🔥🔥 Explore Graph Sampling Techniques using PyTorch Geometric and Weights & Biases 🪄🐝

If you wish to know how to explore and visualize point cloud datasets using PyTorch Geometric and Weights & Biases, you can check out the following notebook:

Install Required Libraries

We now install PyTorch Geometric according to our PyTorch Version. We also install Weights & Biases.

!pip install -q torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}.html !pip install -q torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}.html !pip install -q torch-cluster -f https://data.pyg.org/whl/torch-${TORCH}.html !pip install -q git+https://github.com/pyg-team/pytorch_geometric.git !pip install -q wandb

Import Libraries

We take a single point cloud from the dataset and compare the KNN-sampled subgraph and radius-sampled subgraph by visualizing the subgraphs as wandb.Html on a Weights & Biases Table.

Next, you can check out the following notebook to learn how to train the PointNet++ architecture using PyTorch Geometric and Weights & Biases