import os import sys import random import torch import numpy as np import torch.nn.functional as F from argparse import ArgumentParser from core.registry import register_method from core.base_method import BaseMethod # [Isolation Step 1] 剔除所有路径污染 sys.path = [p for p in sys.path if 'taming-3dgs' not in p] # [Isolation Step 2] 插入正确路径 gsshader_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../gsshader_official')) if gsshader_root not in sys.path: sys.path.insert(0, gsshader_root) # [Isolation Step 3] 强制清理模块缓存，防止重名污染 for m in list(sys.modules.keys()): base_m = m.split('.')[0] if base_m in ["utils", "scene", "gaussian_renderer", "arguments"]: del sys.modules[m] from utils.loss_utils import l1_loss, ssim, predicted_normal_loss, delta_normal_loss, zero_one_loss from gaussian_renderer import render as native_render from scene import Scene, GaussianModel from arguments import ModelParams, PipelineParams, OptimizationParams @register_method("gsshader") class GSShaderWrapper(BaseMethod): def __init__(self, dataset_config, hyperparams): self.parser = ArgumentParser() self.lp = ModelParams(self.parser) self.op = OptimizationParams(self.parser) self.pp = PipelineParams(self.parser) self.args, _ = self.parser.parse_known_args([]) self.args.source_path = dataset_config["source_path"] self.args.model_path = dataset_config["model_path"] self.args.eval = True self.args.resolution = dataset_config.get("resolution", 1) self.track_decoupling = hyperparams.get("track_decoupling", False) self.dataset = self.lp.extract(self.args) self.opt = self.op.extract(self.args) self.pipe = self.pp.extract(self.args) self.gaussians = GaussianModel( self.dataset.sh_degree, self.dataset.brdf_dim, self.dataset.brdf_mode, self.dataset.brdf_envmap_res ) self.scene = Scene(self.dataset, self.gaussians) self.gaussians.training_setup(self.opt) bg_color = [1, 1, 1] if self.dataset.white_background else [0, 0, 0] self.background = torch.tensor(bg_color, dtype=torch.float32, device="cuda") self.viewpoint_stack = self.scene.getTrainCameras().copy() self.last_n_gaussians = len(self.gaussians.get_xyz) def train_iteration(self, step): self.gaussians.update_learning_rate(step) if step % 1000 == 0: self.gaussians.oneupSHdegree() if not self.viewpoint_stack: self.viewpoint_stack = self.scene.getTrainCameras().copy() viewpoint_cam = self.viewpoint_stack.pop(random.randint(0, len(self.viewpoint_stack) - 1)) if self.pipe.brdf: self.gaussians.set_requires_grad("normal", state=step >= self.opt.normal_reg_from_iter) self.gaussians.set_requires_grad("normal2", state=step >= self.opt.normal_reg_from_iter) if self.gaussians.brdf_mode == "envmap": self.gaussians.brdf_mlp.build_mips() render_pkg = native_render(viewpoint_cam, self.gaussians, self.pipe, self.background, debug=False) image = render_pkg["render"] viewspace_point_tensor = render_pkg["viewspace_points"] visibility_filter = render_pkg["visibility_filter"] radii = render_pkg["radii"] gt_image = viewpoint_cam.original_image.cuda() Ll1 = l1_loss(image, gt_image) loss_target = (1.0 - self.opt.lambda_dssim) * Ll1 + self.opt.lambda_dssim * (1.0 - ssim(image, gt_image)) loss_parasitic = torch.tensor(0.0, device="cuda") if self.pipe.brdf and step > self.opt.normal_reg_from_iter: if step < self.opt.normal_reg_util_iter: loss_parasitic += self.opt.lambda_predicted_normal * predicted_normal_loss(render_pkg["normal"], render_pkg["normal_ref"], render_pkg["alpha"]) loss_parasitic += self.opt.lambda_zero_one * zero_one_loss(render_pkg["alpha"]) if "delta_normal_norm" in render_pkg.keys(): loss_parasitic += self.opt.lambda_delta_reg * delta_normal_loss(render_pkg["delta_normal_norm"], render_pkg["alpha"]) loss = loss_target + loss_parasitic grad_cos_sim = 0.0 parasitic_ratio = 0.0 if self.track_decoupling and step % 100 == 0 and loss_parasitic.item() > 0: self.gaussians.optimizer.zero_grad(set_to_none=True) loss_target.backward(retain_graph=True) grad_target = self.gaussians._xyz.grad.clone() if self.gaussians._xyz.grad is not None else torch.zeros_like(self.gaussians._xyz) self.gaussians.optimizer.zero_grad(set_to_none=True) loss_parasitic.backward(retain_graph=True) grad_parasitic = self.gaussians._xyz.grad.clone() if self.gaussians._xyz.grad is not None else torch.zeros_like(self.gaussians._xyz) valid_mask = (torch.norm(grad_target, dim=1) > 0) & (torch.norm(grad_parasitic, dim=1) > 0) if valid_mask.any(): grad_cos_sim = float(F.cosine_similarity(grad_target[valid_mask], grad_parasitic[valid_mask], dim=1).mean()) parasitic_ratio = float(torch.norm(grad_parasitic, dim=1).mean() / (torch.norm(grad_target, dim=1).mean() + 1e-7)) self.gaussians.optimizer.zero_grad(set_to_none=True) loss.backward() else: loss.backward() with torch.no_grad(): if step < self.opt.densify_until_iter: self.gaussians.max_radii2D[visibility_filter] = torch.max(self.gaussians.max_radii2D[visibility_filter], radii[visibility_filter]) self.gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter) if step > self.opt.densify_from_iter and step % self.opt.densification_interval == 0: size_threshold = 20 if step > self.opt.opacity_reset_interval else None self.gaussians.densify_and_prune(self.opt.densify_grad_threshold, 0.005, self.scene.cameras_extent, size_threshold) if step % self.opt.opacity_reset_interval == 0 or (self.dataset.white_background and step == self.opt.densify_from_iter): self.gaussians.reset_opacity() self.gaussians.optimizer.step() self.gaussians.optimizer.zero_grad(set_to_none=True) if self.pipe.brdf and self.pipe.brdf_mode == "envmap": self.gaussians.brdf_mlp.clamp_(min=0.0, max=1.0) num_gaussians = self.gaussians.get_xyz.shape[0] metrics = { "loss": float(loss), "loss_l1": float(loss_target), "loss_ssim": float(loss_parasitic), "num_gaussians": int(num_gaussians), "delta_N": int(num_gaussians - self.last_n_gaussians), "peak_vram_GB": float(torch.cuda.max_memory_allocated() / (1024 ** 3)), "grad_cos_sim": float(grad_cos_sim), "parasitic_ratio": float(parasitic_ratio) } self.last_n_gaussians = num_gaussians histograms = {} if step % 1000 == 0: histograms["opacity"] = torch.sigmoid(self.gaussians._opacity).clone().detach() scales = torch.exp(self.gaussians._scaling).clone().detach() histograms["scaling"] = scales scales_2d = scales[:, :2] if scales.shape[1] >= 2 else scales gamma = scales_2d.max(dim=-1)[0] / (scales_2d.min(dim=-1)[0] + 1e-7) histograms["anisotropy"] = gamma histograms["sh_dc_mag"] = self.gaussians._features_dc.detach().norm(dim=-1) return metrics, histograms def render(self, camera): with torch.no_grad(): render_pkg = native_render(camera, self.gaussians, self.pipe, self.background, debug=False) return {"image": render_pkg["render"], "depth": render_pkg.get("depth", None)} def save(self, save_dir, step): self.scene.save(step) def load(self, model_path, iteration): self.gaussians.load_ply(os.path.join(model_path, 'point_cloud', f'iteration_{iteration}', 'point_cloud.ply')) def get_spatial_centers(self): return self.gaussians._xyz def compute_physical_metrics(self, cameras=None): metrics = {} with torch.no_grad(): raw_scales = self.gaussians._scaling scales = torch.exp(raw_scales) scales_2d = scales[:, :2] if scales.dim() > 1 and scales.shape[1] >= 2 else scales.unsqueeze(-1).expand(-1, 2) max_S, _ = torch.max(scales_2d, dim=1) min_S, _ = torch.min(scales_2d, dim=1) gamma = max_S / (min_S + 1e-7) metrics["gamma_median"] = float(torch.median(gamma)) metrics["gamma_90th_percentile"] = float(torch.quantile(gamma, 0.90)) metrics["scale_mean"] = float(torch.mean(scales_2d)) metrics["alpha_mean"] = float(torch.mean(torch.sigmoid(self.gaussians._opacity))) dc, rest = self.gaussians._features_dc, self.gaussians._features_rest if rest is not None and rest.shape[1] > 0: metrics["sh_energy_ratio"] = float(rest.norm(dim=-1).mean() / (dc.norm(dim=-1).mean() + 1e-7)) if self.pipe.brdf: metrics["roughness_mean"] = float(torch.mean(torch.sigmoid(self.gaussians._roughness + self.gaussians.roughness_bias))) metrics["specular_mean"] = float(torch.mean(torch.sigmoid(self.gaussians._specular))) if cameras is not None and len(cameras) > 0: view_dirs = [] for c in cameras: view_dirs.append(c.world_view_transform[:3, 2].tolist()) view_dirs = F.normalize(torch.tensor(view_dirs, dtype=torch.float32, device="cuda"), dim=1) rots = F.normalize(self.gaussians._rotation.clone(), dim=1) w, x, y, z = rots.unbind(dim=-1) normals = F.normalize(torch.stack([2*(x*z + w*y), 2*(y*z - w*x), 1-2*(x*x + y*y)], dim=-1), dim=1) max_cos, _ = torch.max(torch.abs(torch.matmul(normals, view_dirs.T)), dim=1) metrics["billboard_bias_ratio"] = float((max_cos > 0.90).float().mean()) return metrics def evaluate_spatial_field(self, query_points: torch.Tensor, cameras=None) -> torch.Tensor: with torch.no_grad(): V = query_points.shape[0] densities = torch.zeros(V, device="cuda") xyz, opacities = self.gaussians._xyz, torch.sigmoid(self.gaussians._opacity).squeeze() scales = torch.exp(self.gaussians._scaling) sigma_sq = (scales[:, :2].max(dim=1)[0].pow(2)) if scales.shape[1] >= 2 else scales.squeeze().pow(2) N_gaussians = xyz.shape[0] chunk_size = max(1, 30_000_000 // (N_gaussians + 1)) for i in range(0, V, chunk_size): end = min(i + chunk_size, V) dist_sq = torch.cdist(query_points[i:end], xyz, p=2).pow(2) weights = torch.exp(-0.5 * dist_sq / (sigma_sq.unsqueeze(0) + 1e-7)) densities[i:end] = torch.sum(weights * opacities.unsqueeze(0), dim=1) return densities