File size: 2,883 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 67 68 69 70 71 | import os, subprocess
SPLATATLAS = "/root/autodl-tmp/SplatAtlas"
BASE = "/root/autodl-tmp/SplatAtlas/outputs"
# 映射表：(环境路径, 额外 PYTHONPATH)
ENV_MAP = {
"vanilla_3dgs": ("base", "/root/autodl-tmp/3dgs_official"),
"analyticsplatting": ("/root/autodl-tmp/envs/ana_splatting", "/root/autodl-tmp/analyticsplatting_official"),
"erankgs": ("/root/autodl-tmp/envs/erank_gs", "/root/autodl-tmp/erank_gs"),
"pgsr": ("/root/autodl-tmp/envs/pgsr_v2", "/root/autodl-tmp/pgsr_official")
}
def get_py_and_env(method):
if method not in ENV_MAP:
return "python", os.environ.copy()
env_path, extra_py = ENV_MAP[method]
python_bin = "python" if env_path == "base" else f"{env_path}/bin/python"
env = os.environ.copy()
env["PYTHONPATH"] = f"{SPLATATLAS}:{extra_py}:{env.get('PYTHONPATH', '')}"
# 缓解 LPIPS 评测时的 PyTorch 显存碎片化 OOM
env["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
return python_bin, env
targets = [
("vanilla_3dgs", "bonsai", "/root/autodl-tmp/dataset/360/bonsai", 2),
("analyticsplatting", "bonsai", "/root/autodl-tmp/dataset/360/bonsai", 2),
("analyticsplatting", "Lego", "/root/autodl-tmp/dataset/Synthetic_NeRF_Verified/Synthetic_NeRF/Lego", 1),
("erankgs", "bonsai", "/root/autodl-tmp/dataset/360/bonsai", 2),
("pgsr", "bonsai", "/root/autodl-tmp/dataset/360/bonsai", 2)
]
seeds = ["", "_seed1", "_seed2"]
for method, scene, src, res in targets:
python_bin, custom_env = get_py_and_env(method)
for suffix in seeds:
m_dir = f"{BASE}/{method}{suffix}_{scene}"
metrics_f = f"{m_dir}/metrics_test_iter30000.json"
ply_f = f"{m_dir}/point_cloud/iteration_30000/point_cloud.ply"
if os.path.exists(metrics_f):
continue
if not os.path.exists(ply_f):
print(f"[-] SKIPPING {method}{suffix}_{scene}: PLY not found")
continue
print(f"\n[*] FIXING: {method}{suffix}_{scene}")
print(f" -> Using Python: {python_bin}")
# 1. 渲染 (强制覆盖)
render_cmd = f"{python_bin} {SPLATATLAS}/scripts/main_render.py --method {method} --source_path {src} --model_path {m_dir} --iteration 30000 --resolution {res}"
subprocess.run(render_cmd, shell=True, env=custom_env)
# 2. 评估
renders = f"{m_dir}/renders_test_30000"
gt = f"{m_dir}/gt_test_30000"
depths = f"{m_dir}/depths_test_30000"
eval_cmd = (
f"{python_bin} {SPLATATLAS}/ufd_evalkit/run_eval.py "
f"--method {method} --scene {scene} --render_dir {renders} "
f"--gt_dir {gt} --ply_path {ply_f} --output_json {metrics_f} "
f"--colmap_dir {src} --depth_dir {depths}"
)
subprocess.run(eval_cmd, shell=True, env=custom_env)
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