File size: 3,528 Bytes
0a2e9f2 | 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 | import json
from pathlib import Path
import statistics
from tabulate import tabulate # pip install tabulate
def parse_metrics_dir(model_update_dir: Path):
l1_vals, ssim_vals, lpips_vals, psnr_vals, fid_vals = [], [], [], [], []
per_clip_records = []
fvd_3drn50, fvd_3dinception = None, None
for file in sorted(model_update_dir.iterdir()):
if file.name.endswith("_metrics_l1_ssim_lpips_psnr.json"):
clip_id = file.name.split("_metrics_l1_ssim_lpips_psnr.json")[0]
with open(file) as f:
data = json.load(f)
l1_vals.append(data.get("L1"))
ssim_vals.append(data.get("SSIM"))
lpips_vals.append(data.get("LPIPS"))
psnr_vals.append(data.get("PSNR"))
fid_file = model_update_dir / f"{clip_id}_pytorch_fid.txt"
fid_val = None
if fid_file.exists():
with open(fid_file) as ff:
content = ff.read().strip()
try:
fid_val = float(content.split()[-1])
fid_vals.append(fid_val)
except Exception:
pass
per_clip_records.append([
clip_id,
f"{data.get('L1'):.6f}",
f"{data.get('SSIM'):.6f}",
f"{data.get('LPIPS'):.6f}",
f"{data.get('PSNR'):.6f}",
f"{fid_val:.6f}" if fid_val is not None else "N/A"
])
elif file.name == "metrics_fid-vid_fvd_dtssd_16frames.json":
with open(file) as f:
data = json.load(f)
fvd_3drn50 = data.get("FVD-3DRN50")
fvd_3dinception = data.get("FVD-3DInception")
metrics_summary = [
statistics.mean(l1_vals) if l1_vals else None,
statistics.mean(ssim_vals) if ssim_vals else None,
statistics.mean(lpips_vals) if lpips_vals else None,
statistics.mean(psnr_vals) if psnr_vals else None,
statistics.mean(fid_vals) if fid_vals else None,
fvd_3drn50,
fvd_3dinception
]
return metrics_summary, per_clip_records
def main():
base_dir = Path("../temp_eval_tiktok_frames")
# base_dir = Path("../temp_eval_cartoon_frames")
model_summary_all = []
clip_detail_all = []
for model_name in sorted(base_dir.iterdir()):
if model_name.is_dir():
metrics_summary, per_clip_records = parse_metrics_dir(model_name)
model_summary_all.append(
[model_name] + [f"{v:.6f}" if v is not None else "N/A" for v in metrics_summary]
)
for clip_row in per_clip_records:
clip_detail_all.append([model_name] + clip_row)
summary_header = ["Model", "L1_avg", "SSIM_avg", "LPIPS_avg", "PSNR_avg",
"FID_avg", "FVD-3DRN50", "FVD-3DInception"]
detail_header = ["Model", "VideoID", "L1", "SSIM", "LPIPS", "PSNR", "FID"]
summary_table = tabulate(model_summary_all, headers=summary_header, tablefmt="grid")
detail_table = tabulate(clip_detail_all, headers=detail_header, tablefmt="grid")
(base_dir / "model_summary.txt").write_text(summary_table)
(base_dir / "clip_details.txt").write_text(detail_table)
print("\n===== 模型总体指标 =====")
print(summary_table)
print("\n===== 每视频详细指标 =====")
print(detail_table)
print(f"\n[DONE] 已保存到: {base_dir}/model_summary.txt 和 clip_details.txt")
if __name__ == "__main__":
main()
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