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import sys
import subprocess
import json
import csv
import glob
from tqdm import tqdm
BASE_DIR = "/root/autodl-tmp/SplatAtlas"
OUT_BASE = os.path.join(BASE_DIR, "outputs")
CSV_PATH = os.path.join(BASE_DIR, "scripts/phase1_validation/validation_results_anchor.csv")
ERR_LOG = os.path.join(BASE_DIR, "scripts/phase1_validation/batch_errors.log")
METHODS = [
"3dgsmcmc", "analyticsplatting", "atomgs", "coadaptgs", "conegs",
"erankgs", "ges", "ghap", "gof", "gslpm", "lightgaussian", "opti3dgs",
"pgsr", "reactgs", "steepgs", "vanilla_3dgs", "absgs", "gaussianpro",
"minisplatting", "pixelgs"
]
SCENES = {
"bonsai": {"dataset": "mip360_indoor", "res": 2, "bg": "0,0,0", "source": "/root/autodl-tmp/dataset/360/bonsai"},
"garden": {"dataset": "mip360_outdoor", "res": 4, "bg": "0,0,0", "source": "/root/autodl-tmp/dataset/360/garden"},
"truck": {"dataset": "tnt", "res": 1, "bg": "0,0,0", "source": "/root/autodl-tmp/dataset/tnt/truck"},
"drjohnson": {"dataset": "db", "res": 1, "bg": "0,0,0", "source": "/root/autodl-tmp/dataset/deepblending_clean/DrJohnson"}
}
def log_error(cell_name, phase, stdout, stderr):
with open(ERR_LOG, "a") as f:
f.write(f"\n{'='*50}\n[{phase} ERROR] Cell: {cell_name}\n")
if stdout: f.write(f"--- STDOUT (last 20 lines) ---\n" + "\n".join(stdout.strip().split("\n")[-20:]) + "\n")
if stderr: f.write(f"--- STDERR (last 20 lines) ---\n" + "\n".join(stderr.strip().split("\n")[-20:]) + "\n")
f.write(f"{'='*50}\n")
def get_actual_cell_dir(method, scene):
target = f"{method}_{scene}".lower()
if os.path.exists(OUT_BASE):
for d in os.listdir(OUT_BASE):
if d.lower() == target:
return os.path.join(OUT_BASE, d)
return None
def phase_a_native(method, scene_key, scene_cfg, cell_dir):
json_path = os.path.join(cell_dir, "metrics_test_iter30000.json")
if os.path.exists(json_path):
try:
with open(json_path, 'r') as f:
data = json.load(f)
if "PSNR" in data or ("photometric" in data and "PSNR" in data["photometric"]):
return True
except:
pass
# Step 1: Render
cmd_render = [
"python", "scripts/main_render.py", "--method", method,
"--source_path", scene_cfg["source"], "--model_path", cell_dir,
"--iteration", "30000", "--resolution", str(scene_cfg["res"])
]
res = subprocess.run(cmd_render, cwd=BASE_DIR, capture_output=True, text=True)
if res.returncode != 0:
log_error(f"{method}_{scene_key}", "PHASE_A_RENDER", res.stdout, res.stderr)
return False
# Step 2: Compute Metrics
py_metric = f"""
import sys, json, os
sys.path.append("ufd_evalkit")
from photometric import compute_photometric_metrics
r_dir = "{cell_dir}/renders_test_30000/renders"
g_dir = "{cell_dir}/renders_test_30000/gt"
if not os.path.exists(r_dir):
r_dir = "{cell_dir}/renders_test_30000"
g_dir = "{cell_dir}/gt_test_30000"
metrics = compute_photometric_metrics(r_dir, g_dir)
with open("{json_path}", "w") as f:
json.dump({{"PSNR": metrics.get("PSNR", 0)}}, f, indent=4)
"""
res2 = subprocess.run(["python", "-c", py_metric], cwd=BASE_DIR, capture_output=True, text=True)
if res2.returncode != 0:
log_error(f"{method}_{scene_key}", "PHASE_A_METRIC", res2.stdout, res2.stderr)
return False
return True
def phase_b_gsplat(ply_path, scene_cfg, cell_dir, cell_name):
out_dir = os.path.join(OUT_BASE, "phase1_validation", cell_name)
os.makedirs(out_dir, exist_ok=True)
cmd = [
"python", "scripts/phase1_validation/render_single.py",
"--ply_path", ply_path, "--source_path", scene_cfg["source"],
"--model_path", cell_dir, "--output_dir", out_dir,
"--resolution", str(scene_cfg["res"]), "--bg_color", scene_cfg["bg"]
]
res = subprocess.run(cmd, cwd=BASE_DIR, capture_output=True, text=True)
if res.returncode != 0:
notes = "ERROR_B"
if "CUDA out of memory" in res.stderr or "CUDA out of memory" in res.stdout:
notes = "OOM"
elif "plyfile" in res.stderr and "format" in res.stderr:
notes = "PLY format incompatible"
log_error(cell_name, "PHASE_B_GSPLAT", res.stdout, res.stderr)
return None, None, None, notes
native_psnr, gsplat_psnr, delta = None, None, None
for line in res.stdout.split('\n'):
if "Mean PSNR (ours/gsplat)" in line:
try: gsplat_psnr = float(line.split(":")[1].strip().split()[0])
except: pass
if "Mean PSNR (native baseline)" in line:
try: native_psnr = float(line.split(":")[1].strip().split()[0])
except: pass
if "Delta" in line and "dB" in line and "STATUS" not in line:
try: delta = float(line.split(":")[1].strip().split()[0])
except: pass
# Fallback to json if regex fails
if gsplat_psnr is None:
try:
with open(os.path.join(out_dir, "psnr_results.json"), "r") as f:
d = json.load(f)
gsplat_psnr = float(np.mean(list(d.values())))
except: pass
notes = ""
if gsplat_psnr is not None and gsplat_psnr < 15.0:
notes = "quantized PLY? low PSNR"
return native_psnr, gsplat_psnr, delta, notes
def main():
print("=== SplatAtlas Phase 1 Task B: Anchor Batch Validation ===")
completed = set()
write_header = not os.path.exists(CSV_PATH)
if not write_header:
with open(CSV_PATH, "r") as f:
reader = csv.DictReader(f)
for row in reader:
completed.add((row['method'], row['scene']))
total_cells = len(METHODS) * len(SCENES)
pbar = tqdm(total=total_cells, desc="Validating Cells", unit="cell")
with open(CSV_PATH, "a", newline='') as f:
writer = csv.writer(f)
if write_header:
writer.writerow(["method", "scene", "dataset", "resolution", "has_ply", "native_psnr", "gsplat_psnr", "delta_db", "status", "notes"])
for method in METHODS:
for scene, cfg in SCENES.items():
if (method, scene) in completed:
pbar.update(1)
continue
cell_dir = get_actual_cell_dir(method, scene)
if not cell_dir:
writer.writerow([method, scene, cfg["dataset"], cfg["res"], "False", "", "", "", "SKIP", "Directory missing"])
f.flush()
pbar.set_postfix_str(f"[{method}/{scene}] SKIP (No Dir)")
pbar.update(1)
continue
ply_path = os.path.join(cell_dir, "point_cloud/iteration_30000/point_cloud.ply")
if not os.path.exists(ply_path):
writer.writerow([method, scene, cfg["dataset"], cfg["res"], "False", "", "", "", "SKIP", "PLY missing"])
f.flush()
pbar.set_postfix_str(f"[{method}/{scene}] SKIP (No PLY)")
pbar.update(1)
continue
cell_name = os.path.basename(cell_dir)
# Phase A
if not phase_a_native(method, scene, cfg, cell_dir):
writer.writerow([method, scene, cfg["dataset"], cfg["res"], "True", "", "", "", "ERROR_A", "Native render/eval failed"])
f.flush()
pbar.set_postfix_str(f"[{method}/{scene}] ERROR_A")
pbar.update(1)
continue
# Phase B
native, gsplat, delta, notes = phase_b_gsplat(ply_path, cfg, cell_dir, cell_name)
if native is None and gsplat is None:
writer.writerow([method, scene, cfg["dataset"], cfg["res"], "True", "", "", "", "ERROR_B", notes])
f.flush()
pbar.set_postfix_str(f"[{method}/{scene}] ERROR_B")
else:
if delta is None and native is not None and gsplat is not None:
delta = gsplat - native
status = "FAIL"
if delta is not None:
if abs(delta) < 0.3: status = "PASS"
elif abs(delta) < 0.5: status = "WARN"
writer.writerow([method, scene, cfg["dataset"], cfg["res"], "True",
f"{native:.4f}" if native else "",
f"{gsplat:.4f}" if gsplat else "",
f"{delta:.4f}" if delta else "",
status, notes])
f.flush()
pbar.set_postfix_str(f"[{method}/{scene}] {status} Δ={delta:+.2f}dB")
pbar.update(1)
pbar.close()
# Generate Summary
stats = {"PASS": 0, "WARN": 0, "FAIL": 0, "SKIP": 0, "ERROR": 0}
method_summary = {m: {"PASS": 0, "total_run": 0, "fails": []} for m in METHODS}
with open(CSV_PATH, "r") as f:
reader = csv.DictReader(f)
for row in reader:
st = row['status']
if st.startswith("ERROR"): stats["ERROR"] += 1
elif st in stats: stats[st] += 1
m = row['method']
if m in method_summary and st not in ("SKIP", "ERROR_A"):
method_summary[m]["total_run"] += 1
if st == "PASS": method_summary[m]["PASS"] += 1
elif st in ("FAIL", "WARN", "ERROR_B"):
method_summary[m]["fails"].append(f"{row['scene']} Δ={row['delta_db']} ({st})")
total = sum(stats.values())
print("\n" + "="*40)
print("==== BATCH SUMMARY ====")
print(f" PASS: {stats['PASS']}/{total}")
print(f" WARN: {stats['WARN']}")
print(f" FAIL: {stats['FAIL']}")
print(f" SKIP: {stats['SKIP']}")
print(f" ERROR: {stats['ERROR']}")
print("\nFAIL/WARN Details by Method:")
all_pass = []
all_fail = []
mixed = []
for m, d in method_summary.items():
if d["total_run"] == 0: continue
if d["PASS"] == d["total_run"]:
all_pass.append(m)
elif d["PASS"] == 0:
all_fail.append(m)
print(f" {m}: ALL FAIL/ERROR -> " + ", ".join(d["fails"]))
else:
mixed.append(m)
print(f" {m}: Mixed -> " + ", ".join(d["fails"]))
print("\nMethod Classification:")
print(f" Fully PASS ({len(all_pass)}): " + ", ".join(all_pass))
print(f" Fully FAIL/ERR ({len(all_fail)}) [Needs DataLoader Branch]: " + ", ".join(all_fail))
print(f" Mixed ({len(mixed)}) [Scene-dependent issues]: " + ", ".join(mixed))
print("========================================")
print("BATCH ANCHOR DONE")
print(f"See: {CSV_PATH}")
if __name__ == "__main__":
main()
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