Skip to content

Latest commit

 

History

History
136 lines (102 loc) · 6.85 KB

README.md

File metadata and controls

136 lines (102 loc) · 6.85 KB
Raw

batteryNET

Pipeline for deep-learning based image segmentation of volumetric microCT scans of a lithium-metal battery on NERSC.

Description

Code to organize, prepare, and segment multi-scan, larger-than-memory, volumetric dataset. Deep learning segmentation uses pytorch-lightning for code organization, logging and metrics, and multi-GPU parallelization of training and prediction. Also uses moani for volumetric data augmentation and model implementation (currently U-Net++). Training can be done slice-by-slice with 2D models trained on 2D patches either aligned with or perpendicular to the electrode plane, or with 3D models on 3D patches. Sparse labels can be used to minimize required hand-labeling -- less than 0.1% of the entire dataset was labeled. Also includes code for data preparation (alignment/cropping/patching).

The code also has functionality for estimating segmentation uncertainty, both in the model parameters (systemic) and in the input data (aleatoric). I haven't done as much testing on this feature.

This pipeline was developed to segment a dataset consisting of 25 different gigapixel microCT scans of a lithium-metal battery collected over a single charge/discharge cycle lasting 4 hours. However code can be easily adapted for use general volumetric data.

Getting Started

Dependencies

  • This code is written to be used on NERSC Perlmutter, though nothing wrong with trying on a powerful local workstation.

Installing

  • Download repo
git clone https://github.com/lbl-camera/batteryNET.git
  • Create environment
conda env create -f environment.yml

Executing program

  • On NERSC Perlmutter interactive node

    • create the model using settings specified in setup_files/xsection-unet2d.json:
    conda activate batteryNET
python train.py setup_files/xsection-unet2d.json --num_workers 32 --epochs 200
    • test model on data:
    python predict_2d.py setup_files/xsection-unet2d.json

  • Submitting NERSC batch job

    • create the model using settings specified in setup_files/xsection-unet2d.json:
    sbatch batch_scripts/train_unet2D.sh
    • test model on data:
    sbatch batch_scripts/predict_unet2d.sh

Where is the model and segmentation results saved?

  • For example, suppose your code is in /home/yourcode then a folder /home/battery_data will be created

Authors

David Perlmutter (dperl@lbl.gov), Dani Ushizima (dushizima@lbl.gov)

Copyright Notice

batteryNET Copyright (c) 2023, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.

If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov.

NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.

License Agreement

batteryNET Copyright (c) 2023, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

(1) Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

(2) Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

(3) Neither the name of the University of California, Lawrence Berkeley National Laboratory, U.S. Dept. of Energy nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

You are under no obligation whatsoever to provide any bug fixes, patches, or upgrades to the features, functionality or performance of the source code ("Enhancements") to anyone; however, if you choose to make your Enhancements available either publicly, or directly to Lawrence Berkeley National Laboratory, without imposing a separate written license agreement for such Enhancements, then you hereby grant the following license: a non-exclusive, royalty-free perpetual license to install, use, modify, prepare derivative works, incorporate into other computer software, distribute, and sublicense such enhancements or derivative works thereof, in binary and source code form.

References