File size: 9,026 Bytes
178f61f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | """Read-only structural and provenance validation for a clean KD cache."""
from __future__ import annotations
import argparse
import hashlib
import json
from pathlib import Path
import sys
ROOT = Path(r"E:\Gaze_estimation")
sys.path.insert(0, str(ROOT / ".codex_deps"))
import h5py
import numpy as np
MANIFEST_ROOT = ROOT / "data" / "processed_kd_clean_v1" / "manifests"
CACHE_ROOT = ROOT / "data" / "processed_kd_clean_v1" / "cache"
def file_sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as stream:
for block in iter(lambda: stream.read(1024 * 1024), b""):
digest.update(block)
return digest.hexdigest().upper()
def decode(values: np.ndarray) -> list[str]:
return [value.decode("utf-8") if isinstance(value, bytes) else str(value) for value in values]
def softmax(values: np.ndarray) -> np.ndarray:
shifted = values.astype(np.float64) - values.max(axis=1, keepdims=True)
exp = np.exp(shifted)
return exp / exp.sum(axis=1, keepdims=True)
def vector_from_angles(pitch_deg: np.ndarray, yaw_deg: np.ndarray) -> np.ndarray:
pitch, yaw = np.deg2rad(pitch_deg), np.deg2rad(yaw_deg)
return np.column_stack(
(-np.cos(pitch) * np.sin(yaw), -np.sin(pitch), -np.cos(pitch) * np.cos(yaw))
)
def angular_error(first: np.ndarray, second: np.ndarray) -> np.ndarray:
first = first / np.linalg.norm(first, axis=1, keepdims=True)
second = second / np.linalg.norm(second, axis=1, keepdims=True)
return np.rad2deg(np.arccos(np.clip(np.sum(first * second, axis=1), -1.0, 1.0)))
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--participant", required=True)
parser.add_argument("--tag", required=True)
args = parser.parse_args()
cache_path = CACHE_ROOT / f"{args.participant}.{args.tag}.h5"
processing_path = CACHE_ROOT / f"{args.participant}.{args.tag}.processing.jsonl"
summary_path = CACHE_ROOT / f"{args.participant}.{args.tag}.summary.json"
source_path = MANIFEST_ROOT / f"{args.participant}.source.jsonl"
output_path = CACHE_ROOT / f"{args.participant}.{args.tag}.validation.json"
if output_path.exists():
raise FileExistsError(f"refusing to overwrite validation artifact: {output_path}")
summary = json.loads(summary_path.read_text(encoding="utf-8"))
source_rows = {
row["sample_id"]: row
for row in (json.loads(line) for line in source_path.read_text(encoding="utf-8").splitlines())
}
decisions = [json.loads(line) for line in processing_path.read_text(encoding="utf-8").splitlines()]
errors: list[str] = []
if file_sha256(cache_path) != summary["cache_sha256"]:
errors.append("cache SHA-256 differs from summary")
if file_sha256(processing_path) != summary["processing_manifest_sha256"]:
errors.append("processing manifest SHA-256 differs from summary")
if len(decisions) != summary["source_rows_examined"]:
errors.append("processing-decision count differs from examined-row summary")
if sum(bool(row["accepted"]) for row in decisions) != summary["accepted_rows"]:
errors.append("accepted decision count differs from summary")
required = {
"sample_id", "relative_frame_path", "participant", "day", "frame_id",
"raw_image_sha256", "source_index", "annotation_row", "left_patches",
"right_patches", "landmarks", "left_gaze", "right_gaze",
"left_affine_matrix", "right_affine_matrix", "left_roll_deg", "right_roll_deg",
"teacher_pitch_logits_raw", "teacher_yaw_logits_raw", "teacher_pitch_logits",
"teacher_yaw_logits", "teacher_aligned_vector", "teacher_pitch_deg",
"teacher_yaw_deg", "teacher_error_deg",
}
metrics: dict[str, float | int | bool] = {}
with h5py.File(cache_path, "r") as handle:
missing = sorted(required - set(handle.keys()))
if missing:
errors.append(f"missing datasets: {missing}")
row_counts = {name: int(handle[name].shape[0]) for name in required if name in handle}
if len(set(row_counts.values())) != 1:
errors.append(f"dataset row counts differ: {row_counts}")
count = min(row_counts.values()) if row_counts else 0
if count != summary["accepted_rows"]:
errors.append("H5 row count differs from accepted-row summary")
sample_ids = decode(handle["sample_id"][:])
paths = decode(handle["relative_frame_path"][:])
hashes = decode(handle["raw_image_sha256"][:])
source_indices = handle["source_index"][:]
if len(sample_ids) != len(set(sample_ids)):
errors.append("H5 sample IDs are not unique")
for output_index, sample_id in enumerate(sample_ids):
source = source_rows.get(sample_id)
if source is None:
errors.append(f"H5 sample_id not found in source manifest: {sample_id}")
continue
if source["relative_frame_path"] != paths[output_index]:
errors.append(f"path mismatch at H5 row {output_index}")
if source["raw_image_sha256"] != hashes[output_index]:
errors.append(f"image hash mismatch at H5 row {output_index}")
if source["source_index"] != int(source_indices[output_index]):
errors.append(f"source index mismatch at H5 row {output_index}")
numeric_names = [name for name in required if name in handle and handle[name].dtype.kind not in "OSU"]
if any(not np.isfinite(handle[name][:]).all() for name in numeric_names):
errors.append("one or more numeric datasets contain non-finite values")
if handle["teacher_pitch_logits"].shape[1:] != (90,) or handle["teacher_yaw_logits"].shape[1:] != (90,):
errors.append("aligned teacher logits do not have 90 bins")
if handle["left_patches"].shape[1:] != (4, 16, 16):
errors.append("left patches do not use the declared V16 shape")
if handle["landmarks"].shape[1:] != (478, 2):
errors.append("landmarks do not have shape [N,478,2]")
aligned_pitch_probability = softmax(handle["teacher_pitch_logits"][:])
aligned_yaw_probability = softmax(handle["teacher_yaw_logits"][:])
metrics["max_aligned_pitch_probability_sum_error"] = float(
np.max(np.abs(aligned_pitch_probability.sum(axis=1) - 1.0))
)
metrics["max_aligned_yaw_probability_sum_error"] = float(
np.max(np.abs(aligned_yaw_probability.sum(axis=1) - 1.0))
)
target_deg = np.rad2deg(handle["left_gaze"][:].astype(np.float64))
target_vector = vector_from_angles(target_deg[:, 0], target_deg[:, 1])
aligned_vector = handle["teacher_aligned_vector"][:].astype(np.float64)
recomputed_error = angular_error(aligned_vector, target_vector)
stored_error = handle["teacher_error_deg"][:].astype(np.float64)
metrics["max_teacher_error_recompute_difference_deg"] = float(
np.max(np.abs(recomputed_error - stored_error))
)
metrics["teacher_aligned_error_mean_deg"] = float(recomputed_error.mean())
bin_definition = str(handle.attrs["teacher_bin_centers_deg"])
if bin_definition == "index * 4 - 180":
binwidth, offset = 4.0, 180.0
elif bin_definition == "index * 2 - 90":
binwidth, offset = 2.0, 90.0
else:
errors.append(f"unknown teacher bin definition: {bin_definition}")
binwidth, offset = np.nan, np.nan
raw_pitch = softmax(handle["teacher_pitch_logits_raw"][:]) @ np.arange(90) * binwidth - offset
raw_yaw = softmax(handle["teacher_yaw_logits_raw"][:]) @ np.arange(90) * binwidth - offset
raw_vector = vector_from_angles(raw_pitch, raw_yaw)
metrics["teacher_raw_vs_roll_corrected_target_error_mean_deg"] = float(
angular_error(raw_vector, target_vector).mean()
)
metrics["teacher_alignment_error_change_deg"] = float(
metrics["teacher_aligned_error_mean_deg"]
- metrics["teacher_raw_vs_roll_corrected_target_error_mean_deg"]
)
teacher_audit = json.loads(handle.attrs["teacher_loader_audit"])
if teacher_audit["mapped_tensor_count"] != teacher_audit["checkpoint_tensor_count"]:
errors.append("embedded strict-loader audit does not map every checkpoint tensor")
report = {
"schema": "mpiigaze-kd-clean-cache-validation-v1",
"participant": args.participant,
"tag": args.tag,
"cache_path": str(cache_path.resolve()),
"cache_sha256": file_sha256(cache_path),
"rows": summary["accepted_rows"],
"metrics": metrics,
"errors": errors,
"pass": not errors,
}
output_path.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8", newline="\n")
print(json.dumps(report, indent=2))
if errors:
raise SystemExit(1)
if __name__ == "__main__":
main()
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