python train.py --env FluidShake --dataf /data/vision/torralba/tactile/physics_flex/data_FluidShake
CUDA_VISIBLE_DEVICES=3 python pruning.py --env RiceGrip --pruning_perc 99
- 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
Full data path:
/data/vision/torralba/tactile/physics_flex/data_FluidShake
For Conda users, we provide an installation script:
bash ./scripts/conda_deps.sh
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
.
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
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}
}