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#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import torch
import numpy as np
from utils.general_utils import inverse_sigmoid, get_expon_lr_func, build_rotation
from torch import nn
import os
from utils.system_utils import mkdir_p
from plyfile import PlyData, PlyElement
from utils.sh_utils import RGB2SH
from simple_knn._C import distCUDA2
from utils.graphics_utils import BasicPointCloud
from utils.general_utils import strip_symmetric, build_scaling_rotation
class GaussianModel:
def setup_functions(self):
def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation):
L = build_scaling_rotation(scaling_modifier * scaling, rotation)
actual_covariance = L @ L.transpose(1, 2)
symm = strip_symmetric(actual_covariance)
return symm
self.scaling_activation = torch.exp
self.scaling_inverse_activation = torch.log
self.covariance_activation = build_covariance_from_scaling_rotation
self.opacity_activation = torch.sigmoid
self.inverse_opacity_activation = inverse_sigmoid
self.rotation_activation = torch.nn.functional.normalize
def __init__(self, sh_degree: int):
self.active_sh_degree = 0
self.max_sh_degree = sh_degree
self._xyz = torch.empty(0)
self._features_dc = torch.empty(0)
self._features_rest = torch.empty(0)
self._scaling = torch.empty(0)
self._rotation = torch.empty(0)
self._opacity = torch.empty(0)
self.max_radii2D = torch.empty(0)
self.xyz_gradient_accum = torch.empty(0)
self.denom = torch.empty(0)
self.optimizer = None
self.percent_dense = 0
self.spatial_lr_scale = 0
self.setup_functions()
def capture(self):
return (
self.active_sh_degree,
self._xyz,
self._features_dc,
self._features_rest,
self._scaling,
self._rotation,
self._opacity,
self.max_radii2D,
self.xyz_gradient_accum,
self.denom,
self.optimizer.state_dict(),
self.spatial_lr_scale,
)
def restore(self, model_args, training_args):
(
self.active_sh_degree,
self._xyz,
self._features_dc,
self._features_rest,
self._scaling,
self._rotation,
self._opacity,
self.max_radii2D,
xyz_gradient_accum,
denom,
opt_dict,
self.spatial_lr_scale,
) = model_args
self.training_setup(training_args)
self.xyz_gradient_accum = xyz_gradient_accum
self.denom = denom
self.optimizer.load_state_dict(opt_dict)
@property
def get_scaling(self):
return self.scaling_activation(self._scaling)
@property
def get_rotation(self):
return self.rotation_activation(self._rotation)
@property
def get_xyz(self):
return self._xyz
@property
def get_features(self):
features_dc = self._features_dc
features_rest = self._features_rest
return torch.cat((features_dc, features_rest), dim=1)
@property
def get_opacity(self):
return self.opacity_activation(self._opacity)
def get_covariance(self, scaling_modifier=1):
return self.covariance_activation(
self.get_scaling, scaling_modifier, self._rotation
)
def oneupSHdegree(self):
if self.active_sh_degree < self.max_sh_degree:
self.active_sh_degree += 1
def create_from_pcd(self, pcd: BasicPointCloud, spatial_lr_scale: float):
self.spatial_lr_scale = spatial_lr_scale
fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda()
fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda())
features = (
torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2))
.float()
.cuda()
)
features[:, :3, 0] = fused_color
features[:, 3:, 1:] = 0.0
print("Number of points at initialisation : ", fused_point_cloud.shape[0])
dist2 = torch.clamp_min(
distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()),
0.0000001,
)
scales = torch.log(torch.sqrt(dist2))[..., None].repeat(1, 3)
rots = torch.zeros((fused_point_cloud.shape[0], 4), device="cuda")
rots[:, 0] = 1
opacities = inverse_sigmoid(
0.5
* torch.ones(
(fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda"
)
)
self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True))
self._features_dc = nn.Parameter(
features[:, :, 0:1].transpose(1, 2).contiguous().requires_grad_(True)
)
self._features_rest = nn.Parameter(
features[:, :, 1:].transpose(1, 2).contiguous().requires_grad_(True)
)
self._scaling = nn.Parameter(scales.requires_grad_(True))
self._rotation = nn.Parameter(rots.requires_grad_(True))
self._opacity = nn.Parameter(opacities.requires_grad_(True))
self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")
def training_setup(self, training_args):
self.percent_dense = training_args.percent_dense
self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
l = [
{
"params": [self._xyz],
"lr": training_args.position_lr_init * self.spatial_lr_scale,
"name": "xyz",
},
{
"params": [self._features_dc],
"lr": training_args.feature_lr,
"name": "f_dc",
},
{
"params": [self._features_rest],
"lr": training_args.feature_lr / 20.0,
"name": "f_rest",
},
{
"params": [self._opacity],
"lr": training_args.opacity_lr,
"name": "opacity",
},
{
"params": [self._scaling],
"lr": training_args.scaling_lr,
"name": "scaling",
},
{
"params": [self._rotation],
"lr": training_args.rotation_lr,
"name": "rotation",
},
]
self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15)
self.xyz_scheduler_args = get_expon_lr_func(
lr_init=training_args.position_lr_init * self.spatial_lr_scale,
lr_final=training_args.position_lr_final * self.spatial_lr_scale,
lr_delay_mult=training_args.position_lr_delay_mult,
max_steps=training_args.position_lr_max_steps,
)
def update_learning_rate(self, iteration):
"""Learning rate scheduling per step"""
for param_group in self.optimizer.param_groups:
if param_group["name"] == "xyz":
lr = self.xyz_scheduler_args(iteration)
param_group["lr"] = lr
return lr
def construct_list_of_attributes(self):
l = ["x", "y", "z", "nx", "ny", "nz"]
# All channels except the 3 DC
for i in range(self._features_dc.shape[1] * self._features_dc.shape[2]):
l.append("f_dc_{}".format(i))
for i in range(self._features_rest.shape[1] * self._features_rest.shape[2]):
l.append("f_rest_{}".format(i))
l.append("opacity")
for i in range(self._scaling.shape[1]):
l.append("scale_{}".format(i))
for i in range(self._rotation.shape[1]):
l.append("rot_{}".format(i))
return l
def save_ply(self, path):
mkdir_p(os.path.dirname(path))
xyz = self._xyz.detach().cpu().numpy()
normals = np.zeros_like(xyz)
f_dc = (
self._features_dc.detach()
.transpose(1, 2)
.flatten(start_dim=1)
.contiguous()
.cpu()
.numpy()
)
f_rest = (
self._features_rest.detach()
.transpose(1, 2)
.flatten(start_dim=1)
.contiguous()
.cpu()
.numpy()
)
opacities = self._opacity.detach().cpu().numpy()
scale = self._scaling.detach().cpu().numpy()
rotation = self._rotation.detach().cpu().numpy()
dtype_full = [
(attribute, "f4") for attribute in self.construct_list_of_attributes()
]
elements = np.empty(xyz.shape[0], dtype=dtype_full)
attributes = np.concatenate(
(xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1
)
elements[:] = list(map(tuple, attributes))
el = PlyElement.describe(elements, "vertex")
PlyData([el]).write(path)
def reset_opacity(self):
opacities_new = inverse_sigmoid(
torch.min(self.get_opacity, torch.ones_like(self.get_opacity) * 0.01)
)
optimizable_tensors = self.replace_tensor_to_optimizer(opacities_new, "opacity")
self._opacity = optimizable_tensors["opacity"]
def load_ply(self, path):
plydata = PlyData.read(path)
xyz = np.stack(
(
np.asarray(plydata.elements[0]["x"]),
np.asarray(plydata.elements[0]["y"]),
np.asarray(plydata.elements[0]["z"]),
),
axis=1,
)
opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis]
features_dc = np.zeros((xyz.shape[0], 3, 1))
features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"])
features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"])
features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"])
extra_f_names = [
p.name
for p in plydata.elements[0].properties
if p.name.startswith("f_rest_")
]
extra_f_names = sorted(extra_f_names, key=lambda x: int(x.split("_")[-1]))
assert len(extra_f_names) == 3 * (self.max_sh_degree + 1) ** 2 - 3
features_extra = np.zeros((xyz.shape[0], len(extra_f_names)))
for idx, attr_name in enumerate(extra_f_names):
features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name])
# Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC)
features_extra = features_extra.reshape(
(features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1)
)
scale_names = [
p.name
for p in plydata.elements[0].properties
if p.name.startswith("scale_")
]
scale_names = sorted(scale_names, key=lambda x: int(x.split("_")[-1]))
scales = np.zeros((xyz.shape[0], len(scale_names)))
for idx, attr_name in enumerate(scale_names):
scales[:, idx] = np.asarray(plydata.elements[0][attr_name])
rot_names = [
p.name for p in plydata.elements[0].properties if p.name.startswith("rot")
]
rot_names = sorted(rot_names, key=lambda x: int(x.split("_")[-1]))
rots = np.zeros((xyz.shape[0], len(rot_names)))
for idx, attr_name in enumerate(rot_names):
rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
self._xyz = nn.Parameter(
torch.tensor(xyz, dtype=torch.float, device="cuda").requires_grad_(True)
)
self._features_dc = nn.Parameter(
torch.tensor(features_dc, dtype=torch.float, device="cuda")
.transpose(1, 2)
.contiguous()
.requires_grad_(True)
)
self._features_rest = nn.Parameter(
torch.tensor(features_extra, dtype=torch.float, device="cuda")
.transpose(1, 2)
.contiguous()
.requires_grad_(True)
)
self._opacity = nn.Parameter(
torch.tensor(opacities, dtype=torch.float, device="cuda").requires_grad_(
True
)
)
self._scaling = nn.Parameter(
torch.tensor(scales, dtype=torch.float, device="cuda").requires_grad_(True)
)
self._rotation = nn.Parameter(
torch.tensor(rots, dtype=torch.float, device="cuda").requires_grad_(True)
)
self.active_sh_degree = self.max_sh_degree
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 _prune_optimizer(self, mask):
optimizable_tensors = {}
for group in self.optimizer.param_groups:
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)
self._xyz = optimizable_tensors["xyz"]
self._features_dc = optimizable_tensors["f_dc"]
self._features_rest = optimizable_tensors["f_rest"]
self._opacity = optimizable_tensors["opacity"]
self._scaling = optimizable_tensors["scaling"]
self._rotation = optimizable_tensors["rotation"]
self.xyz_gradient_accum = self.xyz_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:
assert len(group["params"]) == 1
extension_tensor = tensors_dict[group["name"]]
stored_state = self.optimizer.state.get(group["params"][0], None)
if stored_state is not None:
stored_state["exp_avg"] = torch.cat(
(stored_state["exp_avg"], torch.zeros_like(extension_tensor)), dim=0
)
stored_state["exp_avg_sq"] = torch.cat(
(stored_state["exp_avg_sq"], torch.zeros_like(extension_tensor)),
dim=0,
)
del self.optimizer.state[group["params"][0]]
group["params"][0] = nn.Parameter(
torch.cat(
(group["params"][0], extension_tensor), dim=0
).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=0
).requires_grad_(True)
)
optimizable_tensors[group["name"]] = group["params"][0]
return optimizable_tensors
def densification_postfix(
self,
new_xyz,
new_features_dc,
new_features_rest,
new_opacities,
new_scaling,
new_rotation,
):
d = {
"xyz": new_xyz,
"f_dc": new_features_dc,
"f_rest": new_features_rest,
"opacity": new_opacities,
"scaling": new_scaling,
"rotation": new_rotation,
}
optimizable_tensors = self.cat_tensors_to_optimizer(d)
self._xyz = optimizable_tensors["xyz"]
self._features_dc = optimizable_tensors["f_dc"]
self._features_rest = optimizable_tensors["f_rest"]
self._opacity = optimizable_tensors["opacity"]
self._scaling = optimizable_tensors["scaling"]
self._rotation = optimizable_tensors["rotation"]
self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")
def densify_and_split(self, grads, grad_threshold, scene_extent, N=2):
n_init_points = self.get_xyz.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 >= grad_threshold, True, False)
selected_pts_mask = torch.logical_and(
selected_pts_mask,
torch.max(self.get_scaling, dim=1).values
> self.percent_dense * scene_extent,
)
stds = self.get_scaling[selected_pts_mask].repeat(N, 1)
means = torch.zeros((stds.size(0), 3), device="cuda")
samples = torch.normal(mean=means, std=stds)
rots = build_rotation(self._rotation[selected_pts_mask]).repeat(N, 1, 1)
new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self.get_xyz[
selected_pts_mask
].repeat(N, 1)
new_scaling = self.scaling_inverse_activation(
self.get_scaling[selected_pts_mask].repeat(N, 1) / (0.8 * N)
)
new_rotation = self._rotation[selected_pts_mask].repeat(N, 1)
new_features_dc = self._features_dc[selected_pts_mask].repeat(N, 1, 1)
new_features_rest = self._features_rest[selected_pts_mask].repeat(N, 1, 1)
new_opacity = self._opacity[selected_pts_mask].repeat(N, 1)
self.densification_postfix(
new_xyz,
new_features_dc,
new_features_rest,
new_opacity,
new_scaling,
new_rotation,
)
prune_filter = torch.cat(
(
selected_pts_mask,
torch.zeros(N * selected_pts_mask.sum(), device="cuda", dtype=bool),
)
)
self.prune_points(prune_filter)
def densify_and_clone(self, grads, grad_threshold, scene_extent):
# Extract points that satisfy the gradient condition
selected_pts_mask = torch.where(
torch.norm(grads, dim=-1) >= grad_threshold, True, False
)
selected_pts_mask = torch.logical_and(
selected_pts_mask,
torch.max(self.get_scaling, dim=1).values
<= self.percent_dense * scene_extent,
)
new_xyz = self._xyz[selected_pts_mask]
new_features_dc = self._features_dc[selected_pts_mask]
new_features_rest = self._features_rest[selected_pts_mask]
new_opacities = self._opacity[selected_pts_mask]
new_scaling = self._scaling[selected_pts_mask]
new_rotation = self._rotation[selected_pts_mask]
self.densification_postfix(
new_xyz,
new_features_dc,
new_features_rest,
new_opacities,
new_scaling,
new_rotation,
)
def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size):
grads = self.xyz_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.get_opacity < min_opacity).squeeze()
if max_screen_size:
big_points_vs = self.max_radii2D > max_screen_size
big_points_ws = self.get_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 add_densification_stats(self, viewspace_point_tensor, update_filter):
self.xyz_gradient_accum[update_filter] += torch.norm(
viewspace_point_tensor.grad[update_filter, :2], dim=-1, keepdim=True
)
self.denom[update_filter] += 1
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