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import sys
import random
import math
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__), '../../ContextGS_offy')))
from utils.loss_utils import l1_loss, ssim
from gaussian_renderer import prefilter_voxel, render as native_render
from scene import Scene, GaussianModel
from arguments import ModelParams, PipelineParams, OptimizationParams
@register_method("contextgs")
class ContextGS_offyWrapper(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.parser.add_argument('--level_num', type=int, default=3)
self.parser.add_argument('--level_scale', type=int, default=10)
self.parser.add_argument("--n_features", type=int, default=4)
self.parser.add_argument("--lmbda", type=float, default=0.001)
self.parser.add_argument("--lmbda_rec", type=float, default=1)
self.parser.add_argument("--disable_hyper", default=False, action="store_true")
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.args_param = self.args
self.gaussians = GaussianModel(
self.dataset.feat_dim,
self.dataset.n_offsets,
self.dataset.voxel_size,
self.dataset.update_depth,
self.dataset.update_init_factor,
self.dataset.update_hierachy_factor,
self.dataset.use_feat_bank,
n_features_per_level=self.args_param.n_features,
level_num=self.args_param.level_num,
hyper_divisor=self.dataset.hyper_divisor,
target_ratio=self.dataset.target_ratio,
disable_hyper=self.args_param.disable_hyper
)
self.scene = Scene(self.dataset, self.gaussians)
self.gaussians.update_anchor_bound()
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 = self.gaussians.get_anchor.shape[0]
def train_iteration(self, step):
self.gaussians.update_learning_rate(step)
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))
retain_grad = (step < self.opt.update_until and step >= 0)
voxel_visible_mask = prefilter_voxel(viewpoint_cam, self.gaussians, self.pipe, self.background)
render_pkg = native_render(viewpoint_cam, self.gaussians, self.pipe, self.background, visible_mask=voxel_visible_mask, retain_grad=retain_grad, step=step)
image = render_pkg["render"]
viewspace_point_tensor = render_pkg["viewspace_points"]
visibility_filter = render_pkg["visibility_filter"]
offset_selection_mask = render_pkg["selection_mask"]
opacity = render_pkg["neural_opacity"]
scaling = render_pkg["scaling"]
bit_per_param = render_pkg.get("bit_per_param", None)
gt_image = viewpoint_cam.original_image.cuda()
Ll1 = l1_loss(image, gt_image)
ssim_value = ssim(image, gt_image)
scaling_reg = scaling.prod(dim=1).mean()
loss_target = self.args_param.lmbda_rec * ((1.0 - self.opt.lambda_dssim) * Ll1 + self.opt.lambda_dssim * (1.0 - ssim_value))
loss_parasitic = 0.01 * scaling_reg
if bit_per_param is not None:
loss_parasitic = loss_parasitic + self.args_param.lmbda * bit_per_param
loss_parasitic = loss_parasitic + 5e-4 * torch.mean(torch.sigmoid(self.gaussians._mask))
loss = loss_target + loss_parasitic
grad_cos_sim = 0.0
parasitic_ratio = 0.0
stats = {}
if self.track_decoupling and step % 100 == 0:
param_groups_map = {
"spatial": [self.gaussians._anchor, self.gaussians._offset],
"geometry": [self.gaussians._scaling, self.gaussians._rotation],
"opacity": [self.gaussians._opacity, self.gaussians._mask],
"appearance": [self.gaussians._anchor_feat, self.gaussians._hyper_latent],
}
self.gaussians.optimizer.zero_grad(set_to_none=True)
loss_target.backward(retain_graph=True)
grads_target = {}
for group_name, params in param_groups_map.items():
grads_target[group_name] = []
for p in params:
if p.grad is not None:
grads_target[group_name].append(p.grad.clone())
else:
grads_target[group_name].append(torch.zeros_like(p))
self.gaussians.optimizer.zero_grad(set_to_none=True)
loss_parasitic.backward(retain_graph=True)
grads_parasitic = {}
for group_name, params in param_groups_map.items():
grads_parasitic[group_name] = []
for p in params:
if p.grad is not None:
grads_parasitic[group_name].append(p.grad.clone())
else:
grads_parasitic[group_name].append(torch.zeros_like(p))
for group_name, params in param_groups_map.items():
u_t_list = []
u_p_list = []
for i, p in enumerate(params):
state = self.gaussians.optimizer.state.get(p, None)
if state is not None and "exp_avg_sq" in state:
v_t = state["exp_avg_sq"]
else:
v_t = torch.ones_like(p)
for param_group in self.gaussians.optimizer.param_groups:
if id(p) in [id(opt_p) for opt_p in param_group['params']]:
lr = param_group['lr']
break
else:
lr = 1e-3
u_t = (lr / (torch.sqrt(v_t) + 1e-8)) * grads_target[group_name][i]
u_p = (lr / (torch.sqrt(v_t) + 1e-8)) * grads_parasitic[group_name][i]
u_t_list.append(u_t.reshape(-1))
u_p_list.append(u_p.reshape(-1))
gt = torch.cat(u_t_list)
gp = torch.cat(u_p_list)
if gt.norm() > 0 and gp.norm() > 0:
cos = float(F.cosine_similarity(gt.unsqueeze(0), gp.unsqueeze(0)))
r = float(gp.norm() / (gt.norm() + gp.norm() + 1e-7))
ti = r * max(0.0, -cos)
else:
ti = 0.0
stats[f"sti_{group_name}"] = ti
all_gt = torch.cat([torch.cat(u_t_list) for group_name, params in param_groups_map.items() for i, p in enumerate(params)])
all_gp = torch.cat([torch.cat(u_p_list) for group_name, params in param_groups_map.items() for i, p in enumerate(params)])
if all_gt.norm() > 0 and all_gp.norm() > 0:
grad_cos_sim = float(F.cosine_similarity(all_gt.unsqueeze(0), all_gp.unsqueeze(0)))
parasitic_ratio = float(all_gp.norm() / (all_gt.norm() + 1e-7))
self.gaussians.optimizer.zero_grad(set_to_none=True)
loss.backward()
else:
loss.backward()
with torch.no_grad():
if step < self.opt.update_until and step > self.opt.start_stat:
self.gaussians.training_statis(viewspace_point_tensor, opacity, visibility_filter, offset_selection_mask, voxel_visible_mask)
if step not in range(3000, 4000):
if step > self.opt.update_from and step % self.opt.update_interval == 0:
self.gaussians.adjust_anchor(check_interval=self.opt.update_interval, success_threshold=self.opt.success_threshold, grad_threshold=self.opt.densify_grad_threshold, min_opacity=self.opt.min_opacity)
elif step == self.opt.update_until:
del self.gaussians.opacity_accum
del self.gaussians.offset_gradient_accum
del self.gaussians.offset_denom
torch.cuda.empty_cache()
if step < self.opt.iterations:
self.gaussians.optimizer.step()
self.gaussians.optimizer.zero_grad(set_to_none=True)
num_gaussians = self.gaussians.get_anchor.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)
}
metrics.update(stats)
if bit_per_param is not None:
metrics["bit_per_param"] = float(bit_per_param)
self.last_n_gaussians = num_gaussians
histograms = {}
if step % 1000 == 0:
histograms["opacity"] = torch.sigmoid(self.gaussians._opacity).clone().detach()
scales = self.gaussians.get_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["anchor_feat_mag"] = self.gaussians._anchor_feat.detach().norm(dim=-1)
return metrics, histograms
def render(self, camera):
with torch.no_grad():
voxel_visible_mask = prefilter_voxel(camera, self.gaussians, self.pipe, self.background)
render_pkg = native_render(camera, self.gaussians, self.pipe, self.background, visible_mask=voxel_visible_mask)
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_sparse_gaussian(os.path.join(model_path, 'point_cloud', f'iteration_{iteration}', 'point_cloud.ply'))
def get_spatial_centers(self):
return self.gaussians.get_anchor
def compute_physical_metrics(self, cameras=None):
metrics = {}
with torch.no_grad():
scales = self.gaussians.get_scaling
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)))
feat, hyper = self.gaussians._anchor_feat, self.gaussians._hyper_latent
if hyper is not None and hyper.shape[1] > 0:
metrics["hyper_energy_ratio"] = float(hyper.norm(dim=-1).mean() / (feat.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 = self.gaussians.get_anchor
opacities = torch.sigmoid(self.gaussians._opacity).squeeze()
scales = self.gaussians.get_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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