| """Build the video-depth PointDiT-B through the repository's builder and load its EMA2 weights (CPU, no Civo file). |
| |
| python load_point.py <this folder> |
| |
| Run from a clone of github.com/NIRVANALAN/vgg_forecast_4d with PYTHONPATH=<clone>/src. Nothing outside this folder is |
| read: the released PointDiT-B 512 checkpoint that the training config names is NOT read (`model.point.weights: random` |
| builds the same architecture, and every one of the 216 tensors then comes from the EMA2 file; the model after the load is |
| bit-identical to one built over the released checkpoint). The frozen DINOv3 encoder is not needed to load. |
| """ |
| import sys |
| from pathlib import Path |
|
|
| from vggforecast.evaluation.checkpoint_loading import _load_weights, resolve_run_config |
| from vggforecast.training.model_loading import build_point_model |
|
|
| FOLDER = Path(sys.argv[1]) |
| RUN, CKPT = FOLDER / "train", FOLDER / "train" / "ckpt_32500" |
|
|
| cfg = resolve_run_config(RUN, CKPT)["cfg"] |
| cfg.model.point.weights = "random" |
| cfg.model.point.attention_backend = "sdpa" |
| model, core, provenance = build_point_model(cfg) |
| base_hw = tuple(int(v) * core.trunk.patch for v in core.trunk.position_source_grid) |
| loaded = _load_weights(model, core, CKPT, "ema2", base_hw) |
| model.eval() |
| print(model.architecture, core.target_centering, loaded["completed_updates"], loaded["ema_decay"], |
| sum(p.numel() for p in model.parameters())) |
| |
|
|
