scripts/benchmark_2000.py

"""Large-scale resumable benchmark for Perturb attack versions.

Samples N random ImageNet-100 rows, runs the chosen attack on each image,
saves the clean/adversarial PNGs, and records per-image validator scores.
Designed for 2000-image backtests with crash/resume support.

Usage:
    python scripts/benchmark_2000.py --n 2000 --attack v15 --output-dir benchmark_2000
    python scripts/benchmark_2000.py --n 2000 --attack v15 --offline --offline-budget 60
    python scripts/benchmark_2000.py --n 2000 --attack v15 --resume  # skip existing outputs
"""

from __future__ import annotations

import argparse
import base64
import io
import json
import os
import random
import sys
import time
from pathlib import Path
from types import SimpleNamespace
from typing import Any

sys.path.insert(0, str(Path(__file__).resolve().parents[1]))

import numpy as np
import torch
from PIL import Image

from perturbnet import constants as C
from perturbnet.attacks import ATTACKS
from perturbnet.image_io import decode_image_b64, encode_image_b64
from perturbnet.imagenet100_bootstrap import load_imagenet100
from perturbnet.model import load_efficientnet_v2_l, predict_label
from neurons.validator import ChallengeSpec, PerturbValidator

DEFAULT_SEED = 20260819


def build_validator_stub(*, device: torch.device, model: torch.nn.Module) -> PerturbValidator:
    """Minimal PerturbValidator that can run verify_and_score without a wallet."""
    config = SimpleNamespace(
        perturb=SimpleNamespace(
            min_linf_delta=C.MIN_LINF_DELTA,
            max_linf_delta=C.MAX_LINF_DELTA,
            min_ssim=C.MIN_SSIM,
            min_psnr_db=C.MIN_PSNR_DB,
            linf_component_weight=C.LINF_COMPONENT_WEIGHT,
            rmse_component_weight=C.RMSE_COMPONENT_WEIGHT,
            analyze_bucket_margin_weight=C.ANALYZE_BUCKET_MARGIN_WEIGHT,
            analyze_bucket_novelty_weight=C.ANALYZE_BUCKET_NOVELTY_WEIGHT,
            analyze_bucket_novelty_target_pixels=C.ANALYZE_BUCKET_NOVELTY_TARGET_PIXELS,
        )
    )
    stub: PerturbValidator = object.__new__(PerturbValidator)
    stub.config = config
    stub.device = device
    stub.model = model
    return stub


def pick_indices(num_rows: int, n: int, seed: int) -> list[int]:
    rng = random.Random(seed)
    return rng.sample(range(num_rows), min(n, num_rows))


def tensor_to_pil(image_chw: torch.Tensor) -> Image.Image:
    """Convert a float CHW tensor in [0,1] to a PIL RGB image."""
    arr = (image_chw.detach().clamp(0, 1).cpu().numpy() * 255).astype(np.uint8)
    arr = np.transpose(arr, (1, 2, 0))
    return Image.fromarray(arr, mode="RGB")


def save_tensor_png(path: Path, image_chw: torch.Tensor) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    tensor_to_pil(image_chw).save(path, format="PNG")


def load_clean_b64_and_label(dataset: Any, ds_idx: int, device: torch.device, model: torch.nn.Module) -> tuple[str, str]:
    """Return base64 JPEG of the clean image and its model-predicted label."""
    example = dataset[ds_idx]
    buffer = io.BytesIO()
    example["image"].convert("RGB").save(buffer, format="JPEG", quality=95)
    clean_b64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
    clean = decode_image_b64(clean_b64).to(device)
    true_label = predict_label(model=model, image_chw=clean)
    return clean_b64, true_label


def score_image(
    stub: PerturbValidator,
    *,
    clean_b64: str,
    adv_b64: str,
    true_label: str,
    ds_idx: int,
) -> Any:
    """Run the real validator scoring logic on an adversarial image."""
    challenge = ChallengeSpec(
        task_id=f"bench-{ds_idx:07d}",
        image_id=str(ds_idx),
        model_name=C.MODEL_NAME,
        clean_image_b64=clean_b64,
        true_label=true_label,
        epsilon=C.MAX_LINF_DELTA,
        norm_type="Linf",
    )
    return PerturbValidator.verify_and_score(stub, challenge=challenge, perturbed_image_b64=adv_b64)


def run_attack_and_record(
    *,
    ds_idx: int,
    clean_b64: str,
    true_label: str,
    attack_fn: Any,
    time_budget: float,
    model: torch.nn.Module,
    device: torch.device,
    stub: PerturbValidator,
    out_dir: Path,
    save_clean: bool = True,
    prefix: str = "",
    attempt: int | None = None,
) -> dict:
    """Run one attack on one image, save images + score JSON, return record."""
    clean = decode_image_b64(clean_b64).to(device)

    suffix = f"_attempt{attempt}" if attempt is not None else ""
    clean_path: Path | None = None
    if save_clean:
        clean_path = out_dir / f"{prefix}{ds_idx:07d}_clean.png"
        if not clean_path.exists():
            save_tensor_png(clean_path, clean)

    started = time.time()
    adv, info = attack_fn(model, clean, device, time_budget=time_budget)
    elapsed = time.time() - started

    adv_path = out_dir / f"{prefix}{ds_idx:07d}{suffix}_adv.png"
    save_tensor_png(adv_path, adv)

    adv_b64 = encode_image_b64(adv)
    result = score_image(stub, clean_b64=clean_b64, adv_b64=adv_b64, true_label=true_label, ds_idx=ds_idx)

    record: dict[str, Any] = {
        "ds_idx": ds_idx,
        "true_label": true_label,
        "score": result.score,
        "reason": result.reason,
        "prediction": result.model_prediction,
        "norm": result.norm,
        "rmse": result.rmse,
        "ssim": result.ssim,
        "psnr_db": result.psnr_db,
        "margin": result.margin,
        "time": elapsed,
        "attempt": attempt if attempt is not None else 0,
        "clean_path": str(clean_path) if save_clean else None,
        "adv_path": str(adv_path),
    }
    # Merge attack metadata, but avoid overwriting reserved keys.
    for k, v in info.items():
        if k not in record and k != "true_idx":
            record[k] = v

    json_path = out_dir / f"{prefix}{ds_idx:07d}{suffix}.json"
    json_path.write_text(json.dumps(record, indent=2, default=str))
    return record


def summarize(records: list[dict]) -> dict:
    n = len(records)
    succ = [r for r in records if r["reason"] == "success"]
    scores = [r["score"] for r in records]
    mean_score = sum(scores) / n if n else 0.0
    return {
        "n": n,
        "success_rate": len(succ) / n if n else 0.0,
        "avg_score": mean_score,
        "score_std": (sum((s - mean_score) ** 2 for s in scores) / n) ** 0.5 if n else 0.0,
        "min_score": min(scores, default=0.0),
        "max_score": max(scores, default=0.0),
        "p50_score": float(np.median(scores)) if scores else 0.0,
        "p95_score": float(np.percentile(scores, 95)) if scores else 0.0,
        "avg_norm_succ": sum(r["norm"] for r in succ) / len(succ) if succ else 0.0,
        "avg_rmse_succ": sum(r["rmse"] for r in succ) / len(succ) if succ else 0.0,
        "min_ssim_succ": min((r["ssim"] for r in succ), default=0.0),
        "avg_margin_succ": sum(r["margin"] for r in succ) / len(succ) if succ else 0.0,
        "margin_p05": float(np.percentile([r["margin"] for r in succ], 5)) if succ else 0.0,
        "margin_p50": float(np.percentile([r["margin"] for r in succ], 50)) if succ else 0.0,
        "avg_time": sum(r["time"] for r in records) / n if n else 0.0,
        "p95_time": float(np.percentile([r["time"] for r in records], 95)) if records else 0.0,
        "max_time": max((r["time"] for r in records), default=0.0),
        "fail_reasons": {
            reason: sum(1 for r in records if r["reason"] == reason)
            for reason in {r["reason"] for r in records if r["reason"] != "success"}
        },
    }


def run_online_benchmark(
    *,
    indices: list[int],
    attack_name: str,
    time_budget: float,
    output_dir: Path,
    device: torch.device,
    force: bool = False,
    worker_id: int = 0,
    workers: int = 1,
    all_indices: list[int] | None = None,
) -> dict:
    """Run the online attack on every sampled row, saving images and scores."""
    dataset = load_imagenet100()
    model = load_efficientnet_v2_l(device=device)
    stub = build_validator_stub(device=device, model=model)
    attack = ATTACKS[attack_name]

    online_dir = output_dir / attack_name
    online_dir.mkdir(parents=True, exist_ok=True)

    indices_path = output_dir / "indices.json"
    if worker_id == 0 and (not indices_path.exists() or force):
        # Persist the full sampled list so other workers and post-processing can see it.
        full = all_indices if all_indices is not None else indices
        indices_path.write_text(json.dumps({"seed": DEFAULT_SEED, "n": len(full), "indices": full}, indent=2))

    records: list[dict] = []
    prefix = f"[w{worker_id}] "
    for i, ds_idx in enumerate(indices):
        json_path = online_dir / f"{ds_idx:07d}.json"
        if json_path.exists() and not force:
            record = json.loads(json_path.read_text())
            records.append(record)
            print(f"{prefix}[{i + 1:04d}/{len(indices)}] idx={ds_idx} score={record['score']:.4f} (cached)")
            continue

        clean_b64, true_label = load_clean_b64_and_label(dataset, ds_idx, device, model)
        record = run_attack_and_record(
            ds_idx=ds_idx,
            clean_b64=clean_b64,
            true_label=true_label,
            attack_fn=attack,
            time_budget=time_budget,
            model=model,
            device=device,
            stub=stub,
            out_dir=online_dir,
            save_clean=True,
        )
        records.append(record)
        print(
            f"{prefix}[{i + 1:04d}/{len(indices)}] idx={ds_idx} score={record['score']:.4f} "
            f"reason={record['reason']} norm={record['norm']:.5f} rmse={record['rmse']:.5f} "
            f"ssim={record['ssim']:.4f} margin={record['margin']:.2f} time={record['time']:.1f}s "
            f"path={record.get('attack', 'n/a')}"
        )

    summary = summarize(records)
    summary.update({"attack": attack_name, "budget": time_budget, "mode": "online", "worker_id": worker_id, "workers": workers})
    summary_name = "summary.json" if workers == 1 else f"summary_worker{worker_id}.json"
    summary_path = online_dir / summary_name
    summary_path.write_text(json.dumps(summary, indent=2))
    return summary


def run_offline_benchmark(
    *,
    indices: list[int],
    attack_name: str,
    time_budget: float,
    retries: int,
    output_dir: Path,
    device: torch.device,
    force: bool = False,
) -> dict:
    """Run a stronger offline attack with retries on the same rows, keeping the best score."""
    dataset = load_imagenet100()
    model = load_efficientnet_v2_l(device=device)
    stub = build_validator_stub(device=device, model=model)
    attack = ATTACKS[attack_name]

    offline_dir = output_dir / "offline"
    offline_dir.mkdir(parents=True, exist_ok=True)

    records: list[dict] = []
    for i, ds_idx in enumerate(indices):
        json_path = offline_dir / f"{ds_idx:07d}.json"
        if json_path.exists() and not force:
            record = json.loads(json_path.read_text())
            records.append(record)
            print(f"[offline {i + 1:04d}/{len(indices)}] idx={ds_idx} score={record['score']:.4f} (cached)")
            continue

        clean_b64, true_label = load_clean_b64_and_label(dataset, ds_idx, device, model)

        best_record: dict | None = None
        for attempt in range(1 + retries):
            record = run_attack_and_record(
                ds_idx=ds_idx,
                clean_b64=clean_b64,
                true_label=true_label,
                attack_fn=attack,
                time_budget=time_budget,
                model=model,
                device=device,
                stub=stub,
                out_dir=offline_dir,
                save_clean=(attempt == 0),
                attempt=attempt,
            )
            if best_record is None or record["score"] > best_record["score"]:
                best_record = record
            if record["score"] >= 0.965:
                break

        assert best_record is not None
        best_attempt = best_record.get("attempt", 0)

        # Promote the best attempt's files to the canonical names.
        final_adv_path = offline_dir / f"{ds_idx:07d}_adv.png"
        final_json_path = offline_dir / f"{ds_idx:07d}.json"
        best_adv_path = offline_dir / f"{ds_idx:07d}_attempt{best_attempt}_adv.png"
        best_json_path = offline_dir / f"{ds_idx:07d}_attempt{best_attempt}.json"
        if best_adv_path.exists() and best_adv_path != final_adv_path:
            if final_adv_path.exists():
                final_adv_path.unlink()
            os.replace(best_adv_path, final_adv_path)
        if best_json_path.exists() and best_json_path != final_json_path:
            if final_json_path.exists():
                final_json_path.unlink()
            os.replace(best_json_path, final_json_path)

        best_record["attempts"] = 1 + retries
        best_record["best_attempt"] = best_attempt
        best_record["adv_path"] = str(final_adv_path)
        final_json_path.write_text(json.dumps(best_record, indent=2, default=str))
        records.append(best_record)
        print(
            f"[offline {i + 1:04d}/{len(indices)}] idx={ds_idx} score={best_record['score']:.4f} "
            f"best_attempt={best_attempt} reason={best_record['reason']} time={best_record['time']:.1f}s"
        )

    summary = summarize(records)
    summary.update({"attack": attack_name, "budget": time_budget, "retries": retries, "mode": "offline"})
    summary_path = offline_dir / "summary.json"
    summary_path.write_text(json.dumps(summary, indent=2))
    return summary


def main() -> None:
    parser = argparse.ArgumentParser(description="2000-image Perturb attack benchmark")
    parser.add_argument("--n", type=int, default=2000, help="Number of images to sample")
    parser.add_argument("--seed", type=int, default=DEFAULT_SEED, help="Random seed for sampling")
    parser.add_argument("--attack", type=str, default="v15", choices=list(ATTACKS.keys()), help="Attack version")
    parser.add_argument("--budget", type=float, default=25.0, help="Online attack time budget in seconds")
    parser.add_argument("--output-dir", type=Path, default=Path("benchmark_2000"), help="Output directory")
    parser.add_argument("--force", action="store_true", help="Overwrite existing per-image JSONs")
    parser.add_argument("--offline", action="store_true", help="Also run offline precompute benchmark")
    parser.add_argument("--offline-attack", type=str, default="v15", choices=list(ATTACKS.keys()), help="Offline attack version")
    parser.add_argument("--offline-budget", type=float, default=60.0, help="Offline attack time budget per image")
    parser.add_argument("--offline-retries", type=int, default=2, help="Extra retry attempts per image offline")
    parser.add_argument("--workers", type=int, default=1, help="Number of parallel workers (multi-GPU or low-GPU-util fill)")
    parser.add_argument("--worker-id", type=int, default=0, help="Worker ID for this process [0, workers)")
    args = parser.parse_args()

    if args.worker_id < 0 or args.worker_id >= args.workers:
        raise ValueError(f"--worker-id must be in [0, {args.workers}), got {args.worker_id}")

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print(f"[info] worker={args.worker_id}/{args.workers} device={device} n={args.n} seed={args.seed} attack={args.attack}")

    dataset = load_imagenet100()
    total_rows = int(dataset.num_rows)
    all_indices = pick_indices(total_rows, args.n, args.seed)

    # Each worker takes its shard of the full sampled list.
    indices = [idx for i, idx in enumerate(all_indices) if i % args.workers == args.worker_id]
    print(f"[info] sampled {len(all_indices)} rows from {total_rows}; this worker handles {len(indices)}")

    args.output_dir.mkdir(parents=True, exist_ok=True)

    online_summary = run_online_benchmark(
        indices=indices,
        attack_name=args.attack,
        time_budget=args.budget,
        output_dir=args.output_dir,
        device=device,
        force=args.force,
        worker_id=args.worker_id,
        workers=args.workers,
        all_indices=all_indices,
    )
    print("\n" + "=" * 64)
    print(f"ONLINE SUMMARY (worker {args.worker_id}/{args.workers})")
    print("=" * 64)
    print(json.dumps(online_summary, indent=2))

    if args.offline:
        offline_summary = run_offline_benchmark(
            indices=indices,
            attack_name=args.offline_attack,
            time_budget=args.offline_budget,
            retries=args.offline_retries,
            output_dir=args.output_dir,
            device=device,
            force=args.force,
        )
        print("\n" + "=" * 64)
        print("OFFLINE SUMMARY")
        print("=" * 64)
        print(json.dumps(offline_summary, indent=2))

    print(f"\n[info] outputs saved to {args.output_dir}")


if __name__ == "__main__":
    main()