| """Run multi-scale reconstruction inference.""" |
| import importlib.util |
| from pathlib import Path |
| import numpy as np, torch, yaml |
| import argparse |
| import random |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
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|
|
| def validate_config(cfg): |
| data, model = cfg["data"], cfg["model"] |
| if data["image_size"] != model["image_size"] or data["channels"] != model["in_channels"]: |
| raise ValueError("data and model image shape settings must match") |
| if data["scales"] != model["scales"]: |
| raise ValueError("data.scales and model.scales must match") |
|
|
| def main(): |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("--config", type=Path, default=ROOT / "conf/config.yaml") |
| parser.add_argument("--seed", type=int, default=None) |
| parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default=None) |
| parser.add_argument("--data", type=Path, default=None) |
| parser.add_argument("--checkpoint", type=Path, default=None) |
| parser.add_argument("--output-dir", type=Path, default=None) |
| args = parser.parse_args() |
| cfg = yaml.safe_load(args.config.read_text()) |
| validate_config(cfg) |
| seed = cfg["seed"] if args.seed is None else args.seed |
| random.seed(seed); np.random.seed(seed); torch.manual_seed(seed) |
| spec = importlib.util.spec_from_file_location("satmaepp", ROOT / "model/satmaepp.py"); module = importlib.util.module_from_spec(spec); spec.loader.exec_module(module) |
| model_cfg = {k: v for k, v in cfg["model"].items() if k not in {"architecture", "runtime_profile"}} |
| requested = args.device or cfg["runtime"]["device"] |
| if requested == "cuda" and not torch.cuda.is_available(): raise RuntimeError("CUDA was requested but is unavailable") |
| device = torch.device("cuda" if requested == "cuda" or (requested == "auto" and torch.cuda.is_available()) else "cpu") |
| model = module.SatMAEPP(**model_cfg).to(device) |
| checkpoint = args.checkpoint or ROOT / cfg["paths"]["checkpoint"] |
| if not checkpoint.exists(): raise FileNotFoundError("Run training before inference") |
| model.load_state_dict(torch.load(checkpoint, map_location="cpu", weights_only=False)["model"]); model.eval() |
| archive = np.load(args.data or ROOT / cfg["data"]["root"] / "test.npz") |
| images = torch.from_numpy(archive["images"]) |
| expected = (cfg["data"]["channels"], cfg["data"]["image_size"], cfg["data"]["image_size"]) |
| if images.ndim != 4 or tuple(images.shape[1:]) != expected: |
| raise ValueError(f"test images must have shape [N, {expected[0]}, {expected[1]}, {expected[2]}]") |
| targets = {} |
| for scale in cfg["model"]["scales"]: |
| if scale != 1: |
| field = f"images_{scale}x" |
| if field not in archive: |
| raise ValueError(f"test dataset is missing native target {field}") |
| targets[str(scale)] = torch.from_numpy(archive[field]) |
| images, targets = images.to(device), {key: value.to(device) for key, value in targets.items()} |
| with torch.no_grad(): output = model(images, high_resolution_targets=targets) |
| out = args.output_dir or ROOT / cfg["paths"]["inference_dir"]; out.mkdir(parents=True, exist_ok=True) |
| payload = {"target": images.cpu().numpy(), "prediction": output["reconstruction"].cpu().numpy(), "mask": output["mask"].cpu().numpy(), "labels": archive["labels"]} |
| for scale, value in output["predictions"].items(): |
| payload[f"prediction_{scale}"] = value.cpu().numpy() |
| payload[f"target_{scale}"] = output["targets"][scale].cpu().numpy() |
| np.savez_compressed(out / "reconstruction.npz", **payload) |
| print("inference=", out / "reconstruction.npz") |
|
|
| if __name__ == "__main__": main() |
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|
