dev_scripts/benchmark.py

"""Benchmark script for NisabaRelief inference pipeline."""

import argparse
import statistics
import time
from datetime import datetime
from pathlib import Path

import numpy as np
from PIL import Image
from rich.console import Console
from rich.progress import (
    BarColumn,
    MofNCompleteColumn,
    Progress,
    TextColumn,
    TimeElapsedColumn,
)
from rich.table import Table

from nisaba_relief import NisabaRelief
from util.load_val_dataset import load_val_dataset

BENCHMARK_DIR = Path(__file__).parent.parent / "data" / "benchmark"
BASELINE = BENCHMARK_DIR / "benchmark_baseline.png"
WARMUP_RUNS = 2
BENCH_RUNS = 3


def build_timing_table(timings: list[float], n_warmup: int) -> Table:
    bench_timings = timings[n_warmup:]
    mean = statistics.mean(bench_timings)
    stdev = statistics.stdev(bench_timings) if len(bench_timings) > 1 else 0.0
    table = Table(title="Inference Timings")
    table.add_column("Run", justify="right")
    table.add_column("Time", justify="right")
    for i, t in enumerate(timings, 1):
        label = f"[dim]{i} (warmup)[/dim]" if i <= n_warmup else str(i - n_warmup)
        time_str = f"[dim]{t:.2f}s[/dim]" if i <= n_warmup else f"{t:.2f}s"
        table.add_row(label, time_str)
    table.add_section()
    table.add_row("[bold]Mean[/bold]", f"[bold]{mean:.2f} ± {stdev:.2f}s[/bold]")
    return table


def build_diff_table(flat: np.ndarray, max_diff: int) -> Table:
    percentile_vals = np.percentile(flat, [50, 90, 95, 96, 97, 98, 99])
    p98 = percentile_vals[5]
    status = "PASS" if p98 <= 1 else "FAIL"
    status_style = "green" if status == "PASS" else "red"
    table = Table(
        title=f"Pixel Diff vs Baseline  —  [{status_style}]{status}[/{status_style}]"
    )
    table.add_column("Stat", style="bold")
    table.add_column("Value", justify="right")
    table.add_row("Mean", f"{flat.mean():.4f}")
    for label, val in zip(
        ["p50", "p90", "p95", "p96", "p97", "p98", "p99"], percentile_vals
    ):
        table.add_row(label, f"{val:.0f}")
    table.add_row("Max", str(max_diff))
    return table


def main():
    parser = argparse.ArgumentParser(
        description="Benchmark NisabaRelief inference pipeline"
    )
    parser.add_argument(
        "--weights-dir",
        default=".",
        metavar="PATH",
        help="path to weights directory (default: .)",
    )
    parser.add_argument(
        "--device",
        default=None,
        metavar="DEVICE",
        help="device to run inference on, e.g. cuda, cpu (default: cuda if available, else cpu)",
    )
    args = parser.parse_args()

    console = Console()
    rows = load_val_dataset()
    test_image = rows[0]["photo"]
    max_dim = max(test_image.size)
    if max_dim > 2048:
        scale = 2048 / max_dim
        new_size = (round(test_image.width * scale), round(test_image.height * scale))
        test_image = test_image.resize(new_size, Image.LANCZOS)
    console.print(f"Input size: [cyan]{test_image.width}x{test_image.height}[/cyan]")

    model_kwargs = dict(seed=42, weights_dir=Path(args.weights_dir))
    if args.device is not None:
        model_kwargs["device"] = args.device
    model = NisabaRelief(**model_kwargs)

    timings = []
    output = None
    total_runs = WARMUP_RUNS + BENCH_RUNS
    progress = Progress(
        TextColumn("[progress.description]{task.description}"),
        BarColumn(),
        MofNCompleteColumn(),
        TimeElapsedColumn(),
    )
    with progress:
        task = progress.add_task("Benchmarking", total=total_runs)
        for i in range(total_runs):
            t0 = time.perf_counter()
            result = model.process(test_image, show_pbar=False)
            timings.append(time.perf_counter() - t0)
            progress.advance(task)
            if i == WARMUP_RUNS:
                output = result

    console.print(build_timing_table(timings, WARMUP_RUNS))

    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    run_path = BENCHMARK_DIR / f"benchmark_{timestamp}.png"
    run_path.parent.mkdir(parents=True, exist_ok=True)
    output.save(run_path)
    console.print(f"Run image saved to [cyan]{run_path}[/cyan]")

    output_arr = np.array(output)

    if not BASELINE.exists():
        output.save(BASELINE)
        console.print(f"Baseline saved to [cyan]{BASELINE}[/cyan]")
    else:
        baseline_arr = np.array(Image.open(BASELINE))
        diff = np.abs(output_arr.astype(int) - baseline_arr.astype(int))
        flat = diff.flatten()
        max_diff = int(flat.max())
        console.print(build_diff_table(flat, max_diff))

        if max_diff > 0:
            diff_img = Image.fromarray(
                np.clip(diff * (255 // max_diff), 0, 255).astype("uint8")
            )
            diff_path = Path(f"benchmark_{timestamp}_diff.png")
            diff_img.save(diff_path)
            console.print(
                f"Diff image saved to [cyan]{diff_path}[/cyan] (amplified {255 // max_diff}x)"
            )


if __name__ == "__main__":
    main()