d48864033e
102/102 encoder params load with 0 missing and 0 unmapped. A synthetic voxelised sphere shell (16,934 voxels) encodes to 56 latent voxels in 135ms on m3ultra, and the latent comes out mean +0.05 / std 0.92 - the approximately unit-normal distribution a KL-trained VAE should produce, which is decent evidence the graph and the sparse conv path are right. Architecture is inferred from tensor shapes, not constructor defaults: upstream defaults latent_dim to 8 but the released weights say 32, and attn_mode/pe_mode defaults are likewise overridden by the trained config. infer_config() reads it off the checkpoint. Also added SparseDownsample. Upstream's docstring says average pooling but the code passes reduce='amax' - following the code. Kernel orientation: tried latent statistics as a cheap discriminator and it does NOT work. The flip is not a no-op (max delta 3.53) but both orientations give a plausible near-unit-normal latent (std 0.919 vs 0.945). Recorded as a negative result; it needs the decoder and reconstruction quality to settle. |
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| .. | ||
| models | ||
| sparse | ||
| __init__.py | ||
| convert.py | ||