module `onnxrt.onnx_micro_runtime`#

Short summary#

module mlprodict.onnxrt.onnx_micro_runtime

Micro runtime for ONNX.

Classes#

class	truncated documentation
`OnnxMicroRuntime`	Implements a micro runtime for ONNX graphs. It does not implements all the operator types.

Properties#

property	truncated documentation
`input_names`	Returns input names.
`output_names`	Returns output names.

Methods#

method	truncated documentation
`__init__`
`_op_abs`	Runtime for operator Op.Abs.
`_op_add`	Runtime for operator Op.Add.
`_op_concat`	Runtime for operator Op.Concat.
`_op_gather`	Runtime for operator Op.Gather.
`_op_gemm`	Runtime for operator Op.Gemm.
`_op_identity`	Runtime for operator Op.Identity.
`_op_matmul`	Runtime for operator Op.MatMul.
`_op_max`	Runtime for operator Op.Max.
`_op_mul`	Runtime for operator Op.Mul.
`_op_reduceprod`	Runtime for operator Op.ReduceProd.
`_op_reducesum`	Runtime for operator Op.ReduceSum.
`_op_reshape`	Runtime for operator Op.Reshape.
`_op_shape`	Runtime for operator Op.Shape.
`_op_squeeze`	Runtime for operator Op.Squeeze.
`_op_transpose`	Runtime for operator Op.Transpose.
`_op_unsqueeze`	Runtime for operator Op.Unsqueeze.
`run`	Computes the outputs of the graph.

Documentation#

Micro runtime for ONNX.

New in version 0.6.

source on GitHub

class mlprodict.onnxrt.onnx_micro_runtime.OnnxMicroRuntime(model_onnx)#

Bases: object

Implements a micro runtime for ONNX graphs. It does not implements all the operator types.

Parameters:: model_onnx – ONNX model

<<<

import pprint
import numpy
from mlprodict.onnxrt.onnx_micro_runtime import OnnxMicroRuntime
from mlprodict.npy.xop import loadop

OnnxAdd = loadop('Add')

dtype = numpy.float32
opset = 15
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(
    numpy.float32).reshape((3, 2))
cop = OnnxAdd('X', numpy.array([1], dtype=dtype), op_version=opset)
cop4 = OnnxAdd(cop, numpy.array([2], dtype=dtype), op_version=opset,
               output_names=['Y'])
model_def = cop4.to_onnx({'X': x}, target_opset=opset)
rt = OnnxMicroRuntime(model_def)
out = rt.run({'X': x})
pprint.pprint(out)

>>>

    somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/sphinxdoc/source/mlprodict/npy/xop_variable.py:64: DeprecationWarning: `mapping.NP_TYPE_TO_TENSOR_TYPE` is now deprecated and will be removed in the next release or so.To silence this warning, please use `helper.{self._future_function}` instead.
      return NP_TYPE_TO_TENSOR_TYPE[dtype]
    {'X': array([[1., 2.],
           [4., 5.],
           [5., 4.]], dtype=float32),
     'Y': array([[4., 5.],
           [7., 8.],
           [8., 7.]], dtype=float32),
     'init': array([1.], dtype=float32),
     'init_1': array([2.], dtype=float32),
     'out_add_0': array([[2., 3.],
           [5., 6.],
           [6., 5.]], dtype=float32)}

source on GitHub

__init__(model_onnx)#

_op_abs(x)#: Runtime for operator Op.Abs.

_op_add(x, y)#: Runtime for operator Op.Add.

_op_concat(*args, axis=None)#: Runtime for operator Op.Concat.

_op_gather(x, indices, axis=None)#: Runtime for operator Op.Gather.

_op_gemm(a, b, c=None, alpha=None, beta=None, transA=False, transB=False)#: Runtime for operator Op.Gemm.

_op_identity(x)#: Runtime for operator Op.Identity.

_op_matmul(x, y)#: Runtime for operator Op.MatMul.

_op_max(*inps)#: Runtime for operator Op.Max.

_op_mul(x, y)#: Runtime for operator Op.Mul.

_op_reduceprod(data, axes=None, keepdims=None)#: Runtime for operator Op.ReduceProd.

_op_reducesum(data, axes, keepdims=None, noop_with_empty_axes=None)#: Runtime for operator Op.ReduceSum.

_op_reshape(x, shape)#: Runtime for operator Op.Reshape.

_op_shape(x)#: Runtime for operator Op.Shape.

_op_squeeze(x, axes=None)#: Runtime for operator Op.Squeeze.

_op_transpose(x, perm=None)#: Runtime for operator Op.Transpose.

_op_unsqueeze(x, axes=None)#: Runtime for operator Op.Unsqueeze.

property input_names#: Returns input names.

property output_names#: Returns output names.

run(inputs)#

Computes the outputs of the graph.

Parameters:: inputs – dictionary
Returns:: all intermediates results and output as a dictionary

source on GitHub

module onnxrt.onnx_micro_runtime#

Short summary#

Classes#

Properties#

Methods#

Documentation#

module `onnxrt.onnx_micro_runtime`#