Skip to content

Latest commit

 

History

History
168 lines (127 loc) · 5.72 KB

README.md

File metadata and controls

168 lines (127 loc) · 5.72 KB
Raw

Self-Supervised Representation Learning in Point Clouds for Hierarchical Graph-Based Anatomical Structure Identification

by Niels Rocholl

Supervised by Thomas Markus and Matthia Sabatelli

Graph Representation of Advanced Part Encodings (GRAPE) is a novel framework for identifying anatomical structures in 3D point clouds of human bodies. GRAPE integrates self-supervised learning with graph neural networks to create graph-based representations of 3D objects. Each node in the graph is enriched with latent features derived from the object's parts, generated using a Masked Autoencoder (Point-MAE), which captures the geometric and spatial information of the point cloud.

Environment

This code has been tested on

  • PyTorch: 2.2.2
  • Cuda: 12.1
  • Cudnn: 8
  • Python 3.11

The following image was used in our container:

pytorch/pytorch:2.2.2-cuda12.1-cudnn8-devel

Installation steps

python packages

The followin command should install ALL required packages.

pip install -r requirements.txt

This codebase uses some custom cuda implementations to facilitate GPU acceleration of certain algorithms (FPS, KNN, Chamfer Distance). All these are included in the requirements.txt. Below you can find further information on these algorithms.

Chamfer Distance

This project uses a custom loss function based on the Chamfer Distance, which a method to compute the similarity between two sets of points in 3D space. This function is written in cuda and c++. Our implemtation is based on GRNet, credits go to Haozhe Xie. I created a installable python package from this code, which is already included in our requirements.txt.

However, it can also be installed manually via:

pip install git+https://github.com/nielsRocholl/chamfer-distance.git

FPS

This project uses a c++/cuda implementation of FPS to allow for GPU acceleration of this algorithms. The original implemention can be found on Pointnet2_PyTorch.

I created a installable python package from this code, which is already included in our requirements.txt.

However, it can also be installed manually via:

pip install git+https://github.com/nielsRocholl/fps-cuda.git

KNN (knn_cuda)

We use the following implementation of KNN KNN_CUDA which is already installed through the requirements.txt.

However, it can also be installed manually via:

pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl

How to Run

to train Point-MAE:

python main.py --config cfgs/train_ssl/train_ssl_caesar.yaml --wandb

to generate the Graph Dataset using the trained Point-MAE:

python main.py --config cfgs/build_gnn_ds/build_gnn_ds_ceasar.yaml --build_gnn_ds

to train the Graph Neural Network for node classification:

python main.py --config cfgs/train_gnn/train_gnn_caesar.yaml --train_gnn

How to run specific experiments

run embedding noise experiment:

python main.py --config cfgs/noised_embedding_exp.yaml --train_gnn --k_fold --wandb

How to build the default graph:

python main.py --config cfgs/default_graph/build_default_graph_ceasar.yaml --build_default_graph

General Project Information.

We try to produce high quality, understandable code. Key features included in this repository are:

  • Configuration Management: Utilizes YAML files for configuration management.
  • Experiment Tracking: Incorporates Weights and Biases for precise experiment tracking.
  • Configuration Loaders and Writers: Facilitates easy management of configuration data.
  • Custom Logger: A logger designed to support distributed computing environments.
  • Custom Argument Parser: A argument parser to streamline command-line interactions.
  • Class Registry: A registry to effortlessly map strings to classes.
  • Dataset Class: A dataset class to handle data operations seamlessly.
  • IO Class: A dedicated IO class to manage data input and error handling efficiently.

Each function within this project is documented with comprehensive docstrings, comments, and proper typing. The primary adjustment required post-setup is updating the Weights and Biases Project name within the main.py file. In addition to example text in the configuration files.

The project structure is illustrated below:

toy-problem/
│
├── DATASET.md
├── README.md
├── cfgs
│   ├── dataset_cfgs
│   │   └── part_net.yaml
│   └── train.yaml
│
├── dataset
│   ├── build.py
│   ├── io.py
│   └── part_net.py
│
├── experiments
│   └── train
│       └── cfgs
│           ├── TFBoard
│           │   └── experiment
│           └── experiment
│               └── wandb
│
├── main.py
├── models
│
├── output
│   ├── figures
│   └── trained-models
│
├── requirements.txt
├── tools
│   ├── model_tester.py
│   └── model_trainer.py
│
└── utils
    ├── config.py
    ├── logger.py
    ├── misc.py
    ├── parser.py
    └── registry.py

Troubleshooting Errors

If you encounter the following error:

ImportError: libGL.so.1: cannot open shared object file: No such file or directory

Then run:

apt-get update && apt-get install ffmpeg libsm6 libxext6  -y