module `onnxrt.ops_cpu.op_unique`#

Short summary#

module mlprodict.onnxrt.ops_cpu.op_unique

Runtime operator.

Classes#

class	truncated documentation
`Unique`	Unique ====== Find the unique elements of a tensor. When an optional attribute ‘axis’ is provided, unique subtensors sliced …

Functions#

function	truncated documentation
`_specify_int64`

Properties#

property	truncated documentation
`args_default`	Returns the list of arguments as well as the list of parameters with the default values (close to the signature). …
`args_default_modified`	Returns the list of modified parameters.
`args_mandatory`	Returns the list of optional arguments.
`args_optional`	Returns the list of optional arguments.
`atts_value`	Returns all parameters in a dictionary.

Methods#

method	truncated documentation
`__init__`
`_run`

Documentation#

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_unique.Unique(onnx_node, desc=None, **options)#

Bases: OpRun

Find the unique elements of a tensor. When an optional attribute ‘axis’ is provided, unique subtensors sliced along the ‘axis’ are returned. Otherwise the input tensor is flattened and unique values of the flattened tensor are returned.

This operator returns the unique values or sliced unique subtensors of the input tensor and three optional outputs. The first output tensor ‘Y’ contains all unique values or subtensors of the input. The second optional output tensor ‘indices’ contains indices of ‘Y’ elements’ first occurance in ‘X’.. The third optional output tensor ‘inverse_indices’ contains, for elements of ‘X’, its corresponding indices in ‘Y’. “. The fourth optional output tensor ‘counts’ contains the count of each element of ‘Y’ in the input.

Outputs are either sorted in ascending order or optionally in the order of the first occurrence of the values in the input.

https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html

Example 1:

input_X = [2, 1, 1, 3, 4, 3] attribute_sorted = 0 attribute_axis = None output_Y = [2, 1, 3, 4] output_indices = [0, 1, 3, 4] output_inverse_indices = [0, 1, 1, 2, 3, 2] output_counts = [1, 2, 2, 1]

Example 2:

input_X = [[1, 3], [2, 3]] attribute_sorted = 1 attribute_axis = None output_Y = [1, 2, 3] output_indices = [0, 2, 1] output_inverse_indices = [0, 2, 1, 2] output_counts = [1, 1, 2]

Example 3:

input_X = [[1, 0, 0], [1, 0, 0], [2, 3, 4]] attribute_sorted = 1 attribute_axis = 0 output_Y = [[1, 0, 0], [2, 3, 4]] output_indices = [0, 2] output_inverse_indices = [0, 0, 1] output_counts = [2, 1]

Example 4:

input_x = [[[1., 1.], [0., 1.], [2., 1.], [0., 1.]],: [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]]

attribute_sorted = 1 attribute_axis = 1

intermediate data are presented below for better understanding:

there are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4, 2)): A: [[1, 1], [1, 1]],

[[0, 1], [0, 1]], [[2, 1], [2, 1]], [[0, 1], [0, 1]].

there are 3 unique subtensors: [[1, 1], [1, 1]], [[0, 1], [0, 1]], [[2, 1], [2, 1]].

sorted unique subtensors: B: [[0, 1], [0, 1]],

[[1, 1], [1, 1]], [[2, 1], [2, 1]].

output_Y is constructed from B: [[[0. 1.], [1. 1.], [2. 1.]],

[[0. 1.], [1. 1.], [2. 1.]]]

output_indices is to map from B to A: [1, 0, 2]

output_inverse_indices is to map from A to B: [1, 0, 2, 0]

output_counts = [2 1 1]

Attributes

axis: (Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). default value cannot be automatically retrieved (INT)
sorted: (Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default). Default value is namesortedi1typeINT (INT)

Inputs

X (heterogeneous)T: A N-D input tensor that is to be processed.

Outputs

Between 1 and 4 outputs.

Y (heterogeneous)T: A tensor of the same type as ‘X’ containing all the unique values or subtensors sliced along a provided ‘axis’ in ‘X’, either sorted or maintained in the same order they occur in input ‘X’
indices (optional, heterogeneous)tensor(int64): A 1-D INT64 tensor containing indices of ‘Y’ elements’ first occurance in ‘X’. When ‘axis’ is provided, it contains indices to subtensors in input ‘X’ on the ‘axis’. When ‘axis’ is not provided, it contains indices to values in the flattened input tensor.
inverse_indices (optional, heterogeneous)tensor(int64): A 1-D INT64 tensor containing, for elements of ‘X’, its corresponding indices in ‘Y’. When ‘axis’ is provided, it contains indices to subtensors in output ‘Y’ on the ‘axis’. When ‘axis’ is not provided, it contains indices to values in output ‘Y’.
counts (optional, heterogeneous)tensor(int64): A 1-D INT64 tensor containing the count of each element of ‘Y’ in input ‘X’

Type Constraints

T tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Input can be of any tensor type.

Version

Onnx name: Unique

This version of the operator has been available since version 11.

Runtime implementation: Unique

__init__(onnx_node, desc=None, **options)#

_run(x, attributes=None, verbose=0, fLOG=None)#

Should be overwritten.