File size: 5,788 Bytes
eac1b4e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 | from __future__ import annotations
import argparse
import csv
import time
from pathlib import Path
import torch
from common import (
ROOT,
build_model,
count_parameters,
load_config,
make_normalized_loaders,
relative_l2_per_sample,
relative_l2_sum,
resolve,
save_json,
select_device,
set_seed,
)
def evaluate(model, loader, y_normalizer, device):
model.eval()
total = 0.0
count = 0
with torch.no_grad():
for x, y_norm in loader:
x = x.to(device, non_blocking=True)
y_norm = y_norm.to(device, non_blocking=True)
pred = y_normalizer.decode(model(x))
target = y_normalizer.decode(y_norm)
rel = relative_l2_per_sample(pred, target)
total += float(rel.sum().item())
count += int(rel.numel())
return total / max(1, count)
def main() -> None:
parser = argparse.ArgumentParser(description="Train HO-FNO on Pipe horizontal velocity.")
parser.add_argument("--config", default=str(ROOT / "config" / "config.yaml"))
args = parser.parse_args()
cfg = load_config(args.config)
set_seed(int(cfg["experiment"].get("seed", 2026)))
output_dir = resolve(cfg["paths"]["output_dir"])
weight_path = resolve(cfg["paths"]["checkpoint"], output_dir)
results_dir = resolve(cfg["paths"]["results_dir"], output_dir)
weight_path.parent.mkdir(parents=True, exist_ok=True)
results_dir.mkdir(parents=True, exist_ok=True)
require_accelerator = bool(cfg["runtime"].get("require_accelerator", False))
device = select_device(require_accelerator=require_accelerator)
print(f"device={device}", flush=True)
train_loader, test_loader, x_normalizer, y_normalizer = make_normalized_loaders(cfg)
x_normalizer.to(device)
y_normalizer.to(device)
model = build_model(cfg).to(device)
print(f"parameters={count_parameters(model)}", flush=True)
train_cfg = cfg["training"]
optimizer = torch.optim.AdamW(
model.parameters(),
lr=float(train_cfg["learning_rate"]),
weight_decay=float(train_cfg.get("weight_decay", 0.0)),
)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer,
T_max=int(train_cfg["epochs"]),
eta_min=float(train_cfg["final_learning_rate"]),
)
history_path = results_dir / "train_history.csv"
best_metric = float("inf")
best_epoch = -1
train_size = int(cfg["data"]["train_size"])
epochs = int(train_cfg["epochs"])
start_time = time.time()
with history_path.open("w", newline="", encoding="utf-8") as handle:
writer = csv.DictWriter(handle, fieldnames=["epoch", "train_loss", "test_mean_relative_l2", "lr", "seconds"])
writer.writeheader()
for epoch in range(1, epochs + 1):
model.train()
epoch_start = time.time()
train_loss_sum = 0.0
for x, y_norm in train_loader:
x = x.to(device, non_blocking=True)
y_norm = y_norm.to(device, non_blocking=True)
optimizer.zero_grad(set_to_none=True)
pred = y_normalizer.decode(model(x))
target = y_normalizer.decode(y_norm)
loss = relative_l2_sum(pred, target)
loss.backward()
optimizer.step()
train_loss_sum += float(loss.item())
scheduler.step()
train_loss = train_loss_sum / train_size
test_l2 = evaluate(model, test_loader, y_normalizer, device)
lr = float(optimizer.param_groups[0]["lr"])
seconds = time.time() - epoch_start
writer.writerow(
{
"epoch": epoch,
"train_loss": train_loss,
"test_mean_relative_l2": test_l2,
"lr": lr,
"seconds": seconds,
}
)
handle.flush()
print(
f"epoch={epoch:04d} train_loss={train_loss:.8f} "
f"test_mean_relative_l2={test_l2:.8f} lr={lr:.8e}",
flush=True,
)
if test_l2 < best_metric:
best_metric = test_l2
best_epoch = epoch
torch.save(
{
"model_state": model.state_dict(),
"config": cfg,
"epoch": epoch,
"best_metric": best_metric,
"x_normalizer": x_normalizer.state_dict(),
"y_normalizer": y_normalizer.state_dict(),
},
weight_path,
)
save_json(
results_dir / "best_metrics.json",
{"best_epoch": best_epoch, "test_mean_relative_l2": best_metric, "checkpoint": str(weight_path)},
)
torch.save(
{
"model_state": model.state_dict(),
"config": cfg,
"epoch": epochs,
"best_metric": best_metric,
"x_normalizer": x_normalizer.state_dict(),
"y_normalizer": y_normalizer.state_dict(),
},
weight_path.parent / "last_model.pth",
)
save_json(
results_dir / "training_summary.json",
{
"epochs": epochs,
"best_epoch": best_epoch,
"best_test_mean_relative_l2": best_metric,
"total_seconds": time.time() - start_time,
"checkpoint": str(weight_path),
},
)
print(f"best_checkpoint={weight_path}", flush=True)
print(f"best_epoch={best_epoch} best_test_mean_relative_l2={best_metric:.8f}", flush=True)
if __name__ == "__main__":
main()
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