Spaces:
Runtime error
Runtime error
File size: 6,872 Bytes
f3ff4f1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 |
from typing import *
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from ...modules.utils import zero_module, convert_module_to_f16, convert_module_to_f32, convert_module_to_bf16
from ...modules import sparse as sp
from .base import SparseTransformerBase
from ...representations import MeshExtractResult
from ...representations.mesh import SparseFeatures2Mesh
class SparseSubdivideBlock3d(nn.Module):
"""
A 3D subdivide block that can subdivide the sparse tensor.
Args:
channels: channels in the inputs and outputs.
out_channels: if specified, the number of output channels.
num_groups: the number of groups for the group norm.
"""
def __init__(
self,
channels: int,
resolution: int,
out_channels: Optional[int] = None,
num_groups: int = 32
):
super().__init__()
self.channels = channels
self.resolution = resolution
self.out_resolution = resolution * 2
self.out_channels = out_channels or channels
self.act_layers = nn.Sequential(
sp.SparseGroupNorm32(num_groups, channels),
sp.SparseSiLU()
)
self.sub = sp.SparseSubdivide()
self.out_layers = nn.Sequential(
sp.SparseConv3d(channels, self.out_channels, 3, indice_key=f"res_{self.out_resolution}"),
sp.SparseGroupNorm32(num_groups, self.out_channels),
sp.SparseSiLU(),
zero_module(sp.SparseConv3d(self.out_channels, self.out_channels, 3, indice_key=f"res_{self.out_resolution}")),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
else:
self.skip_connection = sp.SparseConv3d(channels, self.out_channels, 1, indice_key=f"res_{self.out_resolution}")
def forward(self, x: sp.SparseTensor) -> sp.SparseTensor:
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
Args:
x: an [N x C x ...] Tensor of features.
Returns:
an [N x C x ...] Tensor of outputs.
"""
h = self.act_layers(x)
h = self.sub(h)
x = self.sub(x)
h = self.out_layers(h)
h = h + self.skip_connection(x)
return h
class SLatMeshDecoder(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_bf16: bool = False,
use_checkpoint: bool = False,
qk_rms_norm: bool = False,
representation_config: dict = None,
mesh_extractor: str = "fc",
):
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_bf16=use_bf16,
use_checkpoint=use_checkpoint,
qk_rms_norm=qk_rms_norm,
)
self.resolution = resolution
self.rep_config = representation_config
if mesh_extractor == "mc":
try:
from ...representations.mesh import SparseFeatures2MCMesh
self.mesh_extractor = SparseFeatures2MCMesh(res=self.resolution*4, use_color=self.rep_config.get('use_color', False))
except ImportError:
raise ValueError("SparseFeatures2MCMesh is not available. Please install the 'mc2mesh' extra.")
elif mesh_extractor == "fc":
self.mesh_extractor = SparseFeatures2Mesh(res=self.resolution*4, use_color=self.rep_config.get('use_color', False))
else:
raise ValueError(f"Invalid mesh extractor {mesh_extractor}")
self.out_channels = self.mesh_extractor.feats_channels
self.upsample = nn.ModuleList([
SparseSubdivideBlock3d(
channels=model_channels,
resolution=resolution,
out_channels=model_channels // 4
),
SparseSubdivideBlock3d(
channels=model_channels // 4,
resolution=resolution * 2,
out_channels=model_channels // 8
)
])
self.out_layer = sp.SparseLinear(model_channels // 8, self.out_channels)
self.initialize_weights()
if use_fp16:
self.convert_to_fp16()
elif use_bf16:
self.convert_to_bf16()
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 convert_to_fp16(self) -> None:
"""
Convert the torso of the model to float16.
"""
self.use_fp16 = True
self.use_bf16 = False
self.dtype = torch.float16
super().convert_to_fp16()
self.upsample.apply(convert_module_to_f16)
def convert_to_bf16(self) -> None:
"""
Convert the torso of the model to bfloat16.
"""
self.use_fp16 = False
self.use_bf16 = True
self.dtype = torch.bfloat16
super().convert_to_bf16()
self.upsample.apply(convert_module_to_bf16)
def convert_to_fp32(self) -> None:
"""
Convert the torso of the model to float32.
"""
self.use_fp16 = False
self.use_bf16 = False
self.dtype = torch.float32
super().convert_to_fp32()
self.upsample.apply(convert_module_to_f32)
def to_representation(self, x: sp.SparseTensor) -> List[MeshExtractResult]:
"""
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]):
mesh = self.mesh_extractor(x[i], training=self.training)
ret.append(mesh)
return ret
def forward(self, x: sp.SparseTensor) -> List[MeshExtractResult]:
h = super().forward(x)
for block in self.upsample:
h = block(h)
h = h.type(x.dtype)
h = self.out_layer(h)
return self.to_representation(h)
|