| --- |
| license: other |
| tags: [phenology, ecology, species-distribution] |
| --- |
| |
| # PhenoField e98 (`e98_large_specdrop_dyn4l`) |
| |
| Frozen phenology field encoder (CrossModalVAE, AdaLN-DiT dynamic branch, species dropout). |
| Image-free interface: `forward_field(species_id, prism_window, alphaearth_emb)` → |
| `(z_static [576], z_dynamic [192])`. The dynamic encoder consumes only the 365×7 trailing PRISM |
| window and the 64-d AlphaEarth embedding (no doy / lat-lon / climplicit weights exist in this |
| checkpoint) — season enters through the PRISM window alone. |
| |
| ## Files |
| |
| | file | what | |
| |---|---| |
| | `last.ckpt` | Lightning checkpoint (weights + full training config in `hyper_parameters`) | |
| | `hparams.yaml` | same config, human-readable | |
| | `models/*.py` | the exact model code (`CrossModalVAE`, `AdaLNDynamicEncoder`) — self-contained, relative imports only | |
| | `species_embeddings_v2.pt` | BioCLIP text matrix [6825, 768]; the constructor loads it via `config['species_embedding_path']` | |
| | `species_vocab.json` | species name → id (indexes rows of the embedding matrix) | |
| | `inputs/prism_weekly.npz` | climatological per-(cell, week) 365×7 PRISM windows for the CONUS 0.5° grid | |
| | `inputs/alphaearth_2017.parquet` | per-point AlphaEarth 64-d embeddings (nearest-cell lookup) | |
| | `inputs/grid_centroids_0.5deg.csv`, `inputs/plant_flowering_events.parquet` | grid definition + plant flowering observations | |
|
|
| ## Loading |
|
|
| ```python |
| import sys, torch |
| from huggingface_hub import snapshot_download |
| from omegaconf import OmegaConf |
| |
| d = snapshot_download('dcher95/phenofield-e98') |
| sys.path.insert(0, d) |
| from models.cross_modal_vae import CrossModalVAE |
| |
| ckpt = torch.load(f'{d}/last.ckpt', map_location='cpu', weights_only=False) |
| cfg = OmegaConf.to_container(OmegaConf.create(ckpt['hyper_parameters']).model, resolve=True) |
| cfg['species_embedding_path'] = f'{d}/species_embeddings_v2.pt' |
| model = CrossModalVAE(cfg) |
| state = {k[len('model.'):]: v for k, v in ckpt['state_dict'].items() if k.startswith('model.')} |
| missing, unexpected = model.load_state_dict(state, strict=False) |
| assert not missing and not unexpected # this models/ copy loads clean |
| model.eval() |
| ``` |
|
|
| Do **not** load this checkpoint with the `models/` in dcher95/PhenoField's top level — that class |
| silently drops `dynamic_encoder.null_species_emb` (the parameter species dropout trains). |
| Use the `models/` shipped here (a copy of `vendor/bwei_ppe`). |
|
|
| Downstream pipeline: `pipelines/` in the ANTHEIA repo (Summer-2026-CfE project). |
|
|
