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on
Zero
Running
on
Zero
| import torch | |
| import numpy as np | |
| from plyfile import PlyData, PlyElement | |
| from .general_utils import inverse_sigmoid, strip_symmetric, build_scaling_rotation | |
| class Gaussian: | |
| def __init__( | |
| self, | |
| aabb : list, | |
| sh_degree : int = 0, | |
| mininum_kernel_size : float = 0.0, | |
| scaling_bias : float = 0.01, | |
| opacity_bias : float = 0.1, | |
| scaling_activation : str = "exp", | |
| device='cuda' | |
| ): | |
| self.init_params = { | |
| 'aabb': aabb, | |
| 'sh_degree': sh_degree, | |
| 'mininum_kernel_size': mininum_kernel_size, | |
| 'scaling_bias': scaling_bias, | |
| 'opacity_bias': opacity_bias, | |
| 'scaling_activation': scaling_activation, | |
| } | |
| self.sh_degree = sh_degree | |
| self.active_sh_degree = sh_degree | |
| self.mininum_kernel_size = mininum_kernel_size | |
| self.scaling_bias = scaling_bias | |
| self.opacity_bias = opacity_bias | |
| self.scaling_activation_type = scaling_activation | |
| self.device = device | |
| self.aabb = torch.tensor(aabb, dtype=torch.float32, device=device) | |
| self.setup_functions() | |
| self._xyz = None | |
| self._features_dc = None | |
| self._features_rest = None | |
| self._scaling = None | |
| self._rotation = None | |
| self._opacity = None | |
| 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 | |
| if self.scaling_activation_type == "exp": | |
| self.scaling_activation = torch.exp | |
| self.inverse_scaling_activation = torch.log | |
| elif self.scaling_activation_type == "softplus": | |
| self.scaling_activation = torch.nn.functional.softplus | |
| self.inverse_scaling_activation = lambda x: x + torch.log(-torch.expm1(-x)) | |
| 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 | |
| self.scale_bias = self.inverse_scaling_activation(torch.tensor(self.scaling_bias)).cuda() | |
| self.rots_bias = torch.zeros((4)).cuda() | |
| self.rots_bias[0] = 1 | |
| self.opacity_bias = self.inverse_opacity_activation(torch.tensor(self.opacity_bias)).cuda() | |
| def get_scaling(self): | |
| scales = self.scaling_activation(self._scaling + self.scale_bias) | |
| scales = torch.square(scales) + self.mininum_kernel_size ** 2 | |
| scales = torch.sqrt(scales) | |
| return scales | |
| def get_rotation(self): | |
| return self.rotation_activation(self._rotation + self.rots_bias[None, :]) | |
| def get_xyz(self): | |
| return self._xyz * self.aabb[None, 3:] + self.aabb[None, :3] | |
| def get_features(self): | |
| return torch.cat((self._features_dc, self._features_rest), dim=2) if self._features_rest is not None else self._features_dc | |
| def get_opacity(self): | |
| return self.opacity_activation(self._opacity + self.opacity_bias) | |
| def get_covariance(self, scaling_modifier = 1): | |
| return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation + self.rots_bias[None, :]) | |
| def from_scaling(self, scales): | |
| scales = torch.sqrt(torch.square(scales) - self.mininum_kernel_size ** 2) | |
| self._scaling = self.inverse_scaling_activation(scales) - self.scale_bias | |
| def from_rotation(self, rots): | |
| self._rotation = rots - self.rots_bias[None, :] | |
| def from_xyz(self, xyz): | |
| self._xyz = (xyz - self.aabb[None, :3]) / self.aabb[None, 3:] | |
| def from_features(self, features): | |
| self._features_dc = features | |
| def from_opacity(self, opacities): | |
| self._opacity = self.inverse_opacity_activation(opacities) - self.opacity_bias | |
| 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)) | |
| 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): | |
| xyz = self.get_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() | |
| opacities = inverse_sigmoid(self.get_opacity).detach().cpu().numpy() | |
| scale = torch.log(self.get_scaling).detach().cpu().numpy() | |
| rotation = (self._rotation + self.rots_bias[None, :]).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) | |
| elements[:] = list(map(tuple, attributes)) | |
| el = PlyElement.describe(elements, 'vertex') | |
| PlyData([el]).write(path) | |
| 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"]) | |
| if self.sh_degree > 0: | |
| 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.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]) | |
| # convert to actual gaussian attributes | |
| xyz = torch.tensor(xyz, dtype=torch.float, device=self.device) | |
| features_dc = torch.tensor(features_dc, dtype=torch.float, device=self.device).transpose(1, 2).contiguous() | |
| if self.sh_degree > 0: | |
| features_extra = torch.tensor(features_extra, dtype=torch.float, device=self.device).transpose(1, 2).contiguous() | |
| opacities = torch.sigmoid(torch.tensor(opacities, dtype=torch.float, device=self.device)) | |
| scales = torch.exp(torch.tensor(scales, dtype=torch.float, device=self.device)) | |
| rots = torch.tensor(rots, dtype=torch.float, device=self.device) | |
| # convert to _hidden attributes | |
| self._xyz = (xyz - self.aabb[None, :3]) / self.aabb[None, 3:] | |
| self._features_dc = features_dc | |
| if self.sh_degree > 0: | |
| self._features_rest = features_extra | |
| else: | |
| self._features_rest = None | |
| self._opacity = self.inverse_opacity_activation(opacities) - self.opacity_bias | |
| self._scaling = self.inverse_scaling_activation(torch.sqrt(torch.square(scales) - self.mininum_kernel_size ** 2)) - self.scale_bias | |
| self._rotation = rots - self.rots_bias[None, :] | |