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Neural Network course project

This project was done as a course project for CS-852, Fall-2024, UNH to better understand diffusion policy.

In the first step for sanity checking I let the diffusion model to generate some simple data. Then I use the same model to generate actions as a diffusion policy.

Quick start: Diffusion Model Sanity check

my_swissroll.ipynb

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Diffusion Policy

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Diffusion policy reimplementation for the course project of the Neural Network course.

Based on the paper: Visuomotor Policy Learning via Action Diffusion

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Installation

  • For pushT environment install the original diffusion policy codebase from here

Data download

  • download the pusht data and put it inside the data folder
wget https://diffusion-policy.cs.columbia.edu/data/training/pusht.zip
  • convert to hdf5 format: Use the zarr2hdf5.ipynb notebook

How to run the code

  • Option 1: jupyter notebook
train_example.ipynb jupyter
  • Option 2: train.py script
python train.py --hdf5_file data/pusht/pusht_v7_zarr_206.hdf5

File structure

File name Description
my_swissroll.ipynb Sanity check for the diffusion model
train.py Main script to train the model
train_example.ipynb Jupyter notebook with the training example
myddpm.py DDPM scheduler and sampler
noise_predictor_model.py UNet model
pos_encoding.py Sinusoidal positional encoding
film.py Fiture wise Linear Modulation (FiLM)
vision_model.py Vision encoder
pusht_data_utils.py Utility functions from original diffusion policy codebase
data/pusht/pusht_v7_zarr_206.hdf5 pusht data in hdf5 format
zarr2hdf5.ipynb Convert zarr to hdf5 format