inkid 0.0.7

Identify ink via machine learning

inkid is a Python package and collection of scripts for identifying ink in volumetric CT data using machine learning.

Requirements

Python >=3.8 is required.

Installation

$ git clone https://code.cs.uky.edu/seales-research/ink-id.git && cd ink-id  # From code.cs server
$ git clone https://gitlab.com/educelab/ink-id.git && cd ink-id # From gitlab.com

$ python -m venv .venv              # Create a virtual environment
$ . .venv/bin/activate              # Activate the environment
(.venv) $ pip install -U pip        # Upgrade pip
(.venv) $ pip install -e .          # Install ink-id and dependencies
(.venv) $ deactivate                # When finished, deactivate the environment

Cython modules are automatically built during the installation. After changes to Cython files (.pyx and .pxd), those modules must be rebuilt:

$ python setup.py build_ext --inplace

Usage

The package can be used as a Python library:

import inkid

params = inkid.ops.json_schema('dataSource0.1')
regions = inkid.data.Dataset(['./training_dataset.txt'])

A script is also included for running a training job and/or generating prediction images:

$ inkid-train-and-predict
usage: inkid-train-and-predict [options]

Examples

SLURM Jobs

This code is often used in Singularity containers, run as SLURM jobs on a compute cluster. For documentation of this usage, see singularity/inkid.def.

K-Fold Cross Validation (and Prediction)

inkid/scripts/train_and_predict.py``typically takes a dataset files as input and trains on the specified training regions, validates on the validation regions, and predicts on the prediction regions. However if the ``--cross-validate-on argument is passed, the behavior is slightly different. The nth training source will be removed from the training set and added to the validation and prediction sets. Example:

$ inkid-train-and-predict --training-set ~/data/dri-datasets-drive/LunateSigma/grid-2x5.txt \
     --cross-validate-on 7 \
     --final-prediction-on-all \
     --output ~/data/LunateSigmaGridTest00

It is possible to schedule all of these jobs with one command if using SLURM’s sbatch. Example:

$ ./submit_with_summary.sh sbatch -p P4V12_SKY32M192_L --time=1-00:00:00 --mem=187G --array=0-1 submit.sh \
     --training-set /pscratch/seales_uksr/dri-datasets-drive/Dummy/DummyTest.volpkg/working/DummyTest_1x2Grid.txt \
     --subvolume-shape-voxels 48 48 48 \
     --final-prediction-on-all \
     --prediction-grid-spacing 2 \
     --label-type rgb_values \
     --subvolume-shape-microns 300 20 20 \
     --output /pscratch/seales_uksr/dri-experiments-drive/inkid/results/DummyTest/check_gpu/03

After performing a run for each value of --cross-validate-on, each will have created a subdirectory of output.

Generating Summary Images

There is a script inkid/scripts/misc/create_summary_images.py that takes the parent output directory and will generate various output images combining the cross-validation results. Example:

$ python inkid/scripts/misc/add_k_fold_prediction_images.py ~/data/out/carbon_phantom_col1_test/

Grid Training

When working with only one surface PPM, it is often desirable to split that single region into a grid to be used with k-fold cross-validation. There is a script to automatically create the grid dataset file:

$ python inkid/scripts/split_region_into_grid.py ~/data/dri-datasets-drive/Dummy/DummyTest.volpkg/working/DummyTest.json 1 2

Then use this dataset for standard k-fold cross validation and prediction.

Miscellaneous

There is a dummy test dataset in the DRI Datasets Drive that is meant to be a small volume to quickly validate training and prediction code. If something major has been broken such as dimensions in the neural network model, this will make that clear without having to wait for large volumes to load. Example:

$ ./submit_with_summary.sh sbatch -p P4V12_SKY32M192_L --time=1-00:00:00 --mem=150G submit.sh \
     --training-set $PSCRATCH/seales_uksr/dri-datasets-drive/Dummy/DummyTest.volpkg/working/DummyTest_grid1x2.txt \
     --subvolume-shape-voxels 48 2 2 \
     --final-prediction-on-all \
     --prediction-grid-spacing 2 \
     --label-type rgb_values \
     --cross-validate-on 0 \
     --output ~/temp/test00

Texture a region using an existing trained model (important parts: --model and --skip-training:

$ ./submit_with_summary.sh sbatch -p P4V12_SKY32M192_L --time=1-00:00:00 --mem=187G submit.sh \
     --training-set $PSCRATCH/seales_uksr/dri-datasets-drive/MorganM910/MS910.volpkg/working/segmentation/quire.json \
     --prediction-set $PSCRATCH/seales_uksr/dri-datasets-drive/MorganM910/MS910.volpkg/working/segmentation/p60.json \
     --subvolume-shape 48 48 48 \
     --final-prediction-on-all \
     --prediction-grid-spacing 8 \
     --label-type rgb_values \
     --skip-training \
     --model $PSCRATCH/seales_uksr/dri-experiments-drive/inkid/results/MS910/p60/initial/09/2021-02-08_09.15.07/checkpoints/checkpoint_0_175000.pt \
     --output $PSCRATCH/seales_uksr/dri-experiments-drive/inkid/results/MS910/p60/fromSavedWeights/02

Contributing

When contributing to this repository, please first discuss the change you wish to make via issue, email, or another method with the owners of this repository.

We follow the git branching model described here and document code based on the Google Python Style Guide standards.

License

This package is licensed under the GNU General Public License (GPLv3) - see LICENSE for details.