| | from typing import * |
| | import torch |
| | import torch.nn as nn |
| | import torch.nn.functional as F |
| | from ...modules import sparse as sp |
| | from ...utils.random_utils import hammersley_sequence |
| | from .base import SparseTransformerBase |
| | from ...representations import Gaussian |
| |
|
| |
|
| | class SLatGaussianDecoder(SparseTransformerBase): |
| | def __init__( |
| | self, |
| | resolution: int, |
| | model_channels: int, |
| | latent_channels: int, |
| | num_blocks: int, |
| | num_heads: Optional[int] = None, |
| | num_head_channels: Optional[int] = 64, |
| | mlp_ratio: float = 4, |
| | attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin", |
| | window_size: int = 8, |
| | pe_mode: Literal["ape", "rope"] = "ape", |
| | use_fp16: bool = False, |
| | use_checkpoint: bool = False, |
| | qk_rms_norm: bool = False, |
| | representation_config: dict = None, |
| | ): |
| | super().__init__( |
| | in_channels=latent_channels, |
| | model_channels=model_channels, |
| | num_blocks=num_blocks, |
| | num_heads=num_heads, |
| | num_head_channels=num_head_channels, |
| | mlp_ratio=mlp_ratio, |
| | attn_mode=attn_mode, |
| | window_size=window_size, |
| | pe_mode=pe_mode, |
| | use_fp16=use_fp16, |
| | use_checkpoint=use_checkpoint, |
| | qk_rms_norm=qk_rms_norm, |
| | ) |
| | self.resolution = resolution |
| | self.rep_config = representation_config |
| | self._calc_layout() |
| | self.out_layer = sp.SparseLinear(model_channels, self.out_channels) |
| | self._build_perturbation() |
| |
|
| | self.initialize_weights() |
| | if use_fp16: |
| | self.convert_to_fp16() |
| |
|
| | def initialize_weights(self) -> None: |
| | super().initialize_weights() |
| | |
| | nn.init.constant_(self.out_layer.weight, 0) |
| | nn.init.constant_(self.out_layer.bias, 0) |
| |
|
| | def _build_perturbation(self) -> None: |
| | perturbation = [hammersley_sequence(3, i, self.rep_config['num_gaussians']) for i in range(self.rep_config['num_gaussians'])] |
| | perturbation = torch.tensor(perturbation).float() * 2 - 1 |
| | perturbation = perturbation / self.rep_config['voxel_size'] |
| | perturbation = torch.atanh(perturbation).to(self.device) |
| | self.register_buffer('offset_perturbation', perturbation) |
| |
|
| | def _calc_layout(self) -> None: |
| | self.layout = { |
| | '_xyz' : {'shape': (self.rep_config['num_gaussians'], 3), 'size': self.rep_config['num_gaussians'] * 3}, |
| | '_features_dc' : {'shape': (self.rep_config['num_gaussians'], 1, 3), 'size': self.rep_config['num_gaussians'] * 3}, |
| | '_scaling' : {'shape': (self.rep_config['num_gaussians'], 3), 'size': self.rep_config['num_gaussians'] * 3}, |
| | '_rotation' : {'shape': (self.rep_config['num_gaussians'], 4), 'size': self.rep_config['num_gaussians'] * 4}, |
| | '_opacity' : {'shape': (self.rep_config['num_gaussians'], 1), 'size': self.rep_config['num_gaussians']}, |
| | } |
| | start = 0 |
| | for k, v in self.layout.items(): |
| | v['range'] = (start, start + v['size']) |
| | start += v['size'] |
| | self.out_channels = start |
| | |
| | def to_representation(self, x: sp.SparseTensor) -> List[Gaussian]: |
| | """ |
| | Convert a batch of network outputs to 3D representations. |
| | |
| | Args: |
| | x: The [N x * x C] sparse tensor output by the network. |
| | |
| | Returns: |
| | list of representations |
| | """ |
| | ret = [] |
| | for i in range(x.shape[0]): |
| | representation = Gaussian( |
| | sh_degree=0, |
| | aabb=[-0.5, -0.5, -0.5, 1.0, 1.0, 1.0], |
| | mininum_kernel_size = self.rep_config['3d_filter_kernel_size'], |
| | scaling_bias = self.rep_config['scaling_bias'], |
| | opacity_bias = self.rep_config['opacity_bias'], |
| | scaling_activation = self.rep_config['scaling_activation'] |
| | ) |
| | xyz = (x.coords[x.layout[i]][:, 1:].float() + 0.5) / self.resolution |
| | for k, v in self.layout.items(): |
| | if k == '_xyz': |
| | offset = x.feats[x.layout[i]][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape']) |
| | offset = offset * self.rep_config['lr'][k] |
| | if self.rep_config['perturb_offset']: |
| | offset = offset + self.offset_perturbation |
| | offset = torch.tanh(offset) / self.resolution * 0.5 * self.rep_config['voxel_size'] |
| | _xyz = xyz.unsqueeze(1) + offset |
| | setattr(representation, k, _xyz.flatten(0, 1)) |
| | else: |
| | feats = x.feats[x.layout[i]][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape']).flatten(0, 1) |
| | feats = feats * self.rep_config['lr'][k] |
| | setattr(representation, k, feats) |
| | ret.append(representation) |
| | return ret |
| |
|
| | def forward(self, x: sp.SparseTensor) -> List[Gaussian]: |
| | h = super().forward(x) |
| | h = h.type(x.dtype) |
| | h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:])) |
| | h = self.out_layer(h) |
| | return self.to_representation(h) |
| |
|
