# # 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 # from lietorch import SO3, SE3, Sim3, LieGroupParameter 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 from scipy.spatial.transform import Rotation as R from utils.pose_utils import rotation2quad, get_tensor_from_camera from utils.graphics_utils import getWorld2View2 import torch.nn.functional as F def quaternion_to_rotation_matrix(quaternion): """ Convert a quaternion to a rotation matrix. Parameters: - quaternion: A tensor of shape (..., 4) representing quaternions. Returns: - A tensor of shape (..., 3, 3) representing rotation matrices. """ # Ensure quaternion is of float type for computation quaternion = quaternion.float() # Normalize the quaternion to unit length quaternion = quaternion / quaternion.norm(p=2, dim=-1, keepdim=True) # Extract components w, x, y, z = quaternion[..., 0], quaternion[..., 1], quaternion[..., 2], quaternion[..., 3] # Compute rotation matrix components xx, yy, zz = x * x, y * y, z * z xy, xz, yz = x * y, x * z, y * z xw, yw, zw = x * w, y * w, z * w # Assemble the rotation matrix R = torch.stack([ torch.stack([1 - 2 * (yy + zz), 2 * (xy - zw), 2 * (xz + yw)], dim=-1), torch.stack([ 2 * (xy + zw), 1 - 2 * (xx + zz), 2 * (yz - xw)], dim=-1), torch.stack([ 2 * (xz - yw), 2 * (yz + xw), 1 - 2 * (xx + yy)], dim=-1) ], dim=-2) return R 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.active_sh_degree = sh_degree 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.param_init = {} 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, self.P, ) 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, self.P) = 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 def compute_relative_world_to_camera(self, R1, t1, R2, t2): # Create a row of zeros with a one at the end, for homogeneous coordinates zero_row = np.array([[0, 0, 0, 1]], dtype=np.float32) # Compute the inverse of the first extrinsic matrix E1_inv = np.hstack([R1.T, -R1.T @ t1.reshape(-1, 1)]) # Transpose and reshape for correct dimensions E1_inv = np.vstack([E1_inv, zero_row]) # Append the zero_row to make it a 4x4 matrix # Compute the second extrinsic matrix E2 = np.hstack([R2, -R2 @ t2.reshape(-1, 1)]) # No need to transpose R2 E2 = np.vstack([E2, zero_row]) # Append the zero_row to make it a 4x4 matrix # Compute the relative transformation E_rel = E2 @ E1_inv return E_rel def init_RT_seq(self, cam_list): poses =[] for cam in cam_list[1.0]: p = get_tensor_from_camera(cam.world_view_transform.transpose(0, 1)) # R T -> quat t poses.append(p) poses = torch.stack(poses) self.P = poses.cuda().requires_grad_(True) # poses_ = torch.randn(poses.detach().clone().shape, device='cuda') # self.P = poses_.cuda().requires_grad_(True) self.param_init['pose'] = poses.detach().clone() def get_RT(self, idx): pose = self.P[idx] return pose def get_RT_test(self, idx): pose = self.test_P[idx] return pose @property def get_features(self): features_dc = self._features_dc # features_rest = self._features_rest return features_dc @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(0.1 * 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.param_init.update({ 'xyz': fused_point_cloud.detach().clone(), 'f_dc': features[:,:,0:1].transpose(1, 2).contiguous().detach().clone(), 'f_rest': features[:,:,1:].transpose(1, 2).contiguous().detach().clone(), 'opacity': opacities.detach().clone(), 'scaling': scales.detach().clone(), 'rotation': rots.detach().clone(), }) 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"}, ] l_cam = [{'params': [self.P],'lr': training_args.rotation_lr*0.1, "name": "pose"},] # l_cam = [{'params': [self.P],'lr': training_args.rotation_lr, "name": "pose"},] l += l_cam 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.cam_scheduler_args = get_expon_lr_func( # lr_init=0, # lr_final=0, lr_init=training_args.rotation_lr*0.1, lr_final=training_args.rotation_lr*0.001, # lr_init=training_args.position_lr_init*self.spatial_lr_scale*10, # lr_final=training_args.position_lr_final*self.spatial_lr_scale*10, lr_delay_mult=training_args.position_lr_delay_mult, max_steps=1000) def update_learning_rate(self, iteration): ''' Learning rate scheduling per step ''' for param_group in self.optimizer.param_groups: if param_group["name"] == "pose": lr = self.cam_scheduler_args(iteration) # print("pose learning rate", iteration, lr) param_group['lr'] = lr 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, opacities, scale, rotation), axis=1) # f_rest, 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: # breakpoint() 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 class Feat2GaussianModel(GaussianModel): def __init__(self, sh_degree : int, feat_dim : int, gs_params_group : dict, noise_std=0): super().__init__(sh_degree) self.noise_std = noise_std self.pc_feat = torch.empty(0) self.param_init = {} self.feat_dim = feat_dim self.gs_params_group = gs_params_group self.active_sh_degree = sh_degree self.sh_coeffs = ((sh_degree + 1) ** 2) * 3-3 net_width = feat_dim out_dim = {'xyz': 3, 'scaling': 3, 'rotation': 4, 'opacity': 1, 'f_dc': 3} # , 'f_rest': self.sh_coeffs for key in gs_params_group.get('head', []): setattr(self, f'head_{key}', conditionalWarp(layers=[feat_dim, net_width, out_dim[key]], skip=[]).cuda()) self.param_key = { 'xyz': '_xyz', 'scaling': '_scaling', 'rotation': '_rotation', 'opacity': '_opacity', 'f_dc': '_features_dc', # 'f_rest': '_features_rest', 'pc_feat': 'pc_feat', } # ## FOR DEBUGGING # self.head_xyz = conditionalWarp(layers=[self.feat_dim, net_width, 3], skip=[]).cuda() # self.head_scaling = conditionalWarp(layers=[self.feat_dim, net_width, 3], skip=[]).cuda() # self.head_rotation = conditionalWarp(layers=[self.feat_dim, net_width, 4], skip=[]).cuda() # self.head_opacity = conditionalWarp(layers=[self.feat_dim, net_width, 1], skip=[]).cuda() # self.head_f_dc = conditionalWarp(layers=[feat_dim, net_width, 3], skip=[]).cuda() # self.head_f_rest = conditionalWarp(layers=[feat_dim, net_width, self.sh_coeffs], skip=[]).cuda() def capture(self): head_state_dicts = {f'head_{key}': getattr(self, f'head_{key}').state_dict() for key in self.gs_params_group.get('head', [])} 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, self.P, head_state_dicts ) 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, self.P, head_state_dicts ) = model_args self.training_setup(training_args) self.xyz_gradient_accum = xyz_gradient_accum self.denom = denom self.optimizer.load_state_dict(opt_dict) for key, state_dict in head_state_dicts.items(): getattr(self, key).load_state_dict(state_dict) def inference(self): feat_in = self.pc_feat for key in self.gs_params_group.get('head', []): if key == 'f_dc': self._features_dc = getattr(self, f'head_{key}')(feat_in, self.param_init[key].view(-1, 3)).reshape(-1, 1, 3) # elif key == 'f_rest': # self._features_rest = getattr(self, f'head_{key}')(feat_in.detach(), self.param_init[key].view(-1, self.sh_coeffs)).reshape(-1, self.sh_coeffs // 3, 3) else: setattr(self, f'_{key}', getattr(self, f'head_{key}')(feat_in, self.param_init[key])) # if key == 'f_dc': # self._features_dc = getattr(self, f'head_{key}')(feat_in, self.param_init[key].view(-1, 3)).reshape(-1, 1, 3) # self._features_dc += self.param_init[key].view(-1, 1, 3).mean(dim=0, keepdim=True) # elif key == 'f_rest': # self._features_rest = getattr(self, f'head_{key}')(feat_in.detach(), self.param_init[key].view(-1, self.sh_coeffs)).reshape(-1, self.sh_coeffs // 3, 3) # self._features_rest += self.param_init[key].view(-1, self.sh_coeffs // 3, 3).mean(dim=0, keepdim=True) # else: # pred = getattr(self, f'head_{key}')(feat_in, self.param_init[key]) # setattr(self, f'_{key}', pred + self.param_init[key].mean(dim=0, keepdim=True)) # ## FOR DEBUGGING # self._xyz = self.head_xyz(pred, self.param_init['xyz']) # self._opacity = self.head_opacity(pred, self.param_init['opacity']) # self._scaling = self.head_scaling(pred, self.param_init['scaling']) # self._rotation = self.head_rotation(pred, self.param_init['rotation']) # self._features_dc = self.head_f_dc(pred, self.param_init['f_dc'].view(-1,3)).reshape(-1, 1, 3) # self._features_rest = self.head_f_rest(pred, self.param_init['f_rest'].view(-1,self.sh_coeffs)).reshape(-1, self.sh_coeffs//3, 3) 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_point_feat = torch.tensor(np.asarray(pcd.features)).float().cuda() # get features from .PLY file assert fused_point_feat.shape[-1] == self.feat_dim, f"Expected feature dimension {self.feat_dim}, but got {fused_point_feat.shape[-1]}" 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.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") self.pc_feat = fused_point_feat#.requires_grad_(True) # fused_point_feat = torch.randn_like(fused_point_feat) # self.pc_feat = fused_point_feat.requires_grad_(True) self.gt_xyz = fused_point_cloud.clone() if self.noise_std != 0: self.noise_std /= 1000.0 torch.manual_seed(0) torch.cuda.manual_seed(0) noise = torch.randn_like(fused_point_cloud) * self.noise_std fused_point_cloud += noise # fused_point_cloud = noise + fused_point_cloud.mean(dim=0, keepdim=True) # fused_point_cloud = torch.zeros_like(fused_point_cloud) + fused_point_cloud.mean(dim=0, keepdim=True) param_init = { 'xyz': fused_point_cloud, 'scaling': scales, 'rotation': rots, 'opacity': opacities, 'f_dc': features[:, :, 0:1].transpose(1, 2).contiguous(), # 'f_rest': features[:, :, 1:].transpose(1, 2).contiguous(), 'pc_feat': fused_point_feat, } for key in self.gs_params_group.get('opt', []): setattr(self, self.param_key[key], nn.Parameter(param_init[key].requires_grad_(True))) self.param_init.update({key: value.detach().clone() for key, value in param_init.items()}) # ## FOR DEBUGGING # self._xyz = nn.Parameter(fused_point_cloud.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._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.param_init.update({ # 'xyz': fused_point_cloud.detach().clone(), # 'f_dc': features[:,:,0:1].transpose(1, 2).contiguous().detach().clone(), # 'f_rest': features[:,:,1:].transpose(1, 2).contiguous().detach().clone(), # 'opacity': opacities.detach().clone(), # 'scaling': scales.detach().clone(), # 'rotation': rots.detach().clone(), # 'pc_feat':fused_point_feat.detach().clone(), # }) 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.param_lr = { "xyz": training_args.position_lr_init * self.spatial_lr_scale, "f_dc": training_args.feature_lr, # "f_rest": training_args.feature_sh_lr, "opacity": training_args.opacity_lr, "scaling": training_args.scaling_lr, "rotation": training_args.rotation_lr } warm_start_lr = 0.01 l = [] for key in self.gs_params_group.get('head', []): l.append({ 'params': getattr(self, f'head_{key}').parameters(), 'lr': warm_start_lr, 'name': key }) for key in self.gs_params_group.get('opt', []): l.append({ 'params': [getattr(self, self.param_key[key])], 'lr': warm_start_lr, 'name': key }) # ## FOR DEBUGGING # l += [ # {'params': self.head_f_dc.parameters(), 'lr': warm_start_lr, "name": "warm_start_f_dc"}, # {'params': self.head_f_rest.parameters(), 'lr': warm_start_lr, "name": "warm_start_f_rest"}, # ] # l = [ # {'params': self.head_xyz.parameters(), 'lr': warm_start_lr, "name": "xyz"}, # # {'params': [self._xyz], 'lr': warm_start_lr, "name": "xyz"}, # {'params': self.head_scaling.parameters(), 'lr': warm_start_lr, "name": "scaling"}, # # {'params': [self._scaling], 'lr': warm_start_lr, "name": "scaling"}, # {'params': self.head_rotation.parameters(), 'lr': warm_start_lr, "name": "rotation"}, # # {'params': [self._rotation], 'lr': warm_start_lr, "name": "rotation"}, # {'params': self.head_opacity.parameters(), 'lr': warm_start_lr, "name": "opacity"}, # # {'params': [self._opacity], 'lr': warm_start_lr, "name": "opacity"}, # # {'params': self.head_f_dc.parameters(), 'lr': warm_start_lr, "name": "f_dc"}, # {'params': [self._features_dc], 'lr': warm_start_lr, "name": "f_dc"}, # # {'params': self.head_f_rest.parameters(), 'lr': warm_start_lr, "name": "f_rest"}, # {'params': [self._features_rest], 'lr': warm_start_lr, "name": "f_rest"}, # # {'params': [self.pc_feat], 'lr': warm_start_lr, "name": "feat"}, # ] l_cam = [{'params': [self.P],'lr': training_args.pose_lr_init, "name": "pose"},] l += l_cam 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.cam_scheduler_args = get_expon_lr_func(lr_init=training_args.pose_lr_init, lr_final=training_args.pose_lr_final, lr_delay_mult=training_args.position_lr_delay_mult, max_steps=1000) self.warm_start_scheduler_args = get_expon_lr_func(lr_init=warm_start_lr, lr_final=warm_start_lr*0.01, max_steps=1000) def setup_rendering_learning_rate(self, ): ''' Setup learning rate scheduling''' for param_group in self.optimizer.param_groups: if param_group["name"] in self.param_lr: param_group['lr'] = self.param_lr[param_group["name"]] # elif param_group["name"] == "feat": # param_group['lr'] = 1e-6 def update_warm_start_learning_rate(self, iteration): ''' Warm start learning rate scheduling per step ''' for param_group in self.optimizer.param_groups: lr = self.warm_start_scheduler_args(iteration) param_group['lr'] = lr def update_learning_rate(self, iteration): ''' Learning rate scheduling per step ''' for param_group in self.optimizer.param_groups: if param_group["name"] == "pose": lr = self.cam_scheduler_args(iteration) param_group['lr'] = lr if param_group["name"] == "xyz": lr = self.xyz_scheduler_args(iteration) param_group['lr'] = lr # return lr class conditionalWarp(torch.nn.Module): def __init__(self, layers, skip, skip_dim=None, res=[], freq=None, zero_init=False): super().__init__() self.skip = skip self.res = res self.freq = freq self.mlp_warp = torch.nn.ModuleList() L = self.get_layer_dims(layers) for li,(k_in,k_out) in enumerate(L): if li in self.skip: k_in += layers[-1] if skip_dim is None else skip_dim linear = torch.nn.Linear(k_in,k_out) # Init network output as 0 if zero_init: if li == (len(L) - 1): torch.nn.init.constant_(linear.weight, 0) torch.nn.init.constant_(linear.bias, 0) self.mlp_warp.append(linear) def get_layer_dims(self, layers): # return a list of tuples (k_in,k_out) return list(zip(layers[:-1],layers[1:])) def positional_encoding(self, input): # [B,...,N] shape = input.shape freq = 2**torch.arange(self.freq, dtype=torch.float32,device=input.device)*np.pi # [L] spectrum = input[...,None]*freq # [B,...,N,L] sin,cos = spectrum.sin(),spectrum.cos() # [B,...,N,L] input_enc = torch.stack([sin,cos],dim=-2) # [B,...,N,2,L] input_enc = input_enc.view(*shape[:-1],-1) # [B,...,2NL] return input_enc def forward(self, feat_in, color): if self.freq != None: feat_in = torch.cat([feat_in, self.positional_encoding(feat_in)], dim=-1) feat = feat_in for li,layer in enumerate(self.mlp_warp): if li in self.skip: feat = torch.cat([feat, color],dim=-1) if li in self.res: feat = feat + feat_in feat = layer(feat) if li!=len(self.mlp_warp)-1: feat = nn.functional.relu(feat) warp = feat return warp