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# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import trimesh
from skimage import measure
from ...modules import sparse as sp
from .encoder import SparseSDFEncoder
from .decoder import SparseSDFDecoder
from .distributions import DiagonalGaussianDistribution
class SparseSDFVAE(nn.Module):
def __init__(self, *,
embed_dim: int = 0,
resolution: int = 64,
model_channels_encoder: int = 512,
num_blocks_encoder: int = 4,
num_heads_encoder: int = 8,
num_head_channels_encoder: int = 64,
model_channels_decoder: int = 512,
num_blocks_decoder: int = 4,
num_heads_decoder: int = 8,
num_head_channels_decoder: int = 64,
out_channels: int = 1,
use_fp16: bool = False,
use_checkpoint: bool = False,
chunk_size: int = 1,
latents_scale: float = 1.0,
latents_shift: float = 0.0):
super().__init__()
self.use_checkpoint = use_checkpoint
self.resolution = resolution
self.latents_scale = latents_scale
self.latents_shift = latents_shift
self.encoder = SparseSDFEncoder(
resolution=resolution,
in_channels=model_channels_encoder,
model_channels=model_channels_encoder,
latent_channels=embed_dim,
num_blocks=num_blocks_encoder,
num_heads=num_heads_encoder,
num_head_channels=num_head_channels_encoder,
use_fp16=use_fp16,
use_checkpoint=use_checkpoint,
)
self.decoder = SparseSDFDecoder(
resolution=resolution,
model_channels=model_channels_decoder,
latent_channels=embed_dim,
num_blocks=num_blocks_decoder,
num_heads=num_heads_decoder,
num_head_channels=num_head_channels_decoder,
out_channels=out_channels,
use_fp16=use_fp16,
use_checkpoint=use_checkpoint,
chunk_size=chunk_size,
)
self.embed_dim = embed_dim
def forward(self, batch):
z, posterior = self.encode(batch)
reconst_x = self.decoder(z)
outputs = {'reconst_x': reconst_x, 'posterior': posterior}
return outputs
def encode(self, batch, sample_posterior: bool = True):
feat, xyz, batch_idx = batch['sparse_sdf'], batch['sparse_index'], batch['batch_idx']
if feat.ndim == 1:
feat = feat.unsqueeze(-1)
coords = torch.cat([batch_idx.unsqueeze(-1), xyz], dim=-1).int()
x = sp.SparseTensor(feat, coords)
h = self.encoder(x, batch.get('factor', None))
posterior = DiagonalGaussianDistribution(h.feats, feat_dim=1)
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
z = h.replace(z)
return z, posterior
def decode_mesh(self,
latents,
voxel_resolution: int = 512,
mc_threshold: float = 0.2,
return_feat: bool = False,
factor: float = 1.0):
voxel_resolution = int(voxel_resolution / factor)
reconst_x = self.decoder(latents, factor=factor, return_feat=return_feat)
if return_feat:
return reconst_x
outputs = self.sparse2mesh(reconst_x, voxel_resolution=voxel_resolution, mc_threshold=mc_threshold)
return outputs
def sparse2mesh(self,
reconst_x: torch.FloatTensor,
voxel_resolution: int = 512,
mc_threshold: float = 0.0):
sparse_sdf, sparse_index = reconst_x.feats.float(), reconst_x.coords
batch_size = int(sparse_index[..., 0].max().cpu().numpy() + 1)
meshes = []
for i in range(batch_size):
idx = sparse_index[..., 0] == i
sparse_sdf_i, sparse_index_i = sparse_sdf[idx].squeeze(-1).cpu(), sparse_index[idx][..., 1:].detach().cpu()
sdf = torch.ones((voxel_resolution, voxel_resolution, voxel_resolution))
sdf[sparse_index_i[..., 0], sparse_index_i[..., 1], sparse_index_i[..., 2]] = sparse_sdf_i
vertices, faces, _, _ = measure.marching_cubes(
sdf.numpy(),
mc_threshold,
method="lewiner",
)
vertices = vertices / voxel_resolution * 2 - 1
meshes.append(trimesh.Trimesh(vertices, faces))
return meshes
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