module onnxrt.ops_cpu.op_array_feature_extractor
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Short summary#
module mlprodict.onnxrt.ops_cpu.op_array_feature_extractor
Runtime operator.
Classes#
class |
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ArrayFeatureExtractor (ai.onnx.ml) ================================== Select elements of the input tensor based on the … |
Functions#
function |
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Implementation of operator ArrayFeatureExtractor with numpy. |
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Properties#
property |
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Returns the list of arguments as well as the list of parameters with the default values (close to the signature). … |
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Returns the list of modified parameters. |
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Returns the list of optional arguments. |
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Returns the list of optional arguments. |
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Returns all parameters in a dictionary. |
Methods#
method |
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Runtime for operator ArrayFeatureExtractor. |
Documentation#
Runtime operator.
- class mlprodict.onnxrt.ops_cpu.op_array_feature_extractor.ArrayFeatureExtractor(ai.onnx.ml)#
Bases:
OpRun
Select elements of the input tensor based on the indices passed.
The indices are applied to the last axes of the tensor.
Inputs
X (heterogeneous)T: Data to be selected
Y (heterogeneous)tensor(int64): The indices, based on 0 as the first index of any dimension.
Outputs
Z (heterogeneous)T: Selected output data as an array
Type Constraints
T tensor(float), tensor(double), tensor(int64), tensor(int32), tensor(string): The input must be a tensor of a numeric type or string. The output will be of the same tensor type.
Version
Onnx name: ArrayFeatureExtractor
This version of the operator has been available since version 1 of domain ai.onnx.ml.
Runtime implementation:
ArrayFeatureExtractor
- __init__(onnx_node, desc=None, **options)#
- _run(data, indices, attributes=None, verbose=0, fLOG=None)#
Runtime for operator ArrayFeatureExtractor.
Warning
ONNX specifications may be imprecise in some cases. When the input data is a vector (one dimension), the output has still two like a matrix with one row. The implementation follows what onnxruntime does in array_feature_extractor.cc.