Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021)

Related tags

Deep Learning TP-Net
Overview

Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021)

By Jinhyung Park, Dohae Lee, In-Kwon Lee from Yonsei University (Seoul, Korea)

To download our paper, click here.

Introduction

This repository accompanies the paper "Flexible Networks for Learning Physical Dynamics of Deformable Objects", which is currenty under review for publication.
We release the code to train, test, and visualize the result of our model.
The implementation is based on python 3.6, tensorflow 2.3.0., CUDA 10.1, and cuDNN 7.6.1

How To Run

1. Configure Environment

pip install -r requirements.txt

2-1. Download Dataset
Download each dataset from the links below.

After downloading and unzipping each dataset, place each folder as below. 

data/synthetic_dataset/preprocessed_data
data/real_world_dataset/preprocessed_data

2-2 (Alternative) Generating the entire Synthetic Dataset
Alternatively, you can generate the synthetic dataset from scratch by executing the following commands.
The entire process of generating the synthetic dataset takes a couple of hours and consumes approximately 12.43GB.

python3 box2d_simulator/simulator.py                     # generates raw point set data
python3 data/simulation/preprocess_code/preprocess.py    # preprocess data

3. Train
To train TP-Net with the parameters that we used for getting the best performance, execute the following command.
You can change the hyperparameters or other training options by changing config.py.

CUDA_VISIBLE_DEVICES=0 python3 train.py

4. Evaluate & Visualize
To evaluate the trained model on test cases, run

CUDA_VISIBLE_DEVICES=0 python3 ./evaluation/evaluate_synthetic.py --init_data_type=ordered
CUDA_VISIBLE_DEVICES=0 python3 ./evaluation/evaluate_real_world.py --init_data_type=unordered

To visualize the results, run 

python3 ./evaluation/visualize_synthetic.py
python3 ./evaluation/visualize_real_world.py
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Comments
  • Bump tensorflow from 2.3.0 to 2.7.2

    Bump tensorflow from 2.3.0 to 2.7.2

    Bumps tensorflow from 2.3.0 to 2.7.2.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.7.2

    Release 2.7.2

    This releases introduces several vulnerability fixes:

    TensorFlow 2.7.1

    Release 2.7.1

    This releases introduces several vulnerability fixes:

    • Fixes a floating point division by 0 when executing convolution operators (CVE-2022-21725)
    • Fixes a heap OOB read in shape inference for ReverseSequence (CVE-2022-21728)
    • Fixes a heap OOB access in Dequantize (CVE-2022-21726)
    • Fixes an integer overflow in shape inference for Dequantize (CVE-2022-21727)
    • Fixes a heap OOB access in FractionalAvgPoolGrad (CVE-2022-21730)
    • Fixes an overflow and divide by zero in UnravelIndex (CVE-2022-21729)
    • Fixes a type confusion in shape inference for ConcatV2 (CVE-2022-21731)
    • Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)
    • Fixes an OOM due to integer overflow in StringNGrams (CVE-2022-21733)
    • Fixes more issues caused by incomplete validation in boosted trees code (CVE-2021-41208)
    • Fixes an integer overflows in most sparse component-wise ops (CVE-2022-23567)
    • Fixes an integer overflows in AddManySparseToTensorsMap (CVE-2022-23568)

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.7.2

    This releases introduces several vulnerability fixes:

    Release 2.6.4

    This releases introduces several vulnerability fixes:

    • Fixes a code injection in saved_model_cli (CVE-2022-29216)
    • Fixes a missing validation which causes TensorSummaryV2 to crash (CVE-2022-29193)
    • Fixes a missing validation which crashes QuantizeAndDequantizeV4Grad (CVE-2022-29192)
    • Fixes a missing validation which causes denial of service via DeleteSessionTensor (CVE-2022-29194)
    • Fixes a missing validation which causes denial of service via GetSessionTensor (CVE-2022-29191)
    • Fixes a missing validation which causes denial of service via StagePeek (CVE-2022-29195)
    • Fixes a missing validation which causes denial of service via UnsortedSegmentJoin (CVE-2022-29197)
    • Fixes a missing validation which causes denial of service via LoadAndRemapMatrix (CVE-2022-29199)
    • Fixes a missing validation which causes denial of service via SparseTensorToCSRSparseMatrix (CVE-2022-29198)
    • Fixes a missing validation which causes denial of service via LSTMBlockCell (CVE-2022-29200)
    • Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29196)
    • Fixes a CHECK failure in depthwise ops via overflows (CVE-2021-41197)
    • Fixes issues arising from undefined behavior stemming from users supplying invalid resource handles (CVE-2022-29207)
    • Fixes a segfault due to missing support for quantized types (CVE-2022-29205)
    • Fixes a missing validation which results in undefined behavior in SparseTensorDenseAdd (CVE-2022-29206)

    ... (truncated)

    Commits
    • dd7b8a3 Merge pull request #56034 from tensorflow-jenkins/relnotes-2.7.2-15779
    • 1e7d6ea Update RELEASE.md
    • 5085135 Merge pull request #56069 from tensorflow/mm-cp-52488e5072f6fe44411d70c6af09e...
    • adafb45 Merge pull request #56060 from yongtang:curl-7.83.1
    • 01cb1b8 Merge pull request #56038 from tensorflow-jenkins/version-numbers-2.7.2-4733
    • 8c90c2f Update version numbers to 2.7.2
    • 43f3cdc Update RELEASE.md
    • 98b0a48 Insert release notes place-fill
    • dfa5cf3 Merge pull request #56028 from tensorflow/disable-tests-on-r2.7
    • 501a65c Disable timing out tests
    • Additional commits viewable in compare view

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Owner
Jinhyung Park
∙Research Intern at the Computer Graphics Laboratory at Yonsei University
Jinhyung Park
GitHub
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