|
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) |
|
|
