File size: 2,706 Bytes
23e73f9 | 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 | import os
import json
import pandas as pd
def main():
outputs_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "outputs"))
data = []
for folder in os.listdir(outputs_dir):
method_dir = os.path.join(outputs_dir, folder)
if not os.path.isdir(method_dir) or folder == "pathology_archive":
continue
# 优先寻找批量管线的终点文件
metrics_file = os.path.join(method_dir, "metrics_test_iter30000.json")
if not os.path.exists(metrics_file):
metrics_file = os.path.join(method_dir, "metrics.json")
train_metrics_file = os.path.join(method_dir, "metrics_train_iter30000.json")
physics_file = os.path.join(method_dir, "offline_physics_30000.json")
row = {"Method": folder.split('_')[0], "Scene": folder.split('_')[1] if '_' in folder else "unknown"}
if os.path.exists(metrics_file):
with open(metrics_file, 'r') as f:
m = json.load(f)
row["PSNR_Test"] = m.get("photometric", {}).get("PSNR", 0)
row["SSIM"] = m.get("photometric", {}).get("SSIM", 0)
row["Prior"] = m.get("prior_type", "none")
row["Flags"] = len(m.get("pathology_flags", []))
# 提取 Train PSNR 并计算你的核心主张：Delta PSNR
if os.path.exists(train_metrics_file):
with open(train_metrics_file, 'r') as tf:
tm = json.load(tf)
row["PSNR_Train"] = tm.get("photometric", {}).get("PSNR", 0)
row["Delta_PSNR"] = row["PSNR_Train"] - row["PSNR_Test"]
if os.path.exists(physics_file):
with open(physics_file, 'r') as f:
p = json.load(f)
# 修正了旧版错误的字典查找，完美对齐新数据
row["Gini"] = p.get("spatial_gini", 0)
row["SH_Ratio"] = p.get("sh_energy_ratio", 0)
row["Condition"] = p.get("scale_condition_mean", 0)
row["Billboard_Bias"] = p.get("billboard_bias_ratio", 0)
if len(row) > 2:
data.append(row)
if not data:
return
df = pd.DataFrame(data).round(3)
csv_path = os.path.join(outputs_dir, "grand_leaderboard.csv")
df.to_csv(csv_path, index=False)
latex_str = df.to_latex(index=False, float_format="%.3f")
with open(os.path.join(outputs_dir, "grand_leaderboard.tex"), "w") as f:
f.write(latex_str)
print(f"📊 [GrandTable] 大榜单生成完毕！共收录 {len(data)} 个模型数据。")
if __name__ == "__main__":
main()
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