Welcome to our Support Center
Updated

Cropping3D

Description

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

 

Input parameters

 

Model in : model architecture.

Parameters : layer parameters.

Depth Crop : cluster, interpreted as (left,Β right).
Default value “(1,1)”.
Β Height Crop :Β cluster, interpreted as two different symmetric cropping values for height and width (top,Β bottom).
Default value β€œ(1,1)”.
Β Width Crop :Β cluster, how many units should be trimmed off at the beginning and end of the cropping dimension (left,Β right).
Default value β€œ(1,1)”.
Β data format : enum, one ofΒ channels_lastΒ orΒ channels_firstΒ (default) . The ordering of the dimensions in the inputs.Β channel_lastΒ corresponds to inputs with shapeΒ (batch, steps, features)Β whileΒ channels_firstΒ corresponds to inputs with shapeΒ (batch, features, steps).
Default value β€œchannels_first”.
training?Β : 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 layer.

 

Output parameters

 

Model out : model architecture.

Dimension

Input shape

5D tensor with shape :

  • If data_format is β€œchannels_last” : (batch_size, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop, depth)
  • If data_format is β€œchannels_first” :Β (batch_size, depth, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)

Output shape

5D tensor with shape :

  • If data_format is β€œchannels_last” : (batch_size, first_cropped_axis, second_cropped_axis, third_cropped_axis, depth)
  • If data_format is β€œchannels_first” :Β (batch_size, depth, first_cropped_axis, second_cropped_axis, third_cropped_axis)

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).

Crooping3D layer

1 – Generate a set of data

We generate an array of data of type single and shape [batch_size = 10, depth = 7, first_axis_to_crop = 5, second_axis_to_crop = 5, third_axis_to_crop = 3].

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 [depth = 7, first_axis_to_crop = 5, second_axis_to_crop = 5, third_axis_to_crop = 3].
Then we add to the graph the Cropping3D layer.

3 – Run graph

We call the forward method and retrieve the result with the β€œPrediction 5D” 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, depth, first_cropped_axis, second_cropped_axis, third_cropped_axis].

 

Cropping3D 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, depth = 7, first_axis_to_crop = 5, second_axis_to_crop = 5, third_axis_to_crop = 3].

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 [depth = 7, first_axis_to_crop = 5, second_axis_to_crop = 5, third_axis_to_crop = 3].
Then we add to the graph the Cropping3D layer.

3 – Run graph

We call the forward method and retrieve the result with the β€œPrediction 5D” 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, depth, first_cropped_axis, second_cropped_axis, third_cropped_axis].

 

Tags:
Table of Contents