File size: 5,727 Bytes
5bb6aee | 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 | #!/usr/bin/env python3
"""Offline end-to-end inference for the frozen unified TartanIMU system."""
from __future__ import annotations
import argparse
import hashlib
import json
import os
import subprocess
import sys
import time
from pathlib import Path
import numpy as np
import pandas as pd
from catboost import CatBoostRegressor
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for block in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(block)
return digest.hexdigest()
def run(command: list[str], env: dict[str, str] | None = None) -> None:
print("RUN", " ".join(command), flush=True)
subprocess.run(command, check=True, env=env)
def load_npz(path: Path) -> dict[str, np.ndarray]:
with np.load(path, allow_pickle=True) as loaded:
return {key: np.asarray(loaded[key]) for key in loaded.files}
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--data-root", type=Path, required=True)
parser.add_argument("--sample", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--work-dir", type=Path, required=True)
parser.add_argument("--scripts-dir", type=Path, required=True)
parser.add_argument("--official-source", type=Path, required=True)
parser.add_argument("--official-config", type=Path, required=True)
parser.add_argument("--model-yaml", type=Path, required=True)
parser.add_argument("--official-checkpoint", type=Path, required=True)
parser.add_argument("--regressor", type=Path, required=True)
parser.add_argument("--expected-sha256", default=None)
parser.add_argument("--reuse-cache", action="store_true")
parser.add_argument("--receipt", type=Path, default=None)
args = parser.parse_args()
started = time.time()
args.work_dir.mkdir(parents=True, exist_ok=True)
raw_dir = args.work_dir / "raw_features"
official_dir = args.work_dir / "official_embeddings"
raw_cache = raw_dir / "test.npz"
official_cache = official_dir / "test.npz"
python = sys.executable
if not args.reuse_cache or not raw_cache.exists():
run(
[
python,
str(args.scripts_dir / "features.py"),
"--root",
str(args.data_root),
"--out-dir",
str(raw_dir),
"--splits",
"test",
]
)
if not args.reuse_cache or not official_cache.exists():
env = os.environ.copy()
previous = env.get("PYTHONPATH")
env["PYTHONPATH"] = (
str(args.official_source)
if not previous
else str(args.official_source) + os.pathsep + previous
)
run(
[
python,
str(args.scripts_dir / "cache_official_embeddings.py"),
"--split",
"test",
"--data-root",
str(args.data_root),
"--config",
str(args.official_config),
"--model-yaml",
str(args.model_yaml),
"--checkpoint",
str(args.official_checkpoint),
"--output",
str(official_cache),
"--batch-seqs",
"32",
],
env=env,
)
raw = load_npz(raw_cache)
official = load_npz(official_cache)
if not np.array_equal(raw["window_id"], official["window_id"]):
raise SystemExit("raw/official window_id order mismatch")
all_heads = official["head_prediction"].reshape(len(raw["X"]), -1)
features = np.concatenate([raw["X"], official["embedding"], all_heads], axis=1)
if features.shape[1] != 633 or not np.isfinite(features).all():
raise SystemExit(f"invalid stacked features: {features.shape}")
model = CatBoostRegressor()
model.load_model(args.regressor)
prediction = np.asarray(model.predict(features), dtype=np.float64)
submission = pd.DataFrame(
{
"window_id": raw["window_id"],
"vx": prediction[:, 0],
"vy": prediction[:, 1],
"vz": prediction[:, 2],
}
)
sample = pd.read_csv(args.sample)
if submission.columns.tolist() != ["window_id", "vx", "vy", "vz"]:
raise SystemExit("wrong output columns")
if not submission["window_id"].equals(sample["window_id"]):
raise SystemExit("output IDs/order differ from sample submission")
if not np.isfinite(submission[["vx", "vy", "vz"]].to_numpy()).all():
raise SystemExit("non-finite output")
args.output.parent.mkdir(parents=True, exist_ok=True)
submission.to_csv(args.output, index=False, float_format="%.8f")
output_hash = sha256(args.output)
if args.expected_sha256 and output_hash != args.expected_sha256:
raise SystemExit(
f"output hash mismatch: {output_hash} != {args.expected_sha256}"
)
receipt = {
"output": str(args.output),
"rows": int(len(submission)),
"features": int(features.shape[1]),
"sha256": output_hash,
"regressor_sha256": sha256(args.regressor),
"official_checkpoint_sha256": sha256(args.official_checkpoint),
"runtime_seconds": time.time() - started,
"offline_ready": True,
"platform_input_or_routing": False,
}
receipt_path = args.receipt or args.output.with_suffix(".receipt.json")
receipt_path.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n")
print(json.dumps(receipt, indent=2, sort_keys=True))
if __name__ == "__main__":
main()
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