| """Generate aligned 264x264 TerraMesh-like multimodal samples.""" |
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| from pathlib import Path |
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| import numpy as np |
| import yaml |
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| ROOT = Path(__file__).resolve().parents[1] |
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| def make_split(path, count, config, seed): |
| rng = np.random.default_rng(seed) |
| size = int(config["data"]["source_size"]) |
| y, x = np.mgrid[-1:1:complex(size), -1:1:complex(size)].astype(np.float32) |
| payload = {"format_version": np.asarray(config["data"]["format_version"]), |
| "data_source": np.asarray("synthetic_terramesh_like")} |
| latent = np.empty((count, size, size), np.float32) |
| for sample in range(count): |
| phase = rng.uniform(0, 2 * np.pi) |
| latent[sample] = np.sin(3 * np.pi * x + phase) * np.cos(2 * np.pi * y - phase) + 0.3 * x + 0.2 * y |
| for index, (name, channels) in enumerate(config["data"]["pixel_modalities"].items()): |
| values = np.empty((count, int(channels), size, size), np.float32) |
| for channel in range(int(channels)): |
| values[:, channel] = latent + 0.08 * channel + 0.12 * index + rng.normal(0, 0.03, latent.shape) |
| payload[f"pixel_{name}"] = values |
| normalized = (latent - latent.min(axis=(1, 2), keepdims=True)) |
| normalized /= normalized.max(axis=(1, 2), keepdims=True).clip(1e-6) |
| payload["token_map_lulc"] = np.floor(normalized * 8).clip(0, 8).astype(np.int64) |
| payload["coords"] = rng.integers(0, int(config["model"]["engineering_vocab_size"]), (count, 2), dtype=np.int64) |
| caption = np.empty((count, 16), np.int64) |
| for sample in range(count): |
| summary = int(normalized[sample].mean() * 127) |
| caption[sample] = (summary + np.arange(16) * 7) % int(config["model"]["engineering_vocab_size"]) |
| payload["caption"] = caption |
| np.savez_compressed(path, **payload) |
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|
| def main(): |
| config = yaml.safe_load((ROOT / "conf/config.yaml").read_text()) |
| output = ROOT / config["data"]["root"] |
| output.mkdir(parents=True, exist_ok=True) |
| for offset, (filename, count) in enumerate((("train.npz", config["data"]["train_samples"]), |
| ("test.npz", config["data"]["test_samples"]))): |
| target = output / filename |
| if not target.exists(): |
| make_split(target, int(count), config, int(config["seed"]) + offset) |
| print(f"generated={target.relative_to(ROOT)} samples={count} source_size={config['data']['source_size']}") |
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| if __name__ == "__main__": |
| main() |
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|
