|
from typing import *
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import numpy as np
|
|
from ...modules import sparse as sp
|
|
from .base import SparseTransformerBase
|
|
from ...representations import Strivec
|
|
|
|
|
|
class SLatRadianceFieldDecoder(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.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 _calc_layout(self) -> None:
|
|
self.layout = {
|
|
'trivec': {'shape': (self.rep_config['rank'], 3, self.rep_config['dim']), 'size': self.rep_config['rank'] * 3 * self.rep_config['dim']},
|
|
'density': {'shape': (self.rep_config['rank'],), 'size': self.rep_config['rank']},
|
|
'features_dc': {'shape': (self.rep_config['rank'], 1, 3), 'size': self.rep_config['rank'] * 3},
|
|
}
|
|
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[Strivec]:
|
|
"""
|
|
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 = Strivec(
|
|
sh_degree=0,
|
|
resolution=self.resolution,
|
|
aabb=[-0.5, -0.5, -0.5, 1, 1, 1],
|
|
rank=self.rep_config['rank'],
|
|
dim=self.rep_config['dim'],
|
|
device='cuda',
|
|
)
|
|
representation.density_shift = 0.0
|
|
representation.position = (x.coords[x.layout[i]][:, 1:].float() + 0.5) / self.resolution
|
|
representation.depth = torch.full((representation.position.shape[0], 1), int(np.log2(self.resolution)), dtype=torch.uint8, device='cuda')
|
|
for k, v in self.layout.items():
|
|
setattr(representation, k, x.feats[x.layout[i]][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape']))
|
|
representation.trivec = representation.trivec + 1
|
|
ret.append(representation)
|
|
return ret
|
|
|
|
def forward(self, x: sp.SparseTensor) -> List[Strivec]:
|
|
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)
|
|
|
