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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
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_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.mesh_extractor = SparseFeatures2Mesh(res=self.resolution*4, use_color=self.rep_config.get('use_color', False))
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()
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.
"""
super().convert_to_fp16()
self.upsample.apply(convert_module_to_f16)
def convert_to_fp32(self) -> None:
"""
Convert the torso of the model to 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)
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