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ADEPT-Dataset-Release

This is the dataset generation code for ADEPT (Approximate Derenderer, Extended Physics, and Tracking).

Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations

Kevin Smith*, Lingjie Mei*, Shunyu Yao, Jiajun Wu, Elizabeth S. Spelke, Joshua B. Tenenbaum, Tomer Ullman (* indicates equal contribution)

NeurIPS 2019, CogSci 2020

Paper BibTeX Website

For the model, see ADEPT-Model-Release

Getting started

  1. Clone this directory and create a conda environment for ADEPT Dataset Generation.

    git clone https://github.com/JerryLingjieMei/ADEPT-Dataset-Release
cd ADEPT-Dataset-Release
conda create -n adept-dataset python=3.10
conda activate adept-dataset

  2. Run the setup script, which install Blender 3.1.2 and python packages used in the scripts.

     bash install.sh

  3. Configure the ffmpeg in dataset.make_video to the one used by your system.

  4. (Optional) By default, the script in run on one machine only. Set up get_host_id in util.misc to run the job on multiple devices. Append to each script with --stride argument will enable job assignment.

  5. (Optional)

    • ShapeNet objects are by default not included unless the scripts below are run.

    • To render ShapeNet objects, please download ShapeNet Core V2 from its official website. Change SHAPE_NET_FOLDER in phys_sim/data/builder/collect_obj.sh to the path of ShapeNet meshes, and run thar script. To turn them into .blend files, run

     # Single machine
 ./blender/blender --background --python render/data/builder/collect_blend.py #Map phase
 ./blender/blender --background --python render/data/builder/collect_blend.py -- --reduce #Reduce phase
 # Multiple machines
 ./blender/blender --background --python render/data/builder/collect_blend.py -- --stride 8 #On each machine
 ./blender/blender --background --python render/data/builder/collect_blend.py -- --reduce --stride 8 #On a single machine

Dataset generation

  1. Generate training set (e.g. with 1000 videos) by running

    # Single machine
./blender/blender --background --python dataset/generate_train.py --end 1000 --
# Multiple machines
./blender/blender --background --python dataset/generate_train.py --end 1000 -- --stride 8 #On each machine

  2. Generate human test set by running

    # Single machine
./blender/blender --background --python dataset/human/generate_human.py --
# Multiple (e.g. 8) machines
./blender/blender --background --python ataset/human/generate_human.py -- --stride 8 #On each machine

Evaluation

  1. Evaluating the relative accuracy on human test set. If you have a experiment output folder that contains .txt files containing the scores of all human test cases, run 
    python3 dataset/human/collect_reuslts.py --summary_folder ${SUMMARY_FOLDER} #Score in SUMMARY_FOLDER/results
python3 dataset/human/collect_reuslts.py --summary_folder ${SUMMARY_FOLDER} --output_folder ${OUTPUT_FOLDER} #Custom output folder
    Or you may get the relative accuracy from a json file that contains a dictionary mapping case name to its score: 
    python3 dataset/human/collect_results.py --summary_file ${SUMMARY_FILE} --output_folder ${OUTPUT_FOLDER} #Custom output folder