Merge branch 'Project-MONAI:dev' into 4980-get-wsi-at-mpp

Author: NikolasSchmitz

.github/workflows/cron.yml

@@ -13,16 +13,12 @@ jobs:
     strategy:
       matrix:
         environment:
-          - "PT110+CUDA113"
           - "PT113+CUDA118"
           - "PT210+CUDA121"
           - "PT240+CUDA126"
           - "PTLATEST+CUDA126"
         include:
           # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes
-          - environment: PT110+CUDA113
-            pytorch: "torch==1.10.2 torchvision==0.11.3 --extra-index-url https://download.pytorch.org/whl/cu113"
-            base: "nvcr.io/nvidia/pytorch:21.06-py3"  # CUDA 11.3
           - environment: PT113+CUDA118
             pytorch: "torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu121"
             base: "nvcr.io/nvidia/pytorch:22.10-py3"  # CUDA 11.8

.github/workflows/pythonapp-gpu.yml

@@ -22,20 +22,10 @@ jobs:
     strategy:
       matrix:
         environment:
-          - "PT19+CUDA114DOCKER"
-          - "PT110+CUDA111"
-          - "PT112+CUDA118DOCKER"
           - "PT113+CUDA116"
           - "PT210+CUDA121DOCKER"
         include:
           # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes
-          - environment: PT110+CUDA111
-            pytorch: "torch==1.10.2 torchvision==0.11.3 --extra-index-url https://download.pytorch.org/whl/cu111"
-            base: "nvcr.io/nvidia/cuda:11.1.1-devel-ubuntu18.04"
-          - environment: PT112+CUDA118DOCKER
-            # 22.09: 1.13.0a0+d0d6b1f
-            pytorch: "-h"  # we explicitly set pytorch to -h to avoid pip install error
-            base: "nvcr.io/nvidia/pytorch:22.09-py3"
           - environment: PT113+CUDA116
             pytorch: "torch==1.13.1 torchvision==0.14.1"
             base: "nvcr.io/nvidia/cuda:11.6.1-devel-ubuntu18.04"
@@ -59,8 +49,7 @@ jobs:
         apt-get update
         apt-get install -y wget
 
-        if [ ${{ matrix.environment }} = "PT110+CUDA111" ] || \
-          [ ${{ matrix.environment }} = "PT113+CUDA116" ]
+        if [ ${{ matrix.environment }} = "PT113+CUDA116" ]
         then
         PYVER=3.9 PYSFX=3 DISTUTILS=python3-distutils && \
         apt-get update && apt-get install -y --no-install-recommends \
@@ -94,9 +83,6 @@ jobs:
         python get-pip.py && \
         rm get-pip.py;
         fi
-    - if: matrix.environment == 'PT19+CUDA114DOCKER'
-      name: Optional Cupy dependency (cuda114)
-      run: echo "cupy-cuda114" >> requirements-dev.txt
     - name: Install dependencies
       if: github.event.pull_request.merged != true
       run: |

.github/workflows/pythonapp-min.yml

@@ -124,7 +124,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        pytorch-version: ['1.10.2', '1.11.0', '1.12.1', '1.13', '2.0.1', 'latest']
+        pytorch-version: ['1.13.1', '2.0.1', '2.2.2', '2.3.1', '2.4.1', 'latest']
     timeout-minutes: 40
     steps:
     - uses: actions/checkout@v4

.github/workflows/pythonapp.yml

@@ -155,7 +155,7 @@ jobs:
         # install the latest pytorch for testing
         # however, "pip install monai*.tar.gz" will build cpp/cuda with an isolated
         # fresh torch installation according to pyproject.toml
-        python -m pip install torch>=1.9 torchvision
+        python -m pip install torch>=1.13.1 torchvision
     - name: Check packages
       run: |
         pip uninstall monai

.pre-commit-config.yaml

@@ -66,7 +66,7 @@ repos:
           )$
 
   - repo: https://github.com/hadialqattan/pycln
-    rev: v2.4.0
+    rev: v2.5.0
     hooks:
       - id: pycln
         args: [--config=pyproject.toml]

README.md

@@ -18,12 +18,13 @@
 
 MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/dev/LICENSE) framework for deep learning in healthcare imaging, part of the [PyTorch Ecosystem](https://pytorch.org/ecosystem/).
 Its ambitions are as follows:
+
 - Developing a community of academic, industrial and clinical researchers collaborating on a common foundation;
 - Creating state-of-the-art, end-to-end training workflows for healthcare imaging;
 - Providing researchers with the optimized and standardized way to create and evaluate deep learning models.
 
-
 ## Features
+
 > _Please see [the technical highlights](https://docs.monai.io/en/latest/highlights.html) and [What's New](https://docs.monai.io/en/latest/whatsnew.html) of the milestone releases._
 
 - flexible pre-processing for multi-dimensional medical imaging data;
@@ -32,7 +33,6 @@ Its ambitions are as follows:
 - customizable design for varying user expertise;
 - multi-GPU multi-node data parallelism support.
 
-
 ## Installation
 
 To install [the current release](https://pypi.org/project/monai/), you can simply run:
@@ -53,30 +53,34 @@ Technical documentation is available at [docs.monai.io](https://docs.monai.io).
 
 ## Citation
 
-If you have used MONAI in your research, please cite us! The citation can be exported from: https://arxiv.org/abs/2211.02701.
+If you have used MONAI in your research, please cite us! The citation can be exported from: <https://arxiv.org/abs/2211.02701>.
 
 ## Model Zoo
+
 [The MONAI Model Zoo](https://github.com/Project-MONAI/model-zoo) is a place for researchers and data scientists to share the latest and great models from the community.
 Utilizing [the MONAI Bundle format](https://docs.monai.io/en/latest/bundle_intro.html) makes it easy to [get started](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo) building workflows with MONAI.
 
 ## Contributing
+
 For guidance on making a contribution to MONAI, see the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md).
 
 ## Community
+
 Join the conversation on Twitter/X [@ProjectMONAI](https://twitter.com/ProjectMONAI) or join our [Slack channel](https://forms.gle/QTxJq3hFictp31UM9).
 
 Ask and answer questions over on [MONAI's GitHub Discussions tab](https://github.com/Project-MONAI/MONAI/discussions).
 
 ## Links
-- Website: https://monai.io/
-- API documentation (milestone): https://docs.monai.io/
-- API documentation (latest dev): https://docs.monai.io/en/latest/
-- Code: https://github.com/Project-MONAI/MONAI
-- Project tracker: https://github.com/Project-MONAI/MONAI/projects
-- Issue tracker: https://github.com/Project-MONAI/MONAI/issues
-- Wiki: https://github.com/Project-MONAI/MONAI/wiki
-- Test status: https://github.com/Project-MONAI/MONAI/actions
-- PyPI package: https://pypi.org/project/monai/
-- conda-forge: https://anaconda.org/conda-forge/monai
-- Weekly previews: https://pypi.org/project/monai-weekly/
-- Docker Hub: https://hub.docker.com/r/projectmonai/monai
+
+- Website: <https://monai.io/>
+- API documentation (milestone): <https://docs.monai.io/>
+- API documentation (latest dev): <https://docs.monai.io/en/latest/>
+- Code: <https://github.com/Project-MONAI/MONAI>
+- Project tracker: <https://github.com/Project-MONAI/MONAI/projects>
+- Issue tracker: <https://github.com/Project-MONAI/MONAI/issues>
+- Wiki: <https://github.com/Project-MONAI/MONAI/wiki>
+- Test status: <https://github.com/Project-MONAI/MONAI/actions>
+- PyPI package: <https://pypi.org/project/monai/>
+- conda-forge: <https://anaconda.org/conda-forge/monai>
+- Weekly previews: <https://pypi.org/project/monai-weekly/>
+- Docker Hub: <https://hub.docker.com/r/projectmonai/monai>

docs/requirements.txt

@@ -1,5 +1,5 @@
--f https://download.pytorch.org/whl/cpu/torch-1.12.1%2Bcpu-cp37-cp37m-linux_x86_64.whl
-torch>=1.9
+-f https://download.pytorch.org/whl/cpu/torch-1.13.1%2Bcpu-cp39-cp39-linux_x86_64.whl
+torch>=1.13.1
 pytorch-ignite==0.4.11
 numpy>=1.20
 itk>=5.2

docs/source/handlers.rst

@@ -53,6 +53,12 @@ ROC AUC metrics handler
     :members:
 
 
+Average Precision metric handler
+--------------------------------
+.. autoclass:: AveragePrecision
+    :members:
+
+
 Confusion matrix metrics handler
 --------------------------------
 .. autoclass:: ConfusionMatrix

docs/source/metrics.rst

@@ -80,6 +80,13 @@ Metrics
 .. autoclass:: ROCAUCMetric
     :members:
 
+`Average Precision`
+-------------------
+.. autofunction:: compute_average_precision
+
+.. autoclass:: AveragePrecisionMetric
+    :members:
+
 `Confusion matrix`
 ------------------
 .. autofunction:: get_confusion_matrix

environment-dev.yml

@@ -6,7 +6,7 @@ channels:
   - conda-forge
 dependencies:
   - numpy>=1.24,<2.0
-  - pytorch>=1.9
+  - pytorch>=1.13.1
   - torchio
   - torchvision
   - pytorch-cuda>=11.6