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import argparse
import sys
from pathlib import Path
from typing import Any
PROJECT_ROOT = Path(__file__).resolve().parent.parent
MODEL_ROOT = PROJECT_ROOT / "model"
SCRIPT_ROOT = PROJECT_ROOT / "scripts"
for path in (PROJECT_ROOT, MODEL_ROOT, SCRIPT_ROOT):
if str(path) not in sys.path:
sys.path.insert(0, str(path))
import h5py
import numpy as np
import torch
import yaml
from stormer import StormCast, edm_heun_sample
from data_loader import StormCastDataset
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Run StormCast inference")
parser.add_argument("--config", type=Path, default=Path("conf/config.yaml"))
parser.add_argument("--regression-weights", type=Path)
parser.add_argument("--diffusion-weights", type=Path)
parser.add_argument("--output", type=Path)
parser.add_argument("--num-steps", type=int)
parser.add_argument("--diffusion-steps", type=int)
parser.add_argument("--seed", type=int)
return parser.parse_args()
def main() -> None:
args = parse_args()
config_path = args.config.resolve()
with config_path.open("r", encoding="utf-8") as handle:
config = yaml.safe_load(handle)
_resolve_paths(config, config_path.parent.parent)
inference_config = config["inference"]
regression_weights = args.regression_weights or Path(
config["model"]["regression_weights"]
)
diffusion_weights = args.diffusion_weights or Path(
config["model"]["diffusion_weights"]
)
output = args.output or Path(inference_config["output_dir"]) / "forecast.h5"
run_inference(
config=config,
regression_weights=regression_weights,
diffusion_weights=diffusion_weights,
output=output,
num_steps=args.num_steps or inference_config["num_steps"],
diffusion_steps=args.diffusion_steps or inference_config["diffusion_steps"],
seed=config["project"]["seed"] if args.seed is None else args.seed,
)
@torch.no_grad()
def run_inference(
config: dict[str, Any],
regression_weights: Path,
diffusion_weights: Path,
output: Path,
num_steps: int,
diffusion_steps: int,
seed: int,
) -> Path:
if not torch.cuda.is_available():
raise RuntimeError("StormCast inference requires a CUDA/HIP device")
if num_steps < 1:
raise ValueError("num_steps must be at least 1")
device = torch.device("cuda", 0)
torch.cuda.set_device(device)
data_config = config["data"]
inference_config = config["inference"]
split = inference_config["split"]
years_key = f"{split}_years"
if years_key not in data_config:
raise ValueError(f"Unknown inference split: {split}")
dataset = StormCastDataset(
data_root=data_config["root_dir"],
years=data_config[years_key],
era5_variables=data_config["era5_variables"],
state_variables=data_config["state_variables"],
invariant_variables=data_config["invariant_variables"],
image_size=data_config["image_size"],
input_steps=data_config["input_steps"],
output_steps=data_config["output_steps"],
normalize=data_config["normalize"],
)
if num_steps > len(dataset):
raise ValueError(f"Requested {num_steps} steps but split contains {len(dataset)}")
regression = _load_weights(regression_weights, "regression").to(device).eval()
diffusion = _load_weights(diffusion_weights, "diffusion").to(device).eval()
model = StormCast(regression, diffusion)
generator = torch.Generator(device=device).manual_seed(seed)
output.parent.mkdir(parents=True, exist_ok=True)
first = dataset[0]
state = first["state"][0].unsqueeze(0).to(device, dtype=torch.float32)
invariant = first["invariant"].to(device, dtype=torch.float32)
state_channels = len(data_config["state_variables"])
background_channels = len(data_config["era5_variables"])
height, width = data_config["image_size"]
if [height, width] != list(config["model"]["image_size"]):
raise ValueError("Data and model image sizes must match")
if list(data_config["era5_image_size"]) != [721, 1440]:
raise ValueError("ERA5 grid must be 721 x 1440")
with h5py.File(output, "w") as handle:
handle.attrs["normalized"] = bool(data_config["normalize"])
handle.attrs["seed"] = seed
handle.attrs["diffusion_steps"] = diffusion_steps
handle.attrs["sigma_min"] = inference_config["sigma_min"]
handle.attrs["sigma_max"] = inference_config["sigma_max"]
handle.attrs["rho"] = inference_config["rho"]
handle.attrs["regression_weights"] = str(regression_weights.resolve())
handle.attrs["diffusion_weights"] = str(diffusion_weights.resolve())
handle.attrs["state_variables"] = np.asarray(
data_config["state_variables"], dtype="S"
)
handle.attrs["background_variables"] = np.asarray(
data_config["era5_variables"], dtype="S"
)
prediction_store = handle.create_dataset(
"prediction", (num_steps, state_channels, height, width), dtype="f4"
)
regression_store = handle.create_dataset(
"regression", (num_steps, state_channels, height, width), dtype="f4"
)
target_store = handle.create_dataset(
"target", (num_steps, state_channels, height, width), dtype="f4"
)
background_store = handle.create_dataset(
"background", (num_steps, background_channels, height, width), dtype="f4"
)
time_store = handle.create_dataset("time_index", (num_steps,), dtype="i8")
for index in range(num_steps):
sample = dataset[index]
background = sample["background"].unsqueeze(0).to(
device, dtype=torch.float32
)
regression_prediction = model.predict_regression(
state, background, invariant
)
condition = model.diffusion_condition(
state, regression_prediction, invariant
)
residual = edm_heun_sample(
diffusion,
condition,
output_channels=state_channels,
num_steps=diffusion_steps,
sigma_min=inference_config["sigma_min"],
sigma_max=inference_config["sigma_max"],
rho=inference_config["rho"],
generator=generator,
)
prediction = regression_prediction + residual
prediction_store[index] = prediction[0].cpu().numpy()
regression_store[index] = regression_prediction[0].cpu().numpy()
target_store[index] = sample["state"][1].numpy()
background_store[index] = sample["background"].numpy()
time_store[index] = int(np.asarray(sample["time_index"]).reshape(-1)[-1])
state = prediction
print(f"forecast_step={index + 1}/{num_steps}")
print(f"output={output}")
return output
def _load_weights(path: Path, kind: str) -> torch.nn.Module:
if not path.is_file():
raise FileNotFoundError(
f"Missing {kind} weights: {path}. "
"Train the corresponding stage first or pass an explicit weight path."
)
checkpoint = torch.load(path, map_location="cpu", weights_only=False)
saved_config = checkpoint.get("config")
if saved_config is None:
raise ValueError("Project .pt weights must include their training config")
model = _build_stage_models_from_config(saved_config, kind)
model.load_state_dict(checkpoint["model_state_dict"], strict=True)
return model
def _build_stage_models_from_config(config: dict[str, Any], kind: str) -> torch.nn.Module:
from stormer import build_diffusion_model, build_regression_model
data_config = config["data"]
model_config = config["model"]
common = {
"image_size": model_config["image_size"],
"state_channels": len(data_config["state_variables"]),
"invariant_channels": len(data_config["invariant_variables"]),
"model_channels": model_config["model_channels"],
"channel_mult": model_config["channel_mult"],
"num_blocks": model_config["num_blocks"],
"attn_resolutions": model_config["attention_resolutions"],
}
if kind == "regression":
return build_regression_model(
**common, background_channels=len(data_config["era5_variables"])
)
return build_diffusion_model(**common)
def _resolve_paths(config: dict[str, Any], project_root: Path) -> None:
for section, key in (("data", "root_dir"), ("inference", "output_dir")):
path = Path(config[section][key])
if not path.is_absolute():
config[section][key] = str((project_root / path).resolve())
for key in ("regression_weights", "diffusion_weights"):
path = Path(config["model"][key])
if not path.is_absolute():
config["model"][key] = str((project_root / path).resolve())
if __name__ == "__main__":
main()
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