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bca45b0 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 | #!/usr/bin/env python3
"""Audit V* benchmark preparation and analyze errors against target scale."""
from __future__ import annotations
import argparse
import csv
import json
import math
import re
from collections import Counter, defaultdict
from pathlib import Path
from statistics import mean, median
from PIL import Image
def read_jsonl(path: Path) -> list[dict]:
return [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line]
def quantile(values: list[float], probability: float) -> float:
ordered = sorted(values)
if not ordered:
raise ValueError("Cannot compute a quantile of an empty list")
position = (len(ordered) - 1) * probability
lower = math.floor(position)
upper = math.ceil(position)
if lower == upper:
return float(ordered[lower])
return float(ordered[lower] * (upper - position) + ordered[upper] * (position - lower))
def summary(values: list[float]) -> dict[str, float]:
return {
"min": min(values),
"p10": quantile(values, 0.10),
"p25": quantile(values, 0.25),
"median": median(values),
"mean": mean(values),
"p75": quantile(values, 0.75),
"p90": quantile(values, 0.90),
"max": max(values),
}
def normalize_text(value: str) -> str:
return re.sub(r"[^a-z0-9]+", " ", value.lower()).strip()
def accuracy_group(rows: list[dict]) -> dict:
samples = len(rows)
correct = sum(bool(row["correct"]) for row in rows)
accuracy = correct / samples if rows else None
lower = None
upper = None
if samples:
z = 1.959963984540054
denominator = 1 + z * z / samples
center = (accuracy + z * z / (2 * samples)) / denominator
margin = (
z
* math.sqrt(accuracy * (1 - accuracy) / samples + z * z / (4 * samples * samples))
/ denominator
)
lower = center - margin
upper = center + margin
return {
"samples": samples,
"correct": correct,
"accuracy": accuracy,
"wilson_95ci": [lower, upper],
}
def grouped(rows: list[dict], key: str) -> dict[str, dict]:
groups: dict[str, list[dict]] = defaultdict(list)
for row in rows:
groups[str(row[key])].append(row)
return {name: accuracy_group(group) for name, group in sorted(groups.items())}
def binned(rows: list[dict], key: str, boundaries: list[float]) -> dict[str, dict]:
groups: dict[str, list[dict]] = defaultdict(list)
for row in rows:
value = float(row[key])
lower = -math.inf
label = ""
for upper in boundaries:
if value <= upper:
label = f"({lower if math.isfinite(lower) else '-inf'}, {upper}]"
break
lower = upper
if not label:
label = f"({boundaries[-1]}, inf)"
groups[label].append(row)
return {name: accuracy_group(group) for name, group in groups.items()}
def mcnemar_exact(discordant_a_only: int, discordant_b_only: int) -> float:
trials = discordant_a_only + discordant_b_only
if trials == 0:
return 1.0
lower = min(discordant_a_only, discordant_b_only)
probability = sum(math.comb(trials, index) for index in range(lower + 1)) / (2**trials)
return min(1.0, 2 * probability)
def paired_comparison(rows: list[dict], first_key: str, second_key: str) -> dict:
first_only = sum(bool(row[first_key]) and not bool(row[second_key]) for row in rows)
second_only = sum(not bool(row[first_key]) and bool(row[second_key]) for row in rows)
both_correct = sum(bool(row[first_key]) and bool(row[second_key]) for row in rows)
both_wrong = sum(not bool(row[first_key]) and not bool(row[second_key]) for row in rows)
first_accuracy = sum(bool(row[first_key]) for row in rows) / len(rows)
second_accuracy = sum(bool(row[second_key]) for row in rows) / len(rows)
return {
"samples": len(rows),
"first_accuracy": first_accuracy,
"second_accuracy": second_accuracy,
"difference_percentage_points": (second_accuracy - first_accuracy) * 100,
"both_correct": both_correct,
"both_wrong": both_wrong,
"first_only_correct": first_only,
"second_only_correct": second_only,
"mcnemar_exact_two_sided_p": mcnemar_exact(first_only, second_only),
}
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--manifest", type=Path, required=True)
parser.add_argument("--results", type=Path, required=True)
parser.add_argument("--dynamic-results", type=Path)
parser.add_argument("--oracle-results", type=Path)
parser.add_argument("--raw-root", type=Path, required=True)
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--eval-width", type=int, default=448)
parser.add_argument("--eval-height", type=int, default=448)
args = parser.parse_args()
manifest = read_jsonl(args.manifest)
results = read_jsonl(args.results)
manifest_by_id = {str(row["sample_id"]): row for row in manifest}
results_by_id = {str(row["sample_id"]): row for row in results}
if len(manifest_by_id) != len(manifest) or len(results_by_id) != len(results):
raise ValueError("Duplicate sample IDs detected")
if manifest_by_id.keys() != results_by_id.keys():
raise ValueError("Manifest/result sample IDs differ")
diagnostic_results = {}
for name, path in (("dynamic", args.dynamic_results), ("oracle", args.oracle_results)):
if path is None:
continue
experiment_rows = read_jsonl(path)
experiment_by_id = {str(row["sample_id"]): row for row in experiment_rows}
if len(experiment_by_id) != len(experiment_rows):
raise ValueError(f"Duplicate sample IDs in {name} results")
if experiment_by_id.keys() != manifest_by_id.keys():
raise ValueError(f"Manifest/{name} result sample IDs differ")
diagnostic_results[name] = experiment_by_id
rows: list[dict] = []
mapping_failures: list[dict] = []
dimension_mismatches: list[dict] = []
for sample_id in sorted(manifest_by_id, key=int):
item = manifest_by_id[sample_id]
result = results_by_id[sample_id]
image_path = Path(item["images"][0])
annotation_path = args.raw_root / str(item["config"]) / f"{image_path.stem}.json"
annotation = json.loads(annotation_path.read_text(encoding="utf-8"))
with Image.open(image_path) as image:
width, height = image.size
recorded_width, recorded_height = item["image_sizes"][0]
if (width, height) != (recorded_width, recorded_height):
dimension_mismatches.append(
{
"sample_id": sample_id,
"actual": [width, height],
"manifest": [recorded_width, recorded_height],
}
)
expected_index = ord(str(item["answer"]).upper()) - ord("A")
expected_choice = str(item["choices"][expected_index])
official_correct_sentence = str(annotation["options"][0])
choice_norm = normalize_text(expected_choice)
sentence_norm = normalize_text(official_correct_sentence)
mapping_valid = choice_norm in sentence_norm or sentence_norm in choice_norm
if not mapping_valid:
mapping_failures.append(
{
"sample_id": sample_id,
"choice": expected_choice,
"official_correct_sentence": official_correct_sentence,
}
)
scale_x = args.eval_width / width
scale_y = args.eval_height / height
boxes = annotation["bbox"]
projected_boxes = [
[float(x) * scale_x, float(y) * scale_y, float(w) * scale_x, float(h) * scale_y]
for x, y, w, h in boxes
]
min_projected_side = min(min(box[2], box[3]) for box in projected_boxes)
min_projected_area = min(box[2] * box[3] for box in projected_boxes)
min_area_fraction = min((float(w) * float(h)) / (width * height) for _, _, w, h in boxes)
target_centers = [
((float(x) + float(w) / 2) / width, (float(y) + float(h) / 2) / height)
for x, y, w, h in boxes
]
max_center_distance = max(
math.sqrt(((cx - 0.5) / 0.5) ** 2 + ((cy - 0.5) / 0.5) ** 2)
for cx, cy in target_centers
)
predicted = result.get("predicted")
predicted_choice = None
if predicted:
predicted_index = ord(str(predicted).upper()) - ord("A")
if 0 <= predicted_index < len(item["choices"]):
predicted_choice = item["choices"][predicted_index]
question = str(item["question"]).lower()
if "color" in question:
question_type = "color"
elif "material" in question:
question_type = "material"
elif str(item["config"]) == "relative_position":
question_type = "relative_position"
elif "kind of" in question or "what animal" in question:
question_type = "object_kind"
else:
question_type = "other_attribute"
output_row = {
"sample_id": sample_id,
"config": item["config"],
"question_type": question_type,
"question": item["question"],
"image_path": str(image_path),
"image_width": width,
"image_height": height,
"image_megapixels": width * height / 1_000_000,
"target_count": len(boxes),
"target_objects": " | ".join(annotation["target_object"]),
"min_target_area_fraction": min_area_fraction,
"min_projected_side_448": min_projected_side,
"min_projected_area_448": min_projected_area,
"max_center_distance": max_center_distance,
"choice_count": len(item["choices"]),
"expected": result["expected"],
"expected_choice": expected_choice,
"predicted": predicted,
"predicted_choice": predicted_choice,
"correct": bool(result["correct"]),
"mapping_valid": mapping_valid,
"error": result.get("error"),
}
for experiment_name, experiment_by_id in diagnostic_results.items():
experiment = experiment_by_id[sample_id]
output_row[f"{experiment_name}_predicted"] = experiment.get("predicted")
output_row[f"{experiment_name}_correct"] = bool(experiment.get("correct"))
output_row[f"{experiment_name}_error"] = experiment.get("error")
rows.append(output_row)
labels = Counter(row["expected"] for row in rows)
predictions = Counter(row["predicted"] for row in rows)
confusion: dict[str, Counter] = defaultdict(Counter)
for row in rows:
confusion[str(row["expected"])][str(row["predicted"])] += 1
task_groups: dict[str, list[dict]] = defaultdict(list)
for row in rows:
task_groups[str(row["config"])].append(row)
chance_by_task = {
task: mean(1 / int(row["choice_count"]) for row in task_rows)
for task, task_rows in sorted(task_groups.items())
}
overall_random_chance = mean(1 / int(row["choice_count"]) for row in rows)
tiny_threshold_counts = {}
for threshold in (4, 8, 12, 16, 24, 32):
subset = [row for row in rows if float(row["min_projected_side_448"]) <= threshold]
tiny_threshold_counts[f"le_{threshold}px"] = accuracy_group(subset)
scale_by_config = {}
for config, config_rows in sorted(task_groups.items()):
correct_rows = [row for row in config_rows if row["correct"]]
wrong_rows = [row for row in config_rows if not row["correct"]]
scale_by_config[config] = {
"accuracy_by_projected_min_side_bin": binned(
config_rows, "min_projected_side_448", [4, 8, 12, 16, 24, 32]
),
"min_projected_side_correct": summary(
[float(row["min_projected_side_448"]) for row in correct_rows]
),
"min_projected_side_wrong": summary(
[float(row["min_projected_side_448"]) for row in wrong_rows]
),
}
wrong_smallest = sorted(
(row for row in rows if not row["correct"]),
key=lambda row: float(row["min_projected_side_448"]),
)[:20]
correct_smallest = sorted(
(row for row in rows if row["correct"]),
key=lambda row: float(row["min_projected_side_448"]),
)[:10]
report = {
"scope": {
"samples": len(rows),
"manifest": str(args.manifest.resolve()),
"results": str(args.results.resolve()),
"raw_root": str(args.raw_root.resolve()),
"simulated_eval_size": [args.eval_width, args.eval_height],
},
"integrity": {
"unique_manifest_ids": len(manifest_by_id),
"unique_result_ids": len(results_by_id),
"id_sets_equal": manifest_by_id.keys() == results_by_id.keys(),
"dimension_mismatches": dimension_mismatches,
"label_mapping_failures": mapping_failures,
"inference_errors": sum(bool(row["error"]) for row in rows),
"unparsed": sum(row["predicted"] is None for row in rows),
},
"performance": {
"overall": accuracy_group(rows),
"by_config": grouped(rows, "config"),
"by_question_type": grouped(rows, "question_type"),
"random_choice_chance": overall_random_chance,
"random_choice_chance_by_config": chance_by_task,
"expected_label_distribution": dict(sorted(labels.items())),
"predicted_label_distribution": dict(sorted(predictions.items())),
"confusion": {
expected: dict(sorted(predicted.items()))
for expected, predicted in sorted(confusion.items())
},
},
"image_and_target_scale": {
"image_width": summary([float(row["image_width"]) for row in rows]),
"image_height": summary([float(row["image_height"]) for row in rows]),
"image_megapixels": summary([float(row["image_megapixels"]) for row in rows]),
"min_projected_target_side_448": summary(
[float(row["min_projected_side_448"]) for row in rows]
),
"min_projected_target_area_448": summary(
[float(row["min_projected_area_448"]) for row in rows]
),
"min_target_area_fraction": summary(
[float(row["min_target_area_fraction"]) for row in rows]
),
"accuracy_by_projected_min_side_bin": binned(
rows, "min_projected_side_448", [4, 8, 12, 16, 24, 32]
),
"accuracy_by_image_megapixels": binned(rows, "image_megapixels", [2, 3, 4, 6]),
"accuracy_by_target_center_distance": binned(
rows, "max_center_distance", [0.5, 0.8, 1.0, 1.2]
),
"cumulative_tiny_target_thresholds": tiny_threshold_counts,
"by_config": scale_by_config,
},
"smallest_wrong_samples": wrong_smallest,
"smallest_correct_samples": correct_smallest,
}
if diagnostic_results:
sensitivity = {}
for experiment_name in diagnostic_results:
correct_key = f"{experiment_name}_correct"
sensitivity[experiment_name] = {
"overall": accuracy_group(
[{**row, "correct": row[correct_key]} for row in rows]
),
"by_config": {
config: accuracy_group(
[{**row, "correct": row[correct_key]} for row in config_rows]
)
for config, config_rows in sorted(task_groups.items())
},
"accuracy_by_baseline_projected_min_side_bin": binned(
[{**row, "correct": row[correct_key]} for row in rows],
"min_projected_side_448",
[4, 8, 12, 16, 24, 32],
),
"errors": sum(bool(row[f"{experiment_name}_error"]) for row in rows),
}
if "dynamic" in diagnostic_results:
sensitivity["baseline_vs_dynamic"] = paired_comparison(
rows, "correct", "dynamic_correct"
)
if "oracle" in diagnostic_results:
sensitivity["baseline_vs_oracle"] = paired_comparison(
rows, "correct", "oracle_correct"
)
if "dynamic" in diagnostic_results and "oracle" in diagnostic_results:
sensitivity["dynamic_vs_oracle"] = paired_comparison(
rows, "dynamic_correct", "oracle_correct"
)
report["sensitivity_experiments"] = sensitivity
args.output_dir.mkdir(parents=True, exist_ok=True)
(args.output_dir / "diagnosis.json").write_text(
json.dumps(report, ensure_ascii=False, indent=2, sort_keys=True) + "\n",
encoding="utf-8",
)
fieldnames = list(rows[0])
with (args.output_dir / "sample_diagnostics.csv").open(
"w", encoding="utf-8", newline=""
) as handle:
writer = csv.DictWriter(handle, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(rows)
print(json.dumps(report, ensure_ascii=False, indent=2, sort_keys=True))
if __name__ == "__main__":
main()
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