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"""Round-4 — renderer-level diagnosis."""
import os, sys, inspect, json, math, struct
import numpy as np
import torch
from PIL import Image
from plyfile import PlyData
sys.path.insert(0, '/root/autodl-tmp/3dgsAtlas_official')
import gsplat
DATASET_ROOT = "/root/autodl-tmp/dataset/tnt"
OUTPUT_ROOT = "/root/autodl-tmp/SplatAtlas/outputs"
SCENES = [("truck", "PASS"), ("lighthouse", "FAIL")]
def sec(t):
print("\n" + "=" * 70); print(f" {t}"); print("=" * 70)
def build_args(source_path, img_dir):
from scene.dataset_readers import readColmapSceneInfo
sig = inspect.signature(readColmapSceneInfo)
a = []
for i, (k, p) in enumerate(sig.parameters.items()):
if i == 0: a.append(source_path)
elif i == 1: a.append(img_dir)
elif k == "eval": a.append(True)
elif k == "train_test_exp": a.append(False)
else: a.append(p.default if p.default != inspect.Parameter.empty else "")
return a
# ---------- Probe 14: cfg_args (训练超参) ----------
def probe_cfg_args(scene):
sec(f"PROBE 14 — Training hyperparameters (cfg_args) [{scene}]")
cell = os.path.join(OUTPUT_ROOT, f"vanilla_3dgs_{scene}")
for name in ["cfg_args", "args.json", "config.json", "training_args.json",
"hparams.json"]:
p = os.path.join(cell, name)
if os.path.exists(p):
print(f" <{name}>")
content = open(p).read()
print(content)
for key in ['white_background', 'sh_degree', 'resolution', 'background',
'convert_SHs_python', 'images']:
if key in content:
print(f" [!] contains key: '{key}'")
return
print(" (no cfg_args-like file found)")
print(" listing cell dir contents:")
for f in sorted(os.listdir(cell))[:30]:
p = os.path.join(cell, f)
t = "DIR" if os.path.isdir(p) else "FIL"
print(f" {t} {f}")
# ---------- Probe 15: PLY SH 完整性 ----------
def probe_ply_struct(scene):
sec(f"PROBE 15 — PLY structure & SH completeness [{scene}]")
ply = os.path.join(OUTPUT_ROOT, f"vanilla_3dgs_{scene}",
"point_cloud", "iteration_30000", "point_cloud.ply")
pd = PlyData.read(ply)
v = pd['vertex']
props = v.data.dtype.names
print(f" N gaussians: {len(v)}")
print(f" N properties: {len(props)}")
f_rest_count = sum(1 for p in props if p.startswith('f_rest_'))
print(f" f_dc_* count: {sum(1 for p in props if p.startswith('f_dc_'))}")
print(f" f_rest_* count: {f_rest_count} "
f"(SH full 3-band needs 45; implied sh_degree="
f"{'3' if f_rest_count == 45 else '?'})")
# opacity/scale/rot 分布
for k in ['opacity', 'scale_0', 'f_dc_0', 'f_rest_0']:
if k in props:
vals = np.asarray(v[k])
print(f" {k:<10} min={vals.min():.3f} max={vals.max():.3f} "
f"mean={vals.mean():.3f} std={vals.std():.3f}")
# ---------- Probe 16: Per-image PSNR breakdown ----------
def probe_per_image(scene):
"""直接复刻 render_single.py 的渲染循环，但这次按 Ours order 逐张
输出 PSNR + 和对应 Native render 同 cam 的 PSNR，找到崩最狠的几张。"""
sec(f"PROBE 16 — Per-image PSNR breakdown & delta per-cam [{scene}]")
from scene.dataset_readers import readColmapSceneInfo
from utils.graphics_utils import getWorld2View2
from utils.general_utils import PILtoTorch
from PIL import Image, ImageOps
source_path = os.path.join(DATASET_ROOT, scene)
cell = os.path.join(OUTPUT_ROOT, f"vanilla_3dgs_{scene}")
img_dir = "images_2"
resolution = 2
scene_info = readColmapSceneInfo(*build_args(source_path, img_dir))
test_cams = scene_info.test_cameras
print(f" test cams: {len(test_cams)}")
# 构建 Native renders index: native_render_name → image
native_render_dir = os.path.join(cell, "renders_test_30000")
if os.path.isdir(os.path.join(native_render_dir, "renders")):
native_render_dir = os.path.join(native_render_dir, "renders")
native_renders = sorted([f for f in os.listdir(native_render_dir)
if f.lower().endswith(('.png', '.jpg'))])
native_gt_dir = os.path.join(cell, "gt_test_30000")
native_gts = sorted([f for f in os.listdir(native_gt_dir)
if f.lower().endswith(('.png', '.jpg'))])
# 用 reverse-lookup (thumb) 找出 Native idx i 对应的真图名，从而把
# Native render 按真图名索引
THUMB = 128
i2_files = sorted([f for f in os.listdir(os.path.join(source_path, img_dir))
if f.lower().endswith(('.png', '.jpg', '.jpeg'))])
i2_thumbs = np.stack([
np.asarray(Image.open(os.path.join(source_path, img_dir, f)).convert('RGB')
.resize((THUMB, THUMB), Image.LANCZOS), dtype=np.float32) / 255.0
for f in i2_files])
native_name_for = {} # native_idx → real image_name
for idx, ng in enumerate(native_gts):
t = np.asarray(Image.open(os.path.join(native_gt_dir, ng)).convert('RGB')
.resize((THUMB, THUMB), Image.LANCZOS), dtype=np.float32) / 255.0
mse = ((i2_thumbs - t) ** 2).mean(axis=(1, 2, 3))
native_name_for[idx] = i2_files[int(np.argmin(mse))]
real_to_native_idx = {v: k for k, v in native_name_for.items()}
# 加载 PLY
pd = PlyData.read(os.path.join(cell, "point_cloud", "iteration_30000",
"point_cloud.ply"))
vv = pd['vertex']
device = torch.device("cuda")
def t32(x): return torch.tensor(x, dtype=torch.float32, device=device)
means = t32(np.stack((vv['x'], vv['y'], vv['z']), -1))
quats = t32(np.stack((vv['rot_0'], vv['rot_1'], vv['rot_2'], vv['rot_3']), -1))
scales = torch.exp(t32(np.stack((vv['scale_0'], vv['scale_1'], vv['scale_2']), -1)))
opacities = torch.sigmoid(t32(np.asarray(vv['opacity'])))
f_dc = t32(np.stack((vv['f_dc_0'], vv['f_dc_1'], vv['f_dc_2']), -1)).unsqueeze(1)
f_rest = t32(np.stack([vv[f'f_rest_{i}'] for i in range(45)], -1))
f_rest = f_rest.view(-1, 3, 15).transpose(1, 2)
shs = torch.cat([f_dc, f_rest], dim=1)
bg_color = torch.tensor([0., 0., 0.], device=device)
print(f"\n {'i':>3} {'cam_name':>15} {'ours_psnr':>10} {'nat_psnr':>10} "
f"{'delta':>8} {'render_vs_render':>18}")
print(" " + "-" * 75)
ours_psnrs, native_psnrs_this = [], []
render_vs_render = []
for i, c in enumerate(test_cams):
w = int(round(c.width / resolution))
h = int(round(c.height / resolution))
viewmat = t32(getWorld2View2(np.array(c.R), np.array(c.T))).unsqueeze(0)
fx = w / (2 * math.tan(c.FovX / 2))
fy = h / (2 * math.tan(c.FovY / 2))
K = t32(np.array([[fx, 0, w/2], [0, fy, h/2], [0, 0, 1]])).unsqueeze(0)
with torch.no_grad():
colors, _, _ = gsplat.rasterization(
means=means, quats=quats, scales=scales, opacities=opacities,
colors=shs, viewmats=viewmat, Ks=K, width=w, height=h,
sh_degree=3, packed=True, render_mode='RGB',
backgrounds=bg_color.unsqueeze(0))
ours_rgb = colors[0].clamp(0, 1)
# 读对应 GT
pil = Image.open(os.path.join(source_path, img_dir, c.image_name))
pil = ImageOps.exif_transpose(pil)
gt_chw = PILtoTorch(pil, (w, h)).to(device)
if gt_chw.shape[0] == 4: gt_chw = gt_chw[:3]
gt_rgb = gt_chw.permute(1, 2, 0)
mse = ((ours_rgb - gt_rgb) ** 2).mean().item()
ours_p = 10 * math.log10(1.0 / max(mse, 1e-10))
ours_psnrs.append(ours_p)
# 对应的 Native render
nat_p_str, vs_str = "—", "—"
if c.image_name in real_to_native_idx:
nidx = real_to_native_idx[c.image_name]
nr_path = os.path.join(native_render_dir, native_renders[nidx])
ng_path = os.path.join(native_gt_dir, native_gts[nidx])
nr = np.asarray(Image.open(nr_path).convert('RGB'), dtype=np.float32) / 255.0
ng = np.asarray(Image.open(ng_path).convert('RGB'), dtype=np.float32) / 255.0
if nr.shape == ng.shape:
nmse = ((nr - ng) ** 2).mean()
nat_p = 10 * math.log10(1.0 / max(nmse, 1e-10))
native_psnrs_this.append(nat_p)
nat_p_str = f"{nat_p:.2f}"
# render vs render
ours_np = (ours_rgb.cpu().numpy() * 255).clip(0, 255).astype(np.uint8)
nr_u8 = (nr * 255).astype(np.uint8)
if ours_np.shape == nr_u8.shape:
vs_mse = ((ours_np.astype(np.float32)/255 - nr_u8.astype(np.float32)/255) ** 2).mean()
vs = 10 * math.log10(1.0 / max(vs_mse, 1e-10))
render_vs_render.append(vs)
vs_str = f"{vs:.2f}"
d = ours_p - (native_psnrs_this[-1] if native_psnrs_this else ours_p)
if i < 10 or (nat_p_str != "—" and abs(ours_p - float(nat_p_str)) > 2):
print(f" {i:>3} {c.image_name:>15} {ours_p:>10.2f} {nat_p_str:>10} "
f"{d:>+8.2f} {vs_str:>18}")
print(f"\n Ours mean PSNR : {np.mean(ours_psnrs):.4f} dB")
if native_psnrs_this:
print(f" Native mean PSNR (recomputed) : {np.mean(native_psnrs_this):.4f} dB")
print(f" Mean delta : "
f"{np.mean(ours_psnrs) - np.mean(native_psnrs_this):+.4f} dB")
if render_vs_render:
arr = np.array(render_vs_render)
print(f"\n gsplat_render vs Native_render (per-cam PSNR):")
print(f" mean={arr.mean():.2f} min={arr.min():.2f} max={arr.max():.2f}")
print(f" >35 dB (near identical): {(arr>35).sum()}/{len(arr)}")
print(f" 25-35 dB : {((arr>=25)&(arr<=35)).sum()}/{len(arr)}")
print(f" <25 dB (visible diff) : {(arr<25).sum()}/{len(arr)}")
if arr.mean() > 35:
print(" [结论] 两个 renderer 输出几乎一致 → 锅不在 renderer")
elif arr.mean() > 25:
print(" [结论] renderer 有明显差异，但在合理范围")
else:
print(" [结论] renderer 输出差异巨大 → gsplat vs diff-gaussian-rasterization 不等价")
def main():
for scene, label in SCENES:
print(f"\n\n{'#'*70}\n# SCENE: {scene} [{label}]\n{'#'*70}")
probe_cfg_args(scene)
probe_ply_struct(scene)
probe_per_image(scene)
if __name__ == "__main__":
main()
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