/DPI-Net

[ICLR 2019] Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids

Primary LanguageJupyter Notebook

Accelerating DPI-Net

Training

python train.py --env FluidShake --dataf /data/vision/torralba/tactile/physics_flex/data_FluidShake

Pruning

CUDA_VISIBLE_DEVICES=3 python pruning.py --env RiceGrip --pruning_perc 99

Evaluation

  • FluidShake:
CUDA_VISIBLE_DEVICES=3 python eval.py --env FluidShake --epoch 4 --iter 500000 --dataf data/small/fluid_shake/ --model_file ./dump_FluidShake/files_FluidShake/net_epoch_4_iter_500000_pruning_95.pth
  • BoxBath:
CUDA_VISIBLE_DEVICES=3 python eval.py --env BoxBath --epoch 4 --iter 370000 --dataf data/small/box_bath/ --model_file ./dump_BoxBath/files_BoxBath/net_epoch_4_iter_370000_pruning_95.pth
  • RiceGrip:
CUDA_VISIBLE_DEVICES=3 python eval.py --env RiceGrip --epoch 18 --iter 130000 --dataf data/small/rice_grip/ --model_file ./dump_RiceGrip/files_RiceGrip/net_epoch_18_iter_130000_pruning_95.pth
  • pruning_perc: 20 | 50 | 95 | 99

Misc

Full data path:

/data/vision/torralba/tactile/physics_flex/data_FluidShake

Install Dependencies

For Conda users, we provide an installation script:

bash ./scripts/conda_deps.sh

Archive

Evaluation

Go to the root folder of DPI-Net. You can direct run the following command to use the pretrained checkpoint.

bash scripts/eval_FluidFall.sh
bash scripts/eval_BoxBath.sh
bash scripts/eval_FluidShake.sh
bash scripts/eval_RiceGrip.sh

It will first show the grount truth followed by the model rollout. The resulting rollouts will be stored in dump_[env]/eval_[env]/rollout_*, where the ground truth is stored in gt_*.tga and the rollout from the model is pred_*.tga.

Training

You can use the following command to train from scratch. Note that if you are running the script for the first time, it will start by generating training and validation data in parallel using num_workers threads. You will need to change --gen_data to 0 if the data has already been generated.

bash scripts/train_FluidFall.sh
bash scripts/train_BoxBath.sh
bash scripts/train_FluidShake.sh
bash scripts/train_RiceGrip.sh

Citing DPI-Net

If you find this codebase useful in your research, please consider citing:

@inproceedings{li2019learning,
    Title={Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids},
    Author={Li, Yunzhu and Wu, Jiajun and Tedrake, Russ and Tenenbaum, Joshua B and Torralba, Antonio},
    Booktitle = {ICLR},
    Year = {2019}
}