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.gitattributes

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+assets/teaser.png filter=lfs diff=lfs merge=lfs -text
+examples/AdventureYouth.glb filter=lfs diff=lfs merge=lfs -text
+examples/Astrologers.glb filter=lfs diff=lfs merge=lfs -text
+examples/Sheep.glb filter=lfs diff=lfs merge=lfs -text
+examples/Spider.glb filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,14 +1,111 @@
----
-title: Bpt
-emoji: 🚀
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 5.6.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: demo for BPT
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Scaling Mesh Generation via Compressive Tokenization
+
+### [Project Page](https://whaohan.github.io/bpt)  | [Paper](https://arxiv.org/abs/2411.07025) | [Weight](https://huggingface.co/whaohan/bpt/tree/main)
+
+
+## 📑 Open-source Plan
+
+- [x] Inference conditioned on point cloud
+- [x] Checkpoints
+- [x] Evaluation metrics
+- [ ] Inference conditioned on images
+- [ ] Training
+
+
+## **Abstract**
+<p align="center">
+  <img src="./assets/teaser.png"  height=450>
+</p>
+
+We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces. BPT compresses mesh sequences by employing block-wise indexing and patch aggregation, reducing their length by approximately 75% compared to the original sequences. This compression milestone unlocks the potential to utilize mesh data with significantly more faces, thereby enhancing detail richness and improving generation robustness. Empowered with the BPT, we have built a foundation mesh generative model training on scaled mesh data to support flexible control for point clouds and images. Our model demonstrates the capability to generate meshes with intricate details and accurate topology, achieving SoTA performance on mesh generation and reaching the level for direct product usage.
+
+## 🎉 **Blocked and Patchified Tokenization (BPT)**
+
+<p align="center">
+  <img src="assets/BPT.png" height=300>
+</p>
+
+
+## Get Started
+
+#### Begin by cloning the repository:
+
+```shell
+git clone https://github.com/whaohan/bpt.git
+cd bpt
+```
+
+#### Installation Guide for Linux
+
+
+Install the packages in `requirements.txt`. The code is tested under CUDA version 12.1 and python 3.9.
+
+```bash
+conda create -n bpt python=3.9
+conda activate bpt
+pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121
+pip install -r requirements.txt
+```
+
+
+#### Download Pretrained Models
+
+The models are available at [huggingface](https://huggingface.co/whaohan/bpt/tree/main). 
+Currently, we resealse a lite version of model with the point-encoder finetuned from [Michelangelo](https://github.com/NeuralCarver/Michelangelo).
+
+To download the model, first install the huggingface-cli. (Detailed instructions are available [here](https://huggingface.co/docs/huggingface_hub/guides/cli).)
+
+```shell
+python3 -m pip install "huggingface_hub[cli]"
+```
+
+Then download the model using the following commands:
+
+```shell
+mkdir weights
+huggingface-cli download whaohan/bpt --local-dir ./weights
+```
+
+#### Inference conditioned on point clouds
+For text to 3d generation, we supports bilingual Chinese and English, you can use the following command to inference.
+```python
+python main.py \
+    --config 'config/BPT-open-8k-8-16.yaml' \
+    --model_path /path/to/model/ckpt \
+    --output_path output/ \
+    --batch_size 1 \
+    --temperature 0.5 \
+    --input_type mesh \
+    --input_dir /path/to/your/dense/meshes
+```
+It requires ~12GB VRAM to run with fp16 precision. It takes averagely 2mins to generate a single mesh.
+
+
+#### Evaluation
+
+```bash
+python metrics.py \
+    --input_dir /path/to/dense/meshes \
+    --output_dir /path/to/output/meshes
+```
+
+### Acknowledgement
+
+- [MeshGPT](https://github.com/lucidrains/meshgpt-pytorch)
+- [PivotMesh](https://github.com/whaohan/pivotmesh)
+- [Michelangelo](https://github.com/NeuralCarver/Michelangelo)
+- [MeshAnything](https://github.com/buaacyw/MeshAnythingV2/)
+- [MeshXL](https://github.com/OpenMeshLab/MeshXL/)
+
+
+## Citation
+
+If you found this repository helpful, please cite our report:
+```bibtex
+@article{weng2024scaling,
+  title={Scaling Mesh Generation via Compressive Tokenization}, 
+  author={Haohan Weng and Zibo Zhao and Biwen Lei and Xianghui Yang and Jian Liu and Zeqiang Lai and Zhuo Chen and Yuhong Liu and Jie Jiang and Chunchao Guo and Tong Zhang and Shenghua Gao and C. L. Philip Chen},
+  journal={arXiv preprint arXiv:2411.07025},
+  year={2024}
+}
+```

app.py

@@ -0,0 +1,243 @@
+from model.data_utils import to_mesh
+from model.serializaiton import BPT_deserialize
+import spaces
+import os
+import torch
+import trimesh
+from accelerate.utils import set_seed
+import numpy as np
+import gradio as gr
+import time
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d.art3d import Poly3DCollection
+from matplotlib.animation import FuncAnimation
+import yaml
+from huggingface_hub import snapshot_download
+from model.model import MeshTransformer
+from utils import apply_normalize, joint_filter, sample_pc
+
+
+CONFIG_PATH = 'config/BPT-open-8k-8-16.yaml'
+with open(CONFIG_PATH, "r") as f:
+    config = yaml.load(f, Loader=yaml.FullLoader)
+
+
+def download_models():
+    os.makedirs("weights", exist_ok=True)
+    try:
+        snapshot_download(
+            repo_id="whaohan/bpt",
+            local_dir="./weights",
+            resume_download=True
+        )
+        print("Successfully downloaded Hunyuan3D-1 model")
+    except Exception as e:
+        print(f"Error downloading Hunyuan3D-1: {e}")
+
+    model_path = 'weights/bpt-8-16-500m.pt'
+    return model_path
+ 
+MODEL_PATH = download_models()
+
+
+# prepare model with fp16 precision
+model = MeshTransformer(
+    dim = config['dim'],
+    attn_depth = config['depth'],
+    max_seq_len = config['max_seq_len'],
+    dropout = config['dropout'],
+    mode = config['mode'],
+    num_discrete_coors= 2**int(config['quant_bit']),
+    block_size = config['block_size'],
+    offset_size = config['offset_size'],
+    conditioned_on_pc = config['conditioned_on_pc'],
+    use_special_block = config['use_special_block'],
+    encoder_name = config['encoder_name'],
+    encoder_freeze = config['encoder_freeze'],
+)
+model.load(MODEL_PATH)
+model = model.eval()
+model = model.half()
+model = model.cuda()
+device = torch.device('cuda')
+print('Model loaded')
+
+
+def create_animation(mesh):
+    mesh.vertices = mesh.vertices[:, [2, 0, 1]]
+
+    bounding_box = mesh.bounds
+    center = mesh.centroid
+    scale = np.ptp(bounding_box, axis=0).max()
+
+    fig = plt.figure(figsize=(10, 10))
+
+    ax = fig.add_subplot(111, projection='3d')
+    ax.set_axis_off()
+
+    # Extract vertices and faces for plotting
+    vertices = mesh.vertices
+    faces = mesh.faces
+
+    # Plot faces
+    ax.add_collection3d(Poly3DCollection(
+        vertices[faces] * 1.4,
+        facecolors=[120/255, 154/255, 192/255, 255/255],
+        edgecolors='k',
+        linewidths=0.5,
+    ))
+
+    # Set limits and center the view on the object
+    ax.set_xlim(center[0] - scale / 2, center[0] + scale / 2)
+    ax.set_ylim(center[1] - scale / 2, center[1] + scale / 2)
+    ax.set_zlim(center[2] - scale / 2, center[2] + scale / 2)
+
+    # Function to update the view angle
+    def update_view(num, ax):
+        ax.view_init(elev=20, azim=num)
+        return ax,
+
+    # Create the animation
+    ani = FuncAnimation(fig, update_view, frames=np.arange(0, 360, 10), interval=100, fargs=(ax,), blit=False)
+
+    # Save the animation as a GIF
+    output_path = f'model_{int(time.time())}.gif'
+    ani.save(output_path, writer='pillow', fps=10)
+
+    # Close the figure
+    plt.close(fig)
+    
+    return output_path
+
+
+@spaces.GPU(duration=480)
+def do_inference(input_3d, sample_seed=0, temperature=0.5, top_k_value=50, top_p_value=0.9):
+    print('Start Inference')
+    set_seed(sample_seed)
+    print("Seed value:", sample_seed)
+
+    mesh = trimesh.load(input_3d, force='mesh')
+    mesh = apply_normalize(mesh)
+    pc_normal = sample_pc(mesh, pc_num=4096, with_normal=True)
+    vertices = mesh.vertices
+
+    pc_coor = pc_normal[:, :3]
+    normals = pc_normal[:, 3:]
+    assert (np.linalg.norm(normals, axis=-1) > 0.99).all(), "normals should be unit vectors, something wrong"
+    normalized_pc_normal = np.concatenate([pc_coor, normals], axis=-1, dtype=np.float16)
+    input = torch.tensor(normalized_pc_normal, dtype=torch.float16, device=device)[None]
+    print("Data loaded")
+
+    with torch.no_grad():
+        code = model.generate(
+            batch_size = 1,
+            temperature = temperature,
+            pc = input,
+            filter_logits_fn = joint_filter,
+            filter_kwargs = dict(k=top_k_value, p=top_p_value),
+            return_codes=True,
+        )[0]
+        
+    print("Model inference done")
+
+    # convert to mesh
+    code = code[code != model.pad_id].cpu().numpy()
+    vertices = BPT_deserialize(
+        code, 
+        block_size = model.block_size, 
+        offset_size = model.offset_size,
+        use_special_block = model.use_special_block,
+    )
+    faces = torch.arange(1, len(vertices) + 1).view(-1, 3)
+    artist_mesh = to_mesh(vertices, faces, transpose=False, post_process=True)
+
+    # add color for visualization
+    num_faces = len(artist_mesh.faces)
+    face_color = np.array([120, 154, 192, 255], dtype=np.uint8)
+    face_colors = np.tile(face_color, (num_faces, 1))
+    artist_mesh.visual.face_colors = face_colors
+    
+    # add time stamp to avoid cache
+    save_name = f"output_{int(time.time())}.obj"
+    artist_mesh.export(save_name)
+    output_render = create_animation(artist_mesh)
+    return save_name, output_render
+
+
+_HEADER_ = '''
+<h2><b>Official 🤗 Gradio Demo for Paper</b> <a href='https://github.com/whaohan/bpt' target='_blank'><b>Scaling Mesh Generation with Compressive Tokenization</b></a></h2>
+'''
+
+_CITE_ = r"""
+If you found our model is helpful, please help to ⭐ the <a href='https://github.com/whaohan/bpt' target='_blank'>Github Repo</a>. Code: <a href='https://github.com/whaohan/bpt' target='_blank'>GitHub</a>. Arxiv Paper: <a href='https://arxiv.org/abs/2411.07025' target='_blank'>ArXiv</a>.
+
+📧 **Contact**
+If you have any questions, feel free to contact <a href='https://whaohan.github.io' target='_blank'>Haohan Weng</a>.
+"""
+
+output_model_obj = gr.Model3D(
+    label="Generated Mesh (OBJ Format)",
+    display_mode="wireframe",
+    scale = 2,
+)
+
+output_image_render = gr.Image(
+    label="Wireframe Render of Generated Mesh",
+    scale = 1,
+)
+
+with gr.Blocks() as demo:
+    gr.Markdown(_HEADER_)
+    with gr.Row(variant="panel"):
+        with gr.Column(scale=1):
+            with gr.Row():
+                input_3d = gr.Model3D(
+                    label="Input Mesh",
+                )
+
+            # with gr.Row():
+            #     # with gr.Group():
+            with gr.Row():
+                sample_seed = gr.Number(value=0, label="Seed Value", precision=0)
+                temperature = gr.Number(value=0.5, label="Temperature For Sampling", precision=None)
+            with gr.Row():
+                top_k_value = gr.Number(value=50, label="TopK For Sampling", precision=0)
+                top_p_value = gr.Number(value=0.9, label="TopP For Sampling", precision=None)
+
+            with gr.Row():
+                submit = gr.Button("Generate", elem_id="generate", variant="primary")
+
+            with gr.Row(variant="panel"):
+                mesh_examples = gr.Examples(
+                    examples=[
+                        os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
+                    ],
+                    inputs=input_3d,
+                    outputs=[output_model_obj, output_image_render],
+                    fn=do_inference,
+                    cache_examples = False,
+                    examples_per_page=10
+                )
+                
+            with gr.Row():
+                gr.Markdown('''Try different <b>Seed Value</b> or <b>Temperature</b> if the result is unsatisfying''')
+                
+        with gr.Column(scale=2):
+            with gr.Row(equal_height=True):
+                output_model_obj.render()
+                output_image_render.render()
+                
+
+    gr.Markdown(_CITE_)
+
+    mv_images = gr.State()
+
+    submit.click(
+        fn=do_inference,
+        inputs=[input_3d, sample_seed, temperature, top_k_value, top_p_value],
+        outputs = [output_model_obj, output_image_render],
+    )
+
+
+demo.launch(share=True)
+

config/BPT-open-8k-8-16.yaml

@@ -0,0 +1,22 @@
+exp_name: 'BPT-open-8k-8-16'
+logdir: '/path/to/log'
+
+# condition
+conditioned_on_pc: True
+encoder_name: miche-256-feature
+encoder_freeze: False
+pc_num: 4096
+
+# representation config
+use_special_block: True
+block_compression: True
+block_size: 8
+offset_size: 16
+quant_bit: 7
+
+# architecture
+mode: 'vertices'
+dim: 1024
+depth: 24
+dropout: 0.0
+max_seq_len: 10000

examples/AdventureYouth.glb

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+oid sha256:ec1363c63948a23fe23173ecd0213dbab3e9e2990b9e4ea8edbe134603e6dd72
+size 15541388

examples/Astrologers.glb

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+size 13683660

examples/Sheep.glb

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+oid sha256:62d36605fca89e316e2a0d8793ecde7a7c46fbdaabc11e80c84b6895586c39fa
+size 15001112

examples/Spider.glb

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+oid sha256:0cbf4e9ec33dacd139c756431b017f7cb79e77d041a60981aa7767b3c651f6eb
+size 16531988

main.py

@@ -0,0 +1,126 @@
+import yaml
+import torch
+import os
+import argparse
+import trimesh
+import numpy as np
+from model.serializaiton import BPT_deserialize
+from model.model import MeshTransformer
+from utils import joint_filter, Dataset
+from model.data_utils import to_mesh
+
+# prepare arguments
+parser = argparse.ArgumentParser()
+parser.add_argument('--config', type=str, default='config/BPT-pc-open-8k-8-16.yaml')
+parser.add_argument('--model_path', type=str)
+parser.add_argument('--input_dir', default=None, type=str)
+parser.add_argument('--input_path', default=None, type=str)
+parser.add_argument('--out_dir', default="output", type=str)
+parser.add_argument('--input_type', choices=['mesh','pc_normal'], default='mesh')
+parser.add_argument('--output_path', type=str, default='output')
+parser.add_argument('--batch_size', type=int, default=1)
+parser.add_argument('--temperature', type=float, default=0.5)  # key sampling parameter
+parser.add_argument('--condition', type=str, default='pc')
+args = parser.parse_args()
+
+
+if __name__ == '__main__':
+    with open(args.config, "r") as f:
+        config = yaml.load(f, Loader=yaml.FullLoader)
+
+    # prepare model with fp16 precision
+    model = MeshTransformer(
+        dim = config['dim'],
+        attn_depth = config['depth'],
+        max_seq_len = config['max_seq_len'],
+        dropout = config['dropout'],
+        mode = config['mode'],
+        num_discrete_coors= 2**int(config['quant_bit']),
+        block_size = config['block_size'],
+        offset_size = config['offset_size'],
+        conditioned_on_pc = config['conditioned_on_pc'],
+        use_special_block = config['use_special_block'],
+        encoder_name = config['encoder_name'],
+        encoder_freeze = config['encoder_freeze'],
+    )
+    model.load(args.model_path)
+    model = model.eval()
+    model = model.half()
+    model = model.cuda()
+    num_params = sum([param.nelement() for param in model.decoder.parameters()])
+    print('Number of parameters: %.2f M' % (num_params / 1e6))
+    print(f'Block Size: {model.block_size} | Offset Size: {model.offset_size}')
+
+    # prepare data
+    if args.input_dir is not None:
+        input_list = sorted(os.listdir(args.input_dir))
+        if args.input_type == 'pc_normal':
+            # npy file with shape (n, 6):
+            # point_cloud (n, 3) + normal (n, 3)
+            input_list = [os.path.join(args.input_dir, x) for x in input_list if x.endswith('.npy')]
+        else:
+            # mesh file (e.g., obj, ply, glb)
+            input_list = [os.path.join(args.input_dir, x) for x in input_list]
+        dataset = Dataset(args.input_type, input_list)
+
+    elif args.input_path is not None:
+        dataset = Dataset(args.input_type, [args.input_path])
+
+    else:
+        raise ValueError("input_dir or input_path must be provided.")
+
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=args.batch_size,
+        drop_last = False,
+        shuffle = False,
+    )
+
+    os.makedirs(args.output_path, exist_ok=True)
+    with torch.no_grad():
+        for it, data in enumerate(dataloader):
+            if args.condition == 'pc':
+                # generate codes with model
+                codes = model.generate(
+                    batch_size = args.batch_size,
+                    temperature = args.temperature,
+                    pc = data['pc_normal'].cuda().half(),
+                    filter_logits_fn = joint_filter,
+                    filter_kwargs = dict(k=50, p=0.95),
+                    return_codes=True,
+                )
+
+            coords = []
+            try:
+                # decoding codes to coordinates
+                for i in range(len(codes)):
+                    code = codes[i]
+                    code = code[code != model.pad_id].cpu().numpy()
+                    vertices = BPT_deserialize(
+                        code, 
+                        block_size = model.block_size, 
+                        offset_size = model.offset_size,
+                        use_special_block = model.use_special_block,
+                    )
+                    coords.append(vertices)
+            except:
+                coords.append(np.zeros(3, 3))
+
+            # convert coordinates to mesh
+            for i in range(args.batch_size):
+                uid = data['uid'][i]
+                vertices = coords[i]
+                faces = torch.arange(1, len(vertices) + 1).view(-1, 3)
+                mesh = to_mesh(vertices, faces, transpose=False, post_process=True)
+                num_faces = len(mesh.faces)
+                # set the color for mesh
+                face_color = np.array([120, 154, 192, 255], dtype=np.uint8)
+                face_colors = np.tile(face_color, (num_faces, 1))
+                mesh.visual.face_colors = face_colors
+                mesh.export(f'{args.output_path}/{uid}_mesh.obj')
+
+                # save pc
+                if args.condition == 'pc':
+                    pcd = data['pc_normal'][i].cpu().numpy()
+                    point_cloud = trimesh.points.PointCloud(pcd[..., 0:3])
+                    point_cloud.export(f'{args.output_path}/{uid}_pc.ply', "ply")

metrics.py

@@ -0,0 +1,39 @@
+import os
+from tqdm import tqdm
+import point_cloud_utils as pcu
+from utils import sample_pc
+import argparse
+
+# prepare augments
+parser = argparse.ArgumentParser()
+parser.add_argument('--input_dir', type=str)  # directory of dense meshes
+parser.add_argument('--output_dir', type=str) # directory of generated meshes
+args = parser.parse_args()
+
+
+def main(sample_dir, ref_dir, pc_num=1024):
+    print(sample_dir, ref_dir)
+    mesh_list = [name for name in os.listdir(ref_dir) if name.endswith('.obj')]
+    
+    hausdorff_dists, chamfer_dists = [], []
+    for mesh_name in tqdm(mesh_list):
+        try:
+            # sample point cloud from input
+            uid = os.path.splitext(mesh_name)[0]
+            ref_path = os.path.join(ref_dir, uid + '.obj')
+            sample_path = os.path.join(sample_dir, uid + '.obj')
+            sample, ref = sample_pc(sample_path, pc_num), sample_pc(ref_path, pc_num)
+            
+            # compute hausdorff and chamfer distance
+            hausdorff_dist = pcu.hausdorff_distance(sample, ref)
+            chamfer_dist = pcu.chamfer_distance(sample, ref)
+            hausdorff_dists.append(hausdorff_dist)
+            chamfer_dists.append(chamfer_dist)
+        except Exception as e:
+            print(e)
+    
+    print('hausdorff distance:', sum(hausdorff_dists) / len(hausdorff_dists))
+    print('chamfer distance:', sum(chamfer_dists) / len(chamfer_dists))
+
+
+main(args.input_dir, args.output_dir)

miche/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  To protect your rights, we need to prevent others from denying you
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+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>.

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="miche/shapevae-256.ckpt", config_path="miche/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))

miche/michelangelo/__init__.py

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

miche/michelangelo/graphics/__init__.py

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

miche/michelangelo/graphics/primitives/__init__.py

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

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
+

miche/michelangelo/models/__init__.py

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

miche/michelangelo/models/modules/__init__.py

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

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.cuda.amp.custom_fwd
+    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.cuda.amp.custom_bwd
+    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

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)
+    )

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}")

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 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

miche/michelangelo/models/tsal/__init__.py

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

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

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 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

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

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 miche.michelangelo.models.modules.distributions import DiagonalGaussianDistribution
+from 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

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 miche.michelangelo.models.modules import checkpoint
+from miche.michelangelo.models.modules.embedder import FourierEmbedder
+from miche.michelangelo.models.modules.distributions import DiagonalGaussianDistribution
+from 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

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

miche/michelangelo/utils/__init__.py

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

miche/michelangelo/utils/misc.py

@@ -0,0 +1,83 @@
+# -*- coding: utf-8 -*-
+
+import importlib
+
+import torch
+import torch.distributed as dist
+
+
+
+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

miche/shapevae-256.yaml

@@ -0,0 +1,46 @@
+model:
+  target: miche.michelangelo.models.tsal.asl_pl_module.AlignedShapeAsLatentPLModule
+  params:
+    shape_module_cfg:
+      target: miche.michelangelo.models.tsal.sal_perceiver.AlignedShapeLatentPerceiver
+      params:
+        num_latents: 256
+        embed_dim: 64
+        point_feats: 3   # normal
+        num_freqs: 8
+        include_pi: false
+        heads: 12
+        width: 768
+        num_encoder_layers: 8
+        num_decoder_layers: 16
+        use_ln_post: true
+        init_scale: 0.25
+        qkv_bias: false
+        use_checkpoint: true
+    aligned_module_cfg:
+      target: miche.michelangelo.models.tsal.clip_asl_module.CLIPAlignedShapeAsLatentModule
+      params:
+        clip_model_version: "./checkpoints/clip/clip-vit-large-patch14"
+
+    loss_cfg:
+      target: miche.michelangelo.models.tsal.loss.ContrastKLNearFar
+      params:
+        contrast_weight: 0.1
+        near_weight: 0.1
+        kl_weight: 0.001
+
+    optimizer_cfg:
+      optimizer:
+        target: torch.optim.AdamW
+        params:
+          betas: [0.9, 0.99]
+          eps: 1.e-6
+          weight_decay: 1.e-2
+
+      scheduler:
+        target: miche.michelangelo.utils.trainings.lr_scheduler.LambdaWarmUpCosineFactorScheduler
+        params:
+          warm_up_steps: 5000
+          f_start: 1.e-6
+          f_min: 1.e-3
+          f_max: 1.0

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
+

model/miche_conditioner.py

@@ -0,0 +1,86 @@
+import torch
+from torch import nn
+from beartype import beartype
+from miche.encode import load_model
+
+# 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
+            config_path = 'miche/shapevae-256.yaml'
+
+            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
+    

model/model.py

@@ -0,0 +1,379 @@
+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]] = 512,  # hidden size of Transformer
+        max_seq_len = 9600,                      # max sequence length
+        flash_attn = True,                       # wether to use flash attention
+        attn_depth = 12,                         # 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.,
+        pad_id = -1,
+        coor_continuous_range = (-1., 1.),
+        num_discrete_coors = 128,
+        block_size = 8,
+        offset_size = 16,
+        mode = 'vertices',
+        special_token = -2,
+        use_special_block = False,
+        conditioned_on_pc = False,
+        encoder_name = 'miche-256-feature',
+        encoder_freeze = True,
+    ):
+        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] = None,
+        filter_logits_fn: Callable = top_k,
+        filter_kwargs: dict = dict(),
+        temperature = 1.,
+        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
+
+        # 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 for all rows to have [eos] to terminate
+
+            is_eos_codes = (codes == self.eos_token_id)
+
+            if is_eos_codes.any(dim = -1).all():
+                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

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')

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==1.26.4
+point-cloud-utils

utils.py

@@ -0,0 +1,88 @@
+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(mesh_path, pc_num, with_normal=False):
+
+    mesh = trimesh.load(mesh_path, force='mesh', process=False)
+    mesh = apply_normalize(mesh)
+    
+    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
+
+