module onnxrt.ops_cpu.op_compress#

Inheritance diagram of mlprodict.onnxrt.ops_cpu.op_compress

Short summary#

module mlprodict.onnxrt.ops_cpu.op_compress

Runtime operator.

source on GitHub

Classes#

class

truncated documentation

Compress

Compress ======== Selects slices from an input tensor along a given axis where condition evaluates to True for each axis …

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__

_infer_shapes

_infer_sizes

_infer_types

_run

to_python

Documentation#

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_compress.Compress(onnx_node, desc=None, **options)#

Bases: OpRun

Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index. In case axis is not provided, input is flattened before elements are selected. Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html

Attributes

  • axis: (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. 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)

Inputs

  • input (heterogeneous)T: Tensor of rank r >= 1.

  • condition (heterogeneous)T1: Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.

Outputs

  • output (heterogeneous)T: Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.

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): Constrain input and output types to all tensor types.

  • T1 tensor(bool): Constrain to boolean tensors.

Version

Onnx name: Compress

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

Runtime implementation: Compress

__init__(onnx_node, desc=None, **options)#
_infer_shapes(x, condition)#

Should be overwritten.

source on GitHub

_infer_sizes(x, condition)#

Should be overwritten.

source on GitHub

_infer_types(x, condition)#

Should be overwritten.

source on GitHub

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

Should be overwritten.

source on GitHub

to_python(inputs)#

Returns a python code equivalent to this operator.

Parameters:

inputs – inputs name

Returns:

imports, python code, both as strings

source on GitHub