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# BPT Installation
Original repo: https://github.com/whaohan/bpt
### Installation
pip install -r requirements.txt
### Download weights (From main Hunyuan3D2 directory)
huggingface-cli download whaohan/bpt --local-dir ./weights

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parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.

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# -*- coding: utf-8 -*-
import argparse
from omegaconf import OmegaConf
import numpy as np
import torch
from .michelangelo.utils.misc import instantiate_from_config
def load_surface(fp):
with np.load(fp) as input_pc:
surface = input_pc['points']
normal = input_pc['normals']
rng = np.random.default_rng()
ind = rng.choice(surface.shape[0], 4096, replace=False)
surface = torch.FloatTensor(surface[ind])
normal = torch.FloatTensor(normal[ind])
surface = torch.cat([surface, normal], dim=-1).unsqueeze(0).cuda()
return surface
def reconstruction(args, model, bounds=(-1.25, -1.25, -1.25, 1.25, 1.25, 1.25), octree_depth=7, num_chunks=10000):
surface = load_surface(args.pointcloud_path)
# old_surface = surface.clone()
# surface[0,:,0]*=-1
# surface[0,:,1]*=-1
surface[0,:,2]*=-1
# encoding
shape_embed, shape_latents = model.model.encode_shape_embed(surface, return_latents=True)
shape_zq, posterior = model.model.shape_model.encode_kl_embed(shape_latents)
# decoding
latents = model.model.shape_model.decode(shape_zq)
# geometric_func = partial(model.model.shape_model.query_geometry, latents=latents)
return 0
def load_model(ckpt_path="shapevae-256.ckpt", config_path="shapevae-256.yaml"):
model_config = OmegaConf.load(config_path)
print(model_config)
if hasattr(model_config, "model"):
model_config = model_config.model
model = instantiate_from_config(model_config, ckpt_path=ckpt_path)
model = model.eval()
return model
if __name__ == "__main__":
'''
1. Reconstruct point cloud
2. Image-conditioned generation
3. Text-conditioned generation
'''
parser = argparse.ArgumentParser()
parser.add_argument("--config_path", type=str, required=True)
parser.add_argument("--ckpt_path", type=str, required=True)
parser.add_argument("--pointcloud_path", type=str, default='./example_data/surface.npz',
help='Path to the input point cloud')
parser.add_argument("--image_path", type=str, help='Path to the input image')
parser.add_argument("--text", type=str,
help='Input text within a format: A 3D model of motorcar; Porsche 911.')
parser.add_argument("--output_dir", type=str, default='./output')
parser.add_argument("-s", "--seed", type=int, default=0)
args = parser.parse_args()
print(f'-----------------------------------------------------------------------------')
print(f'>>> Output directory: {args.output_dir}')
print(f'-----------------------------------------------------------------------------')
reconstruction(args, load_model(args))

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# -*- coding: utf-8 -*-

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# -*- coding: utf-8 -*-
from .volume import generate_dense_grid_points

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# -*- coding: utf-8 -*-
import numpy as np
# produce dense points
def generate_dense_grid_points(bbox_min: np.ndarray,
bbox_max: np.ndarray,
octree_depth: int,
indexing: str = "ij"):
length = bbox_max - bbox_min
num_cells = np.exp2(octree_depth)
x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32)
y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32)
z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32)
[xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing)
xyz = np.stack((xs, ys, zs), axis=-1)
xyz = xyz.reshape(-1, 3)
grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1]
return xyz, grid_size, length

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# -*- coding: utf-8 -*-

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# -*- coding: utf-8 -*-
from .checkpoint import checkpoint

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# -*- coding: utf-8 -*-
import torch
from typing import Callable, Iterable, Sequence, Union
def checkpoint(
func: Callable[..., Union[torch.Tensor, Sequence[torch.Tensor]]],
inputs: Sequence[torch.Tensor],
params: Iterable[torch.Tensor],
flag: bool,
use_deepspeed: bool = False
):
# Evaluate a function without caching intermediate activations, allowing for
# reduced memory at the expense of extra compute in the backward pass.
# :param func: the function to evaluate.
# :param inputs: the argument sequence to pass to `func`.
# :param params: a sequence of parameters `func` depends on but does not
# explicitly take as arguments.
# :param flag: if False, disable gradient checkpointing.
# :param use_deepspeed: if True, use deepspeed
if flag:
if use_deepspeed:
import deepspeed
return deepspeed.checkpointing.checkpoint(func, *inputs)
args = tuple(inputs) + tuple(params)
return CheckpointFunction.apply(func, len(inputs), *args)
else:
return func(*inputs)
class CheckpointFunction(torch.autograd.Function):
@staticmethod
@torch.amp.custom_fwd(device_type="cuda")
def forward(ctx, run_function, length, *args):
ctx.run_function = run_function
ctx.input_tensors = list(args[:length])
ctx.input_params = list(args[length:])
with torch.no_grad():
output_tensors = ctx.run_function(*ctx.input_tensors)
return output_tensors
@staticmethod
@torch.amp.custom_bwd(device_type="cuda")
def backward(ctx, *output_grads):
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
with torch.enable_grad():
# Fixes a bug where the first op in run_function modifies the
# Tensor storage in place, which is not allowed for detach()'d
# Tensors.
shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
output_tensors = ctx.run_function(*shallow_copies)
input_grads = torch.autograd.grad(
output_tensors,
ctx.input_tensors + ctx.input_params,
output_grads,
allow_unused=True,
)
del ctx.input_tensors
del ctx.input_params
del output_tensors
return (None, None) + input_grads

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# -*- coding: utf-8 -*-
import torch
import numpy as np
from typing import Union, List
class DiagonalGaussianDistribution(object):
# Gaussian distribution
def __init__(self, parameters: Union[torch.Tensor, List[torch.Tensor]], deterministic=False, feat_dim=1):
self.feat_dim = feat_dim
self.parameters = parameters
if isinstance(parameters, list):
self.mean = parameters[0]
self.logvar = parameters[1]
else:
self.mean, self.logvar = torch.chunk(parameters, 2, dim=feat_dim)
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
self.deterministic = deterministic
self.std = torch.exp(0.5 * self.logvar)
self.var = torch.exp(self.logvar)
if self.deterministic:
self.var = self.std = torch.zeros_like(self.mean)
# sample from the guassian distribution
def sample(self):
x = self.mean + self.std * torch.randn_like(self.mean)
return x
def kl(self, other=None, dims=(1, 2, 3)):
if self.deterministic:
return torch.Tensor([0.])
else:
if other is None:
return 0.5 * torch.mean(torch.pow(self.mean, 2)
+ self.var - 1.0 - self.logvar,
dim=dims)
else:
return 0.5 * torch.mean(
torch.pow(self.mean - other.mean, 2) / other.var
+ self.var / other.var - 1.0 - self.logvar + other.logvar,
dim=dims)
def nll(self, sample, dims=(1, 2, 3)):
if self.deterministic:
return torch.Tensor([0.])
logtwopi = np.log(2.0 * np.pi)
return 0.5 * torch.sum(
logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
dim=dims)
def mode(self):
return self.mean
def normal_kl(mean1, logvar1, mean2, logvar2):
# Compute the KL divergence between two gaussians.
# Shapes are automatically broadcasted, so batches can be compared to
# scalars, among other use cases.
tensor = None
for obj in (mean1, logvar1, mean2, logvar2):
if isinstance(obj, torch.Tensor):
tensor = obj
break
assert tensor is not None, "at least one argument must be a Tensor"
# Force variances to be Tensors. Broadcasting helps convert scalars to
# Tensors, but it does not work for torch.exp().
logvar1, logvar2 = [
x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
for x in (logvar1, logvar2)
]
return 0.5 * (
-1.0
+ logvar2
- logvar1
+ torch.exp(logvar1 - logvar2)
+ ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
)

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# -*- coding: utf-8 -*-
import numpy as np
import torch
import torch.nn as nn
import math
VALID_EMBED_TYPES = ["identity", "fourier", "hashgrid", "sphere_harmonic", "triplane_fourier"]
class FourierEmbedder(nn.Module):
"""The sin/cosine positional embedding. Given an input tensor `x` of shape [n_batch, ..., c_dim], it converts
each feature dimension of `x[..., i]` into:
[
sin(x[..., i]),
sin(f_1*x[..., i]),
sin(f_2*x[..., i]),
...
sin(f_N * x[..., i]),
cos(x[..., i]),
cos(f_1*x[..., i]),
cos(f_2*x[..., i]),
...
cos(f_N * x[..., i]),
x[..., i] # only present if include_input is True.
], here f_i is the frequency.
Denote the space is [0 / num_freqs, 1 / num_freqs, 2 / num_freqs, 3 / num_freqs, ..., (num_freqs - 1) / num_freqs].
If logspace is True, then the frequency f_i is [2^(0 / num_freqs), ..., 2^(i / num_freqs), ...];
Otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)].
Args:
num_freqs (int): the number of frequencies, default is 6;
logspace (bool): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...],
otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)];
input_dim (int): the input dimension, default is 3;
include_input (bool): include the input tensor or not, default is True.
Attributes:
frequencies (torch.Tensor): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...],
otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1);
out_dim (int): the embedding size, if include_input is True, it is input_dim * (num_freqs * 2 + 1),
otherwise, it is input_dim * num_freqs * 2.
"""
def __init__(self,
num_freqs: int = 6,
logspace: bool = True,
input_dim: int = 3,
include_input: bool = True,
include_pi: bool = True) -> None:
"""The initialization"""
super().__init__()
if logspace:
frequencies = 2.0 ** torch.arange(
num_freqs,
dtype=torch.float32
)
else:
frequencies = torch.linspace(
1.0,
2.0 ** (num_freqs - 1),
num_freqs,
dtype=torch.float32
)
if include_pi:
frequencies *= torch.pi
self.register_buffer("frequencies", frequencies, persistent=False)
self.include_input = include_input
self.num_freqs = num_freqs
self.out_dim = self.get_dims(input_dim)
def get_dims(self, input_dim):
temp = 1 if self.include_input or self.num_freqs == 0 else 0
out_dim = input_dim * (self.num_freqs * 2 + temp)
return out_dim
def forward(self, x: torch.Tensor) -> torch.Tensor:
""" Forward process.
Args:
x: tensor of shape [..., dim]
Returns:
embedding: an embedding of `x` of shape [..., dim * (num_freqs * 2 + temp)]
where temp is 1 if include_input is True and 0 otherwise.
"""
if self.num_freqs > 0:
embed = (x[..., None].contiguous() * self.frequencies).view(*x.shape[:-1], -1)
if self.include_input:
return torch.cat((x, embed.sin(), embed.cos()), dim=-1)
else:
return torch.cat((embed.sin(), embed.cos()), dim=-1)
else:
return x
class LearnedFourierEmbedder(nn.Module):
""" following @crowsonkb "s lead with learned sinusoidal pos emb """
""" https://github.com/crowsonkb/v-diffusion-jax/blob/master/diffusion/models/danbooru_128.py#L8 """
def __init__(self, in_channels, dim):
super().__init__()
assert (dim % 2) == 0
half_dim = dim // 2
per_channel_dim = half_dim // in_channels
self.weights = nn.Parameter(torch.randn(per_channel_dim))
def forward(self, x):
"""
Args:
x (torch.FloatTensor): [..., c]
Returns:
x (torch.FloatTensor): [..., d]
"""
# [b, t, c, 1] * [1, d] = [b, t, c, d] -> [b, t, c * d]
freqs = (x[..., None] * self.weights[None] * 2 * np.pi).view(*x.shape[:-1], -1)
fouriered = torch.cat((x, freqs.sin(), freqs.cos()), dim=-1)
return fouriered
class TriplaneLearnedFourierEmbedder(nn.Module):
def __init__(self, in_channels, dim):
super().__init__()
self.yz_plane_embedder = LearnedFourierEmbedder(in_channels, dim)
self.xz_plane_embedder = LearnedFourierEmbedder(in_channels, dim)
self.xy_plane_embedder = LearnedFourierEmbedder(in_channels, dim)
self.out_dim = in_channels + dim
def forward(self, x):
yz_embed = self.yz_plane_embedder(x)
xz_embed = self.xz_plane_embedder(x)
xy_embed = self.xy_plane_embedder(x)
embed = yz_embed + xz_embed + xy_embed
return embed
def sequential_pos_embed(num_len, embed_dim):
assert embed_dim % 2 == 0
pos = torch.arange(num_len, dtype=torch.float32)
omega = torch.arange(embed_dim // 2, dtype=torch.float32)
omega /= embed_dim / 2.
omega = 1. / 10000 ** omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = torch.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = torch.sin(out) # (M, D/2)
emb_cos = torch.cos(out) # (M, D/2)
embeddings = torch.cat([emb_sin, emb_cos], dim=1) # (M, D)
return embeddings
def timestep_embedding(timesteps, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an [N x dim] Tensor of positional embeddings.
"""
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=timesteps.device)
args = timesteps[:, None].to(timesteps.dtype) * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def get_embedder(embed_type="fourier", num_freqs=-1, input_dim=3, degree=4,
num_levels=16, level_dim=2, per_level_scale=2, base_resolution=16,
log2_hashmap_size=19, desired_resolution=None):
if embed_type == "identity" or (embed_type == "fourier" and num_freqs == -1):
return nn.Identity(), input_dim
elif embed_type == "fourier":
embedder_obj = FourierEmbedder(num_freqs=num_freqs, input_dim=input_dim,
logspace=True, include_input=True)
return embedder_obj, embedder_obj.out_dim
elif embed_type == "hashgrid":
raise NotImplementedError
elif embed_type == "sphere_harmonic":
raise NotImplementedError
else:
raise ValueError(f"{embed_type} is not valid. Currently only supprts {VALID_EMBED_TYPES}")

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# -*- coding: utf-8 -*-
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional
from hy3dgen.shapegen.bpt.miche.michelangelo.models.modules.checkpoint import checkpoint
# Initialize linear layers with normal distribution weights and zero biases
def init_linear(l, stddev):
nn.init.normal_(l.weight, std=stddev)
if l.bias is not None:
nn.init.constant_(l.bias, 0.0)
# Multihead attention module
class MultiheadAttention(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
n_ctx: int, # Context size
width: int, # Width of the input tensor
heads: int, # Number of attention heads
init_scale: float, # Initialization scale for weights
qkv_bias: bool, # Whether to use bias in QKV layers
flash: bool = False # Whether to use flash attention
):
super().__init__()
self.n_ctx = n_ctx
self.width = width
self.heads = heads
self.c_qkv = nn.Linear(width, width * 3, bias=qkv_bias, device=device, dtype=dtype)
self.c_proj = nn.Linear(width, width, device=device, dtype=dtype)
self.attention = QKVMultiheadAttention(device=device, dtype=dtype, heads=heads, n_ctx=n_ctx, flash=flash)
init_linear(self.c_qkv, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x):
x = self.c_qkv(x)
x = checkpoint(self.attention, (x,), (), True)
x = self.c_proj(x)
return x
# QKV multihead attention module
class QKVMultiheadAttention(nn.Module):
def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int, n_ctx: int, flash: bool = False):
super().__init__()
self.device = device
self.dtype = dtype
self.heads = heads
self.n_ctx = n_ctx
self.flash = flash
def forward(self, qkv):
bs, n_ctx, width = qkv.shape
attn_ch = width // self.heads // 3
scale = 1 / math.sqrt(math.sqrt(attn_ch))
qkv = qkv.view(bs, n_ctx, self.heads, -1)
q, k, v = torch.split(qkv, attn_ch, dim=-1)
if self.flash:
out = F.scaled_dot_product_attention(q, k, v)
else:
weight = torch.einsum(
"bthc,bshc->bhts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
wdtype = weight.dtype
weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
out = torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
return out
# Residual attention block module
class ResidualAttentionBlock(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
use_checkpoint: bool = False,
n_ctx: int, # Context size
width: int, # Width of the input tensor
heads: int, # Number of attention heads
init_scale: float, # Initialization scale for weights
qkv_bias: bool, # Whether to use bias in QKV layers
flash: bool = False # Whether to use flash attention
):
super().__init__()
self.use_checkpoint = use_checkpoint
self.attn = MultiheadAttention(
device=device,
dtype=dtype,
n_ctx=n_ctx,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash
)
self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype)
self.mlp = MLP(device=device, dtype=dtype, width=width, init_scale=init_scale)
self.ln_2 = nn.LayerNorm(width, device=device, dtype=dtype)
def _forward(self, x: torch.Tensor):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
def forward(self, x: torch.Tensor):
return checkpoint(self._forward, (x,), self.parameters(), self.use_checkpoint)
# Multihead cross attention module
class MultiheadCrossAttention(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
n_data: Optional[int] = None,
data_width: Optional[int] = None,
width: int, # Width of the input tensor
heads: int, # Number of attention heads
init_scale: float, # Initialization scale for weights
qkv_bias: bool, # Whether to use bias in QKV layers
flash: bool = False # Whether to use flash attention
):
super().__init__()
self.n_data = n_data
self.width = width
self.heads = heads
self.data_width = width if data_width is None else data_width
self.c_q = nn.Linear(width, width, bias=qkv_bias, device=device, dtype=dtype)
self.c_kv = nn.Linear(self.data_width, width * 2, bias=qkv_bias, device=device, dtype=dtype)
self.c_proj = nn.Linear(width, width, device=device, dtype=dtype)
self.attention = QKVMultiheadCrossAttention(
device=device, dtype=dtype, heads=heads, n_data=n_data, flash=flash
)
init_linear(self.c_q, init_scale)
init_linear(self.c_kv, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x, data):
x = self.c_q(x)
data = self.c_kv(data)
x = checkpoint(self.attention, (x, data), (), True)
x = self.c_proj(x)
return x
# QKV multihead cross attention module
class QKVMultiheadCrossAttention(nn.Module):
def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int,
flash: bool = False, n_data: Optional[int] = None):
super().__init__()
self.device = device
self.dtype = dtype
self.heads = heads
self.n_data = n_data
self.flash = flash
def forward(self, q, kv):
_, n_ctx, _ = q.shape
bs, n_data, width = kv.shape
attn_ch = width // self.heads // 2
scale = 1 / math.sqrt(math.sqrt(attn_ch))
q = q.view(bs, n_ctx, self.heads, -1)
kv = kv.view(bs, n_data, self.heads, -1)
k, v = torch.split(kv, attn_ch, dim=-1)
if self.flash:
out = F.scaled_dot_product_attention(q, k, v)
else:
weight = torch.einsum(
"bthc,bshc->bhts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
wdtype = weight.dtype
weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
out = torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
return out
# Residual cross attention block module
class ResidualCrossAttentionBlock(nn.Module):
def __init__(
self,
*,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
n_data: Optional[int] = None,
data_width: Optional[int] = None,
width: int, # Width of the input tensor
heads: int, # Number of attention heads
init_scale: float, # Initialization scale for weights
qkv_bias: bool, # Whether to use bias in QKV layers
flash: bool = False # Whether to use flash attention
):
super().__init__()
if data_width is None:
data_width = width
self.attn = MultiheadCrossAttention(
device=device,
dtype=dtype,
n_data=n_data,
width=width,
heads=heads,
data_width=data_width,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
)
self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype)
self.ln_2 = nn.LayerNorm(data_width, device=device, dtype=dtype)
self.mlp = MLP(device=device, dtype=dtype, width=width, init_scale=init_scale)
self.ln_3 = nn.LayerNorm(width, device=device, dtype=dtype)
def forward(self, x: torch.Tensor, data: torch.Tensor):
x = x + self.attn(self.ln_1(x), self.ln_2(data))
x = x + self.mlp(self.ln_3(x))
return x
# MLP Module
class MLP(nn.Module):
def __init__(self, *,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
width: int,
init_scale: float):
super().__init__()
self.width = width
self.c_fc = nn.Linear(width, width * 4, device=device, dtype=dtype)
self.c_proj = nn.Linear(width * 4, width, device=device, dtype=dtype)
self.gelu = nn.GELU()
init_linear(self.c_fc, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x):
return self.c_proj(self.gelu(self.c_fc(x)))
# Transformer Module
class Transformer(nn.Module):
def __init__(
self,
*,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
layers: int,
use_checkpoint: bool = False,
n_ctx: int, # Context size
width: int, # Width of the input tensor
heads: int, # Number of attention heads
init_scale: float, # Initialization scale for weights
qkv_bias: bool, # Whether to use bias in QKV layers
flash: bool = False # Whether to use flash attention
):
super().__init__()
self.n_ctx = n_ctx
self.width = width
self.layers = layers
self.resblocks = nn.ModuleList(
[
ResidualAttentionBlock(
device=device,
dtype=dtype,
n_ctx=n_ctx,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
use_checkpoint=use_checkpoint
)
for _ in range(layers)
]
)
def forward(self, x: torch.Tensor):
for block in self.resblocks:
x = block(x)
return x

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# -*- coding: utf-8 -*-
from typing import List, Tuple, Dict, Optional
from omegaconf import DictConfig
import torch
import torch.nn.functional as F
from torch import nn
from torch.optim import lr_scheduler
from typing import Union
from functools import partial
from .....miche.michelangelo.utils import instantiate_from_config
from .tsal_base import (
AlignedShapeAsLatentModule,
ShapeAsLatentModule,
Latent2MeshOutput,
AlignedMeshOutput
)
from .....miche.michelangelo.models.tsal.inference_utils import extract_geometry
import trimesh
class AlignedShapeAsLatentPLModule(nn.Module):
def __init__(self, *,
shape_module_cfg,
aligned_module_cfg,
loss_cfg,
optimizer_cfg: Optional[DictConfig] = None,
ckpt_path: Optional[str] = None,
ignore_keys: Union[Tuple[str], List[str]] = ()):
super().__init__()
shape_model: ShapeAsLatentModule = instantiate_from_config(
shape_module_cfg, device=None, dtype=None
)
self.model: AlignedShapeAsLatentModule = instantiate_from_config(
aligned_module_cfg, shape_model=shape_model
)
self.loss = instantiate_from_config(loss_cfg)
self.optimizer_cfg = optimizer_cfg
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
def set_shape_model_only(self):
self.model.set_shape_model_only()
@property
def latent_shape(self):
return self.model.shape_model.latent_shape
@property
def zero_rank(self):
if self._trainer:
zero_rank = self.trainer.local_rank == 0
else:
zero_rank = True
return zero_rank
def init_from_ckpt(self, path, ignore_keys=()):
state_dict = torch.load(path, map_location="cpu")["state_dict"]
keys = list(state_dict.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del state_dict[k]
missing, unexpected = self.load_state_dict(state_dict, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
print(f"Unexpected Keys: {unexpected}")
def configure_optimizers(self) -> Tuple[List, List]:
lr = self.learning_rate
trainable_parameters = list(self.model.parameters())
if self.optimizer_cfg is None:
optimizers = [torch.optim.AdamW(trainable_parameters, lr=lr, betas=(0.9, 0.99), weight_decay=1e-3)]
schedulers = []
else:
optimizer = instantiate_from_config(self.optimizer_cfg.optimizer, params=trainable_parameters)
scheduler_func = instantiate_from_config(
self.optimizer_cfg.scheduler,
max_decay_steps=self.trainer.max_steps,
lr_max=lr
)
scheduler = {
"scheduler": lr_scheduler.LambdaLR(optimizer, lr_lambda=scheduler_func.schedule),
"interval": "step",
"frequency": 1
}
optimizers = [optimizer]
schedulers = [scheduler]
return optimizers, schedulers
def forward(self,
surface: torch.FloatTensor,
image: torch.FloatTensor,
text: torch.FloatTensor,
volume_queries: torch.FloatTensor):
# Args:
# surface (torch.FloatTensor):
# image (torch.FloatTensor):
# text (torch.FloatTensor):
# volume_queries (torch.FloatTensor):
#
# Returns:
embed_outputs, shape_z = self.model(surface, image, text)
shape_zq, posterior = self.model.shape_model.encode_kl_embed(shape_z)
latents = self.model.shape_model.decode(shape_zq)
logits = self.model.shape_model.query_geometry(volume_queries, latents)
return embed_outputs, logits, posterior
def encode(self, surface: torch.FloatTensor, sample_posterior=True):
pc = surface[..., 0:3]
feats = surface[..., 3:6]
shape_embed, shape_zq, posterior = self.model.shape_model.encode(
pc=pc, feats=feats, sample_posterior=sample_posterior
)
return shape_zq
def encode_latents(self, surface: torch.FloatTensor):
pc = surface[..., 0:3]
feats = surface[..., 3:6]
shape_embed, shape_latents = self.model.shape_model.encode_latents(
pc=pc, feats=feats
)
shape_embed = shape_embed.unsqueeze(1)
assert shape_embed.shape[1] == 1 and shape_latents.shape[1] == 256
cat_latents = torch.cat([shape_embed, shape_latents], dim=1)
return cat_latents
def recon(self, surface):
cat_latents = self.encode_latents(surface)
shape_latents = cat_latents[:, 1:]
shape_zq, posterior = self.model.shape_model.encode_kl_embed(shape_latents)
# decoding
latents = self.model.shape_model.decode(shape_zq)
geometric_func = partial(self.model.shape_model.query_geometry, latents=latents)
# reconstruction
mesh_v_f, has_surface = extract_geometry(
geometric_func=geometric_func,
device=surface.device,
batch_size=surface.shape[0],
bounds=(-1.25, -1.25, -1.25, 1.25, 1.25, 1.25),
octree_depth=7,
num_chunks=10000,
)
recon_mesh = trimesh.Trimesh(mesh_v_f[0][0], mesh_v_f[0][1])
return recon_mesh
def to_shape_latents(self, latents):
shape_zq, posterior = self.model.shape_model.encode_kl_embed(latents, sample_posterior = False)
return self.model.shape_model.decode(shape_zq)
def decode(self,
z_q,
bounds: Union[Tuple[float], List[float], float] = 1.1,
octree_depth: int = 7,
num_chunks: int = 10000) -> List[Latent2MeshOutput]:
latents = self.model.shape_model.decode(z_q) # latents: [bs, num_latents, dim]
outputs = self.latent2mesh(latents, bounds=bounds, octree_depth=octree_depth, num_chunks=num_chunks)
return outputs
def training_step(self, batch: Dict[str, torch.FloatTensor],
batch_idx: int, optimizer_idx: int = 0) -> torch.FloatTensor:
#Args:
# batch (dict): the batch sample, and it contains:
# - surface (torch.FloatTensor): [bs, n_surface, (3 + input_dim)]
# - image (torch.FloatTensor): [bs, 3, 224, 224]
# - text (torch.FloatTensor): [bs, num_templates, 77]
# - geo_points (torch.FloatTensor): [bs, n_pts, (3 + 1)]
#
# batch_idx (int):
#
# optimizer_idx (int):
#
# Returns:
# loss (torch.FloatTensor):
surface = batch["surface"]
image = batch["image"]
text = batch["text"]
volume_queries = batch["geo_points"][..., 0:3]
shape_labels = batch["geo_points"][..., -1]
embed_outputs, shape_logits, posteriors = self(surface, image, text, volume_queries)
aeloss, log_dict_ae = self.loss(
**embed_outputs,
posteriors=posteriors,
shape_logits=shape_logits,
shape_labels=shape_labels,
split="train"
)
self.log_dict(log_dict_ae, prog_bar=True, logger=True, batch_size=shape_logits.shape[0],
sync_dist=False, rank_zero_only=True)
return aeloss
def validation_step(self, batch: Dict[str, torch.FloatTensor], batch_idx: int) -> torch.FloatTensor:
surface = batch["surface"]
image = batch["image"]
text = batch["text"]
volume_queries = batch["geo_points"][..., 0:3]
shape_labels = batch["geo_points"][..., -1]
embed_outputs, shape_logits, posteriors = self(surface, image, text, volume_queries)
aeloss, log_dict_ae = self.loss(
**embed_outputs,
posteriors=posteriors,
shape_logits=shape_logits,
shape_labels=shape_labels,
split="val"
)
self.log_dict(log_dict_ae, prog_bar=True, logger=True, batch_size=shape_logits.shape[0],
sync_dist=False, rank_zero_only=True)
return aeloss
def visual_alignment(self,
surface: torch.FloatTensor,
image: torch.FloatTensor,
text: torch.FloatTensor,
description: Optional[List[str]] = None,
bounds: Union[Tuple[float], List[float]] = (-1.25, -1.25, -1.25, 1.25, 1.25, 1.25),
octree_depth: int = 7,
num_chunks: int = 10000) -> List[AlignedMeshOutput]:
"""
Args:
surface:
image:
text:
description:
bounds:
octree_depth:
num_chunks:
Returns:
mesh_outputs (List[AlignedMeshOutput]): the mesh outputs list.
"""
outputs = []
device = surface.device
bs = surface.shape[0]
embed_outputs, shape_z = self.model(surface, image, text)
# calculate the similarity
image_embed = embed_outputs["image_embed"]
text_embed = embed_outputs["text_embed"]
shape_embed = embed_outputs["shape_embed"]
# normalized features
shape_embed = F.normalize(shape_embed, dim=-1, p=2)
text_embed = F.normalize(text_embed, dim=-1, p=2)
image_embed = F.normalize(image_embed, dim=-1, p=2)
# B x B
shape_text_similarity = (100.0 * shape_embed @ text_embed.T).softmax(dim=-1)
# B x B
shape_image_similarity = (100.0 * shape_embed @ image_embed.T).softmax(dim=-1)
# shape reconstruction
shape_zq, posterior = self.model.shape_model.encode_kl_embed(shape_z)
latents = self.model.shape_model.decode(shape_zq)
geometric_func = partial(self.model.shape_model.query_geometry, latents=latents)
# 2. decode geometry
mesh_v_f, has_surface = extract_geometry(
geometric_func=geometric_func,
device=device,
batch_size=bs,
bounds=bounds,
octree_depth=octree_depth,
num_chunks=num_chunks,
disable=not self.zero_rank
)
# 3. decode texture
for i, ((mesh_v, mesh_f), is_surface) in enumerate(zip(mesh_v_f, has_surface)):
if not is_surface:
outputs.append(None)
continue
out = AlignedMeshOutput()
out.mesh_v = mesh_v
out.mesh_f = mesh_f
out.surface = surface[i].cpu().numpy()
out.image = image[i].cpu().numpy()
if description is not None:
out.text = description[i]
out.shape_text_similarity = shape_text_similarity[i, i]
out.shape_image_similarity = shape_image_similarity[i, i]
outputs.append(out)
return outputs
def latent2mesh(self,
latents: torch.FloatTensor,
bounds: Union[Tuple[float], List[float], float] = 1.1,
octree_depth: int = 7,
num_chunks: int = 10000) -> List[Latent2MeshOutput]:
"""
Args:
latents: [bs, num_latents, dim]
bounds:
octree_depth:
num_chunks:
Returns:
mesh_outputs (List[MeshOutput]): the mesh outputs list.
"""
outputs = []
geometric_func = partial(self.model.shape_model.query_geometry, latents=latents)
# 2. decode geometry
device = latents.device
mesh_v_f, has_surface = extract_geometry(
geometric_func=geometric_func,
device=device,
batch_size=len(latents),
bounds=bounds,
octree_depth=octree_depth,
num_chunks=num_chunks,
disable=not self.zero_rank
)
# 3. decode texture
for i, ((mesh_v, mesh_f), is_surface) in enumerate(zip(mesh_v_f, has_surface)):
if not is_surface:
outputs.append(None)
continue
out = Latent2MeshOutput()
out.mesh_v = mesh_v
out.mesh_f = mesh_f
outputs.append(out)
return outputs

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# -*- coding: utf-8 -*-
import torch
from torch import nn
from einops import rearrange
from transformers import CLIPModel
from hy3dgen.shapegen.bpt.miche.michelangelo.models.tsal.tsal_base import AlignedShapeAsLatentModule
class CLIPAlignedShapeAsLatentModule(AlignedShapeAsLatentModule):
def __init__(self, *,
shape_model,
clip_model_version: str = "openai/clip-vit-large-patch14"):
super().__init__()
# self.clip_model: CLIPModel = CLIPModel.from_pretrained(clip_model_version)
# for params in self.clip_model.parameters():
# params.requires_grad = False
self.clip_model = None
self.shape_model = shape_model
self.shape_projection = nn.Parameter(torch.empty(self.shape_model.width, self.shape_model.width))
# nn.init.normal_(self.shape_projection, std=self.shape_model.width ** -0.5)
def set_shape_model_only(self):
self.clip_model = None
def encode_shape_embed(self, surface, return_latents: bool = False):
"""
Args:
surface (torch.FloatTensor): [bs, n, 3 + c]
return_latents (bool):
Returns:
x (torch.FloatTensor): [bs, projection_dim]
shape_latents (torch.FloatTensor): [bs, m, d]
"""
pc = surface[..., 0:3]
feats = surface[..., 3:]
shape_embed, shape_latents = self.shape_model.encode_latents(pc, feats)
x = shape_embed @ self.shape_projection
if return_latents:
return x, shape_latents
else:
return x
def encode_image_embed(self, image):
"""
Args:
image (torch.FloatTensor): [bs, 3, h, w]
Returns:
x (torch.FloatTensor): [bs, projection_dim]
"""
x = self.clip_model.get_image_features(image)
return x
def encode_text_embed(self, text):
x = self.clip_model.get_text_features(text)
return x
def forward(self, surface, image, text):
"""
Args:
surface (torch.FloatTensor):
image (torch.FloatTensor): [bs, 3, 224, 224]
text (torch.LongTensor): [bs, num_templates, 77]
Returns:
embed_outputs (dict): the embedding outputs, and it contains:
- image_embed (torch.FloatTensor):
- text_embed (torch.FloatTensor):
- shape_embed (torch.FloatTensor):
- logit_scale (float):
"""
# # text embedding
# text_embed_all = []
# for i in range(text.shape[0]):
# text_for_one_sample = text[i]
# text_embed = self.encode_text_embed(text_for_one_sample)
# text_embed = text_embed / text_embed.norm(dim=-1, keepdim=True)
# text_embed = text_embed.mean(dim=0)
# text_embed = text_embed / text_embed.norm(dim=-1, keepdim=True)
# text_embed_all.append(text_embed)
# text_embed_all = torch.stack(text_embed_all)
b = text.shape[0]
text_tokens = rearrange(text, "b t l -> (b t) l")
text_embed = self.encode_text_embed(text_tokens)
text_embed = rearrange(text_embed, "(b t) d -> b t d", b=b)
text_embed = text_embed.mean(dim=1)
text_embed = text_embed / text_embed.norm(dim=-1, keepdim=True)
# image embedding
image_embed = self.encode_image_embed(image)
# shape embedding
shape_embed, shape_latents = self.encode_shape_embed(surface, return_latents=True)
embed_outputs = {
"image_embed": image_embed,
"text_embed": text_embed,
"shape_embed": shape_embed,
# "logit_scale": self.clip_model.logit_scale.exp()
}
return embed_outputs, shape_latents

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hy3dgen/shapegen/bpt/miche/michelangelo/models/tsal/inference_utils.py Normal file
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# -*- coding: utf-8 -*-
import torch
from tqdm import tqdm
from einops import repeat
import numpy as np
from typing import Callable, Tuple, List, Union, Optional
from skimage import measure
from .....miche.michelangelo.graphics.primitives import generate_dense_grid_points
@torch.no_grad()
def extract_geometry(geometric_func: Callable,
device: torch.device,
batch_size: int = 1,
bounds: Union[Tuple[float], List[float], float] = (-1.25, -1.25, -1.25, 1.25, 1.25, 1.25),
octree_depth: int = 7,
num_chunks: int = 10000,
disable: bool = True):
# Args:
# geometric_func:
# device:
# bounds:
# octree_depth:
# batch_size:
# num_chunks:
# disable:
# Returns:
if isinstance(bounds, float):
bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
bbox_min = np.array(bounds[0:3])
bbox_max = np.array(bounds[3:6])
bbox_size = bbox_max - bbox_min
xyz_samples, grid_size, length = generate_dense_grid_points(
bbox_min=bbox_min,
bbox_max=bbox_max,
octree_depth=octree_depth,
indexing="ij"
)
xyz_samples = torch.FloatTensor(xyz_samples)
batch_logits = []
for start in tqdm(range(0, xyz_samples.shape[0], num_chunks),
desc="Implicit Function:", disable=disable, leave=False):
queries = xyz_samples[start: start + num_chunks, :].to(device)
batch_queries = repeat(queries, "p c -> b p c", b=batch_size)
logits = geometric_func(batch_queries)
batch_logits.append(logits.cpu())
grid_logits = torch.cat(batch_logits, dim=1).view((batch_size, grid_size[0], grid_size[1], grid_size[2])).numpy()
mesh_v_f = []
has_surface = np.zeros((batch_size,), dtype=np.bool_)
for i in range(batch_size):
try:
vertices, faces, normals, _ = measure.marching_cubes(grid_logits[i], 0, method="lewiner")
vertices = vertices / grid_size * bbox_size + bbox_min
# vertices[:, [0, 1]] = vertices[:, [1, 0]]
mesh_v_f.append((vertices.astype(np.float32), np.ascontiguousarray(faces)))
has_surface[i] = True
except ValueError:
mesh_v_f.append((None, None))
has_surface[i] = False
except RuntimeError:
mesh_v_f.append((None, None))
has_surface[i] = False
return mesh_v_f, has_surface

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# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional
from hy3dgen.shapegen.bpt.miche.michelangelo.models.modules.distributions import DiagonalGaussianDistribution
from hy3dgen.shapegen.bpt.miche.michelangelo.utils import misc
class ContrastKLNearFar(nn.Module):
def __init__(self,
contrast_weight: float = 1.0,
near_weight: float = 0.1,
kl_weight: float = 1.0,
num_near_samples: Optional[int] = None):
super().__init__()
self.labels = None
self.last_local_batch_size = None
self.contrast_weight = contrast_weight
self.near_weight = near_weight
self.kl_weight = kl_weight
self.num_near_samples = num_near_samples
self.geo_criterion = nn.BCEWithLogitsLoss()
def forward(self,
shape_embed: torch.FloatTensor,
text_embed: torch.FloatTensor,
image_embed: torch.FloatTensor,
logit_scale: torch.FloatTensor,
posteriors: Optional[DiagonalGaussianDistribution],
shape_logits: torch.FloatTensor,
shape_labels: torch.FloatTensor,
split: Optional[str] = "train", **kwargs):
# shape_embed: torch.FloatTensor
# text_embed: torch.FloatTensor
# image_embed: torch.FloatTensor
# logit_scale: torch.FloatTensor
# posteriors: Optional[DiagonalGaussianDistribution]
# shape_logits: torch.FloatTensor
# shape_labels: torch.FloatTensor
local_batch_size = shape_embed.size(0)
if local_batch_size != self.last_local_batch_size:
self.labels = local_batch_size * misc.get_rank() + torch.arange(
local_batch_size, device=shape_embed.device
).long()
self.last_local_batch_size = local_batch_size
# normalized features
shape_embed = F.normalize(shape_embed, dim=-1, p=2)
text_embed = F.normalize(text_embed, dim=-1, p=2)
image_embed = F.normalize(image_embed, dim=-1, p=2)
# gather features from all GPUs
shape_embed_all, text_embed_all, image_embed_all = misc.all_gather_batch(
[shape_embed, text_embed, image_embed]
)
# cosine similarity as logits
logits_per_shape_text = logit_scale * shape_embed @ text_embed_all.t()
logits_per_text_shape = logit_scale * text_embed @ shape_embed_all.t()
logits_per_shape_image = logit_scale * shape_embed @ image_embed_all.t()
logits_per_image_shape = logit_scale * image_embed @ shape_embed_all.t()
contrast_loss = (F.cross_entropy(logits_per_shape_text, self.labels) +
F.cross_entropy(logits_per_text_shape, self.labels)) / 2 + \
(F.cross_entropy(logits_per_shape_image, self.labels) +
F.cross_entropy(logits_per_image_shape, self.labels)) / 2
# shape reconstruction
if self.num_near_samples is None:
num_vol = shape_logits.shape[1] // 2
else:
num_vol = shape_logits.shape[1] - self.num_near_samples
vol_logits = shape_logits[:, 0:num_vol]
vol_labels = shape_labels[:, 0:num_vol]
near_logits = shape_logits[:, num_vol:]
near_labels = shape_labels[:, num_vol:]
# occupancy loss
vol_bce = self.geo_criterion(vol_logits.float(), vol_labels.float())
near_bce = self.geo_criterion(near_logits.float(), near_labels.float())
if posteriors is None:
kl_loss = torch.tensor(0.0, dtype=vol_logits.dtype, device=vol_logits.device)
else:
kl_loss = posteriors.kl(dims=(1, 2))
kl_loss = torch.mean(kl_loss)
loss = vol_bce + near_bce * self.near_weight + kl_loss * self.kl_weight + contrast_loss * self.contrast_weight
# compute accuracy
with torch.no_grad():
pred = torch.argmax(logits_per_shape_text, dim=-1)
correct = pred.eq(self.labels).sum()
shape_text_acc = 100 * correct / local_batch_size
pred = torch.argmax(logits_per_shape_image, dim=-1)
correct = pred.eq(self.labels).sum()
shape_image_acc = 100 * correct / local_batch_size
preds = shape_logits >= 0
accuracy = (preds == shape_labels).float()
accuracy = accuracy.mean()
log = {
"{}/contrast".format(split): contrast_loss.clone().detach(),
"{}/near".format(split): near_bce.detach(),
"{}/far".format(split): vol_bce.detach(),
"{}/kl".format(split): kl_loss.detach(),
"{}/shape_text_acc".format(split): shape_text_acc,
"{}/shape_image_acc".format(split): shape_image_acc,
"{}/total_loss".format(split): loss.clone().detach(),
"{}/accuracy".format(split): accuracy,
}
if posteriors is not None:
log[f"{split}/mean"] = posteriors.mean.mean().detach()
log[f"{split}/std_mean"] = posteriors.std.mean().detach()
log[f"{split}/std_max"] = posteriors.std.max().detach()
return loss, log

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# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from typing import Optional
from einops import repeat
import math
from hy3dgen.shapegen.bpt.miche.michelangelo.models.modules import checkpoint
from hy3dgen.shapegen.bpt.miche.michelangelo.models.modules.embedder import FourierEmbedder
from hy3dgen.shapegen.bpt.miche.michelangelo.models.modules.distributions import DiagonalGaussianDistribution
from hy3dgen.shapegen.bpt.miche.michelangelo.models.modules.transformer_blocks import (
ResidualCrossAttentionBlock,
Transformer
)
from .tsal_base import ShapeAsLatentModule
class CrossAttentionEncoder(nn.Module):
def __init__(self, *,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
num_latents: int,
fourier_embedder: FourierEmbedder,
point_feats: int,
width: int,
heads: int,
layers: int,
init_scale: float = 0.25,
qkv_bias: bool = True,
flash: bool = False,
use_ln_post: bool = False,
use_checkpoint: bool = False):
super().__init__()
self.use_checkpoint = use_checkpoint
self.num_latents = num_latents
self.query = nn.Parameter(torch.randn((num_latents, width), device=device, dtype=dtype) * 0.02)
self.fourier_embedder = fourier_embedder
self.input_proj = nn.Linear(self.fourier_embedder.out_dim + point_feats, width, device=device, dtype=dtype)
self.cross_attn = ResidualCrossAttentionBlock(
device=device,
dtype=dtype,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
)
self.self_attn = Transformer(
device=device,
dtype=dtype,
n_ctx=num_latents,
width=width,
layers=layers,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
use_checkpoint=False
)
if use_ln_post:
self.ln_post = nn.LayerNorm(width, dtype=dtype, device=device)
else:
self.ln_post = None
def _forward(self, pc, feats):
# Args:
# pc (torch.FloatTensor): [B, N, 3]
# feats (torch.FloatTensor or None): [B, N, C]
bs = pc.shape[0]
data = self.fourier_embedder(pc)
if feats is not None:
data = torch.cat([data, feats], dim=-1)
data = self.input_proj(data)
query = repeat(self.query, "m c -> b m c", b=bs)
latents = self.cross_attn(query, data)
latents = self.self_attn(latents)
if self.ln_post is not None:
latents = self.ln_post(latents)
return latents, pc
def forward(self, pc: torch.FloatTensor, feats: Optional[torch.FloatTensor] = None):
# Args:
# pc (torch.FloatTensor): [B, N, 3]
# feats (torch.FloatTensor or None): [B, N, C]
return checkpoint(self._forward, (pc, feats), self.parameters(), self.use_checkpoint)
class CrossAttentionDecoder(nn.Module):
def __init__(self, *,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
num_latents: int,
out_channels: int,
fourier_embedder: FourierEmbedder,
width: int,
heads: int,
init_scale: float = 0.25,
qkv_bias: bool = True,
flash: bool = False,
use_checkpoint: bool = False):
super().__init__()
self.use_checkpoint = use_checkpoint
self.fourier_embedder = fourier_embedder
self.query_proj = nn.Linear(self.fourier_embedder.out_dim, width, device=device, dtype=dtype)
self.cross_attn_decoder = ResidualCrossAttentionBlock(
device=device,
dtype=dtype,
n_data=num_latents,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash
)
self.ln_post = nn.LayerNorm(width, device=device, dtype=dtype)
self.output_proj = nn.Linear(width, out_channels, device=device, dtype=dtype)
def _forward(self, queries: torch.FloatTensor, latents: torch.FloatTensor):
queries = self.query_proj(self.fourier_embedder(queries))
x = self.cross_attn_decoder(queries, latents)
x = self.ln_post(x)
x = self.output_proj(x)
return x
def forward(self, queries: torch.FloatTensor, latents: torch.FloatTensor):
return checkpoint(self._forward, (queries, latents), self.parameters(), self.use_checkpoint)
class ShapeAsLatentPerceiver(ShapeAsLatentModule):
def __init__(self, *,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
num_latents: int,
point_feats: int = 0,
embed_dim: int = 0,
num_freqs: int = 8,
include_pi: bool = True,
width: int,
heads: int,
num_encoder_layers: int,
num_decoder_layers: int,
init_scale: float = 0.25,
qkv_bias: bool = True,
flash: bool = False,
use_ln_post: bool = False,
use_checkpoint: bool = False):
super().__init__()
self.use_checkpoint = use_checkpoint
self.num_latents = num_latents
self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi)
init_scale = init_scale * math.sqrt(1.0 / width)
self.encoder = CrossAttentionEncoder(
device=device,
dtype=dtype,
fourier_embedder=self.fourier_embedder,
num_latents=num_latents,
point_feats=point_feats,
width=width,
heads=heads,
layers=num_encoder_layers,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
use_ln_post=use_ln_post,
use_checkpoint=use_checkpoint
)
self.embed_dim = embed_dim
if embed_dim > 0:
# VAE embed
self.pre_kl = nn.Linear(width, embed_dim * 2, device=device, dtype=dtype)
self.post_kl = nn.Linear(embed_dim, width, device=device, dtype=dtype)
self.latent_shape = (num_latents, embed_dim)
else:
self.latent_shape = (num_latents, width)
self.transformer = Transformer(
device=device,
dtype=dtype,
n_ctx=num_latents,
width=width,
layers=num_decoder_layers,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
use_checkpoint=use_checkpoint
)
# geometry decoder
self.geo_decoder = CrossAttentionDecoder(
device=device,
dtype=dtype,
fourier_embedder=self.fourier_embedder,
out_channels=1,
num_latents=num_latents,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
use_checkpoint=use_checkpoint
)
def encode(self,
pc: torch.FloatTensor,
feats: Optional[torch.FloatTensor] = None,
sample_posterior: bool = True):
# Args:
# pc (torch.FloatTensor): [B, N, 3]
# feats (torch.FloatTensor or None): [B, N, C]
# sample_posterior (bool):
# Returns:
# latents (torch.FloatTensor)
# center_pos (torch.FloatTensor or None):
# posterior (DiagonalGaussianDistribution or None):
latents, center_pos = self.encoder(pc, feats)
posterior = None
if self.embed_dim > 0:
moments = self.pre_kl(latents)
posterior = DiagonalGaussianDistribution(moments, feat_dim=-1)
if sample_posterior:
latents = posterior.sample()
else:
latents = posterior.mode()
return latents, center_pos, posterior
def decode(self, latents: torch.FloatTensor):
latents = self.post_kl(latents)
return self.transformer(latents)
def query_geometry(self, queries: torch.FloatTensor, latents: torch.FloatTensor):
logits = self.geo_decoder(queries, latents).squeeze(-1)
return logits
def forward(self,
pc: torch.FloatTensor,
feats: torch.FloatTensor,
volume_queries: torch.FloatTensor,
sample_posterior: bool = True):
# Args:
# pc (torch.FloatTensor): [B, N, 3]
# feats (torch.FloatTensor or None): [B, N, C]
# volume_queries (torch.FloatTensor): [B, P, 3]
# sample_posterior (bool):
# Returns:
# logits (torch.FloatTensor): [B, P]
# center_pos (torch.FloatTensor): [B, M, 3]
# posterior (DiagonalGaussianDistribution or None).
latents, center_pos, posterior = self.encode(pc, feats, sample_posterior=sample_posterior)
latents = self.decode(latents)
logits = self.query_geometry(volume_queries, latents)
return logits, center_pos, posterior
class AlignedShapeLatentPerceiver(ShapeAsLatentPerceiver):
def __init__(self, *,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
num_latents: int,
point_feats: int = 0,
embed_dim: int = 0,
num_freqs: int = 8,
include_pi: bool = True,
width: int,
heads: int,
num_encoder_layers: int,
num_decoder_layers: int,
init_scale: float = 0.25,
qkv_bias: bool = True,
flash: bool = False,
use_ln_post: bool = False,
use_checkpoint: bool = False):
super().__init__(
device=device,
dtype=dtype,
num_latents=1 + num_latents,
point_feats=point_feats,
embed_dim=embed_dim,
num_freqs=num_freqs,
include_pi=include_pi,
width=width,
heads=heads,
num_encoder_layers=num_encoder_layers,
num_decoder_layers=num_decoder_layers,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
use_ln_post=use_ln_post,
use_checkpoint=use_checkpoint
)
self.width = width
def encode(self,
pc: torch.FloatTensor,
feats: Optional[torch.FloatTensor] = None,
sample_posterior: bool = True):
# Args:
# pc (torch.FloatTensor): [B, N, 3]
# feats (torch.FloatTensor or None): [B, N, c]
# sample_posterior (bool):
# Returns:
# shape_embed (torch.FloatTensor)
# kl_embed (torch.FloatTensor):
# posterior (DiagonalGaussianDistribution or None):
shape_embed, latents = self.encode_latents(pc, feats)
kl_embed, posterior = self.encode_kl_embed(latents, sample_posterior)
return shape_embed, kl_embed, posterior
def encode_latents(self,
pc: torch.FloatTensor,
feats: Optional[torch.FloatTensor] = None):
x, _ = self.encoder(pc, feats)
shape_embed = x[:, 0]
latents = x[:, 1:]
return shape_embed, latents
def encode_kl_embed(self, latents: torch.FloatTensor, sample_posterior: bool = True):
posterior = None
if self.embed_dim > 0:
moments = self.pre_kl(latents)
posterior = DiagonalGaussianDistribution(moments, feat_dim=-1)
if sample_posterior:
kl_embed = posterior.sample()
else:
kl_embed = posterior.mode()
else:
kl_embed = latents
return kl_embed, posterior
def forward(self,
pc: torch.FloatTensor,
feats: torch.FloatTensor,
volume_queries: torch.FloatTensor,
sample_posterior: bool = True):
# Args:
# pc (torch.FloatTensor): [B, N, 3]
# feats (torch.FloatTensor or None): [B, N, C]
# volume_queries (torch.FloatTensor): [B, P, 3]
# sample_posterior (bool):
# Returns:
# shape_embed (torch.FloatTensor): [B, projection_dim]
# logits (torch.FloatTensor): [B, M]
# posterior (DiagonalGaussianDistribution or None).
shape_embed, kl_embed, posterior = self.encode(pc, feats, sample_posterior=sample_posterior)
latents = self.decode(kl_embed)
logits = self.query_geometry(volume_queries, latents)
return shape_embed, logits, posterior

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# -*- coding: utf-8 -*-
import torch.nn as nn
from typing import Tuple, List, Optional
# Base class for output of Point to Mesh transformation
class Point2MeshOutput(object):
def __init__(self):
self.mesh_v = None # Vertices of the mesh
self.mesh_f = None # Faces of the mesh
self.center = None # Center of the mesh
self.pc = None # Point cloud data
# Base class for output of Latent to Mesh transformation
class Latent2MeshOutput(object):
def __init__(self):
self.mesh_v = None # Vertices of the mesh
self.mesh_f = None # Faces of the mesh
# Base class for output of Aligned Mesh transformation
class AlignedMeshOutput(object):
def __init__(self):
self.mesh_v = None # Vertices of the mesh
self.mesh_f = None # Faces of the mesh
self.surface = None # Surface data
self.image = None # Aligned image data
self.text: Optional[str] = None # Aligned text data
self.shape_text_similarity: Optional[float] = None # Similarity between shape and text
self.shape_image_similarity: Optional[float] = None # Similarity between shape and image
# Base class for Shape as Latent with Point to Mesh transformation module
class ShapeAsLatentPLModule(nn.Module):
latent_shape: Tuple[int] # Shape of the latent space
def encode(self, surface, *args, **kwargs):
raise NotImplementedError
def decode(self, z_q, *args, **kwargs):
raise NotImplementedError
def latent2mesh(self, latents, *args, **kwargs) -> List[Latent2MeshOutput]:
raise NotImplementedError
def point2mesh(self, *args, **kwargs) -> List[Point2MeshOutput]:
raise NotImplementedError
# Base class for Shape as Latent module
class ShapeAsLatentModule(nn.Module):
latent_shape: Tuple[int, int] # Shape of the latent space
def __init__(self, *args, **kwargs):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
def decode(self, *args, **kwargs):
raise NotImplementedError
def query_geometry(self, *args, **kwargs):
raise NotImplementedError
# Base class for Aligned Shape as Latent with Point to Mesh transformation module
class AlignedShapeAsLatentPLModule(nn.Module):
latent_shape: Tuple[int] # Shape of the latent space
def set_shape_model_only(self):
raise NotImplementedError
def encode(self, surface, *args, **kwargs):
raise NotImplementedError
def decode(self, z_q, *args, **kwargs):
raise NotImplementedError
def latent2mesh(self, latents, *args, **kwargs) -> List[Latent2MeshOutput]:
raise NotImplementedError
def point2mesh(self, *args, **kwargs) -> List[Point2MeshOutput]:
raise NotImplementedError
# Base class for Aligned Shape as Latent module
class AlignedShapeAsLatentModule(nn.Module):
shape_model: ShapeAsLatentModule # Shape model module
latent_shape: Tuple[int, int] # Shape of the latent space
def __init__(self, *args, **kwargs):
super().__init__()
def set_shape_model_only(self):
raise NotImplementedError
def encode_image_embed(self, *args, **kwargs):
raise NotImplementedError
def encode_text_embed(self, *args, **kwargs):
raise NotImplementedError
def encode_shape_embed(self, *args, **kwargs):
raise NotImplementedError
# Base class for Textured Shape as Latent module
class TexturedShapeAsLatentModule(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
def decode(self, *args, **kwargs):
raise NotImplementedError
def query_geometry(self, *args, **kwargs):
raise NotImplementedError
def query_color(self, *args, **kwargs):
raise NotImplementedError

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# -*- coding: utf-8 -*-
from .misc import instantiate_from_config

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# -*- coding: utf-8 -*-
import importlib
import torch
import torch.distributed as dist
import sys
sys.path.append(r"C:\Remade\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-Hunyuan3DWrapper-main")
from hy3dgen.shapegen.bpt.miche.michelangelo.models.tsal import asl_pl_module
def get_obj_from_str(string, reload=False):
module, cls = string.rsplit(".", 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
return getattr(importlib.import_module(module, package=None), cls)
def get_obj_from_config(config):
if "target" not in config:
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])
def instantiate_from_config(config, **kwargs):
if "target" not in config:
raise KeyError("Expected key `target` to instantiate.")
cls = get_obj_from_str(config["target"])
params = config.get("params", dict())
# params.update(kwargs)
# instance = cls(**params)
kwargs.update(params)
instance = cls(**kwargs)
return instance
def is_dist_avail_and_initialized():
if not dist.is_available():
return False
if not dist.is_initialized():
return False
return True
def get_rank():
if not is_dist_avail_and_initialized():
return 0
return dist.get_rank()
def get_world_size():
if not is_dist_avail_and_initialized():
return 1
return dist.get_world_size()
def all_gather_batch(tensors):
"""
Performs all_gather operation on the provided tensors.
"""
# Queue the gathered tensors
world_size = get_world_size()
# There is no need for reduction in the single-proc case
if world_size == 1:
return tensors
tensor_list = []
output_tensor = []
for tensor in tensors:
tensor_all = [torch.ones_like(tensor) for _ in range(world_size)]
dist.all_gather(
tensor_all,
tensor,
async_op=False # performance opt
)
tensor_list.append(tensor_all)
for tensor_all in tensor_list:
output_tensor.append(torch.cat(tensor_all, dim=0))
return output_tensor

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"""Mesh data utilities."""
import random
import networkx as nx
import numpy as np
# import pyrr
from six.moves import range
import trimesh
from scipy.spatial.transform import Rotation
def to_mesh(vertices, faces, transpose=True, post_process=False):
if transpose:
vertices = vertices[:, [1, 2, 0]]
if faces.min() == 1:
faces = (np.array(faces) - 1).tolist()
mesh = trimesh.Trimesh(vertices=vertices, faces=faces, process=False)
if post_process:
mesh.merge_vertices()
mesh.update_faces(mesh.unique_faces())
mesh.fix_normals()
return mesh
def center_vertices(vertices):
"""Translate the vertices so that bounding box is centered at zero."""
vert_min = vertices.min(axis=0)
vert_max = vertices.max(axis=0)
vert_center = 0.5 * (vert_min + vert_max)
# vert_center = np.mean(vertices, axis=0)
return vertices - vert_center
def face_to_cycles(face):
"""Find cycles in face."""
g = nx.Graph()
for v in range(len(face) - 1):
g.add_edge(face[v], face[v + 1])
g.add_edge(face[-1], face[0])
return list(nx.cycle_basis(g))
def block_index(vertex, block_size=32):
return (vertex[2] // block_size, vertex[1] // block_size, vertex[0] // block_size)
def block_id(block_index, num_blocks=4):
return block_index[0] * num_blocks**2 + block_index[1] * num_blocks + block_index[2]
def normalize_vertices_scale(vertices, scale=0.95):
"""Scale the vertices so that the long axis of the bounding box is one."""
vert_min = vertices.min(axis=0)
vert_max = vertices.max(axis=0)
extents = (vert_max - vert_min).max()
return 2.0 * scale * vertices / (extents + 1e-6)
def quantize_process_mesh(vertices, faces, quantization_bits=8, block_first_order=True, block_size=32, num_blocks=4):
"""Quantize vertices, remove resulting duplicates and reindex faces."""
vertices = discretize(vertices, num_discrete=2**quantization_bits)
vertices, inv = np.unique(vertices, axis=0, return_inverse=True)
if block_first_order:
block_indices = np.array([block_index(v, block_size) for v in vertices])
block_ids = np.array([block_id(b, num_blocks) for b in block_indices])
sort_inds = np.lexsort((vertices[:, 0], vertices[:, 1], vertices[:, 2], block_ids))
else:
# Sort vertices by z then y then x.
sort_inds = np.lexsort(vertices.T)
vertices = vertices[sort_inds]
faces = [np.argsort(sort_inds)[inv[f]] for f in faces]
sub_faces = []
for f in faces:
cliques = face_to_cycles(f)
for c in cliques:
c_length = len(c)
if c_length > 2:
d = np.argmin(f)
sub_faces.append([f[(d + i) % c_length] for i in range(c_length)])
faces = sub_faces
# Sort faces by lowest vertex indices. If two faces have the same lowest
# index then sort by next lowest and so on.
faces.sort(key=lambda f: tuple(sorted(f)))
num_verts = vertices.shape[0]
vert_connected = np.equal(
np.arange(num_verts)[:, None], np.hstack(faces)[None]
).any(axis=-1)
vertices = vertices[vert_connected]
# Re-index faces to re-ordered vertices.
vert_indices = np.arange(num_verts) - np.cumsum(1 - vert_connected.astype("int"))
faces = [vert_indices[f].tolist() for f in faces]
return vertices, faces
def process_mesh(vertices, faces, quantization_bits=8, augment=True, augment_dict=None):
"""Process mesh vertices and faces."""
# Transpose so that z-axis is vertical.
vertices = vertices[:, [2, 0, 1]]
# Translate the vertices so that bounding box is centered at zero.
vertices = center_vertices(vertices)
if augment:
vertices = augment_mesh(vertices, **augment_dict)
# Scale the vertices so that the long diagonal of the bounding box is equal
# to one.
vertices = normalize_vertices_scale(vertices)
# Quantize and sort vertices, remove resulting duplicates, sort and reindex
# faces.
vertices, faces = quantize_process_mesh(
vertices, faces, quantization_bits=quantization_bits
)
vertices = undiscretize(vertices, num_discrete=2**quantization_bits)
# Discard degenerate meshes without faces.
return {
"vertices": vertices,
"faces": faces,
}
def load_process_mesh(mesh_obj_path, quantization_bits=8, augment=False, augment_dict=None):
"""Load obj file and process."""
# Load mesh
mesh = trimesh.load(mesh_obj_path, force='mesh', process=False)
return process_mesh(mesh.vertices, mesh.faces, quantization_bits, augment=augment, augment_dict=augment_dict)
def augment_mesh(vertices, scale_min=0.95, scale_max=1.05, rotation=0., jitter_strength=0.):
'''scale vertices by a factor in [0.75, 1.25]'''
# vertices [nv, 3]
for i in range(3):
# Generate a random scale factor
scale = random.uniform(scale_min, scale_max)
# independently applied scaling across each axis of vertices
vertices[:, i] *= scale
if rotation != 0.:
axis = [random.uniform(-1, 1), random.uniform(-1, 1), random.uniform(-1, 1)]
radian = np.pi / 180 * rotation
rotation = Rotation.from_rotvec(radian * np.array(axis))
vertices =rotation.apply(vertices)
if jitter_strength != 0.:
jitter_amount = np.random.uniform(-jitter_strength, jitter_strength)
vertices += jitter_amount
return vertices
def discretize(
t,
continuous_range = (-1, 1),
num_discrete: int = 128
):
lo, hi = continuous_range
assert hi > lo
t = (t - lo) / (hi - lo)
t *= num_discrete
t -= 0.5
return t.round().astype(np.int32).clip(min = 0, max = num_discrete - 1)
def undiscretize(
t,
continuous_range = (-1, 1),
num_discrete: int = 128
):
lo, hi = continuous_range
assert hi > lo
t = t.astype(np.float32)
t += 0.5
t /= num_discrete
return t * (hi - lo) + lo

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hy3dgen/shapegen/bpt/model/miche_conditioner.py Normal file
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import torch
import os
from torch import nn
from beartype import beartype
from ..miche.encode import load_model
from ..miche.michelangelo.models.tsal import asl_pl_module
# helper functions
def exists(val):
return val is not None
def default(*values):
for value in values:
if exists(value):
return value
return None
# point-cloud encoder from Michelangelo
@beartype
class PointConditioner(torch.nn.Module):
def __init__(
self,
*,
dim_latent = None,
model_name = 'miche-256-feature',
cond_dim = 768,
freeze = True,
):
super().__init__()
# open-source version of miche
if model_name == 'miche-256-feature':
ckpt_path = None
dir = os.path.dirname(os.path.abspath(__file__))
model_path = os.path.join(dir, '..\shapevae-256.yaml')
config_path = model_path
self.feature_dim = 1024 # embedding dimension
self.cond_length = 257 # length of embedding
self.point_encoder = load_model(ckpt_path=ckpt_path, config_path=config_path)
# additional layers to connect miche and GPT
self.cond_head_proj = nn.Linear(cond_dim, self.feature_dim)
self.cond_proj = nn.Linear(cond_dim, self.feature_dim)
else:
raise NotImplementedError
# whether to finetuen point-cloud encoder
if freeze:
for parameter in self.point_encoder.parameters():
parameter.requires_grad = False
self.freeze = freeze
self.model_name = model_name
self.dim_latent = default(dim_latent, self.feature_dim)
self.register_buffer('_device_param', torch.tensor(0.), persistent = False)
@property
def device(self):
return next(self.buffers()).device
def embed_pc(self, pc_normal):
# encode point cloud to embeddings
if self.model_name == 'miche-256-feature':
point_feature = self.point_encoder.encode_latents(pc_normal)
pc_embed_head = self.cond_head_proj(point_feature[:, 0:1])
pc_embed = self.cond_proj(point_feature[:, 1:])
pc_embed = torch.cat([pc_embed_head, pc_embed], dim=1)
return pc_embed
def forward(
self,
pc = None,
pc_embeds = None,
):
if pc_embeds is None:
pc_embeds = self.embed_pc(pc.to(next(self.buffers()).dtype))
assert not torch.any(torch.isnan(pc_embeds)), 'NAN values in pc embedings'
return pc_embeds

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hy3dgen/shapegen/bpt/model/model.py Normal file
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import math
import torch
from torch import nn, Tensor
from torch.nn import Module
import torch.nn.functional as F
from einops import rearrange, repeat, pack
from pytorch_custom_utils import save_load
from beartype import beartype
from beartype.typing import Union, Tuple, Callable, Optional, Any
from einops import rearrange, repeat, pack
from x_transformers import Decoder
from x_transformers.x_transformers import LayerIntermediates
from x_transformers.autoregressive_wrapper import (
eval_decorator,
top_k,
)
from .miche_conditioner import PointConditioner
from functools import partial
from tqdm import tqdm
from .data_utils import discretize
# helper functions
def exists(v):
return v is not None
def default(v, d):
return v if exists(v) else d
def first(it):
return it[0]
def divisible_by(num, den):
return (num % den) == 0
def pad_at_dim(t, padding, dim = -1, value = 0):
ndim = t.ndim
right_dims = (ndim - dim - 1) if dim >= 0 else (-dim - 1)
zeros = (0, 0) * right_dims
return F.pad(t, (*zeros, *padding), value = value)
# main class of auto-regressive Transformer
@save_load()
class MeshTransformer(Module):
@beartype
def __init__(
self,
*,
dim: Union[int, Tuple[int, int]] = 1024, # hidden size of Transformer
max_seq_len = 10000, # max sequence length
flash_attn = True, # wether to use flash attention
attn_depth = 24, # number of layers
attn_dim_head = 64, # dim for each head
attn_heads = 16, # number of heads
attn_kwargs: dict = dict(
ff_glu = True,
num_mem_kv = 4,
attn_qk_norm = True,
),
dropout = 0.0,
pad_id = -1,
coor_continuous_range = (-1., 1.),
num_discrete_coors = 2**int(7),
block_size = 8,
offset_size = 16,
mode = 'vertices',
special_token = -2,
use_special_block = True,
conditioned_on_pc = True,
encoder_name = 'miche-256-feature',
encoder_freeze = False,
):
super().__init__()
if use_special_block:
# block_ids, offset_ids, special_block_ids
vocab_size = block_size**3 + offset_size**3 + block_size**3
self.sp_block_embed = nn.Parameter(torch.randn(1, dim))
else:
# block_ids, offset_ids, special_token
vocab_size = block_size**3 + offset_size**3 + 1
self.special_token = special_token
self.special_token_cb = block_size**3 + offset_size**3
self.use_special_block = use_special_block
self.sos_token = nn.Parameter(torch.randn(dim))
self.eos_token_id = vocab_size
self.mode = mode
self.token_embed = nn.Embedding(vocab_size + 1, dim)
self.num_discrete_coors = num_discrete_coors
self.coor_continuous_range = coor_continuous_range
self.block_size = block_size
self.offset_size = offset_size
self.abs_pos_emb = nn.Embedding(max_seq_len, dim)
self.max_seq_len = max_seq_len
self.conditioner = None
self.conditioned_on_pc = conditioned_on_pc
cross_attn_dim_context = None
self.block_embed = nn.Parameter(torch.randn(1, dim))
self.offset_embed = nn.Parameter(torch.randn(1, dim))
assert self.block_size * self.offset_size == self.num_discrete_coors
# load point_cloud encoder
if conditioned_on_pc:
print(f'Point cloud encoder: {encoder_name} | freeze: {encoder_freeze}')
self.conditioner = PointConditioner(model_name=encoder_name, freeze=encoder_freeze)
cross_attn_dim_context = self.conditioner.dim_latent
else:
raise NotImplementedError
# main autoregressive attention network
self.decoder = Decoder(
dim = dim,
depth = attn_depth,
dim_head = attn_dim_head,
heads = attn_heads,
attn_flash = flash_attn,
attn_dropout = dropout,
ff_dropout = dropout,
cross_attend = conditioned_on_pc,
cross_attn_dim_context = cross_attn_dim_context,
cross_attn_num_mem_kv = 4, # needed for preventing nan when dropping out text condition
**attn_kwargs
)
self.to_logits = nn.Linear(dim, vocab_size + 1)
self.pad_id = pad_id
self.discretize_face_coords = partial(
discretize,
num_discrete = num_discrete_coors,
continuous_range = coor_continuous_range
)
@property
def device(self):
return next(self.parameters()).device
@eval_decorator
@torch.no_grad()
@beartype
def generate(
self,
prompt: Optional[Tensor] = None,
pc: Optional[Tensor] = None,
cond_embeds: Optional[Tensor] = None,
batch_size: Optional[int] = 1,
filter_logits_fn: Callable = top_k,
filter_kwargs: dict = dict(),
temperature = 0.5,
return_codes = False,
cache_kv = True,
max_seq_len = None,
face_coords_to_file: Optional[Callable[[Tensor], Any]] = None,
tqdm_position = 0,
):
max_seq_len = default(max_seq_len, self.max_seq_len)
if exists(prompt):
assert not exists(batch_size)
prompt = rearrange(prompt, 'b ... -> b (...)')
assert prompt.shape[-1] <= self.max_seq_len
batch_size = prompt.shape[0]
# encode point cloud
if cond_embeds is None:
if self.conditioned_on_pc:
cond_embeds = self.conditioner(pc = pc)
batch_size = default(batch_size, 1)
codes = default(prompt, torch.empty((batch_size, 0), dtype = torch.long, device = self.device))
curr_length = codes.shape[-1]
cache = None
eos_iter = None
# predict tokens auto-regressively
for i in tqdm(range(curr_length, max_seq_len), position=tqdm_position,
desc=f'Process: {tqdm_position}', dynamic_ncols=True, leave=False):
output = self.forward_on_codes(
codes,
return_loss = False,
return_cache = cache_kv,
append_eos = False,
cond_embeds = cond_embeds,
cache = cache
)
if cache_kv:
logits, cache = output
else:
logits = output
# sample code from logits
logits = logits[:, -1]
filtered_logits = filter_logits_fn(logits, **filter_kwargs)
probs = F.softmax(filtered_logits / temperature, dim=-1)
sample = torch.multinomial(probs, 1)
codes, _ = pack([codes, sample], 'b *')
# Check if all sequences have encountered EOS at least once
is_eos_codes = (codes == self.eos_token_id)
if is_eos_codes.any(dim=-1).all():
# Record the iteration (i.e. current sequence length) when EOS is first detected in all sequences
if eos_iter is None:
eos_iter = codes.shape[-1]
# Once we've generated 20% more tokens than eos_iter, break out of the loop
if codes.shape[-1] >= int(eos_iter * 1.2):
break
# mask out to padding anything after the first eos
mask = is_eos_codes.float().cumsum(dim = -1) >= 1
codes = codes.masked_fill(mask, self.pad_id)
# early return of raw residual quantizer codes
if return_codes:
# codes = rearrange(codes, 'b (n q) -> b n q', q = 2)
if not self.use_special_block:
codes[codes == self.special_token_cb] = self.special_token
return codes
face_coords, face_mask = self.decode_codes(codes)
if not exists(face_coords_to_file):
return face_coords, face_mask
files = [face_coords_to_file(coords[mask]) for coords, mask in zip(face_coords, face_mask)]
return files
def forward(
self,
*,
codes: Optional[Tensor] = None,
cache: Optional[LayerIntermediates] = None,
**kwargs
):
# convert special tokens
if not self.use_special_block:
codes[codes == self.special_token] = self.special_token_cb
return self.forward_on_codes(codes, cache = cache, **kwargs)
def forward_on_codes(
self,
codes = None,
return_loss = True,
return_cache = False,
append_eos = True,
cache = None,
pc = None,
cond_embeds = None,
):
# handle conditions
attn_context_kwargs = dict()
if self.conditioned_on_pc:
assert exists(pc) ^ exists(cond_embeds), 'point cloud should be given'
# preprocess faces and vertices
if not exists(cond_embeds):
cond_embeds = self.conditioner(
pc = pc,
pc_embeds = cond_embeds,
)
attn_context_kwargs = dict(
context = cond_embeds,
context_mask = None,
)
# take care of codes that may be flattened
if codes.ndim > 2:
codes = rearrange(codes, 'b ... -> b (...)')
# prepare mask for position embedding of block and offset tokens
block_mask = (0 <= codes) & (codes < self.block_size**3)
offset_mask = (self.block_size**3 <= codes) & (codes < self.block_size**3 + self.offset_size**3)
if self.use_special_block:
sp_block_mask = (
self.block_size**3 + self.offset_size**3 <= codes
) & (
codes < self.block_size**3 + self.offset_size**3 + self.block_size**3
)
# get some variable
batch, seq_len, device = *codes.shape, codes.device
assert seq_len <= self.max_seq_len, \
f'received codes of length {seq_len} but needs to be less than {self.max_seq_len}'
# auto append eos token
if append_eos:
assert exists(codes)
code_lens = ((codes == self.pad_id).cumsum(dim = -1) == 0).sum(dim = -1)
codes = F.pad(codes, (0, 1), value = 0) # value=-1
batch_arange = torch.arange(batch, device = device)
batch_arange = rearrange(batch_arange, '... -> ... 1')
code_lens = rearrange(code_lens, '... -> ... 1')
codes[batch_arange, code_lens] = self.eos_token_id
# if returning loss, save the labels for cross entropy
if return_loss:
assert seq_len > 0
codes, labels = codes[:, :-1], codes
# token embed
codes = codes.masked_fill(codes == self.pad_id, 0)
codes = self.token_embed(codes)
# codebook embed + absolute positions
seq_arange = torch.arange(codes.shape[-2], device = device)
codes = codes + self.abs_pos_emb(seq_arange)
# add positional embedding for block and offset token
block_embed = repeat(self.block_embed, '1 d -> b n d', n = seq_len, b = batch)
offset_embed = repeat(self.offset_embed, '1 d -> b n d', n = seq_len, b = batch)
codes[block_mask] += block_embed[block_mask]
codes[offset_mask] += offset_embed[offset_mask]
if self.use_special_block:
sp_block_embed = repeat(self.sp_block_embed, '1 d -> b n d', n = seq_len, b = batch)
codes[sp_block_mask] += sp_block_embed[sp_block_mask]
# auto prepend sos token
sos = repeat(self.sos_token, 'd -> b d', b = batch)
codes, _ = pack([sos, codes], 'b * d')
# attention
attended, intermediates_with_cache = self.decoder(
codes,
cache = cache,
return_hiddens = True,
**attn_context_kwargs
)
# logits
logits = self.to_logits(attended)
if not return_loss:
if not return_cache:
return logits
return logits, intermediates_with_cache
# loss
ce_loss = F.cross_entropy(
rearrange(logits, 'b n c -> b c n'),
labels,
ignore_index = self.pad_id
)
return ce_loss

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hy3dgen/shapegen/bpt/model/serializaiton.py Normal file
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import trimesh
import numpy as np
from .data_utils import discretize, undiscretize
def patchified_mesh(mesh: trimesh.Trimesh, special_token = -2, fix_orient=True):
sequence = []
unvisited = np.full(len(mesh.faces), True)
degrees = mesh.vertex_degree.copy()
# with fix_orient=True, the normal would be correct.
# but this may increase the difficulty for learning.
if fix_orient:
face_orient = {}
for ind, face in enumerate(mesh.faces):
v0, v1, v2 = face[0], face[1], face[2]
face_orient['{}-{}-{}'.format(v0, v1, v2)] = True
face_orient['{}-{}-{}'.format(v1, v2, v0)] = True
face_orient['{}-{}-{}'.format(v2, v0, v1)] = True
face_orient['{}-{}-{}'.format(v2, v1, v0)] = False
face_orient['{}-{}-{}'.format(v1, v0, v2)] = False
face_orient['{}-{}-{}'.format(v0, v2, v1)] = False
while sum(unvisited):
unvisited_faces = mesh.faces[unvisited]
# select the patch center
cur_face = unvisited_faces[0]
max_deg_vertex_id = np.argmax(degrees[cur_face])
max_deg_vertex = cur_face[max_deg_vertex_id]
# find all connected faces
selected_faces = []
for face_idx in mesh.vertex_faces[max_deg_vertex]:
if face_idx != -1 and unvisited[face_idx]:
face = mesh.faces[face_idx]
u, v = sorted([vertex for vertex in face if vertex != max_deg_vertex])
selected_faces.append([u, v, face_idx])
face_patch = set()
selected_faces = sorted(selected_faces)
# select the start vertex, select it if it only appears once (the start or end),
# else select the lowest index
cnt = {}
for u, v, _ in selected_faces:
cnt[u] = cnt.get(u, 0) + 1
cnt[v] = cnt.get(v, 0) + 1
starts = []
for vertex, num in cnt.items():
if num == 1:
starts.append(vertex)
start_idx = min(starts) if len(starts) else selected_faces[0][0]
res = [start_idx]
while len(res) <= len(selected_faces):
vertex = res[-1]
for u_i, v_i, face_idx_i in selected_faces:
if face_idx_i not in face_patch and vertex in (u_i, v_i):
u_i, v_i = (u_i, v_i) if vertex == u_i else (v_i, u_i)
res.append(v_i)
face_patch.add(face_idx_i)
break
if res[-1] == vertex:
break
if fix_orient and len(res) >= 2 and not face_orient['{}-{}-{}'.format(max_deg_vertex, res[0], res[1])]:
res = res[::-1]
# reduce the degree of related vertices and mark the visited faces
degrees[max_deg_vertex] = len(selected_faces) - len(res) + 1
for pos_idx, vertex in enumerate(res):
if pos_idx in [0, len(res) - 1]:
degrees[vertex] -= 1
else:
degrees[vertex] -= 2
for face_idx in face_patch:
unvisited[face_idx] = False
sequence.extend(
[mesh.vertices[max_deg_vertex]] +
[mesh.vertices[vertex_idx] for vertex_idx in res] +
[[special_token] * 3]
)
assert sum(degrees) == 0, 'All degrees should be zero'
return np.array(sequence)
def get_block_representation(
sequence,
block_size=8,
offset_size=16,
block_compressed=True,
special_token=-2,
use_special_block=True
):
'''
convert coordinates from Cartesian system to block indexes.
'''
special_block_base = block_size**3 + offset_size**3
# prepare coordinates
sp_mask = sequence != special_token
sp_mask = np.all(sp_mask, axis=1)
coords = sequence[sp_mask].reshape(-1, 3)
coords = discretize(coords)
# convert [x, y, z] to [block_id, offset_id]
block_id = coords // offset_size
block_id = block_id[:, 0] * block_size**2 + block_id[:, 1] * block_size + block_id[:, 2]
offset_id = coords % offset_size
offset_id = offset_id[:, 0] * offset_size**2 + offset_id[:, 1] * offset_size + offset_id[:, 2]
offset_id += block_size**3
block_coords = np.concatenate([block_id[..., None], offset_id[..., None]], axis=-1).astype(np.int64)
sequence[:, :2][sp_mask] = block_coords
sequence = sequence[:, :2]
# convert to codes
codes = []
cur_block_id = sequence[0, 0]
codes.append(cur_block_id)
for i in range(len(sequence)):
if sequence[i, 0] == special_token:
if not use_special_block:
codes.append(special_token)
cur_block_id = special_token
elif sequence[i, 0] == cur_block_id:
if block_compressed:
codes.append(sequence[i, 1])
else:
codes.extend([sequence[i, 0], sequence[i, 1]])
else:
if use_special_block and cur_block_id == special_token:
block_id = sequence[i, 0] + special_block_base
else:
block_id = sequence[i, 0]
codes.extend([block_id, sequence[i, 1]])
cur_block_id = block_id
codes = np.array(codes).astype(np.int64)
sequence = codes
return sequence.flatten()
def BPT_serialize(mesh: trimesh.Trimesh):
# serialize mesh with BPT
# 1. patchify faces into patches
sequence = patchified_mesh(mesh, special_token=-2)
# 2. convert coordinates to block-wise indexes
codes = get_block_representation(
sequence, block_size=8, offset_size=16,
block_compressed=True, special_token=-2, use_special_block=True
)
return codes
def decode_block(sequence, compressed=True, block_size=8, offset_size=16):
# decode from compressed representation
if compressed:
res = []
res_block = 0
for token_id in range(len(sequence)):
if block_size**3 + offset_size**3 > sequence[token_id] >= block_size**3:
res.append([res_block, sequence[token_id]])
elif block_size**3 > sequence[token_id] >= 0:
res_block = sequence[token_id]
else:
print('[Warning] too large offset idx!', token_id, sequence[token_id])
sequence = np.array(res)
block_id, offset_id = np.array_split(sequence, 2, axis=-1)
# from hash representation to xyz
coords = []
offset_id -= block_size**3
for i in [2, 1, 0]:
axis = (block_id // block_size**i) * offset_size + (offset_id // offset_size**i)
block_id %= block_size**i
offset_id %= offset_size**i
coords.append(axis)
coords = np.concatenate(coords, axis=-1) # (nf 3)
# back to continuous space
coords = undiscretize(coords)
return coords
def BPT_deserialize(sequence, block_size=8, offset_size=16, compressed=True, special_token=-2, use_special_block=True):
# decode codes back to coordinates
special_block_base = block_size**3 + offset_size**3
start_idx = 0
vertices = []
for i in range(len(sequence)):
sub_seq = []
if not use_special_block and (sequence[i] == special_token or i == len(sequence) - 1):
sub_seq = sequence[start_idx:i]
sub_seq = decode_block(sub_seq, compressed=compressed, block_size=block_size, offset_size=offset_size)
start_idx = i + 1
elif use_special_block and \
(special_block_base <= sequence[i] < special_block_base + block_size**3 or i == len(sequence)-1):
if i != 0:
sub_seq = sequence[start_idx:i] if i != len(sequence) - 1 else sequence[start_idx: i+1]
if special_block_base <= sub_seq[0] < special_block_base + block_size**3:
sub_seq[0] -= special_block_base
sub_seq = decode_block(sub_seq, compressed=compressed, block_size=block_size, offset_size=offset_size)
start_idx = i
if len(sub_seq):
center, sub_seq = sub_seq[0], sub_seq[1:]
for j in range(len(sub_seq) - 1):
vertices.extend([center.reshape(1, 3), sub_seq[j].reshape(1, 3), sub_seq[j+1].reshape(1, 3)])
# (nf, 3)
return np.concatenate(vertices, axis=0)
if __name__ == '__main__':
# a simple demo for serialize and deserialize mesh with bpt
from data_utils import load_process_mesh, to_mesh
import torch
mesh = load_process_mesh('/path/to/your/mesh', quantization_bits=7)
mesh['faces'] = np.array(mesh['faces'])
mesh = to_mesh(mesh['vertices'], mesh['faces'], transpose=True)
mesh.export('gt.obj')
codes = BPT_serialize(mesh)
coordinates = BPT_deserialize(codes)
faces = torch.arange(1, len(coordinates) + 1).view(-1, 3)
mesh = to_mesh(coordinates, faces, transpose=False, post_process=False)
mesh.export('reconstructed.obj')

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hy3dgen/shapegen/bpt/requirements.txt Normal file
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meshgpt_pytorch==0.6.7
pytorch-custom-utils==0.0.21
accelerate>=0.25.0
beartype
classifier-free-guidance-pytorch==0.5.1
einops>=0.7.0
ema-pytorch
pytorch-warmup
torch_geometric
torchtyping
vector-quantize-pytorch==1.12.8
x-transformers==1.26.6
tqdm
matplotlib
wandb
pyrr
trimesh
opencv-python
pyrender
open3d-python
easydict
chardet
deepspeed
omegaconf
scikit-image
setuptools
pytorch_lightning
mesh2sdf
numpy
point-cloud-utils

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hy3dgen/shapegen/bpt/utils.py Normal file
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import trimesh
import numpy as np
from x_transformers.autoregressive_wrapper import top_p, top_k
class Dataset:
'''
A toy dataset for inference
'''
def __init__(self, input_type, input_list):
super().__init__()
self.data = []
if input_type == 'pc_normal':
for input_path in input_list:
# load npy
cur_data = np.load(input_path)
# sample 4096
assert cur_data.shape[0] >= 4096, "input pc_normal should have at least 4096 points"
idx = np.random.choice(cur_data.shape[0], 4096, replace=False)
cur_data = cur_data[idx]
self.data.append({'pc_normal': cur_data, 'uid': input_path.split('/')[-1].split('.')[0]})
elif input_type == 'mesh':
mesh_list, pc_list = [], []
for input_path in input_list:
# sample point cloud and normal from mesh
cur_data = trimesh.load(input_path, force='mesh')
cur_data = apply_normalize(cur_data)
mesh_list.append(cur_data)
pc_list.append(sample_pc(cur_data, pc_num=4096, with_normal=True))
for input_path, cur_data in zip(input_list, pc_list):
self.data.append({'pc_normal': cur_data, 'uid': input_path.split('/')[-1].split('.')[0]})
print(f"dataset total data samples: {len(self.data)}")
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
data_dict = {}
data_dict['pc_normal'] = self.data[idx]['pc_normal']
data_dict['uid'] = self.data[idx]['uid']
return data_dict
def joint_filter(logits, k = 50, p=0.95):
logits = top_k(logits, k = k)
logits = top_p(logits, thres = p)
return logits
def apply_normalize(mesh):
'''
normalize mesh to [-1, 1]
'''
bbox = mesh.bounds
center = (bbox[1] + bbox[0]) / 2
scale = (bbox[1] - bbox[0]).max()
mesh.apply_translation(-center)
mesh.apply_scale(1 / scale * 2 * 0.95)
return mesh
def sample_pc(trimesh, pc_num, with_normal=False):
mesh = apply_normalize(trimesh)
if not with_normal:
points, _ = mesh.sample(pc_num, return_index=True)
return points
points, face_idx = mesh.sample(50000, return_index=True)
normals = mesh.face_normals[face_idx]
pc_normal = np.concatenate([points, normals], axis=-1, dtype=np.float16)
# random sample point cloud
ind = np.random.choice(pc_normal.shape[0], pc_num, replace=False)
pc_normal = pc_normal[ind]
return pc_normal