File size: 6,907 Bytes
fa2b79f | 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 | """Train, calibrate, and checkpoint WoFS elastic-net logistic models."""
import argparse
import json
import os
import sys
from pathlib import Path
import numpy as np
import torch
import yaml
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, Dataset, DistributedSampler, Subset
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from model.wofsstormcal import WoFSStormCal
class HazardDataset(Dataset):
def __init__(self, path, config):
self.data = np.load(path)
if str(self.data["format_version"]) != config["data"]["format_version"]:
raise ValueError("incompatible WoFS storm-object data format")
count = len(self.data["features"])
if self.data["features"].shape != (count, 113):
raise ValueError("features must have shape [N,113]")
if self.data["targets"].shape != (count, 3):
raise ValueError("targets must have shape [N,3]")
if self.data["lead_group"].shape != (count,):
raise ValueError("lead_group must have shape [N]")
if not np.isfinite(self.data["features"]).all() or not np.isfinite(self.data["targets"]).all():
raise ValueError("data contain NaN or Inf")
def __len__(self):
return len(self.data["features"])
def __getitem__(self, index):
return (torch.from_numpy(self.data["features"][index]).float(),
torch.from_numpy(self.data["targets"][index]).float(),
torch.as_tensor(self.data["lead_group"][index], dtype=torch.long))
def device_from_config(config, rank=0):
requested = config["runtime"]["device"]
if requested == "auto":
return torch.device("cuda", rank) if torch.cuda.is_available() else torch.device("cpu")
return torch.device(requested)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--resume", action="store_true", help="restore model and optimizer state before training")
args = parser.parse_args()
config = yaml.safe_load((ROOT / "conf/config.yaml").read_text())
seed = int(config["seed"])
np.random.seed(seed); torch.manual_seed(seed)
distributed = int(os.environ.get("WORLD_SIZE", "1")) > 1
local_rank = int(os.environ.get("LOCAL_RANK", "0"))
if distributed:
torch.distributed.init_process_group("nccl" if torch.cuda.is_available() else "gloo")
rank = torch.distributed.get_rank() if distributed else 0
device = device_from_config(config, local_rank)
if device.type == "cuda":
torch.cuda.set_device(device)
dataset = HazardDataset(ROOT / config["data"]["root"] / "train.npz", config)
split = int(len(dataset) * (1 - float(config["train"]["calibration_fraction"])))
fit_set = Subset(dataset, range(split))
sampler = DistributedSampler(fit_set, shuffle=True) if distributed else None
loader = DataLoader(fit_set, batch_size=int(config["train"]["batch_size"]), sampler=sampler,
shuffle=sampler is None, num_workers=int(config["train"]["num_workers"]))
model = WoFSStormCal(int(config["model"]["calibration_points"])).to(device)
features = dataset.data["features"][:split]
groups = dataset.data["lead_group"][:split]
means, scales = [], []
for group in range(2):
group_features = features[groups == group]
means.append(group_features.mean(0)); scales.append(group_features.std(0).clip(1e-6))
model.set_normalization(torch.from_numpy(np.stack(means)).to(device), torch.from_numpy(np.stack(scales)).to(device))
wrapped = DistributedDataParallel(model, device_ids=[local_rank] if device.type == "cuda" else None) if distributed else model
optimizer = torch.optim.Adam(wrapped.parameters(), lr=float(config["train"]["learning_rate"]))
checkpoint_path = ROOT / config["paths"]["checkpoint"]
start_epoch, history = 0, []
if args.resume:
checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=False)
model.load_state_dict(checkpoint["model"])
optimizer.load_state_dict(checkpoint["optimizer"])
start_epoch = int(checkpoint["epoch"])
history = checkpoint.get("history", [])
for epoch in range(start_epoch, start_epoch + int(config["train"]["epochs"])):
if sampler is not None:
sampler.set_epoch(epoch)
total, steps = 0.0, 0
for batch_features, targets, lead_group in loader:
batch_features, targets, lead_group = batch_features.to(device), targets.to(device), lead_group.to(device)
active_model = wrapped.module if distributed else wrapped
logits = wrapped(batch_features, lead_group, False)
logits = torch.logit(logits.clamp(1e-6, 1 - 1e-6))
loss = active_model.elastic_net_loss(logits, targets, config["train"]["l1_strength"], config["train"]["l2_strength"])
optimizer.zero_grad(set_to_none=True); loss.backward()
torch.nn.utils.clip_grad_norm_(wrapped.parameters(), float(config["train"]["gradient_clip_norm"]))
optimizer.step(); total += float(loss.detach()); steps += 1
if rank == 0:
history.append({"epoch": epoch + 1, "elastic_net_loss": total / max(steps, 1)})
model = wrapped.module if distributed else wrapped
if rank == 0:
calibration_features = torch.from_numpy(dataset.data["features"][split:]).float().to(device)
calibration_targets = torch.from_numpy(dataset.data["targets"][split:]).float().to(device)
calibration_groups = torch.from_numpy(dataset.data["lead_group"][split:]).long().to(device)
model.fit_calibration(calibration_features, calibration_targets, calibration_groups)
checkpoint_path.parent.mkdir(parents=True, exist_ok=True)
payload = {"model": model.state_dict(), "optimizer": optimizer.state_dict(), "epoch": start_epoch + int(config["train"]["epochs"]),
"history": history, "format_version": config["data"]["format_version"],
"model_metadata": {"input_shape": ["N", 113], "output_shape": ["N", 3],
"hazards": model.hazards, "lead_groups": model.lead_groups,
"ensemble_members": 18, "grid_spacing_km": 3,
"forecast_window_minutes": 30, "forecast_interval_minutes": 5}}
torch.save(payload, checkpoint_path)
metrics = ROOT / config["paths"]["training_metrics"]
metrics.parent.mkdir(parents=True, exist_ok=True)
metrics.write_text(json.dumps({"history": history, "calibration_samples": len(dataset) - split}, indent=2) + "\n")
print(f"checkpoint={checkpoint_path.relative_to(ROOT)} output_shape=(N,3) epoch={payload['epoch']}")
if distributed:
torch.distributed.destroy_process_group()
if __name__ == "__main__":
main()
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