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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
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../HoGS')))
from utils.loss_utils import l1_loss, ssim
from gaussian_renderer import render as native_render
from scene import Scene, GaussianModel
from arguments import ModelParams, PipelineParams, OptimizationParams
@register_method("hogs")
class HoGSWrapper(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_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.opt.iterations = 50000
self.opt.densify_until_iter = 30000
self.opt.opacity_reset_interval = 6000
self.opt.w_lr = 0.0002
self.gaussians = GaussianModel(self.dataset.sh_degree)
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))
render_pkg = native_render(viewpoint_cam, self.gaussians, self.pipe, self.background)
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()
loss_target = (1.0 - self.opt.lambda_dssim) * l1_loss(image, gt_image)
loss_parasitic = self.opt.lambda_dssim * (1.0 - ssim(image, gt_image))
loss = loss_target + loss_parasitic
grad_cos_sim = 0.0
parasitic_ratio = 0.0
if self.track_decoupling and step % 100 == 0:
def get_eff_step(l_val):
self.gaussians.optimizer.zero_grad(set_to_none=True)
l_val.backward(retain_graph=True)
steps = []
for name in ["xyz", "w"]:
for group in self.gaussians.optimizer.param_groups:
if group["name"] == name:
p = group["params"][0]
if p.grad is not None:
state = self.gaussians.optimizer.state.get(p, None)
if state is not None and "exp_avg_sq" in state:
v = state["exp_avg_sq"]
s = (group["lr"] / (torch.sqrt(v) + 1e-15)) * p.grad.clone()
else:
s = group["lr"] * p.grad.clone()
steps.append(s.view(p.shape[0], -1))
return torch.cat(steps, dim=1) if len(steps) > 0 else torch.zeros(self.gaussians.get_xyz.shape[0], 4, device="cuda")
step_target = get_eff_step(loss_target)
step_parasitic = get_eff_step(loss_parasitic)
norm_t = torch.norm(step_target, dim=1)
norm_p = torch.norm(step_parasitic, dim=1)
valid_mask = (norm_t > 0) & (norm_p > 0)
if valid_mask.any():
grad_cos_sim = float(F.cosine_similarity(step_target[valid_mask], step_parasitic[valid_mask], dim=1).mean())
parasitic_ratio = float(norm_p.mean() / (norm_t.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)
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()
w_inv = (1.0 / torch.exp(self.gaussians._w)).clone().detach()
raw_scales = torch.exp(self.gaussians._scaling).clone().detach()
eff_scales = raw_scales * w_inv.unsqueeze(-1)
histograms["scaling"] = eff_scales
scales_2d = eff_scales[:, :2] if eff_scales.shape[1] >= 2 else eff_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)
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):
w_inv = 1.0 / torch.exp(self.gaussians._w)
return self.gaussians._xyz * w_inv.unsqueeze(-1)
def compute_physical_metrics(self, cameras=None):
metrics = {}
with torch.no_grad():
w_inv = 1.0 / torch.exp(self.gaussians._w)
raw_scales = self.gaussians._scaling
scales = torch.exp(raw_scales) * w_inv.unsqueeze(-1)
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)))
w_val = torch.exp(self.gaussians._w)
metrics["w_mean"] = float(torch.mean(w_val))
metrics["w_median"] = float(torch.median(w_val))
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")
w_inv = 1.0 / torch.exp(self.gaussians._w)
xyz = self.gaussians._xyz * w_inv.unsqueeze(-1)
opacities = torch.sigmoid(self.gaussians._opacity).squeeze()
scales = torch.exp(self.gaussians._scaling) * w_inv.unsqueeze(-1)
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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