File size: 6,768 Bytes
2bfd19c | 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 | #!/usr/bin/env python3
"""Generate markdown comparison report from hparam sweep CSV."""
import argparse
import csv
from datetime import datetime
from pathlib import Path
def score_row(r):
try:
psnr = float(r["psnr_db"])
if psnr == float("inf"):
psnr = 100.0
reuse = float(r["reuse_rate_pct"] or 0)
wall = float(r["wall_sec"] or 99999)
# Quality-first with mild speed bonus; penalize very slow
return psnr + 0.015 * reuse - 0.0001 * wall
except (ValueError, TypeError):
return -9999
def fmt_psnr(v):
try:
f = float(v)
if f > 50:
return "∞"
return f"{f:.2f} dB"
except (ValueError, TypeError):
return "N/A"
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--results", required=True)
parser.add_argument("--baseline", required=True)
parser.add_argument("--output", required=True)
parser.add_argument("--sweep_dir", default="")
args = parser.parse_args()
rows = list(csv.DictReader(open(args.results)))
dev3 = [r for r in rows if r["version"] == "dev3" and r["psnr_db"] not in ("NA", "")]
dev4 = [r for r in rows if r["version"] == "dev4" and r["psnr_db"] not in ("NA", "")]
dev3_full = [r for r in rows if r["version"] == "dev3_full" and r["psnr_db"] not in ("NA", "")]
dev4_full = [r for r in rows if r["version"] == "dev4_full" and r["psnr_db"] not in ("NA", "")]
best_dev3 = max(dev3, key=score_row) if dev3 else None
best_dev4 = max(dev4, key=score_row) if dev4 else None
lines = [
"# MotionCache (dev3) vs MotionDetailCache (dev4) 超参对比报告",
"",
f"生成时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
"",
f"Baseline：`{args.baseline}`（FlowCache 全量推理）",
f"Sweep 目录：`{args.sweep_dir}`",
"",
"## 评分方法",
"",
"综合得分 = PSNR + 0.015 × reuse_rate(%) − 0.0001 × wall_time(s)",
"（画质优先，适度奖励更高 reuse、更短耗时）",
"",
]
if best_dev3:
lines += [
"## dev3 最优超参（120 帧 sweep）",
"",
f"| 参数 | 值 |",
f"|------|-----|",
f"| rel_l1_thresh (τ) | **{best_dev3['tau']}** |",
f"| alpha | {best_dev3['alpha']} |",
f"| PSNR | {fmt_psnr(best_dev3['psnr_db'])} |",
f"| SSIM | {best_dev3['ssim']} |",
f"| reuse_rate | {best_dev3['reuse_rate_pct']}% |",
f"| 耗时 | {best_dev3['wall_sec']}s |",
f"| variant | `{best_dev3['variant']}` |",
"",
]
if best_dev4:
lines += [
"## dev4 最优超参（120 帧 sweep，τ 固定为 dev3 最优）",
"",
f"| 参数 | 值 |",
f"|------|-----|",
f"| rel_l1_thresh (τ) | {best_dev4['tau']} |",
f"| detail_alpha | **{best_dev4['detail_alpha']}** |",
f"| detail_window_size | **{best_dev4['detail_window']}** |",
f"| weight_combine_mode | **{best_dev4['combine_mode']}** |",
f"| detail_lambda | {best_dev4['detail_lambda']} |",
f"| PSNR | {fmt_psnr(best_dev4['psnr_db'])} |",
f"| SSIM | {best_dev4['ssim']} |",
f"| reuse_rate | {best_dev4['reuse_rate_pct']}% |",
f"| 耗时 | {best_dev4['wall_sec']}s |",
f"| variant | `{best_dev4['variant']}` |",
"",
]
lines += ["## dev3 全量 τ sweep 结果", "", "| τ | PSNR | reuse% | 耗时(s) | 得分 |", "|---|------|--------|---------|------|"]
for r in sorted(dev3, key=lambda x: float(x["tau"])):
lines.append(
f"| {r['tau']} | {fmt_psnr(r['psnr_db'])} | {r['reuse_rate_pct']} | {r['wall_sec']} | {score_row(r):.3f} |"
)
lines.append("")
lines += ["## dev4 全量 detail sweep 结果", "", "| mode | win | d_α | λ | PSNR | reuse% | 耗时(s) | 得分 |", "|------|-----|-----|---|------|--------|---------|------|"]
for r in sorted(dev4, key=score_row, reverse=True):
lines.append(
f"| {r['combine_mode']} | {r['detail_window']} | {r['detail_alpha']} | {r['detail_lambda']} "
f"| {fmt_psnr(r['psnr_db'])} | {r['reuse_rate_pct']} | {r['wall_sec']} | {score_row(r):.3f} |"
)
lines.append("")
if dev3_full or dev4_full:
lines += ["## 240 帧全分辨率验证", "", "| 版本 | PSNR | reuse% | 耗时(s) |", "|------|------|--------|---------|"]
for r in dev3_full + dev4_full:
lines.append(f"| {r['version']} ({r['variant']}) | {fmt_psnr(r['psnr_db'])} | {r['reuse_rate_pct']} | {r['wall_sec']} |")
lines.append("")
if best_dev3 and best_dev4:
d3p = float(best_dev3["psnr_db"]) if best_dev3["psnr_db"] != "inf" else 100
d4p = float(best_dev4["psnr_db"]) if best_dev4["psnr_db"] != "inf" else 100
d3r = float(best_dev3["reuse_rate_pct"] or 0)
d4r = float(best_dev4["reuse_rate_pct"] or 0)
d3t = float(best_dev3["wall_sec"])
d4t = float(best_dev4["wall_sec"])
lines += [
"## 结论摘要",
"",
f"- **dev3 推荐配置**：τ={best_dev3['tau']}，PSNR {fmt_psnr(best_dev3['psnr_db'])}，reuse {d3r:.1f}%",
f"- **dev4 推荐配置**：mode={best_dev4['combine_mode']}, window={best_dev4['detail_window']}, "
f"detail_α={best_dev4['detail_alpha']}, λ={best_dev4['detail_lambda']}，"
f"PSNR {fmt_psnr(best_dev4['psnr_db'])}，reuse {d4r:.1f}%",
f"- dev4 vs dev3 PSNR 差：{d4p - d3p:+.2f} dB；reuse 差：{d4r - d3r:+.1f}%；耗时差：{d4t - d3t:+.0f}s",
"",
"### 推荐 yaml 片段",
"",
"**dev3** (`motioncache_config.yaml`):",
"```yaml",
f"rel_l1_thresh: {best_dev3['tau']}",
"alpha: 0.5",
"phase1_steps: 9",
"warmup_steps: 5",
"```",
"",
"**dev4** (`motiondetail_config.yaml`):",
"```yaml",
f"rel_l1_thresh: {best_dev4['tau']}",
f"detail_alpha: {best_dev4['detail_alpha']}",
f"detail_window_size: {int(float(best_dev4['detail_window']))}",
f"weight_combine_mode: {best_dev4['combine_mode']}",
f"detail_lambda: {best_dev4['detail_lambda']}",
"alpha: 0.5",
"phase1_steps: 9",
"warmup_steps: 5",
"```",
]
Path(args.output).write_text("\n".join(lines) + "\n", encoding="utf-8")
print(f"Report written to {args.output}")
if __name__ == "__main__":
main()
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