import os import sys import torch import numpy as np import torch.nn.functional as F from omegaconf import OmegaConf from hydra import initialize_config_dir, compose from hydra.core.global_hydra import GlobalHydra from core.registry import register_method from core.base_method import BaseMethod sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../3dgrut'))) from threedgrut.trainer import Trainer3DGRUT from threedgrut.optimizers import SelectiveAdam class MockScene: def getTrainCameras(self): return [] def getTestCameras(self): return [] @register_method("3dgut") class ThreeDGUTWrapper(BaseMethod): def __init__(self, dataset_config, hyperparams): self.track_decoupling = hyperparams.get("track_decoupling", False) GlobalHydra.instance().clear() config_dir = os.path.abspath("/root/autodl-tmp/3dgrut/configs") with initialize_config_dir(version_base=None, config_dir=config_dir): ds_colmap = "dataset=colmap" try: compose(config_name="base_gs", overrides=["dataset=colmap"]) except Exception: ds_colmap = "+dataset=colmap" ds_nerf = "dataset=nerf" try: compose(config_name="base_gs", overrides=["dataset=nerf"]) except Exception: ds_nerf = "+dataset=nerf" overrides = [ "+render=3dgut", f"++dataset.path={dataset_config['source_path']}", f"++path={dataset_config['source_path']}", f"++out_dir={dataset_config['model_path']}", "++experiment_name=benchmark", "++n_iterations=30000", "++loss.use_l1=True", "++loss.lambda_l1=0.8", "++loss.use_ssim=True", "++loss.lambda_ssim=0.2", "++with_gui=False", "++with_viser_gui=False", ] if "Synthetic_NeRF" in str(dataset_config["source_path"]): overrides.append(ds_nerf) else: overrides.append(ds_colmap) overrides.append(f"++dataset.downsample_factor={dataset_config.get('resolution', 1)}") conf = compose(config_name="base_gs", overrides=overrides) OmegaConf.set_struct(conf, False) if "initialization" not in conf: conf.initialization = OmegaConf.create({}) if "Synthetic_NeRF" in str(dataset_config["source_path"]): conf.initialization.method = "random" conf.initialization.num_gaussians = 100000 else: conf.initialization.method = "colmap" conf.initialization.use_observation_points = False if not hasattr(conf.dataset, "test_split_interval"): conf.dataset.test_split_interval = 8 if not hasattr(conf.dataset, "eval"): conf.dataset.eval = True self.trainer = Trainer3DGRUT(conf) self.model = self.trainer.model self.data_iter = iter(self.trainer.train_dataloader) self.last_n_gaussians = self.model.num_gaussians self.scene = MockScene() def train_iteration(self, step): try: batch = next(self.data_iter) except StopIteration: self.data_iter = iter(self.trainer.train_dataloader) batch = next(self.data_iter) gpu_batch = self.trainer.train_dataset.get_gpu_batch_with_intrinsics(batch) outputs = self.model(gpu_batch, train=True, frame_id=step) batch_losses = self.trainer.get_losses(gpu_batch, outputs) loss_target = batch_losses.get("l1_loss", torch.tensor(0.0, device="cuda")) loss_parasitic = batch_losses.get("ssim_loss", torch.tensor(0.0, device="cuda")) loss = batch_losses.get("total_loss", loss_target + loss_parasitic) self.trainer.strategy.pre_backward( step=step, scene_extent=self.trainer.scene_extent, train_dataset=self.trainer.train_dataset, batch=gpu_batch, writer=None, ) grad_cos_sim = 0.0 parasitic_ratio = 0.0 if self.track_decoupling and step % 100 == 0: self.model.optimizer.zero_grad(set_to_none=True) loss_target.backward(retain_graph=True) grad_target = self.model.positions.grad.clone() if self.model.positions.grad is not None else torch.zeros_like(self.model.positions) self.model.optimizer.zero_grad(set_to_none=True) loss_parasitic.backward(retain_graph=True) grad_parasitic = self.model.positions.grad.clone() if self.model.positions.grad is not None else torch.zeros_like(self.model.positions) state = self.model.optimizer.state.get(self.model.positions, None) if state is not None and "exp_avg_sq" in state: v_t = state["exp_avg_sq"] lr = self.model.optimizer.param_groups[0]["lr"] u_t = (lr / (torch.sqrt(v_t) + 1e-8)) * grad_target u_p = (lr / (torch.sqrt(v_t) + 1e-8)) * grad_parasitic else: u_t = grad_target u_p = grad_parasitic valid_mask = (torch.norm(u_t, dim=1) > 0) & (torch.norm(u_p, dim=1) > 0) if valid_mask.any(): grad_cos_sim = float(F.cosine_similarity(u_t[valid_mask], u_p[valid_mask], dim=1).mean()) parasitic_ratio = float(torch.norm(u_p, dim=1).mean() / (torch.norm(u_t, dim=1).mean() + 1e-7)) self.model.optimizer.zero_grad(set_to_none=True) loss.backward() else: loss.backward() self.trainer.strategy.post_backward( step=step, scene_extent=self.trainer.scene_extent, train_dataset=self.trainer.train_dataset, batch=gpu_batch, writer=None, ) if "mog_visibility" in outputs and isinstance(self.model.optimizer, SelectiveAdam): self.model.optimizer.step(outputs["mog_visibility"]) else: self.model.optimizer.step() self.model.optimizer.zero_grad() self.model.scheduler_step(step) self.trainer.strategy.post_optimizer_step( step=step, scene_extent=self.trainer.scene_extent, train_dataset=self.trainer.train_dataset, batch=gpu_batch, writer=None, ) num_gaussians = self.model.num_gaussians 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), "hits_mean": float(outputs.get("hits_count", torch.tensor(0.0)).float().mean()) } self.last_n_gaussians = num_gaussians histograms = {} if step % 1000 == 0: histograms["opacity"] = self.model.get_density().clone().detach() scales = self.model.get_scale().clone().detach() histograms["scaling"] = scales scales_2d = scales[:, :2] if scales.shape[1] >= 2 else scales.unsqueeze(-1).expand(-1, 2) gamma = scales_2d.max(dim=-1)[0] / (scales_2d.min(dim=-1)[0] + 1e-7) histograms["anisotropy"] = gamma histograms["sh_dc_mag"] = self.model.features_albedo.detach().norm(dim=-1) return metrics, histograms def render(self, camera): with torch.no_grad(): gpu_batch = self.trainer.val_dataset.get_gpu_batch_with_intrinsics(camera) outputs = self.model(gpu_batch, train=False) return {"image": outputs["pred_rgb"], "depth": outputs.get("pred_dist", None)} def save(self, save_dir, step): self.trainer.save_checkpoint(last_checkpoint=(step >= 30000)) def load(self, model_path, iteration): ckpt_path = os.path.join(model_path, f"ours_{iteration}", f"ckpt_{iteration}.pt") if not os.path.exists(ckpt_path): ckpt_path = os.path.join(model_path, "ckpt_last.pt") checkpoint = torch.load(ckpt_path, map_location="cuda", weights_only=False) self.model.init_from_checkpoint(checkpoint, setup_optimizer=False) def get_spatial_centers(self): return self.model.positions def compute_physical_metrics(self, cameras=None): metrics = {} with torch.no_grad(): scales = self.model.get_scale() 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(self.model.get_density())) dc = self.model.features_albedo rest = self.model.features_specular 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 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.model.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 = self.model.positions opacities = self.model.get_density().squeeze() scales = self.model.get_scale() 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