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Zero
# | |
# 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 random import randint | |
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 | |
from scene.deformation import deform_network | |
from scene.regulation import compute_plane_smoothness | |
def gaussian_3d_coeff(xyzs, covs): | |
# xyzs: [N, 3] | |
# covs: [N, 6] | |
x, y, z = xyzs[:, 0], xyzs[:, 1], xyzs[:, 2] | |
a, b, c, d, e, f = covs[:, 0], covs[:, 1], covs[:, 2], covs[:, 3], covs[:, 4], covs[:, 5] | |
# eps must be small enough !!! | |
inv_det = 1 / (a * d * f + 2 * e * c * b - e**2 * a - c**2 * d - b**2 * f + 1e-24) | |
inv_a = (d * f - e**2) * inv_det | |
inv_b = (e * c - b * f) * inv_det | |
inv_c = (e * b - c * d) * inv_det | |
inv_d = (a * f - c**2) * inv_det | |
inv_e = (b * c - e * a) * inv_det | |
inv_f = (a * d - b**2) * inv_det | |
power = -0.5 * (x**2 * inv_a + y**2 * inv_d + z**2 * inv_f) - x * y * inv_b - x * z * inv_c - y * z * inv_e | |
power[power > 0] = -1e10 # abnormal values... make weights 0 | |
return torch.exp(power) | |
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, args): | |
self.active_sh_degree = 0 | |
self.max_sh_degree = sh_degree | |
self._xyz = torch.empty(0) | |
# self._deformation = torch.empty(0) | |
self._deformation = deform_network(args) | |
# self.grid = TriPlaneGrid() | |
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._deformation_table = torch.empty(0) | |
self.setup_functions() | |
def capture(self): | |
return ( | |
self.active_sh_degree, | |
self._xyz, | |
self._deformation.state_dict(), | |
self._deformation_table, | |
# self.grid, | |
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._deformation_table, | |
self._deformation, | |
# self.grid, | |
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) | |
def get_scaling(self): | |
return self.scaling_activation(self._scaling) | |
def get_rotation(self): | |
return self.rotation_activation(self._rotation) | |
def get_xyz(self): | |
return self._xyz | |
def get_features(self): | |
features_dc = self._features_dc | |
features_rest = self._features_rest | |
return torch.cat((features_dc, features_rest), dim=1) | |
def get_opacity(self): | |
return self.opacity_activation(self._opacity) | |
def get_deformed_everything(self, time): | |
means3D = self.get_xyz | |
time = torch.tensor(time).to(means3D.device).repeat(means3D.shape[0],1) | |
time = ((time.float() / self.T) - 0.5) * 2 | |
opacity = self._opacity | |
scales = self._scaling | |
rotations = self._rotation | |
deformation_point = self._deformation_table | |
means3D_deform, scales_deform, rotations_deform, opacity_deform = self._deformation(means3D[deformation_point], scales[deformation_point], | |
rotations[deformation_point], opacity[deformation_point], | |
time[deformation_point]) | |
means3D_final = means3D + means3D_deform | |
rotations_final = rotations + rotations_deform | |
scales_final = scales + scales_deform | |
opacity_final = opacity | |
return means3D_final, rotations_final, scales_final, opacity_final | |
def extract_fields_t(self, resolution=128, num_blocks=16, relax_ratio=1.5, t=0): | |
# resolution: resolution of field | |
block_size = 2 / num_blocks | |
assert resolution % block_size == 0 | |
split_size = resolution // num_blocks | |
xyzs, rotation, scale, opacities = self.get_deformed_everything(t) | |
scale = self.scaling_activation(scale) | |
opacities = self.opacity_activation(opacities) | |
# pre-filter low opacity gaussians to save computation | |
mask = (opacities > 0.005).squeeze(1) | |
opacities = opacities[mask] | |
xyzs = xyzs[mask] | |
stds = scale[mask] | |
# normalize to ~ [-1, 1] | |
mn, mx = xyzs.amin(0), xyzs.amax(0) | |
self.center = (mn + mx) / 2 | |
self.scale = 1.8 / (mx - mn).amax().item() | |
xyzs = (xyzs - self.center) * self.scale | |
stds = stds * self.scale | |
covs = self.covariance_activation(stds, 1, rotation[mask]) | |
# tile | |
device = opacities.device | |
occ = torch.zeros([resolution] * 3, dtype=torch.float32, device=device) | |
X = torch.linspace(-1, 1, resolution).split(split_size) | |
Y = torch.linspace(-1, 1, resolution).split(split_size) | |
Z = torch.linspace(-1, 1, resolution).split(split_size) | |
# loop blocks (assume max size of gaussian is small than relax_ratio * block_size !!!) | |
for xi, xs in enumerate(X): | |
for yi, ys in enumerate(Y): | |
for zi, zs in enumerate(Z): | |
xx, yy, zz = torch.meshgrid(xs, ys, zs) | |
# sample points [M, 3] | |
pts = torch.cat([xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1).to(device) | |
# in-tile gaussians mask | |
vmin, vmax = pts.amin(0), pts.amax(0) | |
vmin -= block_size * relax_ratio | |
vmax += block_size * relax_ratio | |
mask = (xyzs < vmax).all(-1) & (xyzs > vmin).all(-1) | |
# if hit no gaussian, continue to next block | |
if not mask.any(): | |
continue | |
mask_xyzs = xyzs[mask] # [L, 3] | |
mask_covs = covs[mask] # [L, 6] | |
mask_opas = opacities[mask].view(1, -1) # [L, 1] --> [1, L] | |
# query per point-gaussian pair. | |
g_pts = pts.unsqueeze(1).repeat(1, mask_covs.shape[0], 1) - mask_xyzs.unsqueeze(0) # [M, L, 3] | |
g_covs = mask_covs.unsqueeze(0).repeat(pts.shape[0], 1, 1) # [M, L, 6] | |
# batch on gaussian to avoid OOM | |
batch_g = 1024 | |
val = 0 | |
for start in range(0, g_covs.shape[1], batch_g): | |
end = min(start + batch_g, g_covs.shape[1]) | |
w = gaussian_3d_coeff(g_pts[:, start:end].reshape(-1, 3), g_covs[:, start:end].reshape(-1, 6)).reshape(pts.shape[0], -1) # [M, l] | |
val += (mask_opas[:, start:end] * w).sum(-1) | |
# kiui.lo(val, mask_opas, w) | |
occ[xi * split_size: xi * split_size + len(xs), | |
yi * split_size: yi * split_size + len(ys), | |
zi * split_size: zi * split_size + len(zs)] = val.reshape(len(xs), len(ys), len(zs)) | |
return occ | |
def extract_mesh_t(self, path, density_thresh=1, t=0, resolution=128, decimate_target=1e5): | |
from mesh import Mesh | |
from mesh_utils import decimate_mesh, clean_mesh | |
os.makedirs(os.path.dirname(path), exist_ok=True) | |
occ = self.extract_fields_t(resolution, t=t).detach().cpu().numpy() | |
import mcubes | |
vertices, triangles = mcubes.marching_cubes(occ, density_thresh) | |
vertices = vertices / (resolution - 1.0) * 2 - 1 | |
# transform back to the original space | |
vertices = vertices / self.scale + self.center.detach().cpu().numpy() | |
vertices, triangles = clean_mesh(vertices, triangles, remesh=True, remesh_size=0.015) | |
if decimate_target > 0 and triangles.shape[0] > decimate_target: | |
vertices, triangles = decimate_mesh(vertices, triangles, decimate_target) | |
v = torch.from_numpy(vertices.astype(np.float32)).contiguous().cuda() | |
f = torch.from_numpy(triangles.astype(np.int32)).contiguous().cuda() | |
print( | |
f"[INFO] marching cubes result: {v.shape} ({v.min().item()}-{v.max().item()}), {f.shape}" | |
) | |
mesh = Mesh(v=v, f=f, device='cuda') | |
return mesh | |
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, time_line: int): | |
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(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda")) | |
self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True)) | |
self._deformation = self._deformation.to("cuda") | |
# self.grid = self.grid.to("cuda") | |
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") | |
self._deformation_table = torch.gt(torch.ones((self.get_xyz.shape[0]),device="cuda"),0) | |
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") | |
self._deformation_accum = torch.zeros((self.get_xyz.shape[0],3),device="cuda") | |
self.T = training_args.batch_size | |
if training_args.optimize_gaussians: | |
l = [ | |
{'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"}, | |
{'params': list(self._deformation.get_mlp_parameters()), 'lr': training_args.deformation_lr_init * self.spatial_lr_scale, "name": "deformation"}, | |
{'params': list(self._deformation.get_grid_parameters()), 'lr': training_args.grid_lr_init * self.spatial_lr_scale, "name": "grid"}, | |
{'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"} | |
] | |
else: | |
l = [ | |
{'params': list(self._deformation.get_mlp_parameters()), 'lr': training_args.deformation_lr_init * self.spatial_lr_scale, "name": "deformation"}, | |
{'params': list(self._deformation.get_grid_parameters()), 'lr': training_args.grid_lr_init * self.spatial_lr_scale, "name": "grid"}, | |
] | |
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) | |
self.deformation_scheduler_args = get_expon_lr_func(lr_init=training_args.deformation_lr_init*self.spatial_lr_scale, | |
lr_final=training_args.deformation_lr_final*self.spatial_lr_scale, | |
lr_delay_mult=training_args.deformation_lr_delay_mult, | |
max_steps=training_args.position_lr_max_steps) | |
self.grid_scheduler_args = get_expon_lr_func(lr_init=training_args.grid_lr_init*self.spatial_lr_scale, | |
lr_final=training_args.grid_lr_final*self.spatial_lr_scale, | |
lr_delay_mult=training_args.deformation_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 | |
if "grid" in param_group["name"]: | |
lr = self.grid_scheduler_args(iteration) | |
param_group['lr'] = lr | |
# return lr | |
elif param_group["name"] == "deformation": | |
lr = self.deformation_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 compute_deformation(self,time): | |
deform = self._deformation[:,:,:time].sum(dim=-1) | |
xyz = self._xyz + deform | |
return xyz | |
def load_model(self, path, name): | |
print("loading model from exists{}".format(path)) | |
weight_dict = torch.load(os.path.join(path, name+"_deformation.pth"),map_location="cuda") | |
self._deformation.load_state_dict(weight_dict) | |
self._deformation = self._deformation.to("cuda") | |
self._deformation_table = torch.gt(torch.ones((self.get_xyz.shape[0]),device="cuda"),0) | |
self._deformation_accum = torch.zeros((self.get_xyz.shape[0],3),device="cuda") | |
if os.path.exists(os.path.join(path, name+"_deformation_table.pth")): | |
self._deformation_table = torch.load(os.path.join(path, name+"_deformation_table.pth"),map_location="cuda") | |
if os.path.exists(os.path.join(path,name+"_deformation_accum.pth")): | |
self._deformation_accum = torch.load(os.path.join(path, name+"_deformation_accum.pth"),map_location="cuda") | |
self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") | |
def save_deformation(self, path, name): | |
torch.save(self._deformation.state_dict(),os.path.join(path, name+"_deformation.pth")) | |
torch.save(self._deformation_table,os.path.join(path, name+"_deformation_table.pth")) | |
torch.save(self._deformation_accum,os.path.join(path, name+"_deformation_accum.pth")) | |
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 save_frame_ply(self, path, t): | |
mkdir_p(os.path.dirname(path)) | |
xyzs, rotation, scale, opacities = self.get_deformed_everything(t) | |
xyz = xyzs.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 = opacities.detach().cpu().numpy() | |
scale = scale.detach().cpu().numpy() | |
rotation = 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 save_frame_ply(self, path, t): | |
# 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): | |
self.spatial_lr_scale = 1 | |
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 | |
self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") | |
self._deformation = self._deformation.to("cuda") | |
self._deformation_table = torch.gt(torch.ones((self.get_xyz.shape[0]),device="cuda"),0) # everything deformed | |
print(self._xyz.shape) | |
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: | |
if len(group["params"]) > 1: | |
continue | |
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._deformation_accum = self._deformation_accum[valid_points_mask] | |
self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask] | |
self._deformation_table = self._deformation_table[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: | |
if len(group["params"])>1:continue | |
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, new_deformation_table): | |
d = {"xyz": new_xyz, | |
"f_dc": new_features_dc, | |
"f_rest": new_features_rest, | |
"opacity": new_opacities, | |
"scaling" : new_scaling, | |
"rotation" : new_rotation, | |
# "deformation": new_deformation | |
} | |
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._deformation = optimizable_tensors["deformation"] | |
self._deformation_table = torch.cat([self._deformation_table,new_deformation_table],-1) | |
self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") | |
self._deformation_accum = torch.zeros((self.get_xyz.shape[0], 3), 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) | |
if not selected_pts_mask.any(): | |
return | |
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) | |
new_deformation_table = self._deformation_table[selected_pts_mask].repeat(N) | |
self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation, new_deformation_table) | |
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] | |
# - 0.001 * self._xyz.grad[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] | |
new_deformation_table = self._deformation_table[selected_pts_mask] | |
self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, new_deformation_table) | |
def prune(self, min_opacity, extent, max_screen_size): | |
prune_mask = (self.get_opacity < min_opacity).squeeze() | |
# prune_mask_2 = torch.logical_and(self.get_opacity <= inverse_sigmoid(0.101 , dtype=torch.float, device="cuda"), self.get_opacity >= inverse_sigmoid(0.999 , dtype=torch.float, device="cuda")) | |
# prune_mask = torch.logical_or(prune_mask, prune_mask_2) | |
# deformation_sum = abs(self._deformation).sum(dim=-1).mean(dim=-1) | |
# deformation_mask = (deformation_sum < torch.quantile(deformation_sum, torch.tensor([0.5]).to("cuda"))) | |
# prune_mask = prune_mask & deformation_mask | |
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(prune_mask, big_points_vs) | |
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 densify(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) | |
def standard_constaint(self): | |
means3D = self._xyz.detach() | |
scales = self._scaling.detach() | |
rotations = self._rotation.detach() | |
opacity = self._opacity.detach() | |
time = torch.tensor(0).to("cuda").repeat(means3D.shape[0],1) | |
means3D_deform, scales_deform, rotations_deform, _ = self._deformation(means3D, scales, rotations, opacity, time) | |
position_error = (means3D_deform - means3D)**2 | |
rotation_error = (rotations_deform - rotations)**2 | |
scaling_erorr = (scales_deform - scales)**2 | |
return position_error.mean() + rotation_error.mean() + scaling_erorr.mean() | |
def add_densification_stats(self, viewspace_point_tensor, update_filter): | |
self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor[update_filter,:2], dim=-1, keepdim=True) | |
self.denom[update_filter] += 1 | |
def update_deformation_table(self,threshold): | |
# print("origin deformation point nums:",self._deformation_table.sum()) | |
self._deformation_table = torch.gt(self._deformation_accum.max(dim=-1).values/100,threshold) | |
def print_deformation_weight_grad(self): | |
for name, weight in self._deformation.named_parameters(): | |
if weight.requires_grad: | |
if weight.grad is None: | |
print(name," :",weight.grad) | |
else: | |
if weight.grad.mean() != 0: | |
print(name," :",weight.grad.mean(), weight.grad.min(), weight.grad.max()) | |
print("-"*50) | |
def _plane_regulation(self): | |
multi_res_grids = self._deformation.deformation_net.grid.grids | |
total = 0 | |
# model.grids is 6 x [1, rank * F_dim, reso, reso] | |
for grids in multi_res_grids: | |
if len(grids) == 3: | |
time_grids = [] | |
else: | |
time_grids = [0,1,3] | |
for grid_id in time_grids: | |
total += compute_plane_smoothness(grids[grid_id]) | |
return total | |
def _time_regulation(self): | |
multi_res_grids = self._deformation.deformation_net.grid.grids | |
total = 0 | |
# model.grids is 6 x [1, rank * F_dim, reso, reso] | |
for grids in multi_res_grids: | |
if len(grids) == 3: | |
time_grids = [] | |
else: | |
time_grids =[2, 4, 5] | |
for grid_id in time_grids: | |
total += compute_plane_smoothness(grids[grid_id]) | |
return total | |
def _l1_regulation(self): | |
# model.grids is 6 x [1, rank * F_dim, reso, reso] | |
multi_res_grids = self._deformation.deformation_net.grid.grids | |
total = 0.0 | |
for grids in multi_res_grids: | |
if len(grids) == 3: | |
continue | |
else: | |
# These are the spatiotemporal grids | |
spatiotemporal_grids = [2, 4, 5] | |
for grid_id in spatiotemporal_grids: | |
total += torch.abs(1 - grids[grid_id]).mean() | |
return total | |
def compute_regulation(self, time_smoothness_weight, l1_time_planes_weight, plane_tv_weight): | |
return plane_tv_weight * self._plane_regulation() + time_smoothness_weight * self._time_regulation() + l1_time_planes_weight * self._l1_regulation() | |
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() | |