#!/usr/bin/env python """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()