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import os
import sys
import json
import csv
_HERE = os.path.dirname(__file__)
_ROOT = os.path.dirname(_HERE)
if _ROOT not in sys.path:
sys.path.insert(0, _ROOT)
import argparse
import time
from typing import Optional
import torch
import torch.nn as nn
import torch.optim as optim
from files.data_io.dataset_loader import get_dataloader, SHDDataset
from files.models.snn import SimpleSNN
from tqdm.auto import tqdm
def _prepare_run_dir(base_dir: str):
ts = time.strftime("%Y%m%d-%H%M%S")
run_dir = os.path.join(base_dir, ts)
os.makedirs(run_dir, exist_ok=True)
return run_dir
def _append_metrics(csv_path: str, row: dict):
write_header = not os.path.exists(csv_path)
with open(csv_path, "a", newline="") as f:
writer = csv.DictWriter(f, fieldnames=row.keys())
if write_header:
writer.writeheader()
writer.writerow(row)
def parse_args():
p = argparse.ArgumentParser(description="MVP training: baseline vs Lyapunov-regularized")
p.add_argument("--cfg", type=str, default="data_io/configs/shd.yaml", help="YAML config for dataloader")
p.add_argument("--epochs", type=int, default=2)
p.add_argument("--hidden", type=int, default=256)
p.add_argument("--classes", type=int, default=20, help="Number of classes")
p.add_argument("--lr", type=float, default=1e-3)
p.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
p.add_argument("--lyapunov", action="store_true", help="Enable Lyapunov regulation")
p.add_argument("--lambda_reg", type=float, default=0.1, help="Weight for Lyapunov penalty")
p.add_argument("--lambda_target", type=float, default=0.0, help="Target average log growth (≈0 for neutral)")
p.add_argument("--no-progress", action="store_true", help="Disable tqdm progress bar")
p.add_argument("--out_dir", type=str, default="runs/mvp", help="Directory to save metrics/checkpoints")
p.add_argument("--log_batches", action="store_true", help="Also log per-batch metrics to CSV")
# Model dynamics and recurrence controls
p.add_argument("--spike_alpha", type=float, default=5.0, help="Surrogate spike sharpness")
p.add_argument("--decay", type=float, default=0.95, help="Membrane decay")
p.add_argument("--v_threshold", type=float, default=1.0, help="Firing threshold")
p.add_argument("--rec_strength", type=float, default=0.0, help="Recurrent coupling strength on spikes")
p.add_argument("--rec_init_scale", type=float, default=1.0, help="Gain for recurrent weight init")
# Lyapunov measurement controls
p.add_argument("--lyap_measure", type=str, default="v", choices=["v", "s"], help="Measure divergence on 'v' or 's'")
p.add_argument("--lyap_eps", type=float, default=1e-3, help="Initial perturbation magnitude")
return p.parse_args()
def train_one_epoch(
model: SimpleSNN,
loader,
optimizer,
device,
ce_loss: nn.Module,
lyapunov: bool,
lambda_reg: float,
lambda_target: float,
progress: bool,
run_dir: str | None = None,
epoch_idx: int | None = None,
log_batches: bool = False,
lyap_measure: str = "v",
lyap_eps: float = 1e-3,
):
model.train()
total = 0
correct = 0
running_loss = 0.0
lyap_vals = []
iterator = tqdm(loader, desc="train", leave=False, dynamic_ncols=True) if progress else loader
for bidx, (x, y) in enumerate(iterator):
x = x.to(device) # (B, T, D)
y = y.to(device)
optimizer.zero_grad(set_to_none=True)
logits, lyap_est = model(
x,
compute_lyapunov=lyapunov,
lyap_eps=lyap_eps,
lyap_measure=lyap_measure,
)
ce = ce_loss(logits, y)
if lyapunov and lyap_est is not None:
reg = (lyap_est - lambda_target) ** 2
loss = ce + lambda_reg * reg
lyap_vals.append(lyap_est.detach().item())
else:
loss = ce
loss.backward()
optimizer.step()
running_loss += loss.item() * x.size(0)
preds = logits.argmax(dim=1)
batch_correct = (preds == y).sum().item()
correct += batch_correct
total += x.size(0)
if log_batches and run_dir is not None and epoch_idx is not None:
_append_metrics(
os.path.join(run_dir, "metrics.csv"),
{
"step": "batch",
"epoch": int(epoch_idx),
"batch": int(bidx),
"loss": float(loss.item()),
"acc": float(batch_correct / max(x.size(0), 1)),
"lyap": float(lyap_est.item()) if (lyapunov and lyap_est is not None) else float("nan"),
"time_sec": float(0.0),
},
)
if progress:
avg_loss = running_loss / max(total, 1)
avg_lyap = (sum(lyap_vals) / len(lyap_vals)) if lyap_vals else None
postfix = {"loss": f"{avg_loss:.4f}"}
if avg_lyap is not None:
postfix["lyap"] = f"{avg_lyap:.4f}"
iterator.set_postfix(postfix)
avg_loss = running_loss / max(total, 1)
acc = correct / max(total, 1)
avg_lyap = sum(lyap_vals) / len(lyap_vals) if lyap_vals else None
return avg_loss, acc, avg_lyap
def main():
args = parse_args()
device = torch.device(args.device)
# Prepare output directory and save run config
run_dir = _prepare_run_dir(args.out_dir)
with open(os.path.join(run_dir, "args.json"), "w") as f:
json.dump(vars(args), f, indent=2)
train_loader, val_loader = get_dataloader(args.cfg)
# Infer input dim and classes from a sample and args
xb, yb = next(iter(train_loader))
_, T, D = xb.shape
C = args.classes
model = SimpleSNN(
input_dim=D,
hidden_dim=args.hidden,
num_classes=C,
v_threshold=args.v_threshold,
decay=args.decay,
spike_alpha=args.spike_alpha,
rec_strength=args.rec_strength,
rec_init_scale=args.rec_init_scale,
).to(device)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
ce_loss = nn.CrossEntropyLoss()
print(f"Starting training on {device} | lyapunov={args.lyapunov} lambda={args.lambda_reg} target={args.lambda_target}")
print(f"Saving run to: {run_dir}")
for epoch in range(1, args.epochs + 1):
t0 = time.time()
tr_loss, tr_acc, tr_lyap = train_one_epoch(
model, train_loader, optimizer, device, ce_loss,
lyapunov=args.lyapunov, lambda_reg=args.lambda_reg, lambda_target=args.lambda_target,
progress=(not args.no_progress),
run_dir=run_dir,
epoch_idx=epoch,
log_batches=args.log_batches,
lyap_measure=args.lyap_measure,
lyap_eps=args.lyap_eps,
)
dt = time.time() - t0
lyap_str = f" lyap={tr_lyap:.4f}" if tr_lyap is not None else ""
print(f"[Epoch {epoch}] loss={tr_loss:.4f} acc={tr_acc:.3f}{lyap_str} ({dt:.1f}s)")
_append_metrics(
os.path.join(run_dir, "metrics.csv"),
{
"step": "epoch",
"epoch": int(epoch),
"batch": int(-1),
"loss": float(tr_loss),
"acc": float(tr_acc),
"lyap": float(tr_lyap) if tr_lyap is not None else float("nan"),
"time_sec": float(dt),
},
)
if __name__ == "__main__":
main()
|
