module `onnxrt.ops_cpu.op_mean`#

Short summary#

module mlprodict.onnxrt.ops_cpu.op_mean

Runtime operator.

Classes#

class	truncated documentation
`Mean`	Mean ==== Element-wise mean of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs …

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__`
`_run`
`_run_inplace`

Documentation#

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_mean.Mean(onnx_node, desc=None, **options)#

Bases: OpRun

Element-wise mean of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.

Inputs

Between 1 and 2147483647 inputs.

data_0 (variadic, heterogeneous)T: List of tensors for mean.

Outputs

mean (heterogeneous)T: Output tensor.

Type Constraints

T tensor(float16), tensor(float), tensor(double), tensor(bfloat16): Constrain input and output types to float tensors.

Version

Onnx name: Mean

This version of the operator has been available since version 13.

Runtime implementation: Mean

__init__(onnx_node, desc=None, **options)#

_run(*args, attributes=None, verbose=0, fLOG=None)#

Should be overwritten.