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import sys
import random
import torch
import numpy as np
import torch.nn.functional as F
from omegaconf import OmegaConf
from hydra import compose, initialize_config_dir
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__), '../../conegs')))
from utils.loss_utils import l1_loss, ssim
from gaussian_renderer import render as native_render
from scene import Scene, GaussianModel
from scene.nerf_model import NeRFModel
from utils.nerf_utils import get_num_rays_to_cast
@register_method("conegs")
class ConeGSWrapper(BaseMethod):
def __init__(self, dataset_config, hyperparams):
conegs_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../conegs'))
if not GlobalHydra().is_initialized():
initialize_config_dir(config_dir=os.path.join(conegs_dir, 'configs'), version_base=None)
self.cfg = compose(config_name="defaults")
OmegaConf.set_struct(self.cfg, False)
self.cfg.gaussian_model.source_path = dataset_config["source_path"]
self.cfg.gaussian_model.model_path = dataset_config["model_path"]
OmegaConf.set_struct(self.cfg, False)
self.cfg.gaussian_model.eval = True
if hasattr(self.cfg, 'dataset'):
self.cfg.dataset.eval = True
self.cfg.optimization.resolution = float(dataset_config.get("resolution", 1))
self.cfg.nerf_model.num_iters_pretrain = 20000
self.cfg.nerf_model.nerf_train_during_3dgs = False
self.cfg.optimization.use_preactivation_opacity_penalty = True
self.cfg.optimization.opacity_penalty = 0.0002
if "max_points" in hyperparams:
self.cfg.optimization.max_points = hyperparams["max_points"]
self.track_decoupling = hyperparams.get("track_decoupling", False)
self.gaussians = GaussianModel(self.cfg.gaussian_model.sh_degree)
self.scene = Scene(self.cfg.gaussian_model, self.gaussians, stack_train_images=self.cfg.nerf_model.stack_images)
self.gaussians.training_setup(self.cfg.optimization)
bg_color = [1, 1, 1] if self.cfg.gaussian_model.white_background else [0, 0, 0]
self.background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
self.nerf_model = NeRFModel(self.cfg.nerf_model)
self.nerf_model.pretrain_model(self.cfg, self.gaussians, self.scene, self.background)
self.nerf_model.prop_optimizer.zero_grad(set_to_none=True)
self.nerf_model.model_optimizer.zero_grad(set_to_none=True)
if hasattr(self.scene, "stacked_images"):
del self.scene.stacked_images
if hasattr(self.scene, "stacked_cam2worlds"):
del self.scene.stacked_cam2worlds
if hasattr(self.scene, "stacked_pix2cams"):
del self.scene.stacked_pix2cams
for i in range(len(self.scene.getTrainCameras())):
self.scene.getTrainCameras()[i].original_image = self.scene.getTrainCameras()[i].original_image.cuda()
for i in range(len(self.scene.getTestCameras())):
self.scene.getTestCameras()[i].original_image = self.scene.getTestCameras()[i].original_image.cuda()
num_rays_to_cast = get_num_rays_to_cast(len(self.gaussians._xyz), gaussian_percentage_increase=self.cfg.optimization.gaussian_percentage_increase, nerf_train_during_3dgs=self.cfg.nerf_model.nerf_train_during_3dgs)
self.nerf_model.estimator_params["n_rays"] = num_rays_to_cast
self.nerf_model.estimator_params["stratified"] = False
self.nerf_model.estimator_params["requires_grad"] = False
self.viewpoint_stack = self.scene.getTrainCameras().copy()
self.last_n_gaussians = len(self.gaussians.get_xyz)
def _get_u_step(self, params, grads):
u_vecs = []
for p, g in zip(params, grads):
if g is None:
continue
state = self.gaussians.optimizer.state.get(p, None)
if state is not None and "exp_avg_sq" in state:
v = state["exp_avg_sq"]
pg_lr = 1e-4
for pg in self.gaussians.optimizer.param_groups:
if any(p is param for param in pg["params"]):
pg_lr = pg["lr"]
break
u = (pg_lr / (torch.sqrt(v) + 1e-15)) * g
else:
u = g
u_vecs.append(u.view(-1))
if len(u_vecs) == 0:
return torch.zeros(1, device="cuda")
return torch.cat(u_vecs)
def train_iteration(self, step):
self.gaussians.update_learning_rate(step)
if step % self.cfg.optimization.SH_increase_iter == 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))
gt_image = viewpoint_cam.original_image.cuda()
bg = torch.rand((3), device="cuda") if self.cfg.optimization.random_background else self.background
render_pkg = native_render(viewpoint_cam, self.gaussians, self.cfg.pipeline, bg)
image = render_pkg["render"]
l1_full = torch.abs((image - gt_image))
ssim_full = ssim(image, gt_image)
loss_target = (1.0 - self.cfg.optimization.lambda_dssim) * l1_full.mean()
loss_ssim_part = self.cfg.optimization.lambda_dssim * (1.0 - ssim_full.mean())
opacity = self.gaussians._opacity if self.cfg.optimization.use_preactivation_opacity_penalty else torch.abs(self.gaussians.get_opacity)
loss_opacity = self.cfg.optimization.opacity_penalty * opacity.mean()
loss = loss_target + loss_ssim_part + loss_opacity
weights = (1.0 - self.cfg.optimization.densification_lambda_dssim) * l1_full.mean(0) + self.cfg.optimization.densification_lambda_dssim * (1.0 - ssim_full.mean(0))
weights = torch.clamp_min(weights, 0.0)
metrics = {}
if self.track_decoupling and step % 100 == 0:
semantic_groups = {
"spatial": [self.gaussians._xyz],
"geometry": [self.gaussians._scaling, self.gaussians._rotation],
"opacity": [self.gaussians._opacity],
"appearance": [self.gaussians._features_dc, self.gaussians._features_rest]
}
self.gaussians.optimizer.zero_grad(set_to_none=True)
loss_target.backward(retain_graph=True)
grad_target_dict = {}
for k, params in semantic_groups.items():
grad_target_dict[k] = [p.grad.clone() if p.grad is not None else torch.zeros_like(p) for p in params]
self.gaussians.optimizer.zero_grad(set_to_none=True)
loss_opacity.backward(retain_graph=True)
grad_parasitic_dict = {}
for k, params in semantic_groups.items():
grad_parasitic_dict[k] = [p.grad.clone() if p.grad is not None else torch.zeros_like(p) for p in params]
for k in semantic_groups.keys():
u_t = self._get_u_step(semantic_groups[k], grad_target_dict[k])
u_p = self._get_u_step(semantic_groups[k], grad_parasitic_dict[k])
norm_t = torch.norm(u_t)
norm_p = torch.norm(u_p)
if norm_p > 1e-7 and norm_t > 1e-7:
s_group = F.cosine_similarity(u_t.unsqueeze(0), u_p.unsqueeze(0)).squeeze()
r_group = norm_p / (norm_t + norm_p + 1e-7)
ti = float(r_group * torch.clamp(-s_group, min=0.0))
else:
ti = 0.0
metrics[f"TI_{k}"] = ti
self.gaussians.optimizer.zero_grad(set_to_none=True)
loss.backward()
else:
loss.backward()
with torch.no_grad():
if self.cfg.optimization.use_3dgs_densification and step < self.cfg.optimization.densify_until_iter:
radii = render_pkg["radii"]
visibility_filter = render_pkg["visibility_filter"]
self.gaussians.max_radii2D[visibility_filter] = torch.max(self.gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
self.gaussians.add_densification_stats(render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg.get("optimized_samples_index", 0))
if step > self.cfg.optimization.densify_from_iter and step % 100 == 0:
size_threshold = 20 if step > 3000 else None
self.gaussians.densify_and_prune(0.0002, 0.005, self.scene.cameras_extent, size_threshold, max_points=self.cfg.optimization.max_points)
if step % 3000 == 0:
self.gaussians.reset_opacity()
self.gaussians.optimizer.step()
self.gaussians.optimizer.zero_grad(set_to_none=True)
if self.cfg.optimization.densify_from_iter < step and step < self.cfg.optimization.densify_until_iter and (self.cfg.optimization.gaussian_percentage_increase or self.cfg.optimization.max_points):
num_rays_to_cast = get_num_rays_to_cast(len(self.gaussians._xyz), gaussian_percentage_increase=self.cfg.optimization.gaussian_percentage_increase, nerf_train_during_3dgs=self.cfg.nerf_model.nerf_train_during_3dgs)
self.nerf_model.cast_rays_during_optimization(weights, num_rays_to_cast, viewpoint_cam, self.cfg, self.gaussians, self.scene, bg, step)
with torch.no_grad():
if self.cfg.optimization.densify_from_iter < step and step % 100 == 0:
dead_mask = (self.gaussians.get_opacity <= 0.005).squeeze(-1)
if step < self.cfg.optimization.densify_until_iter:
self.gaussians.prune_points(dead_mask)
if self.cfg.optimization.gaussian_percentage_increase or self.cfg.optimization.max_points:
self.gaussians.initialize_new_points(use_cone_radius=True, max_points=self.cfg.optimization.max_points, opacity_init_value=self.cfg.optimization.opacity_init_value)
num_gaussians = self.gaussians.get_xyz.shape[0]
stashed_count = len(self.gaussians.stashed_xyz) if hasattr(self.gaussians, "stashed_xyz") and self.gaussians.stashed_xyz is not None else 0
metrics.update({
"loss": float(loss),
"loss_l1": float(loss_target),
"loss_ssim": float(loss_ssim_part),
"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)),
"stashed_points_count": int(stashed_count),
"penalty_loss_ratio": float(loss_opacity / (loss + 1e-7))
})
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.cfg.pipeline, self.background)
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 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
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