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increase code coverage (#10)
* if code coverage * fix issues
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‎.github/workflows/check-release.yml‎

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name: Check Release
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on:
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workflow_dispatch:
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schedule:
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# every first day of the week
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- cron: '0 0 * * *'
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# push:
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jobs:
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release-linux-mac:
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strategy:
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matrix:
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os: [ubuntu-latest, macOS-latest]
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python-version: ['3.12', '3.11', '3.10']
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runs-on: ${{ matrix.os }}
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steps:
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- uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install onnx-diagnostic
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run: pip install onnx-diagnostic
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- name: Version
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run: |
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python -c "import onnx_diagnostic;print(onnx_diagnostic.__version__)"
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- name: Installation path
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run: python -c "import onnx_diagnostic;print(onnx_diagnostic.__file__)"
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- name: git checkout
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run: |
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git init
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git remote add -f origin https://github.com/sdpython/onnx-diagnostic.git
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git config core.sparsecheckout true
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echo _unittests/ >> .git/info/sparse-checkout
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echo _doc/examples/ >> .git/info/sparse-checkout
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echo pyproject.toml >> .git/info/sparse-checkout
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echo requirements-dev.txt >> .git/info/sparse-checkout
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git pull origin main
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VERSION=$(python -c "import onnx_diagnostic;print(onnx_diagnostic.__version__)")
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git checkout tags/${VERSION} -b thistag
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- name: ls
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run: ls .
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- name: Install requirements
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run: pip install -r requirements-dev.txt
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- name: Run pytest
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run: pytest _unittests
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release-windows:
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strategy:
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matrix:
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os: [windows-latest]
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python-version: ['3.12', '3.11', '3.10']
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runs-on: ${{ matrix.os }}
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steps:
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- uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install onnx-diagnostic
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run: pip install onnx-diagnostic
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- name: Version
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run: |
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python -c "import onnx_diagnostic;print(onnx_diagnostic.__version__)"
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- name: Installation path
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run: python -c "import onnx_diagnostic;print(onnx_diagnostic.__file__)"
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- name: git checkout
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run: |
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git init
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git remote add -f origin https://github.com/sdpython/onnx-diagnostic.git
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git config core.sparsecheckout true
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echo _unittests/ >> .git\info\sparse-checkout
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echo _doc/examples/ >> .git\info\sparse-checkout
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echo pyproject.toml >> .git\info\sparse-checkout
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echo requirements-dev.txt >> .git/info/sparse-checkout
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git pull origin main
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git checkout tags/0.2.2 -b thistag
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- name: ls
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run: ls .
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- name: Install requirements
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run: pip install -r requirements-dev.txt
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- name: Run pytest
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run: pytest _unittests

‎_doc/index.rst‎

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@@ -18,7 +18,8 @@ onnx-diagnostic: investigate onnx models
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.. image:: https://codecov.io/gh/sdpython/onnx-diagnostic/branch/main/graph/badge.svg?token=Wb9ZGDta8J
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:target: https://codecov.io/gh/sdpython/onnx-diagnostic
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**onnx-diagnostic** is mostly to experiment ideas.
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**onnx-diagnostic** helps investgating onnx models, exporting models into onnx.
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It implements tools used to understand issues.
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Source are `sdpython/onnx-diagnostic
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<https://github.com/sdpython/onnx-diagnostic>`_.

‎_unittests/ut_reference/test_ort_evaluator.py‎

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import ml_dtypes
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from onnx import ModelProto, TensorProto
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from onnx.checker import check_model
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import onnx
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import onnx.helper as oh
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import onnx.numpy_helper as onh
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import torch
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self.assertIsInstance(got[0], (torch.Tensor, np.ndarray))
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self.assertEqualArray(expected[0], got[0])
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@hide_stdout()
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def test_if(self):
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def _mkv_(name):
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value_info_proto = onnx.ValueInfoProto()
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value_info_proto.name = name
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return value_info_proto
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model = oh.make_model(
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oh.make_graph(
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[
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oh.make_node("ReduceSum", ["X"], ["Xred"]),
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oh.make_node("Add", ["X", "two"], ["X0"]),
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oh.make_node("Add", ["X0", "zero"], ["X00"]),
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oh.make_node("CastLike", ["one", "Xred"], ["one_c"]),
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oh.make_node("Greater", ["Xred", "one_c"], ["cond"]),
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oh.make_node(
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"If",
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["cond"],
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["Z_c"],
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then_branch=oh.make_graph(
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[
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oh.make_node("Constant", [], ["two"], value_floats=[2.1]),
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oh.make_node("Add", ["X00", "two"], ["Y"]),
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],
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"then",
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[],
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[_mkv_("Y")],
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),
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else_branch=oh.make_graph(
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[
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oh.make_node("Constant", [], ["two"], value_floats=[2.2]),
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oh.make_node("Sub", ["X0", "two"], ["Y"]),
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],
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"else",
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[],
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[_mkv_("Y")],
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),
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),
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oh.make_node("CastLike", ["Z_c", "X"], ["Z"]),
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],
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"test",
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[
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oh.make_tensor_value_info("X", TensorProto.FLOAT, ["N"]),
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oh.make_tensor_value_info("one", TensorProto.FLOAT, ["N"]),
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],
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[oh.make_tensor_value_info("Z", TensorProto.UNDEFINED, ["N"])],
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[
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onh.from_array(np.array([0], dtype=np.float32), name="zero"),
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onh.from_array(np.array([2], dtype=np.float32), name="two"),
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],
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),
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opset_imports=[oh.make_operatorsetid("", 18)],
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ir_version=10,
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)
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feeds = {
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"X": np.array([1, 2, 3], dtype=np.float32),
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"one": np.array([1], dtype=np.float32),
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}
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ref = ExtendedReferenceEvaluator(model, verbose=10)
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expected = ref.run(None, feeds)[0]
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sess = OnnxruntimeEvaluator(model, verbose=10)
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got = sess.run(None, feeds)[0]
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self.assertEqualArray(expected[0], got[0])
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if __name__ == "__main__":
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unittest.main(verbosity=2)

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