{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/census/basicblock","entry":"BasicBlock","source":"Syntology differential census (groundwork/55, run v2_2026-09-22), per sample; not an archive number","census_date":"2026-09-22","battery_sha256":["b986f7e04d794a0d88ad4c5f32cf63ec3590b5deff0192150737bc5f1c0b4677"],"runner_sha256":["5a452d0e7c0da5b80771d1be2afe3572e253568cf5c5d59a0d08ebd663d00808"],"bucket_key":"positional (rank, kind, dtype) of each array argument; the argument name is not part of the key because the harness draws the shared array from (rank, kind) and casts it to the dtype, whatever the name","bucket_fields":["rank","kind","dtype"],"claim":"Implementations sharing this entry name were each run on one shared input fixed by the positional (rank, kind, dtype) of their array arguments (the bucket). A cluster is the set whose recorded output digest (sha256 of the output rounded to 6 decimals) is identical. Identical values to six decimals on the shared input are agreement on those inputs, not a statement about the whole domain and not a substitution claim.","n_implementations_compared":6,"n_papers":6,"n_buckets":1,"n_distinct_outputs":5,"n_class_bearing":6,"not_compared":{"not_run_on_shared_input":{"n":4,"by_error":{"RuntimeError":4}},"no_array_argument_ran_on_own_fixture_arguments_only":{"n":0,"n_papers":0,"recorded_shared_digest_equals_own_fixture_digest":0},"output_not_digested_non_numeric":{"n":0,"by_type":{}}},"withdrawn_excluded":0,"code_page":"/code/basicblock","buckets":[{"bucket":[[4,"float","float32"]],"n_implementations_compared":6,"n_papers":6,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"f44376283dae62c3","size":2,"n_papers":2,"shape":[2,4,4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":0.0,"members_with_recorded_values":2,"torch_reference_conventions_with_this_digest":[],"values":[0.0,2.1946566104888916,1.9460337162017822,0.9135919213294983,1.9869762659072876,0.0,1.0342133045196533,0.0,1.406519889831543,0.6406194567680359,0.0,0.0,0.0,0.16641990840435028,0.0,1.3464781045913696,0.0,1.759207010269165,0.0,1.2415794134140015,0.029787510633468628,0.7311146259307861,0.0,0.0,0.7203588485717773,0.0,0.285011887550354,0.0,0.3527040481567383,1.4355517625808716,0.6562255024909973,0.0,0.8262795209884644,0.0,0.0,0.4007524251937866,0.9773152470588684,0.0,0.0,0.14217329025268555,0.0,0.0,3.4216673374176025,0.0,1.373889446258545,2.1709282398223877,2.3652420043945312,1.5882657766342163,0.05682267248630524,0.24440538883209229,1.2434524297714233,0.0,0.5396113395690918,0.2571505308151245,0.20093798637390137,0.2800556719303131,0.0,0.0,0.05230426788330078,1.675096035003662,0.0,0.02000558376312256,0.5989710092544556,0.0,0.541429877281189,1.4046813249588013,0.7608749866485596,0.2379847913980484,0.519077479839325,0.7219001650810242,0.0,0.0,0.6089755892753601,0.0,0.0,0.0,0.0,0.0,0.0,0.028996050357818604,0.0,1.3588833808898926,0.0,0.0,0.0,2.2100374698638916,0.0,0.0,1.2457102537155151,0.7514816522598267,0.0,1.292459487915039,1.292844295501709,0.278521865606308,0.0,0.0,0.44200974702835083,0.9512078762054443,0.0,0.0,0.0,0.0,0.0,0.0,0.19129540026187897,0.0,0.0,2.901747703552246,0.1223439872264862,1.01015305519104,3.0159151554107666,0.0,0.0,0.11982941627502441,3.031184673309326,1.0637023448944092,1.9374651908874512,0.0,0.0,0.49500659108161926,0.17844942212104797,0.028680860996246338,2.8227505683898926,0.0,0.0,0.0,0.0,0.13739217817783356],"values_recorded":128,"members":[{"code_sha256_prefix":"3be83b873ddc9e7a","path":"networks/ensemble_resnet.py","papers":["aaai_16955"],"paper_pages":[{"arxiv_id":"aaai_16955","page":null}],"arg_sig_recorded":[["x",4,"float","float32"]],"scalar_args":{},"class_bearing":true},{"code_sha256_prefix":"89a7e817f74ed365","path":"zebrapose/model/BinaryCodeNet.py","papers":["2203.09418"],"paper_pages":[{"arxiv_id":"2203.09418","page":"/paper/zebrapose-coarse-to-fine-surface-encoding-for"}],"arg_sig_recorded":[["x",4,"float","float32"]],"scalar_args":{},"class_bearing":true}]},{"output_sha":"2b3df4b170d2fb51","size":1,"n_papers":1,"shape":[2,4,4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.0,0.02110259234905243,0.0,0.0,0.0,1.376206874847412,0.7751489877700806,0.6264764070510864,0.0,0.26015007495880127,0.0,0.29229551553726196,2.4133219718933105,1.3968485593795776,3.1324210166931152,0.0,0.6180612444877625,0.18595287203788757,2.969485282897949,0.8814200162887573,1.0166501998901367,0.0,1.2877602577209473,1.5045747756958008,0.3899132013320923,0.7857873439788818,0.8701677322387695,0.0,0.0,0.8253403902053833,0.0,0.8221924901008606,0.0,1.5574692487716675,0.0,0.0,0.0,4.099008560180664,0.08480817079544067,0.3514012098312378,0.0,0.0,0.0,1.1144813299179077,0.28028640151023865,0.0,1.2419615983963013,0.0,0.9297124147415161,0.0,0.5944984555244446,1.97679603099823,1.3713085651397705,0.0,0.1886337548494339,0.31271860003471375,2.5338356494903564,1.3367754220962524,1.4217485189437866,0.0,0.8536617755889893,0.11661780625581741,0.0,3.047699451446533,0.0,0.0,0.0,0.7616114616394043,0.0,0.6920017004013062,1.8357034921646118,0.5054976940155029,0.9001649618148804,1.1416817903518677,2.3548684120178223,0.0,2.922548770904541,1.4473167657852173,0.5543655157089233,0.7849596738815308,1.9129066467285156,0.0,2.354837417602539,2.408298969268799,1.2181029319763184,0.0,0.2229243963956833,0.0,0.0,0.0,0.40719103813171387,0.18942512571811676,0.0,0.0,0.18960736691951752,0.3211144804954529,0.0,0.0,0.0,1.0536692142486572,0.4749516248703003,2.4737048149108887,0.2925300896167755,2.6909406185150146,0.7100595831871033,2.675415277481079,2.1212427616119385,0.0,1.4738105535507202,0.3602483868598938,0.0,0.0,1.3213456869125366,1.0263177156448364,0.3305397033691406,0.0,0.38854900002479553,0.0,0.0,0.0,0.7049307823181152,0.0,0.0,1.2412418127059937,0.0,1.1848853826522827,1.0440173149108887,1.242896556854248],"values_recorded":128,"members":[{"code_sha256_prefix":"301fca3184f8d91b","path":"core/raft.py","papers":["2405.14793"],"paper_pages":[{"arxiv_id":"2405.14793","page":"/paper/sea-raft-simple-efficient-accurate-raft-for"}],"arg_sig_recorded":[["x",4,"float","float32"]],"scalar_args":{},"class_bearing":true}]},{"output_sha":"8a816e5740c08530","size":1,"n_papers":1,"shape":[2,4,4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[-0.899799108505249,2.1496028900146484,0.8422775864601135,-0.42621153593063354,0.7065340280532837,1.0851669311523438,1.0880358219146729,-1.3298990726470947,1.02634596824646,0.2018856704235077,-0.5357305407524109,-1.5334049463272095,0.2970302999019623,0.24279484152793884,-1.1034553050994873,0.056650567799806595,-0.3140512704849243,1.4255168437957764,-0.000935375050175935,0.25722479820251465,-0.2188309282064438,0.6351821422576904,-1.6726268529891968,0.7456499934196472,0.8667719960212708,-0.12458641082048416,-1.8832179307937622,-1.4461218118667603,0.19105233252048492,1.111893653869629,0.18550564348697662,-0.18602940440177917,0.5187951922416687,0.1344667226076126,0.07971540838479996,-0.5434852838516235,0.5246065258979797,-0.8734601140022278,-1.5287816524505615,0.8661280274391174,-1.3036723136901855,-0.46057525277137756,0.4984748363494873,-0.996315598487854,1.210364580154419,1.7393977642059326,1.7060472965240479,0.7266635894775391,0.19550693035125732,-1.0849097967147827,0.3965564966201782,-0.14646536111831665,0.7750476598739624,-1.9183882474899292,0.39938995242118835,-0.07415559142827988,-1.5125007629394531,-1.4197801351547241,2.5456137657165527,0.7604507803916931,-0.6972368955612183,0.4013139605522156,1.4387836456298828,-1.176640272140503,-1.0219324827194214,0.7098553776741028,-0.42105138301849365,0.21329396963119507,-0.20321615040302277,1.5395008325576782,0.8496794104576111,-0.6634721755981445,1.5812678337097168,-0.15385636687278748,0.7259580492973328,-1.5520395040512085,-0.11864639818668365,-1.0929545164108276,-1.9601191282272339,-0.30009135603904724,0.3116442859172821,0.33869460225105286,0.2552895247936249,1.162429690361023,-0.33014634251594543,1.2800153493881226,-0.8746234774589539,-1.035535454750061,0.9826878309249878,-0.08435410261154175,-3.2239720821380615,0.9131590127944946,0.8871453404426575,-0.037736523896455765,0.08351358026266098,-0.20060701668262482,0.11005089432001114,0.2216758280992508,0.0847383588552475,-0.017009973526000977,-0.19788691401481628,-0.8518950939178467,-1.8878347873687744,-1.3725110292434692,-0.03507453575730324,-1.1399141550064087,-0.8371747732162476,1.4733142852783203,-0.34006044268608093,0.8303623199462891,2.1651713848114014,-0.5043212175369263,0.1977800577878952,0.4977215826511383,1.1469082832336426,0.5586000084877014,0.03866635262966156,-0.7361454367637634,0.6028715968132019,-0.31859609484672546,0.4814559519290924,0.7217886447906494,1.7341417074203491,-1.3659230470657349,-0.8058921694755554,-0.8373448848724365,-0.8861727714538574,0.08755449205636978],"values_recorded":128,"members":[{"code_sha256_prefix":"0c07f75568d64e8b","path":"models/base.py","papers":["1603.09382"],"paper_pages":[{"arxiv_id":"1603.09382","page":"/paper/deep-networks-with-stochastic-depth"}],"arg_sig_recorded":[["x",4,"float","float32"]],"scalar_args":{},"class_bearing":true}]},{"output_sha":"8ed7274a387bb47d","size":1,"n_papers":1,"shape":[2,4,4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.7231047749519348,0.3956933617591858,0.25182628631591797,1.0985400676727295,0.6441073417663574,-0.011490721255540848,0.7750469446182251,2.012162446975708,-0.0038538663648068905,0.36892426013946533,-0.009344345889985561,-0.0009828726761043072,1.5696096420288086,1.1559429168701172,-0.011057551018893719,0.5049213171005249,-0.0033638488966971636,-0.00014569579798262566,2.2904648780822754,-0.009931680746376514,-0.004790186882019043,1.2865773439407349,1.5438807010650635,1.2548015117645264,-0.015382333658635616,0.2949342727661133,0.4523601531982422,-0.0036497674882411957,-0.0038908012211322784,1.2640513181686401,-0.007698042318224907,-0.012319543398916721,-0.01384677179157734,2.0107004642486572,-0.012881453149020672,-0.004854680970311165,-0.00523042306303978,4.353240013122559,-0.00849358644336462,0.12927985191345215,-0.017633676528930664,-0.011998859234154224,-0.0038175182417035103,1.4476752281188965,-0.007020825985819101,-0.009705079719424248,-0.013456855900585651,-0.005602426361292601,0.0,-0.004598360043019056,-0.004477760288864374,0.33689582347869873,-0.003367329714819789,0.0,-0.02077099308371544,-0.003411279758438468,2.5002832412719727,1.1494437456130981,-0.016294540837407112,-0.005040982738137245,1.2787436246871948,-0.006127132102847099,-0.010718511417508125,0.0,0.7068794369697571,-0.009072658605873585,1.206670880317688,0.3008113205432892,1.8329517841339111,-0.018280036747455597,-0.048164643347263336,-0.0015220210188999772,-0.03919775038957596,-0.03183527663350105,-0.005850896239280701,0.011103019118309021,-0.01720207929611206,0.447498619556427,0.16413992643356323,1.4083771705627441,0.9652706980705261,-0.01236075721681118,1.399477481842041,3.6681056022644043,0.5487720966339111,-0.006829241290688515,3.6060147285461426,0.29646188020706177,-0.01015994418412447,-0.001229216461069882,2.6254613399505615,-0.005863717757165432,1.0927388668060303,-0.0056374226696789265,-0.00039222894702106714,-0.009098213165998459,-0.023990461602807045,0.41810673475265503,0.24949154257774353,0.4481961131095886,1.0027475357055664,1.2303640842437744,-0.030863426625728607,3.0365023612976074,0.16323891282081604,0.4391850531101227,1.7800346612930298,-0.011686570011079311,-0.0042721787467598915,-0.010534318163990974,-0.02898978628218174,-0.0067772637121379375,-0.005264167208224535,-0.010393590666353703,-0.004006346222013235,-0.01279410906136036,0.3921726644039154,-0.00971924141049385,-0.0013393971603363752,0.1438302844762802,1.6549242734909058,-0.005543294828385115,1.3498618602752686,-0.002796994987875223,0.9860679507255554,1.6686458587646484,0.7427091598510742,1.0903090238571167],"values_recorded":128,"members":[{"code_sha256_prefix":"df935000eb629eca","path":"models.py","papers":["2510.18322"],"paper_pages":[{"arxiv_id":"2510.18322","page":"/paper/arxiv-2510-18322"}],"arg_sig_recorded":[["x",4,"float","float32"]],"scalar_args":{},"class_bearing":true}]},{"output_sha":"ccb953167be961ed","size":1,"n_papers":1,"shape":[2,4,2,2],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.0,2.280085802078247,1.3559497594833374,0.0,0.6808109879493713,0.0,0.31568586826324463,0.3379939794540405,0.5546100735664368,0.8310691118240356,0.046358373016119,0.0,0.9775123596191406,0.0,0.0,0.6081761121749878,0.6352028846740723,0.0,0.0,0.0,0.719954252243042,0.0,1.0986210107803345,0.0,1.201080083847046,0.9268506765365601,0.0,0.0,1.6587638854980469,2.568286418914795,0.0,0.0],"values_recorded":32,"members":[{"code_sha256_prefix":"5237399a3d2dc666","path":"model_RW.py","papers":["2102.09896"],"paper_pages":[{"arxiv_id":"2102.09896","page":"/paper/scribble-supervised-semantic-segmentation-by-1"}],"arg_sig_recorded":[["x",4,"float","float32"]],"scalar_args":{},"class_bearing":true}]}]}]}