mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2024-11-22 16:28:32 +00:00
407 lines
19 KiB
Python
407 lines
19 KiB
Python
#
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# Copyright (C) 2023, Inria
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# GRAPHDECO research group, https://team.inria.fr/graphdeco
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# All rights reserved.
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#
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# This software is free for non-commercial, research and evaluation use
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# under the terms of the LICENSE.md file.
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#
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# For inquiries contact george.drettakis@inria.fr
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#
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import torch
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import numpy as np
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from utils.general_utils import inverse_sigmoid, get_expon_lr_func, build_rotation
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from torch import nn
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import os
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from utils.system_utils import mkdir_p
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from plyfile import PlyData, PlyElement
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from utils.sh_utils import RGB2SH
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from simple_knn._C import distCUDA2
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from utils.graphics_utils import BasicPointCloud
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from utils.general_utils import strip_symmetric, build_scaling_rotation
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class GaussianModel:
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def setup_functions(self):
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def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation):
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L = build_scaling_rotation(scaling_modifier * scaling, rotation)
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actual_covariance = L @ L.transpose(1, 2)
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symm = strip_symmetric(actual_covariance)
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return symm
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self.scaling_activation = torch.exp
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self.scaling_inverse_activation = torch.log
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self.covariance_activation = build_covariance_from_scaling_rotation
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self.opacity_activation = torch.sigmoid
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self.inverse_opacity_activation = inverse_sigmoid
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self.rotation_activation = torch.nn.functional.normalize
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def __init__(self, sh_degree : int):
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self.active_sh_degree = 0
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self.max_sh_degree = sh_degree
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self._xyz = torch.empty(0)
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self._features_dc = torch.empty(0)
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self._features_rest = torch.empty(0)
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self._scaling = torch.empty(0)
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self._rotation = torch.empty(0)
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self._opacity = torch.empty(0)
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self.max_radii2D = torch.empty(0)
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self.xyz_gradient_accum = torch.empty(0)
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self.denom = torch.empty(0)
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self.optimizer = None
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self.percent_dense = 0
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self.spatial_lr_scale = 0
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self.setup_functions()
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def capture(self):
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return (
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self.active_sh_degree,
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self._xyz,
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self._features_dc,
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self._features_rest,
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self._scaling,
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self._rotation,
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self._opacity,
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self.max_radii2D,
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self.xyz_gradient_accum,
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self.denom,
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self.optimizer.state_dict(),
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self.spatial_lr_scale,
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)
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def restore(self, model_args, training_args):
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(self.active_sh_degree,
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self._xyz,
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self._features_dc,
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self._features_rest,
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self._scaling,
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self._rotation,
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self._opacity,
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self.max_radii2D,
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xyz_gradient_accum,
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denom,
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opt_dict,
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self.spatial_lr_scale) = model_args
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self.training_setup(training_args)
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self.xyz_gradient_accum = xyz_gradient_accum
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self.denom = denom
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self.optimizer.load_state_dict(opt_dict)
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@property
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def get_scaling(self):
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return self.scaling_activation(self._scaling)
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@property
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def get_rotation(self):
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return self.rotation_activation(self._rotation)
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@property
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def get_xyz(self):
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return self._xyz
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@property
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def get_features(self):
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features_dc = self._features_dc
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features_rest = self._features_rest
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return torch.cat((features_dc, features_rest), dim=1)
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@property
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def get_opacity(self):
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return self.opacity_activation(self._opacity)
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def get_covariance(self, scaling_modifier = 1):
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return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation)
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def oneupSHdegree(self):
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if self.active_sh_degree < self.max_sh_degree:
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self.active_sh_degree += 1
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def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float):
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self.spatial_lr_scale = spatial_lr_scale
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fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda()
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fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda())
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features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda()
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features[:, :3, 0 ] = fused_color
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features[:, 3:, 1:] = 0.0
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print("Number of points at initialisation : ", fused_point_cloud.shape[0])
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dist2 = torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001)
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scales = torch.log(torch.sqrt(dist2))[...,None].repeat(1, 3)
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rots = torch.zeros((fused_point_cloud.shape[0], 4), device="cuda")
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rots[:, 0] = 1
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opacities = inverse_sigmoid(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda"))
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self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True))
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self._features_dc = nn.Parameter(features[:,:,0:1].transpose(1, 2).contiguous().requires_grad_(True))
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self._features_rest = nn.Parameter(features[:,:,1:].transpose(1, 2).contiguous().requires_grad_(True))
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self._scaling = nn.Parameter(scales.requires_grad_(True))
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self._rotation = nn.Parameter(rots.requires_grad_(True))
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self._opacity = nn.Parameter(opacities.requires_grad_(True))
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self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")
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def training_setup(self, training_args):
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self.percent_dense = training_args.percent_dense
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self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
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self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
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l = [
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{'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"},
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{'params': [self._features_dc], 'lr': training_args.feature_lr, "name": "f_dc"},
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{'params': [self._features_rest], 'lr': training_args.feature_lr / 20.0, "name": "f_rest"},
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{'params': [self._opacity], 'lr': training_args.opacity_lr, "name": "opacity"},
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{'params': [self._scaling], 'lr': training_args.scaling_lr, "name": "scaling"},
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{'params': [self._rotation], 'lr': training_args.rotation_lr, "name": "rotation"}
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]
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self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15)
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self.xyz_scheduler_args = get_expon_lr_func(lr_init=training_args.position_lr_init*self.spatial_lr_scale,
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lr_final=training_args.position_lr_final*self.spatial_lr_scale,
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lr_delay_mult=training_args.position_lr_delay_mult,
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max_steps=training_args.position_lr_max_steps)
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def update_learning_rate(self, iteration):
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''' Learning rate scheduling per step '''
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for param_group in self.optimizer.param_groups:
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if param_group["name"] == "xyz":
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lr = self.xyz_scheduler_args(iteration)
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param_group['lr'] = lr
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return lr
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def construct_list_of_attributes(self):
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l = ['x', 'y', 'z', 'nx', 'ny', 'nz']
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# All channels except the 3 DC
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for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]):
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l.append('f_dc_{}'.format(i))
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for i in range(self._features_rest.shape[1]*self._features_rest.shape[2]):
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l.append('f_rest_{}'.format(i))
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l.append('opacity')
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for i in range(self._scaling.shape[1]):
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l.append('scale_{}'.format(i))
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for i in range(self._rotation.shape[1]):
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l.append('rot_{}'.format(i))
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return l
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def save_ply(self, path):
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mkdir_p(os.path.dirname(path))
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xyz = self._xyz.detach().cpu().numpy()
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normals = np.zeros_like(xyz)
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f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
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f_rest = self._features_rest.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
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opacities = self._opacity.detach().cpu().numpy()
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scale = self._scaling.detach().cpu().numpy()
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rotation = self._rotation.detach().cpu().numpy()
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dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()]
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elements = np.empty(xyz.shape[0], dtype=dtype_full)
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attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1)
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elements[:] = list(map(tuple, attributes))
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el = PlyElement.describe(elements, 'vertex')
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PlyData([el]).write(path)
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def reset_opacity(self):
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opacities_new = inverse_sigmoid(torch.min(self.get_opacity, torch.ones_like(self.get_opacity)*0.01))
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optimizable_tensors = self.replace_tensor_to_optimizer(opacities_new, "opacity")
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self._opacity = optimizable_tensors["opacity"]
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def load_ply(self, path):
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plydata = PlyData.read(path)
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xyz = np.stack((np.asarray(plydata.elements[0]["x"]),
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np.asarray(plydata.elements[0]["y"]),
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np.asarray(plydata.elements[0]["z"])), axis=1)
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opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis]
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features_dc = np.zeros((xyz.shape[0], 3, 1))
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features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"])
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features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"])
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features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"])
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extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("f_rest_")]
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extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1]))
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assert len(extra_f_names)==3*(self.max_sh_degree + 1) ** 2 - 3
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features_extra = np.zeros((xyz.shape[0], len(extra_f_names)))
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for idx, attr_name in enumerate(extra_f_names):
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features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name])
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# Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC)
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features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1))
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scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")]
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scale_names = sorted(scale_names, key = lambda x: int(x.split('_')[-1]))
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scales = np.zeros((xyz.shape[0], len(scale_names)))
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for idx, attr_name in enumerate(scale_names):
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scales[:, idx] = np.asarray(plydata.elements[0][attr_name])
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rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot")]
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rot_names = sorted(rot_names, key = lambda x: int(x.split('_')[-1]))
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rots = np.zeros((xyz.shape[0], len(rot_names)))
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for idx, attr_name in enumerate(rot_names):
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rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
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self._xyz = nn.Parameter(torch.tensor(xyz, dtype=torch.float, device="cuda").requires_grad_(True))
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self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))
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self._features_rest = nn.Parameter(torch.tensor(features_extra, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))
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self._opacity = nn.Parameter(torch.tensor(opacities, dtype=torch.float, device="cuda").requires_grad_(True))
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self._scaling = nn.Parameter(torch.tensor(scales, dtype=torch.float, device="cuda").requires_grad_(True))
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self._rotation = nn.Parameter(torch.tensor(rots, dtype=torch.float, device="cuda").requires_grad_(True))
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self.active_sh_degree = self.max_sh_degree
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def replace_tensor_to_optimizer(self, tensor, name):
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optimizable_tensors = {}
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for group in self.optimizer.param_groups:
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if group["name"] == name:
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stored_state = self.optimizer.state.get(group['params'][0], None)
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stored_state["exp_avg"] = torch.zeros_like(tensor)
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stored_state["exp_avg_sq"] = torch.zeros_like(tensor)
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del self.optimizer.state[group['params'][0]]
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group["params"][0] = nn.Parameter(tensor.requires_grad_(True))
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self.optimizer.state[group['params'][0]] = stored_state
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optimizable_tensors[group["name"]] = group["params"][0]
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return optimizable_tensors
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def _prune_optimizer(self, mask):
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optimizable_tensors = {}
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for group in self.optimizer.param_groups:
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stored_state = self.optimizer.state.get(group['params'][0], None)
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if stored_state is not None:
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stored_state["exp_avg"] = stored_state["exp_avg"][mask]
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stored_state["exp_avg_sq"] = stored_state["exp_avg_sq"][mask]
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del self.optimizer.state[group['params'][0]]
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group["params"][0] = nn.Parameter((group["params"][0][mask].requires_grad_(True)))
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self.optimizer.state[group['params'][0]] = stored_state
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optimizable_tensors[group["name"]] = group["params"][0]
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else:
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group["params"][0] = nn.Parameter(group["params"][0][mask].requires_grad_(True))
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optimizable_tensors[group["name"]] = group["params"][0]
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return optimizable_tensors
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def prune_points(self, mask):
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valid_points_mask = ~mask
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optimizable_tensors = self._prune_optimizer(valid_points_mask)
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self._xyz = optimizable_tensors["xyz"]
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self._features_dc = optimizable_tensors["f_dc"]
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self._features_rest = optimizable_tensors["f_rest"]
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self._opacity = optimizable_tensors["opacity"]
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self._scaling = optimizable_tensors["scaling"]
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self._rotation = optimizable_tensors["rotation"]
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self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask]
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self.denom = self.denom[valid_points_mask]
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self.max_radii2D = self.max_radii2D[valid_points_mask]
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def cat_tensors_to_optimizer(self, tensors_dict):
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optimizable_tensors = {}
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for group in self.optimizer.param_groups:
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assert len(group["params"]) == 1
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extension_tensor = tensors_dict[group["name"]]
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stored_state = self.optimizer.state.get(group['params'][0], None)
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if stored_state is not None:
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stored_state["exp_avg"] = torch.cat((stored_state["exp_avg"], torch.zeros_like(extension_tensor)), dim=0)
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stored_state["exp_avg_sq"] = torch.cat((stored_state["exp_avg_sq"], torch.zeros_like(extension_tensor)), dim=0)
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del self.optimizer.state[group['params'][0]]
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group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True))
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self.optimizer.state[group['params'][0]] = stored_state
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optimizable_tensors[group["name"]] = group["params"][0]
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else:
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group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True))
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optimizable_tensors[group["name"]] = group["params"][0]
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return optimizable_tensors
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def densification_postfix(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation):
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d = {"xyz": new_xyz,
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"f_dc": new_features_dc,
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"f_rest": new_features_rest,
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"opacity": new_opacities,
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"scaling" : new_scaling,
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"rotation" : new_rotation}
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optimizable_tensors = self.cat_tensors_to_optimizer(d)
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self._xyz = optimizable_tensors["xyz"]
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self._features_dc = optimizable_tensors["f_dc"]
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self._features_rest = optimizable_tensors["f_rest"]
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self._opacity = optimizable_tensors["opacity"]
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self._scaling = optimizable_tensors["scaling"]
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self._rotation = optimizable_tensors["rotation"]
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self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
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self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
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self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")
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def densify_and_split(self, grads, grad_threshold, scene_extent, N=2):
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n_init_points = self.get_xyz.shape[0]
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# Extract points that satisfy the gradient condition
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padded_grad = torch.zeros((n_init_points), device="cuda")
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padded_grad[:grads.shape[0]] = grads.squeeze()
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selected_pts_mask = torch.where(padded_grad >= grad_threshold, True, False)
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selected_pts_mask = torch.logical_and(selected_pts_mask,
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torch.max(self.get_scaling, dim=1).values > self.percent_dense*scene_extent)
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stds = self.get_scaling[selected_pts_mask].repeat(N,1)
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means =torch.zeros((stds.size(0), 3),device="cuda")
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samples = torch.normal(mean=means, std=stds)
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rots = build_rotation(self._rotation[selected_pts_mask]).repeat(N,1,1)
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new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self.get_xyz[selected_pts_mask].repeat(N, 1)
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new_scaling = self.scaling_inverse_activation(self.get_scaling[selected_pts_mask].repeat(N,1) / (0.8*N))
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new_rotation = self._rotation[selected_pts_mask].repeat(N,1)
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new_features_dc = self._features_dc[selected_pts_mask].repeat(N,1,1)
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new_features_rest = self._features_rest[selected_pts_mask].repeat(N,1,1)
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new_opacity = self._opacity[selected_pts_mask].repeat(N,1)
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self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation)
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prune_filter = torch.cat((selected_pts_mask, torch.zeros(N * selected_pts_mask.sum(), device="cuda", dtype=bool)))
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self.prune_points(prune_filter)
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def densify_and_clone(self, grads, grad_threshold, scene_extent):
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# Extract points that satisfy the gradient condition
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selected_pts_mask = torch.where(torch.norm(grads, dim=-1) >= grad_threshold, True, False)
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selected_pts_mask = torch.logical_and(selected_pts_mask,
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torch.max(self.get_scaling, dim=1).values <= self.percent_dense*scene_extent)
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new_xyz = self._xyz[selected_pts_mask]
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new_features_dc = self._features_dc[selected_pts_mask]
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new_features_rest = self._features_rest[selected_pts_mask]
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new_opacities = self._opacity[selected_pts_mask]
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new_scaling = self._scaling[selected_pts_mask]
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new_rotation = self._rotation[selected_pts_mask]
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self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation)
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def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size):
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grads = self.xyz_gradient_accum / self.denom
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grads[grads.isnan()] = 0.0
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self.densify_and_clone(grads, max_grad, extent)
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self.densify_and_split(grads, max_grad, extent)
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prune_mask = (self.get_opacity < min_opacity).squeeze()
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if max_screen_size:
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big_points_vs = self.max_radii2D > max_screen_size
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big_points_ws = self.get_scaling.max(dim=1).values > 0.1 * extent
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prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws)
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self.prune_points(prune_mask)
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torch.cuda.empty_cache()
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def add_densification_stats(self, viewspace_point_tensor, update_filter):
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self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor.grad[update_filter,:2], dim=-1, keepdim=True)
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self.denom[update_filter] += 1 |