module onnxrt.ops_cpu.op_shape#
Short summary#
module mlprodict.onnxrt.ops_cpu.op_shape
Runtime operator.
Classes#
class |
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Shape ===== Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional … |
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Shape ===== Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional … |
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 arguments as well as the list of parameters with the default values (close to the signature). … |
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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 modified parameters. |
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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 the list of optional arguments. |
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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 the list of optional arguments. |
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Returns all parameters in a dictionary. |
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Returns all parameters in a dictionary. |
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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_shape.Shape_1(onnx_node, desc=None, **options)#
Bases:
OpRun- __init__(onnx_node, desc=None, **options)#
- _run(data, attributes=None, verbose=0, fLOG=None)#
Should be overwritten.
- class mlprodict.onnxrt.ops_cpu.op_shape.Shape_15(onnx_node, desc=None, **options)#
Bases:
Shape_1Shape#
Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional attributes start and end can be used to compute a slice of the input tensor’s shape. If start axis is omitted, the slice starts from axis 0. The end axis, if specified, is exclusive (and the returned value will not include the size of that axis). If the end axis is omitted, the axes upto the last one will be included. Negative axes indicate counting back from the last axis. Note that axes will be clamped to the range [0, r-1], where r is the rank of the input tensor if they are out-of-range (after adding r in the case of negative axis). Thus, specifying any end value > r is equivalent to specifying an end value of r, and specifying any start value < -r is equivalent to specifying a start value of 0.
For example: Input tensor with shape: [2, 3, 4] No attributes specified. Output: [2, 3, 4]
Input tensor with shape: [2, 3, 4] start: -1 Output: [4]
Input tensor with shape: [2, 3, 4] end: -1 Output: [2, 3]
Input tensor with shape: [2, 3, 4] start: 1 end: 2 Output: [3]
Attributes
end: (Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included. default value cannot be automatically retrieved (INT)
start: (Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back. Default value is
namestarti0typeINT(INT)
Inputs
data (heterogeneous)T: An input tensor.
Outputs
shape (heterogeneous)T1: Shape of the input tensor
Type Constraints
T tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Input tensor can be of arbitrary type.
T1 tensor(int64): Constrain output to int64 tensor.
Version
Onnx name: Shape
This version of the operator has been available since version 15.
Runtime implementation:
Shape- __init__(onnx_node, desc=None, **options)#
- _interval(n)#
- _run(data, attributes=None, verbose=0, fLOG=None)#
Should be overwritten.