{"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/to-torch","entry":"to_torch","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":5,"n_papers":6,"n_buckets":2,"n_distinct_outputs":2,"n_class_bearing":0,"not_compared":{"not_run_on_shared_input":{"n":0,"by_error":{}},"no_array_argument_ran_on_own_fixture_arguments_only":{"n":1,"n_papers":1,"recorded_shared_digest_equals_own_fixture_digest":1},"output_not_digested_non_numeric":{"n":0,"by_type":{}}},"withdrawn_excluded":0,"code_page":"/code/to-torch","buckets":[{"bucket":[[2,"float","float64"]],"n_implementations_compared":3,"n_papers":4,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"d7bd5ceec8a69dcd","size":3,"n_papers":4,"shape":[2,16],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":1.1504320029942505e-07,"members_with_recorded_values":3,"torch_reference_conventions_with_this_digest":[],"values":[1.8026286363601685,-1.5337762832641602,0.47605323791503906,-0.6908870339393616,-0.9028494954109192,-1.0304712057113647,-0.6627609133720398,0.5728892087936401,0.5188759565353394,0.9591456651687622,-0.3971995711326599,-1.5683481693267822,-0.28853508830070496,2.322108745574951,-0.5147839784622192,0.5505285859107971,0.06639543920755386,-1.6228755712509155,0.4321114122867584,-0.4061858654022217,-0.02259422466158867,-1.180782437324524,-0.5368682742118835,0.983674168586731,-0.7956128120422363,0.43327441811561584,0.9449777603149414,1.0175182819366455,-1.225785732269287,1.274217963218689,-1.3222218751907349,-0.5941091179847717],"values_recorded":32,"members":[{"code_sha256_prefix":"7aac4c51e0ceee97","path":"gcc/trainer_v2.py","papers":["2308.09951","2105.04776"],"paper_pages":[{"arxiv_id":"2308.09951","page":"/paper/semantics-meets-temporal-correspondence-self"},{"arxiv_id":"2105.04776","page":"/paper/graph-consistency-based-mean-teaching-for"}],"arg_sig_recorded":[["ndarray",2,"float","float64"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"45e3807991936355","path":"SceneGraphNet/model.py","papers":["1907.11308"],"paper_pages":[{"arxiv_id":"1907.11308","page":"/paper/scenegraphnet-neural-message-passing-for-3d"}],"arg_sig_recorded":[["n",2,"float","float64"]],"scalar_args":{"torch_type":"<class 'torch.FloatTensor'>","requires_grad":"False","dim_0":"2"},"class_bearing":false},{"code_sha256_prefix":"8a5e4e14c09b2ed2","path":"pip/src/demovae/model.py","papers":["2405.07977"],"paper_pages":[{"arxiv_id":"2405.07977","page":"/paper/a-demographic-conditioned-variational"}],"arg_sig_recorded":[["x",2,"float","float64"]],"scalar_args":{},"class_bearing":false}]}]},{"bucket":[[2,"float","float32"]],"n_implementations_compared":2,"n_papers":2,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"d7bd5ceec8a69dcd","size":2,"n_papers":2,"shape":[4,8],"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":[1.8026286363601685,-1.5337762832641602,0.47605323791503906,-0.6908870339393616,-0.9028494954109192,-1.0304712057113647,-0.6627609133720398,0.5728892087936401,0.5188759565353394,0.9591456651687622,-0.3971995711326599,-1.5683481693267822,-0.28853508830070496,2.322108745574951,-0.5147839784622192,0.5505285859107971,0.06639543920755386,-1.6228755712509155,0.4321114122867584,-0.4061858654022217,-0.02259422466158867,-1.180782437324524,-0.5368682742118835,0.983674168586731,-0.7956128120422363,0.43327441811561584,0.9449777603149414,1.0175182819366455,-1.225785732269287,1.274217963218689,-1.3222218751907349,-0.5941091179847717],"values_recorded":32,"members":[{"code_sha256_prefix":"22bc6af5706e2818","path":"src/models.py","papers":["2209.15597"],"paper_pages":[{"arxiv_id":"2209.15597","page":"/paper/meim-multi-partition-embedding-interaction"}],"arg_sig_recorded":[["a",2,"float","float32"]],"scalar_args":{"device":"'cpu'"},"class_bearing":false},{"code_sha256_prefix":"516e33dfd5c4f7df","path":"lib/tracker/lighttrack.py","papers":["2104.14545"],"paper_pages":[{"arxiv_id":"2104.14545","page":"/paper/lighttrack-finding-lightweight-neural"}],"arg_sig_recorded":[["ndarray",2,"float","float32"]],"scalar_args":{},"class_bearing":false}]}]}]}