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Issue 028: Implement MAE pretraining engine with checkpointing
Goal
Implement the Stage 1 strict MAE pretraining loop.
Context
The repository currently has dry-run entrypoints. After this issue, proc/train_mae.py ...
Issue 029: Integrate real MAE training into proc/train_mae.py
Goal
Replace the current dry-run-only proc/train_mae.py behavior with a thin entrypoint that can either validate config or
launch real MAE ...
Issue 027: Implement torch collate and DataLoader helpers for MAE pretraining
Goal
Bridge the NumPy dataset outputs to PyTorch training batches, including variable input attribute counts.
Context
NopimsAttributePretrainDataset ...
Issue 026: Implement MAE reconstruction and gradient losses
Goal
Implement loss functions needed for Stage 1 strict MAE pretraining.
Context
The model predicts patch-level reconstructions:
pred_patches: ...
Issue 025: Implement StrictAttributeSetMAE3D model
Goal
Implement the core strict 3D attribute-set MAE model for MVP pretraining.
Context
The model should use:
- variable input attribute subset ...
Issue 024: Implement transformer blocks and 3D positional encodings
Goal
Add reusable transformer blocks and 3D positional encoding utilities for the strict MAE encoder and decoder.
Context
The MVP ...
Issue 023: Implement context token pooling for local+context MAE
Goal
Implement a lightweight context token pooler that turns low-resolution context crop tokens into a small set of global
context tokens. ...
Issue 021: Implement 3D patchify / unpatchify utilities
Goal
Implement reusable 3D patch utilities for strict MAE targets and decoder outputs.
The utilities must use grid order [x, y, z] consistently. ...
Issue 022: Implement AttributePatchTokenizer3D
Goal
Implement the MVP attribute-set tokenizer that converts a variable number of input attribute volumes into fixed spatial
tokens for strict 3D MAE.
...
The class name of SVIF is incorrect. It should be Interface.
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