| |
| |
| """ |
| DepthART 两路深度输出的相关性/一致性对比。 |
| |
| 支持三种输入格式，自动识别： |
| - 汇总 NPZ（names/depths），如 ONNX 脚本 --dump-npz 生成的文件 |
| - 分块汇总目录（*.npz 中含 names/depths） |
| - 旧的逐图 NPZ 目录（<图片名>.npz 中含 depth） |
| |
| 指标（逐图计算后汇总 mean/p50/p90/min）： |
| pearson 皮尔逊相关系数 |
| spearman 斯皮尔曼秩相关 |
| cosine 余弦相似度 |
| mae / rmse 原始量纲误差 |
| aligned_* 最小二乘 scale-shift 对齐后的误差 |
| rel_error mae / mean(|ref|) |
| |
| 用法： |
| ../../venv/bin/python src/tool/compare_depth_outputs.py \ |
| --ref results/nyu654/fp32/npz --pred results/nyu654/axmodel \ |
| --out results/nyu654/eval/corr_fp32_axmodel.json |
| """ |
|
|
| import argparse |
| import json |
| from pathlib import Path |
|
|
| import numpy as np |
| from scipy.stats import spearmanr |
| from tqdm import tqdm |
|
|
|
|
| def load_predictions(path): |
| path = Path(path) |
| if path.is_file(): |
| files = [path] |
| elif path.is_dir(): |
| files = sorted(path.glob('*.npz')) |
| if not files: |
| raise ValueError(f'目录中没有 NPZ: {path}') |
| else: |
| raise FileNotFoundError(f'路径不存在: {path}') |
|
|
| preds = {} |
| for fp in files: |
| with np.load(fp, allow_pickle=False) as data: |
| if 'depths' in data and 'names' in data: |
| for name, depth in zip(data['names'], data['depths']): |
| preds[str(name)] = np.asarray(depth) |
| elif 'depth' in data: |
| preds[fp.name[:-len('.npz')]] = np.asarray(data['depth']) |
| else: |
| raise ValueError(f'无法识别的 NPZ: {fp}') |
| return preds |
|
|
|
|
| def compare(ref, pred): |
| a = np.asarray(ref, dtype=np.float64).ravel() |
| b = np.asarray(pred, dtype=np.float64).ravel() |
| if a.shape != b.shape: |
| raise ValueError(f'shape 不一致: {a.shape} vs {b.shape}') |
| diff = b - a |
| mae = float(np.mean(np.abs(diff))) |
| rmse = float(np.sqrt(np.mean(diff ** 2))) |
| cosine = float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-12)) |
| pearson = float(np.corrcoef(a, b)[0, 1]) |
| spearman = float(spearmanr(a, b).statistic) |
| slope, offset = np.polyfit(a, b, 1) |
| aligned = b - (slope * a + offset) |
| return { |
| 'pearson': pearson, |
| 'spearman': spearman, |
| 'cosine': cosine, |
| 'mae': mae, |
| 'rmse': rmse, |
| 'rel_error': mae / (np.mean(np.abs(a)) + 1e-12), |
| 'scale': float(slope), |
| 'offset': float(offset), |
| 'aligned_mae': float(np.mean(np.abs(aligned))), |
| 'aligned_rmse': float(np.sqrt(np.mean(aligned ** 2))), |
| } |
|
|
|
|
| def summarize(rows): |
| keys = ('pearson', 'spearman', 'cosine', 'rel_error', 'aligned_rmse') |
| out = {} |
| for k in keys: |
| values = np.array([r[k] for r in rows]) |
| out[k] = {'mean': float(values.mean()), 'p50': float(np.percentile(values, 50)), |
| 'p90': float(np.percentile(values, 90)), 'min': float(values.min())} |
| return out |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description='两路深度输出相关性对比') |
| parser.add_argument('--ref', required=True, help='参考输出（基准后端）') |
| parser.add_argument('--pred', required=True, help='待比较输出') |
| parser.add_argument('--out', help='结果 JSON 路径') |
| args = parser.parse_args() |
|
|
| ref = load_predictions(args.ref) |
| pred = load_predictions(args.pred) |
| names = sorted(set(ref) & set(pred)) |
| if not names: |
| raise ValueError('两路输出没有同名样本') |
| missing = (len(ref) - len(names)) + (len(pred) - len(names)) |
| if missing: |
| print(f'警告: {missing} 个样本名称不匹配，已忽略') |
|
|
| rows = [] |
| for name in tqdm(names, desc='compare', ncols=80): |
| rows.append({'name': name, **compare(ref[name], pred[name])}) |
|
|
| summary = summarize(rows) |
| low = [r for r in rows if r['pearson'] < 0.9] |
| worst = sorted(rows, key=lambda r: r['pearson'])[:5] |
| report = { |
| 'ref': str(args.ref), 'pred': str(args.pred), |
| 'num_samples': len(rows), |
| 'summary': summary, |
| 'pearson_below_0.9': len(low), |
| 'worst': [{'name': r['name'], 'pearson': r['pearson'], |
| 'mae': r['mae'], 'aligned_rmse': r['aligned_rmse']} for r in worst], |
| 'per_image': rows, |
| } |
| print(f'\n样本数 {len(rows)}，pearson<0.9 的 {len(low)} 张') |
| print(f'{"指标":<14}{"mean":>12}{"p50":>12}{"p90":>12}{"min":>12}') |
| for k, v in summary.items(): |
| print(f'{k:<14}{v["mean"]:>12.4f}{v["p50"]:>12.4f}{v["p90"]:>12.4f}{v["min"]:>12.4f}') |
| print('\n最差 5 张:') |
| for r in worst: |
| print(f' {r["name"]:<32} pearson={r["pearson"]:.4f} mae={r["mae"]:.4f}') |
|
|
| if args.out: |
| Path(args.out).parent.mkdir(parents=True, exist_ok=True) |
| with open(args.out, 'w') as fp: |
| json.dump(report, fp, indent=2) |
| print(f'\n结果已保存: {args.out}') |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
