File size: 5,005 Bytes
3050f1b | 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 | """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()
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