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Zero
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from typing import *
import torch
import torch.nn as nn
import torch.nn.functional as F
import trimesh
from skimage import measure
from ...modules.norm import GroupNorm32, ChannelLayerNorm32
from ...modules.spatial import pixel_shuffle_3d
from ...modules.utils import zero_module, convert_module_to_f16, convert_module_to_f32
from .distributions import DiagonalGaussianDistribution
def norm_layer(norm_type: str, *args, **kwargs) -> nn.Module:
"""
Return a normalization layer.
"""
if norm_type == "group":
return GroupNorm32(32, *args, **kwargs)
elif norm_type == "layer":
return ChannelLayerNorm32(*args, **kwargs)
else:
raise ValueError(f"Invalid norm type {norm_type}")
class ResBlock3d(nn.Module):
def __init__(
self,
channels: int,
out_channels: Optional[int] = None,
norm_type: Literal["group", "layer"] = "layer",
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.norm1 = norm_layer(norm_type, channels)
self.norm2 = norm_layer(norm_type, self.out_channels)
self.conv1 = nn.Conv3d(channels, self.out_channels, 3, padding=1)
self.conv2 = zero_module(nn.Conv3d(self.out_channels, self.out_channels, 3, padding=1))
self.skip_connection = nn.Conv3d(channels, self.out_channels, 1) if channels != self.out_channels else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
h = self.norm1(x)
h = F.silu(h)
h = self.conv1(h)
h = self.norm2(h)
h = F.silu(h)
h = self.conv2(h)
h = h + self.skip_connection(x)
return h
class DownsampleBlock3d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
mode: Literal["conv", "avgpool"] = "conv",
):
assert mode in ["conv", "avgpool"], f"Invalid mode {mode}"
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
if mode == "conv":
self.conv = nn.Conv3d(in_channels, out_channels, 2, stride=2)
elif mode == "avgpool":
assert in_channels == out_channels, "Pooling mode requires in_channels to be equal to out_channels"
def forward(self, x: torch.Tensor) -> torch.Tensor:
if hasattr(self, "conv"):
return self.conv(x)
else:
return F.avg_pool3d(x, 2)
class UpsampleBlock3d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
mode: Literal["conv", "nearest"] = "conv",
):
assert mode in ["conv", "nearest"], f"Invalid mode {mode}"
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
if mode == "conv":
self.conv = nn.Conv3d(in_channels, out_channels*8, 3, padding=1)
elif mode == "nearest":
assert in_channels == out_channels, "Nearest mode requires in_channels to be equal to out_channels"
def forward(self, x: torch.Tensor) -> torch.Tensor:
if hasattr(self, "conv"):
x = self.conv(x)
return pixel_shuffle_3d(x, 2)
else:
return F.interpolate(x, scale_factor=2, mode="nearest")
class SparseStructureEncoder(nn.Module):
"""
Encoder for Sparse Structure (\mathcal{E}_S in the paper Sec. 3.3).
Args:
in_channels (int): Channels of the input.
latent_channels (int): Channels of the latent representation.
num_res_blocks (int): Number of residual blocks at each resolution.
channels (List[int]): Channels of the encoder blocks.
num_res_blocks_middle (int): Number of residual blocks in the middle.
norm_type (Literal["group", "layer"]): Type of normalization layer.
use_fp16 (bool): Whether to use FP16.
"""
def __init__(
self,
in_channels: int,
latent_channels: int,
num_res_blocks: int,
channels: List[int],
num_res_blocks_middle: int = 2,
norm_type: Literal["group", "layer"] = "layer",
use_fp16: bool = False,
use_checkpoint: bool = False,
):
super().__init__()
self.in_channels = in_channels
self.latent_channels = latent_channels
self.num_res_blocks = num_res_blocks
self.channels = channels
self.num_res_blocks_middle = num_res_blocks_middle
self.norm_type = norm_type
self.use_fp16 = use_fp16
self.dtype = torch.float16 if use_fp16 else torch.float32
self.use_checkpoint = use_checkpoint
self.input_layer = nn.Conv3d(in_channels, channels[0], 3, padding=1)
self.blocks = nn.ModuleList([])
for i, ch in enumerate(channels):
self.blocks.extend([
ResBlock3d(ch, ch)
for _ in range(num_res_blocks)
])
if i < len(channels) - 1:
self.blocks.append(
DownsampleBlock3d(ch, channels[i+1])
)
self.middle_block = nn.Sequential(*[
ResBlock3d(channels[-1], channels[-1])
for _ in range(num_res_blocks_middle)
])
self.out_layer = nn.Sequential(
norm_layer(norm_type, channels[-1]),
nn.SiLU(),
nn.Conv3d(channels[-1], latent_channels*2, 3, padding=1)
)
if use_fp16:
self.convert_to_fp16()
@property
def device(self) -> torch.device:
"""
Return the device of the model.
"""
return next(self.parameters()).device
def convert_to_fp16(self) -> None:
"""
Convert the torso of the model to float16.
"""
self.use_fp16 = True
self.dtype = torch.float16
self.blocks.apply(convert_module_to_f16)
self.middle_block.apply(convert_module_to_f16)
def convert_to_fp32(self) -> None:
"""
Convert the torso of the model to float32.
"""
self.use_fp16 = False
self.dtype = torch.float32
self.blocks.apply(convert_module_to_f32)
self.middle_block.apply(convert_module_to_f32)
def forward(self, x: torch.Tensor) -> torch.Tensor:
h = self.input_layer(x)
for block in self.blocks:
h = block(h)
h = self.middle_block(h)
h = self.out_layer(h)
return h
class SparseStructureDecoder(nn.Module):
"""
Decoder for Sparse Structure (\mathcal{D}_S in the paper Sec. 3.3).
Args:
out_channels (int): Channels of the output.
latent_channels (int): Channels of the latent representation.
num_res_blocks (int): Number of residual blocks at each resolution.
channels (List[int]): Channels of the decoder blocks.
num_res_blocks_middle (int): Number of residual blocks in the middle.
norm_type (Literal["group", "layer"]): Type of normalization layer.
use_fp16 (bool): Whether to use FP16.
"""
def __init__(
self,
out_channels: int,
latent_channels: int,
num_res_blocks: int,
channels: List[int],
num_res_blocks_middle: int = 2,
norm_type: Literal["group", "layer"] = "layer",
use_fp16: bool = False,
use_checkpoint: bool = False,
):
super().__init__()
self.out_channels = out_channels
self.latent_channels = latent_channels
self.num_res_blocks = num_res_blocks
self.channels = channels
self.num_res_blocks_middle = num_res_blocks_middle
self.norm_type = norm_type
self.use_fp16 = use_fp16
self.dtype = torch.float16 if use_fp16 else torch.float32
self.use_checkpoint = use_checkpoint
self.input_layer = nn.Conv3d(latent_channels, channels[0], 3, padding=1)
self.middle_block = nn.Sequential(*[
ResBlock3d(channels[0], channels[0])
for _ in range(num_res_blocks_middle)
])
self.blocks = nn.ModuleList([])
for i, ch in enumerate(channels):
self.blocks.extend([
ResBlock3d(ch, ch)
for _ in range(num_res_blocks)
])
if i < len(channels) - 1:
self.blocks.append(
UpsampleBlock3d(ch, channels[i+1])
)
self.out_layer = nn.Sequential(
norm_layer(norm_type, channels[-1]),
nn.SiLU(),
nn.Conv3d(channels[-1], out_channels, 3, padding=1)
)
if use_fp16:
self.convert_to_fp16()
@property
def device(self) -> torch.device:
"""
Return the device of the model.
"""
return next(self.parameters()).device
def convert_to_fp16(self) -> None:
"""
Convert the torso of the model to float16.
"""
self.use_fp16 = True
self.dtype = torch.float16
# self.blocks.apply(convert_module_to_f16)
# self.middle_block.apply(convert_module_to_f16)
self.apply(convert_module_to_f16)
def convert_to_fp32(self) -> None:
"""
Convert the torso of the model to float32.
"""
self.use_fp16 = False
self.dtype = torch.float32
self.blocks.apply(convert_module_to_f32)
self.middle_block.apply(convert_module_to_f32)
def forward(self, x: torch.Tensor) -> torch.Tensor:
h = self.input_layer(x)
h = self.middle_block(h)
for block in self.blocks:
h = block(h)
h = self.out_layer(h)
return h
class DenseShapeVAE(nn.Module):
def __init__(self,
embed_dim: int = 0,
model_channels_encoder: list = [32, 128, 512],
model_channels_decoder: list = [512, 128, 32],
num_res_blocks_encoder: int = 2,
num_res_blocks_middle_encoder: int = 2,
num_res_blocks_decoder: int = 2,
num_res_blocks_middle_decoder: int=2,
in_channels: int = 1,
out_channels: int = 1,
use_fp16: bool = False,
use_checkpoint: bool = False,
latents_scale: float = 1.0,
latents_shift: float = 0.0):
super().__init__()
self.use_checkpoint = use_checkpoint
self.latents_scale = latents_scale
self.latents_shift = latents_shift
self.encoder = SparseStructureEncoder(
in_channels=in_channels,
latent_channels=embed_dim,
num_res_blocks=num_res_blocks_encoder,
channels=model_channels_encoder,
num_res_blocks_middle=num_res_blocks_middle_encoder,
use_fp16=use_fp16,
use_checkpoint=use_checkpoint,
)
self.decoder = SparseStructureDecoder(
num_res_blocks=num_res_blocks_decoder,
num_res_blocks_middle=num_res_blocks_middle_decoder,
channels=model_channels_decoder,
latent_channels=embed_dim,
out_channels=out_channels,
use_fp16=use_fp16,
use_checkpoint=use_checkpoint,
)
self.embed_dim = embed_dim
def encode(self, batch, sample_posterior: bool = True):
x = batch['dense_index'] * 2.0 - 1.0
h = self.encoder(x)
posterior = DiagonalGaussianDistribution(h, feat_dim=1)
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
return z, posterior
def forward(self, batch):
z, posterior = self.encode(batch)
reconst_x = self.decoder(z)
outputs = {'reconst_x': reconst_x, 'posterior': posterior}
return outputs
def decode_mesh(self,
latents,
voxel_resolution: int = 64,
mc_threshold: float = 0.5,
return_index: bool = False):
x = self.decoder(latents)
if return_index:
outputs = []
for i in range(len(x)):
occ = x[i].sigmoid()
occ = (occ >= mc_threshold).float().squeeze(0)
index = occ.unsqueeze(0).nonzero()
outputs.append(index)
else:
outputs = self.dense2mesh(x, voxel_resolution=voxel_resolution, mc_threshold=mc_threshold)
return outputs
def dense2mesh(self,
x: torch.FloatTensor,
voxel_resolution: int = 64,
mc_threshold: float = 0.5):
meshes = []
for i in range(len(x)):
occ = x[i].sigmoid()
occ = (occ >= 0.1).float().squeeze(0).cpu().detach().numpy()
vertices, faces, _, _ = measure.marching_cubes(
occ,
mc_threshold,
method="lewiner",
)
vertices = vertices / voxel_resolution * 2 - 1
meshes.append(trimesh.Trimesh(vertices, faces))
return meshes
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