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[fix] train on trial-aligned windows for correct R² evaluation #6

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peterajhgraham merged 1 commit into from
May 18, 2026
Merged

[fix] train on trial-aligned windows for correct R² evaluation #6

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43 changes: 17 additions & 26 deletions scripts/train_benchmark.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@
import torch
from torch.utils.data import DataLoader

from cortex.data.nlb import NLBDataset, collate_events
from cortex.data.nlb import build_trial_aligned_datasets, collate_events
from cortex.models import CortexModel
from cortex.models.config import CORTEX_S
from cortex.training.eval import evaluate, r2_score
Expand All @@ -36,29 +36,16 @@


def build_loaders(
data_root: str, batch_size: int, val_max_samples: int = 2048
nwb_path: str, batch_size: int
) -> tuple[DataLoader, DataLoader]:
kwargs = dict(
data_root=data_root,
dandiset_id="000128",
train_ds, val_ds = build_trial_aligned_datasets(
nwb_path=Path(nwb_path),
bin_size_ms=5,
window_ms=600,
stride_ms=50,
pre_onset_ms=100,
post_onset_ms=500,
max_neurons=CORTEX_S.max_neurons,
spike_value_buckets=CORTEX_S.spike_value_buckets,
download=False,
)
train_ds = NLBDataset(split="train", **kwargs) # type: ignore[arg-type]
val_ds = NLBDataset(split="val", **kwargs) # type: ignore[arg-type]

# Cap val at val_max_samples to keep eval fast during training
if len(val_ds) > val_max_samples:
from torch.utils.data import Subset
import random
rng = random.Random(42)
val_idx = rng.sample(range(len(val_ds)), val_max_samples)
val_ds = Subset(val_ds, val_idx) # type: ignore[assignment]

loader_kw = dict(batch_size=batch_size, num_workers=0, collate_fn=collate_events)
return (
DataLoader(train_ds, shuffle=True, **loader_kw),
Expand All @@ -73,18 +60,22 @@ def main() -> None:
parser.add_argument("--batch-size", type=int, default=32)
parser.add_argument("--lr", type=float, default=3e-4)
parser.add_argument("--warmup-steps", type=int, default=200)
parser.add_argument("--data-root", default="./data")
parser.add_argument(
"--nwb-path",
default="./data/000128/sub-Jenkins/sub-Jenkins_ses-full_desc-train_behavior+ecephys.nwb",
help="Path to the MC_Maze NWB file (train+behavior variant).",
)
parser.add_argument("--out", default="benchmarks/training/results_raw.json")
args = parser.parse_args()

device = select_device(preference=args.device)
log.info("benchmark_start", device=str(device), max_steps=args.max_steps, model_params=f"{sum(p.numel() for p in CortexModel(CORTEX_S).parameters())/1e6:.1f}M")

log.info("loading_data")
log.info("loading_data", nwb_path=args.nwb_path)
t0 = time.time()
train_loader, val_loader = build_loaders(args.data_root, args.batch_size)
log.info("data_loaded", train_windows=len(train_loader.dataset), # type: ignore[arg-type]
val_windows=len(val_loader.dataset), elapsed_s=f"{time.time()-t0:.1f}") # type: ignore[arg-type]
train_loader, val_loader = build_loaders(args.nwb_path, args.batch_size)
log.info("data_loaded", train_trials=len(train_loader.dataset), # type: ignore[arg-type]
val_trials=len(val_loader.dataset), elapsed_s=f"{time.time()-t0:.1f}") # type: ignore[arg-type]

model = CortexModel(CORTEX_S).to(device)
optimizer = build_optimizer(model, _cfg(args.lr))
Expand Down Expand Up @@ -159,8 +150,8 @@ def main() -> None:
"batch_size": args.batch_size,
"final_r2_velocity": eval_log[-1]["r2_velocity"] if eval_log else None,
"final_mse_velocity": eval_log[-1]["mse_velocity"] if eval_log else None,
"train_windows": len(train_loader.dataset), # type: ignore[arg-type]
"val_windows": len(val_loader.dataset), # type: ignore[arg-type]
"train_trials": len(train_loader.dataset), # type: ignore[arg-type]
"val_trials": len(val_loader.dataset), # type: ignore[arg-type]
"total_elapsed_s": round(total_elapsed, 1),
"avg_step_ms": round(avg_step_ms, 1),
"p99_step_ms": round(p99_step_ms, 1),
Expand Down
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