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+                    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
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+
+                            Preamble
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+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

third_partys/Michelangelo/README.md

@@ -0,0 +1,113 @@
+# Michelangelo
+
+## [Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation](https://neuralcarver.github.io/michelangelo)<br/>
+[Zibo Zhao](https://github.com/Maikouuu),
+[Wen Liu](https://github.com/StevenLiuWen),
+[Xin Chen](https://chenxin.tech/),
+[Xianfang Zeng](https://github.com/Zzlongjuanfeng),
+[Rui Wang](https://wrong.wang/),
+[Pei Cheng](https://neuralcarver.github.io/michelangelo),
+[Bin Fu](https://neuralcarver.github.io/michelangelo),
+[Tao Chen](https://eetchen.github.io),
+[Gang Yu](https://www.skicyyu.org),
+[Shenghua Gao](https://sist.shanghaitech.edu.cn/sist_en/2020/0814/c7582a54772/page.htm)<br/>
+### [Hugging Face Demo](https://huggingface.co/spaces/Maikou/Michelangelo) | [Project Page](https://neuralcarver.github.io/michelangelo/) | [Arxiv](https://arxiv.org/abs/2306.17115) | [Paper](https://openreview.net/pdf?id=xmxgMij3LY)<br/>
+
+https://github.com/NeuralCarver/Michelangelo/assets/37449470/123bae2c-fbb1-4d63-bd13-0e300a550868
+
+Visualization of the 3D shape produced by our framework, which splits into triplets with a conditional input on the left, a normal map in the middle, and a triangle mesh on the right. The generated 3D shapes semantically conform to the visual or textural conditional inputs.<br/>
+
+## 🔆 Features
+**Michelangelo** possesses three capabilities: 
+
+1. Representing a shape into shape-image-text aligned space;
+2. Image-conditioned Shape Generation;
+3. Text-conditioned Shape Generation.
+
+<details>
+  <summary><b> Techniques </b></summary>
+
+We present a novel _alignment-before-generation_ approach to tackle the challenging task of generating general 3D shapes based on 2D images or texts. Directly learning a conditional generative model from images or texts to 3D shapes is prone to producing inconsistent results with the conditions because 3D shapes have an additional dimension whose distribution significantly differs from that of 2D images and texts. To bridge the domain gap among the three modalities and facilitate multi-modal-conditioned 3D shape generation, we explore representing 3D shapes in a shape-image-text-aligned space. Our framework comprises two models: a Shape-Image-Text-Aligned Variational Auto-Encoder (SITA-VAE) and a conditional Aligned Shape Latent Diffusion Model (ASLDM). The former model encodes the 3D shapes into the shape latent space aligned to the image and text and reconstructs the fine-grained 3D neural fields corresponding to given shape embeddings via the transformer-based decoder. The latter model learns a probabilistic mapping function from the image or text space to the latent shape space. Our extensive experiments demonstrate that our proposed approach can generate higher-quality and more diverse 3D shapes that better semantically conform to the visual or textural conditional inputs, validating the effectiveness of the shape-image-text-aligned space for cross-modality 3D shape generation.
+
+![newnetwork](https://github.com/NeuralCarver/Michelangelo/assets/16475892/d5231fb7-7768-45ee-92e1-3599a4c43a2c)
+</details>
+
+## 📰 News
+- [2024/1/23] Set up the <a href="https://huggingface.co/spaces/Maikou/Michelangelo">Hugging Face Demo</a> and release the code
+- [2023/09/22] **Michelangelo got accepted by NeurIPS 2023!**
+- [2023/6/29] Upload paper and init project
+
+## ⚙️ Setup
+
+### Installation
+Follow the command below to install the environment. We have tested the installation package on Tesla V100 and Tesla T4. 
+```
+git clone https://github.com/NeuralCarver/Michelangelo.git
+cd Michelangelo
+conda create --name Michelangelo python=3.9
+conda activate Michelangelo 
+pip install -r requirements.txt
+```
+
+### Checkpoints
+Pleasae download weights from <a href="https://huggingface.co/Maikou/Michelangelo/tree/main/checkpoints">Hugging Face Model Space</a> and put it to root folder. We have also uploaded the weights related to CLIP to facilitate quick usage.
+
+<details>
+  <summary><b>  
+    Tips for debugging configureation
+  </b></summary>
+
+- If something goes wrong in the environment configuration process unfortunately, the user may consider skipping those packages, such as pysdf, torch-cluster, and torch-scatter. These packages will not affect the execution of the commands we provide.
+- If you encounter any issues while downloading CLIP, you can consider downloading it from [CLIP's Hugging Face page](https://huggingface.co/openai/clip-vit-large-patch14). Once the download is complete, remember to modify line [26](https://github.com/NeuralCarver/Michelangelo/blob/b53fa004cd4aeb0f4eb4d159ecec8489a4450dab/configs/text_cond_diffuser_asl/text-ASLDM-256.yaml#L26C1-L26C76) and line [34](https://github.com/NeuralCarver/Michelangelo/blob/b53fa004cd4aeb0f4eb4d159ecec8489a4450dab/configs/text_cond_diffuser_asl/text-ASLDM-256.yaml#L34) in the config file for providing correct path of CLIP.
+- From [issue 6](https://github.com/NeuralCarver/Michelangelo/issues/6#issuecomment-1913513382). For Windows users, running wsl2 + ubuntu 22.04, will have issues. As discussed in [issue 786](https://github.com/microsoft/WSL/issues/8587) it is just a matter to add this in the .bashrc:
+```
+export LD_LIBRARY_PATH=/usr/lib/wsl/lib:$LD_LIBRARY_PATH.
+```
+</details>
+
+## ⚡ Quick Start
+
+### Inference
+
+#### Reconstruction a 3D shape
+```
+./scripts/inference/reconstruction.sh
+```
+
+#### Image-conditioned shape generation
+```
+./scripts/inference/image2mesh.sh
+```
+
+#### Text-conditioned shape generation
+```
+./scripts/inference/text2mesh.sh
+```
+
+#### Simply run all the scripts
+```
+./scripts/infer.sh
+```
+
+
+## ❓ FAQ
+
+## Citation
+
+If you find our code or paper helps, please consider citing:
+
+```bibtex
+@inproceedings{
+zhao2023michelangelo,
+title={Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation},
+author={Zibo Zhao and Wen Liu and Xin Chen and Xianfang Zeng and Rui Wang and Pei Cheng and BIN FU and Tao Chen and Gang YU and Shenghua Gao},
+booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
+year={2023},
+url={https://openreview.net/forum?id=xmxgMij3LY}
+}
+```
+
+## License
+
+This code is distributed under an [GPL-3.0 license](LICENSE).
+

third_partys/Michelangelo/configs/shapevae-256.yaml

@@ -0,0 +1,46 @@
+model:
+  target: third_partys.Michelangelo.michelangelo.models.tsal.asl_pl_module.AlignedShapeAsLatentPLModule
+  params:
+    shape_module_cfg:
+      target: third_partys.Michelangelo.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: third_partys.Michelangelo.michelangelo.models.tsal.clip_asl_module.CLIPAlignedShapeAsLatentModule
+      params:
+        clip_model_version: "./checkpoints/clip/clip-vit-large-patch14"
+
+    loss_cfg:
+      target: third_partys.Michelangelo.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: third_partys.Michelangelo.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

third_partys/Michelangelo/encode.py

@@ -0,0 +1,71 @@
+# -*- coding: utf-8 -*-
+import os
+import argparse
+from omegaconf import OmegaConf, DictConfig, ListConfig
+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="third_partys/Michelangelo/checkpoints/aligned_shape_latents/shapevae-256.ckpt"):
+    model_config = OmegaConf.load("third_partys/Michelangelo/configs/shapevae-256.yaml")
+    if hasattr(model_config, "model"):
+        model_config = model_config.model
+
+    model = instantiate_from_config(model_config, ckpt_path=ckpt_path)
+
+    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))

third_partys/Michelangelo/inference.py

@@ -0,0 +1,181 @@
+# -*- coding: utf-8 -*-
+import os
+import time
+from collections import OrderedDict
+from typing import Optional, List
+import argparse
+from functools import partial
+
+from einops import repeat, rearrange
+import numpy as np
+from PIL import Image
+import trimesh
+import cv2
+
+import torch
+import pytorch_lightning as pl
+
+from michelangelo.models.tsal.tsal_base import Latent2MeshOutput
+from michelangelo.models.tsal.inference_utils import extract_geometry
+from michelangelo.utils.misc import get_config_from_file, instantiate_from_config
+from michelangelo.utils.visualizers.pythreejs_viewer import PyThreeJSViewer
+from michelangelo.utils.visualizers import html_util
+
+def load_model(args):
+
+    model_config = get_config_from_file(args.config_path)
+    if hasattr(model_config, "model"):
+        model_config = model_config.model
+
+    model = instantiate_from_config(model_config, ckpt_path=args.ckpt_path)
+    model = model.cuda()
+    model = model.eval()
+
+    return model
+
+def load_surface(fp):
+    
+    with np.load(args.pointcloud_path) 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 prepare_image(args, number_samples=2):
+    
+    image = cv2.imread(f"{args.image_path}")
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    
+    image_pt = torch.tensor(image).float()
+    image_pt = image_pt / 255 * 2 - 1
+    image_pt = rearrange(image_pt, "h w c -> c h w")
+    
+    image_pt = repeat(image_pt, "c h w -> b c h w", b=number_samples)
+
+    return image_pt
+
+def save_output(args, mesh_outputs):
+    
+    os.makedirs(args.output_dir, exist_ok=True)
+    for i, mesh in enumerate(mesh_outputs):
+        mesh.mesh_f = mesh.mesh_f[:, ::-1]
+        mesh_output = trimesh.Trimesh(mesh.mesh_v, mesh.mesh_f)
+
+        name = str(i) + "_out_mesh.obj"
+        mesh_output.export(os.path.join(args.output_dir, name), include_normals=True)
+
+    print(f'-----------------------------------------------------------------------------')
+    print(f'>>> Finished and mesh saved in {args.output_dir}')
+    print(f'-----------------------------------------------------------------------------')        
+
+    return 0
+
+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)
+    
+    # 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)
+    
+    # reconstruction
+    mesh_v_f, has_surface = extract_geometry(
+        geometric_func=geometric_func,
+        device=surface.device,
+        batch_size=surface.shape[0],
+        bounds=bounds,
+        octree_depth=octree_depth,
+        num_chunks=num_chunks,
+    )
+    recon_mesh = trimesh.Trimesh(mesh_v_f[0][0], mesh_v_f[0][1])
+    
+    # save
+    os.makedirs(args.output_dir, exist_ok=True)
+    recon_mesh.export(os.path.join(args.output_dir, 'reconstruction.obj'))    
+    
+    print(f'-----------------------------------------------------------------------------')
+    print(f'>>> Finished and mesh saved in {os.path.join(args.output_dir, "reconstruction.obj")}')
+    print(f'-----------------------------------------------------------------------------')
+    
+    return 0
+
+def image2mesh(args, model, guidance_scale=7.5, box_v=1.1, octree_depth=7):
+
+    sample_inputs = {
+        "image": prepare_image(args)
+    }
+    
+    mesh_outputs = model.sample(
+        sample_inputs,
+        sample_times=1,
+        guidance_scale=guidance_scale,
+        return_intermediates=False,
+        bounds=[-box_v, -box_v, -box_v, box_v, box_v, box_v],
+        octree_depth=octree_depth,
+    )[0]
+    
+    save_output(args, mesh_outputs)
+    
+    return 0
+
+def text2mesh(args, model, num_samples=2, guidance_scale=7.5, box_v=1.1, octree_depth=7):
+
+    sample_inputs = {
+        "text": [args.text] * num_samples
+    }
+    mesh_outputs = model.sample(
+        sample_inputs,
+        sample_times=1,
+        guidance_scale=guidance_scale,
+        return_intermediates=False,
+        bounds=[-box_v, -box_v, -box_v, box_v, box_v, box_v],
+        octree_depth=octree_depth,
+    )[0]
+    
+    save_output(args, mesh_outputs)
+    
+    return 0
+
+task_dick = {
+    'reconstruction': reconstruction,
+    'image2mesh': image2mesh,
+    'text2mesh': text2mesh,
+}
+
+if __name__ == "__main__":
+    '''
+    1. Reconstruct point cloud
+    2. Image-conditioned generation
+    3. Text-conditioned generation
+    '''
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--task", type=str, choices=['reconstruction', 'image2mesh', 'text2mesh'], required=True)
+    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()
+    
+    pl.seed_everything(args.seed)
+
+    print(f'-----------------------------------------------------------------------------')
+    print(f'>>> Running {args.task}')
+    args.output_dir = os.path.join(args.output_dir, args.task)
+    print(f'>>> Output directory: {args.output_dir}')
+    print(f'-----------------------------------------------------------------------------')
+    
+    task_dick[args.task](args, load_model(args))

third_partys/Michelangelo/michelangelo/__init__.py

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

third_partys/Michelangelo/michelangelo/data/__init__.py

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

third_partys/Michelangelo/michelangelo/data/templates.json

@@ -0,0 +1,69 @@
+{
+    "shape": [
+        "a point cloud model of {}.",
+        "There is a {} in the scene.",
+        "There is the {} in the scene.",
+        "a photo of a {} in the scene.",
+        "a photo of the {} in the scene.",
+        "a photo of one {} in the scene.",
+        "itap of a {}.",
+        "itap of my {}.",
+        "itap of the {}.",
+        "a photo of a {}.",
+        "a photo of my {}.",
+        "a photo of the {}.",
+        "a photo of one {}.",
+        "a photo of many {}.",
+        "a good photo of a {}.",
+        "a good photo of the {}.",
+        "a bad photo of a {}.",
+        "a bad photo of the {}.",
+        "a photo of a nice {}.",
+        "a photo of the nice {}.",
+        "a photo of a cool {}.",
+        "a photo of the cool {}.",
+        "a photo of a weird {}.",
+        "a photo of the weird {}.",
+        "a photo of a small {}.",
+        "a photo of the small {}.",
+        "a photo of a large {}.",
+        "a photo of the large {}.",
+        "a photo of a clean {}.",
+        "a photo of the clean {}.",
+        "a photo of a dirty {}.",
+        "a photo of the dirty {}.",
+        "a bright photo of a {}.",
+        "a bright photo of the {}.",
+        "a dark photo of a {}.",
+        "a dark photo of the {}.",
+        "a photo of a hard to see {}.",
+        "a photo of the hard to see {}.",
+        "a low resolution photo of a {}.",
+        "a low resolution photo of the {}.",
+        "a cropped photo of a {}.",
+        "a cropped photo of the {}.",
+        "a close-up photo of a {}.",
+        "a close-up photo of the {}.",
+        "a jpeg corrupted photo of a {}.",
+        "a jpeg corrupted photo of the {}.",
+        "a blurry photo of a {}.",
+        "a blurry photo of the {}.",
+        "a pixelated photo of a {}.",
+        "a pixelated photo of the {}.",
+        "a black and white photo of the {}.",
+        "a black and white photo of a {}",
+        "a plastic {}.",
+        "the plastic {}.",
+        "a toy {}.",
+        "the toy {}.",
+        "a plushie {}.",
+        "the plushie {}.",
+        "a cartoon {}.",
+        "the cartoon {}.",
+        "an embroidered {}.",
+        "the embroidered {}.",
+        "a painting of the {}.",
+        "a painting of a {}."
+    ]
+
+}

third_partys/Michelangelo/michelangelo/data/transforms.py

@@ -0,0 +1,407 @@
+# -*- coding: utf-8 -*-
+import os
+import time
+import numpy as np
+import warnings
+import random
+from omegaconf.listconfig import ListConfig
+from webdataset import pipelinefilter
+import torch
+import torchvision.transforms.functional as TVF
+from torchvision.transforms import InterpolationMode
+from torchvision.transforms.transforms import _interpolation_modes_from_int
+from typing import Sequence
+
+from third_partys.miche.michelangelo.utils import instantiate_from_config
+
+
+def _uid_buffer_pick(buf_dict, rng):
+    uid_keys = list(buf_dict.keys())
+    selected_uid = rng.choice(uid_keys)
+    buf = buf_dict[selected_uid]
+
+    k = rng.randint(0, len(buf) - 1)
+    sample = buf[k]
+    buf[k] = buf[-1]
+    buf.pop()
+
+    if len(buf) == 0:
+        del buf_dict[selected_uid]
+
+    return sample
+
+
+def _add_to_buf_dict(buf_dict, sample):
+    key = sample["__key__"]
+    uid, uid_sample_id = key.split("_")
+    if uid not in buf_dict:
+        buf_dict[uid] = []
+    buf_dict[uid].append(sample)
+
+    return buf_dict
+
+
+def _uid_shuffle(data, bufsize=1000, initial=100, rng=None, handler=None):
+    """Shuffle the data in the stream.
+
+    This uses a buffer of size `bufsize`. Shuffling at
+    startup is less random; this is traded off against
+    yielding samples quickly.
+
+    data: iterator
+    bufsize: buffer size for shuffling
+    returns: iterator
+    rng: either random module or random.Random instance
+
+    """
+    if rng is None:
+        rng = random.Random(int((os.getpid() + time.time()) * 1e9))
+    initial = min(initial, bufsize)
+    buf_dict = dict()
+    current_samples = 0
+    for sample in data:
+        _add_to_buf_dict(buf_dict, sample)
+        current_samples += 1
+
+        if current_samples < bufsize:
+            try:
+                _add_to_buf_dict(buf_dict, next(data))  # skipcq: PYL-R1708
+                current_samples += 1
+            except StopIteration:
+                pass
+
+        if current_samples >= initial:
+            current_samples -= 1
+            yield _uid_buffer_pick(buf_dict, rng)
+
+    while current_samples > 0:
+        current_samples -= 1
+        yield _uid_buffer_pick(buf_dict, rng)
+
+
+uid_shuffle = pipelinefilter(_uid_shuffle)
+
+
+class RandomSample(object):
+    def __init__(self,
+                 num_volume_samples: int = 1024,
+                 num_near_samples: int = 1024):
+
+        super().__init__()
+
+        self.num_volume_samples = num_volume_samples
+        self.num_near_samples = num_near_samples
+
+    def __call__(self, sample):
+        rng = np.random.default_rng()
+
+        # 1. sample surface input
+        total_surface = sample["surface"]
+        ind = rng.choice(total_surface.shape[0], replace=False)
+        surface = total_surface[ind]
+
+        # 2. sample volume/near geometric points
+        vol_points = sample["vol_points"]
+        vol_label = sample["vol_label"]
+        near_points = sample["near_points"]
+        near_label = sample["near_label"]
+
+        ind = rng.choice(vol_points.shape[0], self.num_volume_samples, replace=False)
+        vol_points = vol_points[ind]
+        vol_label = vol_label[ind]
+        vol_points_labels = np.concatenate([vol_points, vol_label[:, np.newaxis]], axis=1)
+
+        ind = rng.choice(near_points.shape[0], self.num_near_samples, replace=False)
+        near_points = near_points[ind]
+        near_label = near_label[ind]
+        near_points_labels = np.concatenate([near_points, near_label[:, np.newaxis]], axis=1)
+
+        # concat sampled volume and near points
+        geo_points = np.concatenate([vol_points_labels, near_points_labels], axis=0)
+
+        sample = {
+            "surface": surface,
+            "geo_points": geo_points
+        }
+
+        return sample
+
+
+class SplitRandomSample(object):
+    def __init__(self,
+                 use_surface_sample: bool = False,
+                 num_surface_samples: int = 4096,
+                 num_volume_samples: int = 1024,
+                 num_near_samples: int = 1024):
+
+        super().__init__()
+
+        self.use_surface_sample = use_surface_sample
+        self.num_surface_samples = num_surface_samples
+        self.num_volume_samples = num_volume_samples
+        self.num_near_samples = num_near_samples
+
+    def __call__(self, sample):
+
+        rng = np.random.default_rng()
+
+        # 1. sample surface input
+        surface = sample["surface"]
+
+        if self.use_surface_sample:
+            replace = surface.shape[0] < self.num_surface_samples
+            ind = rng.choice(surface.shape[0], self.num_surface_samples, replace=replace)
+            surface = surface[ind]
+
+        # 2. sample volume/near geometric points
+        vol_points = sample["vol_points"]
+        vol_label = sample["vol_label"]
+        near_points = sample["near_points"]
+        near_label = sample["near_label"]
+
+        ind = rng.choice(vol_points.shape[0], self.num_volume_samples, replace=False)
+        vol_points = vol_points[ind]
+        vol_label = vol_label[ind]
+        vol_points_labels = np.concatenate([vol_points, vol_label[:, np.newaxis]], axis=1)
+
+        ind = rng.choice(near_points.shape[0], self.num_near_samples, replace=False)
+        near_points = near_points[ind]
+        near_label = near_label[ind]
+        near_points_labels = np.concatenate([near_points, near_label[:, np.newaxis]], axis=1)
+
+        # concat sampled volume and near points
+        geo_points = np.concatenate([vol_points_labels, near_points_labels], axis=0)
+
+        sample = {
+            "surface": surface,
+            "geo_points": geo_points
+        }
+
+        return sample
+
+
+class FeatureSelection(object):
+
+    VALID_SURFACE_FEATURE_DIMS = {
+        "none": [0, 1, 2],                              # xyz
+        "watertight_normal": [0, 1, 2, 3, 4, 5],        # xyz, normal
+        "normal": [0, 1, 2, 6, 7, 8]
+    }
+
+    def __init__(self, surface_feature_type: str):
+
+        self.surface_feature_type = surface_feature_type
+        self.surface_dims = self.VALID_SURFACE_FEATURE_DIMS[surface_feature_type]
+
+    def __call__(self, sample):
+        sample["surface"] = sample["surface"][:, self.surface_dims]
+        return sample
+
+
+class AxisScaleTransform(object):
+    def __init__(self, interval=(0.75, 1.25), jitter=True, jitter_scale=0.005):
+        assert isinstance(interval, (tuple, list, ListConfig))
+        self.interval = interval
+        self.min_val = interval[0]
+        self.max_val = interval[1]
+        self.inter_size = interval[1] - interval[0]
+        self.jitter = jitter
+        self.jitter_scale = jitter_scale
+
+    def __call__(self, sample):
+
+        surface = sample["surface"][..., 0:3]
+        geo_points = sample["geo_points"][..., 0:3]
+
+        scaling = torch.rand(1, 3) * self.inter_size + self.min_val
+        # print(scaling)
+        surface = surface * scaling
+        geo_points = geo_points * scaling
+
+        scale = (1 / torch.abs(surface).max().item()) * 0.999999
+        surface *= scale
+        geo_points *= scale
+
+        if self.jitter:
+            surface += self.jitter_scale * torch.randn_like(surface)
+            surface.clamp_(min=-1.015, max=1.015)
+
+        sample["surface"][..., 0:3] = surface
+        sample["geo_points"][..., 0:3] = geo_points
+
+        return sample
+
+
+class ToTensor(object):
+
+    def __init__(self, tensor_keys=("surface", "geo_points", "tex_points")):
+        self.tensor_keys = tensor_keys
+
+    def __call__(self, sample):
+        for key in self.tensor_keys:
+            if key not in sample:
+                continue
+
+            sample[key] = torch.tensor(sample[key], dtype=torch.float32)
+
+        return sample
+
+
+class AxisScale(object):
+    def __init__(self, interval=(0.75, 1.25), jitter=True, jitter_scale=0.005):
+        assert isinstance(interval, (tuple, list, ListConfig))
+        self.interval = interval
+        self.jitter = jitter
+        self.jitter_scale = jitter_scale
+
+    def __call__(self, surface, *args):
+        scaling = torch.rand(1, 3) * 0.5 + 0.75
+        # print(scaling)
+        surface = surface * scaling
+        scale = (1 / torch.abs(surface).max().item()) * 0.999999
+        surface *= scale
+
+        args_outputs = []
+        for _arg in args:
+            _arg = _arg * scaling * scale
+            args_outputs.append(_arg)
+
+        if self.jitter:
+            surface += self.jitter_scale * torch.randn_like(surface)
+            surface.clamp_(min=-1, max=1)
+
+        if len(args) == 0:
+            return surface
+        else:
+            return surface, *args_outputs
+
+
+class RandomResize(torch.nn.Module):
+    """Apply randomly Resize with a given probability."""
+
+    def __init__(
+        self,
+        size,
+        resize_radio=(0.5, 1),
+        allow_resize_interpolations=(InterpolationMode.BICUBIC, InterpolationMode.BILINEAR, InterpolationMode.BILINEAR),
+        interpolation=InterpolationMode.BICUBIC,
+        max_size=None,
+        antialias=None,
+    ):
+        super().__init__()
+        if not isinstance(size, (int, Sequence)):
+            raise TypeError(f"Size should be int or sequence. Got {type(size)}")
+        if isinstance(size, Sequence) and len(size) not in (1, 2):
+            raise ValueError("If size is a sequence, it should have 1 or 2 values")
+
+        self.size = size
+        self.max_size = max_size
+        # Backward compatibility with integer value
+        if isinstance(interpolation, int):
+            warnings.warn(
+                "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+                "Please use InterpolationMode enum."
+            )
+            interpolation = _interpolation_modes_from_int(interpolation)
+
+        self.interpolation = interpolation
+        self.antialias = antialias
+
+        self.resize_radio = resize_radio
+        self.allow_resize_interpolations = allow_resize_interpolations
+
+    def random_resize_params(self):
+        radio = torch.rand(1) * (self.resize_radio[1] - self.resize_radio[0]) + self.resize_radio[0]
+
+        if isinstance(self.size, int):
+            size = int(self.size * radio)
+        elif isinstance(self.size, Sequence):
+            size = list(self.size)
+            size = (int(size[0] * radio), int(size[1] * radio))
+        else:
+            raise RuntimeError()
+
+        interpolation = self.allow_resize_interpolations[
+            torch.randint(low=0, high=len(self.allow_resize_interpolations), size=(1,))
+        ]
+        return size, interpolation
+
+    def forward(self, img):
+        size, interpolation = self.random_resize_params()
+        img = TVF.resize(img, size, interpolation, self.max_size, self.antialias)
+        img = TVF.resize(img, self.size, self.interpolation, self.max_size, self.antialias)
+        return img
+
+    def __repr__(self) -> str:
+        detail = f"(size={self.size}, interpolation={self.interpolation.value},"
+        detail += f"max_size={self.max_size}, antialias={self.antialias}), resize_radio={self.resize_radio}"
+        return f"{self.__class__.__name__}{detail}"
+
+
+class Compose(object):
+    """Composes several transforms together. This transform does not support torchscript.
+    Please, see the note below.
+
+    Args:
+        transforms (list of ``Transform`` objects): list of transforms to compose.
+
+    Example:
+        >>> transforms.Compose([
+        >>>     transforms.CenterCrop(10),
+        >>>     transforms.ToTensor(),
+        >>> ])
+
+    .. note::
+        In order to script the transformations, please use ``torch.nn.Sequential`` as below.
+
+        >>> transforms = torch.nn.Sequential(
+        >>>     transforms.CenterCrop(10),
+        >>>     transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+        >>> )
+        >>> scripted_transforms = torch.jit.script(transforms)
+
+        Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require
+        `lambda` functions or ``PIL.Image``.
+
+    """
+
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, *args):
+        for t in self.transforms:
+            args = t(*args)
+        return args
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + '('
+        for t in self.transforms:
+            format_string += '\n'
+            format_string += '    {0}'.format(t)
+        format_string += '\n)'
+        return format_string
+
+
+def identity(*args, **kwargs):
+    if len(args) == 1:
+        return args[0]
+    else:
+        return args
+
+
+def build_transforms(cfg):
+
+    if cfg is None:
+        return identity
+
+    transforms = []
+
+    for transform_name, cfg_instance in cfg.items():
+        transform_instance = instantiate_from_config(cfg_instance)
+        transforms.append(transform_instance)
+        print(f"Build transform: {transform_instance}")
+
+    transforms = Compose(transforms)
+
+    return transforms
+

third_partys/Michelangelo/michelangelo/data/utils.py

@@ -0,0 +1,59 @@
+# -*- coding: utf-8 -*-
+
+import torch
+import numpy as np
+
+
+def worker_init_fn(_):
+    worker_info = torch.utils.data.get_worker_info()
+    worker_id = worker_info.id
+
+    # dataset = worker_info.dataset
+    # split_size = dataset.num_records // worker_info.num_workers
+    # # reset num_records to the true number to retain reliable length information
+    # dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size]
+    # current_id = np.random.choice(len(np.random.get_state()[1]), 1)
+    # return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
+
+    return np.random.seed(np.random.get_state()[1][0] + worker_id)
+
+
+def collation_fn(samples, combine_tensors=True, combine_scalars=True):
+    """
+
+    Args:
+        samples (list[dict]):
+        combine_tensors:
+        combine_scalars:
+
+    Returns:
+
+    """
+
+    result = {}
+
+    keys = samples[0].keys()
+
+    for key in keys:
+        result[key] = []
+
+    for sample in samples:
+        for key in keys:
+            val = sample[key]
+            result[key].append(val)
+
+    for key in keys:
+        val_list = result[key]
+        if isinstance(val_list[0], (int, float)):
+            if combine_scalars:
+                result[key] = np.array(result[key])
+
+        elif isinstance(val_list[0], torch.Tensor):
+            if combine_tensors:
+                result[key] = torch.stack(val_list)
+
+        elif isinstance(val_list[0], np.ndarray):
+            if combine_tensors:
+                result[key] = np.stack(val_list)
+
+    return result

third_partys/Michelangelo/michelangelo/graphics/__init__.py

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

third_partys/Michelangelo/michelangelo/graphics/primitives/__init__.py

@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+
+from .volume import generate_dense_grid_points
+
+from .mesh import (
+    MeshOutput,
+    save_obj,
+    savemeshtes2
+)

third_partys/Michelangelo/michelangelo/graphics/primitives/mesh.py

@@ -0,0 +1,114 @@
+# -*- coding: utf-8 -*-
+
+import os
+import cv2
+import numpy as np
+import PIL.Image
+from typing import Optional
+
+import trimesh
+
+
+def save_obj(pointnp_px3, facenp_fx3, fname):
+    fid = open(fname, "w")
+    write_str = ""
+    for pidx, p in enumerate(pointnp_px3):
+        pp = p
+        write_str += "v %f %f %f\n" % (pp[0], pp[1], pp[2])
+
+    for i, f in enumerate(facenp_fx3):
+        f1 = f + 1
+        write_str += "f %d %d %d\n" % (f1[0], f1[1], f1[2])
+    fid.write(write_str)
+    fid.close()
+    return
+
+
+def savemeshtes2(pointnp_px3, tcoords_px2, facenp_fx3, facetex_fx3, tex_map, fname):
+    fol, na = os.path.split(fname)
+    na, _ = os.path.splitext(na)
+
+    matname = "%s/%s.mtl" % (fol, na)
+    fid = open(matname, "w")
+    fid.write("newmtl material_0\n")
+    fid.write("Kd 1 1 1\n")
+    fid.write("Ka 0 0 0\n")
+    fid.write("Ks 0.4 0.4 0.4\n")
+    fid.write("Ns 10\n")
+    fid.write("illum 2\n")
+    fid.write("map_Kd %s.png\n" % na)
+    fid.close()
+    ####
+
+    fid = open(fname, "w")
+    fid.write("mtllib %s.mtl\n" % na)
+
+    for pidx, p in enumerate(pointnp_px3):
+        pp = p
+        fid.write("v %f %f %f\n" % (pp[0], pp[1], pp[2]))
+
+    for pidx, p in enumerate(tcoords_px2):
+        pp = p
+        fid.write("vt %f %f\n" % (pp[0], pp[1]))
+
+    fid.write("usemtl material_0\n")
+    for i, f in enumerate(facenp_fx3):
+        f1 = f + 1
+        f2 = facetex_fx3[i] + 1
+        fid.write("f %d/%d %d/%d %d/%d\n" % (f1[0], f2[0], f1[1], f2[1], f1[2], f2[2]))
+    fid.close()
+
+    PIL.Image.fromarray(np.ascontiguousarray(tex_map), "RGB").save(
+        os.path.join(fol, "%s.png" % na))
+
+    return
+
+
+class MeshOutput(object):
+
+    def __init__(self,
+                 mesh_v: np.ndarray,
+                 mesh_f: np.ndarray,
+                 vertex_colors: Optional[np.ndarray] = None,
+                 uvs: Optional[np.ndarray] = None,
+                 mesh_tex_idx: Optional[np.ndarray] = None,
+                 tex_map: Optional[np.ndarray] = None):
+
+        self.mesh_v = mesh_v
+        self.mesh_f = mesh_f
+        self.vertex_colors = vertex_colors
+        self.uvs = uvs
+        self.mesh_tex_idx = mesh_tex_idx
+        self.tex_map = tex_map
+
+    def contain_uv_texture(self):
+        return (self.uvs is not None) and (self.mesh_tex_idx is not None) and (self.tex_map is not None)
+
+    def contain_vertex_colors(self):
+        return self.vertex_colors is not None
+
+    def export(self, fname):
+
+        if self.contain_uv_texture():
+            savemeshtes2(
+                self.mesh_v,
+                self.uvs,
+                self.mesh_f,
+                self.mesh_tex_idx,
+                self.tex_map,
+                fname
+            )
+
+        elif self.contain_vertex_colors():
+            mesh_obj = trimesh.Trimesh(vertices=self.mesh_v, faces=self.mesh_f, vertex_colors=self.vertex_colors)
+            mesh_obj.export(fname)
+
+        else:
+            save_obj(
+                self.mesh_v,
+                self.mesh_f,
+                fname
+            )
+
+
+

third_partys/Michelangelo/michelangelo/graphics/primitives/volume.py

@@ -0,0 +1,21 @@
+# -*- coding: utf-8 -*-
+
+import numpy as np
+
+
+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
+

third_partys/Michelangelo/michelangelo/models/__init__.py

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

third_partys/Michelangelo/michelangelo/models/asl_diffusion/__init__.py

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

third_partys/Michelangelo/michelangelo/models/asl_diffusion/asl_diffuser_pl_module.py

@@ -0,0 +1,482 @@
+# -*- coding: utf-8 -*-
+
+from omegaconf import DictConfig
+from typing import List, Tuple, Dict, Optional, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.optim import lr_scheduler
+import pytorch_lightning as pl
+from pytorch_lightning.utilities import rank_zero_only
+
+from einops import rearrange
+
+from diffusers.schedulers import (
+    DDPMScheduler,
+    DDIMScheduler,
+    KarrasVeScheduler,
+    DPMSolverMultistepScheduler
+)
+
+from third_partys.Michelangelo.michelangelo.utils import instantiate_from_config
+from third_partys.Michelangelo.michelangelo.models.tsal.tsal_base import AlignedShapeAsLatentPLModule
+from third_partys.Michelangelo.michelangelo.models.asl_diffusion.inference_utils import ddim_sample
+
+SchedulerType = Union[DDIMScheduler, KarrasVeScheduler, DPMSolverMultistepScheduler]
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+class ASLDiffuser(pl.LightningModule):
+    first_stage_model: Optional[AlignedShapeAsLatentPLModule]
+    # cond_stage_model: Optional[Union[nn.Module, pl.LightningModule]]
+    model: nn.Module
+
+    def __init__(self, *,
+                 first_stage_config,
+                 denoiser_cfg,
+                 scheduler_cfg,
+                 optimizer_cfg,
+                 loss_cfg,
+                 first_stage_key: str = "surface",
+                 cond_stage_key: str = "image",
+                 cond_stage_trainable: bool = True,
+                 scale_by_std: bool = False,
+                 z_scale_factor: float = 1.0,
+                 ckpt_path: Optional[str] = None,
+                 ignore_keys: Union[Tuple[str], List[str]] = ()):
+
+        super().__init__()
+
+        self.first_stage_key = first_stage_key
+        self.cond_stage_key = cond_stage_key
+        self.cond_stage_trainable = cond_stage_trainable
+
+        # 1. initialize first stage. 
+        # Note: the condition model contained in the first stage model.
+        self.first_stage_config = first_stage_config
+        self.first_stage_model = None
+        # self.instantiate_first_stage(first_stage_config)
+
+        # 2. initialize conditional stage
+        # self.instantiate_cond_stage(cond_stage_config)
+        self.cond_stage_model = {
+            "image": self.encode_image,
+            "image_unconditional_embedding": self.empty_img_cond,
+            "text": self.encode_text,
+            "text_unconditional_embedding": self.empty_text_cond,
+            "surface": self.encode_surface,
+            "surface_unconditional_embedding": self.empty_surface_cond,
+        }
+
+        # 3. diffusion model
+        self.model = instantiate_from_config(
+            denoiser_cfg, device=None, dtype=None
+        )
+
+        self.optimizer_cfg = optimizer_cfg
+
+        # 4. scheduling strategy
+        self.scheduler_cfg = scheduler_cfg
+
+        self.noise_scheduler: DDPMScheduler = instantiate_from_config(scheduler_cfg.noise)
+        self.denoise_scheduler: SchedulerType = instantiate_from_config(scheduler_cfg.denoise)
+
+        # 5. loss configures
+        self.loss_cfg = loss_cfg
+
+        self.scale_by_std = scale_by_std
+        if scale_by_std:
+            self.register_buffer("z_scale_factor", torch.tensor(z_scale_factor))
+        else:
+            self.z_scale_factor = z_scale_factor
+
+        self.ckpt_path = ckpt_path
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+
+    def instantiate_first_stage(self, config):
+        model = instantiate_from_config(config)
+        self.first_stage_model = model.eval()
+        self.first_stage_model.train = disabled_train
+        for param in self.first_stage_model.parameters():
+            param.requires_grad = False
+
+        self.first_stage_model = self.first_stage_model.to(self.device)
+
+    # def instantiate_cond_stage(self, config):
+    #     if not self.cond_stage_trainable:
+    #         if config == "__is_first_stage__":
+    #             print("Using first stage also as cond stage.")
+    #             self.cond_stage_model = self.first_stage_model
+    #         elif config == "__is_unconditional__":
+    #             print(f"Training {self.__class__.__name__} as an unconditional model.")
+    #             self.cond_stage_model = None
+    #             # self.be_unconditional = True
+    #         else:
+    #             model = instantiate_from_config(config)
+    #             self.cond_stage_model = model.eval()
+    #             self.cond_stage_model.train = disabled_train
+    #             for param in self.cond_stage_model.parameters():
+    #                 param.requires_grad = False
+    #     else:
+    #         assert config != "__is_first_stage__"
+    #         assert config != "__is_unconditional__"
+    #         model = instantiate_from_config(config)
+    #         self.cond_stage_model = model
+
+    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}")
+
+    @property
+    def zero_rank(self):
+        if self._trainer:
+            zero_rank = self.trainer.local_rank == 0
+        else:
+            zero_rank = True
+
+        return zero_rank
+
+    def configure_optimizers(self) -> Tuple[List, List]:
+
+        lr = self.learning_rate
+
+        trainable_parameters = list(self.model.parameters())
+        # if the conditional encoder is trainable
+
+        # if self.cond_stage_trainable:
+        #     conditioner_params = [p for p in self.cond_stage_model.parameters() if p.requires_grad]
+        #     trainable_parameters += conditioner_params
+        #     print(f"number of trainable conditional parameters: {len(conditioner_params)}.")
+
+        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
+
+    @torch.no_grad()
+    def encode_text(self, text):
+
+        b = text.shape[0]
+        text_tokens = rearrange(text, "b t l -> (b t) l")
+        text_embed = self.first_stage_model.model.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)
+
+        return text_embed
+
+    @torch.no_grad()
+    def encode_image(self, img):
+
+        return self.first_stage_model.model.encode_image_embed(img)
+
+    @torch.no_grad()
+    def encode_surface(self, surface):
+
+        return self.first_stage_model.model.encode_shape_embed(surface, return_latents=False)
+
+    @torch.no_grad()
+    def empty_text_cond(self, cond):
+
+        return torch.zeros_like(cond, device=cond.device)
+
+    @torch.no_grad()
+    def empty_img_cond(self, cond):
+
+        return torch.zeros_like(cond, device=cond.device)
+
+    @torch.no_grad()
+    def empty_surface_cond(self, cond):
+
+        return torch.zeros_like(cond, device=cond.device)
+
+    @torch.no_grad()
+    def encode_first_stage(self, surface: torch.FloatTensor, sample_posterior=True):
+
+        z_q = self.first_stage_model.encode(surface, sample_posterior)
+        z_q = self.z_scale_factor * z_q
+
+        return z_q
+
+    @torch.no_grad()
+    def decode_first_stage(self, z_q: torch.FloatTensor, **kwargs):
+
+        z_q = 1. / self.z_scale_factor * z_q
+        latents = self.first_stage_model.decode(z_q, **kwargs)
+        return latents
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_start(self, batch, batch_idx):
+        # only for very first batch
+        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 \
+                and batch_idx == 0 and self.ckpt_path is None:
+            # set rescale weight to 1./std of encodings
+            print("### USING STD-RESCALING ###")
+
+            z_q = self.encode_first_stage(batch[self.first_stage_key])
+            z = z_q.detach()
+
+            del self.z_scale_factor
+            self.register_buffer("z_scale_factor", 1. / z.flatten().std())
+            print(f"setting self.z_scale_factor to {self.z_scale_factor}")
+
+            print("### USING STD-RESCALING ###")
+
+    def compute_loss(self, model_outputs, split):
+        """
+
+        Args:
+            model_outputs (dict):
+                - x_0:
+                - noise:
+                - noise_prior:
+                - noise_pred:
+                - noise_pred_prior:
+
+            split (str):
+
+        Returns:
+
+        """
+
+        pred = model_outputs["pred"]
+
+        if self.noise_scheduler.prediction_type == "epsilon":
+            target = model_outputs["noise"]
+        elif self.noise_scheduler.prediction_type == "sample":
+            target = model_outputs["x_0"]
+        else:
+            raise NotImplementedError(f"Prediction Type: {self.noise_scheduler.prediction_type} not yet supported.")
+
+        if self.loss_cfg.loss_type == "l1":
+            simple = F.l1_loss(pred, target, reduction="mean")
+        elif self.loss_cfg.loss_type in ["mse", "l2"]:
+            simple = F.mse_loss(pred, target, reduction="mean")
+        else:
+            raise NotImplementedError(f"Loss Type: {self.loss_cfg.loss_type} not yet supported.")
+
+        total_loss = simple
+
+        loss_dict = {
+            f"{split}/total_loss": total_loss.clone().detach(),
+            f"{split}/simple": simple.detach(),
+        }
+
+        return total_loss, loss_dict
+
+    def forward(self, batch):
+        """
+
+        Args:
+            batch:
+
+        Returns:
+
+        """
+
+        if self.first_stage_model is None:
+            self.instantiate_first_stage(self.first_stage_config)
+
+        latents = self.encode_first_stage(batch[self.first_stage_key])
+
+        # conditions = self.cond_stage_model.encode(batch[self.cond_stage_key])
+
+        conditions = self.cond_stage_model[self.cond_stage_key](batch[self.cond_stage_key]).unsqueeze(1)
+
+        mask = torch.rand((len(conditions), 1, 1), device=conditions.device, dtype=conditions.dtype) >= 0.1
+        conditions = conditions * mask.to(conditions)
+
+        # Sample noise that we"ll add to the latents
+        # [batch_size, n_token, latent_dim]
+        noise = torch.randn_like(latents)
+        bs = latents.shape[0]
+        # Sample a random timestep for each motion
+        timesteps = torch.randint(
+            0,
+            self.noise_scheduler.config.num_train_timesteps,
+            (bs,),
+            device=latents.device,
+        )
+        timesteps = timesteps.long()
+        # Add noise to the latents according to the noise magnitude at each timestep
+        noisy_z = self.noise_scheduler.add_noise(latents, noise, timesteps)
+
+        # diffusion model forward
+        noise_pred = self.model(noisy_z, timesteps, conditions)
+
+        diffusion_outputs = {
+            "x_0": noisy_z,
+            "noise": noise,
+            "pred": noise_pred
+        }
+
+        return diffusion_outputs
+
+    def training_step(self, batch: Dict[str, Union[torch.FloatTensor, List[str]]],
+                      batch_idx: int, optimizer_idx: int = 0) -> torch.FloatTensor:
+        """
+
+        Args:
+            batch (dict): the batch sample, and it contains:
+                - surface (torch.FloatTensor):
+                - image (torch.FloatTensor): if provide, [bs, 3, h, w], item range [0, 1]
+                - depth (torch.FloatTensor): if provide, [bs, 1, h, w], item range [-1, 1]
+                - normal (torch.FloatTensor): if provide, [bs, 3, h, w], item range [-1, 1]
+                - text (list of str):
+
+            batch_idx (int):
+
+            optimizer_idx (int):
+
+        Returns:
+            loss (torch.FloatTensor):
+
+        """
+
+        diffusion_outputs = self(batch)
+
+        loss, loss_dict = self.compute_loss(diffusion_outputs, "train")
+        self.log_dict(loss_dict, prog_bar=True, logger=True, sync_dist=False, rank_zero_only=True)
+
+        return loss
+
+    def validation_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_pc (torch.FloatTensor): [n_pts, 4]
+                - surface_feats (torch.FloatTensor): [n_pts, c]
+                - text (list of str):
+
+            batch_idx (int):
+
+            optimizer_idx (int):
+
+        Returns:
+            loss (torch.FloatTensor):
+
+        """
+
+        diffusion_outputs = self(batch)
+
+        loss, loss_dict = self.compute_loss(diffusion_outputs, "val")
+        self.log_dict(loss_dict, prog_bar=True, logger=True, sync_dist=False, rank_zero_only=True)
+
+        return loss
+
+    @torch.no_grad()
+    def sample(self,
+               batch: Dict[str, Union[torch.FloatTensor, List[str]]],
+               sample_times: int = 1,
+               steps: Optional[int] = None,
+               guidance_scale: Optional[float] = None,
+               eta: float = 0.0,
+               return_intermediates: bool = False, **kwargs):
+
+        if self.first_stage_model is None:
+            self.instantiate_first_stage(self.first_stage_config)
+
+        if steps is None:
+            steps = self.scheduler_cfg.num_inference_steps
+
+        if guidance_scale is None:
+            guidance_scale = self.scheduler_cfg.guidance_scale
+        do_classifier_free_guidance = guidance_scale > 0
+
+        # conditional encode
+        xc = batch[self.cond_stage_key]
+        # cond = self.cond_stage_model[self.cond_stage_key](xc)
+        cond = self.cond_stage_model[self.cond_stage_key](xc).unsqueeze(1)
+
+        if do_classifier_free_guidance:
+            """
+            Note: There are two kinds of uncond for text. 
+            1: using "" as uncond text; (in SAL diffusion)
+            2: zeros_like(cond) as uncond text; (in MDM)
+            """
+            # un_cond = self.cond_stage_model.unconditional_embedding(batch_size=len(xc))
+            un_cond = self.cond_stage_model[f"{self.cond_stage_key}_unconditional_embedding"](cond)
+            # un_cond = torch.zeros_like(cond, device=cond.device)
+            cond = torch.cat([un_cond, cond], dim=0)
+
+        outputs = []
+        latents = None
+
+        if not return_intermediates:
+            for _ in range(sample_times):
+                sample_loop = ddim_sample(
+                    self.denoise_scheduler,
+                    self.model,
+                    shape=self.first_stage_model.latent_shape,
+                    cond=cond,
+                    steps=steps,
+                    guidance_scale=guidance_scale,
+                    do_classifier_free_guidance=do_classifier_free_guidance,
+                    device=self.device,
+                    eta=eta,
+                    disable_prog=not self.zero_rank
+                )
+                for sample, t in sample_loop:
+                    latents = sample
+                outputs.append(self.decode_first_stage(latents, **kwargs))
+        else:
+
+            sample_loop = ddim_sample(
+                self.denoise_scheduler,
+                self.model,
+                shape=self.first_stage_model.latent_shape,
+                cond=cond,
+                steps=steps,
+                guidance_scale=guidance_scale,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+                device=self.device,
+                eta=eta,
+                disable_prog=not self.zero_rank
+            )
+
+            iter_size = steps // sample_times
+            i = 0
+            for sample, t in sample_loop:
+                latents = sample
+                if i % iter_size == 0 or i == steps - 1:
+                    outputs.append(self.decode_first_stage(latents, **kwargs))
+                i += 1
+
+        return outputs

third_partys/Michelangelo/michelangelo/models/asl_diffusion/asl_udt.py

@@ -0,0 +1,104 @@
+# -*- coding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+from typing import Optional
+from diffusers.models.embeddings import Timesteps
+import math
+
+from third_partys.Michelangelo.michelangelo.models.modules.transformer_blocks import MLP
+from third_partys.Michelangelo.michelangelo.models.modules.diffusion_transformer import UNetDiffusionTransformer
+
+
+class ConditionalASLUDTDenoiser(nn.Module):
+
+    def __init__(self, *,
+                 device: Optional[torch.device],
+                 dtype: Optional[torch.dtype],
+                 input_channels: int,
+                 output_channels: int,
+                 n_ctx: int,
+                 width: int,
+                 layers: int,
+                 heads: int,
+                 context_dim: int,
+                 context_ln: bool = True,
+                 skip_ln: bool = False,
+                 init_scale: float = 0.25,
+                 flip_sin_to_cos: bool = False,
+                 use_checkpoint: bool = False):
+        super().__init__()
+
+        self.use_checkpoint = use_checkpoint
+
+        init_scale = init_scale * math.sqrt(1.0 / width)
+
+        self.backbone = UNetDiffusionTransformer(
+            device=device,
+            dtype=dtype,
+            n_ctx=n_ctx,
+            width=width,
+            layers=layers,
+            heads=heads,
+            skip_ln=skip_ln,
+            init_scale=init_scale,
+            use_checkpoint=use_checkpoint
+        )
+        self.ln_post = nn.LayerNorm(width, device=device, dtype=dtype)
+        self.input_proj = nn.Linear(input_channels, width, device=device, dtype=dtype)
+        self.output_proj = nn.Linear(width, output_channels, device=device, dtype=dtype)
+
+        # timestep embedding
+        self.time_embed = Timesteps(width, flip_sin_to_cos=flip_sin_to_cos, downscale_freq_shift=0)
+        self.time_proj = MLP(
+            device=device, dtype=dtype, width=width, init_scale=init_scale
+        )
+
+        self.context_embed = nn.Sequential(
+            nn.LayerNorm(context_dim, device=device, dtype=dtype),
+            nn.Linear(context_dim, width, device=device, dtype=dtype),
+        )
+
+        if context_ln:
+            self.context_embed = nn.Sequential(
+                nn.LayerNorm(context_dim, device=device, dtype=dtype),
+                nn.Linear(context_dim, width, device=device, dtype=dtype),
+            )
+        else:
+            self.context_embed = nn.Linear(context_dim, width, device=device, dtype=dtype)
+
+    def forward(self,
+                model_input: torch.FloatTensor,
+                timestep: torch.LongTensor,
+                context: torch.FloatTensor):
+
+        r"""
+        Args:
+            model_input (torch.FloatTensor): [bs, n_data, c]
+            timestep (torch.LongTensor): [bs,]
+            context (torch.FloatTensor): [bs, context_tokens, c]
+
+        Returns:
+            sample (torch.FloatTensor): [bs, n_data, c]
+
+        """
+
+        _, n_data, _ = model_input.shape
+
+        # 1. time
+        t_emb = self.time_proj(self.time_embed(timestep)).unsqueeze(dim=1)
+
+        # 2. conditions projector
+        context = self.context_embed(context)
+
+        # 3. denoiser
+        x = self.input_proj(model_input)
+        x = torch.cat([t_emb, context, x], dim=1)
+        x = self.backbone(x)
+        x = self.ln_post(x)
+        x = x[:, -n_data:]
+        sample = self.output_proj(x)
+
+        return sample
+
+

third_partys/Michelangelo/michelangelo/models/asl_diffusion/base.py

@@ -0,0 +1,13 @@
+# -*- coding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+
+
+class BaseDenoiser(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, t, context):
+        raise NotImplementedError

third_partys/Michelangelo/michelangelo/models/asl_diffusion/clip_asl_diffuser_pl_module.py

@@ -0,0 +1,393 @@
+# -*- coding: utf-8 -*-
+
+from omegaconf import DictConfig
+from typing import List, Tuple, Dict, Optional, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.optim import lr_scheduler
+import pytorch_lightning as pl
+from pytorch_lightning.utilities import rank_zero_only
+
+from diffusers.schedulers import (
+    DDPMScheduler,
+    DDIMScheduler,
+    KarrasVeScheduler,
+    DPMSolverMultistepScheduler
+)
+
+from third_partys.Michelangelo.michelangelo.utils import instantiate_from_config
+from third_partys.Michelangelo.michelangelo.models.tsal.tsal_base import AlignedShapeAsLatentPLModule
+from third_partys.Michelangelo.michelangelo.models.asl_diffusion.inference_utils import ddim_sample
+
+SchedulerType = Union[DDIMScheduler, KarrasVeScheduler, DPMSolverMultistepScheduler]
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+class ClipASLDiffuser(pl.LightningModule):
+    first_stage_model: Optional[AlignedShapeAsLatentPLModule]
+    cond_stage_model: Optional[Union[nn.Module, pl.LightningModule]]
+    model: nn.Module
+
+    def __init__(self, *,
+                 first_stage_config,
+                 cond_stage_config,
+                 denoiser_cfg,
+                 scheduler_cfg,
+                 optimizer_cfg,
+                 loss_cfg,
+                 first_stage_key: str = "surface",
+                 cond_stage_key: str = "image",
+                 scale_by_std: bool = False,
+                 z_scale_factor: float = 1.0,
+                 ckpt_path: Optional[str] = None,
+                 ignore_keys: Union[Tuple[str], List[str]] = ()):
+
+        super().__init__()
+
+        self.first_stage_key = first_stage_key
+        self.cond_stage_key = cond_stage_key
+
+        # 1. lazy initialize first stage
+        self.instantiate_first_stage(first_stage_config)
+
+        # 2. initialize conditional stage
+        self.instantiate_cond_stage(cond_stage_config)
+
+        # 3. diffusion model
+        self.model = instantiate_from_config(
+            denoiser_cfg, device=None, dtype=None
+        )
+
+        self.optimizer_cfg = optimizer_cfg
+
+        # 4. scheduling strategy
+        self.scheduler_cfg = scheduler_cfg
+
+        self.noise_scheduler: DDPMScheduler = instantiate_from_config(scheduler_cfg.noise)
+        self.denoise_scheduler: SchedulerType = instantiate_from_config(scheduler_cfg.denoise)
+
+        # 5. loss configures
+        self.loss_cfg = loss_cfg
+
+        self.scale_by_std = scale_by_std
+        if scale_by_std:
+            self.register_buffer("z_scale_factor", torch.tensor(z_scale_factor))
+        else:
+            self.z_scale_factor = z_scale_factor
+
+        self.ckpt_path = ckpt_path
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+
+    def instantiate_non_trainable_model(self, config):
+        model = instantiate_from_config(config)
+        model = model.eval()
+        model.train = disabled_train
+        for param in model.parameters():
+            param.requires_grad = False
+
+        return model
+
+    def instantiate_first_stage(self, first_stage_config):
+        self.first_stage_model = self.instantiate_non_trainable_model(first_stage_config)
+        self.first_stage_model.set_shape_model_only()
+
+    def instantiate_cond_stage(self, cond_stage_config):
+        self.cond_stage_model = self.instantiate_non_trainable_model(cond_stage_config)
+
+    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}")
+
+    @property
+    def zero_rank(self):
+        if self._trainer:
+            zero_rank = self.trainer.local_rank == 0
+        else:
+            zero_rank = True
+
+        return zero_rank
+
+    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
+
+    @torch.no_grad()
+    def encode_first_stage(self, surface: torch.FloatTensor, sample_posterior=True):
+
+        z_q = self.first_stage_model.encode(surface, sample_posterior)
+        z_q = self.z_scale_factor * z_q
+
+        return z_q
+
+    @torch.no_grad()
+    def decode_first_stage(self, z_q: torch.FloatTensor, **kwargs):
+
+        z_q = 1. / self.z_scale_factor * z_q
+        latents = self.first_stage_model.decode(z_q, **kwargs)
+        return latents
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_start(self, batch, batch_idx):
+        # only for very first batch
+        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 \
+                and batch_idx == 0 and self.ckpt_path is None:
+            # set rescale weight to 1./std of encodings
+            print("### USING STD-RESCALING ###")
+
+            z_q = self.encode_first_stage(batch[self.first_stage_key])
+            z = z_q.detach()
+
+            del self.z_scale_factor
+            self.register_buffer("z_scale_factor", 1. / z.flatten().std())
+            print(f"setting self.z_scale_factor to {self.z_scale_factor}")
+
+            print("### USING STD-RESCALING ###")
+
+    def compute_loss(self, model_outputs, split):
+        """
+
+        Args:
+            model_outputs (dict):
+                - x_0:
+                - noise:
+                - noise_prior:
+                - noise_pred:
+                - noise_pred_prior:
+
+            split (str):
+
+        Returns:
+
+        """
+
+        pred = model_outputs["pred"]
+
+        if self.noise_scheduler.prediction_type == "epsilon":
+            target = model_outputs["noise"]
+        elif self.noise_scheduler.prediction_type == "sample":
+            target = model_outputs["x_0"]
+        else:
+            raise NotImplementedError(f"Prediction Type: {self.noise_scheduler.prediction_type} not yet supported.")
+
+        if self.loss_cfg.loss_type == "l1":
+            simple = F.l1_loss(pred, target, reduction="mean")
+        elif self.loss_cfg.loss_type in ["mse", "l2"]:
+            simple = F.mse_loss(pred, target, reduction="mean")
+        else:
+            raise NotImplementedError(f"Loss Type: {self.loss_cfg.loss_type} not yet supported.")
+
+        total_loss = simple
+
+        loss_dict = {
+            f"{split}/total_loss": total_loss.clone().detach(),
+            f"{split}/simple": simple.detach(),
+        }
+
+        return total_loss, loss_dict
+
+    def forward(self, batch):
+        """
+
+        Args:
+            batch:
+
+        Returns:
+
+        """
+
+        latents = self.encode_first_stage(batch[self.first_stage_key])
+        conditions = self.cond_stage_model.encode(batch[self.cond_stage_key])
+
+        # Sample noise that we"ll add to the latents
+        # [batch_size, n_token, latent_dim]
+        noise = torch.randn_like(latents)
+        bs = latents.shape[0]
+        # Sample a random timestep for each motion
+        timesteps = torch.randint(
+            0,
+            self.noise_scheduler.config.num_train_timesteps,
+            (bs,),
+            device=latents.device,
+        )
+        timesteps = timesteps.long()
+        # Add noise to the latents according to the noise magnitude at each timestep
+        noisy_z = self.noise_scheduler.add_noise(latents, noise, timesteps)
+
+        # diffusion model forward
+        noise_pred = self.model(noisy_z, timesteps, conditions)
+
+        diffusion_outputs = {
+            "x_0": noisy_z,
+            "noise": noise,
+            "pred": noise_pred
+        }
+
+        return diffusion_outputs
+
+    def training_step(self, batch: Dict[str, Union[torch.FloatTensor, List[str]]],
+                      batch_idx: int, optimizer_idx: int = 0) -> torch.FloatTensor:
+        """
+
+        Args:
+            batch (dict): the batch sample, and it contains:
+                - surface (torch.FloatTensor):
+                - image (torch.FloatTensor): if provide, [bs, 3, h, w], item range [0, 1]
+                - depth (torch.FloatTensor): if provide, [bs, 1, h, w], item range [-1, 1]
+                - normal (torch.FloatTensor): if provide, [bs, 3, h, w], item range [-1, 1]
+                - text (list of str):
+
+            batch_idx (int):
+
+            optimizer_idx (int):
+
+        Returns:
+            loss (torch.FloatTensor):
+
+        """
+
+        diffusion_outputs = self(batch)
+
+        loss, loss_dict = self.compute_loss(diffusion_outputs, "train")
+        self.log_dict(loss_dict, prog_bar=True, logger=True, sync_dist=False, rank_zero_only=True)
+
+        return loss
+
+    def validation_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_pc (torch.FloatTensor): [n_pts, 4]
+                - surface_feats (torch.FloatTensor): [n_pts, c]
+                - text (list of str):
+
+            batch_idx (int):
+
+            optimizer_idx (int):
+
+        Returns:
+            loss (torch.FloatTensor):
+
+        """
+
+        diffusion_outputs = self(batch)
+
+        loss, loss_dict = self.compute_loss(diffusion_outputs, "val")
+        self.log_dict(loss_dict, prog_bar=True, logger=True, sync_dist=False, rank_zero_only=True)
+
+        return loss
+
+    @torch.no_grad()
+    def sample(self,
+               batch: Dict[str, Union[torch.FloatTensor, List[str]]],
+               sample_times: int = 1,
+               steps: Optional[int] = None,
+               guidance_scale: Optional[float] = None,
+               eta: float = 0.0,
+               return_intermediates: bool = False, **kwargs):
+
+        if steps is None:
+            steps = self.scheduler_cfg.num_inference_steps
+
+        if guidance_scale is None:
+            guidance_scale = self.scheduler_cfg.guidance_scale
+        do_classifier_free_guidance = guidance_scale > 0
+
+        # conditional encode
+        xc = batch[self.cond_stage_key]
+
+        # print(self.first_stage_model.device, self.cond_stage_model.device, self.device)
+
+        cond = self.cond_stage_model(xc)
+
+        if do_classifier_free_guidance:
+            un_cond = self.cond_stage_model.unconditional_embedding(batch_size=len(xc))
+            cond = torch.cat([un_cond, cond], dim=0)
+
+        outputs = []
+        latents = None
+
+        if not return_intermediates:
+            for _ in range(sample_times):
+                sample_loop = ddim_sample(
+                    self.denoise_scheduler,
+                    self.model,
+                    shape=self.first_stage_model.latent_shape,
+                    cond=cond,
+                    steps=steps,
+                    guidance_scale=guidance_scale,
+                    do_classifier_free_guidance=do_classifier_free_guidance,
+                    device=self.device,
+                    eta=eta,
+                    disable_prog=not self.zero_rank
+                )
+                for sample, t in sample_loop:
+                    latents = sample
+                outputs.append(self.decode_first_stage(latents, **kwargs))
+        else:
+
+            sample_loop = ddim_sample(
+                self.denoise_scheduler,
+                self.model,
+                shape=self.first_stage_model.latent_shape,
+                cond=cond,
+                steps=steps,
+                guidance_scale=guidance_scale,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+                device=self.device,
+                eta=eta,
+                disable_prog=not self.zero_rank
+            )
+
+            iter_size = steps // sample_times
+            i = 0
+            for sample, t in sample_loop:
+                latents = sample
+                if i % iter_size == 0 or i == steps - 1:
+                    outputs.append(self.decode_first_stage(latents, **kwargs))
+                i += 1
+
+        return outputs

third_partys/Michelangelo/michelangelo/models/asl_diffusion/inference_utils.py

@@ -0,0 +1,80 @@
+# -*- coding: utf-8 -*-
+
+import torch
+from tqdm import tqdm
+from typing import Tuple, List, Union, Optional
+from diffusers.schedulers import DDIMScheduler
+
+
+__all__ = ["ddim_sample"]
+
+
+def ddim_sample(ddim_scheduler: DDIMScheduler,
+                diffusion_model: torch.nn.Module,
+                shape: Union[List[int], Tuple[int]],
+                cond: torch.FloatTensor,
+                steps: int,
+                eta: float = 0.0,
+                guidance_scale: float = 3.0,
+                do_classifier_free_guidance: bool = True,
+                generator: Optional[torch.Generator] = None,
+                device: torch.device = "cuda:0",
+                disable_prog: bool = True):
+
+    assert steps > 0, f"{steps} must > 0."
+
+    # init latents
+    bsz = cond.shape[0]
+    if do_classifier_free_guidance:
+        bsz = bsz // 2
+
+    latents = torch.randn(
+        (bsz, *shape),
+        generator=generator,
+        device=cond.device,
+        dtype=cond.dtype,
+    )
+    # scale the initial noise by the standard deviation required by the scheduler
+    latents = latents * ddim_scheduler.init_noise_sigma
+    # set timesteps
+    ddim_scheduler.set_timesteps(steps)
+    timesteps = ddim_scheduler.timesteps.to(device)
+    # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+    # eta (η) is only used with the DDIMScheduler, and between [0, 1]
+    extra_step_kwargs = {
+        "eta": eta,
+        "generator": generator
+    }
+
+    # reverse
+    for i, t in enumerate(tqdm(timesteps, disable=disable_prog, desc="DDIM Sampling:", leave=False)):
+        # expand the latents if we are doing classifier free guidance
+        latent_model_input = (
+            torch.cat([latents] * 2)
+            if do_classifier_free_guidance
+            else latents
+        )
+        # latent_model_input = scheduler.scale_model_input(latent_model_input, t)
+        # predict the noise residual
+        timestep_tensor = torch.tensor([t], dtype=torch.long, device=device)
+        timestep_tensor = timestep_tensor.expand(latent_model_input.shape[0])
+        noise_pred = diffusion_model.forward(latent_model_input, timestep_tensor, cond)
+
+        # perform guidance
+        if do_classifier_free_guidance:
+            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+            noise_pred = noise_pred_uncond + guidance_scale * (
+                    noise_pred_text - noise_pred_uncond
+            )
+            # text_embeddings_for_guidance = encoder_hidden_states.chunk(
+            #     2)[1] if do_classifier_free_guidance else encoder_hidden_states
+        # compute the previous noisy sample x_t -> x_t-1
+        latents = ddim_scheduler.step(
+            noise_pred, t, latents, **extra_step_kwargs
+        ).prev_sample
+
+        yield latents, t
+
+
+def karra_sample():
+    pass

third_partys/Michelangelo/michelangelo/models/conditional_encoders/__init__.py

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

third_partys/Michelangelo/michelangelo/models/conditional_encoders/clip.py

@@ -0,0 +1,89 @@
+# -*- coding: utf-8 -*-
+
+import torch
+import numpy as np
+from PIL import Image
+from dataclasses import dataclass
+from torchvision.transforms import Normalize
+from transformers import CLIPModel, CLIPTokenizer
+from transformers.utils import ModelOutput
+from typing import Iterable, Optional, Union, List
+
+
+ImageType = Union[np.ndarray, torch.Tensor, Image.Image]
+
+
+@dataclass
+class CLIPEmbedOutput(ModelOutput):
+    last_hidden_state: torch.FloatTensor = None
+    pooler_output: torch.FloatTensor = None
+    embeds: torch.FloatTensor = None
+
+
+class CLIPEncoder(torch.nn.Module):
+
+    def __init__(self, model_path="openai/clip-vit-base-patch32"):
+
+        super().__init__()
+
+        # Load the CLIP model and processor
+        self.model: CLIPModel = CLIPModel.from_pretrained(model_path)
+        self.tokenizer = CLIPTokenizer.from_pretrained(model_path)
+        self.image_preprocess = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+        self.model.training = False
+        for p in self.model.parameters():
+            p.requires_grad = False
+
+    @torch.no_grad()
+    def encode_image(self, images: Iterable[Optional[ImageType]]):
+        pixel_values = self.image_preprocess(images)
+
+        vision_outputs = self.model.vision_model(pixel_values=pixel_values)
+
+        pooler_output = vision_outputs[1]  # pooled_output
+        image_features = self.model.visual_projection(pooler_output)
+
+        visual_embeds = CLIPEmbedOutput(
+            last_hidden_state=vision_outputs.last_hidden_state,
+            pooler_output=pooler_output,
+            embeds=image_features
+        )
+
+        return visual_embeds
+
+    @torch.no_grad()
+    def encode_text(self, texts: List[str]):
+        text_inputs = self.tokenizer(texts, padding=True, return_tensors="pt")
+
+        text_outputs = self.model.text_model(input_ids=text_inputs)
+
+        pooler_output = text_outputs[1]  # pooled_output
+        text_features = self.model.text_projection(pooler_output)
+
+        text_embeds = CLIPEmbedOutput(
+            last_hidden_state=text_outputs.last_hidden_state,
+            pooler_output=pooler_output,
+            embeds=text_features
+        )
+
+        return text_embeds
+
+    def forward(self,
+                images: Iterable[Optional[ImageType]],
+                texts: List[str]):
+
+        visual_embeds = self.encode_image(images)
+        text_embeds = self.encode_text(texts)
+
+        return visual_embeds, text_embeds
+
+
+
+
+
+
+
+
+
+

third_partys/Michelangelo/michelangelo/models/conditional_encoders/encoder_factory.py

@@ -0,0 +1,562 @@
+# -*- coding: utf-8 -*-
+import os
+
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from transformers import CLIPModel, CLIPTokenizer
+from collections import OrderedDict
+
+from third_partys.Michelangelo.michelangelo.data.transforms import RandomResize
+
+
+class AbstractEncoder(nn.Module):
+    embedding_dim: int
+
+    def __init__(self):
+        super().__init__()
+
+    def encode(self, *args, **kwargs):
+        raise NotImplementedError
+
+
+class ClassEmbedder(nn.Module):
+    def __init__(self, embed_dim, n_classes=1000, key="class"):
+        super().__init__()
+        self.key = key
+        self.embedding = nn.Embedding(n_classes, embed_dim)
+
+    def forward(self, batch, key=None):
+        if key is None:
+            key = self.key
+        # this is for use in crossattn
+        c = batch[key][:, None]
+        c = self.embedding(c)
+        return c
+
+
+class FrozenCLIPTextEmbedder(AbstractEncoder):
+    """Uses the CLIP transformer encoder for text (from Hugging Face)"""
+
+    def __init__(
+        self,
+        version="openai/clip-vit-large-patch14",
+        tokenizer_version=None,
+        device="cuda",
+        max_length=77,
+        zero_embedding_radio: float = 0.1,
+    ):
+        super().__init__()
+        self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_version or version)
+
+        self.device = device
+        self.max_length = max_length
+        self.zero_embedding_radio = zero_embedding_radio
+
+        self.clip_dict = OrderedDict()
+        self.clip_name = os.path.split(version)[-1]
+
+        transformer = CLIPModel.from_pretrained(version).text_model
+
+        for param in transformer.parameters():
+            param.requires_grad = False
+        self.clip_dict[self.clip_name] = transformer
+
+        self._move_flag = False
+
+    @property
+    def clip(self):
+        return self.clip_dict[self.clip_name]
+
+    def move(self):
+        if self._move_flag:
+            return
+
+        self.clip_dict[self.clip_name] = self.clip_dict[self.clip_name].to(self.device)
+        self._move_flag = True
+
+    def unconditional_embedding(self, batch_size):
+        empty_text = [""] * batch_size
+        empty_z = self.forward(empty_text)
+        return empty_z
+
+    def forward(self, text):
+        self.move()
+
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+
+        tokens = batch_encoding["input_ids"].to(self.device)
+        outputs = self.clip(input_ids=tokens)
+
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        batch_size = len(text)
+        batch_mask = torch.rand((batch_size,))
+        for i in range(batch_size):
+            if batch_mask[i] < self.zero_embedding_radio:
+                text[i] = ""
+
+        return self(text)
+
+class FrozenAlignedCLIPTextEmbedder(AbstractEncoder):
+    """Uses the CLIP transformer encoder for text (from Hugging Face)"""
+
+    def __init__(
+        self,
+        version="openai/clip-vit-large-patch14",
+        tokenizer_version=None,
+        device="cuda",
+        max_length=77,
+        zero_embedding_radio: float = 0.1,
+    ):
+        super().__init__()
+        self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_version or version)
+
+        self.device = device
+        self.max_length = max_length
+        self.zero_embedding_radio = zero_embedding_radio
+
+        self.clip_dict = OrderedDict()
+        self.clip_name = os.path.split(version)[-1]
+
+        transformer = CLIPModel.from_pretrained(version).text_model
+
+        for param in transformer.parameters():
+            param.requires_grad = False
+        self.clip_dict[self.clip_name] = transformer
+
+        self._move_flag = False
+
+    @property
+    def clip(self):
+        return self.clip_dict[self.clip_name]
+
+    def move(self):
+        if self._move_flag:
+            return
+
+        self.clip_dict[self.clip_name] = self.clip_dict[self.clip_name].to(self.device)
+        self._move_flag = True
+
+    def unconditional_embedding(self, batch_size):
+        empty_text = [""] * batch_size
+        empty_z = self.forward(empty_text)
+        return empty_z
+
+    def forward(self, text):
+        self.move()
+
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+
+        tokens = batch_encoding["input_ids"].to(self.device)
+        outputs = self.clip(input_ids=tokens)
+
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        batch_size = len(text)
+        batch_mask = torch.rand((batch_size,))
+        for i in range(batch_size):
+            if batch_mask[i] < self.zero_embedding_radio:
+                text[i] = ""
+
+        return self(text)
+
+
+class FrozenCLIPImageEmbedder(AbstractEncoder):
+    """Uses the CLIP transformer encoder for text (from Hugging Face)"""
+
+    def __init__(
+            self,
+            version="openai/clip-vit-large-patch14",
+            device="cuda",
+            zero_embedding_radio=0.1,
+            normalize_embedding=True,
+            num_projection_vector=0,
+            linear_mapping_bias=True,
+            reverse_visual_projection=False,
+    ):
+        super().__init__()
+
+        self.device = device
+
+        self.clip_dict = OrderedDict()
+        self.clip_name = os.path.split(version)[-1]
+
+        clip_model = CLIPModel.from_pretrained(version)
+        clip_model.text_model = None
+        clip_model.text_projection = None
+        clip_model = clip_model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+        self.clip_dict[self.clip_name] = clip_model
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(224, transforms.InterpolationMode.BICUBIC, antialias=True),
+                transforms.CenterCrop(224),  # crop a (224, 224) square
+                transforms.Normalize(
+                    mean=[0.48145466, 0.4578275, 0.40821073],
+                    std=[0.26862954, 0.26130258, 0.27577711],
+                ),
+            ]
+        )
+        self.zero_embedding_radio = zero_embedding_radio
+
+        self.num_projection_vector = num_projection_vector
+        self.reverse_visual_projection = reverse_visual_projection
+        self.normalize_embedding = normalize_embedding
+
+        embedding_dim = (
+            clip_model.visual_projection.in_features
+            if reverse_visual_projection
+            else clip_model.visual_projection.out_features
+        )
+        self.embedding_dim = embedding_dim
+        if self.num_projection_vector > 0:
+            self.projection = nn.Linear(
+                embedding_dim,
+                clip_model.visual_projection.out_features * num_projection_vector,
+                bias=linear_mapping_bias,
+            )
+            nn.init.normal_(self.projection.weight, std=embedding_dim ** -0.5)
+
+        self._move_flag = False
+
+    @property
+    def clip(self):
+        return self.clip_dict[self.clip_name]
+
+    def unconditional_embedding(self, batch_size):
+        zero = torch.zeros(
+            batch_size,
+            1,
+            self.embedding_dim,
+            device=self.device,
+            dtype=self.clip.visual_projection.weight.dtype,
+        )
+        if self.num_projection_vector > 0:
+            zero = self.projection(zero).view(batch_size, self.num_projection_vector, -1)
+        return zero
+
+    def forward(self, image, value_range=(-1, 1), zero_embedding_radio=0):
+        if value_range is not None:
+            low, high = value_range
+            image = (image - low) / (high - low)
+
+        image = image.to(self.device, dtype=self.clip.visual_projection.weight.dtype)
+
+        if self.reverse_visual_projection:
+            z = self.clip.vision_model(self.transform(image))[1]
+        else:
+            z = self.clip.get_image_features(self.transform(image))
+
+        if self.normalize_embedding:
+            z = z / z.norm(dim=-1, keepdim=True)
+        if z.ndim == 2:
+            z = z.unsqueeze(dim=-2)
+
+        if zero_embedding_radio > 0:
+            mask = torch.rand((len(image), 1, 1), device=z.device, dtype=z.dtype) < zero_embedding_radio
+            z = z * mask.to(z)
+
+        if self.num_projection_vector > 0:
+            z = self.projection(z).view(len(image), self.num_projection_vector, -1)
+
+        return z
+
+    def move(self):
+        if self._move_flag:
+            return
+
+        self.clip_dict[self.clip_name] = self.clip_dict[self.clip_name].to(self.device)
+        self._move_flag = True
+
+    def encode(self, image):
+        self.move()
+        return self(image, zero_embedding_radio=self.zero_embedding_radio)
+
+
+class FrozenCLIPImageGridEmbedder(AbstractEncoder):
+
+    def __init__(
+            self,
+            version="openai/clip-vit-large-patch14",
+            device="cuda",
+            zero_embedding_radio=0.1,
+    ):
+        super().__init__()
+
+        self.device = device
+
+        self.clip_dict = OrderedDict()
+        self.clip_name = os.path.split(version)[-1]
+
+        clip_model: CLIPModel = CLIPModel.from_pretrained(version)
+        clip_model.text_model = None
+        clip_model.text_projection = None
+        clip_model = clip_model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+        self.clip_dict[self.clip_name] = clip_model
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(224, transforms.InterpolationMode.BILINEAR, antialias=True),
+                transforms.CenterCrop(224),  # crop a (224, 224) square
+                transforms.Normalize(
+                    mean=[0.48145466, 0.4578275, 0.40821073],
+                    std=[0.26862954, 0.26130258, 0.27577711],
+                ),
+            ]
+        )
+        self.zero_embedding_radio = zero_embedding_radio
+        self.embedding_dim = clip_model.vision_embed_dim
+
+        self._move_flag = False
+
+    @property
+    def clip(self):
+        return self.clip_dict[self.clip_name]
+
+    def move(self):
+        if self._move_flag:
+            return
+
+        self.clip_dict[self.clip_name] = self.clip_dict[self.clip_name].to(self.device)
+        self._move_flag = True
+
+    def unconditional_embedding(self, batch_size):
+        zero = torch.zeros(
+            batch_size,
+            self.clip.vision_model.embeddings.num_positions,
+            self.embedding_dim,
+            device=self.device,
+            dtype=self.clip.visual_projection.weight.dtype,
+        )
+        return zero
+
+    def forward(self, image, value_range=(-1, 1), zero_embedding_radio=0):
+        self.move()
+
+        if value_range is not None:
+            low, high = value_range
+            image = (image - low) / (high - low)
+
+        image = image.to(self.device, dtype=self.clip.visual_projection.weight.dtype)
+
+        z = self.clip.vision_model(self.transform(image)).last_hidden_state
+
+        if zero_embedding_radio > 0:
+            mask = torch.rand((len(image), 1, 1), device=z.device, dtype=z.dtype) >= zero_embedding_radio
+            z = z * mask.to(z)
+
+        return z
+
+    def encode(self, image):
+        return self(image, zero_embedding_radio=self.zero_embedding_radio)
+
+
+class MoECLIPImageEncoder(nn.Module):
+    def __init__(
+            self,
+            versions,
+            hidden_state_dim,
+            num_projection_vector=8,
+            zero_embedding_radio=0.1,
+            device="cuda",
+            precision="fp16",
+            normalize=False,
+            clip_max=0,
+            transform_type="base",
+            argument_p=0.2,
+    ):
+        super().__init__()
+
+        self.device = torch.device(device)
+        self.hidden_state_dim = hidden_state_dim
+        self.zero_embedding_radio = zero_embedding_radio
+        self.num_projection_vector = num_projection_vector
+        self.dtype = dict(fp16=torch.float16, fp32=torch.float32, bf16=torch.bfloat16)[precision]
+        self.normalize = normalize
+        self.clip_max = clip_max
+
+        if transform_type == "base":
+            self.transform = transforms.Compose(
+                [
+                    transforms.Resize(224, transforms.InterpolationMode.BICUBIC, antialias=True),
+                    transforms.CenterCrop(224),  # crop a (224, 224) square
+                    transforms.Normalize(
+                        mean=[0.48145466, 0.4578275, 0.40821073],
+                        std=[0.26862954, 0.26130258, 0.27577711],
+                    ),
+                ]
+            )
+        elif transform_type == "crop_blur_resize":
+            self.transform = transforms.Compose(
+                [
+                    transforms.Resize(224, transforms.InterpolationMode.BICUBIC, antialias=True),
+                    transforms.CenterCrop(224),  # crop a (224, 224) square
+                    transforms.RandomApply(
+                        transforms=[
+                            transforms.RandomResizedCrop(
+                                size=224,
+                                scale=(0.8, 1.0),
+                                ratio=(0.99, 1.01),
+                                interpolation=transforms.InterpolationMode.BICUBIC,
+                            ),
+                        ],
+                        p=argument_p,
+                    ),
+                    transforms.RandomApply(
+                        transforms=[
+                            transforms.GaussianBlur(kernel_size=9, sigma=(0.1, 5)),
+                        ],
+                        p=argument_p,
+                    ),
+                    transforms.RandomApply(
+                        transforms=[
+                            RandomResize(size=224, resize_radio=(0.2, 1)),
+                        ],
+                        p=argument_p,
+                    ),
+                    transforms.Normalize(
+                        mean=[0.48145466, 0.4578275, 0.40821073],
+                        std=[0.26862954, 0.26130258, 0.27577711],
+                    ),
+                ]
+            )
+        else:
+            raise ValueError(f"invalid {transform_type=}")
+
+        if isinstance(versions, str):
+            versions = (versions,)
+
+        # 如果直接把clips定位为当前类的子module，1. 会在保存ckp时存无用的多个权重。 2. pl会调用to，导致layer_norm的权重也被转换成fp16
+        clips = OrderedDict()
+
+        for v in versions:
+            # 因为clips不是子module，直接指定device="cuda"会错误地导致clip模型权重都被放到cuda:0上。
+            clips[v], _ = clip.load(name=v, device="cpu", jit=False, download_root=None)
+            delattr(clips[v], "transformer")
+            clips[v].eval()
+            clips[v].requires_grad_(False)
+
+        self.clips_hidden_dim = sum(clips[v].ln_final.weight.size(0) for v in clips)
+
+        if self.num_projection_vector == 0:
+            self.projection = nn.Identity()
+        else:
+            self.projection = nn.Linear(self.clips_hidden_dim, hidden_state_dim * self.num_projection_vector, bias=True)
+            self.projection.to(dtype=self.dtype)
+            nn.init.normal_(self.projection.weight, std=self.clips_hidden_dim ** -0.5)
+
+        self.clips = clips
+
+        self._move_flag = False
+
+    def move(self):
+        if self._move_flag:
+            return
+
+        def convert_weights(model: nn.Module):
+            """Convert applicable model parameters to fp16"""
+
+            def _convert_weights_to_fp16(l):
+                if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
+                    l.weight.data = l.weight.data.type(self.dtype)
+                    if l.bias is not None:
+                        l.bias.data = l.bias.data.type(self.dtype)
+
+                if isinstance(l, nn.MultiheadAttention):
+                    for attr in [
+                        *[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]],
+                        "in_proj_bias",
+                        "bias_k",
+                        "bias_v",
+                    ]:
+                        tensor = getattr(l, attr)
+                        if tensor is not None:
+                            tensor.data = tensor.data.type(self.dtype)
+
+                for name in ["text_projection", "proj"]:
+                    if hasattr(l, name):
+                        attr = getattr(l, name)
+                        if attr is not None:
+                            attr.data = attr.data.type(self.dtype)
+
+            model.apply(_convert_weights_to_fp16)
+
+        for k in self.clips:
+            self.clips[k].to(self.device)
+            convert_weights(self.clips[k])  # fp32 -> self.dtype
+        self._move_flag = True
+
+    def unconditional_embedding(self, batch_size=None):
+        zero = torch.zeros(
+            batch_size,
+            self.clips_hidden_dim,
+            device=self.device,
+            dtype=self.dtype,
+        )
+        if self.num_projection_vector > 0:
+            zero = self.projection(zero).view(batch_size, self.num_projection_vector, -1)
+        return zero
+
+    def convert_embedding(self, z):
+        if self.num_projection_vector > 0:
+            z = self.projection(z.type(self.projection.weight.dtype)).view(len(z), self.num_projection_vector, -1)
+        return z
+
+    def forward(self, image, value_range=(-1, 1), zero_embedding_radio=0):
+        if value_range is not None:
+            low, high = value_range
+            image = (image - low) / (high - low)
+
+        image = self.transform(image)
+
+        with torch.no_grad():
+            embs = []
+            for v in self.clips:
+                x = self.clips[v].encode_image(image)
+                if self.normalize:
+                    x = x / x.norm(p=2, dim=-1, keepdim=True) * (x.size(-1) ** 0.5)
+                    # clip_max only works with normalization
+                    if self.clip_max > 0:
+                        x = x.clamp(-self.clip_max, self.clip_max)
+                embs.append(x)
+
+            z = torch.cat(embs, dim=-1)
+            if self.normalize:
+                z /= z.size(-1) ** 0.5
+
+        if zero_embedding_radio > 0:
+            mask = torch.rand((len(image), 1, 1), device=z.device, dtype=z.dtype) >= zero_embedding_radio
+            z = z + mask.to(z)
+
+        if self.num_projection_vector > 0:
+            z = self.projection(z).view(len(image), self.num_projection_vector, -1)
+        return z
+
+    def encode(self, image):
+        self.move()
+        return self(image, zero_embedding_radio=self.zero_embedding_radio)

third_partys/Michelangelo/michelangelo/models/modules/__init__.py

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

third_partys/Michelangelo/michelangelo/models/modules/checkpoint.py

@@ -0,0 +1,69 @@
+# -*- coding: utf-8 -*-
+"""
+Adapted from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/nn.py#L124
+"""
+
+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

third_partys/Michelangelo/michelangelo/models/modules/diffusion_transformer.py

@@ -0,0 +1,218 @@
+# -*- coding: utf-8 -*-
+
+import math
+import torch
+import torch.nn as nn
+from typing import Optional
+
+from third_partys.Michelangelo.michelangelo.models.modules.checkpoint import checkpoint
+from third_partys.Michelangelo.michelangelo.models.modules.transformer_blocks import (
+    init_linear,
+    MLP,
+    MultiheadCrossAttention,
+    MultiheadAttention,
+    ResidualAttentionBlock
+)
+
+
+class AdaLayerNorm(nn.Module):
+    def __init__(self,
+                 device: torch.device,
+                 dtype: torch.dtype,
+                 width: int):
+
+        super().__init__()
+
+        self.silu = nn.SiLU(inplace=True)
+        self.linear = nn.Linear(width, width * 2, device=device, dtype=dtype)
+        self.layernorm = nn.LayerNorm(width, elementwise_affine=False, device=device, dtype=dtype)
+
+    def forward(self, x, timestep):
+        emb = self.linear(timestep)
+        scale, shift = torch.chunk(emb, 2, dim=2)
+        x = self.layernorm(x) * (1 + scale) + shift
+        return x
+
+
+class DitBlock(nn.Module):
+    def __init__(
+            self,
+            *,
+            device: torch.device,
+            dtype: torch.dtype,
+            n_ctx: int,
+            width: int,
+            heads: int,
+            context_dim: int,
+            qkv_bias: bool = False,
+            init_scale: float = 1.0,
+            use_checkpoint: bool = False
+    ):
+        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
+        )
+        self.ln_1 = AdaLayerNorm(device, dtype, width)
+
+        if context_dim is not None:
+            self.ln_2 = AdaLayerNorm(device, dtype, width)
+            self.cross_attn = MultiheadCrossAttention(
+                device=device,
+                dtype=dtype,
+                width=width,
+                heads=heads,
+                data_width=context_dim,
+                init_scale=init_scale,
+                qkv_bias=qkv_bias
+            )
+
+        self.mlp = MLP(device=device, dtype=dtype, width=width, init_scale=init_scale)
+        self.ln_3 = AdaLayerNorm(device, dtype, width)
+
+    def forward(self, x: torch.Tensor, t: torch.Tensor, context: Optional[torch.Tensor] = None):
+        return checkpoint(self._forward, (x, t, context), self.parameters(), self.use_checkpoint)
+
+    def _forward(self, x: torch.Tensor, t: torch.Tensor, context: Optional[torch.Tensor] = None):
+        x = x + self.attn(self.ln_1(x, t))
+        if context is not None:
+            x = x + self.cross_attn(self.ln_2(x, t), context)
+        x = x + self.mlp(self.ln_3(x, t))
+        return x
+
+
+class DiT(nn.Module):
+    def __init__(
+            self,
+            *,
+            device: Optional[torch.device],
+            dtype: Optional[torch.dtype],
+            n_ctx: int,
+            width: int,
+            layers: int,
+            heads: int,
+            context_dim: int,
+            init_scale: float = 0.25,
+            qkv_bias: bool = False,
+            use_checkpoint: bool = False
+    ):
+        super().__init__()
+        self.n_ctx = n_ctx
+        self.width = width
+        self.layers = layers
+
+        self.resblocks = nn.ModuleList(
+            [
+                DitBlock(
+                    device=device,
+                    dtype=dtype,
+                    n_ctx=n_ctx,
+                    width=width,
+                    heads=heads,
+                    context_dim=context_dim,
+                    qkv_bias=qkv_bias,
+                    init_scale=init_scale,
+                    use_checkpoint=use_checkpoint
+                )
+                for _ in range(layers)
+            ]
+        )
+
+    def forward(self, x: torch.Tensor, t: torch.Tensor, context: Optional[torch.Tensor] = None):
+        for block in self.resblocks:
+            x = block(x, t, context)
+        return x
+
+
+class UNetDiffusionTransformer(nn.Module):
+    def __init__(
+            self,
+            *,
+            device: Optional[torch.device],
+            dtype: Optional[torch.dtype],
+            n_ctx: int,
+            width: int,
+            layers: int,
+            heads: int,
+            init_scale: float = 0.25,
+            qkv_bias: bool = False,
+            skip_ln: bool = False,
+            use_checkpoint: bool = False
+    ):
+        super().__init__()
+
+        self.n_ctx = n_ctx
+        self.width = width
+        self.layers = layers
+
+        self.encoder = nn.ModuleList()
+        for _ in range(layers):
+            resblock = ResidualAttentionBlock(
+                device=device,
+                dtype=dtype,
+                n_ctx=n_ctx,
+                width=width,
+                heads=heads,
+                init_scale=init_scale,
+                qkv_bias=qkv_bias,
+                use_checkpoint=use_checkpoint
+            )
+            self.encoder.append(resblock)
+
+        self.middle_block = ResidualAttentionBlock(
+            device=device,
+            dtype=dtype,
+            n_ctx=n_ctx,
+            width=width,
+            heads=heads,
+            init_scale=init_scale,
+            qkv_bias=qkv_bias,
+            use_checkpoint=use_checkpoint
+        )
+
+        self.decoder = nn.ModuleList()
+        for _ in range(layers):
+            resblock = ResidualAttentionBlock(
+                device=device,
+                dtype=dtype,
+                n_ctx=n_ctx,
+                width=width,
+                heads=heads,
+                init_scale=init_scale,
+                qkv_bias=qkv_bias,
+                use_checkpoint=use_checkpoint
+            )
+            linear = nn.Linear(width * 2, width, device=device, dtype=dtype)
+            init_linear(linear, init_scale)
+
+            layer_norm = nn.LayerNorm(width, device=device, dtype=dtype) if skip_ln else None
+
+            self.decoder.append(nn.ModuleList([resblock, linear, layer_norm]))
+
+    def forward(self, x: torch.Tensor):
+
+        enc_outputs = []
+        for block in self.encoder:
+            x = block(x)
+            enc_outputs.append(x)
+
+        x = self.middle_block(x)
+
+        for i, (resblock, linear, layer_norm) in enumerate(self.decoder):
+            x = torch.cat([enc_outputs.pop(), x], dim=-1)
+            x = linear(x)
+
+            if layer_norm is not None:
+                x = layer_norm(x)
+
+            x = resblock(x)
+
+        return x

third_partys/Michelangelo/michelangelo/models/modules/distributions.py

@@ -0,0 +1,100 @@
+import torch
+import numpy as np
+from typing import Union, List
+
+
+class AbstractDistribution(object):
+    def sample(self):
+        raise NotImplementedError()
+
+    def mode(self):
+        raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+    def __init__(self, value):
+        self.value = value
+
+    def sample(self):
+        return self.value
+
+    def mode(self):
+        return self.value
+
+
+class DiagonalGaussianDistribution(object):
+    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)
+
+    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):
+    """
+    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+    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)
+    )

third_partys/Michelangelo/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}")

third_partys/Michelangelo/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 third_partys.Michelangelo.michelangelo.models.modules.checkpoint import checkpoint
+
+
+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)
+
+
+class MultiheadAttention(nn.Module):
+    def __init__(
+        self,
+        *,
+        device: torch.device,
+        dtype: torch.dtype,
+        n_ctx: int,
+        width: int,
+        heads: int,
+        init_scale: float,
+        qkv_bias: bool,
+        flash: bool = False
+    ):
+        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
+
+
+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
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        device: torch.device,
+        dtype: torch.dtype,
+        n_ctx: int,
+        width: int,
+        heads: int,
+        init_scale: float = 1.0,
+        qkv_bias: bool = True,
+        flash: bool = False,
+        use_checkpoint: bool = False
+    ):
+        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)
+
+
+class MultiheadCrossAttention(nn.Module):
+    def __init__(
+        self,
+        *,
+        device: torch.device,
+        dtype: torch.dtype,
+        width: int,
+        heads: int,
+        init_scale: float,
+        qkv_bias: bool = True,
+        flash: bool = False,
+        n_data: Optional[int] = None,
+        data_width: Optional[int] = None,
+    ):
+        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
+
+
+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
+
+
+class ResidualCrossAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        device: Optional[torch.device],
+        dtype: Optional[torch.dtype],
+        n_data: Optional[int] = None,
+        width: int,
+        heads: int,
+        data_width: Optional[int] = None,
+        init_scale: float = 0.25,
+        qkv_bias: bool = True,
+        flash: bool = False
+    ):
+        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
+
+
+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)))
+
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        *,
+        device: Optional[torch.device],
+        dtype: Optional[torch.dtype],
+        n_ctx: int,
+        width: int,
+        layers: int,
+        heads: int,
+        init_scale: float = 0.25,
+        qkv_bias: bool = True,
+        flash: bool = False,
+        use_checkpoint: bool = False
+    ):
+        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

third_partys/Michelangelo/michelangelo/models/modules/transformer_vit.py

@@ -0,0 +1,308 @@
+# -*- coding: utf-8 -*-
+
+import math
+import torch
+import torch.nn as nn
+from typing import Optional
+import warnings
+
+from third_partys.Michelangelo.michelangelo.models.modules.checkpoint import checkpoint
+
+
+def _trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+                      "The distribution of values may be incorrect.",
+                      stacklevel=2)
+
+    # Values are generated by using a truncated uniform distribution and
+    # then using the inverse CDF for the normal distribution.
+    # Get upper and lower cdf values
+    l = norm_cdf((a - mean) / std)
+    u = norm_cdf((b - mean) / std)
+
+    # Uniformly fill tensor with values from [l, u], then translate to
+    # [2l-1, 2u-1].
+    tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+    # Use inverse cdf transform for normal distribution to get truncated
+    # standard normal
+    tensor.erfinv_()
+
+    # Transform to proper mean, std
+    tensor.mul_(std * math.sqrt(2.))
+    tensor.add_(mean)
+
+    # Clamp to ensure it's in the proper range
+    tensor.clamp_(min=a, max=b)
+    return tensor
+
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+    # type: (Tensor | nn.Parameter, float, float, float, float) -> Tensor
+    r"""Fills the input Tensor with values drawn from a truncated
+    normal distribution. The values are effectively drawn from the
+    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+    NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are
+    applied while sampling the normal with mean/std applied, therefore a, b args
+    should be adjusted to match the range of mean, std args.
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+    with torch.no_grad():
+        return _trunc_normal_(tensor, mean, std, a, b)
+
+
+def init_weights(m):
+    if isinstance(m, nn.Linear):
+        trunc_normal_(m.weight, std=.02)
+        if isinstance(m, nn.Linear) and m.bias is not None:
+            nn.init.constant_(m.bias, 0)
+    elif isinstance(m, nn.LayerNorm):
+        nn.init.constant_(m.bias, 0)
+        nn.init.constant_(m.weight, 1.0)
+
+
+class MultiheadAttention(nn.Module):
+    def __init__(
+        self,
+        *,
+        device: torch.device,
+        dtype: torch.dtype,
+        n_ctx: int,
+        width: int,
+        heads: int,
+        qkv_bias: bool
+    ):
+        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)
+
+    def forward(self, x):
+        x = self.c_qkv(x)
+        x = checkpoint(self.attention, (x,), (), True)
+        x = self.c_proj(x)
+        return x
+
+
+class QKVMultiheadAttention(nn.Module):
+    def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int, n_ctx: int):
+        super().__init__()
+        self.device = device
+        self.dtype = dtype
+        self.heads = heads
+        self.n_ctx = n_ctx
+
+    def forward(self, qkv):
+        bs, n_ctx, width = qkv.shape
+        attn_ch = width // self.heads // 3
+        scale = 1 / math.sqrt(attn_ch)
+        qkv = qkv.view(bs, n_ctx, self.heads, -1)
+        q, k, v = torch.split(qkv, attn_ch, dim=-1)
+        weight = torch.einsum("bthc,bshc->bhts", q, k) * scale
+        wdtype = weight.dtype
+        weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
+        return torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        device: torch.device,
+        dtype: torch.dtype,
+        n_ctx: int,
+        width: int,
+        heads: int,
+        qkv_bias: bool = True,
+        use_checkpoint: bool = False
+    ):
+        super().__init__()
+
+        self.use_checkpoint = use_checkpoint
+
+        self.attn = MultiheadAttention(
+            device=device,
+            dtype=dtype,
+            n_ctx=n_ctx,
+            width=width,
+            heads=heads,
+            qkv_bias=qkv_bias
+        )
+        self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype)
+        self.mlp = MLP(device=device, dtype=dtype, width=width)
+        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)
+
+
+class MultiheadCrossAttention(nn.Module):
+    def __init__(
+        self,
+        *,
+        device: torch.device,
+        dtype: torch.dtype,
+        width: int,
+        heads: int,
+        qkv_bias: bool = True,
+        n_data: Optional[int] = None,
+        data_width: Optional[int] = None,
+    ):
+        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
+        )
+
+    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
+
+
+class QKVMultiheadCrossAttention(nn.Module):
+    def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int, n_data: Optional[int] = None):
+        super().__init__()
+        self.device = device
+        self.dtype = dtype
+        self.heads = heads
+        self.n_data = n_data
+
+    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(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)
+        weight = torch.einsum("bthc,bshc->bhts", q, k) * scale
+        wdtype = weight.dtype
+        weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
+        return torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
+
+
+class ResidualCrossAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        device: Optional[torch.device],
+        dtype: Optional[torch.dtype],
+        n_data: Optional[int] = None,
+        width: int,
+        heads: int,
+        data_width: Optional[int] = None,
+        qkv_bias: bool = True
+    ):
+        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,
+            qkv_bias=qkv_bias
+        )
+        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)
+        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
+
+
+class MLP(nn.Module):
+    def __init__(self, *,
+                 device: Optional[torch.device],
+                 dtype: Optional[torch.dtype],
+                 width: int):
+        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()
+
+    def forward(self, x):
+        return self.c_proj(self.gelu(self.c_fc(x)))
+
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        *,
+        device: Optional[torch.device],
+        dtype: Optional[torch.dtype],
+        n_ctx: int,
+        width: int,
+        layers: int,
+        heads: int,
+        qkv_bias: bool = True,
+        use_checkpoint: bool = False
+    ):
+        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,
+                    qkv_bias=qkv_bias,
+                    use_checkpoint=use_checkpoint
+                )
+                for _ in range(layers)
+            ]
+        )
+
+        self.apply(init_weights)
+
+    def forward(self, x: torch.Tensor):
+        for block in self.resblocks:
+            x = block(x)
+        return x

third_partys/Michelangelo/michelangelo/models/tsal/__init__.py

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

third_partys/Michelangelo/michelangelo/models/tsal/asl_pl_module.py

@@ -0,0 +1,395 @@
+# -*- 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 third_partys.Michelangelo.michelangelo.utils import instantiate_from_config
+
+from .tsal_base import (
+    AlignedShapeAsLatentModule,
+    ShapeAsLatentModule,
+    Latent2MeshOutput,
+    AlignedMeshOutput
+)
+from third_partys.Michelangelo.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
+

third_partys/Michelangelo/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 third_partys.Michelangelo.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

third_partys/Michelangelo/michelangelo/models/tsal/inference_utils.py

@@ -0,0 +1,80 @@
+# -*- 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 third_partys.Michelangelo.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

third_partys/Michelangelo/michelangelo/models/tsal/loss.py

@@ -0,0 +1,303 @@
+# -*- coding: utf-8 -*-
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from typing import Optional, Tuple, Dict
+
+from third_partys.Michelangelo.michelangelo.models.modules.distributions import DiagonalGaussianDistribution
+from third_partys.Michelangelo.michelangelo.utils.eval import compute_psnr
+from third_partys.Michelangelo.michelangelo.utils import misc
+
+
+class KLNearFar(nn.Module):
+    def __init__(self,
+                 near_weight: float = 0.1,
+                 kl_weight: float = 1.0,
+                 num_near_samples: Optional[int] = None):
+
+        super().__init__()
+
+        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,
+                posteriors: Optional[DiagonalGaussianDistribution],
+                logits: torch.FloatTensor,
+                labels: torch.FloatTensor,
+                split: Optional[str] = "train", **kwargs) -> Tuple[torch.FloatTensor, Dict[str, float]]:
+
+        """
+
+        Args:
+            posteriors (DiagonalGaussianDistribution or torch.distributions.Normal):
+            logits (torch.FloatTensor): [B, 2*N], logits[:, 0:N] is the volume points; logits[:, N:2N] is the near points;
+            labels (torch.FloatTensor): [B, 2*N], labels[:, 0:N] is the volume points; labels[:, N:2N] is the near points;
+            split (str):
+            **kwargs:
+
+        Returns:
+            loss (torch.Tensor): (,)
+            log (dict):
+
+        """
+
+        if self.num_near_samples is None:
+            num_vol = logits.shape[1] // 2
+        else:
+            num_vol = logits.shape[1] - self.num_near_samples
+
+        vol_logits = logits[:, 0:num_vol]
+        vol_labels = labels[:, 0:num_vol]
+
+        near_logits = logits[:, num_vol:]
+        near_labels = labels[:, num_vol:]
+
+        # occupancy loss
+        # vol_bce = self.geo_criterion(vol_logits, vol_labels)
+        # near_bce = self.geo_criterion(near_logits, near_labels)
+        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
+
+        with torch.no_grad():
+            preds = logits >= 0
+            accuracy = (preds == labels).float()
+            accuracy = accuracy.mean()
+            pos_ratio = torch.mean(labels)
+
+        log = {
+            "{}/total_loss".format(split): loss.clone().detach(),
+            "{}/near".format(split): near_bce.detach(),
+            "{}/far".format(split): vol_bce.detach(),
+            "{}/kl".format(split): kl_loss.detach(),
+            "{}/accuracy".format(split): accuracy,
+            "{}/pos_ratio".format(split): pos_ratio
+        }
+
+        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
+
+
+class KLNearFarColor(nn.Module):
+    def __init__(self,
+                 near_weight: float = 0.1,
+                 kl_weight: float = 1.0,
+                 color_weight: float = 1.0,
+                 color_criterion: str = "mse",
+                 num_near_samples: Optional[int] = None):
+
+        super().__init__()
+
+        self.color_weight = color_weight
+        self.near_weight = near_weight
+        self.kl_weight = kl_weight
+        self.num_near_samples = num_near_samples
+
+        if color_criterion == "mse":
+            self.color_criterion = nn.MSELoss()
+
+        elif color_criterion == "l1":
+            self.color_criterion = nn.L1Loss()
+
+        else:
+            raise ValueError(f"{color_criterion} must be [`mse`, `l1`].")
+
+        self.geo_criterion = nn.BCEWithLogitsLoss()
+
+    def forward(self,
+                posteriors: Optional[DiagonalGaussianDistribution],
+                logits: torch.FloatTensor,
+                labels: torch.FloatTensor,
+                pred_colors: torch.FloatTensor,
+                gt_colors: torch.FloatTensor,
+                split: Optional[str] = "train", **kwargs) -> Tuple[torch.FloatTensor, Dict[str, float]]:
+
+        """
+
+        Args:
+            posteriors (DiagonalGaussianDistribution or torch.distributions.Normal):
+            logits (torch.FloatTensor): [B, 2*N], logits[:, 0:N] is the volume points; logits[:, N:2N] is the near points;
+            labels (torch.FloatTensor): [B, 2*N], labels[:, 0:N] is the volume points; labels[:, N:2N] is the near points;
+            pred_colors (torch.FloatTensor): [B, M, 3]
+            gt_colors (torch.FloatTensor): [B, M, 3]
+            split (str):
+            **kwargs:
+
+        Returns:
+            loss (torch.Tensor): (,)
+            log (dict):
+
+        """
+
+        if self.num_near_samples is None:
+            num_vol = logits.shape[1] // 2
+        else:
+            num_vol = logits.shape[1] - self.num_near_samples
+
+        vol_logits = logits[:, 0:num_vol]
+        vol_labels = labels[:, 0:num_vol]
+
+        near_logits = logits[:, num_vol:]
+        near_labels = labels[:, num_vol:]
+
+        # occupancy loss
+        # vol_bce = self.geo_criterion(vol_logits, vol_labels)
+        # near_bce = self.geo_criterion(near_logits, near_labels)
+        vol_bce = self.geo_criterion(vol_logits.float(), vol_labels.float())
+        near_bce = self.geo_criterion(near_logits.float(), near_labels.float())
+
+        # surface color loss
+        color = self.color_criterion(pred_colors, gt_colors)
+
+        if posteriors is None:
+            kl_loss = torch.tensor(0.0, dtype=pred_colors.dtype, device=pred_colors.device)
+        else:
+            kl_loss = posteriors.kl(dims=(1, 2))
+            kl_loss = torch.mean(kl_loss)
+
+        loss = vol_bce + near_bce * self.near_weight + color * self.color_weight + kl_loss * self.kl_weight
+
+        with torch.no_grad():
+            preds = logits >= 0
+            accuracy = (preds == labels).float()
+            accuracy = accuracy.mean()
+            psnr = compute_psnr(pred_colors, gt_colors)
+
+        log = {
+            "{}/total_loss".format(split): loss.clone().detach(),
+            "{}/near".format(split): near_bce.detach(),
+            "{}/far".format(split): vol_bce.detach(),
+            "{}/color".format(split): color.detach(),
+            "{}/kl".format(split): kl_loss.detach(),
+            "{}/psnr".format(split): psnr.detach(),
+            "{}/accuracy".format(split): accuracy
+        }
+
+        return loss, log
+
+
+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):
+
+        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

third_partys/Michelangelo/michelangelo/models/tsal/sal_perceiver.py

@@ -0,0 +1,423 @@
+# -*- coding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+from typing import Optional
+from einops import repeat
+import math
+
+from third_partys.Michelangelo.michelangelo.models.modules import checkpoint
+from third_partys.Michelangelo.michelangelo.models.modules.embedder import FourierEmbedder
+from third_partys.Michelangelo.michelangelo.models.modules.distributions import DiagonalGaussianDistribution
+from third_partys.Michelangelo.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]
+
+        Returns:
+
+        """
+
+        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]
+
+        Returns:
+            dict
+        """
+
+        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

third_partys/Michelangelo/michelangelo/models/tsal/sal_pl_module.py

@@ -0,0 +1,290 @@
+# -*- coding: utf-8 -*-
+
+from typing import List, Tuple, Dict, Optional
+from omegaconf import DictConfig
+
+import torch
+from torch.optim import lr_scheduler
+import pytorch_lightning as pl
+from typing import Union
+from functools import partial
+
+from third_partys.Michelangelo.michelangelo.utils import instantiate_from_config
+
+from .inference_utils import extract_geometry
+from .tsal_base import (
+    ShapeAsLatentModule,
+    Latent2MeshOutput,
+    Point2MeshOutput
+)
+
+
+class ShapeAsLatentPLModule(pl.LightningModule):
+
+    def __init__(self, *,
+                 module_cfg,
+                 loss_cfg,
+                 optimizer_cfg: Optional[DictConfig] = None,
+                 ckpt_path: Optional[str] = None,
+                 ignore_keys: Union[Tuple[str], List[str]] = ()):
+
+        super().__init__()
+
+        self.sal: ShapeAsLatentModule = instantiate_from_config(module_cfg, device=None, dtype=None)
+
+        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)
+
+        self.save_hyperparameters()
+
+    @property
+    def latent_shape(self):
+        return self.sal.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
+
+        # optimizers = [torch.optim.AdamW(self.sal.parameters(), lr=lr, betas=(0.9, 0.99), weight_decay=1e-4)]
+        # optimizers = [torch.optim.AdamW(self.sal.parameters(), lr=lr, betas=(0.9, 0.99), weight_decay=1e-3)]
+
+        if self.optimizer_cfg is None:
+            optimizers = [torch.optim.AdamW(self.sal.parameters(), lr=lr, betas=(0.9, 0.99), weight_decay=1e-3)]
+            schedulers = []
+        else:
+            optimizer = instantiate_from_config(self.optimizer_cfg.optimizer, params=self.sal.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,
+                pc: torch.FloatTensor,
+                feats: torch.FloatTensor,
+                volume_queries: torch.FloatTensor):
+
+        logits, center_pos, posterior = self.sal(pc, feats, volume_queries)
+
+        return posterior, logits
+
+    def encode(self, surface: torch.FloatTensor, sample_posterior=True):
+
+        pc = surface[..., 0:3]
+        feats = surface[..., 3:6]
+
+        latents, center_pos, posterior = self.sal.encode(
+            pc=pc, feats=feats, sample_posterior=sample_posterior
+        )
+
+        return latents
+
+    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.sal.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)]
+                - geo_points (torch.FloatTensor): [bs, n_pts, (3 + 1)]
+
+            batch_idx (int):
+
+            optimizer_idx (int):
+
+        Returns:
+            loss (torch.FloatTensor):
+
+        """
+
+        pc = batch["surface"][..., 0:3]
+        feats = batch["surface"][..., 3:]
+
+        volume_queries = batch["geo_points"][..., 0:3]
+        volume_labels = batch["geo_points"][..., -1]
+
+        posterior, logits = self(
+            pc=pc, feats=feats, volume_queries=volume_queries
+        )
+        aeloss, log_dict_ae = self.loss(posterior, logits, volume_labels, split="train")
+
+        self.log_dict(log_dict_ae, prog_bar=True, logger=True, batch_size=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:
+
+        pc = batch["surface"][..., 0:3]
+        feats = batch["surface"][..., 3:]
+
+        volume_queries = batch["geo_points"][..., 0:3]
+        volume_labels = batch["geo_points"][..., -1]
+
+        posterior, logits = self(
+            pc=pc, feats=feats, volume_queries=volume_queries,
+        )
+        aeloss, log_dict_ae = self.loss(posterior, logits, volume_labels, split="val")
+
+        self.log_dict(log_dict_ae, prog_bar=True, logger=True, batch_size=logits.shape[0],
+                      sync_dist=False, rank_zero_only=True)
+
+        return aeloss
+
+    def point2mesh(self,
+                   pc: torch.FloatTensor,
+                   feats: torch.FloatTensor,
+                   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[Point2MeshOutput]:
+
+        """
+
+        Args:
+            pc:
+            feats:
+            bounds:
+            octree_depth:
+            num_chunks:
+
+        Returns:
+            mesh_outputs (List[MeshOutput]): the mesh outputs list.
+
+        """
+
+        outputs = []
+
+        device = pc.device
+        bs = pc.shape[0]
+
+        # 1. point encoder + latents transformer
+        latents, center_pos, posterior = self.sal.encode(pc, feats)
+        latents = self.sal.decode(latents)  # latents: [bs, num_latents, dim]
+
+        geometric_func = partial(self.sal.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 = Point2MeshOutput()
+            out.mesh_v = mesh_v
+            out.mesh_f = mesh_f
+            out.pc = torch.cat([pc[i], feats[i]], dim=-1).cpu().numpy()
+
+            if center_pos is not None:
+                out.center = center_pos[i].cpu().numpy()
+
+            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.sal.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

third_partys/Michelangelo/michelangelo/models/tsal/tsal_base.py

@@ -0,0 +1,120 @@
+# -*- coding: utf-8 -*-
+
+import torch.nn as nn
+from typing import Tuple, List, Optional
+
+
+class Point2MeshOutput(object):
+    def __init__(self):
+        self.mesh_v = None
+        self.mesh_f = None
+        self.center = None
+        self.pc = None
+
+
+class Latent2MeshOutput(object):
+
+    def __init__(self):
+        self.mesh_v = None
+        self.mesh_f = None
+
+
+class AlignedMeshOutput(object):
+
+    def __init__(self):
+        self.mesh_v = None
+        self.mesh_f = None
+        self.surface = None
+        self.image = None
+        self.text: Optional[str] = None
+        self.shape_text_similarity: Optional[float] = None
+        self.shape_image_similarity: Optional[float] = None
+
+
+class ShapeAsLatentPLModule(nn.Module):
+    latent_shape: Tuple[int]
+
+    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
+
+
+class ShapeAsLatentModule(nn.Module):
+    latent_shape: Tuple[int, int]
+
+    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
+
+
+class AlignedShapeAsLatentPLModule(nn.Module):
+    latent_shape: Tuple[int]
+
+    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
+
+
+class AlignedShapeAsLatentModule(nn.Module):
+    shape_model: ShapeAsLatentModule
+    latent_shape: Tuple[int, int]
+
+    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
+
+
+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

third_partys/Michelangelo/michelangelo/utils/__init__.py

@@ -0,0 +1,4 @@
+# -*- coding: utf-8 -*-
+
+from .misc import get_config_from_file
+from .misc import instantiate_from_config

third_partys/Michelangelo/michelangelo/utils/eval.py

@@ -0,0 +1,12 @@
+# -*- coding: utf-8 -*-
+
+import torch
+
+
+def compute_psnr(x, y, data_range: float = 2, eps: float = 1e-7):
+
+    mse = torch.mean((x - y) ** 2)
+    psnr = 10 * torch.log10(data_range / (mse + eps))
+
+    return psnr
+

third_partys/Michelangelo/michelangelo/utils/io.py

@@ -0,0 +1,47 @@
+# -*- coding: utf-8 -*-
+
+import os
+import io
+import tarfile
+import json
+import numpy as np
+import numpy.lib.format
+
+
+def mkdir(path):
+    os.makedirs(path, exist_ok=True)
+    return path
+
+
+def npy_loads(data):
+    stream = io.BytesIO(data)
+    return np.lib.format.read_array(stream)
+
+
+def npz_loads(data):
+    return np.load(io.BytesIO(data))
+
+
+def json_loads(data):
+    return json.loads(data)
+
+
+def load_json(filepath):
+    with open(filepath, "r") as f:
+        data = json.load(f)
+        return data
+
+
+def write_json(filepath, data):
+    with open(filepath, "w") as f:
+        json.dump(data, f, indent=2)
+
+
+def extract_tar(tar_path, tar_cache_folder):
+
+    with tarfile.open(tar_path, "r") as tar:
+        tar.extractall(path=tar_cache_folder)
+
+    tar_uids = sorted(os.listdir(tar_cache_folder))
+    print(f"extract tar: {tar_path} to {tar_cache_folder}")
+    return tar_uids

third_partys/Michelangelo/michelangelo/utils/misc.py

@@ -0,0 +1,103 @@
+# -*- coding: utf-8 -*-
+
+import importlib
+from omegaconf import OmegaConf, DictConfig, ListConfig
+
+import torch
+import torch.distributed as dist
+from typing import Union
+
+
+def get_config_from_file(config_file: str) -> Union[DictConfig, ListConfig]:
+    config_file = OmegaConf.load(config_file)
+
+    if 'base_config' in config_file.keys():
+        if config_file['base_config'] == "default_base":
+            base_config = OmegaConf.create()
+            # base_config = get_default_config()
+        elif config_file['base_config'].endswith(".yaml"):
+            base_config = get_config_from_file(config_file['base_config'])
+        else:
+            raise ValueError(f"{config_file} must be `.yaml` file or it contains `base_config` key.")
+
+        config_file = {key: value for key, value in config_file if key != "base_config"}
+
+        return OmegaConf.merge(base_config, config_file)
+
+    return config_file
+
+
+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

third_partys/Michelangelo/michelangelo/utils/visualizers/__init__.py

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

third_partys/Michelangelo/michelangelo/utils/visualizers/color_util.py

@@ -0,0 +1,43 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+# Helper functions
+def get_colors(inp, colormap="viridis", normalize=True, vmin=None, vmax=None):
+    colormap = plt.cm.get_cmap(colormap)
+    if normalize:
+        vmin = np.min(inp)
+        vmax = np.max(inp)
+
+    norm = plt.Normalize(vmin, vmax)
+    return colormap(norm(inp))[:, :3]
+
+
+def gen_checkers(n_checkers_x, n_checkers_y, width=256, height=256):
+    # tex dims need to be power of two.
+    array = np.ones((width, height, 3), dtype='float32')
+
+    # width in texels of each checker
+    checker_w = width / n_checkers_x
+    checker_h = height / n_checkers_y
+
+    for y in range(height):
+        for x in range(width):
+            color_key = int(x / checker_w) + int(y / checker_h)
+            if color_key % 2 == 0:
+                array[x, y, :] = [1., 0.874, 0.0]
+            else:
+                array[x, y, :] = [0., 0., 0.]
+    return array
+
+
+def gen_circle(width=256, height=256):
+    xx, yy = np.mgrid[:width, :height]
+    circle = (xx - width / 2 + 0.5) ** 2 + (yy - height / 2 + 0.5) ** 2
+    array = np.ones((width, height, 4), dtype='float32')
+    array[:, :, 0] = (circle <= width)
+    array[:, :, 1] = (circle <= width)
+    array[:, :, 2] = (circle <= width)
+    array[:, :, 3] = circle <= width
+    return array
+

third_partys/Michelangelo/michelangelo/utils/visualizers/html_util.py

@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+import io
+import base64
+import numpy as np
+from PIL import Image
+
+
+def to_html_frame(content):
+
+    html_frame = f"""
+    <html>
+      <body>
+        {content}
+      </body>
+    </html>
+    """
+
+    return html_frame
+
+
+def to_single_row_table(caption: str, content: str):
+
+    table_html = f"""
+    <table border = "1">
+        <caption>{caption}</caption>
+        <tr>
+            <td>{content}</td>
+        </tr>
+    </table>
+    """
+
+    return table_html
+
+
+def to_image_embed_tag(image: np.ndarray):
+
+    # Convert np.ndarray to bytes
+    img = Image.fromarray(image)
+    raw_bytes = io.BytesIO()
+    img.save(raw_bytes, "PNG")
+
+    # Encode bytes to base64
+    image_base64 = base64.b64encode(raw_bytes.getvalue()).decode("utf-8")
+
+    image_tag = f"""
+    <img src="data:image/png;base64,{image_base64}" alt="Embedded Image">
+    """
+
+    return image_tag

third_partys/Michelangelo/michelangelo/utils/visualizers/pythreejs_viewer.py

@@ -0,0 +1,534 @@
+import numpy as np
+from ipywidgets import embed
+import pythreejs as p3s
+import uuid
+
+from .color_util import get_colors, gen_circle, gen_checkers
+
+
+EMBED_URL = "https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@1.0.1/dist/embed-amd.js"
+
+
+class PyThreeJSViewer(object):
+
+    def __init__(self, settings, render_mode="WEBSITE"):
+        self.render_mode = render_mode
+        self.__update_settings(settings)
+        self._light = p3s.DirectionalLight(color='white', position=[0, 0, 1], intensity=0.6)
+        self._light2 = p3s.AmbientLight(intensity=0.5)
+        self._cam = p3s.PerspectiveCamera(position=[0, 0, 1], lookAt=[0, 0, 0], fov=self.__s["fov"],
+                                          aspect=self.__s["width"] / self.__s["height"], children=[self._light])
+        self._orbit = p3s.OrbitControls(controlling=self._cam)
+        self._scene = p3s.Scene(children=[self._cam, self._light2], background=self.__s["background"])  # "#4c4c80"
+        self._renderer = p3s.Renderer(camera=self._cam, scene=self._scene, controls=[self._orbit],
+                                      width=self.__s["width"], height=self.__s["height"],
+                                      antialias=self.__s["antialias"])
+
+        self.__objects = {}
+        self.__cnt = 0
+
+    def jupyter_mode(self):
+        self.render_mode = "JUPYTER"
+
+    def offline(self):
+        self.render_mode = "OFFLINE"
+
+    def website(self):
+        self.render_mode = "WEBSITE"
+
+    def __get_shading(self, shading):
+        shad = {"flat": True, "wireframe": False, "wire_width": 0.03, "wire_color": "black",
+                "side": 'DoubleSide', "colormap": "viridis", "normalize": [None, None],
+                "bbox": False, "roughness": 0.5, "metalness": 0.25, "reflectivity": 1.0,
+                "line_width": 1.0, "line_color": "black",
+                "point_color": "red", "point_size": 0.01, "point_shape": "circle",
+                "text_color": "red"
+                }
+        for k in shading:
+            shad[k] = shading[k]
+        return shad
+
+    def __update_settings(self, settings={}):
+        sett = {"width": 600, "height": 600, "antialias": True, "scale": 1.5, "background": "#ffffff",
+                "fov": 30}
+        for k in settings:
+            sett[k] = settings[k]
+        self.__s = sett
+
+    def __add_object(self, obj, parent=None):
+        if not parent:  # Object is added to global scene and objects dict
+            self.__objects[self.__cnt] = obj
+            self.__cnt += 1
+            self._scene.add(obj["mesh"])
+        else:  # Object is added to parent object and NOT to objects dict
+            parent.add(obj["mesh"])
+
+        self.__update_view()
+
+        if self.render_mode == "JUPYTER":
+            return self.__cnt - 1
+        elif self.render_mode == "WEBSITE":
+            return self
+
+    def __add_line_geometry(self, lines, shading, obj=None):
+        lines = lines.astype("float32", copy=False)
+        mi = np.min(lines, axis=0)
+        ma = np.max(lines, axis=0)
+
+        geometry = p3s.LineSegmentsGeometry(positions=lines.reshape((-1, 2, 3)))
+        material = p3s.LineMaterial(linewidth=shading["line_width"], color=shading["line_color"])
+        # , vertexColors='VertexColors'),
+        lines = p3s.LineSegments2(geometry=geometry, material=material)  # type='LinePieces')
+        line_obj = {"geometry": geometry, "mesh": lines, "material": material,
+                    "max": ma, "min": mi, "type": "Lines", "wireframe": None}
+
+        if obj:
+            return self.__add_object(line_obj, obj), line_obj
+        else:
+            return self.__add_object(line_obj)
+
+    def __update_view(self):
+        if len(self.__objects) == 0:
+            return
+        ma = np.zeros((len(self.__objects), 3))
+        mi = np.zeros((len(self.__objects), 3))
+        for r, obj in enumerate(self.__objects):
+            ma[r] = self.__objects[obj]["max"]
+            mi[r] = self.__objects[obj]["min"]
+        ma = np.max(ma, axis=0)
+        mi = np.min(mi, axis=0)
+        diag = np.linalg.norm(ma - mi)
+        mean = ((ma - mi) / 2 + mi).tolist()
+        scale = self.__s["scale"] * (diag)
+        self._orbit.target = mean
+        self._cam.lookAt(mean)
+        self._cam.position = [mean[0], mean[1], mean[2] + scale]
+        self._light.position = [mean[0], mean[1], mean[2] + scale]
+
+        self._orbit.exec_three_obj_method('update')
+        self._cam.exec_three_obj_method('updateProjectionMatrix')
+
+    def __get_bbox(self, v):
+        m = np.min(v, axis=0)
+        M = np.max(v, axis=0)
+
+        # Corners of the bounding box
+        v_box = np.array([[m[0], m[1], m[2]], [M[0], m[1], m[2]], [M[0], M[1], m[2]], [m[0], M[1], m[2]],
+                          [m[0], m[1], M[2]], [M[0], m[1], M[2]], [M[0], M[1], M[2]], [m[0], M[1], M[2]]])
+
+        f_box = np.array([[0, 1], [1, 2], [2, 3], [3, 0], [4, 5], [5, 6], [6, 7], [7, 4],
+                          [0, 4], [1, 5], [2, 6], [7, 3]], dtype=np.uint32)
+        return v_box, f_box
+
+    def __get_colors(self, v, f, c, sh):
+        coloring = "VertexColors"
+        if type(c) == np.ndarray and c.size == 3:  # Single color
+            colors = np.ones_like(v)
+            colors[:, 0] = c[0]
+            colors[:, 1] = c[1]
+            colors[:, 2] = c[2]
+            # print("Single colors")
+        elif type(c) == np.ndarray and len(c.shape) == 2 and c.shape[1] == 3:  # Color values for
+            if c.shape[0] == f.shape[0]:  # faces
+                colors = np.hstack([c, c, c]).reshape((-1, 3))
+                coloring = "FaceColors"
+                # print("Face color values")
+            elif c.shape[0] == v.shape[0]:  # vertices
+                colors = c
+                # print("Vertex color values")
+            else:  # Wrong size, fallback
+                print("Invalid color array given! Supported are numpy arrays.", type(c))
+                colors = np.ones_like(v)
+                colors[:, 0] = 1.0
+                colors[:, 1] = 0.874
+                colors[:, 2] = 0.0
+        elif type(c) == np.ndarray and c.size == f.shape[0]:  # Function values for faces
+            normalize = sh["normalize"][0] != None and sh["normalize"][1] != None
+            cc = get_colors(c, sh["colormap"], normalize=normalize,
+                            vmin=sh["normalize"][0], vmax=sh["normalize"][1])
+            # print(cc.shape)
+            colors = np.hstack([cc, cc, cc]).reshape((-1, 3))
+            coloring = "FaceColors"
+            # print("Face function values")
+        elif type(c) == np.ndarray and c.size == v.shape[0]:  # Function values for vertices
+            normalize = sh["normalize"][0] != None and sh["normalize"][1] != None
+            colors = get_colors(c, sh["colormap"], normalize=normalize,
+                                vmin=sh["normalize"][0], vmax=sh["normalize"][1])
+            # print("Vertex function values")
+
+        else:
+            colors = np.ones_like(v)
+            colors[:, 0] = 1.0
+            colors[:, 1] = 0.874
+            colors[:, 2] = 0.0
+
+            # No color
+            if c is not None:
+                print("Invalid color array given! Supported are numpy arrays.", type(c))
+
+        return colors, coloring
+
+    def __get_point_colors(self, v, c, sh):
+        v_color = True
+        if c is None:  # No color given, use global color
+            # conv = mpl.colors.ColorConverter()
+            colors = sh["point_color"]  # np.array(conv.to_rgb(sh["point_color"]))
+            v_color = False
+        elif isinstance(c, str):  # No color given, use global color
+            # conv = mpl.colors.ColorConverter()
+            colors = c  # np.array(conv.to_rgb(c))
+            v_color = False
+        elif type(c) == np.ndarray and len(c.shape) == 2 and c.shape[0] == v.shape[0] and c.shape[1] == 3:
+            # Point color
+            colors = c.astype("float32", copy=False)
+
+        elif isinstance(c, np.ndarray) and len(c.shape) == 2 and c.shape[0] == v.shape[0] and c.shape[1] != 3:
+            # Function values for vertices, but the colors are features
+            c_norm = np.linalg.norm(c, ord=2, axis=-1)
+            normalize = sh["normalize"][0] != None and sh["normalize"][1] != None
+            colors = get_colors(c_norm, sh["colormap"], normalize=normalize,
+                                vmin=sh["normalize"][0], vmax=sh["normalize"][1])
+            colors = colors.astype("float32", copy=False)
+
+        elif type(c) == np.ndarray and c.size == v.shape[0]:  # Function color
+            normalize = sh["normalize"][0] != None and sh["normalize"][1] != None
+            colors = get_colors(c, sh["colormap"], normalize=normalize,
+                                vmin=sh["normalize"][0], vmax=sh["normalize"][1])
+            colors = colors.astype("float32", copy=False)
+            # print("Vertex function values")
+
+        else:
+            print("Invalid color array given! Supported are numpy arrays.", type(c))
+            colors = sh["point_color"]
+            v_color = False
+
+        return colors, v_color
+
+    def add_mesh(self, v, f, c=None, uv=None, n=None, shading={}, texture_data=None, **kwargs):
+        shading.update(kwargs)
+        sh = self.__get_shading(shading)
+        mesh_obj = {}
+
+        # it is a tet
+        if v.shape[1] == 3 and f.shape[1] == 4:
+            f_tmp = np.ndarray([f.shape[0] * 4, 3], dtype=f.dtype)
+            for i in range(f.shape[0]):
+                f_tmp[i * 4 + 0] = np.array([f[i][1], f[i][0], f[i][2]])
+                f_tmp[i * 4 + 1] = np.array([f[i][0], f[i][1], f[i][3]])
+                f_tmp[i * 4 + 2] = np.array([f[i][1], f[i][2], f[i][3]])
+                f_tmp[i * 4 + 3] = np.array([f[i][2], f[i][0], f[i][3]])
+            f = f_tmp
+
+        if v.shape[1] == 2:
+            v = np.append(v, np.zeros([v.shape[0], 1]), 1)
+
+        # Type adjustment vertices
+        v = v.astype("float32", copy=False)
+
+        # Color setup
+        colors, coloring = self.__get_colors(v, f, c, sh)
+
+        # Type adjustment faces and colors
+        c = colors.astype("float32", copy=False)
+
+        # Material and geometry setup
+        ba_dict = {"color": p3s.BufferAttribute(c)}
+        if coloring == "FaceColors":
+            verts = np.zeros((f.shape[0] * 3, 3), dtype="float32")
+            for ii in range(f.shape[0]):
+                # print(ii*3, f[ii])
+                verts[ii * 3] = v[f[ii, 0]]
+                verts[ii * 3 + 1] = v[f[ii, 1]]
+                verts[ii * 3 + 2] = v[f[ii, 2]]
+            v = verts
+        else:
+            f = f.astype("uint32", copy=False).ravel()
+            ba_dict["index"] = p3s.BufferAttribute(f, normalized=False)
+
+        ba_dict["position"] = p3s.BufferAttribute(v, normalized=False)
+
+        if uv is not None:
+            uv = (uv - np.min(uv)) / (np.max(uv) - np.min(uv))
+            if texture_data is None:
+                texture_data = gen_checkers(20, 20)
+            tex = p3s.DataTexture(data=texture_data, format="RGBFormat", type="FloatType")
+            material = p3s.MeshStandardMaterial(map=tex, reflectivity=sh["reflectivity"], side=sh["side"],
+                                                roughness=sh["roughness"], metalness=sh["metalness"],
+                                                flatShading=sh["flat"],
+                                                polygonOffset=True, polygonOffsetFactor=1, polygonOffsetUnits=5)
+            ba_dict["uv"] = p3s.BufferAttribute(uv.astype("float32", copy=False))
+        else:
+            material = p3s.MeshStandardMaterial(vertexColors=coloring, reflectivity=sh["reflectivity"],
+                                                side=sh["side"], roughness=sh["roughness"], metalness=sh["metalness"],
+                                                flatShading=sh["flat"],
+                                                polygonOffset=True, polygonOffsetFactor=1, polygonOffsetUnits=5)
+
+        if type(n) != type(None) and coloring == "VertexColors":  # TODO: properly handle normals for FaceColors as well
+            ba_dict["normal"] = p3s.BufferAttribute(n.astype("float32", copy=False), normalized=True)
+
+        geometry = p3s.BufferGeometry(attributes=ba_dict)
+
+        if coloring == "VertexColors" and type(n) == type(None):
+            geometry.exec_three_obj_method('computeVertexNormals')
+        elif coloring == "FaceColors" and type(n) == type(None):
+            geometry.exec_three_obj_method('computeFaceNormals')
+
+        # Mesh setup
+        mesh = p3s.Mesh(geometry=geometry, material=material)
+
+        # Wireframe setup
+        mesh_obj["wireframe"] = None
+        if sh["wireframe"]:
+            wf_geometry = p3s.WireframeGeometry(mesh.geometry)  # WireframeGeometry
+            wf_material = p3s.LineBasicMaterial(color=sh["wire_color"], linewidth=sh["wire_width"])
+            wireframe = p3s.LineSegments(wf_geometry, wf_material)
+            mesh.add(wireframe)
+            mesh_obj["wireframe"] = wireframe
+
+        # Bounding box setup
+        if sh["bbox"]:
+            v_box, f_box = self.__get_bbox(v)
+            _, bbox = self.add_edges(v_box, f_box, sh, mesh)
+            mesh_obj["bbox"] = [bbox, v_box, f_box]
+
+        # Object setup
+        mesh_obj["max"] = np.max(v, axis=0)
+        mesh_obj["min"] = np.min(v, axis=0)
+        mesh_obj["geometry"] = geometry
+        mesh_obj["mesh"] = mesh
+        mesh_obj["material"] = material
+        mesh_obj["type"] = "Mesh"
+        mesh_obj["shading"] = sh
+        mesh_obj["coloring"] = coloring
+        mesh_obj["arrays"] = [v, f, c]  # TODO replays with proper storage or remove if not needed
+
+        return self.__add_object(mesh_obj)
+
+    def add_lines(self, beginning, ending, shading={}, obj=None, **kwargs):
+        shading.update(kwargs)
+        if len(beginning.shape) == 1:
+            if len(beginning) == 2:
+                beginning = np.array([[beginning[0], beginning[1], 0]])
+        else:
+            if beginning.shape[1] == 2:
+                beginning = np.append(
+                    beginning, np.zeros([beginning.shape[0], 1]), 1)
+        if len(ending.shape) == 1:
+            if len(ending) == 2:
+                ending = np.array([[ending[0], ending[1], 0]])
+        else:
+            if ending.shape[1] == 2:
+                ending = np.append(
+                    ending, np.zeros([ending.shape[0], 1]), 1)
+
+        sh = self.__get_shading(shading)
+        lines = np.hstack([beginning, ending])
+        lines = lines.reshape((-1, 3))
+        return self.__add_line_geometry(lines, sh, obj)
+
+    def add_edges(self, vertices, edges, shading={}, obj=None, **kwargs):
+        shading.update(kwargs)
+        if vertices.shape[1] == 2:
+            vertices = np.append(
+                vertices, np.zeros([vertices.shape[0], 1]), 1)
+        sh = self.__get_shading(shading)
+        lines = np.zeros((edges.size, 3))
+        cnt = 0
+        for e in edges:
+            lines[cnt, :] = vertices[e[0]]
+            lines[cnt + 1, :] = vertices[e[1]]
+            cnt += 2
+        return self.__add_line_geometry(lines, sh, obj)
+
+    def add_points(self, points, c=None, shading={}, obj=None, **kwargs):
+        shading.update(kwargs)
+        if len(points.shape) == 1:
+            if len(points) == 2:
+                points = np.array([[points[0], points[1], 0]])
+        else:
+            if points.shape[1] == 2:
+                points = np.append(
+                    points, np.zeros([points.shape[0], 1]), 1)
+        sh = self.__get_shading(shading)
+        points = points.astype("float32", copy=False)
+        mi = np.min(points, axis=0)
+        ma = np.max(points, axis=0)
+
+        g_attributes = {"position": p3s.BufferAttribute(points, normalized=False)}
+        m_attributes = {"size": sh["point_size"]}
+
+        if sh["point_shape"] == "circle":  # Plot circles
+            tex = p3s.DataTexture(data=gen_circle(16, 16), format="RGBAFormat", type="FloatType")
+            m_attributes["map"] = tex
+            m_attributes["alphaTest"] = 0.5
+            m_attributes["transparency"] = True
+        else:  # Plot squares
+            pass
+
+        colors, v_colors = self.__get_point_colors(points, c, sh)
+        if v_colors:  # Colors per point
+            m_attributes["vertexColors"] = 'VertexColors'
+            g_attributes["color"] = p3s.BufferAttribute(colors, normalized=False)
+
+        else:  # Colors for all points
+            m_attributes["color"] = colors
+
+        material = p3s.PointsMaterial(**m_attributes)
+        geometry = p3s.BufferGeometry(attributes=g_attributes)
+        points = p3s.Points(geometry=geometry, material=material)
+        point_obj = {"geometry": geometry, "mesh": points, "material": material,
+                     "max": ma, "min": mi, "type": "Points", "wireframe": None}
+
+        if obj:
+            return self.__add_object(point_obj, obj), point_obj
+        else:
+            return self.__add_object(point_obj)
+
+    def remove_object(self, obj_id):
+        if obj_id not in self.__objects:
+            print("Invalid object id. Valid ids are: ", list(self.__objects.keys()))
+            return
+        self._scene.remove(self.__objects[obj_id]["mesh"])
+        del self.__objects[obj_id]
+        self.__update_view()
+
+    def reset(self):
+        for obj_id in list(self.__objects.keys()).copy():
+            self._scene.remove(self.__objects[obj_id]["mesh"])
+            del self.__objects[obj_id]
+        self.__update_view()
+
+    def update_object(self, oid=0, vertices=None, colors=None, faces=None):
+        obj = self.__objects[oid]
+        if type(vertices) != type(None):
+            if obj["coloring"] == "FaceColors":
+                f = obj["arrays"][1]
+                verts = np.zeros((f.shape[0] * 3, 3), dtype="float32")
+                for ii in range(f.shape[0]):
+                    # print(ii*3, f[ii])
+                    verts[ii * 3] = vertices[f[ii, 0]]
+                    verts[ii * 3 + 1] = vertices[f[ii, 1]]
+                    verts[ii * 3 + 2] = vertices[f[ii, 2]]
+                v = verts
+
+            else:
+                v = vertices.astype("float32", copy=False)
+            obj["geometry"].attributes["position"].array = v
+            # self.wireframe.attributes["position"].array = v # Wireframe updates?
+            obj["geometry"].attributes["position"].needsUpdate = True
+        #           obj["geometry"].exec_three_obj_method('computeVertexNormals')
+        if type(colors) != type(None):
+            colors, coloring = self.__get_colors(obj["arrays"][0], obj["arrays"][1], colors, obj["shading"])
+            colors = colors.astype("float32", copy=False)
+            obj["geometry"].attributes["color"].array = colors
+            obj["geometry"].attributes["color"].needsUpdate = True
+        if type(faces) != type(None):
+            if obj["coloring"] == "FaceColors":
+                print("Face updates are currently only possible in vertex color mode.")
+                return
+            f = faces.astype("uint32", copy=False).ravel()
+            print(obj["geometry"].attributes)
+            obj["geometry"].attributes["index"].array = f
+            # self.wireframe.attributes["position"].array = v # Wireframe updates?
+            obj["geometry"].attributes["index"].needsUpdate = True
+        #            obj["geometry"].exec_three_obj_method('computeVertexNormals')
+        # self.mesh.geometry.verticesNeedUpdate = True
+        # self.mesh.geometry.elementsNeedUpdate = True
+        # self.update()
+        if self.render_mode == "WEBSITE":
+            return self
+
+    #    def update(self):
+    #        self.mesh.exec_three_obj_method('update')
+    #        self.orbit.exec_three_obj_method('update')
+    #        self.cam.exec_three_obj_method('updateProjectionMatrix')
+    #        self.scene.exec_three_obj_method('update')
+
+    def add_text(self, text, shading={}, **kwargs):
+        shading.update(kwargs)
+        sh = self.__get_shading(shading)
+        tt = p3s.TextTexture(string=text, color=sh["text_color"])
+        sm = p3s.SpriteMaterial(map=tt)
+        text = p3s.Sprite(material=sm, scaleToTexture=True)
+        self._scene.add(text)
+
+    # def add_widget(self, widget, callback):
+    #    self.widgets.append(widget)
+    #    widget.observe(callback, names='value')
+
+    #    def add_dropdown(self, options, default, desc, cb):
+    #        widget = widgets.Dropdown(options=options, value=default, description=desc)
+    #        self.__widgets.append(widget)
+    #        widget.observe(cb, names="value")
+    #        display(widget)
+
+    #    def add_button(self, text, cb):
+    #        button = widgets.Button(description=text)
+    #        self.__widgets.append(button)
+    #        button.on_click(cb)
+    #        display(button)
+
+    def to_html(self, imports=True, html_frame=True):
+        # Bake positions (fixes centering bug in offline rendering)
+        if len(self.__objects) == 0:
+            return
+        ma = np.zeros((len(self.__objects), 3))
+        mi = np.zeros((len(self.__objects), 3))
+        for r, obj in enumerate(self.__objects):
+            ma[r] = self.__objects[obj]["max"]
+            mi[r] = self.__objects[obj]["min"]
+        ma = np.max(ma, axis=0)
+        mi = np.min(mi, axis=0)
+        diag = np.linalg.norm(ma - mi)
+        mean = (ma - mi) / 2 + mi
+        for r, obj in enumerate(self.__objects):
+            v = self.__objects[obj]["geometry"].attributes["position"].array
+            v -= mean
+            v += np.array([0.0, .9, 0.0]) #! to move the obj to the center of window
+
+        scale = self.__s["scale"] * (diag)
+        self._orbit.target = [0.0, 0.0, 0.0]
+        self._cam.lookAt([0.0, 0.0, 0.0])
+        # self._cam.position = [0.0, 0.0, scale]
+        self._cam.position = [0.0, 0.5, scale * 1.3] #! show four complete meshes in the window
+        self._light.position = [0.0, 0.0, scale]
+
+        state = embed.dependency_state(self._renderer)
+
+        # Somehow these entries are missing when the state is exported in python.
+        # Exporting from the GUI works, so we are inserting the missing entries.
+        for k in state:
+            if state[k]["model_name"] == "OrbitControlsModel":
+                state[k]["state"]["maxAzimuthAngle"] = "inf"
+                state[k]["state"]["maxDistance"] = "inf"
+                state[k]["state"]["maxZoom"] = "inf"
+                state[k]["state"]["minAzimuthAngle"] = "-inf"
+
+        tpl = embed.load_requirejs_template
+        if not imports:
+            embed.load_requirejs_template = ""
+
+        s = embed.embed_snippet(self._renderer, state=state, embed_url=EMBED_URL)
+        # s = embed.embed_snippet(self.__w, state=state)
+        embed.load_requirejs_template = tpl
+
+        if html_frame:
+            s = "<html>\n<body>\n" + s + "\n</body>\n</html>"
+
+        # Revert changes
+        for r, obj in enumerate(self.__objects):
+            v = self.__objects[obj]["geometry"].attributes["position"].array
+            v += mean
+        self.__update_view()
+
+        return s
+
+    def save(self, filename=""):
+        if filename == "":
+            uid = str(uuid.uuid4()) + ".html"
+        else:
+            filename = filename.replace(".html", "")
+            uid = filename + '.html'
+        with open(uid, "w") as f:
+            f.write(self.to_html())
+        print("Plot saved to file %s." % uid)

third_partys/Michelangelo/requirements.txt

@@ -0,0 +1,248 @@
+absl-py==1.4.0
+accelerate==0.20.3
+addict==2.4.0
+aiofiles==23.1.0
+aiohttp==3.8.4
+aiosignal==1.3.1
+altair==5.0.1
+antlr4-python3-runtime==4.9.3
+anyio==3.6.2
+appdirs==1.4.4
+argon2-cffi==21.3.0
+argon2-cffi-bindings==21.2.0
+arrow==1.2.3
+asttokens==2.2.1
+async-timeout==4.0.2
+attrs==22.2.0
+backcall==0.2.0
+beautifulsoup4==4.11.2
+bleach==6.0.0
+braceexpand==0.1.7
+cachetools==5.3.0
+cffi==1.15.1
+charset-normalizer==3.0.1
+click==8.1.3
+coloredlogs==15.0.1
+comm==0.1.2
+configargparse==1.5.3
+contourpy==1.0.7
+controlnet-aux==0.0.5
+cycler==0.11.0
+cython==0.29.33
+dash==2.8.1
+dash-core-components==2.0.0
+dash-html-components==2.0.0
+dash-table==5.0.0
+dataclasses-json==0.6.0
+debugpy==1.6.6
+decorator==5.1.1
+deepspeed==0.8.1
+defusedxml==0.7.1
+deprecated==1.2.14
+diffusers==0.18.2
+docker-pycreds==0.4.0
+einops==0.6.0
+executing==1.2.0
+fastapi==0.101.0
+fastjsonschema==2.16.2
+ffmpy==0.3.1
+filelock==3.9.0
+flask==2.2.3
+flatbuffers==23.5.26
+fonttools==4.38.0
+fqdn==1.5.1
+frozenlist==1.3.3
+fsspec==2023.1.0
+ftfy==6.1.1
+fvcore==0.1.5.post20221221
+gitdb==4.0.10
+gitpython==3.1.31
+google-auth==2.16.1
+google-auth-oauthlib==0.4.6
+gradio==3.39.0
+gradio-client==0.3.0
+grpcio==1.51.3
+h11==0.14.0
+hjson==3.1.0
+httpcore==0.17.3
+httpx==0.24.1
+huggingface-hub==0.16.4
+humanfriendly==10.0
+idna==3.4
+imageio==2.25.1
+importlib-metadata==6.0.0
+iopath==0.1.10
+ipydatawidgets==4.3.3
+ipykernel==6.21.2
+ipython==8.10.0
+ipython-genutils==0.2.0
+ipywidgets==8.0.4
+isoduration==20.11.0
+itsdangerous==2.1.2
+jedi==0.18.2
+jinja2==3.1.2
+joblib==1.2.0
+jsonpointer==2.3
+jsonschema==4.17.3
+jupyter==1.0.0
+jupyter-client==8.0.3
+jupyter-console==6.6.1
+jupyter-core==5.2.0
+jupyter-events==0.6.3
+jupyter-server==2.3.0
+jupyter-server-terminals==0.4.4
+jupyterlab-pygments==0.2.2
+jupyterlab-widgets==3.0.5
+kiwisolver==1.4.4
+lightning-utilities==0.7.1
+linkify-it-py==2.0.2
+lmdb==1.4.1
+markdown==3.4.1
+markdown-it-py==2.2.0
+markupsafe==2.1.2
+marshmallow==3.20.1
+matplotlib==3.6.3
+matplotlib-inline==0.1.6
+mdit-py-plugins==0.3.3
+mdurl==0.1.2
+mesh2sdf==1.1.0
+mistune==2.0.5
+mpmath==1.3.0
+multidict==6.0.4
+mypy-extensions==1.0.0
+nbclassic==0.5.2
+nbclient==0.7.2
+nbconvert==7.2.9
+nbformat==5.5.0
+nest-asyncio==1.5.6
+networkx==3.0
+ninja==1.11.1
+notebook==6.5.2
+notebook-shim==0.2.2
+numpy==1.23.1
+oauthlib==3.2.2
+objaverse==0.0.7
+omegaconf==2.3.0
+onnxruntime==1.15.1
+opencv-contrib-python==4.8.0.74
+opencv-python==4.7.0.72
+orjson==3.9.2
+packaging==21.3
+pandas==1.4.4
+pandocfilters==1.5.0
+parso==0.8.3
+pathtools==0.1.2
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow==9.2.0
+platformdirs==3.0.0
+plotly==5.13.0
+portalocker==2.7.0
+prometheus-client==0.16.0
+prompt-toolkit==3.0.37
+protobuf==3.19.6
+psutil==5.9.4
+ptyprocess==0.7.0
+pure-eval==0.2.2
+py-cpuinfo==9.0.0
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pycparser==2.21
+pydantic==1.10.5
+pydub==0.25.1
+pygltflib==1.16.0
+pygments==2.14.0
+pymeshlab==2022.2.post3
+pyparsing==3.0.9
+pyquaternion==0.9.9
+pyrsistent==0.19.3
+pysdf==0.1.8
+python-dateutil==2.8.2
+python-json-logger==2.0.7
+python-multipart==0.0.6
+pythreejs==2.4.2
+pytz==2022.7.1
+pywavelets==1.4.1
+pyyaml==6.0
+pyzmq==25.0.0
+qtconsole==5.4.0
+qtpy==2.3.0
+regex==2022.10.31
+requests==2.28.2
+requests-oauthlib==1.3.1
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rsa==4.9
+rtree==1.0.1
+safetensors==0.3.1
+scikit-image==0.19.3
+scikit-learn==1.2.1
+scipy==1.10.1
+semantic-version==2.10.0
+send2trash==1.8.0
+sentencepiece==0.1.97
+sentry-sdk==1.15.0
+setproctitle==1.3.2
+setuptools==63.4.3
+sh==2.0.2
+shapely==2.0.1
+six==1.16.0
+smmap==5.0.0
+sniffio==1.3.0
+soupsieve==2.4
+stack-data==0.6.2
+starlette==0.27.0
+sympy==1.12
+tabulate==0.9.0
+tenacity==8.2.1
+tensorboard==2.10.1
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+termcolor==2.3.0
+terminado==0.17.1
+threadpoolctl==3.1.0
+tifffile==2023.2.3
+timm==0.9.2
+tinycss2==1.2.1
+tokenizers==0.13.2
+toolz==0.12.0
+tornado==6.2
+tqdm==4.64.1
+traitlets==5.9.0
+traittypes==0.2.1
+transformers==4.30.2
+trimesh==3.18.3
+triton==1.1.1
+typing-extensions==4.5.0
+typing-inspect==0.9.0
+uc-micro-py==1.0.2
+uri-template==1.2.0
+urllib3==1.26.14
+uvicorn==0.23.2
+wandb==0.13.10
+wcwidth==0.2.6
+webcolors==1.12
+webdataset==0.2.33
+webencodings==0.5.1
+websocket-client==1.5.1
+websockets==11.0.3
+werkzeug==2.2.3
+widgetsnbextension==4.0.5
+wrapt==1.15.0
+xatlas==0.0.7
+yacs==0.1.8
+yarl==1.8.2
+zipp==3.14.0
+# torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
+https://download.pytorch.org/whl/cu116/torch-1.13.1%2Bcu116-cp39-cp39-linux_x86_64.whl#sha256=db457a822d736013b6ffe509053001bc918bdd78fe68967b605f53984a9afac5
+https://download.pytorch.org/whl/cu116/torchvision-0.14.1%2Bcu116-cp39-cp39-linux_x86_64.whl#sha256=a9fc38040e133d1779f131b4497caef830e9e699faf89cd323cd58794ffb305b
+https://download.pytorch.org/whl/cu116/torchaudio-0.13.1%2Bcu116-cp39-cp39-linux_x86_64.whl#sha256=5bc0e29cb78f7c452eeb4f27029c40049770d51553bf840b4ca2edd63da289ee
+# torch-cluster
+https://data.pyg.org/whl/torch-1.13.0%2Bcu116/torch_cluster-1.6.1%2Bpt113cu116-cp39-cp39-linux_x86_64.whl
+# torch-scatter
+https://data.pyg.org/whl/torch-1.13.0%2Bcu116/torch_scatter-2.1.1%2Bpt113cu116-cp39-cp39-linux_x86_64.whl
+torchmetrics==0.11.1
+pytorch_lightning~=1.9.3
+git+https://github.com/pyvista/fast-simplification.git
+git+https://github.com/skoch9/meshplot.git
+git+https://github.com/NVlabs/nvdiffrast/

third_partys/Michelangelo/scripts/infer.sh

@@ -0,0 +1,17 @@
+python inference.py \
+--task reconstruction \
+--config_path ./configs/aligned_shape_latents/shapevae-256.yaml \
+--ckpt_path ./checkpoints/aligned_shape_latents/shapevae-256.ckpt \
+--pointcloud_path ./example_data/surface/surface.npz
+
+python inference.py \
+--task image2mesh \
+--config_path ./configs/image_cond_diffuser_asl/image-ASLDM-256.yaml \
+--ckpt_path ./checkpoints/image_cond_diffuser_asl/image-ASLDM-256.ckpt \
+--image_path ./example_data/image/car.jpg
+
+python inference.py \
+--task text2mesh \
+--config_path ./configs/text_cond_diffuser_asl/text-ASLDM-256.yaml \
+--ckpt_path ./checkpoints/text_cond_diffuser_asl/text-ASLDM-256.ckpt \
+--text "A 3D model of motorcar; Porche Cayenne Turbo."

third_partys/Michelangelo/scripts/inference/image2mesh.sh

@@ -0,0 +1,5 @@
+python inference.py \
+--task image2mesh \
+--config_path ./configs/image_cond_diffuser_asl/image-ASLDM-256.yaml \
+--ckpt_path ./checkpoints/image_cond_diffuser_asl/image-ASLDM-256.ckpt \
+--image_path ./example_data/image/car.jpg