Merge remote-tracking branch 'upstream/dev' into releasing/1.1

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

.github/workflows/cron-mmar.yml → .github/workflows/cron-ngc-bundle.yml

@@ -1,15 +1,15 @@
-# daily tests for clara mmar models
-name: cron-mmar
+# daily tests for ngc bundles
+name: cron-ngc-bundle
 
 on:
-  # schedule:
-  #   - cron: "0 2 * * *"  # at 02:00 UTC
+  schedule:
+    - cron: "0 2 * * *"  # at 02:00 UTC
   # Allows you to run this workflow manually from the Actions tab
   workflow_dispatch:
 
 concurrency:
   # automatically cancel the previously triggered workflows when there's a newer version
-  group: mmar-tests-${{ github.event.pull_request.number || github.ref }}
+  group: bundle-tests-${{ github.event.pull_request.number || github.ref }}
   cancel-in-progress: true
 
 jobs:
@@ -33,12 +33,12 @@ jobs:
         key: ${{ runner.os }}-pip-${{ steps.pip-cache.outputs.datew }}
     - name: Install dependencies
       run: |
-        rm -rf /github/home/.cache/torch/hub/mmars/
+        rm -rf /github/home/.cache/torch/hub/bundle/
         python -m pip install --upgrade pip wheel
         python -m pip install -r requirements-dev.txt
-    - name: Loading MMARs
+    - name: Loading Bundles
       run: |
         # clean up temporary files
         $(pwd)/runtests.sh --build --clean
         # run tests
-        python -m tests.ngc_mmar_loading
+        python -m tests.ngc_bundle_download

.github/workflows/pythonapp-gpu.yml

@@ -44,7 +44,7 @@ jobs:
             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==1.13.0 torchvision==0.14.0"
+            pytorch: "torch==1.13.1 torchvision==0.14.1"
             base: "nvcr.io/nvidia/cuda:11.6.1-devel-ubuntu18.04"
     container:
       image: ${{ matrix.base }}

.github/workflows/pythonapp-min.yml

@@ -52,11 +52,11 @@ jobs:
     - if: runner.os == 'windows'
       name: Install torch cpu from pytorch.org (Windows only)
       run: |
-        python -m pip install torch==1.13+cpu -f https://download.pytorch.org/whl/torch_stable.html
+        python -m pip install torch==1.13.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
     - name: Install the dependencies
       run: |
         # min. requirements
-        python -m pip install torch==1.13
+        python -m pip install torch==1.13.1
         python -m pip install -r requirements-min.txt
         python -m pip list
         BUILD_MONAI=0 python setup.py develop  # no compile of extensions

.github/workflows/pythonapp.yml

@@ -88,14 +88,14 @@ jobs:
     - if: runner.os == 'windows'
       name: Install torch cpu from pytorch.org (Windows only)
       run: |
-        python -m pip install torch==1.13.0+cpu torchvision==0.14.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
+        python -m pip install torch==1.13.1+cpu torchvision==0.14.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
     - if: runner.os == 'Linux'
       name: Install itk pre-release (Linux only)
       run: |
         python -m pip install --pre -U itk
     - name: Install the dependencies
       run: |
-        python -m pip install torch==1.13.0 torchvision==0.14.0
+        python -m pip install torch==1.13.1 torchvision==0.14.1
         cat "requirements-dev.txt"
         python -m pip install -r requirements-dev.txt
         python -m pip list

.github/workflows/setupapp.yml

@@ -100,7 +100,7 @@ jobs:
     - name: Install the dependencies
       run: |
         python -m pip install --upgrade pip wheel
-        python -m pip install torch==1.13.0 torchvision==0.14.0
+        python -m pip install torch==1.13.1 torchvision==0.14.1
         python -m pip install -r requirements-dev.txt
     - name: Run quick tests CPU ubuntu
       run: |

CITATION.cff

@@ -48,8 +48,6 @@ preferred-citation:
     family-names: "Xu"
   - given-names: "Ali"
     family-names: "Hatamizadeh"
-  - given-names: "Andriy"
-    family-names: "Myronenko"
   - given-names: "Wentao"
     family-names: "Zhu"
   - given-names: "Yun"

README.md

@@ -4,12 +4,17 @@
 
 **M**edical **O**pen **N**etwork for **AI**
 
+![Supported Python versions](https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/python.svg)
 [![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
-[![CI Build](https://github.com/Project-MONAI/MONAI/workflows/build/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/commits/dev)
-[![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://docs.monai.io/en/latest/?badge=latest)
-[![codecov](https://codecov.io/gh/Project-MONAI/MONAI/branch/dev/graph/badge.svg?token=6FTC7U1JJ4)](https://codecov.io/gh/Project-MONAI/MONAI)
 [![PyPI version](https://badge.fury.io/py/monai.svg)](https://badge.fury.io/py/monai)
-[![conda](https://img.shields.io/conda/vn/conda-forge/monai)](https://anaconda.org/conda-forge/monai)
+[![docker](https://img.shields.io/badge/docker-pull-green.svg?logo=docker&logoColor=white)](https://hub.docker.com/r/projectmonai/monai)
+[![conda](https://img.shields.io/conda/vn/conda-forge/monai?color=green)](https://anaconda.org/conda-forge/monai)
+
+[![premerge](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml)
+[![postmerge](https://img.shields.io/github/checks-status/project-monai/monai/dev?label=postmerge)](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev)
+[![docker](https://github.com/Project-MONAI/MONAI/actions/workflows/docker.yml/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/actions/workflows/docker.yml)
+[![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://docs.monai.io/en/latest/)
+[![codecov](https://codecov.io/gh/Project-MONAI/MONAI/branch/dev/graph/badge.svg?token=6FTC7U1JJ4)](https://codecov.io/gh/Project-MONAI/MONAI)
 
 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 [PyTorch Ecosystem](https://pytorch.org/ecosystem/).
 Its ambitions are:

docs/source/whatsnew.rst

@@ -6,6 +6,7 @@ What's New
 .. toctree::
    :maxdepth: 1
 
+   whatsnew_1_1.md
    whatsnew_1_0.md
    whatsnew_0_9.md
    whatsnew_0_8.md

docs/source/whatsnew_1_0.md

@@ -1,4 +1,4 @@
-# What's new in 1.0 🎉🎉
+# What's new in 1.0
 
 - Model Zoo
 - Auto3DSeg

docs/source/whatsnew_1_1.md

@@ -0,0 +1,75 @@
+# What's new in 1.1 🎉🎉
+
+- Digital pathology workflows
+- Experiment management for MONAI bundle
+- Auto3dSeg enhancements
+- New models in MONAI Model Zoo
+- State-of-the-art SurgToolLoc solution
+
+## Digital pathology workflows
+
+![hovernet](../images/hovernet_diagram.png)
+
+Hover-Net is a model for simultaneous segmentation and classification of nuclei in multi-tissue histology images (Graham et al. Medical Image Analysis, 2019).
+We have added support for this model in MONAI by implementing several new components, enhancing existing ones and providing pipelines and examples for training, validation and inference.
+
+Along with the modules release, new digital pathology analysis tutorials are made available:
+
+- [HoVerNet pipelines](https://github.com/Project-MONAI/tutorials/tree/main/pathology/hovernet) based on MONAI workflows for training, validation and inference
+- [HoVerNet tutorial](https://github.com/Project-MONAI/tutorials/blob/main/pathology/hovernet/hovernet_torch.ipynb) for training, validation and inference
+- NuClick (Interactive Annotation for Pathology) tutorials for [training](https://github.com/Project-MONAI/tutorials/blob/main/pathology/nuclick/nuclick_training_notebook.ipynb)
+and [inference](https://github.com/Project-MONAI/tutorials/blob/main/pathology/nuclick/nuclick_infer.ipynb)
+- Nuclei classification tutorials for [training](https://github.com/Project-MONAI/tutorials/blob/main/pathology/nuclick/nuclei_classification_training_notebook.ipynb)
+and [inference](https://github.com/Project-MONAI/tutorials/blob/main/pathology/nuclick/nuclei_classification_infer.ipynb)
+
+## Experiment management for MONAI bundle
+
+![exp_mgmt](../images/exp_mgmt.png)
+
+In this release, experiment management features are integrated with MONAI bundle.
+It provides essential APIs for managing the end-to-end model bundle lifecycle.
+Users can start tracking experiments by, for example, appending `--tracking "mlflow"` to the training or inference commands to enable the MLFlow-based management.
+By default, MLFlow will track the executed bundle config, model quality measurements, and source code versioning.
+For more details, please refer to the [tutorial](https://github.com/Project-MONAI/tutorials/blob/main/experiment_management/bundle_integrate_mlflow.ipynb).
+
+## Auto3dSeg enhancements
+
+Multiple improvements have been added in `Auto3DSeg` both in terms of
+usability and performance.
+- Multi-modality support is added and applied for
+automated segmentation of the HECKTOR22 challenge dataset, which includes input 3D
+CT and PET images of various resolutions and sizes. A tutorial example of
+running Auto3DSeg on the HECKTOR22 challenge dataset is available in MONAI
+Tutorials. The tutorial is based on [the HECKTOR22 challenge 1st place solution](https://arxiv.org/abs/2209.10809).
+- A new improved version of `Segresnet` Algo is now available in `AutoRunner`.
+In this version, data caching is more efficient and the preprocessing transforms are more flexible.
+The workflow progresses including the timings of steps are written to console output as well as a YAML file.
+- Automatic customization and optimization of the model training configuration
+can be achieved according to the GPU devices used. The feature
+focuses on determining parameters including batch size of model
+training and sliding-window inference, allocated devices for
+data in sliding-window inference. For more details about how to enable it, please see [the tutorials](https://github.com/Project-MONAI/tutorials/tree/main/auto3dseg).
+
+## New models in MONAI Model Zoo
+
+New pretrained models are being created and released [in the Model Zoo](https://monai.io/model-zoo.html).
+Notably,
+
+- The `mednist_reg` model demonstrates how to build image registration workflows in MONAI bundle
+format. The model uses a ResNet and spatial transformer for hand X-ray image registration based on
+[the registration_mednist tutorial](https://github.com/Project-MONAI/tutorials/blob/main/2d_registration/registration_mednist.ipynb),
+- `pathology_nuclei_segmentation_and_classification`,
+  `pathology_nuclick_annotation`, and `pathology_nuclei_classification` bundles
+  are built for [digital pathology image
+  analysis](https://github.com/Project-MONAI/model-zoo/tree/dev/models/pathology_nuclei_segmentation_classification).
+
+For more details about how to use the models, please see [the tutorials](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo).
+
+## State-of-the-art SurgToolLoc solution
+
+[SurgToolLoc](https://surgtoolloc.grand-challenge.org/Home/) is a part of the
+[EndoVis](https://endovis.grand-challenge.org/) challenge at [MICCAI 2022](https://conferences.miccai.org/2022/en/).
+The challenge focuses on endoscopic video analysis and is divided into (1) fully supervised tool classification
+and (2) weakly supervised tool classification/localization.
+Team NVIDIA won prizes by finishing [third](https://surgtoolloc.grand-challenge.org/results/) in both categories.
+The core components of the solutions [are released in MONAI](https://github.com/Project-MONAI/tutorials/tree/main/competitions/MICCAI/surgtoolloc).

monai/apps/auto3dseg/bundle_gen.py

@@ -32,7 +32,7 @@
 from monai.utils import ensure_tuple
 
 logger = get_logger(module_name=__name__)
-ALGO_HASH = os.environ.get("MONAI_ALGO_HASH", "c812e5f")
+ALGO_HASH = os.environ.get("MONAI_ALGO_HASH", "1dde7a1")
 
 __all__ = ["BundleAlgo", "BundleGen"]