Key | Value |
---|---|
MD5 | FE661AC3D7ECAB49C8F5C9B3E7D8CBC5 |
PackageArch | x86_64 |
PackageDescription | Open format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners. |
PackageMaintainer | https://bugs.opensuse.org |
PackageName | python3-onnx |
PackageRelease | bp153.1.19 |
PackageVersion | 1.6.0 |
SHA-1 | 64BBAEBBE07A35B55ED1DBAE956112EC21F32418 |
SHA-256 | C398D545ADE0517EB347ED6CDCA57BFF11B4BA78A3096F11F603B8E8123BD8CB |
hashlookup:children-total | 2620 |
hashlookup:trust | 50 |
The searched file hash includes 2620 children files known and seen by metalookup. A sample is included below:
Key | Value |
---|---|
FileName | ./usr/lib64/python3.6/site-packages/onnx/backend/test/case/node/__pycache__/less.cpython-36.pyc |
FileSize | 1379 |
MD5 | 1757CBB625FF405377A6E98FCB4B78E6 |
SHA-1 | 0017469F0D0BF30B3D74E9E7E57F039759A84883 |
SHA-256 | 976DEEFD318BE39E13DCF909B5FAB2718A47B06C51928B38AD02D05E10F1E9E9 |
SSDEEP | 24:gS/RFDz1uGv1UkiR7FRMQLELHb//BWFY/WYngBwFyLCcbaWMuoZns8BRYTL7J53n:gS/nD1107hLETb3BWKx6LCcmRu0sqYTT |
TLSH | T1962184D0E44BEEB5FD59F4BDC1FB09A44838226F0741A1273BA850463F953F115906AD |
Key | Value |
---|---|
FileName | snap-hashlookup-import/app/machine-learning/python-packages/onnx/backend/test/data/node/test_unique_not_sorted_without_axis/test_data_set_0/input_0.pb |
FileSize | 33 |
MD5 | 744756E3B1EEA26D2734BF9EA1062270 |
SHA-1 | 00249AEF2BFD0861B303F9731A71AE2B599CEA3E |
SHA-256 | 36CF0EC69194FE4F0FB0DF6612A387CAA57A529A5E0885DA9F0978DAED006F58 |
SHA-512 | 58242C9D6A342A868078152674FE1DAB1464994C9376C526480FC15470D239C1A1FC97247CBFF8513FDCE64B0BC3605AF261005A291B5B4A33EBBFD2D4B2ADA0 |
SSDEEP | 3:TknOlQotlXlln:onKQ49 |
TLSH | |
insert-timestamp | 1721647816.5803854 |
mimetype | application/octet-stream |
source | snap:RE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_143 |
Key | Value |
---|---|
FileName | snap-hashlookup-import/app/machine-learning/python-packages/onnx/backend/test/data/node/test_pow/test_data_set_0/input_0.pb |
FileSize | 254 |
MD5 | 1236606A29D429E5853E78CF5A383D37 |
SHA-1 | 004A45FCBA63BB2681290081A128A54F31ADBB54 |
SHA-256 | 018FD53069CA24142BC3DF2DCFFA9815166EEA86EE5C2F9D190C898CAACA8CAA |
SHA-512 | D7F6A8DE3DA5E32FCDD77AFD266FCD4E677270E5F164EED514133529A43F579B92D16C474D8B24C69BCAB8A483BCC3CBB5E07250201BD0514258872F109F2447 |
SSDEEP | 3:0hGhVEtWlvvFl/lk9sl6cl/slqMlPsla0u1N2dl+ldGN1OVSlTnZ7n1TnRrntznR:0/WM9EtEd91MdslsNMVSf |
TLSH | T141D08053774897D1E1685D304DE33D1763474C7C51B5300C4F67031B15D4157550DC87 |
insert-timestamp | 1721647809.7144208 |
mimetype | application/octet-stream |
source | snap:RE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_143 |
Key | Value |
---|---|
FileName | ./usr/lib64/python3.6/site-packages/onnx/backend/test/data/node/test_tan_example/model.onnx |
FileSize | 87 |
MD5 | 9E4F287C0ED66271FD53BDB80FC4DA27 |
SHA-1 | 0100A9052740D8D2732DE346B73147533265A52A |
SHA-256 | ABE8800BB5A6BB436087FB185E020ECF721D4E4DC88349433D80C2242597B6B5 |
SSDEEP | 3:upkZAthd3VmZBUu3Mptm0VNtkptmnmn:uq+7dhDm8tkDmnmn |
TLSH | T175B012CA0A1204014B32C01FC8110633900DC551448CF7C51385C44D4D82CA14886344 |
Key | Value |
---|---|
FileName | snap-hashlookup-import/app/machine-learning/python-packages/onnx/backend/test/data/pytorch-converted/test_AvgPool3d_stride/model.onnx |
FileSize | 196 |
MD5 | C35533FBAF16988482DDAE450CCBF859 |
SHA-1 | 015CFE60A9DA4E71A8B2F2B6D13545E27C764893 |
SHA-256 | 8B80276BBF2F4E3E031BFEEF5A8F5342AA7CAD1D52B0C250CCD3ED119B6771D6 |
SHA-512 | 3EECBD63E9A613DD0519D1F7B55B566685D4EE5BC8BCBD835C744379EA280A250B8E9B1162D6B203DB703BECD798374472D6EC308BFF8B6D4744CB96080ECBD8 |
SSDEEP | 6:ui2RKXfhEUiMJWWeRg1JRU99u4LPAvb6kcAvr:ui2qEfRy0Sb69Kr |
TLSH | T122D01213048914D8EB092014F49659984543E00D15CD87E155CAEF07BC44D4D4A845D9 |
insert-timestamp | 1721647789.7102919 |
mimetype | application/octet-stream |
source | snap:RE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_143 |
Key | Value |
---|---|
FileName | snap-hashlookup-import/app/machine-learning/python-packages/onnx/backend/test/data/node/test_scatter_with_axis/model.onnx |
FileSize | 199 |
MD5 | 1848210776A05751E8F7C5A8705889EC |
SHA-1 | 01602490EEDAED1C47366D487CB4E7247769C76D |
SHA-256 | 98278975EB8059E4367087D4D78678024E2F9E52B3D429951F870B4AC167D497 |
SHA-512 | C5C141F57D8BF404678B2208018929B06F35B1C655F6D757AD7AAABD9C54928907317879190984071ADAD64D475F74FD30BD2C97053C7F57CC8EF5273D823695 |
SSDEEP | 3:E9vyTXBBjwzRB9uS9zX6z2WwiL4d/G/TN7D0/GlQVB4XptS/GoV3rv4d/Gs3n:E9vyNr+exwi8Y717lQV0ptNoV374Ys3 |
TLSH | T19CD022B3D020A80E072F10182892B7987412F41908C64B908108C7370D62DB4F8EE333 |
insert-timestamp | 1721647811.0299919 |
mimetype | application/octet-stream |
source | snap:RE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_143 |
Key | Value |
---|---|
FileName | snap-hashlookup-import/app/machine-learning/python-packages/onnx/backend/test/data/node/test_reduce_sum_square_negative_axes_keepdims_random_expanded/test_data_set_0/output_0.pb |
FileSize | 43 |
MD5 | FC666F6BDA36D47375D022C6C615F46D |
SHA-1 | 017EC79D5C208495B80277B2AC4605AA8525D707 |
SHA-256 | 4FEB9DD68CA76CFAD4EA4F286D40EF62AC50A02B2AD0DF92FFC4892B1B61B819 |
SHA-512 | 953283E3F750F17C939295FC042700063639B9D5845ED26E37E5F3C9DB4A6B68479034AA50F1BA65940C48811EBBA6B121EBF6549D68BE414276FA63B5DFB427 |
SSDEEP | 3:0kflfsanjunlPnn:0kVJnj4Pnn |
TLSH | |
insert-timestamp | 1721647814.033244 |
mimetype | application/octet-stream |
source | snap:RE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_143 |
Key | Value |
---|---|
FileName | ./usr/lib64/python3.6/site-packages/onnx/backend/test/data/node/test_mod_mixed_sign_float16/model.onnx |
FileSize | 131 |
MD5 | 65EC34E5AE89A1E16D4EA7E20440E2B2 |
SHA-1 | 0183F846EF9F6BEA20749AECE6769B487996CDED |
SHA-256 | C115075EC02FEEF6EC5062F4D3266D8EB361F730C1647A07C632BC4AF866F569 |
SSDEEP | 3:E9vpLV3WGRPudx9Mk/WR6/XTt//HHptD+f1tvpt79f3ptbln:E9vnpudx90R6bNGNtdF |
TLSH | T160C09B82F57565340F34916DEC39127B0218CAC5267433CFC745640E45C8D21914D744 |
Key | Value |
---|---|
FileName | ./usr/lib64/python3.6/site-packages/onnx/backend/test/data/node/test_constant/model.onnx |
FileSize | 222 |
MD5 | F9D5AF5A9AEEBA9F76255D04F573D97C |
SHA-1 | 01A30793235F28B58017BAB4C4E525DE8B3E54C7 |
SHA-256 | DAFF6D1B87494A7C1334E47E590C10836591F69943C769E15BA8C964941C784E |
SSDEEP | 6:uq+ATf1tmKvk+/JLPMLfLnXi7MvqZ5DlXILF7UX0QAc21s:u49JFPMrLS7MvE5ZIhUXpALm |
TLSH | T197D0A7CAE172265617DAC9C791D53374511C1D0833188ED2C00996591B59D6B812FA7B |
Key | Value |
---|---|
FileName | ./usr/lib64/python3.6/site-packages/onnx/backend/test/data/node/test_scatter_elements_with_negative_indices/model.onnx |
FileSize | 226 |
MD5 | 80594521587233302283F7F8038905B3 |
SHA-1 | 01C81A38C0789A4630AA47F00FF9A642D4A5DC7A |
SHA-256 | 0EF95FA6380940BA7B947DE67E8C0DABAF0A1D77C78C03EF3282C2E662C6EE84 |
SSDEEP | 6:M+0mLrzRWVdnMMSIJvPX0BEi8Y717lQV0ptNoV374Ycu:lLfRW/Jh8YdMO |
TLSH | T199D0A7B2A020B80D076F104914E5B3AD7505F51405844760C20CC5370D72D70BA9E337 |