module onnxrt.onnx_shape_inference

Short summary

module mlprodict.onnxrt.onnx_shape_inference

Runtime to infer shapes.

Classes

class	truncated documentation
OnnxShapeInference	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.

Static Methods

staticmethod	truncated documentation
_get_shape

Methods

method	truncated documentation
__init__
__repr__	Usual
_run_empty	Computes shape and types of all results.
run	Runs shape inference and type given known inputs.

Documentation

Runtime to infer shapes.

New in version 0.9.

source on GitHub

class mlprodict.onnxrt.onnx_shape_inference.OnnxShapeInference(model_onnx)

Bases: object

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

Parameters:

model_onnx – ONNX model

Other attributes:

• known_shapes_: shapes which can be inferred without any input

• cache_: keeps track of the function used to infer the shapes

• is_isfunction: tells if the graph is a function or a model

<<<

import pprint
import numpy
from mlprodict.onnxrt.onnx_shape_inference import OnnxShapeInference
from mlprodict.npy.xop_variable import Variable
from mlprodict.npy.xop import loadop

opset = 15
OnnxAdd = loadop('Add')
dtype = numpy.float32

cop = OnnxAdd('X', numpy.array(
    [[1]], dtype=dtype), op_version=opset)
cop4 = OnnxAdd(cop, numpy.array([[2]], dtype=dtype),
               output_names=['Y'])
vari = Variable('X', numpy.float32, [None, 3])
model_def = cop4.to_onnx([vari], run_shape=False)
rt = OnnxShapeInference(model_def)
out = rt.run()
pprint.pprint(out.get())

>>>

    somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/sphinxdoc/source/mlprodict/npy/xop_variable.py:67: 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[dt]
    {'X': ShapeResult('X', ['_0', 3], dtype('float32')),
     'Y': ShapeResult('Y', ['_0', 3], dtype('float32')),
     'init': ShapeResult('init', [1, 1], dtype('float32')),
     'init_1': ShapeResult('init_1', [1, 1], dtype('float32')),
     'out_add_0': ShapeResult('out_add_0', ['_0', 3], dtype('float32'))}

source on GitHub

__init__(model_onnx)

__repr__()

Usual

static _get_shape(obj, known_shapes=None, result_name=None)

_run_empty()

Computes shape and types of all results.

Returns:

all intermediates results and output as a dictionary

source on GitHub

property input_names

Returns input names.

property output_names

Returns output names.

run(inputs=None)

Runs shape inference and type given known inputs.

Parameters:

inputs – inputs

Returns:

all results

source on GitHub