| r"""train.py — SSL masked-reconstruction pretrain, then supervised Student-t
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| fine-tune on TRAIN-fold labeled cells (spatial split). Run on GPU VM.
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| Usage: python data\models\train.py --tensors data\tensors --epochs 30
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| """
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| from __future__ import annotations
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| import os, sys, argparse, pathlib
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| import numpy as np, torch
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| sys.path.insert(0, str(pathlib.Path(__file__).resolve().parent))
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| from model import AtmosIQNet, student_t_nll, ssl_masked_mse
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| from dataset import AtmosIQData, ssl_corrupt
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|
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| def run():
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| ap = argparse.ArgumentParser()
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| ap.add_argument("--tensors", default="data/tensors")
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| ap.add_argument("--window", type=int, default=7)
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| ap.add_argument("--ssl_epochs", type=int, default=10)
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| ap.add_argument("--epochs", type=int, default=30)
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| ap.add_argument("--lr", type=float, default=1e-3)
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| a = ap.parse_args()
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| dev = "cuda" if torch.cuda.is_available() else "cpu"
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| print("device:", dev)
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| data = AtmosIQData(a.tensors, window=a.window, device=dev)
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| net = AtmosIQNet(data.F).to(dev)
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| ei, e_hat, n_hat = data.edges()
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| opt = torch.optim.AdamW(net.parameters(), lr=a.lr, weight_decay=1e-4)
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|
|
|
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| all_days = list(range(a.window-1, data.D))
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| for ep in range(a.ssl_epochs):
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| net.train(); np.random.shuffle(all_days); tot = 0
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| for t in all_days:
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| b = data.window(t); Xc, fm = ssl_corrupt(b["X"])
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| out = net(Xc, ei, e_hat, n_hat, b["wu"], b["wv"], b["blh"])
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| loss = ssl_masked_mse(out["recon"], b["X"][-1], fm)
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| opt.zero_grad(); loss.backward(); opt.step(); tot += loss.item()
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| print(f"[SSL {ep+1}/{a.ssl_epochs}] recon_mse={tot/len(all_days):.4f}")
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|
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| tr_days = data.target_days(fold=0); va_days = data.target_days(fold=1)
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| tr_nodes = (data.split == 0).float().unsqueeze(-1)
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| va_nodes = (data.split == 1).float().unsqueeze(-1)
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| best = 1e9
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| for ep in range(a.epochs):
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| net.train(); np.random.shuffle(tr_days); tot = 0
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| for t in tr_days:
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| b = data.window(t)
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| out = net(b["X"], ei, e_hat, n_hat, b["wu"], b["wv"], b["blh"])
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| nll = student_t_nll(out["loc"], out["log_scale"], out["log_df"], b["y"], b["m"]*tr_nodes)
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| opt.zero_grad(); nll.backward(); opt.step(); tot += nll.item()
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|
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| net.eval(); vtot = 0
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| with torch.no_grad():
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| for t in va_days:
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| b = data.window(t)
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| out = net(b["X"], ei, e_hat, n_hat, b["wu"], b["wv"], b["blh"])
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| vtot += student_t_nll(out["loc"], out["log_scale"], out["log_df"], b["y"], b["m"]*va_nodes).item()
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| vmean = vtot/max(len(va_days),1)
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| print(f"[FT {ep+1}/{a.epochs}] train_nll={tot/len(tr_days):.3f} val_nll={vmean:.3f}")
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| if vmean < best:
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| best = vmean; torch.save(net.state_dict(), pathlib.Path(a.tensors).parent/"atmosiq_best.pt")
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| print(f"done. best val_nll={best:.3f} -> atmosiq_best.pt")
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|
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| if __name__ == "__main__": run() |
