module onnxrt.ops_cpu.op_scatter_elements
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Short summary#
module mlprodict.onnxrt.ops_cpu.op_scatter_elements
Runtime operator.
Classes#
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ScatterElements =============== ScatterElements takes three inputs data, updates, and indices of the same rank r … |
Functions#
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Properties#
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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#
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Documentation#
Runtime operator.
- class mlprodict.onnxrt.ops_cpu.op_scatter_elements.ScatterElements(onnx_node, desc=None, **options)#
Bases:
OpRun
ScatterElements takes three inputs data, updates, and indices of the same rank r >= 1 and an optional attribute axis that identifies an axis of data (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input data, and then updating its value to values specified by updates at specific index positions specified by indices. Its output shape is the same as the shape of data.
For each entry in updates, the target index in data is obtained by combining the corresponding entry in indices with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in indices and the index-value for dimension != axis is obtained from the index of the entry itself.
reduction allows specification of an optional reduction operation, which is applied to all values in updates tensor into output at the specified indices. In cases where reduction is set to “none”, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``
output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1,
`` When reduction is set to some reduction function f, the update corresponding to the [i][j] entry is performed as below: ``
output[indices[i][j]][j] += f(output[indices[i][j]][j], updates[i][j]) if axis = 0, output[i][indices[i][j]] += f(output[i][indices[i][j]], updates[i][j]) if axis = 1,
`` where the f is +/*/max/min as specified.
This operator is the inverse of GatherElements. It is similar to Torch’s Scatter operation.
(Opset 18 change): Adds max/min to the set of allowed reduction ops.
Example 1: ``
- data = [
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0],
] indices = [
[1, 0, 2], [0, 2, 1],
] updates = [
[1.0, 1.1, 1.2], [2.0, 2.1, 2.2],
] output = [
[2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2]
]
`` Example 2: ``
data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
Attributes
axis: Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Default value is
nameaxisi0typeINT
(INT)reduction: Type of reduction to apply: none (default), add, mul, max, min. ‘none’: no reduction applied. ‘add’: reduction using the addition operation. ‘mul’: reduction using the multiplication operation.’max’: reduction using the maximum operation.’min’: reduction using the minimum operation. Default value is
namereductionsnonetypeSTRING
(STRING)
Inputs
data (heterogeneous)T: Tensor of rank r >= 1.
indices (heterogeneous)Tind: Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
updates (heterogeneous)T: Tensor of rank r >=1 (same rank and shape as indices)
Outputs
output (heterogeneous)T: Tensor of rank r >= 1 (same rank as input).
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 and output types can be of any tensor type.
Tind tensor(int32), tensor(int64): Constrain indices to integer types
Version
Onnx name: ScatterElements
This version of the operator has been available since version 18.
Runtime implementation:
ScatterElements
- __init__(onnx_node, desc=None, **options)#
- _run(data, indices, updates, attributes=None, verbose=0, fLOG=None)#
Should be overwritten.
- mlprodict.onnxrt.ops_cpu.op_scatter_elements.scatter_elements(data, indices, updates, axis=0)#
- ::
// for 3-dim and axis=0 // output[indices[i][j][k]][j][k] = updates[i][j][k] // for axis 1 // output[i][indices[i][j][k]][k] = updates[i][j][k] // and so on