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Bug#1011033: marked as done (onnx: flaky autopkgtest on armhf: Arrays are not almost equal to 7 decimals)

Your message dated Sun, 10 Sep 2023 16:40:57 +0000
with message-id <E1qfNUX-004vH7-3s@fasolo.debian.org>
and subject line Bug#1011033: fixed in onnx 1.13.1-2
has caused the Debian Bug report #1011033,
regarding onnx: flaky autopkgtest on armhf: Arrays are not almost equal to 7 decimals
to be marked as done.

This means that you claim that the problem has been dealt with.
If this is not the case it is now your responsibility to reopen the
Bug report if necessary, and/or fix the problem forthwith.

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-- 
1011033: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=1011033
Debian Bug Tracking System
Contact owner@bugs.debian.org with problems
--- Begin Message --- 
Source: onnx
Version: 1.7.0+dfsg-3
Severity: serious
X-Debbugs-CC: debian-ci@lists.debian.org
User: debian-ci@lists.debian.org
Usertags: flaky

Dear maintainer(s),
I looked at the results of the autopkgtest of you package on armhf because it was showing up as a regression for the upload of gcc-12. I noticed that the test regularly fails, it seems to be restricted to this architecture. 

Because the unstable-to-testing migration software now blocks on
regressions in testing, flaky tests, i.e. tests that flip between
passing and failing without changes to the list of installed packages,
are causing people unrelated to your package to spend time on these
tests.

Don't hesitate to reach out if you need help and some more information
from our infrastructure.

Paul

https://ci.debian.net/packages/o/onnx/

https://ci.debian.net/data/autopkgtest/testing/armhf/o/onnx/21152474/log.gz
=================================== FAILURES =================================== _________________ TestOptimizer.test_fuse_bn_into_conv_simple __________________
self = <optimizer_test.TestOptimizer testMethod=test_fuse_bn_into_conv_simple> 

    def test_fuse_bn_into_conv_simple(self):  # type: () -> None
for (tensor_type, np_type) in [(TensorProto.FLOAT, np.float32), (TensorProto.DOUBLE, np.float64)]: 
            conv = helper.make_node("Conv", ["X", "W", "B"], ["Y"])
            bn = helper.make_node("BatchNormalization", [
                                  "Y", "scale", "b", "mean", "var"], ["Z"])

            W = np.random.randn(3, 2, 5, 5).astype(np_type) + 2
            B = np.random.randn(3,).astype(np_type) + 2
            scale = np.random.randn(3,).astype(np_type) + 2
            b = np.random.randn(3,).astype(np_type) + 2
            mean = np.random.randn(3,).astype(np_type) + 2
            var = np.abs(np.random.randn(3,).astype(np_type)) + 2

            initializers = [
                helper.make_tensor(name, tensor_type,
                                   npa.shape, npa.tobytes(), raw=True)
for name, npa in [('W', W), ('B', B), ('scale', scale), ('b', b), ('mean', mean), ('var', var)] 
            ]
            graph = helper.make_graph(
                [conv, bn],
                "test",
[helper.make_tensor_value_info("X", tensor_type, (5, 2, 28, 28)), helper.make_tensor_value_info("W", tensor_type, (3, 2, 5, 5)), 
                 helper.make_tensor_value_info("B", tensor_type, (3,)),
                 helper.make_tensor_value_info("scale", tensor_type, (3,)),
                 helper.make_tensor_value_info("b", tensor_type, (3,)),
                 helper.make_tensor_value_info("mean", tensor_type, (3,)),
                 helper.make_tensor_value_info("var", tensor_type, (3,))],
                [helper.make_tensor_value_info(
                    "Z", tensor_type, (5, 3, 24, 24))],
                initializer=initializers,
                value_info=[
                    helper.make_tensor_value_info(
                        "Y", tensor_type, (5, 3, 24, 24))
                ]
            )
            optimized_model = self._optimized(graph, ["fuse_bn_into_conv"])

            self.assertEqual(len(optimized_model.graph.node), 1)
            self.assertEqual(optimized_model.graph.node[0].op_type, 'Conv')
            self.assertEqual(len(optimized_model.graph.initializer), 2)
new_W = numpy_helper.to_array(optimized_model.graph.initializer[0]) new_b = numpy_helper.to_array(optimized_model.graph.initializer[1]) 

            f = scale / np.sqrt(var + 1e-5)
            np.testing.assert_almost_equal((B - mean) * f + b, new_b)
>           np.testing.assert_almost_equal(
                W * f[:, np.newaxis, np.newaxis, np.newaxis], new_W)
E           AssertionError:
E           Arrays are not almost equal to 7 decimals
E
E           Mismatched elements: 2 / 150 (1.33%)
E           Max absolute difference: 2.3841858e-07
E           Max relative difference: 1.1832779e-07
E x: array([[[[ 1.3602041, 0.6074747, 0.5127016, 1.3461237, 0.3262223], E [ 1.1601448, 1.40072 , 2.0301225, 1.1147949, 1.3020884], E [ 0.6207906, 1.1024365, 1.3334007, 1.5556062, 1.4650011],... E y: array([[[[ 1.3602041, 0.6074747, 0.5127015, 1.3461237, 0.3262222], E [ 1.1601447, 1.40072 , 2.0301223, 1.1147949, 1.3020883], E [ 0.6207906, 1.1024364, 1.3334006, 1.5556061, 1.465001 ],... 

onnx/test/optimizer_test.py:1510: AssertionError
=============================== warnings summary ===============================

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--- End Message ---
--- Begin Message --- 
Source: onnx
Source-Version: 1.13.1-2
Done: Mo Zhou <lumin@debian.org>

We believe that the bug you reported is fixed in the latest version of
onnx, which is due to be installed in the Debian FTP archive.

A summary of the changes between this version and the previous one is
attached.

Thank you for reporting the bug, which will now be closed.  If you
have further comments please address them to 1011033@bugs.debian.org,
and the maintainer will reopen the bug report if appropriate.

Debian distribution maintenance software
pp.
Mo Zhou <lumin@debian.org> (supplier of updated onnx package)

(This message was generated automatically at their request; if you
believe that there is a problem with it please contact the archive
administrators by mailing ftpmaster@ftp-master.debian.org)


-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA512

Format: 1.8
Date: Sun, 10 Sep 2023 11:24:11 -0400
Source: onnx
Architecture: source
Version: 1.13.1-2
Distribution: unstable
Urgency: medium
Maintainer: Debian Deep Learning Team <debian-ai@lists.debian.org>
Changed-By: Mo Zhou <lumin@debian.org>
Closes: 1011033 1048619 1051585
Changes:
 onnx (1.13.1-2) unstable; urgency=medium
 .
   * rules: Make rules less confusing.
   * rules: Toggle verbose makefile.
   * Autopkgtest: omit numerical precision issue on non-amd64 archs.
     (Closes: #1011033, #1051585)
   * Update dh_auto_clean to fix twice in a row FTBS. (Closes: #1048619)
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 f56e28c15b33bf07c673e6c45c372a30f0c8fa8e 2392 onnx_1.13.1-2.dsc
 360954e7029186078247d085b11762b8e7360c4d 12284 onnx_1.13.1-2.debian.tar.xz
 f5403ad6fc9896cb61dd8e615409b197ea57fa19 7574 onnx_1.13.1-2_source.buildinfo
Checksums-Sha256:
 bd26e5f8cbefaee216046b22b74cd89b8b3a8b3d0a8361e7af017309b0adae67 2392 onnx_1.13.1-2.dsc
 c2ccf181b6d90932865b16117aa330c1e1a643555043b573bf96643542173b51 12284 onnx_1.13.1-2.debian.tar.xz
 03590f31b86a373bc5a14a0943ba0a273ad417d8060f676632252024ffa6d01f 7574 onnx_1.13.1-2_source.buildinfo
Files:
 3ff2acdd35dd5a0964482f46275774e8 2392 science optional onnx_1.13.1-2.dsc
 b094178c2841234755d16526f1d84dd3 12284 science optional onnx_1.13.1-2.debian.tar.xz
 1c704e94ef6ead9eee11955affcee74f 7574 science optional onnx_1.13.1-2_source.buildinfo

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--- End Message ---
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