| """Quick checkpoint load and forward-pass sanity check for two-stage models. |
| |
| Example: |
| python experiments/test_model.py --checkpoint-dir output/two_stage_encoding |
| """ |
|
|
| import argparse |
| from pathlib import Path |
|
|
| import torch |
|
|
| ROOT = Path(__file__).resolve().parent.parent |
| import sys |
|
|
| sys.path.append(str(ROOT)) |
|
|
| from src.flowfm.checkpointing import load_state_dict, resolve_stage2_checkpoint |
| from src.flowfm.config import load_config, resolve_subjects |
| from src.flowfm.data_pipeline import make_data_loaders |
| from src.flowfm.model_factory import ( |
| build_stage1_model, |
| build_stage2_models, |
| infer_feature_dims, |
| infer_target_dim, |
| ) |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser(description="Sanity-check two-stage checkpoint loading") |
| parser.add_argument( |
| "--checkpoint-dir", |
| type=str, |
| default=str(ROOT / "output" / "two_stage_encoding"), |
| help="Directory containing config.yaml and stage checkpoints", |
| ) |
| parser.add_argument("--cfg-path", type=str, default=None) |
| parser.add_argument("--stage2-ckpt", type=str, default=None) |
| parser.add_argument("--device", type=str, default=None) |
| parser.add_argument("--n-timesteps", type=int, default=25) |
| args = parser.parse_args() |
|
|
| checkpoint_dir = Path(args.checkpoint_dir) |
| cfg_path = args.cfg_path or str(checkpoint_dir / "config.yaml") |
|
|
| cfg = load_config(cfg_path) |
| if args.device is not None: |
| cfg.device = args.device |
|
|
| device = torch.device(cfg.device) |
| subjects = resolve_subjects(cfg) |
|
|
| data_loaders = make_data_loaders(cfg) |
| sample_batch = next(iter(data_loaders["train"])) |
|
|
| feat_dims = infer_feature_dims(sample_batch) |
| target_dim = infer_target_dim(sample_batch) |
|
|
| stage1_model = build_stage1_model( |
| cfg=cfg, |
| feat_dims=feat_dims, |
| subjects=subjects, |
| device=device, |
| ) |
| stage1_path = checkpoint_dir / "stage1_best.pt" |
| stage1_model.load_state_dict(load_state_dict(stage1_path, device)) |
| stage1_model.eval() |
|
|
| stage2_models = build_stage2_models( |
| cfg=cfg, |
| target_dim=target_dim, |
| subjects=subjects, |
| device=device, |
| ) |
| stage2_path = resolve_stage2_checkpoint(checkpoint_dir, args.stage2_ckpt) |
| stage2_models.load_state_dict(load_state_dict(stage2_path, device)) |
| stage2_models.eval() |
|
|
| print(f"Loaded Stage 1 checkpoint: {stage1_path}") |
| print(f"Loaded Stage 2 checkpoint: {stage2_path}") |
|
|
| feats = [f.to(device) for f in sample_batch["features"]] |
|
|
| with torch.no_grad(): |
| mu_anchor = stage1_model(feats) |
| preds = [] |
| for i, sub in enumerate(subjects): |
| cfm = stage2_models[str(sub)] |
| mu = mu_anchor[:, i].transpose(1, 2) |
| pred = cfm(mu, n_timesteps=int(args.n_timesteps)).transpose(1, 2) |
| preds.append(pred.unsqueeze(1)) |
|
|
| pred_all = torch.cat(preds, dim=1) |
|
|
| print(f"Stage 1 output shape: {tuple(mu_anchor.shape)}") |
| print(f"Stage 2 output shape: {tuple(pred_all.shape)}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
