0s autopkgtest [20:48:26]: starting date and time: 2024-03-25 20:48:26+0000 0s autopkgtest [20:48:26]: git checkout: 4a1cd702 l/adt_testbed: don't blame the testbed for unsolvable build deps 0s autopkgtest [20:48:26]: host juju-7f2275-prod-proposed-migration-environment-3; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.afszsryl/out --timeout-copy=6000 --setup-commands /home/ubuntu/autopkgtest-cloud/worker-config-production/setup-canonical.sh --setup-commands /home/ubuntu/autopkgtest/setup-commands/setup-testbed --apt-pocket=proposed=src:onnx,src:protobuf --apt-upgrade onnx --timeout-short=300 --timeout-copy=20000 --timeout-build=20000 '--env=ADT_TEST_TRIGGERS=onnx/1.14.1-2.1build1 protobuf/3.21.12-8.2' -- ssh -s /home/ubuntu/autopkgtest/ssh-setup/nova -- --flavor autopkgtest --security-groups autopkgtest-juju-7f2275-prod-proposed-migration-environment-3@bos02-s390x-16.secgroup --name adt-noble-s390x-onnx-20240325-204826-juju-7f2275-prod-proposed-migration-environment-3 --image adt/ubuntu-noble-s390x-server --keyname testbed-juju-7f2275-prod-proposed-migration-environment-3 --net-id=net_prod-proposed-migration -e TERM=linux -e ''"'"'http_proxy=http://squid.internal:3128'"'"'' -e ''"'"'https_proxy=http://squid.internal:3128'"'"'' -e ''"'"'no_proxy=127.0.0.1,127.0.1.1,login.ubuntu.com,localhost,localdomain,novalocal,internal,archive.ubuntu.com,ports.ubuntu.com,security.ubuntu.com,ddebs.ubuntu.com,changelogs.ubuntu.com,launchpadlibrarian.net,launchpadcontent.net,launchpad.net,10.24.0.0/24,keystone.ps5.canonical.com,objectstorage.prodstack5.canonical.com'"'"'' --mirror=http://ftpmaster.internal/ubuntu/ 160s autopkgtest [20:51:06]: testbed dpkg architecture: s390x 160s autopkgtest [20:51:06]: testbed apt version: 2.7.12 160s autopkgtest [20:51:06]: @@@@@@@@@@@@@@@@@@@@ test bed setup 161s Get:1 http://ftpmaster.internal/ubuntu noble-proposed InRelease [117 kB] 162s Get:2 http://ftpmaster.internal/ubuntu noble-proposed/universe Sources [4041 kB] 164s Get:3 http://ftpmaster.internal/ubuntu noble-proposed/multiverse Sources [56.0 kB] 164s Get:4 http://ftpmaster.internal/ubuntu noble-proposed/restricted Sources [7608 B] 164s Get:5 http://ftpmaster.internal/ubuntu noble-proposed/main Sources [497 kB] 164s Get:6 http://ftpmaster.internal/ubuntu noble-proposed/main s390x Packages [693 kB] 164s Get:7 http://ftpmaster.internal/ubuntu noble-proposed/main s390x c-n-f Metadata [3032 B] 164s Get:8 http://ftpmaster.internal/ubuntu noble-proposed/restricted s390x Packages [1372 B] 164s Get:9 http://ftpmaster.internal/ubuntu noble-proposed/restricted s390x c-n-f Metadata [116 B] 164s Get:10 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x Packages [4136 kB] 165s Get:11 http://ftpmaster.internal/ubuntu noble-proposed/universe s390x c-n-f Metadata [7292 B] 165s Get:12 http://ftpmaster.internal/ubuntu noble-proposed/multiverse s390x Packages [47.8 kB] 165s Get:13 http://ftpmaster.internal/ubuntu noble-proposed/multiverse s390x c-n-f Metadata [116 B] 166s Fetched 9607 kB in 5s (1981 kB/s) 167s Reading package lists... 170s Reading package lists... 170s Building dependency tree... 170s Reading state information... 170s Calculating upgrade... 170s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 170s Reading package lists... 170s Building dependency tree... 170s Reading state information... 171s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 171s Unknown architecture, assuming PC-style ttyS0 171s sh: Attempting to set up Debian/Ubuntu apt sources automatically 171s sh: Distribution appears to be Ubuntu 172s Reading package lists... 172s Building dependency tree... 172s Reading state information... 172s eatmydata is already the newest version (131-1). 172s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 172s Reading package lists... 172s Building dependency tree... 172s Reading state information... 172s dbus is already the newest version (1.14.10-4ubuntu1). 172s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 172s Reading package lists... 172s Building dependency tree... 172s Reading state information... 172s rng-tools-debian is already the newest version (2.4). 172s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 173s Reading package lists... 173s Building dependency tree... 173s Reading state information... 173s The following packages will be REMOVED: 173s cloud-init* python3-configobj* python3-debconf* 173s 0 upgraded, 0 newly installed, 3 to remove and 0 not upgraded. 173s After this operation, 3256 kB disk space will be freed. 173s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 52170 files and directories currently installed.) 173s Removing cloud-init (24.1.2-0ubuntu1) ... 173s Removing python3-configobj (5.0.8-3) ... 174s Removing python3-debconf (1.5.86) ... 174s Processing triggers for man-db (2.12.0-3) ... 174s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 51781 files and directories currently installed.) 174s Purging configuration files for cloud-init (24.1.2-0ubuntu1) ... 174s dpkg: warning: while removing cloud-init, directory '/etc/cloud/cloud.cfg.d' not empty so not removed 174s Processing triggers for rsyslog (8.2312.0-3ubuntu3) ... 174s invoke-rc.d: policy-rc.d denied execution of try-restart. 175s Reading package lists... 175s Building dependency tree... 175s Reading state information... 175s linux-generic is already the newest version (6.8.0-11.11+1). 175s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 176s Hit:1 http://ftpmaster.internal/ubuntu noble InRelease 176s Hit:2 http://ftpmaster.internal/ubuntu noble-updates InRelease 176s Hit:3 http://ftpmaster.internal/ubuntu noble-security InRelease 177s Reading package lists... 177s Reading package lists... 177s Building dependency tree... 177s Reading state information... 178s Calculating upgrade... 178s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 178s Reading package lists... 178s Building dependency tree... 178s Reading state information... 178s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 178s autopkgtest [20:51:24]: rebooting testbed after setup commands that affected boot 196s autopkgtest [20:51:42]: testbed running kernel: Linux 6.8.0-11-generic #11-Ubuntu SMP Tue Feb 13 23:45:46 UTC 2024 199s autopkgtest [20:51:45]: @@@@@@@@@@@@@@@@@@@@ apt-source onnx 206s Get:1 http://ftpmaster.internal/ubuntu noble/universe onnx 1.14.1-2.1 (dsc) [2426 B] 206s Get:2 http://ftpmaster.internal/ubuntu noble/universe onnx 1.14.1-2.1 (tar) [11.6 MB] 206s Get:3 http://ftpmaster.internal/ubuntu noble/universe onnx 1.14.1-2.1 (diff) [12.4 kB] 207s gpgv: Signature made Thu Feb 29 02:30:33 2024 UTC 207s gpgv: using RSA key A62D2CFBD50B9B5BF360D54B159EB5C4EFC8774C 207s gpgv: Can't check signature: No public key 207s dpkg-source: warning: cannot verify inline signature for ./onnx_1.14.1-2.1.dsc: no acceptable signature found 207s autopkgtest [20:51:53]: testing package onnx version 1.14.1-2.1 208s autopkgtest [20:51:54]: build not needed 223s autopkgtest [20:52:09]: test command1: preparing testbed 226s Reading package lists... 226s Building dependency tree... 226s Reading state information... 227s Starting pkgProblemResolver with broken count: 0 227s Starting 2 pkgProblemResolver with broken count: 0 227s Done 227s The following additional packages will be installed: 227s libblas3 libgfortran5 liblapack3 libonnx-dev libonnx-testdata libonnx1t64 227s libprotobuf32 python3-iniconfig python3-numpy python3-onnx python3-packaging 227s python3-parameterized python3-pluggy python3-protobuf python3-pytest 227s Suggested packages: 227s gcc gfortran python3-dev 227s The following NEW packages will be installed: 227s autopkgtest-satdep libblas3 libgfortran5 liblapack3 libonnx-dev 227s libonnx-testdata libonnx1t64 libprotobuf32 python3-iniconfig python3-numpy 227s python3-onnx python3-packaging python3-parameterized python3-pluggy 227s python3-protobuf python3-pytest 227s 0 upgraded, 16 newly installed, 0 to remove and 0 not upgraded. 227s Need to get 18.8 MB/18.8 MB of archives. 227s After this operation, 81.9 MB of additional disk space will be used. 227s Get:1 /tmp/autopkgtest.buTYAJ/1-autopkgtest-satdep.deb autopkgtest-satdep s390x 0 [748 B] 227s Get:2 http://ftpmaster.internal/ubuntu noble/main s390x libblas3 s390x 3.12.0-3 [245 kB] 228s Get:3 http://ftpmaster.internal/ubuntu noble/main s390x libgfortran5 s390x 14-20240303-1ubuntu1 [598 kB] 228s Get:4 http://ftpmaster.internal/ubuntu noble/main s390x liblapack3 s390x 3.12.0-3 [2979 kB] 231s Get:5 http://ftpmaster.internal/ubuntu noble/main s390x libprotobuf32 s390x 3.21.12-8ubuntu5 [954 kB] 231s Get:6 http://ftpmaster.internal/ubuntu noble/universe s390x libonnx1t64 s390x 1.14.1-2.1 [842 kB] 232s Get:7 http://ftpmaster.internal/ubuntu noble/universe s390x libonnx-dev s390x 1.14.1-2.1 [121 kB] 232s Get:8 http://ftpmaster.internal/ubuntu noble/universe s390x libonnx-testdata all 1.14.1-2.1 [6616 kB] 235s Get:9 http://ftpmaster.internal/ubuntu noble/universe s390x python3-iniconfig all 1.1.1-2 [6024 B] 235s Get:10 http://ftpmaster.internal/ubuntu noble/main s390x python3-numpy s390x 1:1.24.2-2 [5137 kB] 237s Get:11 http://ftpmaster.internal/ubuntu noble/universe s390x python3-protobuf s390x 3.21.12-8ubuntu5 [245 kB] 237s Get:12 http://ftpmaster.internal/ubuntu noble/universe s390x python3-onnx s390x 1.14.1-2.1 [633 kB] 237s Get:13 http://ftpmaster.internal/ubuntu noble/main s390x python3-packaging all 23.2-1 [40.6 kB] 237s Get:14 http://ftpmaster.internal/ubuntu noble/universe s390x python3-parameterized all 0.8.1-6 [19.5 kB] 237s Get:15 http://ftpmaster.internal/ubuntu noble/universe s390x python3-pluggy all 1.4.0-1 [20.4 kB] 237s Get:16 http://ftpmaster.internal/ubuntu noble/universe s390x python3-pytest all 7.4.4-1 [305 kB] 238s Fetched 18.8 MB in 11s (1762 kB/s) 238s Selecting previously unselected package libblas3:s390x. 238s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 51726 files and directories currently installed.) 238s Preparing to unpack .../00-libblas3_3.12.0-3_s390x.deb ... 238s Unpacking libblas3:s390x (3.12.0-3) ... 238s Selecting previously unselected package libgfortran5:s390x. 238s Preparing to unpack .../01-libgfortran5_14-20240303-1ubuntu1_s390x.deb ... 238s Unpacking libgfortran5:s390x (14-20240303-1ubuntu1) ... 238s Selecting previously unselected package liblapack3:s390x. 238s Preparing to unpack .../02-liblapack3_3.12.0-3_s390x.deb ... 238s Unpacking liblapack3:s390x (3.12.0-3) ... 238s Selecting previously unselected package libprotobuf32:s390x. 238s Preparing to unpack .../03-libprotobuf32_3.21.12-8ubuntu5_s390x.deb ... 238s Unpacking libprotobuf32:s390x (3.21.12-8ubuntu5) ... 238s Selecting previously unselected package libonnx1t64:s390x. 238s Preparing to unpack .../04-libonnx1t64_1.14.1-2.1_s390x.deb ... 238s Unpacking libonnx1t64:s390x (1.14.1-2.1) ... 238s Selecting previously unselected package libonnx-dev:s390x. 238s Preparing to unpack .../05-libonnx-dev_1.14.1-2.1_s390x.deb ... 238s Unpacking libonnx-dev:s390x (1.14.1-2.1) ... 238s Selecting previously unselected package libonnx-testdata. 238s Preparing to unpack .../06-libonnx-testdata_1.14.1-2.1_all.deb ... 238s Unpacking libonnx-testdata (1.14.1-2.1) ... 239s Selecting previously unselected package python3-iniconfig. 239s Preparing to unpack .../07-python3-iniconfig_1.1.1-2_all.deb ... 239s Unpacking python3-iniconfig (1.1.1-2) ... 239s Selecting previously unselected package python3-numpy. 239s Preparing to unpack .../08-python3-numpy_1%3a1.24.2-2_s390x.deb ... 239s Unpacking python3-numpy (1:1.24.2-2) ... 239s Selecting previously unselected package python3-protobuf. 239s Preparing to unpack .../09-python3-protobuf_3.21.12-8ubuntu5_s390x.deb ... 239s Unpacking python3-protobuf (3.21.12-8ubuntu5) ... 239s Selecting previously unselected package python3-onnx. 239s Preparing to unpack .../10-python3-onnx_1.14.1-2.1_s390x.deb ... 239s Unpacking python3-onnx (1.14.1-2.1) ... 239s Selecting previously unselected package python3-packaging. 239s Preparing to unpack .../11-python3-packaging_23.2-1_all.deb ... 239s Unpacking python3-packaging (23.2-1) ... 239s Selecting previously unselected package python3-parameterized. 239s Preparing to unpack .../12-python3-parameterized_0.8.1-6_all.deb ... 239s Unpacking python3-parameterized (0.8.1-6) ... 239s Selecting previously unselected package python3-pluggy. 239s Preparing to unpack .../13-python3-pluggy_1.4.0-1_all.deb ... 239s Unpacking python3-pluggy (1.4.0-1) ... 239s Selecting previously unselected package python3-pytest. 239s Preparing to unpack .../14-python3-pytest_7.4.4-1_all.deb ... 239s Unpacking python3-pytest (7.4.4-1) ... 239s Selecting previously unselected package autopkgtest-satdep. 239s Preparing to unpack .../15-1-autopkgtest-satdep.deb ... 239s Unpacking autopkgtest-satdep (0) ... 239s Setting up python3-iniconfig (1.1.1-2) ... 240s Setting up python3-parameterized (0.8.1-6) ... 240s Setting up libblas3:s390x (3.12.0-3) ... 240s update-alternatives: using /usr/lib/s390x-linux-gnu/blas/libblas.so.3 to provide /usr/lib/s390x-linux-gnu/libblas.so.3 (libblas.so.3-s390x-linux-gnu) in auto mode 240s Setting up python3-packaging (23.2-1) ... 240s Setting up libgfortran5:s390x (14-20240303-1ubuntu1) ... 240s Setting up python3-pluggy (1.4.0-1) ... 240s Setting up libprotobuf32:s390x (3.21.12-8ubuntu5) ... 240s Setting up libonnx1t64:s390x (1.14.1-2.1) ... 240s Setting up libonnx-testdata (1.14.1-2.1) ... 240s Setting up python3-protobuf (3.21.12-8ubuntu5) ... 240s Setting up libonnx-dev:s390x (1.14.1-2.1) ... 240s Setting up liblapack3:s390x (3.12.0-3) ... 240s update-alternatives: using /usr/lib/s390x-linux-gnu/lapack/liblapack.so.3 to provide /usr/lib/s390x-linux-gnu/liblapack.so.3 (liblapack.so.3-s390x-linux-gnu) in auto mode 240s Setting up python3-pytest (7.4.4-1) ... 240s Setting up python3-numpy (1:1.24.2-2) ... 242s Setting up python3-onnx (1.14.1-2.1) ... 243s Setting up autopkgtest-satdep (0) ... 243s Processing triggers for man-db (2.12.0-3) ... 243s Processing triggers for libc-bin (2.39-0ubuntu6) ... 246s (Reading database ... 61780 files and directories currently installed.) 246s Removing autopkgtest-satdep (0) ... 246s autopkgtest [20:52:32]: test command1: test $(arch) = 'x86_64' && (pytest-3 -v -k 'not OnnxBackendRealModelTest and not examples') || (pytest-3 -v -k 'not OnnxBackendRealModelTest and not examples' || true) 246s autopkgtest [20:52:32]: test command1: [----------------------- 247s ============================= test session starts ============================== 247s platform linux -- Python 3.12.2, pytest-7.4.4, pluggy-1.4.0 -- /usr/bin/python3 247s cachedir: .pytest_cache 247s rootdir: /tmp/autopkgtest.buTYAJ/build.YML/src 247s configfile: setup.cfg 247s testpaths: onnx/test, onnx/examples 248s collecting ... collected 7727 items / 36 deselected / 7691 selected 248s 248s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Abs PASSED [ 0%] 248s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Acos PASSED [ 0%] 248s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Acosh PASSED [ 0%] 248s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Add_1 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Add_2 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Add_3 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_And PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_ArgMax_1 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_ArgMax_2 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_ArgMin_1 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_ArgMin_2 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Asin PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Asinh PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Atan PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Atanh PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_AveragePool PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_BatchNormalization_1 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_BatchNormalization_2 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Bernoulli PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_BitShift PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_BitwiseAnd PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_BitwiseNot PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_BitwiseOr PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_BitwiseXor PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_BlackmanWindow PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Cast PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_CastLike PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Ceil PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Celu PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_CenterCropPad PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Clip_1 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Clip_2 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Clip_3 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Clip_4 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Col2Im_4D PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Col2Im_5D PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Compress PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Concat PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_ConstantOfShape PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_ConvTranspose PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Conv_1 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Conv_2 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Conv_3 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Convinteger PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Cos PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Cosh PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Cumsum PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_DFT PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_DeformConv PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_DepthToSpace PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_DequantizeLinear PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Det_1 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Det_2 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Div PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Dropout PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_DynamicQuantizeLinear PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Einsum_1 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Einsum_2 PASSED [ 0%] 249s onnx/test/automatic_upgrade_test.py::TestAutomaticUpgrade::test_Elu PASSED 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onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_max_uint64_cpu PASSED [ 78%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_max_uint64_cuda SKIPPED [ 78%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_max_uint8_cpu PASSED [ 78%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_max_uint8_cuda SKIPPED [ 78%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_1d_default_cpu PASSED [ 78%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_1d_default_cuda SKIPPED [ 78%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_ceil_cpu PASSED [ 78%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_ceil_cuda SKIPPED [ 78%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_default_cpu PASSED [ 78%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_default_cuda SKIPPED [ 78%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_dilations_cpu PASSED [ 78%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_dilations_cuda SKIPPED [ 78%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_pads_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_pads_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_precomputed_pads_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_precomputed_pads_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_precomputed_same_upper_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_precomputed_same_upper_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_precomputed_strides_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_precomputed_strides_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_same_lower_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_same_lower_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_same_upper_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_same_upper_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_strides_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_strides_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_uint8_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_2d_uint8_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_3d_default_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_3d_default_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_with_argmax_2d_precomputed_pads_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_with_argmax_2d_precomputed_pads_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_with_argmax_2d_precomputed_strides_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxpool_with_argmax_2d_precomputed_strides_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxunpool_export_with_output_shape_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxunpool_export_with_output_shape_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxunpool_export_without_output_shape_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_maxunpool_export_without_output_shape_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mean_example_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mean_example_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mean_one_input_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mean_one_input_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mean_two_inputs_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mean_two_inputs_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_melweightmatrix_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_melweightmatrix_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_example_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_example_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_float16_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_float16_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_float32_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_float32_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_float64_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_float64_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_int16_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_int16_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_int32_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_int32_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_int64_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_int64_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_int8_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_int8_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_one_input_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_one_input_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_two_inputs_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_two_inputs_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_uint16_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_uint16_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_uint32_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_uint32_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_uint64_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_uint64_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_uint8_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_min_uint8_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mish_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mish_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mish_expanded_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mish_expanded_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_broadcast_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_broadcast_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_int64_fmod_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_int64_fmod_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_mixed_sign_float16_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_mixed_sign_float16_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_mixed_sign_float32_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_mixed_sign_float32_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_mixed_sign_float64_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_mixed_sign_float64_cuda SKIPPED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_mixed_sign_int16_cpu PASSED [ 79%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_mixed_sign_int16_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_mixed_sign_int32_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_mixed_sign_int32_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_mixed_sign_int64_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_mixed_sign_int64_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_mixed_sign_int8_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_mixed_sign_int8_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_uint16_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_uint16_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_uint32_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_uint32_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_uint64_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_uint64_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_uint8_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mod_uint8_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_momentum_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_momentum_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_momentum_multiple_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_momentum_multiple_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mul_bcast_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mul_bcast_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mul_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mul_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mul_example_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mul_example_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mul_uint8_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mul_uint8_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mvn_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mvn_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mvn_expanded_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mvn_expanded_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mvn_expanded_ver18_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_mvn_expanded_ver18_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_neg_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_neg_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_neg_example_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_neg_example_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nesterov_momentum_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nesterov_momentum_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NC_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NC_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NC_expanded_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NC_expanded_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_expanded_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_expanded_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_ii_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_ii_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_ii_expanded_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_ii_expanded_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_mean_weight_negative_ii_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_mean_weight_negative_ii_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_mean_weight_negative_ii_expanded_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_mean_weight_negative_ii_expanded_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_weight_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_weight_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_weight_expanded_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_weight_expanded_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_weight_ii_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_weight_ii_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_weight_ii_expanded_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1_weight_ii_expanded_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_expanded_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_expanded_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_no_weight_reduction_mean_ii_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_no_weight_reduction_mean_ii_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_reduction_mean_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_reduction_mean_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_reduction_mean_expanded_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_reduction_mean_expanded_cuda SKIPPED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_reduction_sum_cpu PASSED [ 80%] 335s onnx/test/test_backend_test.py::OnnxBackendNodeModelTest::test_nllloss_NCd1d2_reduction_sum_cuda SKIPPED [ 80%] 335s 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onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_AvgPool2d_cpu PASSED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_AvgPool2d_cuda SKIPPED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_AvgPool2d_stride_cpu PASSED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_AvgPool2d_stride_cuda SKIPPED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_AvgPool3d_cpu PASSED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_AvgPool3d_cuda SKIPPED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_AvgPool3d_stride1_pad0_gpu_input_cpu PASSED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_AvgPool3d_stride1_pad0_gpu_input_cuda SKIPPED [ 95%] 336s 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onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_BatchNorm3d_eval_cpu PASSED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_BatchNorm3d_eval_cuda SKIPPED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_BatchNorm3d_momentum_eval_cpu PASSED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_BatchNorm3d_momentum_eval_cuda SKIPPED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_ConstantPad2d_cpu PASSED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_ConstantPad2d_cuda SKIPPED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Conv1d_cpu PASSED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Conv1d_cuda SKIPPED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Conv1d_dilated_cpu PASSED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Conv1d_dilated_cuda SKIPPED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Conv1d_groups_cpu PASSED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Conv1d_groups_cuda SKIPPED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Conv1d_pad1_cpu PASSED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Conv1d_pad1_cuda SKIPPED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Conv1d_pad1size1_cpu PASSED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Conv1d_pad1size1_cuda SKIPPED [ 95%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Conv1d_pad2_cpu 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onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_LeakyReLU_with_negval_cpu PASSED [ 96%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_LeakyReLU_with_negval_cuda SKIPPED [ 96%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Linear_cpu PASSED [ 96%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Linear_cuda SKIPPED [ 96%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Linear_no_bias_cpu PASSED [ 96%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_Linear_no_bias_cuda SKIPPED [ 96%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_LogSoftmax_cpu PASSED [ 96%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_LogSoftmax_cuda SKIPPED [ 96%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_MaxPool1d_cpu PASSED [ 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SKIPPED [ 97%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_PReLU_3d_multiparam_cpu PASSED [ 97%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_PReLU_3d_multiparam_cuda SKIPPED [ 97%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_PixelShuffle_cpu PASSED [ 97%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_PixelShuffle_cuda SKIPPED [ 97%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_PoissonNLLLLoss_no_reduce_cpu PASSED [ 97%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_PoissonNLLLLoss_no_reduce_cuda SKIPPED [ 97%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_ReLU_cpu PASSED [ 97%] 336s onnx/test/test_backend_test.py::OnnxBackendPyTorchConvertedModelTest::test_ReLU_cuda SKIPPED [ 97%] 336s 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onnx/test/version_converter_test.py::TestVersionConverter::test_backwards_incompatible PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_batch_normalization_5_8 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_batch_normalization_8_5 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_batch_normalization_8_9 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_batchnormalization_9_8 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_cast_8_9 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_concat_3_5 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_concat_5_3 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_constant_8_9 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_constant_9_8 PASSED [ 99%] 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onnx/test/version_converter_test.py::TestVersionConverter::test_gru_13_14 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_gru_14_13 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_if_subgraph_10_11 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_initializer_not_in_input_above_ir4 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_less_8_9 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_less_9_8 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_lstm_13_14 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_lstm_14_13 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_matmul_8_9 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_matmul_9_8 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_max_7_8 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_maxpool_down PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_maxpool_up PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_mean_7_8 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_min_7_8 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_mul_5_8 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_mul_8_5 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_non_existent_op PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_pad_10_11 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_pad_with_value_10_11 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_prelu_8_9 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_prelu_9_8 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_relu_5_7 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_relu_7_5 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_reshape_4_6 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_reshape_6_4 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_rnn_13_14 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_rnn_14_13 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_scan_8_9 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_slice_9_10 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_softmax_12_13 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_split_12_13 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_split_13_12 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_sum_5_8 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_sum_7_8 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_sum_8_5 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_upsample_6_7 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_upsample_8_9 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_upsample_with_constant_node_9_8 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_upsample_with_initializer_9_8 PASSED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_upsample_with_raw_constant_node_9_8 FAILED [ 99%] 337s onnx/test/version_converter_test.py::TestVersionConverter::test_upsample_with_raw_initializer_9_8 FAILED [100%] 337s 337s ==================================== ERRORS ==================================== 337s _ ERROR at teardown of TestOnnxBackEndWithReferenceEvaluator.test_xor_bcast4v4d _ 337s 337s self = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_Conv2d_depthwise_padded') 337s index = 0, i_output = 0 337s desired = array([[[[ 9.04451311e-02, 2.59667486e-01, -1.09607518e-01, 337s 2.27060467e-01, -5.80047965e-02, -5.29188275e-...-1.63738191e-01, -2.90462047e-01, 337s -3.69208872e-01, -3.35327953e-01, -1.56381518e-01]]]], 337s dtype=float32) 337s output = array([[[[ 9.04451162e-02, 2.59667546e-01, -1.09607533e-01, 337s 2.27060467e-01, -5.80047816e-02, -5.29188275e-...-1.63738191e-01, -2.90462047e-01, 337s -3.69208872e-01, -3.35327953e-01, -1.56381518e-01]]]], 337s dtype=float32) 337s rtol = 0, atol = 0 337s comment = '[runtime=ReferenceEvaluator]\nir_version: 3\nproducer_name: "pytorch"\nproducer_version: "0.3"\ngraph {\n node {\n ... dim {\n dim_value: 6\n }\n }\n }\n }\n }\n}\nopset_import {\n version: 6\n}\n' 337s inputs = [array([[[[ 0.69232553, 0.45320687, 2.1692784 , -0.8968519 , 337s 1.3547171 , -0.5139638 ], 337s [ 2.21389..., 337s [-0.13081397, -0.1452413 , -1.5837705 , -1.4981829 , 337s 1.263025 , -0.08155061]]]], dtype=float32)] 337s 337s def _compare_results( 337s self, index, i_output, desired, output, rtol=0, atol=0, comment="", inputs=None 337s ): 337s """ 337s Compares the expected output and the output produced 337s by the runtime. Raises an exception if not equal. 337s 337s :param index: test index 337s :param i_output: output index 337s :param desired: expected output 337s :param output: output 337s :param rtol: relative tolerance 337s :param atol: absolute tolerance 337s :param comment: addition text to give more insights to the user 337s :param inputs: inputs to the model 337s """ 337s if comment == "": 337s raise RuntimeError("Argument comment should be filled.") 337s if atol is None: 337s atol = 0 337s if rtol is None: 337s rtol = 0 337s if isinstance(desired, np.ndarray): 337s if isinstance(output, np.ndarray): 337s if rtol == 0: 337s if desired.dtype == np.float32: 337s rtl = 1e-5 337s elif desired.dtype == np.float64: 337s rtl = 1e-12 337s else: 337s rtl = rtol 337s else: 337s rtl = rtol 337s if desired.dtype == dtype_object: 337s try: 337s assert_allclose_string(desired, output) 337s except AssertionError as ex: 337s raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed, comment={comment}." 337s ) from ex 337s else: 337s equal_nan = desired.dtype in (np.float16, np.float32, np.float64) 337s if equal_nan: 337s try: 337s > assert_allclose( 337s desired, 337s output, 337s atol=atol, 337s rtol=rtl, 337s equal_nan=equal_nan, 337s ) 337s 337s onnx/test/reference_evaluator_backend_test.py:279: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (.compare at 0x3ff9ce2fba0>, array([[[[ 9.04451311e-02, 2.59667486e-01, -1.09607518...1.63738191e-01, -2.90462047e-01, 337s -3.69208872e-01, -3.35327953e-01, -1.56381518e-01]]]], 337s dtype=float32)) 337s kwds = {'equal_nan': True, 'err_msg': '', 'header': 'Not equal to tolerance rtol=1e-05, atol=0', 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Not equal to tolerance rtol=1e-05, atol=0 337s E 337s E Mismatched elements: 1 / 288 (0.347%) 337s E Max absolute difference: 5.9604645e-08 337s E Max relative difference: 1.3763678e-05 337s E x: array([[[[ 9.044513e-02, 2.596675e-01, -1.096075e-01, 2.270605e-01, 337s E -5.800480e-02, -5.291883e-01], 337s E [ 2.394726e-01, 1.705277e-01, 1.725401e-01, -1.005543e+00,... 337s E y: array([[[[ 9.044512e-02, 2.596675e-01, -1.096075e-01, 2.270605e-01, 337s E -5.800478e-02, -5.291883e-01], 337s E [ 2.394726e-01, 1.705277e-01, 1.725401e-01, -1.005543e+00,... 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s 337s The above exception was the direct cause of the following exception: 337s 337s self = 337s te = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_Conv2d_depthwise_padded') 337s check_other_runtime = None, verbose = 0, print_io = False 337s 337s def _test_( 337s self, te=te, check_other_runtime=None, verbose=0, print_io=False 337s ): 337s if te.fname in getattr(cls, "skip_test", set()): 337s cls.skipped.append((te, None)) 337s return 337s rtol = getattr(cls, "rtol", {}) 337s atol = getattr(cls, "atol", {}) 337s if len(rtol) == 0 or len(atol) == 0: 337s raise AssertionError("rtol or atol is empty.") 337s > self.common_test_onnx_test_run( 337s te, 337s getattr(cls, "successes", []), 337s getattr(cls, "missed", []), 337s getattr(cls, "skipped", []), 337s getattr(cls, "load_failed", []), 337s getattr(cls, "exec_failed", []), 337s getattr(cls, "mismatch", []), 337s verbose=verbose, 337s rtol=rtol, 337s atol=atol, 337s check_other_runtime=check_other_runtime, 337s print_io=print_io, 337s ) 337s 337s onnx/test/reference_evaluator_backend_test.py:494: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_backend_test.py:649: in common_test_onnx_test_run 337s raise e 337s onnx/test/reference_evaluator_backend_test.py:623: in common_test_onnx_test_run 337s te.run( 337s onnx/test/reference_evaluator_backend_test.py:382: in run 337s self.run( 337s onnx/test/reference_evaluator_backend_test.py:429: in run 337s self._compare_results( 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s self = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_Conv2d_depthwise_padded') 337s index = 0, i_output = 0 337s desired = array([[[[ 9.04451311e-02, 2.59667486e-01, -1.09607518e-01, 337s 2.27060467e-01, -5.80047965e-02, -5.29188275e-...-1.63738191e-01, -2.90462047e-01, 337s -3.69208872e-01, -3.35327953e-01, -1.56381518e-01]]]], 337s dtype=float32) 337s output = array([[[[ 9.04451162e-02, 2.59667546e-01, -1.09607533e-01, 337s 2.27060467e-01, -5.80047816e-02, -5.29188275e-...-1.63738191e-01, -2.90462047e-01, 337s -3.69208872e-01, -3.35327953e-01, -1.56381518e-01]]]], 337s dtype=float32) 337s rtol = 0, atol = 0 337s comment = '[runtime=ReferenceEvaluator]\nir_version: 3\nproducer_name: "pytorch"\nproducer_version: "0.3"\ngraph {\n node {\n ... dim {\n dim_value: 6\n }\n }\n }\n }\n }\n}\nopset_import {\n version: 6\n}\n' 337s inputs = [array([[[[ 0.69232553, 0.45320687, 2.1692784 , -0.8968519 , 337s 1.3547171 , -0.5139638 ], 337s [ 2.21389..., 337s [-0.13081397, -0.1452413 , -1.5837705 , -1.4981829 , 337s 1.263025 , -0.08155061]]]], dtype=float32)] 337s 337s def _compare_results( 337s self, index, i_output, desired, output, rtol=0, atol=0, comment="", inputs=None 337s ): 337s """ 337s Compares the expected output and the output produced 337s by the runtime. Raises an exception if not equal. 337s 337s :param index: test index 337s :param i_output: output index 337s :param desired: expected output 337s :param output: output 337s :param rtol: relative tolerance 337s :param atol: absolute tolerance 337s :param comment: addition text to give more insights to the user 337s :param inputs: inputs to the model 337s """ 337s if comment == "": 337s raise RuntimeError("Argument comment should be filled.") 337s if atol is None: 337s atol = 0 337s if rtol is None: 337s rtol = 0 337s if isinstance(desired, np.ndarray): 337s if isinstance(output, np.ndarray): 337s if rtol == 0: 337s if desired.dtype == np.float32: 337s rtl = 1e-5 337s elif desired.dtype == np.float64: 337s rtl = 1e-12 337s else: 337s rtl = rtol 337s else: 337s rtl = rtol 337s if desired.dtype == dtype_object: 337s try: 337s assert_allclose_string(desired, output) 337s except AssertionError as ex: 337s raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed, comment={comment}." 337s ) from ex 337s else: 337s equal_nan = desired.dtype in (np.float16, np.float32, np.float64) 337s if equal_nan: 337s try: 337s assert_allclose( 337s desired, 337s output, 337s atol=atol, 337s rtol=rtl, 337s equal_nan=equal_nan, 337s ) 337s except AssertionError as ex: 337s try: 337s diff = output - desired 337s except ValueError: 337s diff = None 337s > raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed " 337s f"(rtol={rtl}, atol={atol}), comment={comment}\n---\n{desired}\n----" 337s f"\n{output}\n-----\n{diff}\n------INPUTS----\n{pprint.pformat(inputs)}." 337s ) from ex 337s E AssertionError: Output 0 of test 0 in folder '/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_Conv2d_depthwise_padded' failed (rtol=1e-05, atol=0), comment=[runtime=ReferenceEvaluator] 337s E ir_version: 3 337s E producer_name: "pytorch" 337s E producer_version: "0.3" 337s E graph { 337s E node { 337s E input: "0" 337s E input: "1" 337s E input: "2" 337s E output: "3" 337s E op_type: "Conv" 337s E attribute { 337s E name: "dilations" 337s E ints: 1 337s E ints: 1 337s E type: INTS 337s E } 337s E attribute { 337s E name: "group" 337s E i: 4 337s E type: INT 337s E } 337s E attribute { 337s E name: "kernel_shape" 337s E ints: 3 337s E ints: 3 337s E type: INTS 337s E } 337s E attribute { 337s E name: "pads" 337s E ints: 1 337s E ints: 1 337s E ints: 1 337s E ints: 1 337s E type: INTS 337s E } 337s E attribute { 337s E name: "strides" 337s E ints: 1 337s E ints: 1 337s E type: INTS 337s E } 337s E } 337s E name: "torch-jit-export" 337s E initializer { 337s E dims: 4 337s E dims: 1 337s E dims: 3 337s E dims: 3 337s E data_type: 1 337s E name: "1" 337s E raw_data: "\274\326\365\030=\232\357\216\275\003s\360\275\262[\033=\304\332\n>\tg\017>#\271%=\035NT\276\006\336\322\275\215\033\256>\0200\021\275\030@|<\375\257\370<\330\027\340\275\2707^=\240U^\275\200W\356=\334*\262<\245\264@\2752U\306\275\340\005\000\276\020ff\275\n\373L=\370\234\356<\350\203\030>\r\370\335\275xs\226\275\205[\230\275u>\006\274\003\332p=\226Tb=\005\273l\007\312\307\274\266\022\250\275\344\266s" 337s E } 337s E initializer { 337s E dims: 4 337s E data_type: 1 337s E name: "2" 337s E raw_data: "\276\025J\332\275\2317\026\275qD\254\275\365\007#" 337s E } 337s E input { 337s E name: "0" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 2 337s E } 337s E dim { 337s E dim_value: 4 337s E } 337s E dim { 337s E dim_value: 6 337s E } 337s E dim { 337s E dim_value: 6 337s E } 337s E } 337s E } 337s E } 337s E } 337s E input { 337s E name: "1" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 4 337s E } 337s E dim { 337s E dim_value: 1 337s E } 337s E dim { 337s E dim_value: 3 337s E } 337s E dim { 337s E dim_value: 3 337s E } 337s E } 337s E } 337s E } 337s E } 337s E input { 337s E name: "2" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 4 337s E } 337s E } 337s E } 337s E } 337s E } 337s E output { 337s E name: "3" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 2 337s E } 337s E dim { 337s E dim_value: 4 337s E } 337s E dim { 337s E dim_value: 6 337s E } 337s E dim { 337s E dim_value: 6 337s E } 337s E } 337s E } 337s E } 337s E } 337s E } 337s E opset_import { 337s E version: 6 337s E } 337s E 337s E --- 337s E [[[[ 9.04451311e-02 2.59667486e-01 -1.09607518e-01 2.27060467e-01 337s E -5.80047965e-02 -5.29188275e-01] 337s E [ 2.39472598e-01 1.70527726e-01 1.72540098e-01 -1.00554287e+00 337s E -1.45831883e-01 1.02312207e-01] 337s E [ 2.02663988e-01 -6.19730473e-01 -2.97909021e-01 -2.83862919e-01 337s E -2.86865771e-01 1.29058421e-01] 337s E [-1.29076704e-01 -1.43824309e-01 -4.05187309e-01 -1.89752936e-01 337s E 3.55474323e-01 -2.14935020e-01] 337s E [-1.72179520e-01 -7.07473040e-01 -8.05565342e-02 4.65433747e-01 337s E 2.56773859e-01 -3.54018092e-01] 337s E [-5.10721505e-02 1.98414564e-01 -2.05287337e-02 -3.09055001e-01 337s E -4.44793105e-01 -6.08029366e-02]] 337s E 337s E [[-1.43441930e-02 -1.13900051e-01 -1.62280291e-01 9.72877294e-02 337s E -1.85675710e-01 7.04685599e-02] 337s E [-1.28756613e-01 -2.59187162e-01 3.07682574e-01 -3.36851358e-01 337s E -4.52385768e-02 4.70410287e-03] 337s E [-3.75674367e-01 4.05638397e-01 -4.88643229e-01 -1.19740270e-01 337s E 2.87702620e-01 -1.19759806e-01] 337s E [-2.88968146e-01 1.89473972e-01 -5.11745632e-01 1.00332975e-01 337s E -2.22260937e-01 -1.45938426e-01] 337s E [ 3.45454514e-01 -3.50505948e-01 4.04806852e-01 -3.70131969e-01 337s E 4.38449621e-01 -1.81537271e-01] 337s E [ 6.64843470e-02 -4.06239867e-01 -1.99985325e-01 -5.37078753e-02 337s E 3.04264575e-02 -2.15892836e-01]] 337s E 337s E [[ 1.71425313e-01 -6.04883075e-01 -1.37853324e-01 -1.07623890e-01 337s E -1.83807969e-01 4.51174498e-01] 337s E [-6.56127036e-01 5.22963047e-01 1.60877779e-02 9.89070088e-02 337s E 3.22297931e-01 -2.47981355e-01] 337s E [ 6.15023494e-01 -5.12455165e-01 -5.22559695e-02 -1.54784083e-01 337s E -4.52445924e-01 8.29571933e-02] 337s E [-4.77082551e-01 8.46077055e-02 -4.89401817e-02 -1.22576952e-01 337s E 1.61036760e-01 -4.61338937e-01] 337s E [-5.19442335e-02 -5.32882996e-02 5.26649505e-02 -7.68996924e-02 337s E -3.59155595e-01 -1.02642462e-01] 337s E [ 1.32276267e-01 8.81795883e-02 -2.74113417e-01 5.96636087e-02 337s E 5.32687455e-02 5.51304072e-02]] 337s E 337s E [[-3.28978747e-01 -5.36455572e-01 6.78425357e-02 2.60163322e-02 337s E -2.26633072e-01 1.46028310e-01] 337s E [ 5.82031086e-02 -1.28552586e-01 -2.92895660e-02 -1.61742628e-01 337s E -1.10788979e-01 -1.28600836e-01] 337s E [-6.71031252e-02 -5.89863122e-01 -6.05481923e-01 -1.67134598e-01 337s E 1.23714365e-01 -3.53690356e-01] 337s E [-6.70233518e-02 3.96840185e-01 9.51160565e-02 -4.25221175e-01 337s E -2.10134983e-01 -1.34419113e-01] 337s E [-2.07145065e-01 -6.50114566e-03 -5.34719378e-02 -6.41830683e-01 337s E -1.97601706e-01 1.87940925e-01] 337s E [-1.28388315e-01 -7.43171200e-02 1.81218475e-01 7.43379965e-02 337s E -6.82289079e-02 -6.87058866e-02]]] 337s E 337s E 337s E [[[-2.54434407e-01 -6.20997667e-01 9.52176154e-02 -1.27491891e-01 337s E -3.86431515e-01 -1.11279331e-01] 337s E [-1.27466172e-01 2.46243864e-01 -4.02579159e-02 -2.86341459e-02 337s E -6.06886744e-01 -4.30824369e-01] 337s E [-2.14729771e-01 -5.89762449e-01 -5.54056644e-01 3.35670114e-02 337s E 6.02487206e-01 -2.11963341e-01] 337s E [ 4.65372205e-03 1.90699041e-01 -1.46585554e-01 -2.31421903e-01 337s E -9.66671228e-01 -1.22498229e-01] 337s E [-3.44695896e-01 -3.10551673e-01 2.81475484e-02 -1.52723372e-01 337s E -3.14325899e-01 5.18683493e-02] 337s E [ 4.15668935e-02 -1.79213017e-01 -1.76665634e-01 -3.42608273e-01 337s E 8.91897827e-02 1.84001744e-01]] 337s E 337s E [[-3.56191337e-01 -8.89556631e-02 1.00753546e-01 -1.27870396e-01 337s E -2.18738198e-01 3.11021134e-02] 337s E [-3.05862784e-01 2.18692020e-01 -2.21859396e-01 -1.81461871e-01 337s E -6.12358600e-02 2.41194859e-01] 337s E [-1.27627924e-01 2.92178765e-02 -1.20258585e-01 -2.39791647e-01 337s E 5.02879173e-02 -8.04869235e-02] 337s E [-9.84959304e-03 -2.23187104e-01 -2.78030932e-01 -1.69486195e-01 337s E 2.13004485e-01 -3.21269512e-01] 337s E [ 3.67447019e-01 -2.89493918e-01 -5.41329384e-04 2.14935884e-01 337s E -2.42685035e-01 -6.74540922e-02] 337s E [ 8.16626102e-03 -5.33410981e-02 -2.32338011e-01 -2.13834181e-01 337s E -1.05647273e-01 -2.24549621e-02]] 337s E 337s E [[ 2.31772140e-02 -5.74476905e-02 7.26949722e-02 -2.56419815e-02 337s E -1.18937083e-01 -4.36923563e-01] 337s E [-3.88662294e-02 -6.16406620e-01 -2.21821114e-01 7.05406070e-03 337s E -2.52409726e-01 7.56103992e-02] 337s E [-1.77475125e-01 2.17536017e-01 5.86686313e-01 -4.28955138e-01 337s E 2.81287208e-02 3.85538876e-01] 337s E [ 4.84748930e-03 -1.89253539e-01 -3.15744877e-01 3.58607888e-01 337s E 1.50398433e-01 -1.75513521e-01] 337s E [-1.63466215e-01 -2.57279463e-02 -2.31721118e-01 2.48190686e-02 337s E -2.61522114e-01 -1.26382530e-01] 337s E [-1.08048759e-01 1.95850879e-02 2.51228690e-01 -1.54807299e-01 337s E -4.14312601e-01 -3.87236103e-02]] 337s E 337s E [[-1.75931811e-01 3.42793986e-02 -2.07133889e-01 -1.79836541e-01 337s E 9.50343385e-02 -1.02944896e-01] 337s E [-6.30943403e-02 -1.67409420e-01 -8.46790969e-02 2.90238038e-02 337s E 2.81909034e-02 -1.76308379e-02] 337s E [-3.98619846e-02 -3.75581175e-01 -2.27725536e-01 7.23285154e-02 337s E -3.70147735e-01 1.87434182e-02] 337s E [-2.41644502e-01 -2.92109311e-01 -2.84525871e-01 -1.42950714e-01 337s E -1.17604621e-01 -6.97315484e-03] 337s E [-1.10194474e-01 7.82676861e-02 1.64821118e-01 -4.69337881e-01 337s E -1.81303352e-01 5.01427427e-02] 337s E [-7.60068670e-02 -1.63738191e-01 -2.90462047e-01 -3.69208872e-01 337s E -3.35327953e-01 -1.56381518e-01]]]] 337s E ---- 337s E [[[[ 9.04451162e-02 2.59667546e-01 -1.09607533e-01 2.27060467e-01 337s E -5.80047816e-02 -5.29188275e-01] 337s E [ 2.39472568e-01 1.70527726e-01 1.72540098e-01 -1.00554287e+00 337s E -1.45831883e-01 1.02312237e-01] 337s E [ 2.02664018e-01 -6.19730473e-01 -2.97909021e-01 -2.83862948e-01 337s E -2.86865771e-01 1.29058421e-01] 337s E [-1.29076704e-01 -1.43824309e-01 -4.05187309e-01 -1.89752951e-01 337s E 3.55474323e-01 -2.14935005e-01] 337s E [-1.72179520e-01 -7.07473040e-01 -8.05565342e-02 4.65433747e-01 337s E 2.56773859e-01 -3.54018092e-01] 337s E [-5.10721430e-02 1.98414564e-01 -2.05287337e-02 -3.09055030e-01 337s E -4.44793105e-01 -6.08029366e-02]] 337s E 337s E [[-1.43441893e-02 -1.13900051e-01 -1.62280291e-01 9.72877294e-02 337s E -1.85675710e-01 7.04685599e-02] 337s E [-1.28756613e-01 -2.59187162e-01 3.07682574e-01 -3.36851358e-01 337s E -4.52385768e-02 4.70410287e-03] 337s E [-3.75674307e-01 4.05638397e-01 -4.88643169e-01 -1.19740255e-01 337s E 2.87702620e-01 -1.19759806e-01] 337s E [-2.88968146e-01 1.89473972e-01 -5.11745572e-01 1.00332975e-01 337s E -2.22260937e-01 -1.45938426e-01] 337s E [ 3.45454514e-01 -3.50505948e-01 4.04806852e-01 -3.70131969e-01 337s E 4.38449621e-01 -1.81537285e-01] 337s E [ 6.64843470e-02 -4.06239867e-01 -1.99985325e-01 -5.37078753e-02 337s E 3.04264575e-02 -2.15892851e-01]] 337s E 337s E [[ 1.71425328e-01 -6.04883075e-01 -1.37853324e-01 -1.07623890e-01 337s E -1.83807984e-01 4.51174498e-01] 337s E [-6.56127036e-01 5.22962987e-01 1.60877779e-02 9.89070237e-02 337s E 3.22297931e-01 -2.47981355e-01] 337s E [ 6.15023494e-01 -5.12455106e-01 -5.22559695e-02 -1.54784083e-01 337s E -4.52445924e-01 8.29571784e-02] 337s E [-4.77082551e-01 8.46076906e-02 -4.89401780e-02 -1.22576952e-01 337s E 1.61036760e-01 -4.61338937e-01] 337s E [-5.19442335e-02 -5.32882884e-02 5.26649505e-02 -7.68996999e-02 337s E -3.59155595e-01 -1.02642477e-01] 337s E [ 1.32276252e-01 8.81795883e-02 -2.74113417e-01 5.96636012e-02 337s E 5.32687828e-02 5.51303998e-02]] 337s E 337s E [[-3.28978747e-01 -5.36455572e-01 6.78425208e-02 2.60163322e-02 337s E -2.26633057e-01 1.46028340e-01] 337s E [ 5.82030937e-02 -1.28552616e-01 -2.92895585e-02 -1.61742613e-01 337s E -1.10788971e-01 -1.28600836e-01] 337s E [-6.71031177e-02 -5.89863062e-01 -6.05481923e-01 -1.67134598e-01 337s E 1.23714380e-01 -3.53690356e-01] 337s E [-6.70233518e-02 3.96840185e-01 9.51160565e-02 -4.25221205e-01 337s E -2.10134983e-01 -1.34419113e-01] 337s E [-2.07145065e-01 -6.50114566e-03 -5.34719303e-02 -6.41830683e-01 337s E -1.97601721e-01 1.87940925e-01] 337s E [-1.28388315e-01 -7.43171200e-02 1.81218475e-01 7.43379965e-02 337s E -6.82289079e-02 -6.87058941e-02]]] 337s E 337s E 337s E [[[-2.54434407e-01 -6.20997667e-01 9.52176154e-02 -1.27491876e-01 337s E -3.86431485e-01 -1.11279339e-01] 337s E [-1.27466172e-01 2.46243834e-01 -4.02579159e-02 -2.86341608e-02 337s E -6.06886744e-01 -4.30824369e-01] 337s E [-2.14729786e-01 -5.89762449e-01 -5.54056644e-01 3.35670263e-02 337s E 6.02487206e-01 -2.11963326e-01] 337s E [ 4.65370715e-03 1.90699011e-01 -1.46585569e-01 -2.31421888e-01 337s E -9.66671228e-01 -1.22498222e-01] 337s E [-3.44695896e-01 -3.10551703e-01 2.81475484e-02 -1.52723372e-01 337s E -3.14325899e-01 5.18683493e-02] 337s E [ 4.15668935e-02 -1.79213017e-01 -1.76665649e-01 -3.42608273e-01 337s E 8.91897827e-02 1.84001744e-01]] 337s E 337s E [[-3.56191337e-01 -8.89556631e-02 1.00753561e-01 -1.27870396e-01 337s E -2.18738198e-01 3.11021134e-02] 337s E [-3.05862784e-01 2.18692020e-01 -2.21859396e-01 -1.81461871e-01 337s E -6.12358451e-02 2.41194829e-01] 337s E [-1.27627909e-01 2.92178467e-02 -1.20258585e-01 -2.39791647e-01 337s E 5.02879173e-02 -8.04869309e-02] 337s E [-9.84960794e-03 -2.23187089e-01 -2.78030932e-01 -1.69486195e-01 337s E 2.13004485e-01 -3.21269512e-01] 337s E [ 3.67447019e-01 -2.89493918e-01 -5.41321933e-04 2.14935884e-01 337s E -2.42685035e-01 -6.74540699e-02] 337s E [ 8.16626102e-03 -5.33410981e-02 -2.32338011e-01 -2.13834181e-01 337s E -1.05647281e-01 -2.24549621e-02]] 337s E 337s E [[ 2.31772140e-02 -5.74476905e-02 7.26949722e-02 -2.56419852e-02 337s E -1.18937090e-01 -4.36923563e-01] 337s E [-3.88662294e-02 -6.16406620e-01 -2.21821114e-01 7.05409050e-03 337s E -2.52409697e-01 7.56103992e-02] 337s E [-1.77475125e-01 2.17536047e-01 5.86686254e-01 -4.28955138e-01 337s E 2.81287208e-02 3.85538876e-01] 337s E [ 4.84748930e-03 -1.89253539e-01 -3.15744817e-01 3.58607888e-01 337s E 1.50398403e-01 -1.75513506e-01] 337s E [-1.63466215e-01 -2.57279426e-02 -2.31721118e-01 2.48190612e-02 337s E -2.61522114e-01 -1.26382530e-01] 337s E [-1.08048767e-01 1.95850879e-02 2.51228690e-01 -1.54807284e-01 337s E -4.14312601e-01 -3.87236029e-02]] 337s E 337s E [[-1.75931811e-01 3.42793986e-02 -2.07133889e-01 -1.79836556e-01 337s E 9.50343236e-02 -1.02944896e-01] 337s E [-6.30943403e-02 -1.67409420e-01 -8.46790969e-02 2.90238187e-02 337s E 2.81909034e-02 -1.76308304e-02] 337s E [-3.98619846e-02 -3.75581145e-01 -2.27725536e-01 7.23285154e-02 337s E -3.70147705e-01 1.87434033e-02] 337s E [-2.41644502e-01 -2.92109311e-01 -2.84525901e-01 -1.42950714e-01 337s E -1.17604621e-01 -6.97315484e-03] 337s E [-1.10194474e-01 7.82676861e-02 1.64821118e-01 -4.69337881e-01 337s E -1.81303337e-01 5.01427427e-02] 337s E [-7.60068670e-02 -1.63738191e-01 -2.90462047e-01 -3.69208872e-01 337s E -3.35327953e-01 -1.56381518e-01]]]] 337s E ----- 337s E [[[[-1.4901161e-08 5.9604645e-08 -1.4901161e-08 0.0000000e+00 337s E 1.4901161e-08 0.0000000e+00] 337s E [-2.9802322e-08 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 2.9802322e-08] 337s E [ 2.9802322e-08 0.0000000e+00 0.0000000e+00 -2.9802322e-08 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 -1.4901161e-08 337s E 0.0000000e+00 1.4901161e-08] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 7.4505806e-09 0.0000000e+00 0.0000000e+00 -2.9802322e-08 337s E 0.0000000e+00 0.0000000e+00]] 337s E 337s E [[ 3.7252903e-09 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 5.9604645e-08 0.0000000e+00 5.9604645e-08 1.4901161e-08 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 5.9604645e-08 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 -1.4901161e-08] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 -1.4901161e-08]] 337s E 337s E [[ 1.4901161e-08 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E -1.4901161e-08 0.0000000e+00] 337s E [ 0.0000000e+00 -5.9604645e-08 0.0000000e+00 1.4901161e-08 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 5.9604645e-08 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 -1.4901161e-08] 337s E [ 0.0000000e+00 -1.4901161e-08 3.7252903e-09 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 1.1175871e-08 0.0000000e+00 -7.4505806e-09 337s E 0.0000000e+00 -1.4901161e-08] 337s E [-1.4901161e-08 0.0000000e+00 0.0000000e+00 -7.4505806e-09 337s E 3.7252903e-08 -7.4505806e-09]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00 -1.4901161e-08 0.0000000e+00 337s E 1.4901161e-08 2.9802322e-08] 337s E [-1.4901161e-08 -2.9802322e-08 7.4505806e-09 1.4901161e-08 337s E 7.4505806e-09 0.0000000e+00] 337s E [ 7.4505806e-09 5.9604645e-08 0.0000000e+00 0.0000000e+00 337s E 1.4901161e-08 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 -2.9802322e-08 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 7.4505806e-09 0.0000000e+00 337s E -1.4901161e-08 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 -7.4505806e-09]]] 337s E 337s E 337s E [[[ 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.4901161e-08 337s E 2.9802322e-08 -7.4505806e-09] 337s E [ 0.0000000e+00 -2.9802322e-08 0.0000000e+00 -1.4901161e-08 337s E 0.0000000e+00 0.0000000e+00] 337s E [-1.4901161e-08 0.0000000e+00 0.0000000e+00 1.4901161e-08 337s E 0.0000000e+00 1.4901161e-08] 337s E [-1.4901161e-08 -2.9802322e-08 -1.4901161e-08 1.4901161e-08 337s E 0.0000000e+00 7.4505806e-09] 337s E [ 0.0000000e+00 -2.9802322e-08 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 -1.4901161e-08 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00 1.4901161e-08 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 1.4901161e-08 -2.9802322e-08] 337s E [ 1.4901161e-08 -2.9802322e-08 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 -7.4505806e-09] 337s E [-1.4901161e-08 1.4901161e-08 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 7.4505806e-09 0.0000000e+00 337s E 0.0000000e+00 2.2351742e-08] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E -7.4505806e-09 0.0000000e+00]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00 0.0000000e+00 -3.7252903e-09 337s E -7.4505806e-09 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.9802322e-08 337s E 2.9802322e-08 0.0000000e+00] 337s E [ 0.0000000e+00 2.9802322e-08 -5.9604645e-08 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 5.9604645e-08 0.0000000e+00 337s E -2.9802322e-08 1.4901161e-08] 337s E [ 0.0000000e+00 3.7252903e-09 0.0000000e+00 -7.4505806e-09 337s E 0.0000000e+00 0.0000000e+00] 337s E [-7.4505806e-09 0.0000000e+00 0.0000000e+00 1.4901161e-08 337s E 0.0000000e+00 7.4505806e-09]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00 0.0000000e+00 -1.4901161e-08 337s E -1.4901161e-08 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.4901161e-08 337s E 0.0000000e+00 7.4505806e-09] 337s E [ 0.0000000e+00 2.9802322e-08 0.0000000e+00 0.0000000e+00 337s E 2.9802322e-08 -1.4901161e-08] 337s E [ 0.0000000e+00 0.0000000e+00 -2.9802322e-08 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 1.4901161e-08 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00]]]] 337s E ------INPUTS---- 337s E [array([[[[ 0.69232553, 0.45320687, 2.1692784 , -0.8968519 , 337s E 1.3547171 , -0.5139638 ], 337s E [ 2.2138987 , -0.18112804, 1.6392266 , 0.35310313, 337s E -1.4467232 , 0.39830685], 337s E [ 1.3330269 , -0.8180627 , -1.3542233 , -0.75486296, 337s E 1.0860974 , -0.40080443], 337s E [ 0.07577234, -1.1683928 , -0.45294595, -0.72029716, 337s E 1.9682063 , 0.65563005], 337s E [ 0.52641505, -0.14344929, -1.1874508 , 0.72630656, 337s E 0.4546233 , 0.6608454 ], 337s E [-0.38869128, 0.65324336, 1.7448293 , 0.92278606, 337s E -1.2782667 , -0.6699633 ]], 337s E 337s E [[-1.2309015 , -0.41122258, -0.28716785, 0.46576646, 337s E 0.80649656, -0.22816706], 337s E [ 0.69429 , 0.92515934, -0.11469138, -0.97559094, 337s E 1.758934 , 0.18157808], 337s E [-0.7838291 , 1.1150683 , -1.3667834 , 1.168168 , 337s E 0.46770123, -1.138334 ], 337s E [ 0.73462844, -1.8345634 , 0.8668746 , -0.6107124 , 337s E 0.9056958 , 0.07475986], 337s E [ 0.89749336, -1.7719518 , -0.6997398 , -0.46831298, 337s E 0.27938223, -1.1089259 ], 337s E [-0.15051004, 0.52221483, 0.6137759 , 2.206822 , 337s E 0.3112967 , 0.9354072 ]], 337s E 337s E [[ 0.3082965 , 2.1008935 , -1.322245 , 0.39408472, 337s E -1.1989535 , -1.0104884 ], 337s E [-0.69566435, -1.3552536 , 1.0396894 , -0.3119677 , 337s E 1.0306792 , 2.001276 ], 337s E [-0.8205169 , 1.6374258 , -0.7985877 , 0.58831173, 337s E -0.37220407, 0.10811766], 337s E [ 1.0189639 , -2.1021051 , 1.4085556 , -0.69187266, 337s E 0.15292606, 1.1178733 ], 337s E [ 0.06130087, -0.10653395, -0.96363544, 0.3560341 , 337s E -1.1863015 , -2.1424072 ], 337s E [-0.5962941 , 1.3341993 , -0.02486269, -0.23697156, 337s E 0.10091614, -1.7383071 ]], 337s E 337s E [[-0.02264192, -0.82033 , -0.44958127, 1.3961492 , 337s E 1.9654402 , 0.7194433 ], 337s E [-1.1099433 , 1.9968332 , 1.7118304 , -0.16253375, 337s E 1.0937141 , 2.1202888 ], 337s E [-1.4686924 , -1.3296591 , -0.8680727 , 0.1982869 , 337s E -0.12106275, -1.2888205 ], 337s E [-1.2358515 , -0.3517313 , 0.8149394 , 0.5946754 , 337s E -0.2413678 , -2.1188636 ], 337s E [ 0.5008174 , -0.0883439 , -3.2045536 , -0.090054 , 337s E 0.0400042 , 0.8204074 ], 337s E [ 0.01335039, 1.6053296 , -0.52211934, 0.13791034, 337s E 1.1237203 , 0.61801046]]], 337s E 337s E 337s E [[[ 1.0750546 , -0.954712 , -0.63092005, 1.666651 , 337s E -0.3859544 , -1.8635256 ], 337s E [-1.2980901 , 0.25826585, 0.05715753, 0.3690894 , 337s E 1.2783152 , -0.6343572 ], 337s E [ 0.7739016 , 0.25243253, -1.4672629 , -1.254006 , 337s E -0.3254153 , 1.8266025 ], 337s E [-1.175393 , 0.1940818 , 0.5712985 , 0.87811434, 337s E -0.79959625, -1.5806178 ], 337s E [ 0.7510618 , -1.2003088 , -0.14388844, -0.89734876, 337s E -0.6863968 , 1.7864239 ], 337s E [ 0.56827146, 0.27870733, 0.7093103 , -0.9255147 , 337s E -0.02981607, 1.8106778 ]], 337s E 337s E [[-0.19713244, 2.4600215 , 0.4524925 , -0.7070368 , 337s E 0.27978238, 1.1567475 ], 337s E [-0.75985795, -0.9536486 , -0.30235928, 0.74008656, 337s E 0.43530285, -0.5211254 ], 337s E [ 0.38508797, -1.4274461 , -1.3744974 , 0.57809246, 337s E 1.9284421 , -0.34622356], 337s E [ 1.3067752 , -0.35285166, -0.44282648, 0.84895235, 337s E 0.13772313, -0.579692 ], 337s E [ 1.4768968 , -0.14861327, -1.5614916 , -0.68640924, 337s E -1.0805118 , -0.4911891 ], 337s E [-1.1829529 , 1.1039604 , -1.0408776 , -0.22080167, 337s E 1.6905397 , -0.20013234]], 337s E 337s E [[ 0.75512385, -0.8794844 , 0.6111656 , -0.49052906, 337s E 1.9594527 , 0.83461833], 337s E [ 1.4407539 , -0.24256405, 0.05532105, -1.4355502 , 337s E -1.028706 , -0.31872627], 337s E [-0.6505729 , -2.064766 , 0.84508765, 1.4639692 , 337s E -1.7076899 , 0.17171867], 337s E [ 0.9472312 , -0.24513735, 0.23904401, -0.48614362, 337s E 1.8041528 , 1.191407 ], 337s E [-0.8447745 , 0.18803704, 0.33802363, 0.62303495, 337s E 0.33649364, 1.5196944 ], 337s E [-1.432193 , -0.9387139 , -0.7667283 , 1.9013181 , 337s E -0.67890865, 0.4773699 ]], 337s E 337s E [[-0.59322566, 0.32661358, -0.13737763, -0.06013466, 337s E 0.42408347, -1.2199674 ], 337s E [ 1.3118391 , 0.10613123, -0.15149905, 1.10739 , 337s E 0.07836985, -0.67522025], 337s E [-0.26078302, 0.23547728, 1.1281868 , -0.2208668 , 337s E 0.36713317, -1.0883863 ], 337s E [ 0.08185866, -1.4915061 , 1.8988248 , -0.7817656 , 337s E 0.8984357 , 0.33715042], 337s E [-1.0013785 , 1.2713685 , -0.12268564, 1.7714666 , 337s E 0.2648192 , 1.829561 ], 337s E [-0.13081397, -0.1452413 , -1.5837705 , -1.4981829 , 337s E 1.263025 , -0.08155061]]]], dtype=float32)]. 337s 337s onnx/test/reference_evaluator_backend_test.py:291: AssertionError 337s 337s The above exception was the direct cause of the following exception: 337s 337s cls = 337s 337s @classmethod 337s def tearDownClass(cls): 337s if len(cls.successes) == 0: 337s failed = cls.mismatch + cls.missed + cls.load_failed + cls.exec_failed 337s if len(failed) > 0: 337s raise RuntimeError( 337s f"No test was successful, {len(failed)} failed." 337s ) from failed[0][1] 337s raise RuntimeError("No test was successful.") 337s > cls._postprocess( 337s cls.successes, 337s cls.missed, 337s cls.skipped, 337s cls.load_failed, 337s cls.exec_failed, 337s cls.mismatch, 337s 10, 337s ) 337s 337s onnx/test/reference_evaluator_backend_test.py:853: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s successes = [(OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_AvgPool1d'), None, Non...est('/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_AvgPool3d_stride'), None, None), ...] 337s missed = [] 337s skipped = [(OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/simple/test_gradient_of_add'), None), (OnnxBa...ons'), None), (OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/node/test_scan_sum'), None), ...] 337s load_failed = [], exec_failed = [] 337s mismatch = [(OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_Conv2d_depthwise_padde..., 0.],\n [96., 0.],\n [97., 0.],\n [98., 0.],\n [99., 0.]]]], dtype=float32)].'))] 337s verbose = 10 337s 337s @staticmethod 337s def _postprocess( 337s successes, missed, skipped, load_failed, exec_failed, mismatch, verbose 337s ): 337s success = len(successes) 337s failed = [ 337s len(missed), 337s len(skipped), 337s len(load_failed), 337s len(exec_failed), 337s len(mismatch), 337s ] 337s coverage = success / (success + sum(failed)) 337s 337s if verbose: 337s path = os.path.dirname(onnx_file) 337s print("-----------") 337s print( 337s f"success={success}, skipped={len(skipped)}, missed={len(missed)}, load_failed={len(load_failed)}, " 337s f"exec_failed={len(exec_failed)}, mismatch={len(mismatch)}" 337s ) 337s print( 337s f"coverage {coverage * 100:.1f}% out of {success + sum(failed)} tests" 337s ) 337s 337s if verbose > 3: 337s 337s def _print(s, path): 337s return ( 337s str(s) 337s .replace("\\\\", "\\") 337s .replace(path, "onnx") 337s .replace("\\", "/") 337s ) 337s 337s print("-----------") 337s for t in sorted(load_failed, key=lambda m: m[0].fname): 337s print("loading failed", t[0].fname, "---", _print(t[0], path)) 337s for t in sorted(exec_failed, key=lambda m: m[0].fname): 337s print("execution failed", t[0].fname, "---", _print(t[0], path)) 337s for t in sorted(mismatch, key=lambda m: m[0].fname): 337s print("mismatch", t[0].fname, "---", _print(t[0], path)) 337s for t in sorted(missed, key=lambda m: m[0].fname): 337s print("missed ", t[0].fname, "---", _print(t[0], path)) 337s for t in sorted(skipped, key=lambda m: m[0].fname): 337s print("skipped", t[0].fname, "---", _print(t[0], path)) 337s 337s if success > 30: 337s print("-----------") 337s print( 337s f"success={success}, skipped={len(skipped)}, missed={len(missed)}, load_failed={len(load_failed)}, " 337s f"exec_failed={len(exec_failed)}, mismatch={len(mismatch)}" 337s ) 337s print( 337s f"coverage {coverage * 100:.1f}% out of {success + sum(failed)} tests" 337s ) 337s print("-----------") 337s 337s if len(mismatch) > 0: 337s te, e = mismatch[0] 337s > raise AssertionError( 337s f"Mismatch in test {te.name!r}\n{te.onnx_model}." 337s ) from e 337s E AssertionError: Mismatch in test 'test_Conv2d_depthwise_padded' 337s E ir_version: 3 337s E producer_name: "pytorch" 337s E producer_version: "0.3" 337s E graph { 337s E node { 337s E input: "0" 337s E input: "1" 337s E input: "2" 337s E output: "3" 337s E op_type: "Conv" 337s E attribute { 337s E name: "dilations" 337s E ints: 1 337s E ints: 1 337s E type: INTS 337s E } 337s E attribute { 337s E name: "group" 337s E i: 4 337s E type: INT 337s E } 337s E attribute { 337s E name: "kernel_shape" 337s E ints: 3 337s E ints: 3 337s E type: INTS 337s E } 337s E attribute { 337s E name: "pads" 337s E ints: 1 337s E ints: 1 337s E ints: 1 337s E ints: 1 337s E type: INTS 337s E } 337s E attribute { 337s E name: "strides" 337s E ints: 1 337s E ints: 1 337s E type: INTS 337s E } 337s E } 337s E name: "torch-jit-export" 337s E initializer { 337s E dims: 4 337s E dims: 1 337s E dims: 3 337s E dims: 3 337s E data_type: 1 337s E name: "1" 337s E raw_data: "\274\326\365\030=\232\357\216\275\003s\360\275\262[\033=\304\332\n>\tg\017>#\271%=\035NT\276\006\336\322\275\215\033\256>\0200\021\275\030@|<\375\257\370<\330\027\340\275\2707^=\240U^\275\200W\356=\334*\262<\245\264@\2752U\306\275\340\005\000\276\020ff\275\n\373L=\370\234\356<\350\203\030>\r\370\335\275xs\226\275\205[\230\275u>\006\274\003\332p=\226Tb=\005\273l\007\312\307\274\266\022\250\275\344\266s" 337s E } 337s E initializer { 337s E dims: 4 337s E data_type: 1 337s E name: "2" 337s E raw_data: "\276\025J\332\275\2317\026\275qD\254\275\365\007#" 337s E } 337s E input { 337s E name: "0" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 2 337s E } 337s E dim { 337s E dim_value: 4 337s E } 337s E dim { 337s E dim_value: 6 337s E } 337s E dim { 337s E dim_value: 6 337s E } 337s E } 337s E } 337s E } 337s E } 337s E input { 337s E name: "1" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 4 337s E } 337s E dim { 337s E dim_value: 1 337s E } 337s E dim { 337s E dim_value: 3 337s E } 337s E dim { 337s E dim_value: 3 337s E } 337s E } 337s E } 337s E } 337s E } 337s E input { 337s E name: "2" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 4 337s E } 337s E } 337s E } 337s E } 337s E } 337s E output { 337s E name: "3" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 2 337s E } 337s E dim { 337s E dim_value: 4 337s E } 337s E dim { 337s E dim_value: 6 337s E } 337s E dim { 337s E dim_value: 6 337s E } 337s E } 337s E } 337s E } 337s E } 337s E } 337s E opset_import { 337s E version: 6 337s E } 337s E . 337s 337s onnx/test/reference_evaluator_backend_test.py:753: AssertionError 337s --------------------------- Captured stdout teardown --------------------------- 337s ----------- 337s success=1295, skipped=8, missed=0, load_failed=0, exec_failed=0, mismatch=5 337s coverage 99.0% out of 1308 tests 337s ----------- 337s mismatch test__pytorch_converted_Conv2d_depthwise_padded --- OnnxBackendTest('onnx/backend/test/data/pytorch-converted/test_Conv2d_depthwise_padded') 337s mismatch test__pytorch_converted_Conv2d_dilated --- OnnxBackendTest('onnx/backend/test/data/pytorch-converted/test_Conv2d_dilated') 337s mismatch test_dft --- OnnxBackendTest('onnx/backend/test/data/node/test_dft') 337s mismatch test_dft_axis --- OnnxBackendTest('onnx/backend/test/data/node/test_dft_axis') 337s mismatch test_dft_inverse --- OnnxBackendTest('onnx/backend/test/data/node/test_dft_inverse') 337s skipped test__simple_gradient_of_add --- OnnxBackendTest('onnx/backend/test/data/simple/test_gradient_of_add') 337s skipped test__simple_gradient_of_add_and_mul --- OnnxBackendTest('onnx/backend/test/data/simple/test_gradient_of_add_and_mul') 337s skipped test_averagepool_2d_dilations --- OnnxBackendTest('onnx/backend/test/data/node/test_averagepool_2d_dilations') 337s skipped test_center_crop_pad_crop_axes_hwc_expanded --- OnnxBackendTest('onnx/backend/test/data/node/test_center_crop_pad_crop_axes_hwc_expanded') 337s skipped test_lppool_2d_dilations --- OnnxBackendTest('onnx/backend/test/data/node/test_lppool_2d_dilations') 337s skipped test_scan_sum --- OnnxBackendTest('onnx/backend/test/data/node/test_scan_sum') 337s skipped test_scatter_with_axis --- OnnxBackendTest('onnx/backend/test/data/node/test_scatter_with_axis') 337s skipped test_scatter_without_axis --- OnnxBackendTest('onnx/backend/test/data/node/test_scatter_without_axis') 337s ----------- 337s success=1295, skipped=8, missed=0, load_failed=0, exec_failed=0, mismatch=5 337s coverage 99.0% out of 1308 tests 337s ----------- 337s =================================== FAILURES =================================== 337s ___________ TestHelperTensorFunctions.test_make_bfloat16_tensor_raw ____________ 337s 337s self = 337s 337s def test_make_bfloat16_tensor_raw(self) -> None: 337s # numpy doesn't support bf16, so we have to compute the correct result manually 337s np_array = np.array( 337s [ 337s [1.0, 2.0], 337s [3.0, 4.0], 337s [0.099853515625, 0.099365234375], 337s [0.0998535081744, 0.1], 337s [np.nan, np.inf], 337s ], 337s dtype=np.float32, 337s ) 337s np_results = np.array( 337s [ 337s [ 337s struct.unpack("!f", bytes.fromhex("3F800000"))[0], # 1.0 337s struct.unpack("!f", bytes.fromhex("40000000"))[0], 337s ], # 2.0 337s [ 337s struct.unpack("!f", bytes.fromhex("40400000"))[0], # 3.0 337s struct.unpack("!f", bytes.fromhex("40800000"))[0], 337s ], # 4.0 337s [ 337s struct.unpack("!f", bytes.fromhex("3DCC0000"))[0], # truncated 337s struct.unpack("!f", bytes.fromhex("3DCB0000"))[0], 337s ], # truncated 337s [ 337s struct.unpack("!f", bytes.fromhex("3DCC0000"))[0], # truncated 337s struct.unpack("!f", bytes.fromhex("3DCC0000"))[0], 337s ], # truncated 337s [ 337s struct.unpack("!f", bytes.fromhex("7FC00000"))[0], # NaN 337s struct.unpack("!f", bytes.fromhex("7F800000"))[0], 337s ], # inf 337s ] 337s ) 337s 337s # write out 16-bit of fp32 to create bf16 using truncation, no rounding 337s def truncate(x): 337s return x >> 16 337s 337s values_as_ints = np_array.astype(np.float32).view(np.uint32).flatten() 337s packed_values = truncate(values_as_ints).astype(np.uint16).tobytes() 337s tensor = helper.make_tensor( 337s name="test", 337s data_type=TensorProto.BFLOAT16, 337s dims=np_array.shape, 337s vals=packed_values, 337s raw=True, 337s ) 337s self.assertEqual(tensor.name, "test") 337s > np.testing.assert_equal(np_results, numpy_helper.to_array(tensor)) 337s 337s onnx/test/helper_test.py:552: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (, array([[1. , 2. ], 337s [3. , 4. ], 337s [0.09960938, 0.09912...07, -4.9545216e+07], 337s [-4.9545216e+07, -4.9545216e+07], 337s [-1.1663108e-38, -3.9843750e+00]], dtype=float32)) 337s kwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Arrays are not equal 337s E 337s E x and y nan location mismatch: 337s E x: array([[1. , 2. ], 337s E [3. , 4. ], 337s E [0.099609, 0.099121],... 337s E y: array([[ 5.877472e-39, -5.785636e-39], 337s E [-5.877472e-39, 3.000000e+00], 337s E [-1.238630e+07, -4.954522e+07],... 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s _________ TestHelperTensorFunctions.test_make_float8e4m3fn_tensor_raw __________ 337s 337s self = 337s 337s def test_make_float8e4m3fn_tensor_raw(self) -> None: 337s expected = np.array([0, 0.5, 1, 448, 10], dtype=np.float32) 337s f8 = np.array( 337s [helper.float32_to_float8e4m3(x) for x in expected], dtype=np.uint8 337s ) 337s packed_values = f8.tobytes() 337s y = helper.make_tensor( 337s name="test", 337s data_type=TensorProto.FLOAT8E4M3FN, 337s dims=list(expected.shape), 337s vals=packed_values, 337s raw=True, 337s ) 337s > ynp = numpy_helper.to_array(y) 337s 337s onnx/test/helper_test.py:567: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s /usr/lib/python3/dist-packages/onnx/numpy_helper.py:213: in to_array 337s convert_endian(tensor) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s tensor = dims: 5 337s data_type: 17 337s name: "test" 337s raw_data: "\00008~R" 337s 337s 337s def convert_endian(tensor: TensorProto) -> None: 337s """ 337s Call to convert endianess of raw data in tensor. 337s 337s Arguments: 337s tensor (TensorProto): TensorProto to be converted. 337s """ 337s tensor_dtype = tensor.data_type 337s np_dtype = helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s tensor.raw_data = ( 337s > np.frombuffer(tensor.raw_data, dtype=np_dtype).byteswap().tobytes() 337s ) 337s E ValueError: buffer size must be a multiple of element size 337s 337s /usr/lib/python3/dist-packages/onnx/numpy_helper.py:586: ValueError 337s ________ TestHelperTensorFunctions.test_make_float8e4m3fnuz_tensor_raw _________ 337s 337s self = 337s 337s def test_make_float8e4m3fnuz_tensor_raw(self) -> None: 337s expected = np.array([0, 0.5, 1, 240, 10], dtype=np.float32) 337s f8 = np.array( 337s [helper.float32_to_float8e4m3(x, uz=True) for x in expected], dtype=np.uint8 337s ) 337s packed_values = f8.tobytes() 337s y = helper.make_tensor( 337s name="test", 337s data_type=TensorProto.FLOAT8E4M3FNUZ, 337s dims=list(expected.shape), 337s vals=packed_values, 337s raw=True, 337s ) 337s > ynp = numpy_helper.to_array(y) 337s 337s onnx/test/helper_test.py:583: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s /usr/lib/python3/dist-packages/onnx/numpy_helper.py:213: in to_array 337s convert_endian(tensor) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s tensor = dims: 5 337s data_type: 18 337s name: "test" 337s raw_data: "\0008@\177Z" 337s 337s 337s def convert_endian(tensor: TensorProto) -> None: 337s """ 337s Call to convert endianess of raw data in tensor. 337s 337s Arguments: 337s tensor (TensorProto): TensorProto to be converted. 337s """ 337s tensor_dtype = tensor.data_type 337s np_dtype = helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s tensor.raw_data = ( 337s > np.frombuffer(tensor.raw_data, dtype=np_dtype).byteswap().tobytes() 337s ) 337s E ValueError: buffer size must be a multiple of element size 337s 337s /usr/lib/python3/dist-packages/onnx/numpy_helper.py:586: ValueError 337s __________ TestHelperTensorFunctions.test_make_float8e5m2_tensor_raw ___________ 337s 337s self = 337s 337s def test_make_float8e5m2_tensor_raw(self) -> None: 337s expected = np.array([0, 0.5, 1, 49152, 10], dtype=np.float32) 337s f8 = np.array( 337s [helper.float32_to_float8e5m2(x) for x in expected], dtype=np.uint8 337s ) 337s packed_values = f8.tobytes() 337s y = helper.make_tensor( 337s name="test", 337s data_type=TensorProto.FLOAT8E5M2, 337s dims=list(expected.shape), 337s vals=packed_values, 337s raw=True, 337s ) 337s > ynp = numpy_helper.to_array(y) 337s 337s onnx/test/helper_test.py:599: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s /usr/lib/python3/dist-packages/onnx/numpy_helper.py:213: in to_array 337s convert_endian(tensor) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s tensor = dims: 5 337s data_type: 19 337s name: "test" 337s raw_data: "\0008 None: 337s """ 337s Call to convert endianess of raw data in tensor. 337s 337s Arguments: 337s tensor (TensorProto): TensorProto to be converted. 337s """ 337s tensor_dtype = tensor.data_type 337s np_dtype = helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s tensor.raw_data = ( 337s > np.frombuffer(tensor.raw_data, dtype=np_dtype).byteswap().tobytes() 337s ) 337s E ValueError: buffer size must be a multiple of element size 337s 337s /usr/lib/python3/dist-packages/onnx/numpy_helper.py:586: ValueError 337s ________ TestHelperTensorFunctions.test_make_float8e5m2fnuz_tensor_raw _________ 337s 337s self = 337s 337s def test_make_float8e5m2fnuz_tensor_raw(self) -> None: 337s expected = np.array([0, 0.5, 1, 49152, 10], dtype=np.float32) 337s f8 = np.array( 337s [helper.float32_to_float8e5m2(x, fn=True, uz=True) for x in expected], 337s dtype=np.uint8, 337s ) 337s packed_values = f8.tobytes() 337s y = helper.make_tensor( 337s name="test", 337s data_type=TensorProto.FLOAT8E5M2FNUZ, 337s dims=list(expected.shape), 337s vals=packed_values, 337s raw=True, 337s ) 337s > ynp = numpy_helper.to_array(y) 337s 337s onnx/test/helper_test.py:616: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s /usr/lib/python3/dist-packages/onnx/numpy_helper.py:213: in to_array 337s convert_endian(tensor) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s tensor = dims: 5 337s data_type: 20 337s name: "test" 337s raw_data: "\000<@~M" 337s 337s 337s def convert_endian(tensor: TensorProto) -> None: 337s """ 337s Call to convert endianess of raw data in tensor. 337s 337s Arguments: 337s tensor (TensorProto): TensorProto to be converted. 337s """ 337s tensor_dtype = tensor.data_type 337s np_dtype = helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s tensor.raw_data = ( 337s > np.frombuffer(tensor.raw_data, dtype=np_dtype).byteswap().tobytes() 337s ) 337s E ValueError: buffer size must be a multiple of element size 337s 337s /usr/lib/python3/dist-packages/onnx/numpy_helper.py:586: ValueError 337s ___________________ test_make_tensor_raw[TensorProto.FLOAT] ____________________ 337s 337s tensor_dtype = 1 337s 337s @pytest.mark.parametrize( 337s "tensor_dtype", 337s [ 337s t 337s for t in helper.get_all_tensor_dtypes() 337s if t 337s not in { 337s TensorProto.BFLOAT16, 337s TensorProto.STRING, 337s TensorProto.FLOAT8E4M3FN, 337s TensorProto.FLOAT8E4M3FNUZ, 337s TensorProto.FLOAT8E5M2, 337s TensorProto.FLOAT8E5M2FNUZ, 337s } 337s ], 337s ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype), 337s ) 337s def test_make_tensor_raw(tensor_dtype: int) -> None: 337s np_array = np.random.randn(2, 3).astype( 337s helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s ) 337s tensor = helper.make_tensor( 337s name="test", 337s data_type=tensor_dtype, 337s dims=np_array.shape, 337s vals=np_array.tobytes(), 337s raw=True, 337s ) 337s > np.testing.assert_equal(np_array, numpy_helper.to_array(tensor)) 337s 337s onnx/test/helper_test.py:841: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (, array([[-0.3119761 , -0.84029037, -1.0068318 ], 337s [ 1.6815767 , -0.79228663, -0.5316059 ...5786845e+13, 2.5174841e+03, -2.0131353e+18], 337s [-3.5859914e+24, 1.1077785e+08, 6.4868680e+11]], dtype=float32)) 337s kwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Arrays are not equal 337s E 337s E Mismatched elements: 6 / 6 (100%) 337s E Max absolute difference: 3.5859914e+24 337s E Max relative difference: 1.0003338 337s E x: array([[-0.311976, -0.84029 , -1.006832], 337s E [ 1.681577, -0.792287, -0.531606]], dtype=float32) 337s E y: array([[ 2.578685e+13, 2.517484e+03, -2.013135e+18], 337s E [-3.585991e+24, 1.107778e+08, 6.486868e+11]], dtype=float32) 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s ___________________ test_make_tensor_raw[TensorProto.UINT16] ___________________ 337s 337s tensor_dtype = 4 337s 337s @pytest.mark.parametrize( 337s "tensor_dtype", 337s [ 337s t 337s for t in helper.get_all_tensor_dtypes() 337s if t 337s not in { 337s TensorProto.BFLOAT16, 337s TensorProto.STRING, 337s TensorProto.FLOAT8E4M3FN, 337s TensorProto.FLOAT8E4M3FNUZ, 337s TensorProto.FLOAT8E5M2, 337s TensorProto.FLOAT8E5M2FNUZ, 337s } 337s ], 337s ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype), 337s ) 337s def test_make_tensor_raw(tensor_dtype: int) -> None: 337s np_array = np.random.randn(2, 3).astype( 337s helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s ) 337s tensor = helper.make_tensor( 337s name="test", 337s data_type=tensor_dtype, 337s dims=np_array.shape, 337s vals=np_array.tobytes(), 337s raw=True, 337s ) 337s > np.testing.assert_equal(np_array, numpy_helper.to_array(tensor)) 337s 337s onnx/test/helper_test.py:841: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (, array([[1, 0, 0], 337s [0, 0, 0]], dtype=uint16), array([[256, 0, 0], 337s [ 0, 0, 0]], dtype=uint16)) 337s kwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Arrays are not equal 337s E 337s E Mismatched elements: 1 / 6 (16.7%) 337s E Max absolute difference: 255 337s E Max relative difference: 0.99609375 337s E x: array([[1, 0, 0], 337s E [0, 0, 0]], dtype=uint16) 337s E y: array([[256, 0, 0], 337s E [ 0, 0, 0]], dtype=uint16) 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s ___________________ test_make_tensor_raw[TensorProto.INT16] ____________________ 337s 337s tensor_dtype = 5 337s 337s @pytest.mark.parametrize( 337s "tensor_dtype", 337s [ 337s t 337s for t in helper.get_all_tensor_dtypes() 337s if t 337s not in { 337s TensorProto.BFLOAT16, 337s TensorProto.STRING, 337s TensorProto.FLOAT8E4M3FN, 337s TensorProto.FLOAT8E4M3FNUZ, 337s TensorProto.FLOAT8E5M2, 337s TensorProto.FLOAT8E5M2FNUZ, 337s } 337s ], 337s ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype), 337s ) 337s def test_make_tensor_raw(tensor_dtype: int) -> None: 337s np_array = np.random.randn(2, 3).astype( 337s helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s ) 337s tensor = helper.make_tensor( 337s name="test", 337s data_type=tensor_dtype, 337s dims=np_array.shape, 337s vals=np_array.tobytes(), 337s raw=True, 337s ) 337s > np.testing.assert_equal(np_array, numpy_helper.to_array(tensor)) 337s 337s onnx/test/helper_test.py:841: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (, array([[ 2, -1, 0], 337s [ 0, 0, 0]], dtype=int16), array([[512, -1, 0], 337s [ 0, 0, 0]], dtype=int16)) 337s kwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Arrays are not equal 337s E 337s E Mismatched elements: 1 / 6 (16.7%) 337s E Max absolute difference: 510 337s E Max relative difference: 0.99609375 337s E x: array([[ 2, -1, 0], 337s E [ 0, 0, 0]], dtype=int16) 337s E y: array([[512, -1, 0], 337s E [ 0, 0, 0]], dtype=int16) 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s ___________________ test_make_tensor_raw[TensorProto.INT32] ____________________ 337s 337s tensor_dtype = 6 337s 337s @pytest.mark.parametrize( 337s "tensor_dtype", 337s [ 337s t 337s for t in helper.get_all_tensor_dtypes() 337s if t 337s not in { 337s TensorProto.BFLOAT16, 337s TensorProto.STRING, 337s TensorProto.FLOAT8E4M3FN, 337s TensorProto.FLOAT8E4M3FNUZ, 337s TensorProto.FLOAT8E5M2, 337s TensorProto.FLOAT8E5M2FNUZ, 337s } 337s ], 337s ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype), 337s ) 337s def test_make_tensor_raw(tensor_dtype: int) -> None: 337s np_array = np.random.randn(2, 3).astype( 337s helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s ) 337s tensor = helper.make_tensor( 337s name="test", 337s data_type=tensor_dtype, 337s dims=np_array.shape, 337s vals=np_array.tobytes(), 337s raw=True, 337s ) 337s > np.testing.assert_equal(np_array, numpy_helper.to_array(tensor)) 337s 337s onnx/test/helper_test.py:841: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (, array([[0, 0, 0], 337s [1, 0, 0]], dtype=int32), array([[ 0, 0, 0], 337s [16777216, 0, 0]], dtype=int32)) 337s kwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Arrays are not equal 337s E 337s E Mismatched elements: 1 / 6 (16.7%) 337s E Max absolute difference: 16777215 337s E Max relative difference: 0.99999994 337s E x: array([[0, 0, 0], 337s E [1, 0, 0]], dtype=int32) 337s E y: array([[ 0, 0, 0], 337s E [16777216, 0, 0]], dtype=int32) 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s ___________________ test_make_tensor_raw[TensorProto.INT64] ____________________ 337s 337s tensor_dtype = 7 337s 337s @pytest.mark.parametrize( 337s "tensor_dtype", 337s [ 337s t 337s for t in helper.get_all_tensor_dtypes() 337s if t 337s not in { 337s TensorProto.BFLOAT16, 337s TensorProto.STRING, 337s TensorProto.FLOAT8E4M3FN, 337s TensorProto.FLOAT8E4M3FNUZ, 337s TensorProto.FLOAT8E5M2, 337s TensorProto.FLOAT8E5M2FNUZ, 337s } 337s ], 337s ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype), 337s ) 337s def test_make_tensor_raw(tensor_dtype: int) -> None: 337s np_array = np.random.randn(2, 3).astype( 337s helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s ) 337s tensor = helper.make_tensor( 337s name="test", 337s data_type=tensor_dtype, 337s dims=np_array.shape, 337s vals=np_array.tobytes(), 337s raw=True, 337s ) 337s > np.testing.assert_equal(np_array, numpy_helper.to_array(tensor)) 337s 337s onnx/test/helper_test.py:841: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (, array([[0, 0, 0], 337s [1, 2, 0]]), array([[ 0, 0, 0], 337s [ 72057594037927936, 144115188075855872, 0]])) 337s kwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Arrays are not equal 337s E 337s E Mismatched elements: 2 / 6 (33.3%) 337s E Max absolute difference: 144115188075855870 337s E Max relative difference: 1. 337s E x: array([[0, 0, 0], 337s E [1, 2, 0]]) 337s E y: array([[ 0, 0, 0], 337s E [ 72057594037927936, 144115188075855872, 0]]) 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s __________________ test_make_tensor_raw[TensorProto.FLOAT16] ___________________ 337s 337s tensor_dtype = 10 337s 337s @pytest.mark.parametrize( 337s "tensor_dtype", 337s [ 337s t 337s for t in helper.get_all_tensor_dtypes() 337s if t 337s not in { 337s TensorProto.BFLOAT16, 337s TensorProto.STRING, 337s TensorProto.FLOAT8E4M3FN, 337s TensorProto.FLOAT8E4M3FNUZ, 337s TensorProto.FLOAT8E5M2, 337s TensorProto.FLOAT8E5M2FNUZ, 337s } 337s ], 337s ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype), 337s ) 337s def test_make_tensor_raw(tensor_dtype: int) -> None: 337s np_array = np.random.randn(2, 3).astype( 337s helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s ) 337s tensor = helper.make_tensor( 337s name="test", 337s data_type=tensor_dtype, 337s dims=np_array.shape, 337s vals=np_array.tobytes(), 337s raw=True, 337s ) 337s > np.testing.assert_equal(np_array, numpy_helper.to_array(tensor)) 337s 337s onnx/test/helper_test.py:841: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (, array([[-0.3616, 1.064 , -0.938 ], 337s [ 0.433 , -0.406 , 0.7246]], dtype=float16), array([[-1.1414e+01, 3.1172e+00, -2.6405e-05], 337s [-6.3600e+03, nan, -1.6891e+01]], dtype=float16)) 337s kwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Arrays are not equal 337s E 337s E x and y nan location mismatch: 337s E x: array([[-0.3616, 1.064 , -0.938 ], 337s E [ 0.433 , -0.406 , 0.7246]], dtype=float16) 337s E y: array([[-1.1414e+01, 3.1172e+00, -2.6405e-05], 337s E [-6.3600e+03, nan, -1.6891e+01]], dtype=float16) 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s ___________________ test_make_tensor_raw[TensorProto.DOUBLE] ___________________ 337s 337s tensor_dtype = 11 337s 337s @pytest.mark.parametrize( 337s "tensor_dtype", 337s [ 337s t 337s for t in helper.get_all_tensor_dtypes() 337s if t 337s not in { 337s TensorProto.BFLOAT16, 337s TensorProto.STRING, 337s TensorProto.FLOAT8E4M3FN, 337s TensorProto.FLOAT8E4M3FNUZ, 337s TensorProto.FLOAT8E5M2, 337s TensorProto.FLOAT8E5M2FNUZ, 337s } 337s ], 337s ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype), 337s ) 337s def test_make_tensor_raw(tensor_dtype: int) -> None: 337s np_array = np.random.randn(2, 3).astype( 337s helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s ) 337s tensor = helper.make_tensor( 337s name="test", 337s data_type=tensor_dtype, 337s dims=np_array.shape, 337s vals=np_array.tobytes(), 337s raw=True, 337s ) 337s > np.testing.assert_equal(np_array, numpy_helper.to_array(tensor)) 337s 337s onnx/test/helper_test.py:841: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (, array([[ 1.38526155, -0.30309825, 0.44103291], 337s [ 0.17879287, -0.7994224 , 0.24078751...-2.26348247e+242, 5.92135182e+118, -2.19324740e+061], 337s [ 7.11604926e-157, 3.92347574e-023, 1.96790549e+295]])) 337s kwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Arrays are not equal 337s E 337s E Mismatched elements: 6 / 6 (100%) 337s E Max absolute difference: 1.96790549e+295 337s E Max relative difference: 2.51252991e+155 337s E x: array([[ 1.385262, -0.303098, 0.441033], 337s E [ 0.178793, -0.799422, 0.240788]]) 337s E y: array([[-2.263482e+242, 5.921352e+118, -2.193247e+061], 337s E [ 7.116049e-157, 3.923476e-023, 1.967905e+295]]) 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s _________________ test_make_tensor_raw[TensorProto.COMPLEX64] __________________ 337s 337s tensor_dtype = 14 337s 337s @pytest.mark.parametrize( 337s "tensor_dtype", 337s [ 337s t 337s for t in helper.get_all_tensor_dtypes() 337s if t 337s not in { 337s TensorProto.BFLOAT16, 337s TensorProto.STRING, 337s TensorProto.FLOAT8E4M3FN, 337s TensorProto.FLOAT8E4M3FNUZ, 337s TensorProto.FLOAT8E5M2, 337s TensorProto.FLOAT8E5M2FNUZ, 337s } 337s ], 337s ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype), 337s ) 337s def test_make_tensor_raw(tensor_dtype: int) -> None: 337s np_array = np.random.randn(2, 3).astype( 337s helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s ) 337s tensor = helper.make_tensor( 337s name="test", 337s data_type=tensor_dtype, 337s dims=np_array.shape, 337s vals=np_array.tobytes(), 337s raw=True, 337s ) 337s > np.testing.assert_equal(np_array, numpy_helper.to_array(tensor)) 337s 337s onnx/test/helper_test.py:841: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (, array([[ 0.2891205 +0.j, 0.41287082+0.j, -0.1983989 +0.j], 337s [ 0.0941923 +0.j, -1.14761...0.j, 3.4189028e-26+0.j], 337s [-1.3680160e+23+0.j, -1.3816404e+26+0.j, -8.3434198e-04+0.j]], 337s dtype=complex64)) 337s kwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Arrays are not equal 337s E 337s E Mismatched elements: 6 / 6 (100%) 337s E Max absolute difference: 1.3816404e+26 337s E Max relative difference: 5.802999e+24 337s E x: array([[ 0.28912 +0.j, 0.412871+0.j, -0.198399+0.j], 337s E [ 0.094192+0.j, -1.147611+0.j, -0.358114+0.j]], dtype=complex64) 337s E y: array([[-2.803709e-23+0.j, -9.555680e+08+0.j, 3.418903e-26+0.j], 337s E [-1.368016e+23+0.j, -1.381640e+26+0.j, -8.343420e-04+0.j]], 337s E dtype=complex64) 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s _________________ test_make_tensor_raw[TensorProto.COMPLEX128] _________________ 337s 337s tensor_dtype = 15 337s 337s @pytest.mark.parametrize( 337s "tensor_dtype", 337s [ 337s t 337s for t in helper.get_all_tensor_dtypes() 337s if t 337s not in { 337s TensorProto.BFLOAT16, 337s TensorProto.STRING, 337s TensorProto.FLOAT8E4M3FN, 337s TensorProto.FLOAT8E4M3FNUZ, 337s TensorProto.FLOAT8E5M2, 337s TensorProto.FLOAT8E5M2FNUZ, 337s } 337s ], 337s ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype), 337s ) 337s def test_make_tensor_raw(tensor_dtype: int) -> None: 337s np_array = np.random.randn(2, 3).astype( 337s helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s ) 337s tensor = helper.make_tensor( 337s name="test", 337s data_type=tensor_dtype, 337s dims=np_array.shape, 337s vals=np_array.tobytes(), 337s raw=True, 337s ) 337s > np.testing.assert_equal(np_array, numpy_helper.to_array(tensor)) 337s 337s onnx/test/helper_test.py:841: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (, array([[ 0.55596268+0.j, 0.89247389+0.j, -0.42231482+0.j], 337s [ 0.10471403+0.j, 0.22805....56142554e+178+0.j, 8.25483079e+086+0.j], 337s [-7.05106886e-149+0.j, 1.05603273e-012+0.j, 2.25749570e-026+0.j]])) 337s kwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Arrays are not equal 337s E 337s E Mismatched elements: 6 / 6 (100%) 337s E Max absolute difference: 1.56142554e+178 337s E Max relative difference: 1.48508023e+147 337s E x: array([[ 0.555963+0.j, 0.892474+0.j, -0.422315+0.j], 337s E [ 0.104714+0.j, 0.228053+0.j, 0.20148 +0.j]]) 337s E y: array([[-2.545543e-107+0.j, -1.561426e+178+0.j, 8.254831e+086+0.j], 337s E [-7.051069e-149+0.j, 1.056033e-012+0.j, 2.257496e-026+0.j]]) 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s ___________________ test_make_tensor_raw[TensorProto.UINT32] ___________________ 337s 337s tensor_dtype = 12 337s 337s @pytest.mark.parametrize( 337s "tensor_dtype", 337s [ 337s t 337s for t in helper.get_all_tensor_dtypes() 337s if t 337s not in { 337s TensorProto.BFLOAT16, 337s TensorProto.STRING, 337s TensorProto.FLOAT8E4M3FN, 337s TensorProto.FLOAT8E4M3FNUZ, 337s TensorProto.FLOAT8E5M2, 337s TensorProto.FLOAT8E5M2FNUZ, 337s } 337s ], 337s ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype), 337s ) 337s def test_make_tensor_raw(tensor_dtype: int) -> None: 337s np_array = np.random.randn(2, 3).astype( 337s helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s ) 337s tensor = helper.make_tensor( 337s name="test", 337s data_type=tensor_dtype, 337s dims=np_array.shape, 337s vals=np_array.tobytes(), 337s raw=True, 337s ) 337s > np.testing.assert_equal(np_array, numpy_helper.to_array(tensor)) 337s 337s onnx/test/helper_test.py:841: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (, array([[0, 0, 0], 337s [0, 0, 1]], dtype=uint32), array([[ 0, 0, 0], 337s [ 0, 0, 16777216]], dtype=uint32)) 337s kwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Arrays are not equal 337s E 337s E Mismatched elements: 1 / 6 (16.7%) 337s E Max absolute difference: 16777215 337s E Max relative difference: 0.99999994 337s E x: array([[0, 0, 0], 337s E [0, 0, 1]], dtype=uint32) 337s E y: array([[ 0, 0, 0], 337s E [ 0, 0, 16777216]], dtype=uint32) 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s ___________________ test_make_tensor_raw[TensorProto.UINT64] ___________________ 337s 337s tensor_dtype = 13 337s 337s @pytest.mark.parametrize( 337s "tensor_dtype", 337s [ 337s t 337s for t in helper.get_all_tensor_dtypes() 337s if t 337s not in { 337s TensorProto.BFLOAT16, 337s TensorProto.STRING, 337s TensorProto.FLOAT8E4M3FN, 337s TensorProto.FLOAT8E4M3FNUZ, 337s TensorProto.FLOAT8E5M2, 337s TensorProto.FLOAT8E5M2FNUZ, 337s } 337s ], 337s ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype), 337s ) 337s def test_make_tensor_raw(tensor_dtype: int) -> None: 337s np_array = np.random.randn(2, 3).astype( 337s helper.tensor_dtype_to_np_dtype(tensor_dtype) 337s ) 337s tensor = helper.make_tensor( 337s name="test", 337s data_type=tensor_dtype, 337s dims=np_array.shape, 337s vals=np_array.tobytes(), 337s raw=True, 337s ) 337s > np.testing.assert_equal(np_array, numpy_helper.to_array(tensor)) 337s 337s onnx/test/helper_test.py:841: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (, array([[0, 1, 0], 337s [0, 0, 0]], dtype=uint64), array([[ 0, 72057594037927936, 0], 337s [ 0, 0, 0]], 337s dtype=uint64)) 337s kwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Arrays are not equal 337s E 337s E Mismatched elements: 1 / 6 (16.7%) 337s E Max absolute difference: 72057594037927935 337s E Max relative difference: 1. 337s E x: array([[0, 1, 0], 337s E [0, 0, 0]], dtype=uint64) 337s E y: array([[ 0, 72057594037927936, 0], 337s E [ 0, 0, 0]], 337s E dtype=uint64) 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s _ TestOnnxBackEndWithReferenceEvaluator.test__pytorch_converted_Conv2d_depthwise_padded _ 337s 337s self = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_Conv2d_depthwise_padded') 337s index = 0, i_output = 0 337s desired = array([[[[ 9.04451311e-02, 2.59667486e-01, -1.09607518e-01, 337s 2.27060467e-01, -5.80047965e-02, -5.29188275e-...-1.63738191e-01, -2.90462047e-01, 337s -3.69208872e-01, -3.35327953e-01, -1.56381518e-01]]]], 337s dtype=float32) 337s output = array([[[[ 9.04451162e-02, 2.59667546e-01, -1.09607533e-01, 337s 2.27060467e-01, -5.80047816e-02, -5.29188275e-...-1.63738191e-01, -2.90462047e-01, 337s -3.69208872e-01, -3.35327953e-01, -1.56381518e-01]]]], 337s dtype=float32) 337s rtol = 0, atol = 0 337s comment = '[runtime=ReferenceEvaluator]\nir_version: 3\nproducer_name: "pytorch"\nproducer_version: "0.3"\ngraph {\n node {\n ... dim {\n dim_value: 6\n }\n }\n }\n }\n }\n}\nopset_import {\n version: 6\n}\n' 337s inputs = [array([[[[ 0.69232553, 0.45320687, 2.1692784 , -0.8968519 , 337s 1.3547171 , -0.5139638 ], 337s [ 2.21389..., 337s [-0.13081397, -0.1452413 , -1.5837705 , -1.4981829 , 337s 1.263025 , -0.08155061]]]], dtype=float32)] 337s 337s def _compare_results( 337s self, index, i_output, desired, output, rtol=0, atol=0, comment="", inputs=None 337s ): 337s """ 337s Compares the expected output and the output produced 337s by the runtime. Raises an exception if not equal. 337s 337s :param index: test index 337s :param i_output: output index 337s :param desired: expected output 337s :param output: output 337s :param rtol: relative tolerance 337s :param atol: absolute tolerance 337s :param comment: addition text to give more insights to the user 337s :param inputs: inputs to the model 337s """ 337s if comment == "": 337s raise RuntimeError("Argument comment should be filled.") 337s if atol is None: 337s atol = 0 337s if rtol is None: 337s rtol = 0 337s if isinstance(desired, np.ndarray): 337s if isinstance(output, np.ndarray): 337s if rtol == 0: 337s if desired.dtype == np.float32: 337s rtl = 1e-5 337s elif desired.dtype == np.float64: 337s rtl = 1e-12 337s else: 337s rtl = rtol 337s else: 337s rtl = rtol 337s if desired.dtype == dtype_object: 337s try: 337s assert_allclose_string(desired, output) 337s except AssertionError as ex: 337s raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed, comment={comment}." 337s ) from ex 337s else: 337s equal_nan = desired.dtype in (np.float16, np.float32, np.float64) 337s if equal_nan: 337s try: 337s > assert_allclose( 337s desired, 337s output, 337s atol=atol, 337s rtol=rtl, 337s equal_nan=equal_nan, 337s ) 337s 337s onnx/test/reference_evaluator_backend_test.py:279: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (.compare at 0x3ff9ce2fba0>, array([[[[ 9.04451311e-02, 2.59667486e-01, -1.09607518...1.63738191e-01, -2.90462047e-01, 337s -3.69208872e-01, -3.35327953e-01, -1.56381518e-01]]]], 337s dtype=float32)) 337s kwds = {'equal_nan': True, 'err_msg': '', 'header': 'Not equal to tolerance rtol=1e-05, atol=0', 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Not equal to tolerance rtol=1e-05, atol=0 337s E 337s E Mismatched elements: 1 / 288 (0.347%) 337s E Max absolute difference: 5.9604645e-08 337s E Max relative difference: 1.3763678e-05 337s E x: array([[[[ 9.044513e-02, 2.596675e-01, -1.096075e-01, 2.270605e-01, 337s E -5.800480e-02, -5.291883e-01], 337s E [ 2.394726e-01, 1.705277e-01, 1.725401e-01, -1.005543e+00,... 337s E y: array([[[[ 9.044512e-02, 2.596675e-01, -1.096075e-01, 2.270605e-01, 337s E -5.800478e-02, -5.291883e-01], 337s E [ 2.394726e-01, 1.705277e-01, 1.725401e-01, -1.005543e+00,... 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s 337s The above exception was the direct cause of the following exception: 337s 337s self = 337s te = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_Conv2d_depthwise_padded') 337s check_other_runtime = None, verbose = 0, print_io = False 337s 337s def _test_( 337s self, te=te, check_other_runtime=None, verbose=0, print_io=False 337s ): 337s if te.fname in getattr(cls, "skip_test", set()): 337s cls.skipped.append((te, None)) 337s return 337s rtol = getattr(cls, "rtol", {}) 337s atol = getattr(cls, "atol", {}) 337s if len(rtol) == 0 or len(atol) == 0: 337s raise AssertionError("rtol or atol is empty.") 337s > self.common_test_onnx_test_run( 337s te, 337s getattr(cls, "successes", []), 337s getattr(cls, "missed", []), 337s getattr(cls, "skipped", []), 337s getattr(cls, "load_failed", []), 337s getattr(cls, "exec_failed", []), 337s getattr(cls, "mismatch", []), 337s verbose=verbose, 337s rtol=rtol, 337s atol=atol, 337s check_other_runtime=check_other_runtime, 337s print_io=print_io, 337s ) 337s 337s onnx/test/reference_evaluator_backend_test.py:494: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_backend_test.py:649: in common_test_onnx_test_run 337s raise e 337s onnx/test/reference_evaluator_backend_test.py:623: in common_test_onnx_test_run 337s te.run( 337s onnx/test/reference_evaluator_backend_test.py:382: in run 337s self.run( 337s onnx/test/reference_evaluator_backend_test.py:429: in run 337s self._compare_results( 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s self = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_Conv2d_depthwise_padded') 337s index = 0, i_output = 0 337s desired = array([[[[ 9.04451311e-02, 2.59667486e-01, -1.09607518e-01, 337s 2.27060467e-01, -5.80047965e-02, -5.29188275e-...-1.63738191e-01, -2.90462047e-01, 337s -3.69208872e-01, -3.35327953e-01, -1.56381518e-01]]]], 337s dtype=float32) 337s output = array([[[[ 9.04451162e-02, 2.59667546e-01, -1.09607533e-01, 337s 2.27060467e-01, -5.80047816e-02, -5.29188275e-...-1.63738191e-01, -2.90462047e-01, 337s -3.69208872e-01, -3.35327953e-01, -1.56381518e-01]]]], 337s dtype=float32) 337s rtol = 0, atol = 0 337s comment = '[runtime=ReferenceEvaluator]\nir_version: 3\nproducer_name: "pytorch"\nproducer_version: "0.3"\ngraph {\n node {\n ... dim {\n dim_value: 6\n }\n }\n }\n }\n }\n}\nopset_import {\n version: 6\n}\n' 337s inputs = [array([[[[ 0.69232553, 0.45320687, 2.1692784 , -0.8968519 , 337s 1.3547171 , -0.5139638 ], 337s [ 2.21389..., 337s [-0.13081397, -0.1452413 , -1.5837705 , -1.4981829 , 337s 1.263025 , -0.08155061]]]], dtype=float32)] 337s 337s def _compare_results( 337s self, index, i_output, desired, output, rtol=0, atol=0, comment="", inputs=None 337s ): 337s """ 337s Compares the expected output and the output produced 337s by the runtime. Raises an exception if not equal. 337s 337s :param index: test index 337s :param i_output: output index 337s :param desired: expected output 337s :param output: output 337s :param rtol: relative tolerance 337s :param atol: absolute tolerance 337s :param comment: addition text to give more insights to the user 337s :param inputs: inputs to the model 337s """ 337s if comment == "": 337s raise RuntimeError("Argument comment should be filled.") 337s if atol is None: 337s atol = 0 337s if rtol is None: 337s rtol = 0 337s if isinstance(desired, np.ndarray): 337s if isinstance(output, np.ndarray): 337s if rtol == 0: 337s if desired.dtype == np.float32: 337s rtl = 1e-5 337s elif desired.dtype == np.float64: 337s rtl = 1e-12 337s else: 337s rtl = rtol 337s else: 337s rtl = rtol 337s if desired.dtype == dtype_object: 337s try: 337s assert_allclose_string(desired, output) 337s except AssertionError as ex: 337s raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed, comment={comment}." 337s ) from ex 337s else: 337s equal_nan = desired.dtype in (np.float16, np.float32, np.float64) 337s if equal_nan: 337s try: 337s assert_allclose( 337s desired, 337s output, 337s atol=atol, 337s rtol=rtl, 337s equal_nan=equal_nan, 337s ) 337s except AssertionError as ex: 337s try: 337s diff = output - desired 337s except ValueError: 337s diff = None 337s > raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed " 337s f"(rtol={rtl}, atol={atol}), comment={comment}\n---\n{desired}\n----" 337s f"\n{output}\n-----\n{diff}\n------INPUTS----\n{pprint.pformat(inputs)}." 337s ) from ex 337s E AssertionError: Output 0 of test 0 in folder '/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_Conv2d_depthwise_padded' failed (rtol=1e-05, atol=0), comment=[runtime=ReferenceEvaluator] 337s E ir_version: 3 337s E producer_name: "pytorch" 337s E producer_version: "0.3" 337s E graph { 337s E node { 337s E input: "0" 337s E input: "1" 337s E input: "2" 337s E output: "3" 337s E op_type: "Conv" 337s E attribute { 337s E name: "dilations" 337s E ints: 1 337s E ints: 1 337s E type: INTS 337s E } 337s E attribute { 337s E name: "group" 337s E i: 4 337s E type: INT 337s E } 337s E attribute { 337s E name: "kernel_shape" 337s E ints: 3 337s E ints: 3 337s E type: INTS 337s E } 337s E attribute { 337s E name: "pads" 337s E ints: 1 337s E ints: 1 337s E ints: 1 337s E ints: 1 337s E type: INTS 337s E } 337s E attribute { 337s E name: "strides" 337s E ints: 1 337s E ints: 1 337s E type: INTS 337s E } 337s E } 337s E name: "torch-jit-export" 337s E initializer { 337s E dims: 4 337s E dims: 1 337s E dims: 3 337s E dims: 3 337s E data_type: 1 337s E name: "1" 337s E raw_data: "\274\326\365\030=\232\357\216\275\003s\360\275\262[\033=\304\332\n>\tg\017>#\271%=\035NT\276\006\336\322\275\215\033\256>\0200\021\275\030@|<\375\257\370<\330\027\340\275\2707^=\240U^\275\200W\356=\334*\262<\245\264@\2752U\306\275\340\005\000\276\020ff\275\n\373L=\370\234\356<\350\203\030>\r\370\335\275xs\226\275\205[\230\275u>\006\274\003\332p=\226Tb=\005\273l\007\312\307\274\266\022\250\275\344\266s" 337s E } 337s E initializer { 337s E dims: 4 337s E data_type: 1 337s E name: "2" 337s E raw_data: "\276\025J\332\275\2317\026\275qD\254\275\365\007#" 337s E } 337s E input { 337s E name: "0" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 2 337s E } 337s E dim { 337s E dim_value: 4 337s E } 337s E dim { 337s E dim_value: 6 337s E } 337s E dim { 337s E dim_value: 6 337s E } 337s E } 337s E } 337s E } 337s E } 337s E input { 337s E name: "1" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 4 337s E } 337s E dim { 337s E dim_value: 1 337s E } 337s E dim { 337s E dim_value: 3 337s E } 337s E dim { 337s E dim_value: 3 337s E } 337s E } 337s E } 337s E } 337s E } 337s E input { 337s E name: "2" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 4 337s E } 337s E } 337s E } 337s E } 337s E } 337s E output { 337s E name: "3" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 2 337s E } 337s E dim { 337s E dim_value: 4 337s E } 337s E dim { 337s E dim_value: 6 337s E } 337s E dim { 337s E dim_value: 6 337s E } 337s E } 337s E } 337s E } 337s E } 337s E } 337s E opset_import { 337s E version: 6 337s E } 337s E 337s E --- 337s E [[[[ 9.04451311e-02 2.59667486e-01 -1.09607518e-01 2.27060467e-01 337s E -5.80047965e-02 -5.29188275e-01] 337s E [ 2.39472598e-01 1.70527726e-01 1.72540098e-01 -1.00554287e+00 337s E -1.45831883e-01 1.02312207e-01] 337s E [ 2.02663988e-01 -6.19730473e-01 -2.97909021e-01 -2.83862919e-01 337s E -2.86865771e-01 1.29058421e-01] 337s E [-1.29076704e-01 -1.43824309e-01 -4.05187309e-01 -1.89752936e-01 337s E 3.55474323e-01 -2.14935020e-01] 337s E [-1.72179520e-01 -7.07473040e-01 -8.05565342e-02 4.65433747e-01 337s E 2.56773859e-01 -3.54018092e-01] 337s E [-5.10721505e-02 1.98414564e-01 -2.05287337e-02 -3.09055001e-01 337s E -4.44793105e-01 -6.08029366e-02]] 337s E 337s E [[-1.43441930e-02 -1.13900051e-01 -1.62280291e-01 9.72877294e-02 337s E -1.85675710e-01 7.04685599e-02] 337s E [-1.28756613e-01 -2.59187162e-01 3.07682574e-01 -3.36851358e-01 337s E -4.52385768e-02 4.70410287e-03] 337s E [-3.75674367e-01 4.05638397e-01 -4.88643229e-01 -1.19740270e-01 337s E 2.87702620e-01 -1.19759806e-01] 337s E [-2.88968146e-01 1.89473972e-01 -5.11745632e-01 1.00332975e-01 337s E -2.22260937e-01 -1.45938426e-01] 337s E [ 3.45454514e-01 -3.50505948e-01 4.04806852e-01 -3.70131969e-01 337s E 4.38449621e-01 -1.81537271e-01] 337s E [ 6.64843470e-02 -4.06239867e-01 -1.99985325e-01 -5.37078753e-02 337s E 3.04264575e-02 -2.15892836e-01]] 337s E 337s E [[ 1.71425313e-01 -6.04883075e-01 -1.37853324e-01 -1.07623890e-01 337s E -1.83807969e-01 4.51174498e-01] 337s E [-6.56127036e-01 5.22963047e-01 1.60877779e-02 9.89070088e-02 337s E 3.22297931e-01 -2.47981355e-01] 337s E [ 6.15023494e-01 -5.12455165e-01 -5.22559695e-02 -1.54784083e-01 337s E -4.52445924e-01 8.29571933e-02] 337s E [-4.77082551e-01 8.46077055e-02 -4.89401817e-02 -1.22576952e-01 337s E 1.61036760e-01 -4.61338937e-01] 337s E [-5.19442335e-02 -5.32882996e-02 5.26649505e-02 -7.68996924e-02 337s E -3.59155595e-01 -1.02642462e-01] 337s E [ 1.32276267e-01 8.81795883e-02 -2.74113417e-01 5.96636087e-02 337s E 5.32687455e-02 5.51304072e-02]] 337s E 337s E [[-3.28978747e-01 -5.36455572e-01 6.78425357e-02 2.60163322e-02 337s E -2.26633072e-01 1.46028310e-01] 337s E [ 5.82031086e-02 -1.28552586e-01 -2.92895660e-02 -1.61742628e-01 337s E -1.10788979e-01 -1.28600836e-01] 337s E [-6.71031252e-02 -5.89863122e-01 -6.05481923e-01 -1.67134598e-01 337s E 1.23714365e-01 -3.53690356e-01] 337s E [-6.70233518e-02 3.96840185e-01 9.51160565e-02 -4.25221175e-01 337s E -2.10134983e-01 -1.34419113e-01] 337s E [-2.07145065e-01 -6.50114566e-03 -5.34719378e-02 -6.41830683e-01 337s E -1.97601706e-01 1.87940925e-01] 337s E [-1.28388315e-01 -7.43171200e-02 1.81218475e-01 7.43379965e-02 337s E -6.82289079e-02 -6.87058866e-02]]] 337s E 337s E 337s E [[[-2.54434407e-01 -6.20997667e-01 9.52176154e-02 -1.27491891e-01 337s E -3.86431515e-01 -1.11279331e-01] 337s E [-1.27466172e-01 2.46243864e-01 -4.02579159e-02 -2.86341459e-02 337s E -6.06886744e-01 -4.30824369e-01] 337s E [-2.14729771e-01 -5.89762449e-01 -5.54056644e-01 3.35670114e-02 337s E 6.02487206e-01 -2.11963341e-01] 337s E [ 4.65372205e-03 1.90699041e-01 -1.46585554e-01 -2.31421903e-01 337s E -9.66671228e-01 -1.22498229e-01] 337s E [-3.44695896e-01 -3.10551673e-01 2.81475484e-02 -1.52723372e-01 337s E -3.14325899e-01 5.18683493e-02] 337s E [ 4.15668935e-02 -1.79213017e-01 -1.76665634e-01 -3.42608273e-01 337s E 8.91897827e-02 1.84001744e-01]] 337s E 337s E [[-3.56191337e-01 -8.89556631e-02 1.00753546e-01 -1.27870396e-01 337s E -2.18738198e-01 3.11021134e-02] 337s E [-3.05862784e-01 2.18692020e-01 -2.21859396e-01 -1.81461871e-01 337s E -6.12358600e-02 2.41194859e-01] 337s E [-1.27627924e-01 2.92178765e-02 -1.20258585e-01 -2.39791647e-01 337s E 5.02879173e-02 -8.04869235e-02] 337s E [-9.84959304e-03 -2.23187104e-01 -2.78030932e-01 -1.69486195e-01 337s E 2.13004485e-01 -3.21269512e-01] 337s E [ 3.67447019e-01 -2.89493918e-01 -5.41329384e-04 2.14935884e-01 337s E -2.42685035e-01 -6.74540922e-02] 337s E [ 8.16626102e-03 -5.33410981e-02 -2.32338011e-01 -2.13834181e-01 337s E -1.05647273e-01 -2.24549621e-02]] 337s E 337s E [[ 2.31772140e-02 -5.74476905e-02 7.26949722e-02 -2.56419815e-02 337s E -1.18937083e-01 -4.36923563e-01] 337s E [-3.88662294e-02 -6.16406620e-01 -2.21821114e-01 7.05406070e-03 337s E -2.52409726e-01 7.56103992e-02] 337s E [-1.77475125e-01 2.17536017e-01 5.86686313e-01 -4.28955138e-01 337s E 2.81287208e-02 3.85538876e-01] 337s E [ 4.84748930e-03 -1.89253539e-01 -3.15744877e-01 3.58607888e-01 337s E 1.50398433e-01 -1.75513521e-01] 337s E [-1.63466215e-01 -2.57279463e-02 -2.31721118e-01 2.48190686e-02 337s E -2.61522114e-01 -1.26382530e-01] 337s E [-1.08048759e-01 1.95850879e-02 2.51228690e-01 -1.54807299e-01 337s E -4.14312601e-01 -3.87236103e-02]] 337s E 337s E [[-1.75931811e-01 3.42793986e-02 -2.07133889e-01 -1.79836541e-01 337s E 9.50343385e-02 -1.02944896e-01] 337s E [-6.30943403e-02 -1.67409420e-01 -8.46790969e-02 2.90238038e-02 337s E 2.81909034e-02 -1.76308379e-02] 337s E [-3.98619846e-02 -3.75581175e-01 -2.27725536e-01 7.23285154e-02 337s E -3.70147735e-01 1.87434182e-02] 337s E [-2.41644502e-01 -2.92109311e-01 -2.84525871e-01 -1.42950714e-01 337s E -1.17604621e-01 -6.97315484e-03] 337s E [-1.10194474e-01 7.82676861e-02 1.64821118e-01 -4.69337881e-01 337s E -1.81303352e-01 5.01427427e-02] 337s E [-7.60068670e-02 -1.63738191e-01 -2.90462047e-01 -3.69208872e-01 337s E -3.35327953e-01 -1.56381518e-01]]]] 337s E ---- 337s E [[[[ 9.04451162e-02 2.59667546e-01 -1.09607533e-01 2.27060467e-01 337s E -5.80047816e-02 -5.29188275e-01] 337s E [ 2.39472568e-01 1.70527726e-01 1.72540098e-01 -1.00554287e+00 337s E -1.45831883e-01 1.02312237e-01] 337s E [ 2.02664018e-01 -6.19730473e-01 -2.97909021e-01 -2.83862948e-01 337s E -2.86865771e-01 1.29058421e-01] 337s E [-1.29076704e-01 -1.43824309e-01 -4.05187309e-01 -1.89752951e-01 337s E 3.55474323e-01 -2.14935005e-01] 337s E [-1.72179520e-01 -7.07473040e-01 -8.05565342e-02 4.65433747e-01 337s E 2.56773859e-01 -3.54018092e-01] 337s E [-5.10721430e-02 1.98414564e-01 -2.05287337e-02 -3.09055030e-01 337s E -4.44793105e-01 -6.08029366e-02]] 337s E 337s E [[-1.43441893e-02 -1.13900051e-01 -1.62280291e-01 9.72877294e-02 337s E -1.85675710e-01 7.04685599e-02] 337s E [-1.28756613e-01 -2.59187162e-01 3.07682574e-01 -3.36851358e-01 337s E -4.52385768e-02 4.70410287e-03] 337s E [-3.75674307e-01 4.05638397e-01 -4.88643169e-01 -1.19740255e-01 337s E 2.87702620e-01 -1.19759806e-01] 337s E [-2.88968146e-01 1.89473972e-01 -5.11745572e-01 1.00332975e-01 337s E -2.22260937e-01 -1.45938426e-01] 337s E [ 3.45454514e-01 -3.50505948e-01 4.04806852e-01 -3.70131969e-01 337s E 4.38449621e-01 -1.81537285e-01] 337s E [ 6.64843470e-02 -4.06239867e-01 -1.99985325e-01 -5.37078753e-02 337s E 3.04264575e-02 -2.15892851e-01]] 337s E 337s E [[ 1.71425328e-01 -6.04883075e-01 -1.37853324e-01 -1.07623890e-01 337s E -1.83807984e-01 4.51174498e-01] 337s E [-6.56127036e-01 5.22962987e-01 1.60877779e-02 9.89070237e-02 337s E 3.22297931e-01 -2.47981355e-01] 337s E [ 6.15023494e-01 -5.12455106e-01 -5.22559695e-02 -1.54784083e-01 337s E -4.52445924e-01 8.29571784e-02] 337s E [-4.77082551e-01 8.46076906e-02 -4.89401780e-02 -1.22576952e-01 337s E 1.61036760e-01 -4.61338937e-01] 337s E [-5.19442335e-02 -5.32882884e-02 5.26649505e-02 -7.68996999e-02 337s E -3.59155595e-01 -1.02642477e-01] 337s E [ 1.32276252e-01 8.81795883e-02 -2.74113417e-01 5.96636012e-02 337s E 5.32687828e-02 5.51303998e-02]] 337s E 337s E [[-3.28978747e-01 -5.36455572e-01 6.78425208e-02 2.60163322e-02 337s E -2.26633057e-01 1.46028340e-01] 337s E [ 5.82030937e-02 -1.28552616e-01 -2.92895585e-02 -1.61742613e-01 337s E -1.10788971e-01 -1.28600836e-01] 337s E [-6.71031177e-02 -5.89863062e-01 -6.05481923e-01 -1.67134598e-01 337s E 1.23714380e-01 -3.53690356e-01] 337s E [-6.70233518e-02 3.96840185e-01 9.51160565e-02 -4.25221205e-01 337s E -2.10134983e-01 -1.34419113e-01] 337s E [-2.07145065e-01 -6.50114566e-03 -5.34719303e-02 -6.41830683e-01 337s E -1.97601721e-01 1.87940925e-01] 337s E [-1.28388315e-01 -7.43171200e-02 1.81218475e-01 7.43379965e-02 337s E -6.82289079e-02 -6.87058941e-02]]] 337s E 337s E 337s E [[[-2.54434407e-01 -6.20997667e-01 9.52176154e-02 -1.27491876e-01 337s E -3.86431485e-01 -1.11279339e-01] 337s E [-1.27466172e-01 2.46243834e-01 -4.02579159e-02 -2.86341608e-02 337s E -6.06886744e-01 -4.30824369e-01] 337s E [-2.14729786e-01 -5.89762449e-01 -5.54056644e-01 3.35670263e-02 337s E 6.02487206e-01 -2.11963326e-01] 337s E [ 4.65370715e-03 1.90699011e-01 -1.46585569e-01 -2.31421888e-01 337s E -9.66671228e-01 -1.22498222e-01] 337s E [-3.44695896e-01 -3.10551703e-01 2.81475484e-02 -1.52723372e-01 337s E -3.14325899e-01 5.18683493e-02] 337s E [ 4.15668935e-02 -1.79213017e-01 -1.76665649e-01 -3.42608273e-01 337s E 8.91897827e-02 1.84001744e-01]] 337s E 337s E [[-3.56191337e-01 -8.89556631e-02 1.00753561e-01 -1.27870396e-01 337s E -2.18738198e-01 3.11021134e-02] 337s E [-3.05862784e-01 2.18692020e-01 -2.21859396e-01 -1.81461871e-01 337s E -6.12358451e-02 2.41194829e-01] 337s E [-1.27627909e-01 2.92178467e-02 -1.20258585e-01 -2.39791647e-01 337s E 5.02879173e-02 -8.04869309e-02] 337s E [-9.84960794e-03 -2.23187089e-01 -2.78030932e-01 -1.69486195e-01 337s E 2.13004485e-01 -3.21269512e-01] 337s E [ 3.67447019e-01 -2.89493918e-01 -5.41321933e-04 2.14935884e-01 337s E -2.42685035e-01 -6.74540699e-02] 337s E [ 8.16626102e-03 -5.33410981e-02 -2.32338011e-01 -2.13834181e-01 337s E -1.05647281e-01 -2.24549621e-02]] 337s E 337s E [[ 2.31772140e-02 -5.74476905e-02 7.26949722e-02 -2.56419852e-02 337s E -1.18937090e-01 -4.36923563e-01] 337s E [-3.88662294e-02 -6.16406620e-01 -2.21821114e-01 7.05409050e-03 337s E -2.52409697e-01 7.56103992e-02] 337s E [-1.77475125e-01 2.17536047e-01 5.86686254e-01 -4.28955138e-01 337s E 2.81287208e-02 3.85538876e-01] 337s E [ 4.84748930e-03 -1.89253539e-01 -3.15744817e-01 3.58607888e-01 337s E 1.50398403e-01 -1.75513506e-01] 337s E [-1.63466215e-01 -2.57279426e-02 -2.31721118e-01 2.48190612e-02 337s E -2.61522114e-01 -1.26382530e-01] 337s E [-1.08048767e-01 1.95850879e-02 2.51228690e-01 -1.54807284e-01 337s E -4.14312601e-01 -3.87236029e-02]] 337s E 337s E [[-1.75931811e-01 3.42793986e-02 -2.07133889e-01 -1.79836556e-01 337s E 9.50343236e-02 -1.02944896e-01] 337s E [-6.30943403e-02 -1.67409420e-01 -8.46790969e-02 2.90238187e-02 337s E 2.81909034e-02 -1.76308304e-02] 337s E [-3.98619846e-02 -3.75581145e-01 -2.27725536e-01 7.23285154e-02 337s E -3.70147705e-01 1.87434033e-02] 337s E [-2.41644502e-01 -2.92109311e-01 -2.84525901e-01 -1.42950714e-01 337s E -1.17604621e-01 -6.97315484e-03] 337s E [-1.10194474e-01 7.82676861e-02 1.64821118e-01 -4.69337881e-01 337s E -1.81303337e-01 5.01427427e-02] 337s E [-7.60068670e-02 -1.63738191e-01 -2.90462047e-01 -3.69208872e-01 337s E -3.35327953e-01 -1.56381518e-01]]]] 337s E ----- 337s E [[[[-1.4901161e-08 5.9604645e-08 -1.4901161e-08 0.0000000e+00 337s E 1.4901161e-08 0.0000000e+00] 337s E [-2.9802322e-08 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 2.9802322e-08] 337s E [ 2.9802322e-08 0.0000000e+00 0.0000000e+00 -2.9802322e-08 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 -1.4901161e-08 337s E 0.0000000e+00 1.4901161e-08] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 7.4505806e-09 0.0000000e+00 0.0000000e+00 -2.9802322e-08 337s E 0.0000000e+00 0.0000000e+00]] 337s E 337s E [[ 3.7252903e-09 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 5.9604645e-08 0.0000000e+00 5.9604645e-08 1.4901161e-08 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 5.9604645e-08 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 -1.4901161e-08] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 -1.4901161e-08]] 337s E 337s E [[ 1.4901161e-08 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E -1.4901161e-08 0.0000000e+00] 337s E [ 0.0000000e+00 -5.9604645e-08 0.0000000e+00 1.4901161e-08 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 5.9604645e-08 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 -1.4901161e-08] 337s E [ 0.0000000e+00 -1.4901161e-08 3.7252903e-09 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 1.1175871e-08 0.0000000e+00 -7.4505806e-09 337s E 0.0000000e+00 -1.4901161e-08] 337s E [-1.4901161e-08 0.0000000e+00 0.0000000e+00 -7.4505806e-09 337s E 3.7252903e-08 -7.4505806e-09]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00 -1.4901161e-08 0.0000000e+00 337s E 1.4901161e-08 2.9802322e-08] 337s E [-1.4901161e-08 -2.9802322e-08 7.4505806e-09 1.4901161e-08 337s E 7.4505806e-09 0.0000000e+00] 337s E [ 7.4505806e-09 5.9604645e-08 0.0000000e+00 0.0000000e+00 337s E 1.4901161e-08 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 -2.9802322e-08 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 7.4505806e-09 0.0000000e+00 337s E -1.4901161e-08 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 -7.4505806e-09]]] 337s E 337s E 337s E [[[ 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.4901161e-08 337s E 2.9802322e-08 -7.4505806e-09] 337s E [ 0.0000000e+00 -2.9802322e-08 0.0000000e+00 -1.4901161e-08 337s E 0.0000000e+00 0.0000000e+00] 337s E [-1.4901161e-08 0.0000000e+00 0.0000000e+00 1.4901161e-08 337s E 0.0000000e+00 1.4901161e-08] 337s E [-1.4901161e-08 -2.9802322e-08 -1.4901161e-08 1.4901161e-08 337s E 0.0000000e+00 7.4505806e-09] 337s E [ 0.0000000e+00 -2.9802322e-08 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 -1.4901161e-08 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00 1.4901161e-08 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 1.4901161e-08 -2.9802322e-08] 337s E [ 1.4901161e-08 -2.9802322e-08 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 -7.4505806e-09] 337s E [-1.4901161e-08 1.4901161e-08 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 7.4505806e-09 0.0000000e+00 337s E 0.0000000e+00 2.2351742e-08] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E -7.4505806e-09 0.0000000e+00]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00 0.0000000e+00 -3.7252903e-09 337s E -7.4505806e-09 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.9802322e-08 337s E 2.9802322e-08 0.0000000e+00] 337s E [ 0.0000000e+00 2.9802322e-08 -5.9604645e-08 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 5.9604645e-08 0.0000000e+00 337s E -2.9802322e-08 1.4901161e-08] 337s E [ 0.0000000e+00 3.7252903e-09 0.0000000e+00 -7.4505806e-09 337s E 0.0000000e+00 0.0000000e+00] 337s E [-7.4505806e-09 0.0000000e+00 0.0000000e+00 1.4901161e-08 337s E 0.0000000e+00 7.4505806e-09]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00 0.0000000e+00 -1.4901161e-08 337s E -1.4901161e-08 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.4901161e-08 337s E 0.0000000e+00 7.4505806e-09] 337s E [ 0.0000000e+00 2.9802322e-08 0.0000000e+00 0.0000000e+00 337s E 2.9802322e-08 -1.4901161e-08] 337s E [ 0.0000000e+00 0.0000000e+00 -2.9802322e-08 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 1.4901161e-08 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 337s E 0.0000000e+00 0.0000000e+00]]]] 337s E ------INPUTS---- 337s E [array([[[[ 0.69232553, 0.45320687, 2.1692784 , -0.8968519 , 337s E 1.3547171 , -0.5139638 ], 337s E [ 2.2138987 , -0.18112804, 1.6392266 , 0.35310313, 337s E -1.4467232 , 0.39830685], 337s E [ 1.3330269 , -0.8180627 , -1.3542233 , -0.75486296, 337s E 1.0860974 , -0.40080443], 337s E [ 0.07577234, -1.1683928 , -0.45294595, -0.72029716, 337s E 1.9682063 , 0.65563005], 337s E [ 0.52641505, -0.14344929, -1.1874508 , 0.72630656, 337s E 0.4546233 , 0.6608454 ], 337s E [-0.38869128, 0.65324336, 1.7448293 , 0.92278606, 337s E -1.2782667 , -0.6699633 ]], 337s E 337s E [[-1.2309015 , -0.41122258, -0.28716785, 0.46576646, 337s E 0.80649656, -0.22816706], 337s E [ 0.69429 , 0.92515934, -0.11469138, -0.97559094, 337s E 1.758934 , 0.18157808], 337s E [-0.7838291 , 1.1150683 , -1.3667834 , 1.168168 , 337s E 0.46770123, -1.138334 ], 337s E [ 0.73462844, -1.8345634 , 0.8668746 , -0.6107124 , 337s E 0.9056958 , 0.07475986], 337s E [ 0.89749336, -1.7719518 , -0.6997398 , -0.46831298, 337s E 0.27938223, -1.1089259 ], 337s E [-0.15051004, 0.52221483, 0.6137759 , 2.206822 , 337s E 0.3112967 , 0.9354072 ]], 337s E 337s E [[ 0.3082965 , 2.1008935 , -1.322245 , 0.39408472, 337s E -1.1989535 , -1.0104884 ], 337s E [-0.69566435, -1.3552536 , 1.0396894 , -0.3119677 , 337s E 1.0306792 , 2.001276 ], 337s E [-0.8205169 , 1.6374258 , -0.7985877 , 0.58831173, 337s E -0.37220407, 0.10811766], 337s E [ 1.0189639 , -2.1021051 , 1.4085556 , -0.69187266, 337s E 0.15292606, 1.1178733 ], 337s E [ 0.06130087, -0.10653395, -0.96363544, 0.3560341 , 337s E -1.1863015 , -2.1424072 ], 337s E [-0.5962941 , 1.3341993 , -0.02486269, -0.23697156, 337s E 0.10091614, -1.7383071 ]], 337s E 337s E [[-0.02264192, -0.82033 , -0.44958127, 1.3961492 , 337s E 1.9654402 , 0.7194433 ], 337s E [-1.1099433 , 1.9968332 , 1.7118304 , -0.16253375, 337s E 1.0937141 , 2.1202888 ], 337s E [-1.4686924 , -1.3296591 , -0.8680727 , 0.1982869 , 337s E -0.12106275, -1.2888205 ], 337s E [-1.2358515 , -0.3517313 , 0.8149394 , 0.5946754 , 337s E -0.2413678 , -2.1188636 ], 337s E [ 0.5008174 , -0.0883439 , -3.2045536 , -0.090054 , 337s E 0.0400042 , 0.8204074 ], 337s E [ 0.01335039, 1.6053296 , -0.52211934, 0.13791034, 337s E 1.1237203 , 0.61801046]]], 337s E 337s E 337s E [[[ 1.0750546 , -0.954712 , -0.63092005, 1.666651 , 337s E -0.3859544 , -1.8635256 ], 337s E [-1.2980901 , 0.25826585, 0.05715753, 0.3690894 , 337s E 1.2783152 , -0.6343572 ], 337s E [ 0.7739016 , 0.25243253, -1.4672629 , -1.254006 , 337s E -0.3254153 , 1.8266025 ], 337s E [-1.175393 , 0.1940818 , 0.5712985 , 0.87811434, 337s E -0.79959625, -1.5806178 ], 337s E [ 0.7510618 , -1.2003088 , -0.14388844, -0.89734876, 337s E -0.6863968 , 1.7864239 ], 337s E [ 0.56827146, 0.27870733, 0.7093103 , -0.9255147 , 337s E -0.02981607, 1.8106778 ]], 337s E 337s E [[-0.19713244, 2.4600215 , 0.4524925 , -0.7070368 , 337s E 0.27978238, 1.1567475 ], 337s E [-0.75985795, -0.9536486 , -0.30235928, 0.74008656, 337s E 0.43530285, -0.5211254 ], 337s E [ 0.38508797, -1.4274461 , -1.3744974 , 0.57809246, 337s E 1.9284421 , -0.34622356], 337s E [ 1.3067752 , -0.35285166, -0.44282648, 0.84895235, 337s E 0.13772313, -0.579692 ], 337s E [ 1.4768968 , -0.14861327, -1.5614916 , -0.68640924, 337s E -1.0805118 , -0.4911891 ], 337s E [-1.1829529 , 1.1039604 , -1.0408776 , -0.22080167, 337s E 1.6905397 , -0.20013234]], 337s E 337s E [[ 0.75512385, -0.8794844 , 0.6111656 , -0.49052906, 337s E 1.9594527 , 0.83461833], 337s E [ 1.4407539 , -0.24256405, 0.05532105, -1.4355502 , 337s E -1.028706 , -0.31872627], 337s E [-0.6505729 , -2.064766 , 0.84508765, 1.4639692 , 337s E -1.7076899 , 0.17171867], 337s E [ 0.9472312 , -0.24513735, 0.23904401, -0.48614362, 337s E 1.8041528 , 1.191407 ], 337s E [-0.8447745 , 0.18803704, 0.33802363, 0.62303495, 337s E 0.33649364, 1.5196944 ], 337s E [-1.432193 , -0.9387139 , -0.7667283 , 1.9013181 , 337s E -0.67890865, 0.4773699 ]], 337s E 337s E [[-0.59322566, 0.32661358, -0.13737763, -0.06013466, 337s E 0.42408347, -1.2199674 ], 337s E [ 1.3118391 , 0.10613123, -0.15149905, 1.10739 , 337s E 0.07836985, -0.67522025], 337s E [-0.26078302, 0.23547728, 1.1281868 , -0.2208668 , 337s E 0.36713317, -1.0883863 ], 337s E [ 0.08185866, -1.4915061 , 1.8988248 , -0.7817656 , 337s E 0.8984357 , 0.33715042], 337s E [-1.0013785 , 1.2713685 , -0.12268564, 1.7714666 , 337s E 0.2648192 , 1.829561 ], 337s E [-0.13081397, -0.1452413 , -1.5837705 , -1.4981829 , 337s E 1.263025 , -0.08155061]]]], dtype=float32)]. 337s 337s onnx/test/reference_evaluator_backend_test.py:291: AssertionError 337s _ TestOnnxBackEndWithReferenceEvaluator.test__pytorch_converted_Conv2d_dilated _ 337s 337s self = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_Conv2d_dilated') 337s index = 0, i_output = 0 337s desired = array([[[[ 0.19884917, -0.500968 , 0.1547932 ], 337s [ 0.19672862, -0.04575066, -0.64228106], 337s [-0.75121..., 337s [-0.44971293, -0.02588271, 0.8156379 ], 337s [-0.1473616 , -1.0839633 , -0.31312275]]]], dtype=float32) 337s output = array([[[[ 0.19884914, -0.500968 , 0.1547932 ], 337s [ 0.1967286 , -0.04575062, -0.642281 ], 337s [-0.75121..., 337s [-0.44971287, -0.0258827 , 0.81563795], 337s [-0.14736164, -1.0839632 , -0.31312272]]]], dtype=float32) 337s rtol = 0, atol = 0 337s comment = '[runtime=ReferenceEvaluator]\nir_version: 3\nproducer_name: "pytorch"\nproducer_version: "0.3"\ngraph {\n node {\n ... dim {\n dim_value: 3\n }\n }\n }\n }\n }\n}\nopset_import {\n version: 6\n}\n' 337s inputs = [array([[[[-1.84089541e-02, -6.06993377e-01, -2.80845433e-01, 337s -9.91596878e-01, -8.03171992e-01, 6.04136407e... 1.93696809e+00, 1.27714181e+00, 1.25448251e+00, 337s -4.39563662e-01, 6.25209332e-01]]]], dtype=float32)] 337s 337s def _compare_results( 337s self, index, i_output, desired, output, rtol=0, atol=0, comment="", inputs=None 337s ): 337s """ 337s Compares the expected output and the output produced 337s by the runtime. Raises an exception if not equal. 337s 337s :param index: test index 337s :param i_output: output index 337s :param desired: expected output 337s :param output: output 337s :param rtol: relative tolerance 337s :param atol: absolute tolerance 337s :param comment: addition text to give more insights to the user 337s :param inputs: inputs to the model 337s """ 337s if comment == "": 337s raise RuntimeError("Argument comment should be filled.") 337s if atol is None: 337s atol = 0 337s if rtol is None: 337s rtol = 0 337s if isinstance(desired, np.ndarray): 337s if isinstance(output, np.ndarray): 337s if rtol == 0: 337s if desired.dtype == np.float32: 337s rtl = 1e-5 337s elif desired.dtype == np.float64: 337s rtl = 1e-12 337s else: 337s rtl = rtol 337s else: 337s rtl = rtol 337s if desired.dtype == dtype_object: 337s try: 337s assert_allclose_string(desired, output) 337s except AssertionError as ex: 337s raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed, comment={comment}." 337s ) from ex 337s else: 337s equal_nan = desired.dtype in (np.float16, np.float32, np.float64) 337s if equal_nan: 337s try: 337s > assert_allclose( 337s desired, 337s output, 337s atol=atol, 337s rtol=rtl, 337s equal_nan=equal_nan, 337s ) 337s 337s onnx/test/reference_evaluator_backend_test.py:279: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (.compare at 0x3ff9ce88540>, array([[[[ 0.19884917, -0.500968 , 0.1547932 ], 337s ... 337s [-0.44971287, -0.0258827 , 0.81563795], 337s [-0.14736164, -1.0839632 , -0.31312272]]]], dtype=float32)) 337s kwds = {'equal_nan': True, 'err_msg': '', 'header': 'Not equal to tolerance rtol=1e-05, atol=0', 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Not equal to tolerance rtol=1e-05, atol=0 337s E 337s E Mismatched elements: 1 / 36 (2.78%) 337s E Max absolute difference: 1.4901161e-07 337s E Max relative difference: 1.1947545e-05 337s E x: array([[[[ 0.198849, -0.500968, 0.154793], 337s E [ 0.196729, -0.045751, -0.642281], 337s E [-0.751213, 0.561376, 0.012472]],... 337s E y: array([[[[ 0.198849, -0.500968, 0.154793], 337s E [ 0.196729, -0.045751, -0.642281], 337s E [-0.751213, 0.561376, 0.012472]],... 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s 337s The above exception was the direct cause of the following exception: 337s 337s self = 337s te = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_Conv2d_dilated') 337s check_other_runtime = None, verbose = 0, print_io = False 337s 337s def _test_( 337s self, te=te, check_other_runtime=None, verbose=0, print_io=False 337s ): 337s if te.fname in getattr(cls, "skip_test", set()): 337s cls.skipped.append((te, None)) 337s return 337s rtol = getattr(cls, "rtol", {}) 337s atol = getattr(cls, "atol", {}) 337s if len(rtol) == 0 or len(atol) == 0: 337s raise AssertionError("rtol or atol is empty.") 337s > self.common_test_onnx_test_run( 337s te, 337s getattr(cls, "successes", []), 337s getattr(cls, "missed", []), 337s getattr(cls, "skipped", []), 337s getattr(cls, "load_failed", []), 337s getattr(cls, "exec_failed", []), 337s getattr(cls, "mismatch", []), 337s verbose=verbose, 337s rtol=rtol, 337s atol=atol, 337s check_other_runtime=check_other_runtime, 337s print_io=print_io, 337s ) 337s 337s onnx/test/reference_evaluator_backend_test.py:494: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_backend_test.py:649: in common_test_onnx_test_run 337s raise e 337s onnx/test/reference_evaluator_backend_test.py:623: in common_test_onnx_test_run 337s te.run( 337s onnx/test/reference_evaluator_backend_test.py:382: in run 337s self.run( 337s onnx/test/reference_evaluator_backend_test.py:429: in run 337s self._compare_results( 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s self = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_Conv2d_dilated') 337s index = 0, i_output = 0 337s desired = array([[[[ 0.19884917, -0.500968 , 0.1547932 ], 337s [ 0.19672862, -0.04575066, -0.64228106], 337s [-0.75121..., 337s [-0.44971293, -0.02588271, 0.8156379 ], 337s [-0.1473616 , -1.0839633 , -0.31312275]]]], dtype=float32) 337s output = array([[[[ 0.19884914, -0.500968 , 0.1547932 ], 337s [ 0.1967286 , -0.04575062, -0.642281 ], 337s [-0.75121..., 337s [-0.44971287, -0.0258827 , 0.81563795], 337s [-0.14736164, -1.0839632 , -0.31312272]]]], dtype=float32) 337s rtol = 0, atol = 0 337s comment = '[runtime=ReferenceEvaluator]\nir_version: 3\nproducer_name: "pytorch"\nproducer_version: "0.3"\ngraph {\n node {\n ... dim {\n dim_value: 3\n }\n }\n }\n }\n }\n}\nopset_import {\n version: 6\n}\n' 337s inputs = [array([[[[-1.84089541e-02, -6.06993377e-01, -2.80845433e-01, 337s -9.91596878e-01, -8.03171992e-01, 6.04136407e... 1.93696809e+00, 1.27714181e+00, 1.25448251e+00, 337s -4.39563662e-01, 6.25209332e-01]]]], dtype=float32)] 337s 337s def _compare_results( 337s self, index, i_output, desired, output, rtol=0, atol=0, comment="", inputs=None 337s ): 337s """ 337s Compares the expected output and the output produced 337s by the runtime. Raises an exception if not equal. 337s 337s :param index: test index 337s :param i_output: output index 337s :param desired: expected output 337s :param output: output 337s :param rtol: relative tolerance 337s :param atol: absolute tolerance 337s :param comment: addition text to give more insights to the user 337s :param inputs: inputs to the model 337s """ 337s if comment == "": 337s raise RuntimeError("Argument comment should be filled.") 337s if atol is None: 337s atol = 0 337s if rtol is None: 337s rtol = 0 337s if isinstance(desired, np.ndarray): 337s if isinstance(output, np.ndarray): 337s if rtol == 0: 337s if desired.dtype == np.float32: 337s rtl = 1e-5 337s elif desired.dtype == np.float64: 337s rtl = 1e-12 337s else: 337s rtl = rtol 337s else: 337s rtl = rtol 337s if desired.dtype == dtype_object: 337s try: 337s assert_allclose_string(desired, output) 337s except AssertionError as ex: 337s raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed, comment={comment}." 337s ) from ex 337s else: 337s equal_nan = desired.dtype in (np.float16, np.float32, np.float64) 337s if equal_nan: 337s try: 337s assert_allclose( 337s desired, 337s output, 337s atol=atol, 337s rtol=rtl, 337s equal_nan=equal_nan, 337s ) 337s except AssertionError as ex: 337s try: 337s diff = output - desired 337s except ValueError: 337s diff = None 337s > raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed " 337s f"(rtol={rtl}, atol={atol}), comment={comment}\n---\n{desired}\n----" 337s f"\n{output}\n-----\n{diff}\n------INPUTS----\n{pprint.pformat(inputs)}." 337s ) from ex 337s E AssertionError: Output 0 of test 0 in folder '/usr/lib/python3/dist-packages/onnx/backend/test/data/pytorch-converted/test_Conv2d_dilated' failed (rtol=1e-05, atol=0), comment=[runtime=ReferenceEvaluator] 337s E ir_version: 3 337s E producer_name: "pytorch" 337s E producer_version: "0.3" 337s E graph { 337s E node { 337s E input: "0" 337s E input: "1" 337s E input: "2" 337s E output: "3" 337s E op_type: "Conv" 337s E attribute { 337s E name: "dilations" 337s E ints: 2 337s E ints: 2 337s E type: INTS 337s E } 337s E attribute { 337s E name: "group" 337s E i: 1 337s E type: INT 337s E } 337s E attribute { 337s E name: "kernel_shape" 337s E ints: 3 337s E ints: 3 337s E type: INTS 337s E } 337s E attribute { 337s E name: "pads" 337s E ints: 1 337s E ints: 1 337s E ints: 1 337s E ints: 1 337s E type: INTS 337s E } 337s E attribute { 337s E name: "strides" 337s E ints: 2 337s E ints: 2 337s E type: INTS 337s E } 337s E } 337s E name: "torch-jit-export" 337s E initializer { 337s E dims: 2 337s E dims: 3 337s E dims: 3 337s E dims: 3 337s E data_type: 1 337s E name: "1" 337s E raw_data: "\274\347}P\275\275\250j=\200c\016\275x3\250=\250N\276=(]\304>=\242=\274\266`\270\275o\242\254>=\252==>\245\220\276\010\n.\275\351\247\020=\232\264f=\376\322\372>\034\236\035\273\226* =\272g\332\016\2731\275\275\232\237\274\346\276\210\275\257\274\363\275\205\374\366\275q\254\260\272\247L\000\275\2528\203\275\215\006\271>\032mG>$OW\276\003@\\=\331\036B\275\331\037,\275\331\363\204>!\243\r\274\363\371\230=\270W\032>\021\021\245\276\002\366\000\276\024\302\366\275\367k)\272\225R\000>>\006/>\021\351\177>%\266]>\030\026\027\2769\013d\275\360\010\345=\355wz=V\010\300\275\316\030\321\275\030 \264" 337s E } 337s E initializer { 337s E dims: 2 337s E data_type: 1 337s E name: "2" 337s E raw_data: "\275\351_\364\275\022\036<" 337s E } 337s E input { 337s E name: "0" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 2 337s E } 337s E dim { 337s E dim_value: 3 337s E } 337s E dim { 337s E dim_value: 8 337s E } 337s E dim { 337s E dim_value: 8 337s E } 337s E } 337s E } 337s E } 337s E } 337s E input { 337s E name: "1" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 2 337s E } 337s E dim { 337s E dim_value: 3 337s E } 337s E dim { 337s E dim_value: 3 337s E } 337s E dim { 337s E dim_value: 3 337s E } 337s E } 337s E } 337s E } 337s E } 337s E input { 337s E name: "2" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 2 337s E } 337s E } 337s E } 337s E } 337s E } 337s E output { 337s E name: "3" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 2 337s E } 337s E dim { 337s E dim_value: 2 337s E } 337s E dim { 337s E dim_value: 3 337s E } 337s E dim { 337s E dim_value: 3 337s E } 337s E } 337s E } 337s E } 337s E } 337s E } 337s E opset_import { 337s E version: 6 337s E } 337s E 337s E --- 337s E [[[[ 0.19884917 -0.500968 0.1547932 ] 337s E [ 0.19672862 -0.04575066 -0.64228106] 337s E [-0.7512127 0.56137574 0.012472 ]] 337s E 337s E [[ 0.2878182 0.8317861 0.06981868] 337s E [-0.2874698 -0.4874791 -2.0593507 ] 337s E [-0.14513923 -0.58563787 -0.04213482]]] 337s E 337s E 337s E [[[-0.35236087 0.27741024 -0.09119175] 337s E [ 0.10173823 -0.07381938 -0.5995018 ] 337s E [-0.27918646 -0.05465417 -0.14519052]] 337s E 337s E [[-0.11914223 0.11774506 0.3094418 ] 337s E [-0.44971293 -0.02588271 0.8156379 ] 337s E [-0.1473616 -1.0839633 -0.31312275]]]] 337s E ---- 337s E [[[[ 0.19884914 -0.500968 0.1547932 ] 337s E [ 0.1967286 -0.04575062 -0.642281 ] 337s E [-0.7512127 0.56137574 0.01247215]] 337s E 337s E [[ 0.28781825 0.8317861 0.06981871] 337s E [-0.2874698 -0.4874791 -2.0593507 ] 337s E [-0.14513925 -0.58563787 -0.04213482]]] 337s E 337s E 337s E [[[-0.3523609 0.2774102 -0.09119175] 337s E [ 0.10173822 -0.07381937 -0.5995018 ] 337s E [-0.27918646 -0.05465416 -0.1451905 ]] 337s E 337s E [[-0.11914225 0.11774503 0.30944183] 337s E [-0.44971287 -0.0258827 0.81563795] 337s E [-0.14736164 -1.0839632 -0.31312272]]]] 337s E ----- 337s E [[[[-2.9802322e-08 0.0000000e+00 0.0000000e+00] 337s E [-1.4901161e-08 4.4703484e-08 5.9604645e-08] 337s E [ 0.0000000e+00 0.0000000e+00 1.4901161e-07]] 337s E 337s E [[ 5.9604645e-08 0.0000000e+00 2.9802322e-08] 337s E [ 0.0000000e+00 0.0000000e+00 0.0000000e+00] 337s E [-1.4901161e-08 0.0000000e+00 0.0000000e+00]]] 337s E 337s E 337s E [[[-2.9802322e-08 -2.9802322e-08 0.0000000e+00] 337s E [-7.4505806e-09 1.4901161e-08 0.0000000e+00] 337s E [ 0.0000000e+00 3.7252903e-09 1.4901161e-08]] 337s E 337s E [[-1.4901161e-08 -2.9802322e-08 2.9802322e-08] 337s E [ 5.9604645e-08 7.4505806e-09 5.9604645e-08] 337s E [-2.9802322e-08 1.1920929e-07 2.9802322e-08]]]] 337s E ------INPUTS---- 337s E [array([[[[-1.84089541e-02, -6.06993377e-01, -2.80845433e-01, 337s E -9.91596878e-01, -8.03171992e-01, 6.04136407e-01, 337s E 1.95692968e+00, 7.02132881e-01], 337s E [-7.53322005e-01, 3.93163115e-01, -6.98806345e-01, 337s E -2.01078206e-02, -1.49016798e-01, 6.59724414e-01, 337s E 7.12246358e-01, 4.35850114e-01], 337s E [ 9.70853746e-01, -5.79747200e-01, -1.06287432e+00, 337s E -7.49351561e-01, 3.10038269e-01, 2.46185274e-03, 337s E 2.45189685e-02, 1.21982419e+00], 337s E [-1.49756181e+00, -1.44562110e-01, -5.20062864e-01, 337s E -1.14678419e+00, -3.32032847e+00, -1.88837957e+00, 337s E -1.37703991e+00, 1.16923726e+00], 337s E [ 3.46965551e-01, 1.24434137e+00, -1.81637377e-01, 337s E -1.74748039e+00, -3.23677152e-01, -8.78200531e-01, 337s E -8.76391381e-02, -7.85675526e-01], 337s E [ 2.58958310e-01, -1.74128675e+00, -1.45944476e+00, 337s E -6.24294043e-01, -9.39373195e-01, 1.00421607e+00, 337s E -7.60454684e-02, 2.56109893e-01], 337s E [ 1.66016591e+00, 1.81584716e+00, 1.21443057e+00, 337s E 6.07176244e-01, -1.68237650e+00, 2.03667164e+00, 337s E -9.06268120e-01, 1.21096039e+00], 337s E [-1.32197869e+00, 4.35015321e-01, -1.16008675e+00, 337s E -9.40110311e-02, 9.61145818e-01, 7.33145297e-01, 337s E 1.69223702e+00, 1.59750319e+00]], 337s E 337s E [[-3.44303995e-01, -5.66431403e-01, 2.18902755e+00, 337s E -3.84048045e-01, -9.33285594e-01, 4.01929170e-01, 337s E 5.55357397e-01, -4.58694339e-01], 337s E [ 7.93777525e-01, 2.13403249e+00, -1.70946217e+00, 337s E -1.11389136e+00, -8.16507697e-01, -4.78101760e-01, 337s E 5.40830374e-01, -1.01887274e+00], 337s E [ 7.10313618e-01, 1.26507151e+00, -5.18325210e-01, 337s E 3.44350934e-02, 1.36374938e+00, -1.54354274e+00, 337s E -1.08630598e+00, 1.01969898e+00], 337s E [ 5.90652168e-01, -7.07596362e-01, -2.25643858e-01, 337s E 1.78402984e+00, -3.01748663e-01, -8.33105966e-02, 337s E -1.72071919e-01, 5.97092390e-01], 337s E [ 9.46228564e-01, -4.43124354e-01, 7.46440813e-02, 337s E 5.57505429e-01, -3.54245991e-01, 1.29892910e+00, 337s E 5.86976230e-01, 2.74700618e+00], 337s E [-4.41147625e-01, -1.28794849e-01, 1.03072906e+00, 337s E 3.11763436e-02, 1.34020793e+00, 7.11821437e-01, 337s E -8.21646154e-01, 1.11300014e-01], 337s E [ 8.02602649e-01, -5.30144155e-01, 2.17412925e+00, 337s E 1.13929152e+00, -4.74923521e-01, -1.31615257e+00, 337s E 9.18630600e-01, -4.64901447e-01], 337s E [-8.16343606e-01, 1.80840755e+00, 2.10819530e+00, 337s E -7.42249429e-01, 3.63734365e-01, 1.08723867e+00, 337s E 1.73769903e+00, 3.47246528e-01]], 337s E 337s E [[-1.60526848e+00, -1.51294038e-01, -2.20157206e-01, 337s E -1.62309873e+00, 8.26928139e-01, -2.83010066e-01, 337s E -2.40287876e+00, 1.08568799e+00], 337s E [ 2.47091308e-01, -3.25748384e-01, 8.05480421e-01, 337s E -8.50878119e-01, -5.79763412e-01, -1.24171877e+00, 337s E 4.74905431e-01, -1.75174057e+00], 337s E [ 7.03765512e-01, -2.63655573e-01, 2.28555202e-01, 337s E 5.28985597e-02, 1.01544082e+00, 2.30165899e-01, 337s E 1.32510400e+00, 3.91562581e-01], 337s E [ 7.44979560e-01, 3.37985456e-01, 3.92973751e-01, 337s E 1.56505182e-01, 1.70469552e-01, 2.69769400e-01, 337s E 4.62709755e-01, -2.16480708e+00], 337s E [-1.07009970e-02, -3.98567170e-01, 1.87917307e-01, 337s E 7.15260565e-01, 5.89413941e-01, -3.73822629e-01, 337s E -1.58367670e+00, 9.14475620e-01], 337s E [ 9.11280215e-01, 7.91276574e-01, 1.82653320e+00, 337s E -3.07196885e-01, -4.75410938e-01, 8.71947587e-01, 337s E 6.36164665e-01, 1.49430227e+00], 337s E [ 1.11014581e+00, -3.09581142e-02, 6.46823704e-01, 337s E 8.48748147e-01, 4.04983759e-01, -6.71743989e-01, 337s E 1.16180308e-01, 1.04450870e+00], 337s E [-3.46652180e-01, -3.47928673e-01, 8.31448793e-01, 337s E 8.98963436e-02, 1.60748541e+00, 7.72266686e-01, 337s E 3.48914206e-01, 1.25842774e+00]]], 337s E 337s E 337s E [[[-9.86703515e-01, -9.61175442e-01, -3.48520011e-01, 337s E -9.32746604e-02, 9.93260086e-01, 2.80397326e-01, 337s E 7.08821058e-01, -9.46948290e-01], 337s E [-2.32704312e-01, -1.40872705e+00, -3.35144222e-01, 337s E -4.16244268e-01, 1.90874326e+00, -9.05852497e-01, 337s E 3.00531924e-01, -5.91438055e-01], 337s E [-6.09940171e-01, 5.33465564e-01, -7.19619453e-01, 337s E 1.51262641e+00, -1.60433590e+00, 4.07322824e-01, 337s E -4.90560919e-01, 5.57049811e-01], 337s E [ 7.02997088e-01, 4.28079069e-02, -2.16196299e-01, 337s E 1.40599281e-01, 7.33201504e-02, 4.87709045e-01, 337s E 1.45439625e-01, -1.52859271e+00], 337s E [ 1.05933440e+00, 2.33970851e-01, -4.36405033e-01, 337s E -3.59165102e-01, 1.40006506e+00, 1.28190327e+00, 337s E 6.73339128e-01, 2.18242094e-01], 337s E [-6.86184540e-02, 1.92074463e-01, 2.28142273e-03, 337s E 9.10970867e-01, 2.52341247e+00, 3.88432324e-01, 337s E 4.17997718e-01, 1.17579317e+00], 337s E [-1.21906567e+00, -6.46158516e-01, 6.47064149e-01, 337s E -7.89307654e-02, -7.50259280e-01, 2.17474163e-01, 337s E -3.73793058e-02, -8.67561460e-01], 337s E [ 8.14000845e-01, -1.86957315e-01, -9.75255251e-01, 337s E 1.28601864e-01, -4.20513719e-01, 1.36356878e+00, 337s E 3.01291108e-01, -1.24178207e+00]], 337s E 337s E [[ 5.71294539e-02, 1.17094362e+00, -2.04255843e+00, 337s E 3.45637381e-01, -1.14476645e+00, 5.35143793e-01, 337s E 1.67810738e-01, -1.46595791e-01], 337s E [-2.41434097e-01, -5.16245008e-01, 1.00135207e+00, 337s E -1.46028376e+00, -4.64059770e-01, -9.16023374e-01, 337s E -2.08662629e+00, -9.73922014e-03], 337s E [ 2.39866182e-01, 3.05685580e-01, 9.20619428e-01, 337s E -1.83800924e+00, -1.07721031e+00, -4.69573796e-01, 337s E 1.20165005e-01, 1.75057679e-01], 337s E [ 2.73236871e-01, -1.71641856e-01, 6.66544855e-01, 337s E 6.74948215e-01, 1.94579673e+00, -3.99168760e-01, 337s E 1.37970459e+00, 9.51515362e-02], 337s E [-1.23496103e+00, -1.53505123e+00, -3.46692502e-02, 337s E 1.53225064e+00, -1.02160668e+00, -3.69328499e-01, 337s E 6.71232402e-01, 8.55082497e-02], 337s E [-2.52362043e-01, -7.65702665e-01, 1.56919920e+00, 337s E -9.42868460e-03, 1.26481807e+00, 7.44326040e-02, 337s E -2.12592080e-01, 2.81800091e-01], 337s E [ 1.18065226e+00, -1.88142464e-01, 8.03538561e-02, 337s E 1.20947093e-01, 2.03098699e-01, -2.37693501e+00, 337s E 3.93933535e-01, 7.86497235e-01], 337s E [ 1.01274538e+00, -7.16164947e-01, -2.95670837e-01, 337s E 4.66427296e-01, -1.92912698e+00, 1.00682437e+00, 337s E 4.19114918e-01, -3.01186740e-01]], 337s E 337s E [[ 3.54592413e-01, 7.92109072e-01, 1.23173857e+00, 337s E -1.71544015e+00, 5.32944977e-01, -4.12495553e-01, 337s E 1.68892398e-01, -1.04830337e+00], 337s E [-6.42338514e-01, 1.55933714e+00, -6.95014000e-01, 337s E -2.14242578e+00, 1.93508923e-01, 4.61062342e-01, 337s E -5.73715031e-01, 1.57160163e+00], 337s E [ 1.48312485e+00, 1.59501398e+00, 2.41567001e-01, 337s E -2.47953907e-01, 4.68793213e-02, -6.74864173e-01, 337s E -4.94332165e-01, 2.57136011e+00], 337s E [-9.47363079e-01, -7.45053232e-01, -1.81410766e+00, 337s E -1.65076637e+00, 9.36650097e-01, -9.76263523e-01, 337s E 1.13908231e+00, 6.47034228e-01], 337s E [ 7.23084211e-01, 5.51324129e-01, 2.98724324e-01, 337s E 8.29814613e-01, -8.46945465e-01, 5.13816178e-01, 337s E -1.49861455e+00, 1.57700670e+00], 337s E [-8.43423247e-01, 4.46644008e-01, -1.29535317e-01, 337s E 6.30050898e-01, -2.05498409e+00, -2.69786865e-01, 337s E 5.88357329e-01, 6.34424686e-01], 337s E [ 2.04229541e-02, 5.50112963e-01, 1.08304214e+00, 337s E -4.03787255e-01, -1.09886980e+00, -1.03948748e+00, 337s E 1.10723603e+00, -1.65091252e+00], 337s E [-1.60025680e+00, -1.44891286e+00, 8.04004192e-01, 337s E 1.93696809e+00, 1.27714181e+00, 1.25448251e+00, 337s E -4.39563662e-01, 6.25209332e-01]]]], dtype=float32)]. 337s 337s onnx/test/reference_evaluator_backend_test.py:291: AssertionError 337s ________________ TestOnnxBackEndWithReferenceEvaluator.test_dft ________________ 337s 337s self = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/node/test_dft') 337s index = 0, i_output = 0 337s desired = array([[[[ 4.5000000e+02, 0.0000000e+00], 337s [ 4.6000000e+02, 0.0000000e+00], 337s [ 4.7000000e+02, 0.000...1.5388417e+02], 337s [-5.0000000e+01, -1.5388417e+02], 337s [-5.0000000e+01, -1.5388417e+02]]]], dtype=float32) 337s output = array([[[[ 4.5000000e+02, 0.0000000e+00], 337s [ 4.6000000e+02, 0.0000000e+00], 337s [ 4.7000000e+02, 0.000...1.5388417e+02], 337s [-5.0000000e+01, -1.5388417e+02], 337s [-5.0000000e+01, -1.5388417e+02]]]], dtype=float32) 337s rtol = 0, atol = 0 337s comment = '[runtime=ReferenceEvaluator]\nir_version: 8\nproducer_name: "backend-test"\ngraph {\n node {\n input: "x"\n ou... dim_value: 2\n }\n }\n }\n }\n }\n}\nopset_import {\n domain: ""\n version: 17\n}\n' 337s inputs = [array([[[[ 0.], 337s [ 1.], 337s [ 2.], 337s [ 3.], 337s [ 4.], 337s [ 5.], 337s [ 6.], 337s ...3.], 337s [94.], 337s [95.], 337s [96.], 337s [97.], 337s [98.], 337s [99.]]]], dtype=float32)] 337s 337s def _compare_results( 337s self, index, i_output, desired, output, rtol=0, atol=0, comment="", inputs=None 337s ): 337s """ 337s Compares the expected output and the output produced 337s by the runtime. Raises an exception if not equal. 337s 337s :param index: test index 337s :param i_output: output index 337s :param desired: expected output 337s :param output: output 337s :param rtol: relative tolerance 337s :param atol: absolute tolerance 337s :param comment: addition text to give more insights to the user 337s :param inputs: inputs to the model 337s """ 337s if comment == "": 337s raise RuntimeError("Argument comment should be filled.") 337s if atol is None: 337s atol = 0 337s if rtol is None: 337s rtol = 0 337s if isinstance(desired, np.ndarray): 337s if isinstance(output, np.ndarray): 337s if rtol == 0: 337s if desired.dtype == np.float32: 337s rtl = 1e-5 337s elif desired.dtype == np.float64: 337s rtl = 1e-12 337s else: 337s rtl = rtol 337s else: 337s rtl = rtol 337s if desired.dtype == dtype_object: 337s try: 337s assert_allclose_string(desired, output) 337s except AssertionError as ex: 337s raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed, comment={comment}." 337s ) from ex 337s else: 337s equal_nan = desired.dtype in (np.float16, np.float32, np.float64) 337s if equal_nan: 337s try: 337s > assert_allclose( 337s desired, 337s output, 337s atol=atol, 337s rtol=rtl, 337s equal_nan=equal_nan, 337s ) 337s 337s onnx/test/reference_evaluator_backend_test.py:279: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (.compare at 0x3ff9ca1c4a0>, array([[[[ 4.5000000e+02, 0.0000000e+00], 337s [ 4....5388417e+02], 337s [-5.0000000e+01, -1.5388417e+02], 337s [-5.0000000e+01, -1.5388417e+02]]]], dtype=float32)) 337s kwds = {'equal_nan': True, 'err_msg': '', 'header': 'Not equal to tolerance rtol=1e-05, atol=0', 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Not equal to tolerance rtol=1e-05, atol=0 337s E 337s E Mismatched elements: 10 / 200 (5%) 337s E Max absolute difference: 3.5527137e-15 337s E Max relative difference: 0.33333334 337s E x: array([[[[ 4.500000e+02, 0.000000e+00], 337s E [ 4.600000e+02, 0.000000e+00], 337s E [ 4.700000e+02, 0.000000e+00],... 337s E y: array([[[[ 4.500000e+02, 0.000000e+00], 337s E [ 4.600000e+02, 0.000000e+00], 337s E [ 4.700000e+02, 0.000000e+00],... 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s 337s The above exception was the direct cause of the following exception: 337s 337s self = 337s te = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/node/test_dft') 337s check_other_runtime = None, verbose = 0, print_io = False 337s 337s def _test_( 337s self, te=te, check_other_runtime=None, verbose=0, print_io=False 337s ): 337s if te.fname in getattr(cls, "skip_test", set()): 337s cls.skipped.append((te, None)) 337s return 337s rtol = getattr(cls, "rtol", {}) 337s atol = getattr(cls, "atol", {}) 337s if len(rtol) == 0 or len(atol) == 0: 337s raise AssertionError("rtol or atol is empty.") 337s > self.common_test_onnx_test_run( 337s te, 337s getattr(cls, "successes", []), 337s getattr(cls, "missed", []), 337s getattr(cls, "skipped", []), 337s getattr(cls, "load_failed", []), 337s getattr(cls, "exec_failed", []), 337s getattr(cls, "mismatch", []), 337s verbose=verbose, 337s rtol=rtol, 337s atol=atol, 337s check_other_runtime=check_other_runtime, 337s print_io=print_io, 337s ) 337s 337s onnx/test/reference_evaluator_backend_test.py:494: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_backend_test.py:649: in common_test_onnx_test_run 337s raise e 337s onnx/test/reference_evaluator_backend_test.py:623: in common_test_onnx_test_run 337s te.run( 337s onnx/test/reference_evaluator_backend_test.py:382: in run 337s self.run( 337s onnx/test/reference_evaluator_backend_test.py:429: in run 337s self._compare_results( 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s self = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/node/test_dft') 337s index = 0, i_output = 0 337s desired = array([[[[ 4.5000000e+02, 0.0000000e+00], 337s [ 4.6000000e+02, 0.0000000e+00], 337s [ 4.7000000e+02, 0.000...1.5388417e+02], 337s [-5.0000000e+01, -1.5388417e+02], 337s [-5.0000000e+01, -1.5388417e+02]]]], dtype=float32) 337s output = array([[[[ 4.5000000e+02, 0.0000000e+00], 337s [ 4.6000000e+02, 0.0000000e+00], 337s [ 4.7000000e+02, 0.000...1.5388417e+02], 337s [-5.0000000e+01, -1.5388417e+02], 337s [-5.0000000e+01, -1.5388417e+02]]]], dtype=float32) 337s rtol = 0, atol = 0 337s comment = '[runtime=ReferenceEvaluator]\nir_version: 8\nproducer_name: "backend-test"\ngraph {\n node {\n input: "x"\n ou... dim_value: 2\n }\n }\n }\n }\n }\n}\nopset_import {\n domain: ""\n version: 17\n}\n' 337s inputs = [array([[[[ 0.], 337s [ 1.], 337s [ 2.], 337s [ 3.], 337s [ 4.], 337s [ 5.], 337s [ 6.], 337s ...3.], 337s [94.], 337s [95.], 337s [96.], 337s [97.], 337s [98.], 337s [99.]]]], dtype=float32)] 337s 337s def _compare_results( 337s self, index, i_output, desired, output, rtol=0, atol=0, comment="", inputs=None 337s ): 337s """ 337s Compares the expected output and the output produced 337s by the runtime. Raises an exception if not equal. 337s 337s :param index: test index 337s :param i_output: output index 337s :param desired: expected output 337s :param output: output 337s :param rtol: relative tolerance 337s :param atol: absolute tolerance 337s :param comment: addition text to give more insights to the user 337s :param inputs: inputs to the model 337s """ 337s if comment == "": 337s raise RuntimeError("Argument comment should be filled.") 337s if atol is None: 337s atol = 0 337s if rtol is None: 337s rtol = 0 337s if isinstance(desired, np.ndarray): 337s if isinstance(output, np.ndarray): 337s if rtol == 0: 337s if desired.dtype == np.float32: 337s rtl = 1e-5 337s elif desired.dtype == np.float64: 337s rtl = 1e-12 337s else: 337s rtl = rtol 337s else: 337s rtl = rtol 337s if desired.dtype == dtype_object: 337s try: 337s assert_allclose_string(desired, output) 337s except AssertionError as ex: 337s raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed, comment={comment}." 337s ) from ex 337s else: 337s equal_nan = desired.dtype in (np.float16, np.float32, np.float64) 337s if equal_nan: 337s try: 337s assert_allclose( 337s desired, 337s output, 337s atol=atol, 337s rtol=rtl, 337s equal_nan=equal_nan, 337s ) 337s except AssertionError as ex: 337s try: 337s diff = output - desired 337s except ValueError: 337s diff = None 337s > raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed " 337s f"(rtol={rtl}, atol={atol}), comment={comment}\n---\n{desired}\n----" 337s f"\n{output}\n-----\n{diff}\n------INPUTS----\n{pprint.pformat(inputs)}." 337s ) from ex 337s E AssertionError: Output 0 of test 0 in folder '/usr/lib/python3/dist-packages/onnx/backend/test/data/node/test_dft' failed (rtol=1e-05, atol=0), comment=[runtime=ReferenceEvaluator] 337s E ir_version: 8 337s E producer_name: "backend-test" 337s E graph { 337s E node { 337s E input: "x" 337s E output: "y" 337s E op_type: "DFT" 337s E attribute { 337s E name: "axis" 337s E i: 1 337s E type: INT 337s E } 337s E } 337s E name: "test_dft" 337s E input { 337s E name: "x" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 1 337s E } 337s E dim { 337s E dim_value: 10 337s E } 337s E dim { 337s E dim_value: 10 337s E } 337s E dim { 337s E dim_value: 1 337s E } 337s E } 337s E } 337s E } 337s E } 337s E output { 337s E name: "y" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 1 337s E } 337s E dim { 337s E dim_value: 10 337s E } 337s E dim { 337s E dim_value: 10 337s E } 337s E dim { 337s E dim_value: 2 337s E } 337s E } 337s E } 337s E } 337s E } 337s E } 337s E opset_import { 337s E domain: "" 337s E version: 17 337s E } 337s E 337s E --- 337s E [[[[ 4.5000000e+02 0.0000000e+00] 337s E [ 4.6000000e+02 0.0000000e+00] 337s E [ 4.7000000e+02 0.0000000e+00] 337s E [ 4.8000000e+02 0.0000000e+00] 337s E [ 4.9000000e+02 0.0000000e+00] 337s E [ 5.0000000e+02 0.0000000e+00] 337s E [ 5.1000000e+02 0.0000000e+00] 337s E [ 5.2000000e+02 0.0000000e+00] 337s E [ 5.3000000e+02 0.0000000e+00] 337s E [ 5.4000000e+02 0.0000000e+00]] 337s E 337s E [[-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02]] 337s E 337s E [[-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01]] 337s E 337s E [[-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01]] 337s E 337s E [[-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01]] 337s E 337s E [[-5.0000000e+01 -1.4210855e-14] 337s E [-5.0000000e+01 -1.4210855e-14] 337s E [-5.0000000e+01 -1.4210855e-14] 337s E [-5.0000000e+01 -1.4210855e-14] 337s E [-5.0000000e+01 -1.4210855e-14] 337s E [-5.0000000e+01 -1.4210855e-14] 337s E [-5.0000000e+01 -1.4210855e-14] 337s E [-5.0000000e+01 -1.4210855e-14] 337s E [-5.0000000e+01 -1.4210855e-14] 337s E [-5.0000000e+01 -1.4210855e-14]] 337s E 337s E [[-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01]] 337s E 337s E [[-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01]] 337s E 337s E [[-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01]] 337s E 337s E [[-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02]]]] 337s E ---- 337s E [[[[ 4.5000000e+02 0.0000000e+00] 337s E [ 4.6000000e+02 0.0000000e+00] 337s E [ 4.7000000e+02 0.0000000e+00] 337s E [ 4.8000000e+02 0.0000000e+00] 337s E [ 4.9000000e+02 0.0000000e+00] 337s E [ 5.0000000e+02 0.0000000e+00] 337s E [ 5.1000000e+02 0.0000000e+00] 337s E [ 5.2000000e+02 0.0000000e+00] 337s E [ 5.3000000e+02 0.0000000e+00] 337s E [ 5.4000000e+02 0.0000000e+00]] 337s E 337s E [[-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02] 337s E [-5.0000000e+01 1.5388417e+02]] 337s E 337s E [[-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01] 337s E [-5.0000000e+01 6.8819099e+01]] 337s E 337s E [[-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01] 337s E [-5.0000000e+01 3.6327126e+01]] 337s E 337s E [[-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01] 337s E [-5.0000000e+01 1.6245985e+01]] 337s E 337s E [[-5.0000000e+01 -1.0658141e-14] 337s E [-5.0000000e+01 -1.0658141e-14] 337s E [-5.0000000e+01 -1.0658141e-14] 337s E [-5.0000000e+01 -1.0658141e-14] 337s E [-5.0000000e+01 -1.0658141e-14] 337s E [-5.0000000e+01 -1.0658141e-14] 337s E [-5.0000000e+01 -1.0658141e-14] 337s E [-5.0000000e+01 -1.0658141e-14] 337s E [-5.0000000e+01 -1.0658141e-14] 337s E [-5.0000000e+01 -1.0658141e-14]] 337s E 337s E [[-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01] 337s E [-5.0000000e+01 -1.6245985e+01]] 337s E 337s E [[-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01] 337s E [-5.0000000e+01 -3.6327126e+01]] 337s E 337s E [[-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01] 337s E [-5.0000000e+01 -6.8819099e+01]] 337s E 337s E [[-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02] 337s E [-5.0000000e+01 -1.5388417e+02]]]] 337s E ----- 337s E [[[[0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00]] 337s E 337s E [[0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00]] 337s E 337s E [[0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00]] 337s E 337s E [[0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00]] 337s E 337s E [[0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00]] 337s E 337s E [[0.0000000e+00 3.5527137e-15] 337s E [0.0000000e+00 3.5527137e-15] 337s E [0.0000000e+00 3.5527137e-15] 337s E [0.0000000e+00 3.5527137e-15] 337s E [0.0000000e+00 3.5527137e-15] 337s E [0.0000000e+00 3.5527137e-15] 337s E [0.0000000e+00 3.5527137e-15] 337s E [0.0000000e+00 3.5527137e-15] 337s E [0.0000000e+00 3.5527137e-15] 337s E [0.0000000e+00 3.5527137e-15]] 337s E 337s E [[0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00]] 337s E 337s E [[0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00]] 337s E 337s E [[0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00]] 337s E 337s E [[0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00] 337s E [0.0000000e+00 0.0000000e+00]]]] 337s E ------INPUTS---- 337s E [array([[[[ 0.], 337s E [ 1.], 337s E [ 2.], 337s E [ 3.], 337s E [ 4.], 337s E [ 5.], 337s E [ 6.], 337s E [ 7.], 337s E [ 8.], 337s E [ 9.]], 337s E 337s E [[10.], 337s E [11.], 337s E [12.], 337s E [13.], 337s E [14.], 337s E [15.], 337s E [16.], 337s E [17.], 337s E [18.], 337s E [19.]], 337s E 337s E [[20.], 337s E [21.], 337s E [22.], 337s E [23.], 337s E [24.], 337s E [25.], 337s E [26.], 337s E [27.], 337s E [28.], 337s E [29.]], 337s E 337s E [[30.], 337s E [31.], 337s E [32.], 337s E [33.], 337s E [34.], 337s E [35.], 337s E [36.], 337s E [37.], 337s E [38.], 337s E [39.]], 337s E 337s E [[40.], 337s E [41.], 337s E [42.], 337s E [43.], 337s E [44.], 337s E [45.], 337s E [46.], 337s E [47.], 337s E [48.], 337s E [49.]], 337s E 337s E [[50.], 337s E [51.], 337s E [52.], 337s E [53.], 337s E [54.], 337s E [55.], 337s E [56.], 337s E [57.], 337s E [58.], 337s E [59.]], 337s E 337s E [[60.], 337s E [61.], 337s E [62.], 337s E [63.], 337s E [64.], 337s E [65.], 337s E [66.], 337s E [67.], 337s E [68.], 337s E [69.]], 337s E 337s E [[70.], 337s E [71.], 337s E [72.], 337s E [73.], 337s E [74.], 337s E [75.], 337s E [76.], 337s E [77.], 337s E [78.], 337s E [79.]], 337s E 337s E [[80.], 337s E [81.], 337s E [82.], 337s E [83.], 337s E [84.], 337s E [85.], 337s E [86.], 337s E [87.], 337s E [88.], 337s E [89.]], 337s E 337s E [[90.], 337s E [91.], 337s E [92.], 337s E [93.], 337s E [94.], 337s E [95.], 337s E [96.], 337s E [97.], 337s E [98.], 337s E [99.]]]], dtype=float32)]. 337s 337s onnx/test/reference_evaluator_backend_test.py:291: AssertionError 337s _____________ TestOnnxBackEndWithReferenceEvaluator.test_dft_axis ______________ 337s 337s self = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/node/test_dft_axis') 337s index = 0, i_output = 0 337s desired = array([[[[ 4.5000000e+01, 0.0000000e+00], 337s [-5.0000000e+00, 1.5388417e+01], 337s [-5.0000000e+00, 6.881...3.6327126e+00], 337s [-5.0000000e+00, -6.8819094e+00], 337s [-5.0000000e+00, -1.5388417e+01]]]], dtype=float32) 337s output = array([[[[ 4.5000000e+01, 0.0000000e+00], 337s [-5.0000000e+00, 1.5388417e+01], 337s [-5.0000000e+00, 6.881...3.6327126e+00], 337s [-5.0000000e+00, -6.8819094e+00], 337s [-5.0000000e+00, -1.5388417e+01]]]], dtype=float32) 337s rtol = 0, atol = 0 337s comment = '[runtime=ReferenceEvaluator]\nir_version: 8\nproducer_name: "backend-test"\ngraph {\n node {\n input: "x"\n ou... dim_value: 2\n }\n }\n }\n }\n }\n}\nopset_import {\n domain: ""\n version: 17\n}\n' 337s inputs = [array([[[[ 0.], 337s [ 1.], 337s [ 2.], 337s [ 3.], 337s [ 4.], 337s [ 5.], 337s [ 6.], 337s ...3.], 337s [94.], 337s [95.], 337s [96.], 337s [97.], 337s [98.], 337s [99.]]]], dtype=float32)] 337s 337s def _compare_results( 337s self, index, i_output, desired, output, rtol=0, atol=0, comment="", inputs=None 337s ): 337s """ 337s Compares the expected output and the output produced 337s by the runtime. Raises an exception if not equal. 337s 337s :param index: test index 337s :param i_output: output index 337s :param desired: expected output 337s :param output: output 337s :param rtol: relative tolerance 337s :param atol: absolute tolerance 337s :param comment: addition text to give more insights to the user 337s :param inputs: inputs to the model 337s """ 337s if comment == "": 337s raise RuntimeError("Argument comment should be filled.") 337s if atol is None: 337s atol = 0 337s if rtol is None: 337s rtol = 0 337s if isinstance(desired, np.ndarray): 337s if isinstance(output, np.ndarray): 337s if rtol == 0: 337s if desired.dtype == np.float32: 337s rtl = 1e-5 337s elif desired.dtype == np.float64: 337s rtl = 1e-12 337s else: 337s rtl = rtol 337s else: 337s rtl = rtol 337s if desired.dtype == dtype_object: 337s try: 337s assert_allclose_string(desired, output) 337s except AssertionError as ex: 337s raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed, comment={comment}." 337s ) from ex 337s else: 337s equal_nan = desired.dtype in (np.float16, np.float32, np.float64) 337s if equal_nan: 337s try: 337s > assert_allclose( 337s desired, 337s output, 337s atol=atol, 337s rtol=rtl, 337s equal_nan=equal_nan, 337s ) 337s 337s onnx/test/reference_evaluator_backend_test.py:279: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (.compare at 0x3ff9c950e00>, array([[[[ 4.5000000e+01, 0.0000000e+00], 337s [-5....6327126e+00], 337s [-5.0000000e+00, -6.8819094e+00], 337s [-5.0000000e+00, -1.5388417e+01]]]], dtype=float32)) 337s kwds = {'equal_nan': True, 'err_msg': '', 'header': 'Not equal to tolerance rtol=1e-05, atol=0', 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Not equal to tolerance rtol=1e-05, atol=0 337s E 337s E Mismatched elements: 10 / 200 (5%) 337s E Max absolute difference: 2.220446e-16 337s E Max relative difference: 0.14285715 337s E x: array([[[[ 4.500000e+01, 0.000000e+00], 337s E [-5.000000e+00, 1.538842e+01], 337s E [-5.000000e+00, 6.881909e+00],... 337s E y: array([[[[ 4.500000e+01, 0.000000e+00], 337s E [-5.000000e+00, 1.538842e+01], 337s E [-5.000000e+00, 6.881909e+00],... 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s 337s The above exception was the direct cause of the following exception: 337s 337s self = 337s te = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/node/test_dft_axis') 337s check_other_runtime = None, verbose = 0, print_io = False 337s 337s def _test_( 337s self, te=te, check_other_runtime=None, verbose=0, print_io=False 337s ): 337s if te.fname in getattr(cls, "skip_test", set()): 337s cls.skipped.append((te, None)) 337s return 337s rtol = getattr(cls, "rtol", {}) 337s atol = getattr(cls, "atol", {}) 337s if len(rtol) == 0 or len(atol) == 0: 337s raise AssertionError("rtol or atol is empty.") 337s > self.common_test_onnx_test_run( 337s te, 337s getattr(cls, "successes", []), 337s getattr(cls, "missed", []), 337s getattr(cls, "skipped", []), 337s getattr(cls, "load_failed", []), 337s getattr(cls, "exec_failed", []), 337s getattr(cls, "mismatch", []), 337s verbose=verbose, 337s rtol=rtol, 337s atol=atol, 337s check_other_runtime=check_other_runtime, 337s print_io=print_io, 337s ) 337s 337s onnx/test/reference_evaluator_backend_test.py:494: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_backend_test.py:649: in common_test_onnx_test_run 337s raise e 337s onnx/test/reference_evaluator_backend_test.py:623: in common_test_onnx_test_run 337s te.run( 337s onnx/test/reference_evaluator_backend_test.py:382: in run 337s self.run( 337s onnx/test/reference_evaluator_backend_test.py:429: in run 337s self._compare_results( 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s self = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/node/test_dft_axis') 337s index = 0, i_output = 0 337s desired = array([[[[ 4.5000000e+01, 0.0000000e+00], 337s [-5.0000000e+00, 1.5388417e+01], 337s [-5.0000000e+00, 6.881...3.6327126e+00], 337s [-5.0000000e+00, -6.8819094e+00], 337s [-5.0000000e+00, -1.5388417e+01]]]], dtype=float32) 337s output = array([[[[ 4.5000000e+01, 0.0000000e+00], 337s [-5.0000000e+00, 1.5388417e+01], 337s [-5.0000000e+00, 6.881...3.6327126e+00], 337s [-5.0000000e+00, -6.8819094e+00], 337s [-5.0000000e+00, -1.5388417e+01]]]], dtype=float32) 337s rtol = 0, atol = 0 337s comment = '[runtime=ReferenceEvaluator]\nir_version: 8\nproducer_name: "backend-test"\ngraph {\n node {\n input: "x"\n ou... dim_value: 2\n }\n }\n }\n }\n }\n}\nopset_import {\n domain: ""\n version: 17\n}\n' 337s inputs = [array([[[[ 0.], 337s [ 1.], 337s [ 2.], 337s [ 3.], 337s [ 4.], 337s [ 5.], 337s [ 6.], 337s ...3.], 337s [94.], 337s [95.], 337s [96.], 337s [97.], 337s [98.], 337s [99.]]]], dtype=float32)] 337s 337s def _compare_results( 337s self, index, i_output, desired, output, rtol=0, atol=0, comment="", inputs=None 337s ): 337s """ 337s Compares the expected output and the output produced 337s by the runtime. Raises an exception if not equal. 337s 337s :param index: test index 337s :param i_output: output index 337s :param desired: expected output 337s :param output: output 337s :param rtol: relative tolerance 337s :param atol: absolute tolerance 337s :param comment: addition text to give more insights to the user 337s :param inputs: inputs to the model 337s """ 337s if comment == "": 337s raise RuntimeError("Argument comment should be filled.") 337s if atol is None: 337s atol = 0 337s if rtol is None: 337s rtol = 0 337s if isinstance(desired, np.ndarray): 337s if isinstance(output, np.ndarray): 337s if rtol == 0: 337s if desired.dtype == np.float32: 337s rtl = 1e-5 337s elif desired.dtype == np.float64: 337s rtl = 1e-12 337s else: 337s rtl = rtol 337s else: 337s rtl = rtol 337s if desired.dtype == dtype_object: 337s try: 337s assert_allclose_string(desired, output) 337s except AssertionError as ex: 337s raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed, comment={comment}." 337s ) from ex 337s else: 337s equal_nan = desired.dtype in (np.float16, np.float32, np.float64) 337s if equal_nan: 337s try: 337s assert_allclose( 337s desired, 337s output, 337s atol=atol, 337s rtol=rtl, 337s equal_nan=equal_nan, 337s ) 337s except AssertionError as ex: 337s try: 337s diff = output - desired 337s except ValueError: 337s diff = None 337s > raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed " 337s f"(rtol={rtl}, atol={atol}), comment={comment}\n---\n{desired}\n----" 337s f"\n{output}\n-----\n{diff}\n------INPUTS----\n{pprint.pformat(inputs)}." 337s ) from ex 337s E AssertionError: Output 0 of test 0 in folder '/usr/lib/python3/dist-packages/onnx/backend/test/data/node/test_dft_axis' failed (rtol=1e-05, atol=0), comment=[runtime=ReferenceEvaluator] 337s E ir_version: 8 337s E producer_name: "backend-test" 337s E graph { 337s E node { 337s E input: "x" 337s E output: "y" 337s E op_type: "DFT" 337s E attribute { 337s E name: "axis" 337s E i: 2 337s E type: INT 337s E } 337s E } 337s E name: "test_dft_axis" 337s E input { 337s E name: "x" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 1 337s E } 337s E dim { 337s E dim_value: 10 337s E } 337s E dim { 337s E dim_value: 10 337s E } 337s E dim { 337s E dim_value: 1 337s E } 337s E } 337s E } 337s E } 337s E } 337s E output { 337s E name: "y" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 1 337s E } 337s E dim { 337s E dim_value: 10 337s E } 337s E dim { 337s E dim_value: 10 337s E } 337s E dim { 337s E dim_value: 2 337s E } 337s E } 337s E } 337s E } 337s E } 337s E } 337s E opset_import { 337s E domain: "" 337s E version: 17 337s E } 337s E 337s E --- 337s E [[[[ 4.5000000e+01 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.3322676e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 1.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.3322676e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 2.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.3322676e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 3.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.3322676e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 4.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.3322676e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 5.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.3322676e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 6.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.3322676e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 7.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.3322676e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 8.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.3322676e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 9.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.3322676e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]]]] 337s E ---- 337s E [[[[ 4.5000000e+01 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.5543122e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 1.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.5543122e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 2.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.5543122e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 3.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.5543122e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 4.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.5543122e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 5.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.5543122e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 6.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.5543122e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 7.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.5543122e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 8.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.5543122e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[ 9.4500000e+02 0.0000000e+00] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 -1.5543122e-15] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -1.5388417e+01]]]] 337s E ----- 337s E [[[[ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 -2.220446e-16] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00]] 337s E 337s E [[ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 -2.220446e-16] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00]] 337s E 337s E [[ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 -2.220446e-16] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00]] 337s E 337s E [[ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 -2.220446e-16] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00]] 337s E 337s E [[ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 -2.220446e-16] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00]] 337s E 337s E [[ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 -2.220446e-16] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00]] 337s E 337s E [[ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 -2.220446e-16] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00]] 337s E 337s E [[ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 -2.220446e-16] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00]] 337s E 337s E [[ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 -2.220446e-16] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00]] 337s E 337s E [[ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 -2.220446e-16] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00] 337s E [ 0.000000e+00 0.000000e+00]]]] 337s E ------INPUTS---- 337s E [array([[[[ 0.], 337s E [ 1.], 337s E [ 2.], 337s E [ 3.], 337s E [ 4.], 337s E [ 5.], 337s E [ 6.], 337s E [ 7.], 337s E [ 8.], 337s E [ 9.]], 337s E 337s E [[10.], 337s E [11.], 337s E [12.], 337s E [13.], 337s E [14.], 337s E [15.], 337s E [16.], 337s E [17.], 337s E [18.], 337s E [19.]], 337s E 337s E [[20.], 337s E [21.], 337s E [22.], 337s E [23.], 337s E [24.], 337s E [25.], 337s E [26.], 337s E [27.], 337s E [28.], 337s E [29.]], 337s E 337s E [[30.], 337s E [31.], 337s E [32.], 337s E [33.], 337s E [34.], 337s E [35.], 337s E [36.], 337s E [37.], 337s E [38.], 337s E [39.]], 337s E 337s E [[40.], 337s E [41.], 337s E [42.], 337s E [43.], 337s E [44.], 337s E [45.], 337s E [46.], 337s E [47.], 337s E [48.], 337s E [49.]], 337s E 337s E [[50.], 337s E [51.], 337s E [52.], 337s E [53.], 337s E [54.], 337s E [55.], 337s E [56.], 337s E [57.], 337s E [58.], 337s E [59.]], 337s E 337s E [[60.], 337s E [61.], 337s E [62.], 337s E [63.], 337s E [64.], 337s E [65.], 337s E [66.], 337s E [67.], 337s E [68.], 337s E [69.]], 337s E 337s E [[70.], 337s E [71.], 337s E [72.], 337s E [73.], 337s E [74.], 337s E [75.], 337s E [76.], 337s E [77.], 337s E [78.], 337s E [79.]], 337s E 337s E [[80.], 337s E [81.], 337s E [82.], 337s E [83.], 337s E [84.], 337s E [85.], 337s E [86.], 337s E [87.], 337s E [88.], 337s E [89.]], 337s E 337s E [[90.], 337s E [91.], 337s E [92.], 337s E [93.], 337s E [94.], 337s E [95.], 337s E [96.], 337s E [97.], 337s E [98.], 337s E [99.]]]], dtype=float32)]. 337s 337s onnx/test/reference_evaluator_backend_test.py:291: AssertionError 337s ____________ TestOnnxBackEndWithReferenceEvaluator.test_dft_inverse ____________ 337s 337s self = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/node/test_dft_inverse') 337s index = 0, i_output = 0 337s desired = array([[[[ 4.5000000e+01, 0.0000000e+00], 337s [ 4.6000000e+01, 0.0000000e+00], 337s [ 4.7000000e+01, 0.000...1.5388417e+01], 337s [-5.0000000e+00, 1.5388417e+01], 337s [-5.0000000e+00, 1.5388417e+01]]]], dtype=float32) 337s output = array([[[[ 4.5000000e+01, 0.0000000e+00], 337s [ 4.6000000e+01, 0.0000000e+00], 337s [ 4.7000000e+01, 0.000...1.5388417e+01], 337s [-5.0000000e+00, 1.5388417e+01], 337s [-5.0000000e+00, 1.5388417e+01]]]], dtype=float32) 337s rtol = 0, atol = 0 337s comment = '[runtime=ReferenceEvaluator]\nir_version: 8\nproducer_name: "backend-test"\ngraph {\n node {\n input: "x"\n ou... dim_value: 2\n }\n }\n }\n }\n }\n}\nopset_import {\n domain: ""\n version: 17\n}\n' 337s inputs = [array([[[[ 0., 0.], 337s [ 1., 0.], 337s [ 2., 0.], 337s [ 3., 0.], 337s [ 4., 0.], 337s [ 5... [95., 0.], 337s [96., 0.], 337s [97., 0.], 337s [98., 0.], 337s [99., 0.]]]], dtype=float32)] 337s 337s def _compare_results( 337s self, index, i_output, desired, output, rtol=0, atol=0, comment="", inputs=None 337s ): 337s """ 337s Compares the expected output and the output produced 337s by the runtime. Raises an exception if not equal. 337s 337s :param index: test index 337s :param i_output: output index 337s :param desired: expected output 337s :param output: output 337s :param rtol: relative tolerance 337s :param atol: absolute tolerance 337s :param comment: addition text to give more insights to the user 337s :param inputs: inputs to the model 337s """ 337s if comment == "": 337s raise RuntimeError("Argument comment should be filled.") 337s if atol is None: 337s atol = 0 337s if rtol is None: 337s rtol = 0 337s if isinstance(desired, np.ndarray): 337s if isinstance(output, np.ndarray): 337s if rtol == 0: 337s if desired.dtype == np.float32: 337s rtl = 1e-5 337s elif desired.dtype == np.float64: 337s rtl = 1e-12 337s else: 337s rtl = rtol 337s else: 337s rtl = rtol 337s if desired.dtype == dtype_object: 337s try: 337s assert_allclose_string(desired, output) 337s except AssertionError as ex: 337s raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed, comment={comment}." 337s ) from ex 337s else: 337s equal_nan = desired.dtype in (np.float16, np.float32, np.float64) 337s if equal_nan: 337s try: 337s > assert_allclose( 337s desired, 337s output, 337s atol=atol, 337s rtol=rtl, 337s equal_nan=equal_nan, 337s ) 337s 337s onnx/test/reference_evaluator_backend_test.py:279: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (.compare at 0x3ff9ca1ce00>, array([[[[ 4.5000000e+01, 0.0000000e+00], 337s [ 4....5388417e+01], 337s [-5.0000000e+00, 1.5388417e+01], 337s [-5.0000000e+00, 1.5388417e+01]]]], dtype=float32)) 337s kwds = {'equal_nan': True, 'err_msg': '', 'header': 'Not equal to tolerance rtol=1e-05, atol=0', 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Not equal to tolerance rtol=1e-05, atol=0 337s E 337s E Mismatched elements: 10 / 200 (5%) 337s E Max absolute difference: 3.5527135e-16 337s E Max relative difference: 0.3333333 337s E x: array([[[[ 4.500000e+01, 0.000000e+00], 337s E [ 4.600000e+01, 0.000000e+00], 337s E [ 4.700000e+01, 0.000000e+00],... 337s E y: array([[[[ 4.500000e+01, 0.000000e+00], 337s E [ 4.600000e+01, 0.000000e+00], 337s E [ 4.700000e+01, 0.000000e+00],... 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s 337s The above exception was the direct cause of the following exception: 337s 337s self = 337s te = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/node/test_dft_inverse') 337s check_other_runtime = None, verbose = 0, print_io = False 337s 337s def _test_( 337s self, te=te, check_other_runtime=None, verbose=0, print_io=False 337s ): 337s if te.fname in getattr(cls, "skip_test", set()): 337s cls.skipped.append((te, None)) 337s return 337s rtol = getattr(cls, "rtol", {}) 337s atol = getattr(cls, "atol", {}) 337s if len(rtol) == 0 or len(atol) == 0: 337s raise AssertionError("rtol or atol is empty.") 337s > self.common_test_onnx_test_run( 337s te, 337s getattr(cls, "successes", []), 337s getattr(cls, "missed", []), 337s getattr(cls, "skipped", []), 337s getattr(cls, "load_failed", []), 337s getattr(cls, "exec_failed", []), 337s getattr(cls, "mismatch", []), 337s verbose=verbose, 337s rtol=rtol, 337s atol=atol, 337s check_other_runtime=check_other_runtime, 337s print_io=print_io, 337s ) 337s 337s onnx/test/reference_evaluator_backend_test.py:494: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_backend_test.py:649: in common_test_onnx_test_run 337s raise e 337s onnx/test/reference_evaluator_backend_test.py:623: in common_test_onnx_test_run 337s te.run( 337s onnx/test/reference_evaluator_backend_test.py:382: in run 337s self.run( 337s onnx/test/reference_evaluator_backend_test.py:429: in run 337s self._compare_results( 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s self = OnnxBackendTest('/usr/lib/python3/dist-packages/onnx/backend/test/data/node/test_dft_inverse') 337s index = 0, i_output = 0 337s desired = array([[[[ 4.5000000e+01, 0.0000000e+00], 337s [ 4.6000000e+01, 0.0000000e+00], 337s [ 4.7000000e+01, 0.000...1.5388417e+01], 337s [-5.0000000e+00, 1.5388417e+01], 337s [-5.0000000e+00, 1.5388417e+01]]]], dtype=float32) 337s output = array([[[[ 4.5000000e+01, 0.0000000e+00], 337s [ 4.6000000e+01, 0.0000000e+00], 337s [ 4.7000000e+01, 0.000...1.5388417e+01], 337s [-5.0000000e+00, 1.5388417e+01], 337s [-5.0000000e+00, 1.5388417e+01]]]], dtype=float32) 337s rtol = 0, atol = 0 337s comment = '[runtime=ReferenceEvaluator]\nir_version: 8\nproducer_name: "backend-test"\ngraph {\n node {\n input: "x"\n ou... dim_value: 2\n }\n }\n }\n }\n }\n}\nopset_import {\n domain: ""\n version: 17\n}\n' 337s inputs = [array([[[[ 0., 0.], 337s [ 1., 0.], 337s [ 2., 0.], 337s [ 3., 0.], 337s [ 4., 0.], 337s [ 5... [95., 0.], 337s [96., 0.], 337s [97., 0.], 337s [98., 0.], 337s [99., 0.]]]], dtype=float32)] 337s 337s def _compare_results( 337s self, index, i_output, desired, output, rtol=0, atol=0, comment="", inputs=None 337s ): 337s """ 337s Compares the expected output and the output produced 337s by the runtime. Raises an exception if not equal. 337s 337s :param index: test index 337s :param i_output: output index 337s :param desired: expected output 337s :param output: output 337s :param rtol: relative tolerance 337s :param atol: absolute tolerance 337s :param comment: addition text to give more insights to the user 337s :param inputs: inputs to the model 337s """ 337s if comment == "": 337s raise RuntimeError("Argument comment should be filled.") 337s if atol is None: 337s atol = 0 337s if rtol is None: 337s rtol = 0 337s if isinstance(desired, np.ndarray): 337s if isinstance(output, np.ndarray): 337s if rtol == 0: 337s if desired.dtype == np.float32: 337s rtl = 1e-5 337s elif desired.dtype == np.float64: 337s rtl = 1e-12 337s else: 337s rtl = rtol 337s else: 337s rtl = rtol 337s if desired.dtype == dtype_object: 337s try: 337s assert_allclose_string(desired, output) 337s except AssertionError as ex: 337s raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed, comment={comment}." 337s ) from ex 337s else: 337s equal_nan = desired.dtype in (np.float16, np.float32, np.float64) 337s if equal_nan: 337s try: 337s assert_allclose( 337s desired, 337s output, 337s atol=atol, 337s rtol=rtl, 337s equal_nan=equal_nan, 337s ) 337s except AssertionError as ex: 337s try: 337s diff = output - desired 337s except ValueError: 337s diff = None 337s > raise AssertionError( 337s f"Output {i_output} of test {index} in folder {self.folder!r} failed " 337s f"(rtol={rtl}, atol={atol}), comment={comment}\n---\n{desired}\n----" 337s f"\n{output}\n-----\n{diff}\n------INPUTS----\n{pprint.pformat(inputs)}." 337s ) from ex 337s E AssertionError: Output 0 of test 0 in folder '/usr/lib/python3/dist-packages/onnx/backend/test/data/node/test_dft_inverse' failed (rtol=1e-05, atol=0), comment=[runtime=ReferenceEvaluator] 337s E ir_version: 8 337s E producer_name: "backend-test" 337s E graph { 337s E node { 337s E input: "x" 337s E output: "y" 337s E op_type: "DFT" 337s E attribute { 337s E name: "axis" 337s E i: 1 337s E type: INT 337s E } 337s E attribute { 337s E name: "inverse" 337s E i: 1 337s E type: INT 337s E } 337s E } 337s E name: "test_dft_inverse" 337s E input { 337s E name: "x" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 1 337s E } 337s E dim { 337s E dim_value: 10 337s E } 337s E dim { 337s E dim_value: 10 337s E } 337s E dim { 337s E dim_value: 2 337s E } 337s E } 337s E } 337s E } 337s E } 337s E output { 337s E name: "y" 337s E type { 337s E tensor_type { 337s E elem_type: 1 337s E shape { 337s E dim { 337s E dim_value: 1 337s E } 337s E dim { 337s E dim_value: 10 337s E } 337s E dim { 337s E dim_value: 10 337s E } 337s E dim { 337s E dim_value: 2 337s E } 337s E } 337s E } 337s E } 337s E } 337s E } 337s E opset_import { 337s E domain: "" 337s E version: 17 337s E } 337s E 337s E --- 337s E [[[[ 4.5000000e+01 0.0000000e+00] 337s E [ 4.6000000e+01 0.0000000e+00] 337s E [ 4.7000000e+01 0.0000000e+00] 337s E [ 4.8000000e+01 0.0000000e+00] 337s E [ 4.9000000e+01 0.0000000e+00] 337s E [ 5.0000000e+01 0.0000000e+00] 337s E [ 5.1000000e+01 0.0000000e+00] 337s E [ 5.2000000e+01 0.0000000e+00] 337s E [ 5.3000000e+01 0.0000000e+00] 337s E [ 5.4000000e+01 0.0000000e+00]] 337s E 337s E [[-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00]] 337s E 337s E [[-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00]] 337s E 337s E [[-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00]] 337s E 337s E [[-5.0000000e+00 1.4210855e-15] 337s E [-5.0000000e+00 1.4210855e-15] 337s E [-5.0000000e+00 1.4210855e-15] 337s E [-5.0000000e+00 1.4210855e-15] 337s E [-5.0000000e+00 1.4210855e-15] 337s E [-5.0000000e+00 1.4210855e-15] 337s E [-5.0000000e+00 1.4210855e-15] 337s E [-5.0000000e+00 1.4210855e-15] 337s E [-5.0000000e+00 1.4210855e-15] 337s E [-5.0000000e+00 1.4210855e-15]] 337s E 337s E [[-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00]] 337s E 337s E [[-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00]] 337s E 337s E [[-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00]] 337s E 337s E [[-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01]]]] 337s E ---- 337s E [[[[ 4.5000000e+01 0.0000000e+00] 337s E [ 4.6000000e+01 0.0000000e+00] 337s E [ 4.7000000e+01 0.0000000e+00] 337s E [ 4.8000000e+01 0.0000000e+00] 337s E [ 4.9000000e+01 0.0000000e+00] 337s E [ 5.0000000e+01 0.0000000e+00] 337s E [ 5.1000000e+01 0.0000000e+00] 337s E [ 5.2000000e+01 0.0000000e+00] 337s E [ 5.3000000e+01 0.0000000e+00] 337s E [ 5.4000000e+01 0.0000000e+00]] 337s E 337s E [[-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01] 337s E [-5.0000000e+00 -1.5388417e+01]] 337s E 337s E [[-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00] 337s E [-5.0000000e+00 -6.8819094e+00]] 337s E 337s E [[-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00] 337s E [-5.0000000e+00 -3.6327126e+00]] 337s E 337s E [[-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00] 337s E [-5.0000000e+00 -1.6245985e+00]] 337s E 337s E [[-5.0000000e+00 1.0658141e-15] 337s E [-5.0000000e+00 1.0658141e-15] 337s E [-5.0000000e+00 1.0658141e-15] 337s E [-5.0000000e+00 1.0658141e-15] 337s E [-5.0000000e+00 1.0658141e-15] 337s E [-5.0000000e+00 1.0658141e-15] 337s E [-5.0000000e+00 1.0658141e-15] 337s E [-5.0000000e+00 1.0658141e-15] 337s E [-5.0000000e+00 1.0658141e-15] 337s E [-5.0000000e+00 1.0658141e-15]] 337s E 337s E [[-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00] 337s E [-5.0000000e+00 1.6245985e+00]] 337s E 337s E [[-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00] 337s E [-5.0000000e+00 3.6327126e+00]] 337s E 337s E [[-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00] 337s E [-5.0000000e+00 6.8819094e+00]] 337s E 337s E [[-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01] 337s E [-5.0000000e+00 1.5388417e+01]]]] 337s E ----- 337s E [[[[ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00]] 337s E 337s E [[ 0.0000000e+00 -3.5527135e-16] 337s E [ 0.0000000e+00 -3.5527135e-16] 337s E [ 0.0000000e+00 -3.5527135e-16] 337s E [ 0.0000000e+00 -3.5527135e-16] 337s E [ 0.0000000e+00 -3.5527135e-16] 337s E [ 0.0000000e+00 -3.5527135e-16] 337s E [ 0.0000000e+00 -3.5527135e-16] 337s E [ 0.0000000e+00 -3.5527135e-16] 337s E [ 0.0000000e+00 -3.5527135e-16] 337s E [ 0.0000000e+00 -3.5527135e-16]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00]] 337s E 337s E [[ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00] 337s E [ 0.0000000e+00 0.0000000e+00]]]] 337s E ------INPUTS---- 337s E [array([[[[ 0., 0.], 337s E [ 1., 0.], 337s E [ 2., 0.], 337s E [ 3., 0.], 337s E [ 4., 0.], 337s E [ 5., 0.], 337s E [ 6., 0.], 337s E [ 7., 0.], 337s E [ 8., 0.], 337s E [ 9., 0.]], 337s E 337s E [[10., 0.], 337s E [11., 0.], 337s E [12., 0.], 337s E [13., 0.], 337s E [14., 0.], 337s E [15., 0.], 337s E [16., 0.], 337s E [17., 0.], 337s E [18., 0.], 337s E [19., 0.]], 337s E 337s E [[20., 0.], 337s E [21., 0.], 337s E [22., 0.], 337s E [23., 0.], 337s E [24., 0.], 337s E [25., 0.], 337s E [26., 0.], 337s E [27., 0.], 337s E [28., 0.], 337s E [29., 0.]], 337s E 337s E [[30., 0.], 337s E [31., 0.], 337s E [32., 0.], 337s E [33., 0.], 337s E [34., 0.], 337s E [35., 0.], 337s E [36., 0.], 337s E [37., 0.], 337s E [38., 0.], 337s E [39., 0.]], 337s E 337s E [[40., 0.], 337s E [41., 0.], 337s E [42., 0.], 337s E [43., 0.], 337s E [44., 0.], 337s E [45., 0.], 337s E [46., 0.], 337s E [47., 0.], 337s E [48., 0.], 337s E [49., 0.]], 337s E 337s E [[50., 0.], 337s E [51., 0.], 337s E [52., 0.], 337s E [53., 0.], 337s E [54., 0.], 337s E [55., 0.], 337s E [56., 0.], 337s E [57., 0.], 337s E [58., 0.], 337s E [59., 0.]], 337s E 337s E [[60., 0.], 337s E [61., 0.], 337s E [62., 0.], 337s E [63., 0.], 337s E [64., 0.], 337s E [65., 0.], 337s E [66., 0.], 337s E [67., 0.], 337s E [68., 0.], 337s E [69., 0.]], 337s E 337s E [[70., 0.], 337s E [71., 0.], 337s E [72., 0.], 337s E [73., 0.], 337s E [74., 0.], 337s E [75., 0.], 337s E [76., 0.], 337s E [77., 0.], 337s E [78., 0.], 337s E [79., 0.]], 337s E 337s E [[80., 0.], 337s E [81., 0.], 337s E [82., 0.], 337s E [83., 0.], 337s E [84., 0.], 337s E [85., 0.], 337s E [86., 0.], 337s E [87., 0.], 337s E [88., 0.], 337s E [89., 0.]], 337s E 337s E [[90., 0.], 337s E [91., 0.], 337s E [92., 0.], 337s E [93., 0.], 337s E [94., 0.], 337s E [95., 0.], 337s E [96., 0.], 337s E [97., 0.], 337s E [98., 0.], 337s E [99., 0.]]]], dtype=float32)]. 337s 337s onnx/test/reference_evaluator_backend_test.py:291: AssertionError 337s ___________________ TestReferenceEvaluator.test_op_reduce_00 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:123: in _run 337s return _CommonTopK._common_run(self, data, ink, axis=axis, largest=largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:69: in _common_run 337s sort, sorti = topk_sorted_implementation(data, k, axis, largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:26: in topk_sorted_implementation 337s sorted_indices = np.argpartition(X, axis=axis, kth=k - 1) 337s <__array_function__ internals>:200: in argpartition 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:871: in argpartition 337s return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s obj = array([[1.0000062e+00, nan, nan, 1.0000000e+00, 337s nan, nan, na....0000000e+00, nan, 337s nan, nan, nan, 2.4633040e+00]], 337s dtype=float32) 337s method = 'argpartition', args = (144115188075855871,) 337s kwds = {'axis': 1, 'kind': 'introselect', 'order': None} 337s bound = 337s 337s def _wrapfunc(obj, method, *args, **kwds): 337s bound = getattr(obj, method, None) 337s if bound is None: 337s return _wrapit(obj, method, *args, **kwds) 337s 337s try: 337s > return bound(*args, **kwds) 337s E ValueError: kth(=144115188075855871) out of bounds (20) 337s 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:57: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_01 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_scan.py:129: in _run 337s outputs_list = self._run_body(inputs) # type: ignore 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:295: in 337s lambda context, value=value, attributes=None: OpRun._evaluate_subgraph( 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:274: in _evaluate_subgraph 337s return value.run(None, context or {}, attributes=attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s self = 337s data = array([[1.9882580e+21, 1.0000124e+00, 9.1296865e+03, 3.0004878e+00], 337s [1.9882580e+21, 5.0000124e+00, 9.1336865e+03, 7.0004878e+00]], 337s dtype=float32) 337s axes = (72057594037927936,), keepdims = False, noop_with_empty_axes = 0 337s 337s def _run(self, data, axes=None, keepdims=1, noop_with_empty_axes=0): # type: ignore 337s if self.is_axes_empty(axes) and noop_with_empty_axes != 0: # type: ignore 337s return (data,) 337s 337s axes = self.handle_axes(axes) 337s keepdims = keepdims != 0 # type: ignore 337s > res = np.minimum.reduce(data, axis=axes, keepdims=keepdims) 337s E ValueError: integer won't fit into a C int 337s 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_min.py:32: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_02 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:123: in _run 337s return _CommonTopK._common_run(self, data, ink, axis=axis, largest=largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:69: in _common_run 337s sort, sorti = topk_sorted_implementation(data, k, axis, largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:26: in topk_sorted_implementation 337s sorted_indices = np.argpartition(X, axis=axis, kth=k - 1) 337s <__array_function__ internals>:200: in argpartition 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:871: in argpartition 337s return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s obj = array([[4.45898875e+10, 1.93811040e+07, 1.14237411e+02, 1.94702948e+18, 337s 1.56581077e+19, 2.44948983e+00, 3.4212...957e+14, 1.41788843e+16, 337s 7.04623389e+03, 3.09094426e+09, 1.22782781e+17, 1.74263882e+15]], 337s dtype=float32) 337s method = 'argpartition', args = (144115188075855871,) 337s kwds = {'axis': 1, 'kind': 'introselect', 'order': None} 337s bound = 337s 337s def _wrapfunc(obj, method, *args, **kwds): 337s bound = getattr(obj, method, None) 337s if bound is None: 337s return _wrapit(obj, method, *args, **kwds) 337s 337s try: 337s > return bound(*args, **kwds) 337s E ValueError: kth(=144115188075855871) out of bounds (20) 337s 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:57: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_03 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_scan.py:129: in _run 337s outputs_list = self._run_body(inputs) # type: ignore 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:295: in 337s lambda context, value=value, attributes=None: OpRun._evaluate_subgraph( 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:274: in _evaluate_subgraph 337s return value.run(None, context or {}, attributes=attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_l1.py:30: in _run 337s res = np.sum(np.abs(data), axis=axes, keepdims=keepdims).astype( 337s <__array_function__ internals>:200: in sum 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:2324: in sum 337s return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims, 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s obj = array([[1.9882580e+21, 1.0000124e+00, 9.1296865e+03, 3.0004878e+00], 337s [1.9882580e+21, 5.0000124e+00, 9.1336865e+03, 7.0004878e+00]], 337s dtype=float32) 337s ufunc = , method = 'sum', axis = (72057594037927936,), dtype = None 337s out = None 337s kwargs = {'initial': , 'keepdims': False, 'where': } 337s passkwargs = {'keepdims': False} 337s 337s def _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs): 337s passkwargs = {k: v for k, v in kwargs.items() 337s if v is not np._NoValue} 337s 337s if type(obj) is not mu.ndarray: 337s try: 337s reduction = getattr(obj, method) 337s except AttributeError: 337s pass 337s else: 337s # This branch is needed for reductions like any which don't 337s # support a dtype. 337s if dtype is not None: 337s return reduction(axis=axis, dtype=dtype, out=out, **passkwargs) 337s else: 337s return reduction(axis=axis, out=out, **passkwargs) 337s 337s > return ufunc.reduce(obj, axis, dtype, out, **passkwargs) 337s E ValueError: integer won't fit into a C int 337s 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:86: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_04 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:123: in _run 337s return _CommonTopK._common_run(self, data, ink, axis=axis, largest=largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:69: in _common_run 337s sort, sorti = topk_sorted_implementation(data, k, axis, largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:26: in topk_sorted_implementation 337s sorted_indices = np.argpartition(X, axis=axis, kth=k - 1) 337s <__array_function__ internals>:200: in argpartition 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:871: in argpartition 337s return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s obj = array([[ inf, 1.9381100e+07, 1.1421990e+02, inf, 337s inf, 1.9343364e+00, in... inf, inf, 337s 7.0462329e+03, 2.8027436e+09, inf, inf]], 337s dtype=float32) 337s method = 'argpartition', args = (144115188075855871,) 337s kwds = {'axis': 1, 'kind': 'introselect', 'order': None} 337s bound = 337s 337s def _wrapfunc(obj, method, *args, **kwds): 337s bound = getattr(obj, method, None) 337s if bound is None: 337s return _wrapit(obj, method, *args, **kwds) 337s 337s try: 337s > return bound(*args, **kwds) 337s E ValueError: kth(=144115188075855871) out of bounds (20) 337s 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:57: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_05 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_scan.py:129: in _run 337s outputs_list = self._run_body(inputs) # type: ignore 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:295: in 337s lambda context, value=value, attributes=None: OpRun._evaluate_subgraph( 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:274: in _evaluate_subgraph 337s return value.run(None, context or {}, attributes=attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_l2.py:30: in _run 337s res = np.sqrt(np.sum(np.square(data), axis=axes, keepdims=keepdims)).astype( 337s <__array_function__ internals>:200: in sum 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:2324: in sum 337s return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims, 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s obj = array([[ inf, 1.0000248e+00, 8.3351176e+07, 9.0029268e+00], 337s [ inf, 2.5000124e+01, 8.3424232e+07, 4.9006828e+01]], 337s dtype=float32) 337s ufunc = , method = 'sum', axis = (72057594037927936,), dtype = None 337s out = None 337s kwargs = {'initial': , 'keepdims': False, 'where': } 337s passkwargs = {'keepdims': False} 337s 337s def _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs): 337s passkwargs = {k: v for k, v in kwargs.items() 337s if v is not np._NoValue} 337s 337s if type(obj) is not mu.ndarray: 337s try: 337s reduction = getattr(obj, method) 337s except AttributeError: 337s pass 337s else: 337s # This branch is needed for reductions like any which don't 337s # support a dtype. 337s if dtype is not None: 337s return reduction(axis=axis, dtype=dtype, out=out, **passkwargs) 337s else: 337s return reduction(axis=axis, out=out, **passkwargs) 337s 337s > return ufunc.reduce(obj, axis, dtype, out, **passkwargs) 337s E ValueError: integer won't fit into a C int 337s 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:86: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_06 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:123: in _run 337s return _CommonTopK._common_run(self, data, ink, axis=axis, largest=largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:69: in _common_run 337s sort, sorti = topk_sorted_implementation(data, k, axis, largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:26: in topk_sorted_implementation 337s sorted_indices = np.argpartition(X, axis=axis, kth=k - 1) 337s <__array_function__ internals>:200: in argpartition 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:871: in argpartition 337s return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s obj = array([[7.002967 , nan, nan, 9.177454 , 9.401864 , 1.3385662, 337s 8.457988 , nan, nan, ... 9.179568 , nan, 8.14973 , 8.624445 , nan, nan, 337s 8.871211 , 8.377849 ]], dtype=float32) 337s method = 'argpartition', args = (144115188075855871,) 337s kwds = {'axis': 1, 'kind': 'introselect', 'order': None} 337s bound = 337s 337s def _wrapfunc(obj, method, *args, **kwds): 337s bound = getattr(obj, method, None) 337s if bound is None: 337s return _wrapit(obj, method, *args, **kwds) 337s 337s try: 337s > return bound(*args, **kwds) 337s E ValueError: kth(=144115188075855871) out of bounds (20) 337s 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:57: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_07 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_scan.py:129: in _run 337s outputs_list = self._run_body(inputs) # type: ignore 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:295: in 337s lambda context, value=value, attributes=None: OpRun._evaluate_subgraph( 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:274: in _evaluate_subgraph 337s return value.run(None, context or {}, attributes=attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_log_sum.py:28: in _run 337s res = np.sum(data, axis=axes, keepdims=keepdims) 337s <__array_function__ internals>:200: in sum 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:2324: in sum 337s return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims, 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s obj = array([[1.9882580e+21, 1.0000124e+00, 9.1296865e+03, 3.0004878e+00], 337s [1.9882580e+21, 5.0000124e+00, 9.1336865e+03, 7.0004878e+00]], 337s dtype=float32) 337s ufunc = , method = 'sum', axis = (72057594037927936,), dtype = None 337s out = None 337s kwargs = {'initial': , 'keepdims': False, 'where': } 337s passkwargs = {'keepdims': False} 337s 337s def _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs): 337s passkwargs = {k: v for k, v in kwargs.items() 337s if v is not np._NoValue} 337s 337s if type(obj) is not mu.ndarray: 337s try: 337s reduction = getattr(obj, method) 337s except AttributeError: 337s pass 337s else: 337s # This branch is needed for reductions like any which don't 337s # support a dtype. 337s if dtype is not None: 337s return reduction(axis=axis, dtype=dtype, out=out, **passkwargs) 337s else: 337s return reduction(axis=axis, out=out, **passkwargs) 337s 337s > return ufunc.reduce(obj, axis, dtype, out, **passkwargs) 337s E ValueError: integer won't fit into a C int 337s 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:86: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_08 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:123: in _run 337s return _CommonTopK._common_run(self, data, ink, axis=axis, largest=largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:69: in _common_run 337s sort, sorti = topk_sorted_implementation(data, k, axis, largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:26: in topk_sorted_implementation 337s sorted_indices = np.argpartition(X, axis=axis, kth=k - 1) 337s <__array_function__ internals>:200: in argpartition 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:871: in argpartition 337s return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s obj = array([[4.4589887e+10, 1.1262204e+04, 1.7804061e+00, 1.9470295e+18, 337s 1.5658108e+19, 1.8547748e+00, 3.4212279e+1....6454796e+14, 1.4178865e+16, 337s 2.5313249e+00, 2.9871724e+00, 1.2278278e+17, 1.7415429e+15]], 337s dtype=float32) 337s method = 'argpartition', args = (144115188075855871,) 337s kwds = {'axis': 1, 'kind': 'introselect', 'order': None} 337s bound = 337s 337s def _wrapfunc(obj, method, *args, **kwds): 337s bound = getattr(obj, method, None) 337s if bound is None: 337s return _wrapit(obj, method, *args, **kwds) 337s 337s try: 337s > return bound(*args, **kwds) 337s E ValueError: kth(=144115188075855871) out of bounds (20) 337s 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:57: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_09 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_scan.py:129: in _run 337s outputs_list = self._run_body(inputs) # type: ignore 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:295: in 337s lambda context, value=value, attributes=None: OpRun._evaluate_subgraph( 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:274: in _evaluate_subgraph 337s return value.run(None, context or {}, attributes=attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_log_sum_exp.py:39: in _run 337s return compute_log_sum_exp(data, axes, keepdims) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_log_sum_exp.py:15: in compute_log_sum_exp 337s mx = data_max.max(axis=axes, keepdims=True) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s a = array([[1.9882580e+21, 1.0000124e+00, 9.1296865e+03, 3.0004878e+00], 337s [1.9882580e+21, 5.0000124e+00, 9.1336865e+03, 7.0004878e+00]], 337s dtype=float32) 337s axis = (72057594037927936,), out = None, keepdims = True, initial = 337s where = True 337s 337s def _amax(a, axis=None, out=None, keepdims=False, 337s initial=_NoValue, where=True): 337s > return umr_maximum(a, axis, None, out, keepdims, initial, where) 337s E ValueError: integer won't fit into a C int 337s 337s /usr/lib/python3/dist-packages/numpy/core/_methods.py:41: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_10 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:123: in _run 337s return _CommonTopK._common_run(self, data, ink, axis=axis, largest=largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:69: in _common_run 337s sort, sorti = topk_sorted_implementation(data, k, axis, largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:26: in topk_sorted_implementation 337s sorted_indices = np.argpartition(X, axis=axis, kth=k - 1) 337s <__array_function__ internals>:200: in argpartition 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:871: in argpartition 337s return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s obj = array([[4.4589887e+10, 1.1262204e+04, 1.7320508e+00, 1.9470295e+18, 337s 1.5658108e+19, 1.7320508e+00, 3.4212279e+1....6454796e+14, 1.4178865e+16, 337s 2.4494898e+00, 2.9606102e+00, 1.2278278e+17, 1.7415429e+15]], 337s dtype=float32) 337s method = 'argpartition', args = (144115188075855871,) 337s kwds = {'axis': 1, 'kind': 'introselect', 'order': None} 337s bound = 337s 337s def _wrapfunc(obj, method, *args, **kwds): 337s bound = getattr(obj, method, None) 337s if bound is None: 337s return _wrapit(obj, method, *args, **kwds) 337s 337s try: 337s > return bound(*args, **kwds) 337s E ValueError: kth(=144115188075855871) out of bounds (20) 337s 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:57: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_11 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_scan.py:129: in _run 337s outputs_list = self._run_body(inputs) # type: ignore 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:295: in 337s lambda context, value=value, attributes=None: OpRun._evaluate_subgraph( 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:274: in _evaluate_subgraph 337s return value.run(None, context or {}, attributes=attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s self = 337s data = array([[1.9882580e+21, 1.0000124e+00, 9.1296865e+03, 3.0004878e+00], 337s [1.9882580e+21, 5.0000124e+00, 9.1336865e+03, 7.0004878e+00]], 337s dtype=float32) 337s axes = (72057594037927936,), keepdims = False, noop_with_empty_axes = 0 337s 337s def _run(self, data, axes=None, keepdims=1, noop_with_empty_axes=0): # type: ignore 337s if self.is_axes_empty(axes) and noop_with_empty_axes != 0: # type: ignore 337s return (data,) 337s 337s axes = self.handle_axes(axes) 337s keepdims = keepdims != 0 # type: ignore 337s > res = np.maximum.reduce(data, axis=axes, keepdims=keepdims) 337s E ValueError: integer won't fit into a C int 337s 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_max.py:28: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_12 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:123: in _run 337s return _CommonTopK._common_run(self, data, ink, axis=axis, largest=largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:69: in _common_run 337s sort, sorti = topk_sorted_implementation(data, k, axis, largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:26: in topk_sorted_implementation 337s sorted_indices = np.argpartition(X, axis=axis, kth=k - 1) 337s <__array_function__ internals>:200: in argpartition 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:871: in argpartition 337s return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s obj = array([[2.2294944e+10, nan, nan, 9.7351474e+17, 337s 7.8290539e+18, 1.2247449e+00, 1.7106139e+1....3227398e+14, 7.0894228e+15, 337s nan, nan, 6.1391391e+16, 8.7131941e+14]], 337s dtype=float32) 337s method = 'argpartition', args = (144115188075855871,) 337s kwds = {'axis': 1, 'kind': 'introselect', 'order': None} 337s bound = 337s 337s def _wrapfunc(obj, method, *args, **kwds): 337s bound = getattr(obj, method, None) 337s if bound is None: 337s return _wrapit(obj, method, *args, **kwds) 337s 337s try: 337s > return bound(*args, **kwds) 337s E ValueError: kth(=144115188075855871) out of bounds (20) 337s 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:57: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_13 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_scan.py:129: in _run 337s outputs_list = self._run_body(inputs) # type: ignore 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:295: in 337s lambda context, value=value, attributes=None: OpRun._evaluate_subgraph( 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:274: in _evaluate_subgraph 337s return value.run(None, context or {}, attributes=attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_mean.py:29: in _run 337s res = np.mean(data, axis=axes, keepdims=keepdims, dtype=data.dtype) # type: ignore 337s <__array_function__ internals>:200: in mean 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3464: in mean 337s return _methods._mean(a, axis=axis, dtype=dtype, 337s /usr/lib/python3/dist-packages/numpy/core/_methods.py:169: in _mean 337s rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s arr = array([[1.9882580e+21, 1.0000124e+00, 9.1296865e+03, 3.0004878e+00], 337s [1.9882580e+21, 5.0000124e+00, 9.1336865e+03, 7.0004878e+00]], 337s dtype=float32) 337s axis = (72057594037927936,), keepdims = False, where = True 337s 337s def _count_reduce_items(arr, axis, keepdims=False, where=True): 337s # fast-path for the default case 337s if where is True: 337s # no boolean mask given, calculate items according to axis 337s if axis is None: 337s axis = tuple(range(arr.ndim)) 337s elif not isinstance(axis, tuple): 337s axis = (axis,) 337s items = 1 337s for ax in axis: 337s > items *= arr.shape[mu.normalize_axis_index(ax, arr.ndim)] 337s E OverflowError: Python int too large to convert to C int 337s 337s /usr/lib/python3/dist-packages/numpy/core/_methods.py:77: OverflowError 337s ___________________ TestReferenceEvaluator.test_op_reduce_14 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:123: in _run 337s return _CommonTopK._common_run(self, data, ink, axis=axis, largest=largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:69: in _common_run 337s sort, sorti = topk_sorted_implementation(data, k, axis, largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:26: in topk_sorted_implementation 337s sorted_indices = np.argpartition(X, axis=axis, kth=k - 1) 337s <__array_function__ internals>:200: in argpartition 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:871: in argpartition 337s return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s obj = array([[ inf, 3.7562707e+14, 1.3046187e+04, inf, 337s inf, 3.7416575e+00, in... inf, inf, 337s 4.9649396e+07, 7.8553712e+18, inf, inf]], 337s dtype=float32) 337s method = 'argpartition', args = (144115188075855871,) 337s kwds = {'axis': 1, 'kind': 'introselect', 'order': None} 337s bound = 337s 337s def _wrapfunc(obj, method, *args, **kwds): 337s bound = getattr(obj, method, None) 337s if bound is None: 337s return _wrapit(obj, method, *args, **kwds) 337s 337s try: 337s > return bound(*args, **kwds) 337s E ValueError: kth(=144115188075855871) out of bounds (20) 337s 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:57: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_15 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_scan.py:129: in _run 337s outputs_list = self._run_body(inputs) # type: ignore 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:295: in 337s lambda context, value=value, attributes=None: OpRun._evaluate_subgraph( 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:274: in _evaluate_subgraph 337s return value.run(None, context or {}, attributes=attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_sum_square.py:28: in _run 337s res = np.sum(np.square(data), axis=axes, keepdims=keepdims) 337s <__array_function__ internals>:200: in sum 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:2324: in sum 337s return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims, 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s obj = array([[ inf, 1.0000248e+00, 8.3351176e+07, 9.0029268e+00], 337s [ inf, 2.5000124e+01, 8.3424232e+07, 4.9006828e+01]], 337s dtype=float32) 337s ufunc = , method = 'sum', axis = (72057594037927936,), dtype = None 337s out = None 337s kwargs = {'initial': , 'keepdims': False, 'where': } 337s passkwargs = {'keepdims': False} 337s 337s def _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs): 337s passkwargs = {k: v for k, v in kwargs.items() 337s if v is not np._NoValue} 337s 337s if type(obj) is not mu.ndarray: 337s try: 337s reduction = getattr(obj, method) 337s except AttributeError: 337s pass 337s else: 337s # This branch is needed for reductions like any which don't 337s # support a dtype. 337s if dtype is not None: 337s return reduction(axis=axis, dtype=dtype, out=out, **passkwargs) 337s else: 337s return reduction(axis=axis, out=out, **passkwargs) 337s 337s > return ufunc.reduce(obj, axis, dtype, out, **passkwargs) 337s E ValueError: integer won't fit into a C int 337s 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:86: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_16 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:123: in _run 337s return _CommonTopK._common_run(self, data, ink, axis=axis, largest=largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:69: in _common_run 337s sort, sorti = topk_sorted_implementation(data, k, axis, largest) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_topk.py:26: in topk_sorted_implementation 337s sorted_indices = np.argpartition(X, axis=axis, kth=k - 1) 337s <__array_function__ internals>:200: in argpartition 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:871: in argpartition 337s return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order) 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s obj = array([[7.3801117e+12, 8.8048560e+07, nan, 4.7692290e+18, 337s inf, nan, na....8979760e+15, inf, 337s nan, inf, nan, inf]], 337s dtype=float32) 337s method = 'argpartition', args = (144115188075855871,) 337s kwds = {'axis': 1, 'kind': 'introselect', 'order': None} 337s bound = 337s 337s def _wrapfunc(obj, method, *args, **kwds): 337s bound = getattr(obj, method, None) 337s if bound is None: 337s return _wrapit(obj, method, *args, **kwds) 337s 337s try: 337s > return bound(*args, **kwds) 337s E ValueError: kth(=144115188075855871) out of bounds (20) 337s 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:57: ValueError 337s ___________________ TestReferenceEvaluator.test_op_reduce_17 ___________________ 337s 337s a = (,) 337s 337s @wraps(func) 337s def standalone_func(*a): 337s > return func(*(a + p.args), **p.kwargs) 337s 337s /usr/lib/python3/dist-packages/parameterized/parameterized.py:637: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/reference_evaluator_test.py:2849: in test_op_reduce 337s got = sess.run(None, {"input": X}) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_scan.py:129: in _run 337s outputs_list = self._run_body(inputs) # type: ignore 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:295: in 337s lambda context, value=value, attributes=None: OpRun._evaluate_subgraph( 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:274: in _evaluate_subgraph 337s return value.run(None, context or {}, attributes=attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/reference_evaluator.py:462: in run 337s outputs = node.run(*inputs, **linked_attributes) 337s /usr/lib/python3/dist-packages/onnx/reference/op_run.py:467: in run 337s res = self._run(*args, **kwargs) 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_prod.py:28: in _run 337s res = np.prod(data, axis=axes, keepdims=keepdims, dtype=data.dtype) 337s <__array_function__ internals>:200: in prod 337s ??? 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3076: in prod 337s return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out, 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s obj = array([[1.9882580e+21, 1.0000124e+00, 9.1296865e+03, 3.0004878e+00], 337s [1.9882580e+21, 5.0000124e+00, 9.1336865e+03, 7.0004878e+00]], 337s dtype=float32) 337s ufunc = , method = 'prod', axis = (72057594037927936,) 337s dtype = dtype('float32'), out = None 337s kwargs = {'initial': , 'keepdims': False, 'where': } 337s passkwargs = {'keepdims': False} 337s 337s def _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs): 337s passkwargs = {k: v for k, v in kwargs.items() 337s if v is not np._NoValue} 337s 337s if type(obj) is not mu.ndarray: 337s try: 337s reduction = getattr(obj, method) 337s except AttributeError: 337s pass 337s else: 337s # This branch is needed for reductions like any which don't 337s # support a dtype. 337s if dtype is not None: 337s return reduction(axis=axis, dtype=dtype, out=out, **passkwargs) 337s else: 337s return reduction(axis=axis, out=out, **passkwargs) 337s 337s > return ufunc.reduce(obj, axis, dtype, out, **passkwargs) 337s E ValueError: integer won't fit into a C int 337s 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:86: ValueError 337s __ TestExternalDataToArray.test_save_model_with_external_data_multiple_times ___ 337s 337s self = 337s 337s def test_save_model_with_external_data_multiple_times(self) -> None: 337s # Test onnx.save should respectively handle typical tensor and external tensor properly 337s # 1st save: save two tensors which have raw_data 337s # Only w_large will be stored as external tensors since it's larger than 1024 337s onnx.save_model( 337s self.model, 337s self.model_file_path, 337s save_as_external_data=True, 337s all_tensors_to_one_file=False, 337s location=None, 337s size_threshold=1024, 337s convert_attribute=True, 337s ) 337s model_without_loading_external = onnx.load( 337s self.model_file_path, load_external_data=False 337s ) 337s large_input_tensor = model_without_loading_external.graph.initializer[0] 337s self.assertTrue(large_input_tensor.HasField("data_location")) 337s > self.assertTrue( 337s np.allclose(to_array(large_input_tensor, self.temp_dir), self.large_data) 337s ) 337s E AssertionError: False is not true 337s 337s onnx/test/test_external_data.py:579: AssertionError 337s ___________ TestExternalDataToArray.test_to_array_with_external_data ___________ 337s 337s self = 337s 337s def test_to_array_with_external_data(self) -> None: 337s onnx.save_model( 337s self.model, 337s self.model_file_path, 337s save_as_external_data=True, 337s all_tensors_to_one_file=False, 337s size_threshold=0, 337s ) 337s # raw_data of external tensor is not loaded 337s model = onnx.load(self.model_file_path, load_external_data=False) 337s # Specify self.temp_dir to load external tensor 337s loaded_large_data = to_array(model.graph.initializer[0], self.temp_dir) 337s > self.assertTrue(np.allclose(loaded_large_data, self.large_data)) 337s E AssertionError: False is not true 337s 337s onnx/test/test_external_data.py:559: AssertionError 337s ___________________ TestToolsFunctions.test_replace_constant ___________________ 337s 337s self = 337s 337s def test_replace_constant(self): 337s dtype = np.float32 337s value = np.random.randn(2, 100).astype(dtype) 337s A = numpy_helper.from_array(value, name="A") 337s value = np.array([1], dtype=dtype) 337s C = numpy_helper.from_array(value, name="C") 337s 337s X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, None]) 337s Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None]) 337s node0 = helper.make_node("Constant", [], ["A"], value=A) 337s node1 = helper.make_node("MatMul", ["X", "A"], ["AX"]) 337s node2 = helper.make_node("Sub", ["AX", "C"], ["Y"]) 337s graph = helper.make_graph([node0, node1, node2], "lr", [X], [Y], [C]) 337s model_def = helper.make_model(graph) 337s 337s x = np.array([1, 2, 4, 5, 5, 4]).astype(np.float32).reshape((3, 2)) 337s oinf1 = ReferenceEvaluator(model_def) 337s y1 = oinf1.run(None, {"X": x})[0] 337s repl = replace_initializer_by_constant_of_shape(model_def) 337s node_types = set(n.op_type for n in repl.graph.node) 337s self.assertIn("ConstantOfShape", node_types) 337s oinf2 = ReferenceEvaluator(repl) 337s y1[:, :] = 3.5 337s y1[0, :] = 0.5 337s y2 = oinf2.run(None, {"X": x})[0] 337s > assert_allclose(y1, y2) 337s 337s onnx/test/tools_test.py:112: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (.compare at 0x3ff9bed7d80>, array([[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.....5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 337s 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5]], dtype=float32)) 337s kwds = {'equal_nan': True, 'err_msg': '', 'header': 'Not equal to tolerance rtol=1e-07, atol=0', 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Not equal to tolerance rtol=1e-07, atol=0 337s E 337s E Mismatched elements: 300 / 300 (100%) 337s E Max absolute difference: 1. 337s E Max relative difference: 0.6666667 337s E x: array([[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 337s E 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 337s E 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,... 337s E y: array([[1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 337s E 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 337s E 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,... 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s ______________ TestToolsFunctions.test_replace_constant_function _______________ 337s 337s self = 337s 337s def test_replace_constant_function(self): 337s dtype = np.float32 337s value = np.random.randn(2, 100).astype(dtype) 337s A = numpy_helper.from_array(value, name="A") 337s value = np.array([1], dtype=dtype) 337s C = numpy_helper.from_array(value, name="C") 337s 337s X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, None]) 337s Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None]) 337s nodeC = helper.make_node("Constant", [], ["C"], value=C) 337s node0 = helper.make_node("Constant", [], ["A"], value=A) 337s node1 = helper.make_node("MatMul", ["X", "A"], ["AX"]) 337s node2 = helper.make_node("Sub", ["AX", "C"], ["Y"]) 337s opset_imports = [ 337s helper.make_opsetid("", onnx_opset_version()), 337s helper.make_opsetid("custom", 1), 337s ] 337s fct = helper.make_function( 337s "custom", 337s "unittest", 337s ["X"], 337s ["Y"], 337s [nodeC, node0, node1, node2], 337s opset_imports, 337s ) 337s 337s node = helper.make_node("unittest", ["X"], ["Y"], domain="custom") 337s graph = helper.make_graph([node], "lr", [X], [Y], [C]) 337s model_def = helper.make_model( 337s graph, functions=[fct], opset_imports=opset_imports 337s ) 337s 337s x = np.array([1, 2, 4, 5, 5, 4]).astype(np.float32).reshape((3, 2)) 337s oinf1 = ReferenceEvaluator(model_def) 337s y1 = oinf1.run(None, {"X": x})[0] 337s repl = replace_initializer_by_constant_of_shape(model_def) 337s node_types = set(n.op_type for n in repl.functions[0].node) 337s self.assertIn("ConstantOfShape", node_types) 337s oinf2 = ReferenceEvaluator(repl) 337s y1[:, :] = 3.5 337s y1[0, :] = 0.5 337s y2 = oinf2.run(None, {"X": x})[0] 337s > assert_allclose(y1, y2) 337s 337s onnx/test/tools_test.py:182: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (.compare at 0x3ff9be82340>, array([[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.....5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 337s 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5]], dtype=float32)) 337s kwds = {'equal_nan': True, 'err_msg': '', 'header': 'Not equal to tolerance rtol=1e-07, atol=0', 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Not equal to tolerance rtol=1e-07, atol=0 337s E 337s E Mismatched elements: 300 / 300 (100%) 337s E Max absolute difference: 1. 337s E Max relative difference: 0.6666667 337s E x: array([[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 337s E 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 337s E 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,... 337s E y: array([[1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 337s E 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 337s E 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,... 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s _________________ TestToolsFunctions.test_replace_initializer __________________ 337s 337s self = 337s 337s def test_replace_initializer(self): 337s dtype = np.float32 337s value = np.random.randn(2, 100).astype(dtype) 337s A = numpy_helper.from_array(value, name="A") 337s value = np.array([1], dtype=dtype) 337s C = numpy_helper.from_array(value, name="C") 337s 337s X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, None]) 337s Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None]) 337s node1 = helper.make_node("MatMul", ["X", "A"], ["AX"]) 337s node2 = helper.make_node("Sub", ["AX", "C"], ["Y"]) 337s graph = helper.make_graph([node1, node2], "lr", [X], [Y], [A, C]) 337s model_def = helper.make_model(graph) 337s 337s x = np.array([1, 2, 4, 5, 5, 4]).astype(np.float32).reshape((3, 2)) 337s oinf1 = ReferenceEvaluator(model_def) 337s y1 = oinf1.run(None, {"X": x})[0] 337s repl = replace_initializer_by_constant_of_shape(model_def) 337s node_types = set(n.op_type for n in repl.graph.node) 337s self.assertIn("ConstantOfShape", node_types) 337s oinf2 = ReferenceEvaluator(repl) 337s y1[:, :] = 3.5 337s y1[0, :] = 0.5 337s y2 = oinf2.run(None, {"X": x})[0] 337s > assert_allclose(y1, y2) 337s 337s onnx/test/tools_test.py:85: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s args = (.compare at 0x3ff9c1d4400>, array([[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.....5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 337s 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5]], dtype=float32)) 337s kwds = {'equal_nan': True, 'err_msg': '', 'header': 'Not equal to tolerance rtol=1e-07, atol=0', 'verbose': True} 337s 337s @wraps(func) 337s def inner(*args, **kwds): 337s with self._recreate_cm(): 337s > return func(*args, **kwds) 337s E AssertionError: 337s E Not equal to tolerance rtol=1e-07, atol=0 337s E 337s E Mismatched elements: 300 / 300 (100%) 337s E Max absolute difference: 1. 337s E Max relative difference: 0.6666667 337s E x: array([[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 337s E 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 337s E 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,... 337s E y: array([[1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 337s E 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 337s E 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,... 337s 337s /usr/lib/python3.12/contextlib.py:81: AssertionError 337s ________ TestVersionConverter.test_upsample_with_raw_constant_node_9_8 _________ 337s 337s self = 337s 337s def test_upsample_with_raw_constant_node_9_8(self) -> None: 337s > self.helper_upsample_with_constant(raw_scale=True) 337s 337s onnx/test/version_converter_test.py:1328: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/version_converter_test.py:1304: in helper_upsample_with_constant 337s converted_model = self._converted( 337s onnx/test/version_converter_test.py:32: in _converted 337s converted_model = onnx.version_converter.convert_version( 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s model = ir_version: 9 337s producer_name: "onnx-test" 337s graph { 337s node { 337s output: "Constant_Output" 337s op_type: "Constant" 337s at... dim { 337s dim_value: 4 337s } 337s } 337s } 337s } 337s } 337s } 337s opset_import { 337s domain: "" 337s version: 9 337s } 337s 337s target_version = 8 337s 337s def convert_version(model: ModelProto, target_version: int) -> ModelProto: 337s """Apply the version conversion on the serialized ModelProto. 337s 337s Arguments: 337s input (ModelProto): model 337s target_version (int): target opset version 337s 337s Returns: 337s return (ModelProto) converted model 337s 337s Raises Exceptions: 337s RuntimeError when some necessary conversion is not supported 337s 337s Supported adapters: 337s - Add from Opset 7 to Opset 6 337s - Add from Opset 6 to Opset 5 337s - Add from Opset 6 to Opset 7 337s - Add from Opset 5 to Opset 6 337s - Mul from Opset 6 to Opset 7 337s - Mul from Opset 7 to Opset 6 337s - Mul from Opset 6 to Opset 5 337s - Mul from Opset 5 to Opset 6 337s - Gemm from Opset 7 to Opset 6 337s - Gemm from Opset 6 to Opset 5 337s - Gemm from Opset 6 to Opset 7 337s - Gemm from Opset 5 to Opset 6 337s - Relu from Opset 6 to Opset 5 337s - Relu from Opset 5 to Opset 6 337s - BatchNorm from Opset 7 to Opset 6 337s - BatchNorm from Opset 6 to Opset 7 337s - BatchNorm from Opset 6 to Opset 5 337s - BatchNorm from Opset 5 to Opset 6 337s - Concat from Opset 4 to Opset 3 337s - Concat from Opset 3 to Opset 4 337s - Reshape from Opset 5 to Opset 4 337s - Reshape from Opset 4 to Opset 5 337s - Sum from Opset 7 to Opset 8 337s - Sum from Opset 8 to Opset 7 337s - Sum from Opset 6 to Opset 5 337s - Sum from Opset 5 to Opset 6 337s - MaxPool from Opset 8 to Opset 7 337s - MaxPool from Opset 7 to Opset 8 337s - AveragePool from Opset 7 to Opset 6 337s - AveragePool from Opset 6 to Opset 7 337s - Dropout from Opset 7 to Opset 6 337s - Dropout from Opset 6 to Opset 5 337s - Dropout from Opset 6 to Opset 7 337s - Dropout from Opset 5 to Opset 6 337s - RNN from Opset 13 to Opset 14 337s - RNN from Opset 14 to Opset 13 337s - GRU from Opset 13 to Opset 14 337s - GRU from Opset 14 to Opset 13 337s - LSTM from Opset 13 to Opset 14 337s - LSTM from Opset 14 to Opset 13 337s 337s Unsupported adapters: 337s - Min from Opset 8 to Opset 7 337s - Min from Opset 7 to Opset 8 337s - Min from Opset 6 to Opset 5 337s - Min from Opset 5 to Opset 6 337s - Mean from Opset 8 to Opset 7 337s - Mean from Opset 7 to Opset 8 337s - Mean from Opset 6 to Opset 5 337s - Mean from Opset 5 to Opset 6 337s - Max from Opset 8 to Opset 7 337s - Max from Opset 7 to Opset 8 337s - Max from Opset 6 to Opset 5 337s - Max from Opset 5 to Opset 6 337s - Xor from Opset 6 to Opset 7 337s - Xor from Opset 7 to Opset 6 337s - Upsample from Opset 6 to Opset 7 337s - Upsample from Opset 7 to Opset 6 337s - Sub from Opset 6 to Opset 7 337s - Sub from Opset 7 to Opset 6 337s - Sub from Opset 6 to Opset 5 337s - Sub from Opset 5 to Opset 6 337s - RNN from Opset 6 to Opset 7 337s - RNN from Opset 7 to Opset 6 337s - Pow from Opset 6 to Opset 7 337s - Pow from Opset 7 to Opset 6 337s - PRelu from Opset 6 to Opset 7 337s - PRelu from Opset 7 to Opset 6 337s - PRelu from Opset 6 to Opset 5 337s - PRelu from Opset 5 to Opset 6 337s - Or from Opset 6 to Opset 7 337s - Or from Opset 7 to Opset 6 337s - Less from Opset 6 to Opset 7 337s - Less from Opset 7 to Opset 6 337s - LSTM from Opset 6 to Opset 7 337s - LSTM from Opset 7 to Opset 6 337s - Greater from Opset 6 to Opset 7 337s - Greater from Opset 7 to Opset 6 337s - GRU from Opset 6 to Opset 7 337s - GRU from Opset 7 to Opset 6 337s - GRU from Opset 3 to Opset 2 337s - GRU from Opset 2 to Opset 3 337s - Equal from Opset 6 to Opset 7 337s - Equal from Opset 7 to Opset 6 337s - Div from Opset 6 to Opset 7 337s - Div from Opset 7 to Opset 6 337s - Div from Opset 6 to Opset 5 337s - Div from Opset 5 to Opset 6 337s - And from Opset 6 to Opset 7 337s - And from Opset 7 to Opset 6 337s - And from Opset 6 to Opset 5 337s - And from Opset 5 to Opset 6 337s - Tile from Opset 6 to Opset 5 337s - Tile from Opset 5 to Opset 6 337s - Sqrt from Opset 6 to Opset 5 337s - Sqrt from Opset 5 to Opset 6 337s - Sigmoid from opset 6 to opset 5 337s - Sigmoid from opset 5 to opset 6 337s - Selu from opset 6 to opset 5 337s - Selu from opset 5 to opset 6 337s - Reciprocal from opset 6 to opset 5 337s - Reciprocal from opset 5 to opset 6 337s - Neg from opset 6 to opset 5 337s - Neg from opset 5 to opset 6 337s - Log from opset 6 to opset 5 337s - Log from opset 5 to opset 6 337s - LeakyRelu from opset 6 to opset 5 337s - LeakyRelu from opset 5 to opset 6 337s - InstanceNormalization from opset 6 to opset 5 337s - InstanceNormalization from opset 5 to opset 6 337s - HardSigmoid from opset 6 to opset 5 337s - HardSigmoid from opset 5 to opset 6 337s - Floor from opset 6 to opset 5 337s - Floor from opset 5 to opset 6 337s - Exp from opset 6 to opset 5 337s - Exp from opset 5 to opset 6 337s - Elu from opset 6 to opset 5 337s - Elu from opset 5 to opset 6 337s - Clip from opset 6 to opset 5 337s - Clip from opset 5 to opset 6 337s - Ceil from opset 6 to opset 5 337s - Ceil from opset 5 to opset 6 337s - Cast from opset 6 to opset 5 337s - Cast from opset 5 to opset 6 337s - Abs from opset 6 to opset 5 337s - Abs from opset 5 to opset 6 337s - Split from opset 2 to opset 1 337s - Split from opset 1 to opset 2 337s - Pad from opset 2 to opset 1 337s - Pad from opset 1 to opset 2 337s - LpPool from opset 2 to opset 1 337s - LpPool from opset 1 to opset 2 337s - GlobalLpPool from opset 2 to opset 1 337s - GlobalLpPool from opset 1 to opset 2 337s """ 337s if not isinstance(model, ModelProto): 337s raise ValueError( 337s f"VersionConverter only accepts ModelProto as model, incorrect type: {type(model)}" 337s ) 337s if not isinstance(target_version, int): 337s raise ValueError( 337s f"VersionConverter only accepts int as target_version, incorrect type: {type(target_version)}" 337s ) 337s model_str = model.SerializeToString() 337s > converted_model_str = C.convert_version(model_str, target_version) 337s E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Upsample): [ShapeInferenceError] Inferred shape and existing shape differ in dimension 0: (0) vs (1) 337s 337s /usr/lib/python3/dist-packages/onnx/version_converter.py:173: InferenceError 337s _________ TestVersionConverter.test_upsample_with_raw_initializer_9_8 __________ 337s 337s self = 337s 337s def test_upsample_with_raw_initializer_9_8(self) -> None: 337s > self.helper_upsample_with_constant(raw_scale=True) 337s 337s onnx/test/version_converter_test.py:1324: 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s onnx/test/version_converter_test.py:1304: in helper_upsample_with_constant 337s converted_model = self._converted( 337s onnx/test/version_converter_test.py:32: in _converted 337s converted_model = onnx.version_converter.convert_version( 337s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 337s 337s model = ir_version: 9 337s producer_name: "onnx-test" 337s graph { 337s node { 337s output: "Constant_Output" 337s op_type: "Constant" 337s at... dim { 337s dim_value: 4 337s } 337s } 337s } 337s } 337s } 337s } 337s opset_import { 337s domain: "" 337s version: 9 337s } 337s 337s target_version = 8 337s 337s def convert_version(model: ModelProto, target_version: int) -> ModelProto: 337s """Apply the version conversion on the serialized ModelProto. 337s 337s Arguments: 337s input (ModelProto): model 337s target_version (int): target opset version 337s 337s Returns: 337s return (ModelProto) converted model 337s 337s Raises Exceptions: 337s RuntimeError when some necessary conversion is not supported 337s 337s Supported adapters: 337s - Add from Opset 7 to Opset 6 337s - Add from Opset 6 to Opset 5 337s - Add from Opset 6 to Opset 7 337s - Add from Opset 5 to Opset 6 337s - Mul from Opset 6 to Opset 7 337s - Mul from Opset 7 to Opset 6 337s - Mul from Opset 6 to Opset 5 337s - Mul from Opset 5 to Opset 6 337s - Gemm from Opset 7 to Opset 6 337s - Gemm from Opset 6 to Opset 5 337s - Gemm from Opset 6 to Opset 7 337s - Gemm from Opset 5 to Opset 6 337s - Relu from Opset 6 to Opset 5 337s - Relu from Opset 5 to Opset 6 337s - BatchNorm from Opset 7 to Opset 6 337s - BatchNorm from Opset 6 to Opset 7 337s - BatchNorm from Opset 6 to Opset 5 337s - BatchNorm from Opset 5 to Opset 6 337s - Concat from Opset 4 to Opset 3 337s - Concat from Opset 3 to Opset 4 337s - Reshape from Opset 5 to Opset 4 337s - Reshape from Opset 4 to Opset 5 337s - Sum from Opset 7 to Opset 8 337s - Sum from Opset 8 to Opset 7 337s - Sum from Opset 6 to Opset 5 337s - Sum from Opset 5 to Opset 6 337s - MaxPool from Opset 8 to Opset 7 337s - MaxPool from Opset 7 to Opset 8 337s - AveragePool from Opset 7 to Opset 6 337s - AveragePool from Opset 6 to Opset 7 337s - Dropout from Opset 7 to Opset 6 337s - Dropout from Opset 6 to Opset 5 337s - Dropout from Opset 6 to Opset 7 337s - Dropout from Opset 5 to Opset 6 337s - RNN from Opset 13 to Opset 14 337s - RNN from Opset 14 to Opset 13 337s - GRU from Opset 13 to Opset 14 337s - GRU from Opset 14 to Opset 13 337s - LSTM from Opset 13 to Opset 14 337s - LSTM from Opset 14 to Opset 13 337s 337s Unsupported adapters: 337s - Min from Opset 8 to Opset 7 337s - Min from Opset 7 to Opset 8 337s - Min from Opset 6 to Opset 5 337s - Min from Opset 5 to Opset 6 337s - Mean from Opset 8 to Opset 7 337s - Mean from Opset 7 to Opset 8 337s - Mean from Opset 6 to Opset 5 337s - Mean from Opset 5 to Opset 6 337s - Max from Opset 8 to Opset 7 337s - Max from Opset 7 to Opset 8 337s - Max from Opset 6 to Opset 5 337s - Max from Opset 5 to Opset 6 337s - Xor from Opset 6 to Opset 7 337s - Xor from Opset 7 to Opset 6 337s - Upsample from Opset 6 to Opset 7 337s - Upsample from Opset 7 to Opset 6 337s - Sub from Opset 6 to Opset 7 337s - Sub from Opset 7 to Opset 6 337s - Sub from Opset 6 to Opset 5 337s - Sub from Opset 5 to Opset 6 337s - RNN from Opset 6 to Opset 7 337s - RNN from Opset 7 to Opset 6 337s - Pow from Opset 6 to Opset 7 337s - Pow from Opset 7 to Opset 6 337s - PRelu from Opset 6 to Opset 7 337s - PRelu from Opset 7 to Opset 6 337s - PRelu from Opset 6 to Opset 5 337s - PRelu from Opset 5 to Opset 6 337s - Or from Opset 6 to Opset 7 337s - Or from Opset 7 to Opset 6 337s - Less from Opset 6 to Opset 7 337s - Less from Opset 7 to Opset 6 337s - LSTM from Opset 6 to Opset 7 337s - LSTM from Opset 7 to Opset 6 337s - Greater from Opset 6 to Opset 7 337s - Greater from Opset 7 to Opset 6 337s - GRU from Opset 6 to Opset 7 337s - GRU from Opset 7 to Opset 6 337s - GRU from Opset 3 to Opset 2 337s - GRU from Opset 2 to Opset 3 337s - Equal from Opset 6 to Opset 7 337s - Equal from Opset 7 to Opset 6 337s - Div from Opset 6 to Opset 7 337s - Div from Opset 7 to Opset 6 337s - Div from Opset 6 to Opset 5 337s - Div from Opset 5 to Opset 6 337s - And from Opset 6 to Opset 7 337s - And from Opset 7 to Opset 6 337s - And from Opset 6 to Opset 5 337s - And from Opset 5 to Opset 6 337s - Tile from Opset 6 to Opset 5 337s - Tile from Opset 5 to Opset 6 337s - Sqrt from Opset 6 to Opset 5 337s - Sqrt from Opset 5 to Opset 6 337s - Sigmoid from opset 6 to opset 5 337s - Sigmoid from opset 5 to opset 6 337s - Selu from opset 6 to opset 5 337s - Selu from opset 5 to opset 6 337s - Reciprocal from opset 6 to opset 5 337s - Reciprocal from opset 5 to opset 6 337s - Neg from opset 6 to opset 5 337s - Neg from opset 5 to opset 6 337s - Log from opset 6 to opset 5 337s - Log from opset 5 to opset 6 337s - LeakyRelu from opset 6 to opset 5 337s - LeakyRelu from opset 5 to opset 6 337s - InstanceNormalization from opset 6 to opset 5 337s - InstanceNormalization from opset 5 to opset 6 337s - HardSigmoid from opset 6 to opset 5 337s - HardSigmoid from opset 5 to opset 6 337s - Floor from opset 6 to opset 5 337s - Floor from opset 5 to opset 6 337s - Exp from opset 6 to opset 5 337s - Exp from opset 5 to opset 6 337s - Elu from opset 6 to opset 5 337s - Elu from opset 5 to opset 6 337s - Clip from opset 6 to opset 5 337s - Clip from opset 5 to opset 6 337s - Ceil from opset 6 to opset 5 337s - Ceil from opset 5 to opset 6 337s - Cast from opset 6 to opset 5 337s - Cast from opset 5 to opset 6 337s - Abs from opset 6 to opset 5 337s - Abs from opset 5 to opset 6 337s - Split from opset 2 to opset 1 337s - Split from opset 1 to opset 2 337s - Pad from opset 2 to opset 1 337s - Pad from opset 1 to opset 2 337s - LpPool from opset 2 to opset 1 337s - LpPool from opset 1 to opset 2 337s - GlobalLpPool from opset 2 to opset 1 337s - GlobalLpPool from opset 1 to opset 2 337s """ 337s if not isinstance(model, ModelProto): 337s raise ValueError( 337s f"VersionConverter only accepts ModelProto as model, incorrect type: {type(model)}" 337s ) 337s if not isinstance(target_version, int): 337s raise ValueError( 337s f"VersionConverter only accepts int as target_version, incorrect type: {type(target_version)}" 337s ) 337s model_str = model.SerializeToString() 337s > converted_model_str = C.convert_version(model_str, target_version) 337s E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Upsample): [ShapeInferenceError] Inferred shape and existing shape differ in dimension 0: (0) vs (1) 337s 337s /usr/lib/python3/dist-packages/onnx/version_converter.py:173: InferenceError 337s =============================== warnings summary =============================== 337s :488 337s :488: DeprecationWarning: Type google.protobuf.pyext._message.ScalarMapContainer uses PyType_Spec with a metaclass that has custom tp_new. This is deprecated and will no longer be allowed in Python 3.14. 337s 337s :488 337s :488: DeprecationWarning: Type google.protobuf.pyext._message.MessageMapContainer uses PyType_Spec with a metaclass that has custom tp_new. This is deprecated and will no longer be allowed in Python 3.14. 337s 337s ../../../../usr/lib/python3/dist-packages/google/protobuf/internal/well_known_types.py:91 337s /usr/lib/python3/dist-packages/google/protobuf/internal/well_known_types.py:91: DeprecationWarning: datetime.datetime.utcfromtimestamp() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.fromtimestamp(timestamp, datetime.UTC). 337s _EPOCH_DATETIME_NAIVE = datetime.datetime.utcfromtimestamp(0) 337s 337s onnx/test/helper_test.py::test_make_tensor_vals[TensorProto.UINT8] 337s onnx/test/helper_test.py::test_make_tensor_vals[TensorProto.UINT64] 337s /tmp/autopkgtest.buTYAJ/build.YML/src/onnx/test/helper_test.py:804: RuntimeWarning: invalid value encountered in cast 337s np_array = np.random.randn(2, 3).astype( 337s 337s onnx/test/helper_test.py::test_make_tensor_raw[TensorProto.UINT32] 337s onnx/test/helper_test.py::test_make_tensor_raw[TensorProto.UINT64] 337s /tmp/autopkgtest.buTYAJ/build.YML/src/onnx/test/helper_test.py:831: RuntimeWarning: invalid value encountered in cast 337s np_array = np.random.randn(2, 3).astype( 337s 337s onnx/test/numpy_helper_test.py::TestNumpyHelper::test_bfloat16_to_float32_5 337s /tmp/autopkgtest.buTYAJ/build.YML/src/onnx/test/numpy_helper_test.py:25: RuntimeWarning: overflow encountered in cast 337s return np.float32(fval) 337s 337s onnx/test/reference_evaluator_backend_test.py::TestOnnxBackEndWithReferenceEvaluator::test_maxpool_2d_uint8 337s onnx/test/test_backend_reference.py::OnnxBackendNodeModelTest::test_maxpool_2d_uint8_cpu 337s /usr/lib/python3/dist-packages/numpy/lib/arraypad.py:147: RuntimeWarning: invalid value encountered in cast 337s padded[left_slice] = value_pair[0] 337s 337s onnx/test/reference_evaluator_backend_test.py::TestOnnxBackEndWithReferenceEvaluator::test_maxpool_2d_uint8 337s onnx/test/test_backend_reference.py::OnnxBackendNodeModelTest::test_maxpool_2d_uint8_cpu 337s /usr/lib/python3/dist-packages/numpy/lib/arraypad.py:151: RuntimeWarning: invalid value encountered in cast 337s padded[right_slice] = value_pair[1] 337s 337s onnx/test/reference_evaluator_ml_test.py::TestReferenceEvaluatorAiOnnxMl::test_svm_classifier_linear 337s onnx/test/reference_evaluator_ml_test.py::TestReferenceEvaluatorAiOnnxMl::test_svm_classifier_noprob_linear 337s /usr/lib/python3/dist-packages/onnx/reference/ops/aionnxml/_common_classifier.py:65: RuntimeWarning: invalid value encountered in log 337s log = np.log(x) 337s 337s onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_04 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_l2.py:14: RuntimeWarning: overflow encountered in square 337s res = np.sqrt(np.sum(np.square(data), axis=axes, keepdims=keepdims)).astype( 337s 337s onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_05 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_l2.py:30: RuntimeWarning: overflow encountered in square 337s res = np.sqrt(np.sum(np.square(data), axis=axes, keepdims=keepdims)).astype( 337s 337s onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_06 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_log_sum.py:16: RuntimeWarning: invalid value encountered in log 337s return (np.log(res, out=res),) 337s 337s onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_14 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_sum_square.py:14: RuntimeWarning: overflow encountered in square 337s res = np.sum(np.square(data), axis=axes, keepdims=keepdims) 337s 337s onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_15 337s /usr/lib/python3/dist-packages/onnx/reference/ops/op_reduce_sum_square.py:28: RuntimeWarning: overflow encountered in square 337s res = np.sum(np.square(data), axis=axes, keepdims=keepdims) 337s 337s onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_16 337s /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:86: RuntimeWarning: overflow encountered in reduce 337s return ufunc.reduce(obj, axis, dtype, out, **passkwargs) 337s 337s onnx/test/test_external_data.py::TestExternalDataToArray::test_save_model_with_external_data_multiple_times 337s onnx/test/test_external_data.py::TestExternalDataToArray::test_to_array_with_external_data 337s /usr/lib/python3/dist-packages/numpy/core/numeric.py:2387: RuntimeWarning: invalid value encountered in multiply 337s x = x * ones_like(cond) 337s 337s -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html 337s =========================== short test summary info ============================ 337s FAILED onnx/test/helper_test.py::TestHelperTensorFunctions::test_make_bfloat16_tensor_raw 337s FAILED onnx/test/helper_test.py::TestHelperTensorFunctions::test_make_float8e4m3fn_tensor_raw 337s FAILED onnx/test/helper_test.py::TestHelperTensorFunctions::test_make_float8e4m3fnuz_tensor_raw 337s FAILED onnx/test/helper_test.py::TestHelperTensorFunctions::test_make_float8e5m2_tensor_raw 337s FAILED onnx/test/helper_test.py::TestHelperTensorFunctions::test_make_float8e5m2fnuz_tensor_raw 337s FAILED onnx/test/helper_test.py::test_make_tensor_raw[TensorProto.FLOAT] - As... 337s FAILED onnx/test/helper_test.py::test_make_tensor_raw[TensorProto.UINT16] - A... 337s FAILED onnx/test/helper_test.py::test_make_tensor_raw[TensorProto.INT16] - As... 337s FAILED onnx/test/helper_test.py::test_make_tensor_raw[TensorProto.INT32] - As... 337s FAILED onnx/test/helper_test.py::test_make_tensor_raw[TensorProto.INT64] - As... 337s FAILED onnx/test/helper_test.py::test_make_tensor_raw[TensorProto.FLOAT16] - ... 337s FAILED onnx/test/helper_test.py::test_make_tensor_raw[TensorProto.DOUBLE] - A... 337s FAILED onnx/test/helper_test.py::test_make_tensor_raw[TensorProto.COMPLEX64] 337s FAILED onnx/test/helper_test.py::test_make_tensor_raw[TensorProto.COMPLEX128] 337s FAILED onnx/test/helper_test.py::test_make_tensor_raw[TensorProto.UINT32] - A... 337s FAILED onnx/test/helper_test.py::test_make_tensor_raw[TensorProto.UINT64] - A... 337s FAILED onnx/test/reference_evaluator_backend_test.py::TestOnnxBackEndWithReferenceEvaluator::test__pytorch_converted_Conv2d_depthwise_padded 337s FAILED onnx/test/reference_evaluator_backend_test.py::TestOnnxBackEndWithReferenceEvaluator::test__pytorch_converted_Conv2d_dilated 337s FAILED onnx/test/reference_evaluator_backend_test.py::TestOnnxBackEndWithReferenceEvaluator::test_dft 337s FAILED onnx/test/reference_evaluator_backend_test.py::TestOnnxBackEndWithReferenceEvaluator::test_dft_axis 337s FAILED onnx/test/reference_evaluator_backend_test.py::TestOnnxBackEndWithReferenceEvaluator::test_dft_inverse 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_00 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_01 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_02 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_03 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_04 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_05 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_06 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_07 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_08 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_09 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_10 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_11 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_12 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_13 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_14 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_15 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_16 337s FAILED onnx/test/reference_evaluator_test.py::TestReferenceEvaluator::test_op_reduce_17 337s FAILED onnx/test/test_external_data.py::TestExternalDataToArray::test_save_model_with_external_data_multiple_times 337s FAILED onnx/test/test_external_data.py::TestExternalDataToArray::test_to_array_with_external_data 337s FAILED onnx/test/tools_test.py::TestToolsFunctions::test_replace_constant - A... 337s FAILED onnx/test/tools_test.py::TestToolsFunctions::test_replace_constant_function 337s FAILED onnx/test/tools_test.py::TestToolsFunctions::test_replace_initializer 337s FAILED onnx/test/version_converter_test.py::TestVersionConverter::test_upsample_with_raw_constant_node_9_8 337s FAILED onnx/test/version_converter_test.py::TestVersionConverter::test_upsample_with_raw_initializer_9_8 337s ERROR onnx/test/reference_evaluator_backend_test.py::TestOnnxBackEndWithReferenceEvaluator::test_xor_bcast4v4d 337s = 46 failed, 4940 passed, 2705 skipped, 36 deselected, 22 warnings, 1 error in 90.37s (0:01:30) = 338s autopkgtest [20:54:04]: test command1: -----------------------] 339s autopkgtest [20:54:05]: test command1: - - - - - - - - - - results - - - - - - - - - - 339s command1 PASS 339s autopkgtest [20:54:05]: @@@@@@@@@@@@@@@@@@@@ summary 339s command1 PASS 357s Creating nova instance adt-noble-s390x-onnx-20240325-204826-juju-7f2275-prod-proposed-migration-environment-3 from image adt/ubuntu-noble-s390x-server-20240325.img (UUID 9f25d9bc-613c-4979-9452-2ea8e4d84cd0)...