Added BPT

hy3dgen/shapegen/bpt/README.md

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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

hy3dgen/shapegen/bpt/miche/LICENSE

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+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>.

hy3dgen/shapegen/bpt/miche/encode.py

@@ -0,0 +1,74 @@
+# -*- 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))

hy3dgen/shapegen/bpt/miche/michelangelo/__init__.py

@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-

hy3dgen/shapegen/bpt/miche/michelangelo/graphics/__init__.py

@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-

hy3dgen/shapegen/bpt/miche/michelangelo/graphics/primitives/__init__.py

@@ -0,0 +1,4 @@
+# -*- coding: utf-8 -*-
+
+from .volume import generate_dense_grid_points
+

hy3dgen/shapegen/bpt/miche/michelangelo/graphics/primitives/volume.py

@@ -0,0 +1,21 @@
+# -*- 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
+

hy3dgen/shapegen/bpt/miche/michelangelo/models/__init__.py

@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-

hy3dgen/shapegen/bpt/miche/michelangelo/models/modules/__init__.py

@@ -0,0 +1,3 @@
+# -*- coding: utf-8 -*-
+
+from .checkpoint import checkpoint

hy3dgen/shapegen/bpt/miche/michelangelo/models/modules/checkpoint.py

@@ -0,0 +1,64 @@
+# -*- 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

hy3dgen/shapegen/bpt/miche/michelangelo/models/modules/distributions.py

@@ -0,0 +1,83 @@
+# -*- 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)
+    )

hy3dgen/shapegen/bpt/miche/michelangelo/models/modules/embedder.py

@@ -0,0 +1,213 @@
+# -*- 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}")

hy3dgen/shapegen/bpt/miche/michelangelo/models/modules/transformer_blocks.py

@@ -0,0 +1,286 @@
+# -*- 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

hy3dgen/shapegen/bpt/miche/michelangelo/models/tsal/__init__.py

@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-

hy3dgen/shapegen/bpt/miche/michelangelo/models/tsal/asl_pl_module.py

@@ -0,0 +1,383 @@
+# -*- 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

hy3dgen/shapegen/bpt/miche/michelangelo/models/tsal/clip_asl_module.py

@@ -0,0 +1,118 @@
+# -*- 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

hy3dgen/shapegen/bpt/miche/michelangelo/models/tsal/inference_utils.py

@@ -0,0 +1,76 @@
+# -*- 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

hy3dgen/shapegen/bpt/miche/michelangelo/models/tsal/loss.py

@@ -0,0 +1,130 @@
+# -*- 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

hy3dgen/shapegen/bpt/miche/michelangelo/models/tsal/sal_perceiver.py

@@ -0,0 +1,410 @@
+# -*- 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

hy3dgen/shapegen/bpt/miche/michelangelo/models/tsal/tsal_base.py

@@ -0,0 +1,125 @@
+# -*- 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

hy3dgen/shapegen/bpt/miche/michelangelo/utils/__init__.py

@@ -0,0 +1,3 @@
+# -*- coding: utf-8 -*-
+
+from .misc import instantiate_from_config

hy3dgen/shapegen/bpt/miche/michelangelo/utils/misc.py

@@ -0,0 +1,86 @@
+# -*- 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

hy3dgen/shapegen/bpt/model/data_utils.py

@@ -0,0 +1,194 @@
+"""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
+

hy3dgen/shapegen/bpt/model/miche_conditioner.py

@@ -0,0 +1,90 @@
+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
+    

hy3dgen/shapegen/bpt/model/model.py

@@ -0,0 +1,382 @@
+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

hy3dgen/shapegen/bpt/model/serializaiton.py

@@ -0,0 +1,241 @@
+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')

hy3dgen/shapegen/bpt/requirements.txt

@@ -0,0 +1,30 @@
+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

hy3dgen/shapegen/bpt/utils.py

@@ -0,0 +1,86 @@
+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
+
+