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Moves OnnxMicroRuntime to onnxrt #348

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Feb 5, 2022
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Moves OnnxMicroRuntime to onnxrt #348

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5 changes: 4 additions & 1 deletion _doc/sphinxdoc/source/api/onnxrt.rst
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Expand Up @@ -18,7 +18,10 @@ implementated in :epkg:`Python`. The :epkg:`ONNX` model relies
on the following operators :ref:`l-onnx-runtime-operators`.

.. autosignature:: mlprodict.onnxrt.onnx_inference.OnnxInference
:members:
:members: run, shape_inference, check_model, run2onnx, get_profiling

.. autosignature:: mlprodict.onnxrt.onnx_micro_inference.OnnxMicroRuntime
:members: run

Python to ONNX
++++++++++++++
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7 changes: 0 additions & 7 deletions _doc/sphinxdoc/source/api/tools.rst
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Expand Up @@ -83,13 +83,6 @@ Serialization

.. autosignature:: mlprodict.onnx_tools.onnx2py_helper.to_bytes

Runtime
=======

.. autosignature:: mlprodict.onnxrt.onnx_inference.OnnxInference

.. autosignature:: mlprodict.tools.onnx_micro_runtime.OnnxMicroRuntime

Validation
++++++++++

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242 changes: 121 additions & 121 deletions ...tests/ut_tools/test_onnx_micro_runtime.py → ...ests/ut_onnxrt/test_onnx_micro_runtime.py
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@@ -1,121 +1,121 @@
"""
@brief test log(time=3s)
"""
import unittest
import numpy
from pyquickhelper.pycode import ExtTestCase
from skl2onnx.algebra.onnx_ops import ( # pylint: disable=E0611
OnnxAdd, OnnxTranspose, OnnxShape, OnnxPow, OnnxMatMul, OnnxGemm,
OnnxSqueeze, OnnxUnsqueeze)
from mlprodict.tools.onnx_micro_runtime import OnnxMicroRuntime
class TestOnnxMicroRuntime(ExtTestCase):
opset = 15 # opset=13, 14, ...
def test_onnx_micro_runtime(self):
opset = TestOnnxMicroRuntime.opset
dtype = numpy.float32
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})
self.assertIn('X', out)
self.assertIn('Y', out)
self.assertIn('Ad_Addcst', out)
self.assertEqual(len(out), 5)
def test_onnx_micro_runtime_exc1(self):
self.assertRaise(lambda: OnnxMicroRuntime(None), TypeError)
def test_onnx_micro_runtime_exc2(self):
opset = TestOnnxMicroRuntime.opset
dtype = numpy.float32
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 = OnnxPow(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)
self.assertRaise(lambda: rt.run({'X': x}), NotImplementedError)
self.assertRaise(lambda: rt.run(x), TypeError)
def test_onnx_micro_runtime_shape(self):
opset = TestOnnxMicroRuntime.opset
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(
numpy.float32).reshape((3, 2))
cop = OnnxShape('X', op_version=opset, output_names=['Y'])
model_def = cop.to_onnx({'X': x}, target_opset=opset)
rt = OnnxMicroRuntime(model_def)
out = rt.run({'X': x})
self.assertEqual(numpy.array(x.shape, dtype=numpy.int64), out['Y'])
def test_onnx_micro_runtime_transpose(self):
opset = TestOnnxMicroRuntime.opset
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(
numpy.float32).reshape((3, 2))
cop = OnnxTranspose('X', perm=[1, 0], op_version=opset,
output_names=['Y'])
model_def = cop.to_onnx({'X': x}, target_opset=opset)
rt = OnnxMicroRuntime(model_def)
out = rt.run({'X': x})
self.assertEqual(x.T, out['Y'])
def test_onnx_micro_runtime_matmul(self):
opset = TestOnnxMicroRuntime.opset
x = numpy.array([1, 2, 4, 5]).astype(
numpy.float32).reshape((2, 2))
cop = OnnxMatMul('X', 'X', op_version=opset,
output_names=['Y'])
model_def = cop.to_onnx({'X': x}, target_opset=opset)
rt = OnnxMicroRuntime(model_def)
out = rt.run({'X': x})
self.assertEqual(numpy.matmul(x, x), out['Y'])
def test_onnx_micro_runtime_squeeze(self):
opset = TestOnnxMicroRuntime.opset
x = numpy.array([1, 2, 4, 5]).astype(
numpy.float32).reshape((2, 2, 1))
cop = OnnxSqueeze('X', numpy.array([2], dtype=numpy.int64),
op_version=opset, output_names=['Y'])
model_def = cop.to_onnx({'X': x}, target_opset=opset)
rt = OnnxMicroRuntime(model_def)
out = rt.run({'X': x})
self.assertEqual(numpy.squeeze(x), out['Y'])
def test_onnx_micro_runtime_unsqueeze(self):
opset = TestOnnxMicroRuntime.opset
x = numpy.array([1, 2, 4, 5]).astype(
numpy.float32).reshape((2, 2))
cop = OnnxUnsqueeze('X', numpy.array([2], dtype=numpy.int64),
op_version=opset, output_names=['Y'])
model_def = cop.to_onnx({'X': x}, target_opset=opset)
rt = OnnxMicroRuntime(model_def)
out = rt.run({'X': x})
self.assertEqual(x.reshape((2, 2, 1)), out['Y'])
def test_onnx_micro_runtime_gemm(self):
opset = TestOnnxMicroRuntime.opset
x = numpy.array([1, 2, 4, 5]).astype(
numpy.float32).reshape((2, 2))
for ta in [0, 1]:
for tb in [0, 1]:
cop = OnnxGemm(
'X', 'X', 'X', op_version=opset, alpha=1., beta=1.,
output_names=['Y'], transA=ta, transB=tb)
model_def = cop.to_onnx({'X': x}, target_opset=opset)
rt = OnnxMicroRuntime(model_def)
out = rt.run({'X': x})
xa = x.T if ta else x
xb = x.T if tb else x
self.assertEqual(numpy.matmul(xa, xb) + x, out['Y'])
if __name__ == "__main__":
unittest.main()
"""
@brief test log(time=3s)
"""
import unittest
import numpy
from pyquickhelper.pycode import ExtTestCase
from skl2onnx.algebra.onnx_ops import ( # pylint: disable=E0611
OnnxAdd, OnnxTranspose, OnnxShape, OnnxPow, OnnxMatMul, OnnxGemm,
OnnxSqueeze, OnnxUnsqueeze)
from mlprodict.onnxrt.onnx_micro_runtime import OnnxMicroRuntime


class TestOnnxMicroRuntime(ExtTestCase):

opset = 15 # opset=13, 14, ...

def test_onnx_micro_runtime(self):
opset = TestOnnxMicroRuntime.opset
dtype = numpy.float32
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})
self.assertIn('X', out)
self.assertIn('Y', out)
self.assertIn('Ad_Addcst', out)
self.assertEqual(len(out), 5)

def test_onnx_micro_runtime_exc1(self):
self.assertRaise(lambda: OnnxMicroRuntime(None), TypeError)

def test_onnx_micro_runtime_exc2(self):
opset = TestOnnxMicroRuntime.opset
dtype = numpy.float32
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 = OnnxPow(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)
self.assertRaise(lambda: rt.run({'X': x}), NotImplementedError)
self.assertRaise(lambda: rt.run(x), TypeError)

def test_onnx_micro_runtime_shape(self):
opset = TestOnnxMicroRuntime.opset
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(
numpy.float32).reshape((3, 2))
cop = OnnxShape('X', op_version=opset, output_names=['Y'])
model_def = cop.to_onnx({'X': x}, target_opset=opset)
rt = OnnxMicroRuntime(model_def)
out = rt.run({'X': x})
self.assertEqual(numpy.array(x.shape, dtype=numpy.int64), out['Y'])

def test_onnx_micro_runtime_transpose(self):
opset = TestOnnxMicroRuntime.opset
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(
numpy.float32).reshape((3, 2))
cop = OnnxTranspose('X', perm=[1, 0], op_version=opset,
output_names=['Y'])
model_def = cop.to_onnx({'X': x}, target_opset=opset)
rt = OnnxMicroRuntime(model_def)
out = rt.run({'X': x})
self.assertEqual(x.T, out['Y'])

def test_onnx_micro_runtime_matmul(self):
opset = TestOnnxMicroRuntime.opset
x = numpy.array([1, 2, 4, 5]).astype(
numpy.float32).reshape((2, 2))
cop = OnnxMatMul('X', 'X', op_version=opset,
output_names=['Y'])
model_def = cop.to_onnx({'X': x}, target_opset=opset)
rt = OnnxMicroRuntime(model_def)
out = rt.run({'X': x})
self.assertEqual(numpy.matmul(x, x), out['Y'])

def test_onnx_micro_runtime_squeeze(self):
opset = TestOnnxMicroRuntime.opset
x = numpy.array([1, 2, 4, 5]).astype(
numpy.float32).reshape((2, 2, 1))
cop = OnnxSqueeze('X', numpy.array([2], dtype=numpy.int64),
op_version=opset, output_names=['Y'])
model_def = cop.to_onnx({'X': x}, target_opset=opset)
rt = OnnxMicroRuntime(model_def)
out = rt.run({'X': x})
self.assertEqual(numpy.squeeze(x), out['Y'])

def test_onnx_micro_runtime_unsqueeze(self):
opset = TestOnnxMicroRuntime.opset
x = numpy.array([1, 2, 4, 5]).astype(
numpy.float32).reshape((2, 2))
cop = OnnxUnsqueeze('X', numpy.array([2], dtype=numpy.int64),
op_version=opset, output_names=['Y'])
model_def = cop.to_onnx({'X': x}, target_opset=opset)
rt = OnnxMicroRuntime(model_def)
out = rt.run({'X': x})
self.assertEqual(x.reshape((2, 2, 1)), out['Y'])

def test_onnx_micro_runtime_gemm(self):
opset = TestOnnxMicroRuntime.opset
x = numpy.array([1, 2, 4, 5]).astype(
numpy.float32).reshape((2, 2))
for ta in [0, 1]:
for tb in [0, 1]:
cop = OnnxGemm(
'X', 'X', 'X', op_version=opset, alpha=1., beta=1.,
output_names=['Y'], transA=ta, transB=tb)
model_def = cop.to_onnx({'X': x}, target_opset=opset)
rt = OnnxMicroRuntime(model_def)
out = rt.run({'X': x})
xa = x.T if ta else x
xb = x.T if tb else x
self.assertEqual(numpy.matmul(xa, xb) + x, out['Y'])


if __name__ == "__main__":
unittest.main()
2 changes: 2 additions & 0 deletions mlprodict/onnxrt/__init__.py
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Expand Up @@ -4,3 +4,5 @@
@brief Shortcut to *onnxrt*.
"""
from .onnx_inference import OnnxInference
from .onnx_micro_runtime import OnnxMicroRuntime

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