a set of data

We generate an array of data of type single and shape [batch size = 10, channels = 7, width = 5].

2 – Define graph

First, we define the first layer of the graph which is an Input layer (explicit input layer method). This layer is setup as an input array shaped [channels = 7, width = 5].
Then we add to the graph the Permute3D layer.

3 – Run graph

We call the forward method and retrieve the result with the “Prediction 3D” method.
This method returns two variables, the first one is the layer information (cluster composed of the layer name, the graph index and the shape of the output layer) and the second one is the prediction with a shape of [batch_size, width, channels].

 

Permute3D layer, batch and dimension

1 – Generate a set of data

We generate an array of data of type single and shape [number of batch = 9, batch size = 10, channels = 7, width = 5].

2 – Define graph

First, we define the first layer of the graph which is an Input layer (explicit input layer method). This layer is setup as an input array shaped [channels = 7, width = 5].
Then we add to the graph the Permute3D layer.

3 – Run graph

We call the forward method and retrieve the result with the “Prediction 3D” method.
This method returns two variables, the first one is the layer information (cluster composed of the layer name, the graph index and the shape of the output layer) and the second one is the prediction with a shape of [batch_size, width, channels].

 

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