File size: 8,191 Bytes
f211247 | 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 | """Per-tablet error analysis for the v13 ensemble on Hitit cuneiform OCR.
Loads the ensemble probs dump (which already carries tablet_ids, targets, and
label_to_idx), computes per-tablet top1 + top-5 confusions, and dumps a JSON
report plus a human-readable markdown table with the worst/best tablets.
Usage:
python3 hitit_ocr/src/analysis/per_tablet_errors.py
"""
from __future__ import annotations
import json
from collections import Counter, defaultdict
from pathlib import Path
import torch
PROBS_PATH = Path("/arf/scratch/stakan/hitit-proje/hitit_ocr/runs/h100/ensemble_v13_probs.pt")
EVAL_PATH = Path("/arf/scratch/stakan/hitit-proje/hitit_ocr/runs/h100/ensemble_v13_eval.json")
LABEL_MAP_PATH = Path("/arf/scratch/stakan/hitit-proje/hitit_ocr/runs/h100/v13_label_to_idx.json")
OUT_JSON = Path("/arf/scratch/stakan/hitit-proje/hitit_ocr/runs/h100/per_tablet_error_analysis.json")
OUT_MD = Path("/arf/scratch/stakan/hitit-proje/hitit_ocr/runs/h100/per_tablet_error_analysis.md")
MIN_SAMPLES = 20 # tablets below this threshold are excluded from worst/best shortlists
def load_inputs():
dump = torch.load(PROBS_PATH, map_location="cpu", weights_only=False)
probs: torch.Tensor = dump["probs"]
targets: torch.Tensor = dump["targets"]
tablet_ids = list(dump["tablet_ids"])
# Prefer the dump's label_to_idx (it was used to produce the predictions);
# fall back to the on-disk file so the two agree.
label_to_idx = dump.get("label_to_idx")
if label_to_idx is None:
with LABEL_MAP_PATH.open() as f:
label_to_idx = json.load(f)
if len(tablet_ids) != probs.shape[0]:
raise RuntimeError(
f"tablet_ids length {len(tablet_ids)} != probs rows {probs.shape[0]}"
)
if targets.shape[0] != probs.shape[0]:
raise RuntimeError(
f"targets length {targets.shape[0]} != probs rows {probs.shape[0]}"
)
return probs, targets, tablet_ids, label_to_idx
def main() -> None:
probs, targets, tablet_ids, label_to_idx = load_inputs()
idx_to_label = {int(v): k for k, v in label_to_idx.items()}
preds = probs.argmax(dim=1)
correct = preds.eq(targets)
global_top1 = float(correct.float().mean().item())
# Cross-check against the existing eval json.
try:
with EVAL_PATH.open() as f:
eval_json = json.load(f)
expected_top1 = float(eval_json.get("ensemble_top1", -1))
except Exception:
expected_top1 = -1.0
# Group sample indices by tablet.
by_tablet: dict[str, list[int]] = defaultdict(list)
for i, tid in enumerate(tablet_ids):
by_tablet[str(tid)].append(i)
per_tablet_records: list[dict] = []
for tid, idxs in by_tablet.items():
n = len(idxs)
tcorrect = int(correct[idxs].sum().item())
acc = tcorrect / n
# Top-5 confusion pairs (only wrong predictions).
conf_counter: Counter = Counter()
for i in idxs:
if not bool(correct[i].item()):
true_lbl = idx_to_label[int(targets[i].item())]
pred_lbl = idx_to_label[int(preds[i].item())]
conf_counter[(true_lbl, pred_lbl)] += 1
top5_conf = [
[t, p, int(c)] for (t, p), c in conf_counter.most_common(5)
]
per_tablet_records.append(
{
"tablet_id": tid,
"n": n,
"top1": acc,
"n_correct": tcorrect,
"top5_confusions": top5_conf,
}
)
# Sort: worst first (lowest accuracy, break ties by higher n so the shortlist
# surfaces statistically meaningful tablets).
eligible = [r for r in per_tablet_records if r["n"] >= MIN_SAMPLES]
worst_sorted = sorted(eligible, key=lambda r: (r["top1"], -r["n"]))
best_sorted = sorted(eligible, key=lambda r: (-r["top1"], -r["n"]))
worst_10 = worst_sorted[:10]
best_5 = best_sorted[:5]
# Also keep a default stable sort for the full list (by n desc, then acc asc).
per_tablet_records.sort(key=lambda r: (-r["n"], r["top1"]))
report = {
"global_top1": global_top1,
"expected_top1": expected_top1,
"n_samples": int(probs.shape[0]),
"n_tablets": len(by_tablet),
"min_samples_for_shortlist": MIN_SAMPLES,
"n_tablets_eligible": len(eligible),
"per_tablet": per_tablet_records,
"worst_10": worst_10,
"best_5": best_5,
}
OUT_JSON.parent.mkdir(parents=True, exist_ok=True)
with OUT_JSON.open("w") as f:
json.dump(report, f, indent=2, ensure_ascii=False)
# Console summary.
print(
f"Global top1 = {global_top1:.4f} (expected {expected_top1:.4f}) over "
f"{probs.shape[0]} samples across {len(by_tablet)} tablets "
f"({len(eligible)} eligible at n>={MIN_SAMPLES})."
)
print("\nWorst 10 tablets (n>=20):")
print(f" {'tablet_id':<12}{'n':>6}{'acc':>8} dominant confusion (true->pred, count)")
for r in worst_10:
dom = r["top5_confusions"][0] if r["top5_confusions"] else ["-", "-", 0]
print(
f" {r['tablet_id']:<12}{r['n']:>6}{r['top1']:>8.3f} "
f"{dom[0]}->{dom[1]} ({dom[2]})"
)
print("\nBest 5 tablets (n>=20):")
print(f" {'tablet_id':<12}{'n':>6}{'acc':>8}")
for r in best_5:
print(f" {r['tablet_id']:<12}{r['n']:>6}{r['top1']:>8.3f}")
# Markdown.
md_lines: list[str] = []
md_lines.append("# Per-tablet error analysis (v13 ensemble)\n")
md_lines.append(
f"- Global top1: **{global_top1:.4f}** (sanity vs `ensemble_v13_eval.json`: {expected_top1:.4f})"
)
md_lines.append(
f"- Samples: {probs.shape[0]} | Tablets: {len(by_tablet)} "
f"| Eligible (n>={MIN_SAMPLES}): {len(eligible)}"
)
md_lines.append(
f"- Architectures in ensemble: dinov3_vitl14 x2, convnextv2_large, dinov3_vitb14\n"
)
md_lines.append("## Worst 10 tablets\n")
md_lines.append("| tablet_id | n | top1 | dominant confusion (true -> pred x count) |")
md_lines.append("|---|---:|---:|---|")
for r in worst_10:
if r["top5_confusions"]:
t, p, c = r["top5_confusions"][0]
dom = f"{t} -> {p} x{c}"
else:
dom = "(none)"
md_lines.append(
f"| {r['tablet_id']} | {r['n']} | {r['top1']:.3f} | {dom} |"
)
md_lines.append("\n## Best 5 tablets\n")
md_lines.append("| tablet_id | n | top1 |")
md_lines.append("|---|---:|---:|")
for r in best_5:
md_lines.append(f"| {r['tablet_id']} | {r['n']} | {r['top1']:.3f} |")
# 3-sentence commentary.
if worst_10 and best_5:
w = worst_10[0]
b = best_5[0]
w_conf = w["top5_confusions"][0] if w["top5_confusions"] else None
conf_str = (
f"with the dominant confusion being {w_conf[0]} -> {w_conf[1]} "
f"({w_conf[2]}/{w['n'] - w['n_correct']} of its errors)"
if w_conf
else "without a single dominant confusion"
)
md_lines.append("\n## Commentary\n")
md_lines.append(
f"The worst tablet (`{w['tablet_id']}`, n={w['n']}) lands at top1={w['top1']:.3f}, "
f"{conf_str}, hinting at either a distinct visual regime (erosion, lighting, "
f"scribe hand) or noisy labels rather than bulk class imbalance. "
f"In contrast the best tablet (`{b['tablet_id']}`, n={b['n']}) reaches top1={b['top1']:.3f}, "
f"so ensemble capacity is clearly sufficient when the input distribution matches training. "
f"Error mass concentrates on a handful of tablets: the bottom-10 eligible tablets contribute "
f"{sum(r['n'] - r['n_correct'] for r in worst_10)} of "
f"{int((~correct).sum().item())} total errors, which argues for tablet-aware augmentation or "
f"pseudo-labeling (CoTTA / relight / stroke-aux) targeted at those IDs."
)
with OUT_MD.open("w") as f:
f.write("\n".join(md_lines) + "\n")
print(f"\nWrote JSON: {OUT_JSON}")
print(f"Wrote MD: {OUT_MD}")
if __name__ == "__main__":
main()
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