module onnxrt.ops_cpu.op_range
#
Short summary#
module mlprodict.onnxrt.ops_cpu.op_range
Runtime operator.
Classes#
class |
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Range ===== Generate a tensor containing a sequence of numbers that begin at start and extends by increments of delta … |
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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Documentation#
Runtime operator.
- class mlprodict.onnxrt.ops_cpu.op_range.Range(onnx_node, desc=None, **options)#
Bases:
OpRun
Generate a tensor containing a sequence of numbers that begin at start and extends by increments of delta up to limit (exclusive).
The number of elements in the output of range is computed as below-
number_of_elements = max( ceil( (limit - start) / delta ) , 0 )
The pseudocode determining the contents of the output is shown below-
for(int i=0; i<number_of_elements; ++i)
{
` output[i] = start + (i * delta); `
}
Example 1 Inputs: start = 3, limit = 9, delta = 3 Output: [3, 6]
Example 2 Inputs: start = 10, limit = 4, delta = -2 Output: [10, 8, 6]
Inputs
start (heterogeneous)T: Scalar. First entry for the range of output values.
limit (heterogeneous)T: Scalar. Exclusive upper limit for the range of output values.
delta (heterogeneous)T: Scalar. Value to step by.
Outputs
output (heterogeneous)T: A 1-D tensor with same type as the inputs containing generated range of values.
Type Constraints
T tensor(float), tensor(double), tensor(int16), tensor(int32), tensor(int64): Constrain input types to common numeric type tensors.
Version
Onnx name: Range
This version of the operator has been available since version 11.
Runtime implementation:
Range
- __init__(onnx_node, desc=None, **options)#
- _run(starts, ends, steps, attributes=None, verbose=0, fLOG=None)#
Should be overwritten.