| import os, json, numpy as np |
| from datetime import datetime, timezone |
|
|
| OUTPUT_ROOT = "/root/autodl-tmp/SplatAtlas/outputs/error_responsibility" |
| PROF_DIR = os.path.join(OUTPUT_ROOT, "_profiles") |
|
|
| METHODS = ['vanilla_3dgs', 'analyticsplatting', 'erankgs', 'ges', 'lightgaussian', 'minisplatting', 'opti3dgs', 'pgsr', 'steepgs'] |
| SCENES = ['bicycle','bonsai','counter','flowers','garden','kitchen','room','stump','treehill', |
| 'auditorium','ballroom','barn','caterpillar','courtroom','lighthouse','museum','palace','playground','temple','train','truck', |
| 'DrJohnson','Playroom', |
| 'Chair','Drums','Ficus','Hotdog','Lego','Materials','Mic','Ship'] |
|
|
| METRICS = ["psnr_full", "sh_net_effect", "sh_corruption_rate", "opacity_net_effect", "coverage", "coverage_error_fraction", "residual_error"] |
|
|
| |
| method_medians = {m: {} for m in METHODS} |
| for m in METHODS: |
| for metric in METRICS: |
| vals = [] |
| for s in SCENES: |
| p = os.path.join(OUTPUT_ROOT, f"{m}_{s}", "counterfactual_summary.json") |
| if os.path.exists(p): |
| with open(p) as f: |
| d = json.load(f) |
| if d.get(metric) is not None: |
| vals.append(d[metric]) |
| method_medians[m][metric] = float(np.median(vals)) if vals else 0.0 |
|
|
| |
| def calc_iqr_stats(vals_list): |
| if not vals_list: return None, None, None |
| med = float(np.median(vals_list)) |
| abs_iqr = float(np.percentile(vals_list, 75) - np.percentile(vals_list, 25)) |
| rel_iqr = (abs_iqr / abs(med) * 100) if med != 0 else None |
| return med, abs_iqr, rel_iqr |
|
|
| cross_method_analysis = {"n_methods": len(METHODS), "methods_included": METHODS, "metrics": {}} |
|
|
| |
| psnr_vals = [method_medians[m]["psnr_full"] for m in METHODS] |
| _, _, psnr_rel_iqr = calc_iqr_stats(psnr_vals) |
|
|
| for metric in METRICS: |
| vals = [method_medians[m][metric] for m in METHODS] |
| med, abs_iqr, rel_iqr = calc_iqr_stats(vals) |
| ratio_rel = (rel_iqr / psnr_rel_iqr) if (rel_iqr is not None and psnr_rel_iqr) else None |
| |
| cross_method_analysis["metrics"][metric] = { |
| "median_of_9_methods": med, |
| "relative_iqr_pct": rel_iqr, |
| "ratio_vs_psnr_relative": ratio_rel |
| } |
|
|
| |
| md_lines = [ |
| "# Phase 2 Part 3b — Final Heterogeneous Array Findings", |
| f"\n**Scope**: {len(METHODS)} methods (Vanilla + 8 heterogeneous), {len(SCENES)} scenes.", |
| "\n## Ultimate Discriminative Power (Relative IQR)", |
| f"> **Baseline**: PSNR Relative IQR across 9 methods = **{psnr_rel_iqr:.2f}%**", |
| "\n| Metric | Relative IQR (%) | Ratio vs PSNR (Discriminative Multiplier) |", |
| "|---|---|---|" |
| ] |
|
|
| for metric in METRICS: |
| if metric == "psnr_full": continue |
| d = cross_method_analysis["metrics"][metric] |
| rel_str = f"{d['relative_iqr_pct']:.2f}%" if d['relative_iqr_pct'] is not None else "N/A" |
| ratio_str = f"**{d['ratio_vs_psnr_relative']:.1f}x**" if d['ratio_vs_psnr_relative'] is not None else "N/A" |
| md_lines.append(f"| {metric} | {rel_str} | {ratio_str} |") |
|
|
| md_content = "\n".join(md_lines) |
| with open(os.path.join(PROF_DIR, "part3b_findings.md"), "w") as f: |
| f.write(md_content) |
|
|
| print(md_content) |
|
|
