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Running
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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 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) | |
| 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_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 | |