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Add an export to convert an onnx graph into light API code (#46)
* Add an export to convert an onnx graph into light API code * fix unit tests * fix annotations * fix documentation * doc
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‎CHANGELOGS.rst

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0.1.3
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+++++
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* :pr:`46`: adds an export to convert an onnx graph into light API code
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* :pr:`45`: fixes light API for operators with two outputs
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0.1.2

‎README.rst

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@@ -141,4 +141,4 @@ The euclidean distance looks like the following:
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The library is released on
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`pypi/onnx-array-api <https://pypi.org/project/onnx-array-api/>`_
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and its documentation is published at
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`(Numpy) Array API for ONNX <https://sdpython.github.io/doc/onnx-array-api/dev/>`_.
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`APIs to create ONNX Graphs <https://sdpython.github.io/doc/onnx-array-api/dev/>`_.

‎_doc/api/light_api.rst

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@@ -2,33 +2,67 @@
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onnx_array_api.light_api
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========================
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Main API
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========
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start
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=====
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+++++
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.. autofunction:: onnx_array_api.light_api.start
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translate
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+++++++++
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.. autofunction:: onnx_array_api.light_api.translate
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Classes for the Light API
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=========================
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OnnxGraph
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=========
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+++++++++
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.. autoclass:: onnx_array_api.light_api.OnnxGraph
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:members:
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BaseVar
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=======
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+++++++
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.. autoclass:: onnx_array_api.light_api.var.BaseVar
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:members:
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Var
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===
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+++
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.. autoclass:: onnx_array_api.light_api.Var
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:members:
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:inherited-members:
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Vars
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====
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++++
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.. autoclass:: onnx_array_api.light_api.Vars
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:members:
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:inherited-members:
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Classes for the Translater
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==========================
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Emitter
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+++++++
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.. autoclass:: onnx_array_api.light_api.translate.Emitter
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:members:
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EventType
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+++++++++
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.. autoclass:: onnx_array_api.light_api.translate.EventType
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:members:
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Translater
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++++++++++
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.. autoclass:: onnx_array_api.light_api.translate.Translater
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:members:
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‎_doc/index.rst

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CHANGELOGS
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license
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**Numpy API**
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Numpy API
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+++++++++
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Sources available on
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`github/onnx-array-api <https://github.com/sdpython/onnx-array-api>`_.
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res = jitted_myloss(x, y)
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print(to_dot(jitted_myloss.get_onnx()))
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**Light API**
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Light API
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+++++++++
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.. runpython::
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:showcode:
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)
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print(onnx_simple_text_plot(model))
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Older versions
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++++++++++++++
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* `0.1.2 <../v0.1.2/index.html>`_

‎_unittests/ut_light_api/test_translate.py

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import unittest
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from textwrap import dedent
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import numpy as np
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from onnx import ModelProto, TensorProto
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from onnx.defs import onnx_opset_version
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from onnx.reference import ReferenceEvaluator
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from onnx_array_api.ext_test_case import ExtTestCase
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from onnx_array_api.light_api import start, translate
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OPSET_API = min(19, onnx_opset_version() - 1)
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class TestTranslate(ExtTestCase):
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def test_exp(self):
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onx = start(opset=19).vin("X").Exp().rename("Y").vout().to_onnx()
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self.assertIsInstance(onx, ModelProto)
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self.assertIn("Exp", str(onx))
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ref = ReferenceEvaluator(onx)
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a = np.arange(10).astype(np.float32)
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got = ref.run(None, {"X": a})[0]
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self.assertEqualArray(np.exp(a), got)
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code = translate(onx)
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expected = dedent(
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"""
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(
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start(opset=19)
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.vin('X', elem_type=TensorProto.FLOAT)
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.bring('X')
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.Exp()
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.rename('Y')
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.bring('Y')
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.vout(elem_type=TensorProto.FLOAT)
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.to_onnx()
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)"""
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).strip("\n")
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self.assertEqual(expected, code)
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onx2 = (
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start(opset=19)
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.vin("X", elem_type=TensorProto.FLOAT)
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.bring("X")
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.Exp()
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.rename("Y")
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.bring("Y")
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.vout(elem_type=TensorProto.FLOAT)
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.to_onnx()
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)
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ref = ReferenceEvaluator(onx2)
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a = np.arange(10).astype(np.float32)
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got = ref.run(None, {"X": a})[0]
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self.assertEqualArray(np.exp(a), got)
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def test_transpose(self):
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onx = (
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start(opset=19)
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.vin("X")
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.reshape((-1, 1))
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.Transpose(perm=[1, 0])
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.rename("Y")
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.vout()
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.to_onnx()
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)
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self.assertIsInstance(onx, ModelProto)
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self.assertIn("Transpose", str(onx))
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ref = ReferenceEvaluator(onx)
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a = np.arange(10).astype(np.float32)
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got = ref.run(None, {"X": a})[0]
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self.assertEqualArray(a.reshape((-1, 1)).T, got)
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code = translate(onx)
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expected = dedent(
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"""
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(
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start(opset=19)
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.vin('X', elem_type=TensorProto.FLOAT)
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.bring('X', 'r')
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.Reshape()
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.rename('r0_0')
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.bring('r0_0')
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.Transpose(perm=[1, 0])
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.rename('Y')
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.bring('Y')
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.vout(elem_type=TensorProto.FLOAT)
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.to_onnx()
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)"""
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).strip("\n")
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self.assertEqual(expected, code)
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def test_topk_reverse(self):
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onx = (
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start(opset=19)
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.vin("X", np.float32)
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.vin("K", np.int64)
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.bring("X", "K")
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.TopK(largest=0)
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.rename("Values", "Indices")
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.vout()
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.to_onnx()
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)
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self.assertIsInstance(onx, ModelProto)
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ref = ReferenceEvaluator(onx)
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x = np.array([[0, 1, 2, 3], [9, 8, 7, 6]], dtype=np.float32)
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k = np.array([2], dtype=np.int64)
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got = ref.run(None, {"X": x, "K": k})
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self.assertEqualArray(np.array([[0, 1], [6, 7]], dtype=np.float32), got[0])
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self.assertEqualArray(np.array([[0, 1], [3, 2]], dtype=np.int64), got[1])
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code = translate(onx)
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expected = dedent(
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"""
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(
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start(opset=19)
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.vin('X', elem_type=TensorProto.FLOAT)
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.vin('K', elem_type=TensorProto.INT64)
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.bring('X', 'K')
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.TopK(axis=-1, largest=0, sorted=1)
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.rename('Values', 'Indices')
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.bring('Values')
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.vout(elem_type=TensorProto.FLOAT)
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.bring('Indices')
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.vout(elem_type=TensorProto.FLOAT)
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.to_onnx()
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)"""
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).strip("\n")
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self.assertEqual(expected, code)
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if __name__ == "__main__":
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# TestLightApi().test_topk()
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unittest.main(verbosity=2)

‎onnx_array_api/light_api/__init__.py

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from typing import Dict, Optional
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from onnx import ModelProto
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from .model import OnnxGraph
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from .translate import Translater
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from .var import Var, Vars
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from onnx_array_api.light_api import start
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onx = (
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start().vin("X").vin("Y").bring("X", "Y").Add().rename("Z").vout().to_onnx()
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start()
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.vin("X")
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.vin("Y")
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.bring("X", "Y")
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.Add()
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.rename("Z")
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.vout()
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.to_onnx()
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)
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print(onx)
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"""
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return OnnxGraph(opset=opset, opsets=opsets, is_function=is_function)
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def translate(proto: ModelProto, single_line=False) -> str:
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"""
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Translates an ONNX proto into a code using :ref:`l-light-api`
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to describe the ONNX graph.
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:param proto: model to translate
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:param single_line: as a single line or not
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:return: code
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.. runpython::
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:showcode:
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from onnx_array_api.light_api import start, translate
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onx = (
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start()
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.vin("X")
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.reshape((-1, 1))
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.Transpose(perm=[1, 0])
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.rename("Y")
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.vout()
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.to_onnx()
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)
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code = translate(onx)
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print(code)
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"""
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tr = Translater(proto)
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rows = tr.export()
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if single_line:
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return ".".join(rows)
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return "".join(["(\n ", "\n .".join(rows), "\n)"])

‎onnx_array_api/light_api/annotations.py

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ELEMENT_TYPE_NAME = {
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getattr(TensorProto, k): k
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for k in dir(TensorProto)
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if isinstance(getattr(TensorProto, k), int)
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if isinstance(getattr(TensorProto, k), int) and "_" not in k
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}
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_type_numpy = {

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