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import numpy as np
import torch
import torch.nn as nn
from pytorch3d.transforms import quaternion_to_matrix, matrix_to_quaternion
from sugar.sugar_scene.sugar_model import SuGaR
from sugar.sugar_scene.sugar_optimizer import SuGaROptimizer
from sugar.sugar_utils.general_utils import inverse_sigmoid
class SuGaRDensifier():
"""Wrapper of the densification functions used for Gaussian Splatting and SuGaR optimization.
Largely inspired by the original implementation of the 3D Gaussian Splatting paper:
https://github.com/graphdeco-inria/gaussian-splatting
"""
def __init__(
self,
sugar_model:SuGaR,
sugar_optimizer:SuGaROptimizer,
max_grad=0.0002,
min_opacity:float=0.005,
max_screen_size:int=20,
scene_extent:float=None,
percent_dense:float=0.01,
) -> None:
pass
self.model = sugar_model
self.optimizer = sugar_optimizer.optimizer
self.points_gradient_accum = torch.zeros((self.model.points.shape[0], 1), device=self.model.device)
self.denom = torch.zeros((self.model.points.shape[0], 1), device=self.model.device)
self.max_radii2D = torch.zeros((self.model.points.shape[0]), device=self.model.device)
self.max_grad = max_grad
self.min_opacity = min_opacity
self.max_screen_size = max_screen_size
if scene_extent is None:
self.scene_extent = sugar_model.get_cameras_spatial_extent()
else:
self.scene_extent = scene_extent
self.percent_dense = percent_dense
self.params_to_densify = []
if not self.model.freeze_gaussians:
self.params_to_densify.extend(["points", "all_densities", "scales", "quaternions"])
self.params_to_densify.extend(["sh_coordinates_dc", "sh_coordinates_rest"])
def _prune_optimizer(self, mask):
optimizable_tensors = {}
for group in self.optimizer.param_groups:
name = group["name"]
if name in self.params_to_densify:
stored_state = self.optimizer.state.get(group['params'][0], None)
if stored_state is not None:
stored_state["exp_avg"] = stored_state["exp_avg"][mask]
stored_state["exp_avg_sq"] = stored_state["exp_avg_sq"][mask]
del self.optimizer.state[group['params'][0]]
group["params"][0] = nn.Parameter((group["params"][0][mask].requires_grad_(True)))
self.optimizer.state[group['params'][0]] = stored_state
optimizable_tensors[group["name"]] = group["params"][0]
else:
group["params"][0] = nn.Parameter(group["params"][0][mask].requires_grad_(True))
optimizable_tensors[group["name"]] = group["params"][0]
return optimizable_tensors
def prune_points(self, mask):
valid_points_mask = ~mask
optimizable_tensors = self._prune_optimizer(valid_points_mask)
if "points" in self.params_to_densify:
self.model._points = optimizable_tensors["points"]
if "all_densities" in self.params_to_densify:
self.model.all_densities = optimizable_tensors["all_densities"]
if "scales" in self.params_to_densify:
self.model._scales = optimizable_tensors["scales"]
if "quaternions" in self.params_to_densify:
self.model._quaternions = optimizable_tensors["quaternions"]
if "sh_coordinates_dc" in self.params_to_densify:
self.model._sh_coordinates_dc = optimizable_tensors["sh_coordinates_dc"]
if "sh_coordinates_rest" in self.params_to_densify:
self.model._sh_coordinates_rest = optimizable_tensors["sh_coordinates_rest"]
self.points_gradient_accum = self.points_gradient_accum[valid_points_mask]
self.denom = self.denom[valid_points_mask]
self.max_radii2D = self.max_radii2D[valid_points_mask]
def cat_tensors_to_optimizer(self, tensors_dict):
optimizable_tensors = {}
for group in self.optimizer.param_groups:
name = group["name"]
if name in self.params_to_densify:
assert len(group["params"]) == 1
extension_tensor = tensors_dict[group["name"]]
stored_state = self.optimizer.state.get(group['params'][0], None)
dim_to_cat = 0
if stored_state is not None:
stored_state["exp_avg"] = torch.cat((stored_state["exp_avg"], torch.zeros_like(extension_tensor)), dim=dim_to_cat)
stored_state["exp_avg_sq"] = torch.cat((stored_state["exp_avg_sq"], torch.zeros_like(extension_tensor)), dim=dim_to_cat)
del self.optimizer.state[group['params'][0]]
group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=dim_to_cat).requires_grad_(True))
self.optimizer.state[group['params'][0]] = stored_state
optimizable_tensors[group["name"]] = group["params"][0]
else:
group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=dim_to_cat).requires_grad_(True))
optimizable_tensors[group["name"]] = group["params"][0]
return optimizable_tensors
def replace_tensor_to_optimizer(self, tensor, name):
optimizable_tensors = {}
for group in self.optimizer.param_groups:
if group["name"] == name:
stored_state = self.optimizer.state.get(group['params'][0], None)
stored_state["exp_avg"] = torch.zeros_like(tensor)
stored_state["exp_avg_sq"] = torch.zeros_like(tensor)
del self.optimizer.state[group['params'][0]]
group["params"][0] = nn.Parameter(tensor.requires_grad_(True))
self.optimizer.state[group['params'][0]] = stored_state
optimizable_tensors[group["name"]] = group["params"][0]
return optimizable_tensors
def densification_postfix(self, new_points,
new_densities, new_scales, new_quaternions,
new_sh_coordinates_dc=None, new_sh_coordinates_rest=None,
):
tensors_dict = {
"points": new_points,
"all_densities": new_densities,
"scales": new_scales,
"quaternions": new_quaternions
}
tensors_dict["sh_coordinates_dc"] = new_sh_coordinates_dc
tensors_dict["sh_coordinates_rest"] = new_sh_coordinates_rest
optimizable_tensors = self.cat_tensors_to_optimizer(tensors_dict)
self.model._points = optimizable_tensors["points"]
self.model.all_densities = optimizable_tensors["all_densities"]
self.model._scales = optimizable_tensors["scales"]
self.model._quaternions = optimizable_tensors["quaternions"]
self.model._sh_coordinates_dc = optimizable_tensors["sh_coordinates_dc"]
self.model._sh_coordinates_rest = optimizable_tensors["sh_coordinates_rest"]
self.points_gradient_accum = torch.zeros((self.model.points.shape[0], 1), device=self.model.device)
self.denom = torch.zeros((self.model.points.shape[0], 1), device=self.model.device)
self.max_radii2D = torch.zeros((self.model.points.shape[0]), device=self.model.device)
def update_densification_stats(self, viewspace_point_tensor, radii, visibility_filter):
# Updates maximum observed 2D radii of all gaussians
self.max_radii2D[visibility_filter] = torch.max(self.max_radii2D[visibility_filter], radii[visibility_filter])
# Accumulate gradient magnitudes of all points
self.points_gradient_accum[visibility_filter] += torch.norm(viewspace_point_tensor.grad[visibility_filter, :2], dim=-1, keepdim=True)
# Counts number of updates for each point
self.denom[visibility_filter] += 1
def densify_and_clone(self, grads, max_grad, extent):
max_grad = self.max_grad if max_grad is None else max_grad
extent = self.scene_extent if extent is None else extent
# Extract points that satisfy the gradient condition
selected_pts_mask = torch.where(torch.norm(grads, dim=-1) >= max_grad, True, False)
selected_pts_mask = torch.logical_and(selected_pts_mask,
torch.max(self.model.scaling, dim=1).values <= self.percent_dense * extent)
new_points = self.model._points[selected_pts_mask]
new_densities = self.model.all_densities[selected_pts_mask]
new_scales = self.model._scales[selected_pts_mask]
new_quaternions = self.model._quaternions[selected_pts_mask]
new_sh_coordinates_dc = self.model._sh_coordinates_dc[selected_pts_mask]
new_sh_coordinates_rest = self.model._sh_coordinates_rest[selected_pts_mask]
self.densification_postfix(
new_points=new_points,
new_densities=new_densities,
new_scales=new_scales,
new_quaternions=new_quaternions,
new_sh_coordinates_dc=new_sh_coordinates_dc,
new_sh_coordinates_rest=new_sh_coordinates_rest,
)
def densify_and_split(self, grads, max_grad, extent, N=2):
max_grad = self.max_grad if max_grad is None else max_grad
extent = self.scene_extent if extent is None else extent
n_init_points = self.model._points.shape[0]
# Extract points that satisfy the gradient condition
padded_grad = torch.zeros((n_init_points), device="cuda")
padded_grad[:grads.shape[0]] = grads.squeeze()
selected_pts_mask = torch.where(padded_grad >= max_grad, True, False)
selected_pts_mask = torch.logical_and(selected_pts_mask,
torch.max(self.model.scaling, dim=1).values > self.percent_dense*extent)
stds = self.model.scaling[selected_pts_mask].repeat(N,1)
means = torch.zeros((stds.size(0), 3),device="cuda")
samples = torch.normal(mean=means, std=stds)
rots = quaternion_to_matrix(self.model.quaternions[selected_pts_mask]).repeat(N, 1, 1)
new_points = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self.model.points[selected_pts_mask].repeat(N, 1)
new_scales = self.model.scale_inverse_activation(self.model.scaling[selected_pts_mask].repeat(N,1) / (0.8*N))
new_quaternions = self.model._quaternions[selected_pts_mask].repeat(N,1)
new_densities = self.model.all_densities[selected_pts_mask].repeat(N,1)
new_sh_coordinates_dc = self.model._sh_coordinates_dc[selected_pts_mask].repeat(N,1,1)
new_sh_coordinates_rest = self.model._sh_coordinates_rest[selected_pts_mask].repeat(N,1,1)
self.densification_postfix(
new_points=new_points,
new_densities=new_densities,
new_scales=new_scales,
new_quaternions=new_quaternions,
new_sh_coordinates_dc=new_sh_coordinates_dc,
new_sh_coordinates_rest=new_sh_coordinates_rest,
)
prune_filter = torch.cat((selected_pts_mask, torch.zeros(N * selected_pts_mask.sum(), device=self.model.device, dtype=bool)))
self.prune_points(prune_filter)
def densify_and_prune(self, max_grad:float=None, min_opacity:float=None, extent:float=None, max_screen_size:int=None):
max_grad = self.max_grad if max_grad is None else max_grad
min_opacity = self.min_opacity if min_opacity is None else min_opacity
extent = self.scene_extent if extent is None else extent
grads = self.points_gradient_accum / self.denom
grads[grads.isnan()] = 0.0
self.densify_and_clone(grads, max_grad, extent)
self.densify_and_split(grads, max_grad, extent)
prune_mask = (self.model.strengths < min_opacity).squeeze()
if max_screen_size:
big_points_vs = self.max_radii2D > max_screen_size
big_points_ws = self.model.scaling.max(dim=1).values > 0.1 * extent
prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws)
self.prune_points(prune_mask)
torch.cuda.empty_cache()
def reset_opacity(self):
opacities_new = inverse_sigmoid(torch.min(self.model.strengths, torch.ones_like(self.model.all_densities.view(-1, 1))*0.01))
optimizable_tensors = self.replace_tensor_to_optimizer(opacities_new, "all_densities")
self.all_densities = optimizable_tensors["all_densities"]
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