Tensor
Root class of all tensors (global and pipeline).
Parameters
Configuration at initialization.
Inheritors
Types
Initialization for a RGB or RGBA color array tensor.
The color type (combining DataType and number of channels) used to define a color array.
Data type enums.
Initialization of a tensor for double array. If the double values to be contained by the tensor is supposed to be static, you should consider use the String.tensor defined in Pipeline which allows you to
Initialization of a tensor for float array. If the float values to be contained by the tensor is supposed to be static, you should consider use the String.tensor defined in Pipeline which allows you to
The most fundamental initialization configuration for Tensor.
Initialization of a tensor for int array. If the int values to be contained by the tensor is supposed to be static, you should consider use the String.tensor defined in Pipeline which allows you to
Initialization config to declare a multi-dimensional tensor. This type of tensors is the conventionally defined ones in mathematics and physics applications. The data given to the tensor will be interpreted as an array of the declared data type and channels. This is the only type that support arithmetic operations.
Initialization for a POINT2 array tensor.
Initialization for a POINT3 array tensor.
Initialization for a SCALAR array tensor.
Initialization of a tensor for short array. If the short values to be contained by the tensor is supposed to be static, you should consider use the String.tensor defined in Pipeline which allows you to
Initialization for a SLICE array tensor.
Tensor's initialization config for special usage (usages other then the conventional multi-dimensional tensors).
Initialization of a tensor for String. If the string to be contained by the tensor is supposed to be static, you should consider use the String.tensor defined in Pipeline which allows you to
Enum to declare tensor's usage. By default, the tensor shall all be of multi-dimensional usage, which creates non-structured data arrays. Such a tensor observes the conventional definition of tensors in linear algebra, where the values it contains are non-structured.
Initialization for a timestamp