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‎.gitignore

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# Mask and other files
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clinicadl/clinicadl/resources/masks/*.nii
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clinicadl/clinicadl/resources/masks/*.nii.gz
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‎CHANGELOG

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# Changelog
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Main changes to this code/ project are documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [Unreleased]
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### Added
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-
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-
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-
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### Changed
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-
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-
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### Deprecated
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### Removed
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### Fixed
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### Security
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## ClinicaDL 0.2.0
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### Added
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- New functionality `clinica interpret` to generate saliency maps linked
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to pretrained models based on groups of individual images.
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- New functionality `clinicadl random-search` to sample random networks from a
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predefined hyperparameter space.
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- Slice subparsers for `autoencoder`/`cnn`/`multicnn` to be homogeneous with other
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parsers.
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- roi parser has now `multicnn` option.
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- Add generic options to command line: `--verbose`, `--version` and
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`--logname`.
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### Changed
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- Behaviour of `clinicadl quality-check t1-volume`.
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- Simplify `clinicadl tsvtools` behaviour when using getlabels, split and
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analysis.
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- Update documentation.
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### Fixed
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- Fix broken file when running preprocessing in t1-extensive.
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‎Jenkinsfile

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@@ -157,41 +157,150 @@ pipeline {
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}
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}
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}
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}
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stage('Train tests Linux') {
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agent { label 'linux && gpu' }
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environment {
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PATH = "$HOME/miniconda3/bin:$HOME/miniconda/bin:$PATH"
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}
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steps {
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echo 'Testing train task...'
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sh 'echo "Agent name: ${NODE_NAME}"'
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//sh 'conda env remove --name "clinicadl_test"'
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sh '''#!/usr/bin/env bash
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set +x
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eval "$(conda shell.bash hook)"
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source ./.jenkins/scripts/find_env.sh
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conda activate clinicadl_test
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clinicadl --help
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cd $WORKSPACE/clinicadl/tests
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mkdir -p ./data/dataset
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tar xf /mnt/data/data_CI/dataset/RandomCaps.tar.gz -C ./data/dataset
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cp -r /mnt/data/data_CI/labels_list ./data/
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pytest \
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--junitxml=../../test-reports/test_train_report.xml \
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--verbose \
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--disable-warnings \
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-k "test_train"
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conda deactivate
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'''
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}
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post {
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always {
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junit 'test-reports/test_train_report.xml'
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sh 'rm -rf $WORKSPACE/clinicadl/tests/data/dataset'
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sh 'rm -rf $WORKSPACE/clinicadl/tests/data/labels_list'
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}
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stage('Train / transfer learning / interpretation / random search') {
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stages{
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stage('Train tests Linux') {
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agent { label 'linux && gpu' }
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environment {
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PATH = "$HOME/miniconda3/bin:$HOME/miniconda/bin:$PATH"
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}
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steps {
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echo 'Testing train task...'
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sh 'echo "Agent name: ${NODE_NAME}"'
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//sh 'conda env remove --name "clinicadl_test"'
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sh '''#!/usr/bin/env bash
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set +x
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eval "$(conda shell.bash hook)"
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source ./.jenkins/scripts/find_env.sh
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conda activate clinicadl_test
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clinicadl --help
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cd $WORKSPACE/clinicadl/tests
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mkdir -p ./data/dataset
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tar xf /mnt/data/data_CI/dataset/RandomCaps.tar.gz -C ./data/dataset
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cp -r /mnt/data/data_CI/labels_list ./data/
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pytest \
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--junitxml=../../test-reports/test_train_report.xml \
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--verbose \
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--disable-warnings \
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-k "test_train"
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conda deactivate
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'''
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}
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post {
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always {
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junit 'test-reports/test_train_report.xml'
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sh 'rm -rf $WORKSPACE/clinicadl/tests/data/dataset'
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sh 'rm -rf $WORKSPACE/clinicadl/tests/data/labels_list'
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}
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}
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}
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}
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stage('Transfer learning tests Linux') {
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agent { label 'linux && gpu' }
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environment {
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PATH = "$HOME/miniconda3/bin:$HOME/miniconda/bin:$PATH"
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}
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steps {
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echo 'Testing transfer learning...'
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sh 'echo "Agent name: ${NODE_NAME}"'
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//sh 'conda env remove --name "clinicadl_test"'
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sh '''#!/usr/bin/env bash
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set +x
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eval "$(conda shell.bash hook)"
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source ./.jenkins/scripts/find_env.sh
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conda activate clinicadl_test
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clinicadl --help
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cd $WORKSPACE/clinicadl/tests
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mkdir -p ./data/dataset
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tar xf /mnt/data/data_CI/dataset/RandomCaps.tar.gz -C ./data/dataset
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cp -r /mnt/data/data_CI/labels_list ./data/
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pytest \
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--junitxml=../../test-reports/test_transfer_learning_report.xml \
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--verbose \
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--disable-warnings \
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test_transfer_learning.py
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conda deactivate
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'''
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}
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post {
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always {
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junit 'test-reports/test_transfer_learning_report.xml'
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sh 'rm -rf $WORKSPACE/clinicadl/tests/data/dataset'
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sh 'rm -rf $WORKSPACE/clinicadl/tests/data/labels_list'
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}
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}
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}
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stage('Interpretation tests Linux') {
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agent { label 'linux && gpu' }
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environment {
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PATH = "$HOME/miniconda3/bin:$HOME/miniconda/bin:$PATH"
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}
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steps {
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echo 'Testing interpret task...'
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sh 'echo "Agent name: ${NODE_NAME}"'
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//sh 'conda env remove --name "clinicadl_test"'
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sh '''#!/usr/bin/env bash
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set +x
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eval "$(conda shell.bash hook)"
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source ./.jenkins/scripts/find_env.sh
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conda activate clinicadl_test
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clinicadl --help
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cd $WORKSPACE/clinicadl/tests
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mkdir -p ./data/dataset
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tar xf /mnt/data/data_CI/dataset/RandomCaps.tar.gz -C ./data/dataset
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cp -r /mnt/data/data_CI/labels_list ./data/
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pytest \
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--junitxml=../../test-reports/test_interpret_report.xml \
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--verbose \
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--disable-warnings \
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test_interpret.py
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conda deactivate
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'''
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}
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post {
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always {
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junit 'test-reports/test_interpret_report.xml'
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sh 'rm -rf $WORKSPACE/clinicadl/tests/data/dataset'
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sh 'rm -rf $WORKSPACE/clinicadl/tests/data/labels_list'
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}
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}
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}
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stage('Random search tests Linux') {
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agent { label 'linux && gpu' }
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environment {
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PATH = "$HOME/miniconda3/bin:$HOME/miniconda/bin:$PATH"
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}
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steps {
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echo 'Testing random search...'
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sh 'echo "Agent name: ${NODE_NAME}"'
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//sh 'conda env remove --name "clinicadl_test"'
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sh '''#!/usr/bin/env bash
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set +x
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eval "$(conda shell.bash hook)"
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source ./.jenkins/scripts/find_env.sh
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conda activate clinicadl_test
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clinicadl --help
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cd $WORKSPACE/clinicadl/tests
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mkdir -p ./data/dataset
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tar xf /mnt/data/data_CI/dataset/RandomCaps.tar.gz -C ./data/dataset
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cp -r /mnt/data/data_CI/labels_list ./data/
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pytest \
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--junitxml=../../test-reports/test_random_search_report.xml \
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--verbose \
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--disable-warnings \
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test_random_search.py
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conda deactivate
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'''
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}
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post {
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always {
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junit 'test-reports/test_random_search_report.xml'
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sh 'rm -rf $WORKSPACE/clinicadl/tests/data/dataset'
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sh 'rm -rf $WORKSPACE/clinicadl/tests/data/labels_list'
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}
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}
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}
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}
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}
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}
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}

‎MANIFEST.in

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include clinicadl/clinicadl/preprocessing/t1_extensive/info.json

‎README.md

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* **T1w-weighted MR image preprocessing.** The `preprocessing` task
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processes a dataset of T1 images stored in BIDS format and prepares to
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extract the tensors (see paper for details on the preprocessing). Output
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is stored using the [CAPS](http://www.clinica.run/doc/CAPS/Introduction/)
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is stored using the [CAPS](https://aramislab.paris.inria.fr/clinica/docs/public/latest/CAPS/Introduction/)
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hierarchy.
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* **Quality check of preprocessed data.** The `quality_check` task uses a
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pretrained network [(Fonov et al,
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* **Tensor extraction from preprocessed data.** The `extract` task allows
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to create files in PyTorch format (`.pt`) with different options: the
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complete MRI, 2D slices and/or 3D patches. This files are also stored in
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the [CAPS](http://www.clinica.run/doc/CAPS/Introduction/) hierarchy.
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the [CAPS](https://aramislab.paris.inria.fr/clinica/docs/public/latest/CAPS/Introduction/) hierarchy.
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- **Train & test your classifier**
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* **Train neural networks.** The `train` task is designed to perform

‎clinicadl/clinicadl/VERSION

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0.1.1
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0.2.0rc1

‎clinicadl/clinicadl/__init__.py

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__all__ = ['__version__']
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# Load the Clinica package version
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import sys
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import pkgutil
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__version__ = pkgutil.get_data(__package__, 'VERSION').decode('ascii').strip()
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version = __version__
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# import pkg_resources
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# version = pkg_resources.require("Clinica")[0].version
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# __version__ = version
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# python 3.6 minimum version is required
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if sys.version_info < (3, 6):
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print(f"ClinicaDL {__version__} requires Python 3.6")
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sys.exit(1)

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