# # 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 import json from utils.system_utils import mkdir_p import meshio 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 try: from diff_gaussian_rasterization import SparseGaussianAdam except: pass 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, optimizer_type="default"): self.active_sh_degree = 0 self.optimizer_type = optimizer_type 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_features_dc(self): return self._features_dc @property def get_features_rest(self): return self._features_rest @property def get_opacity(self): return self.opacity_activation(self._opacity) @property def get_exposure(self): return self._exposure def get_exposure_from_name(self, image_name): if self.pretrained_exposures is None: return self._exposure[self.exposure_mapping[image_name]] else: return self.pretrained_exposures[image_name] 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, cam_infos : int, 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 = self.inverse_opacity_activation(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._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.exposure_mapping = {cam_info.image_name: idx for idx, cam_info in enumerate(cam_infos)} self.pretrained_exposures = None exposure = torch.eye(3, 4, device="cuda")[None].repeat(len(cam_infos), 1, 1) self._exposure = nn.Parameter(exposure.requires_grad_(True)) 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"} ] if self.optimizer_type == "default": self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15) elif self.optimizer_type == "sparse_adam": try: self.optimizer = SparseGaussianAdam(l, lr=0.0, eps=1e-15) except: # A special version of the rasterizer is required to enable sparse adam self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15) self.exposure_optimizer = torch.optim.Adam([self._exposure]) 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.exposure_scheduler_args = get_expon_lr_func(training_args.exposure_lr_init, training_args.exposure_lr_final, lr_delay_steps=training_args.exposure_lr_delay_steps, lr_delay_mult=training_args.exposure_lr_delay_mult, max_steps=training_args.iterations) def update_learning_rate(self, iteration): ''' Learning rate scheduling per step ''' if self.pretrained_exposures is None: for param_group in self.exposure_optimizer.param_groups: param_group['lr'] = self.exposure_scheduler_args(iteration) 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() point_data = {} attribs_no_pos = [attribute for attribute in self.construct_list_of_attributes() if attribute not in ["x", "y", "z"]] values = np.concatenate((normals, f_dc, f_rest, opacities, scale, rotation), axis=1) for index, attribute in enumerate(attribs_no_pos): point_data[attribute] = values[..., index] mesh = meshio.Mesh(points=xyz.astype(np.float32), point_data=point_data, cells=[]) meshio.write(path, mesh) def reset_opacity(self): opacities_new = self.inverse_opacity_activation(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, use_train_test_exp = False): if use_train_test_exp: exposure_file = os.path.join(os.path.dirname(path), os.pardir, os.pardir, "exposure.json") if os.path.exists(exposure_file): with open(exposure_file, "r") as f: exposures = json.load(f) self.pretrained_exposures = {image_name: torch.FloatTensor(exposures[image_name]).requires_grad_(False).cuda() for image_name in exposures} print(f"Pretrained exposures loaded.") else: print(f"No exposure to be loaded at {exposure_file}") self.pretrained_exposures = None vertices = meshio.read(path) xyz = vertices.points point_data = vertices.point_data opacities = np.asarray(point_data["opacity"])[..., np.newaxis] features_dc = np.zeros((xyz.shape[0], 3, 1)) features_dc[:, 0, 0] = np.asarray(point_data["f_dc_0"]) features_dc[:, 1, 0] = np.asarray(point_data["f_dc_1"]) features_dc[:, 2, 0] = np.asarray(point_data["f_dc_2"]) extra_f_names = [p for p in point_data if p.startswith("f_rest_")] extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1])) 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(point_data[attr_name]) features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1)) scale_names = [p for p in point_data if p.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(point_data[attr_name]) rot_names = [p for p in point_data if p.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(point_data[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] self.tmp_radii = self.tmp_radii[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, new_tmp_radii): 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.tmp_radii = torch.cat((self.tmp_radii, new_tmp_radii)) 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) new_tmp_radii = self.tmp_radii[selected_pts_mask].repeat(N) self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation, new_tmp_radii) 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] new_tmp_radii = self.tmp_radii[selected_pts_mask] self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, new_tmp_radii) def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size, radii): grads = self.xyz_gradient_accum / self.denom grads[grads.isnan()] = 0.0 self.tmp_radii = radii 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) tmp_radii = self.tmp_radii self.tmp_radii = None 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