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README.md
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environment.yml
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main.py
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tester.py
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README.md

Training and Testing ConDor (PyTorch version)

This README contains instructions to train and test the PyTorch version of ConDor.

Loading the environment

Make sure you have Anaconda or Miniconda installed before you proceed to load this environment.

# Creating the conda environment and loading it
conda env create -f environment.yml
conda activate ConDor_torch

ConDor (Full)

Training and testing ConDor full model.

Training

  1. In configs/ConDor.yaml change the dataset path to the downloaded dataset.
# In configs/ConDor.yaml
dataset:
  root: <change_path_to_dataset>
  ...
  ...
  train_files: ["train_plane.h5"]
  test_files: ["val_plane.h5"]
  val_files: ["val_plane.h5"]
  1. Run the code below
# Run the code to train
CUDA_VISIBLE_DEVICES=0 python3 main.py

Testing

  1. The test script tests the model on the validation set and saves the output as ply files for visualization.
# Test the trained model
# weight files are stored at path outputs/<date_of_run_train>/<time_of_run_train>/checkpoints/ 
CUDA_VISIBLE_DEVICES=0 python3 tester.py 'test.weights="<model_weights_path>"' 'test.skip=1'
  1. After running the test script you will find a new directory with stored pointclouds at location outputs/<date_of_run_test>/<time_of_run_test>/pointclouds/