Conv2d
Conv2d: 7 implementations from 7 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 6 distinct outputs.
Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence. Implementations are compared only within one bucket, the 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; each bucket's shared input is fixed by that key, so members of one bucket saw bitwise-identical inputs under their own scalar arguments. A cluster is the set of members whose recorded output digest is identical. Nothing here says which computation a paper's method intended, and nothing reproduces a paper's results.
Not compared, and not in the tables or the counts above:
- Every examined implementation of this name that took an array argument ran on the shared input.
- No implementation of this name ran on its own fixture arguments only: every one that ran took an array argument.
- Every compared output was digested.
- 7 compared members are class-bearing (tagged class): the class was constructed with seeded weights in evaluation mode and then called, so its recorded output depends on that initialisation as well as on the forward computation.
Bucket 1 of 1: arg 1: rank 4, kind float, dtype float32
7 implementations from 7 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 6 distinct outputs, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 2 implementations 2 papers 669bfe71a452 recorded values identical |
class visualDet3D/networks/detectors/MonoWAD.py 9f54fd54 recorded x:4/float/float32 class src/model.py cfc4ea99 recorded x:4/float/float32 |
[1.95945, 0, 0, 0.0236183, 0.776935, 0, 0, 0.864413, …] shape [2, 4, 4, 4] · float32 · Tensor |
| 1 implementation 1 paper 34a4619bc419 |
class models/crapcn.py 111d7d0d recorded input:4/float/float32 |
[0, 0.391968, 0.623181, 0, 0, 0, 1.75938, 0, …] shape [2, 4, 2, 2] · float32 · Tensor |
| 1 implementation 1 paper 7680a2ee1f94 |
class aff_block_LL.py 95bbde41 recorded input:4/float/float32 |
[0.778376, -0.129074, -0.191526, -0.0636253, 0.264035, -0.876927, -0.279699, 0.302084, …] shape [2, 4, 4, 4] · float32 · Tensor |
| 1 implementation 1 paper 9fed0d8fcb98 |
class dispu/generator.py b0d00cfb recorded x:4/float/float32 |
[1.95434, 0, 0, 0.0235568, 0.77491, 0, 0, 0.86216, …] shape [2, 16, 4, 4] · float32 · Tensor |
| 1 implementation 1 paper b0c5f595fade |
class lib/pruners.py d526bce0 recorded input:4/float/float32 |
[0.767772, -0.139678, -0.20213, -0.0742292, 0.253431, -0.887531, -0.290302, 0.29148, …] shape [2, 4, 4, 4] · float32 · Tensor |
| 1 implementation 1 paper d9af1ca8a39e |
models/MaskPoint.py 64ccb021 recorded input:4/float/float32 |
[0, 0.404552, 0.754689, 0, 0, 1.01219, 0.881081, 0, …] shape [2, 4, 4, 4] · float32 · Tensor |
Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence; where a cluster's members carry recorded values, the largest difference among them is shown under the cluster. Paper titles are the archive's archive 2025-07-28 where the paper is in the archive and the graph's where it was added by Syntology; papers with no page here are shown by their recorded paper id only. A paper count above the implementation count means one implementation (one code sha) is held from several papers' repositories and counts once. Per-sample status, licence and fingerprint records for each paper are on its paper page. JSON twin: /census/conv2d.json.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections