Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,946 Bytes
db6a3b7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 |
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()
# Zero-out output layers:
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)
|