Spaces:
Running
on
L40S
Running
on
L40S
| 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() | |
| # Zero-out output layers: | |
| 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) | |