Merge branch 'dev' into mednext

.github/workflows/blossom-ci.yml

@@ -34,8 +34,7 @@ jobs:
       (
         github.actor == 'Nic-Ma' ||
         github.actor == 'wyli' ||
-        github.actor == 'pxLi' ||
-        github.actor == 'YanxuanLiu' ||
+        github.actor == 'wendell-hom' ||
         github.actor == 'KumoLiu'
       )
     steps:

.github/workflows/cron.yml

@@ -16,7 +16,8 @@ jobs:
           - "PT110+CUDA113"
           - "PT113+CUDA118"
           - "PT210+CUDA121"
-          - "PTLATEST+CUDA124"
+          - "PT240+CUDA126"
+          - "PTLATEST+CUDA126"
         include:
           # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes
           - environment: PT110+CUDA113
@@ -28,9 +29,12 @@ jobs:
           - environment: PT210+CUDA121
             pytorch: "pytorch==2.1.0 torchvision==0.16.0 --extra-index-url https://download.pytorch.org/whl/cu121"
             base: "nvcr.io/nvidia/pytorch:23.08-py3"  # CUDA 12.1
-          - environment: PTLATEST+CUDA124
+          - environment: PT240+CUDA126
+            pytorch: "pytorch==2.4.0 torchvision==0.19.0 --extra-index-url https://download.pytorch.org/whl/cu121"
+            base: "nvcr.io/nvidia/pytorch:24.08-py3"  # CUDA 12.6
+          - environment: PTLATEST+CUDA126
             pytorch: "-U torch torchvision --extra-index-url https://download.pytorch.org/whl/cu121"
-            base: "nvcr.io/nvidia/pytorch:24.08-py3"  # CUDA 12.4
+            base: "nvcr.io/nvidia/pytorch:24.10-py3"  # CUDA 12.6
     container:
       image: ${{ matrix.base }}
       options: "--gpus all"
@@ -80,7 +84,7 @@ jobs:
     if: github.repository == 'Project-MONAI/MONAI'
     strategy:
       matrix:
-        container: ["pytorch:23.08", "pytorch:24.08"]
+        container: ["pytorch:23.08", "pytorch:24.08", "pytorch:24.10"]
     container:
       image: nvcr.io/nvidia/${{ matrix.container }}-py3  # testing with the latest pytorch base image
       options: "--gpus all"
@@ -129,7 +133,7 @@ jobs:
     if: github.repository == 'Project-MONAI/MONAI'
     strategy:
       matrix:
-        container: ["pytorch:24.08"]
+        container: ["pytorch:24.10"]
     container:
       image: nvcr.io/nvidia/${{ matrix.container }}-py3  # testing with the latest pytorch base image
       options: "--gpus all"
@@ -233,7 +237,7 @@ jobs:
     if: github.repository == 'Project-MONAI/MONAI'
     needs: cron-gpu  # so that monai itself is verified first
     container:
-      image: nvcr.io/nvidia/pytorch:24.08-py3  # testing with the latest pytorch base image
+      image: nvcr.io/nvidia/pytorch:24.10-py3  # testing with the latest pytorch base image
       options: "--gpus all --ipc=host"
     runs-on: [self-hosted, linux, x64, integration]
     steps:

.github/workflows/weekly-preview.yml

@@ -66,7 +66,7 @@ jobs:
         export YEAR_WEEK=$(date +'%y%U')
         echo "Year week for tag is ${YEAR_WEEK}"
         if ! [[ $YEAR_WEEK =~ ^[0-9]{4}$ ]] ; then echo "Wrong 'year week' format.  Should be 4 digits."; exit 1 ; fi
-        git tag "1.4.dev${YEAR_WEEK}"
+        git tag "1.5.dev${YEAR_WEEK}"
         git log -1
         git tag --list
         python setup.py sdist bdist_wheel

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v5.0.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
@@ -26,33 +26,30 @@ repos:
         args: ['--autofix', '--no-sort-keys', '--indent=4']
       - id: end-of-file-fixer
       - id: mixed-line-ending
-  - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.3.5
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.7.0
     hooks:
     -   id: ruff
         args:
         - --fix
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.3.1
+    rev: v3.19.0
     hooks:
       - id: pyupgrade
-        args: [--py37-plus]
-        name: Upgrade code excluding monai networks
+        args: [--py39-plus, --keep-runtime-typing]
+        name: Upgrade code with exceptions
         exclude: |
           (?x)(
               ^versioneer.py|
               ^monai/_version.py|
-              ^monai/networks/|  # no PEP 604 for torchscript tensorrt
-              ^monai/losses/  # no PEP 604 for torchscript tensorrt
+              ^monai/networks/| # avoid typing rewrites
+              ^monai/apps/detection/utils/anchor_utils.py| # avoid typing rewrites
+              ^tests/test_compute_panoptic_quality.py # avoid typing rewrites
           )
-      - id: pyupgrade
-        args: [--py37-plus, --keep-runtime-typing]
-        name: Upgrade monai networks
-        files: (?x)(^monai/networks/)
 
   - repo: https://github.com/asottile/yesqa
-    rev: v1.4.0
+    rev: v1.5.0
     hooks:
       - id: yesqa
         name: Unused noqa

CHANGELOG.md

@@ -5,6 +5,98 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ## [Unreleased]
 
+## [1.4.0] - 2024-10-17
+## What's Changed
+### Added
+* Implemented Conjugate Gradient Solver to generate confidence maps. (#7876)
+* Added norm parameter to `ResNet` (#7752, #7805)
+* Introduced alpha parameter to `DiceFocalLoss` for improved flexibility (#7841)
+* Integrated Tailored ControlNet Implementations (#7875)
+* Integrated Tailored Auto-Encoder Model (#7861)
+* Integrated Tailored Diffusion U-Net Model (7867)
+* Added Maisi morphological functions (#7893)
+* Added support for downloading bundles from NGC private registry (#7907, #7929, #8076)
+* Integrated generative refactor into the core (#7886, #7962)
+* Made `ViT` and `UNETR` models compatible with TorchScript (#7937)
+* Implemented post-download checks for MONAI bundles and compatibility warnings (#7938)
+* Added NGC prefix argument when downloading bundles (#7974)
+* Added flash attention support in the attention block for improved performance (#7977)
+* Enhanced `MLPBlock` for compatibility with VISTA-3D (#7995)
+* Added support for Neighbor-Aware Calibration Loss (NACL) for calibrated models in segmentation tasks (#7819)
+* Added label_smoothing parameter to `DiceCELoss` for enhanced model calibration (#8000)
+* Add `include_fc` and `use_combined_linear` argument in the `SABlock` (#7996)
+* Added utilities, networks, and an inferer specific to VISTA-3D (#7999, #7987, #8047, #8059, #8021)
+* Integrated a new network, `CellSamWrapper`, for cell-based applications (#7981)
+* Introduced `WriteFileMapping` transform to map between input image paths and their corresponding output paths (#7769)
+* Added `TrtHandler` to accelerate models using TensorRT (#7990, #8064)
+* Added box and points conversion transforms for more flexible spatial manipulation (#8053)
+* Enhanced `RandSimulateLowResolutiond` transform with deterministic support (#8057)
+* Added a contiguous argument to the `Fourier` class to facilitate contiguous tensor outputs (#7969)
+* Allowed `ApplyTransformToPointsd` to receive a sequence of reference keys for more versatile point manipulation (#8063)
+* Made `MetaTensor` an optional print in `DataStats` and `DataStatsd` for more concise logging (#7814)
+#### misc.
+* Refactored Dataset to utilize Compose for handling transforms. (#7784)
+* Combined `map_classes_to_indices` and `generate_label_classes_crop_centers` into a unified function (#7712)
+* Introduced metadata schema directly into the codebase for improved structure and validation (#7409)
+* Renamed `optional_packages_version` to `required_packages_version` for clearer package dependency management. (#7253)
+* Replaced `pkg_resources` with the more modern packaging module for package handling (#7953)
+* Refactored MAISI-related networks to align with the new generative components (#7989, #7993, #8005)
+* Added a badge displaying monthly download statistics to enhance project visibility (#7891)
+### Fixed
+#### transforms
+* Ensured deterministic behavior in `MixUp`, `CutMix`, and `CutOut` transforms (#7813)
+* Applied a minor correction to `AsDiscrete` transform (#7984)
+* Fixed handling of integer weightmaps in `RandomWeightedCrop` (#8097)
+* Resolved data type bug in `ScaleIntensityRangePercentile` (#8109)
+#### data
+* Fixed negative strides issue in the `NrrdReader` (#7809)
+* Addressed wsireader issue with retrieving MPP (7921)
+* Ensured location is returned as a tuple in wsireader (#8007)
+* Corrected interpretation of space directions in nrrd reader (#8091)
+#### metrics and losses
+* Improved memory management for `NACLLoss` (#8020)
+* Fixed reduction logic in `GeneralizedDiceScore` (#7970)
+#### networks
+* Resolved issue with loading pre-trained weights in `ResNet` (#7924)
+* Fixed error where `torch.device` object had no attribute gpu_id during TensorRT export (#8019)
+* Corrected function for loading older weights in `DiffusionModelUNet` (#8031)
+* Switched to `torch_tensorrt.Device` instead of `torch.device` during TensorRT compilation (#8051)
+#### engines and handlers
+* Attempted to resolve the "experiment already exists" issue in `MLFlowHandler` (#7916)
+* Refactored the model export process for conversion and saving (#7934)
+#### misc.
+* Adjusted requirements to exclude Numpy version 2.0 (#7859)
+* Updated deprecated `scipy.ndimage` namespaces in optional imports (#7847, #7897)
+* Resolved `load_module()` deprecation in Python 3.12 (#7881)
+* Fixed Ruff type check issues (#7885)
+* Cleaned disk space in the conda test pipeline (#7902)
+* Replaced deprecated `pkgutil.find_loader` usage  (#7906)
+* Enhanced docstrings in various modules (#7913, #8055)
+* Test cases fixing (#7905, #7794, #7808)
+* Fix mypy issue introduced in 1.11.0 (#7941)
+* Cleaned up warnings during test collection (#7914)
+* Fix incompatible types in assignment issue (#7950)
+* Fix outdated link in the docs (#7971)
+* Addressed CI issues (#7983, #8013)
+* Fix module can not import correctly issue (#8015)
+* Fix AttributeError when using torch.min and max (#8041)
+* Ensure synchronization by adding `cuda.synchronize` (#8058)
+* Ignore warning from nptyping as workaround (#8062)
+* Suppress deprecated warning when importing monai (#8067)
+* Fix link in test bundle under MONAI-extra-test-data (#8092)
+### Changed
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:24.08-py3` from `nvcr.io/nvidia/pytorch:23.08-py3`
+* Change blossom-ci to ACL security format (#7843)
+* Move PyType test to weekly test (#8025)
+* Adjusted to meet Numpy 2.0 requirements (#7857)
+### Deprecated
+* Dropped support for Python 3.8 (#7909)
+* Remove deprecated arguments and class for v1.4 (#8079)
+### Removed
+* Remove use of deprecated python 3.12 strtobool (#7900)
+* Removed the pipeline for publishing to testpypi (#8086)
+* Cleaning up some very old and now obsolete infrastructure (#8113, #8118, #8121)
+
 ## [1.3.2] - 2024-06-25
 ### Fixed
 #### misc.
@@ -1040,7 +1132,8 @@ the postprocessing steps should be used before calling the metrics methods
 
 [highlights]: https://github.com/Project-MONAI/MONAI/blob/master/docs/source/highlights.md
 
-[Unreleased]: https://github.com/Project-MONAI/MONAI/compare/1.3.2...HEAD
+[Unreleased]: https://github.com/Project-MONAI/MONAI/compare/1.4.0...HEAD
+[1.4.0]: https://github.com/Project-MONAI/MONAI/compare/1.3.2...1.4.0
 [1.3.2]: https://github.com/Project-MONAI/MONAI/compare/1.3.1...1.3.2
 [1.3.1]: https://github.com/Project-MONAI/MONAI/compare/1.3.0...1.3.1
 [1.3.0]: https://github.com/Project-MONAI/MONAI/compare/1.2.0...1.3.0

CITATION.cff

@@ -6,8 +6,8 @@ title: "MONAI: Medical Open Network for AI"
 abstract: "AI Toolkit for Healthcare Imaging"
 authors:
   - name: "MONAI Consortium"
-date-released: 2024-06-26
-version: "1.3.2"
+date-released: 2024-10-17
+version: "1.4.0"
 identifiers:
   - description: "This DOI represents all versions of MONAI, and will always resolve to the latest one."
     type: doi

CONTRIBUTING.md

@@ -383,3 +383,8 @@ then make PRs to the `releasing/[version number]` to fix the bugs via the regula
 If any error occurs after the release process, first check out a new hotfix branch from the `main` branch,
 make a patch version release following the semantic versioning, for example, `releasing/0.1.1`.
 Make sure the `releasing/0.1.1` is merged back into both `dev` and `main` and all the test pipelines succeed.
+
+
+<p align="right">
+  <a href="#introduction">⬆️ Back to Top</a>
+</p>

Dockerfile

@@ -11,7 +11,7 @@
 
 # To build with a different base image
 # please run `docker build` using the `--build-arg PYTORCH_IMAGE=...` flag.
-ARG PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:24.08-py3
+ARG PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:24.10-py3
 FROM ${PYTORCH_IMAGE}
 
 LABEL maintainer="[email protected]"

README.md

@@ -16,11 +16,11 @@
 [![codecov](https://codecov.io/gh/Project-MONAI/MONAI/branch/dev/graph/badge.svg?token=6FTC7U1JJ4)](https://codecov.io/gh/Project-MONAI/MONAI)
 [![monai Downloads Last Month](https://assets.piptrends.com/get-last-month-downloads-badge/monai.svg 'monai Downloads Last Month by pip Trends')](https://piptrends.com/package/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:
-- 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.
+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

docs/source/installation.md

@@ -254,10 +254,10 @@ Since MONAI v0.2.0, the extras syntax such as `pip install 'monai[nibabel]'` is
 - The options are
 
 ```
-[nibabel, skimage, scipy, pillow, tensorboard, gdown, ignite, torchvision, itk, tqdm, lmdb, psutil, cucim, openslide, pandas, einops, transformers, mlflow, clearml, matplotlib, tensorboardX, tifffile, imagecodecs, pyyaml, fire, jsonschema, ninja, pynrrd, pydicom, h5py, nni, optuna, onnx, onnxruntime, zarr, lpips, pynvml, huggingface_hub, segment-anything]
+[nibabel, skimage, scipy, pillow, tensorboard, gdown, ignite, torchvision, itk, tqdm, lmdb, psutil, cucim, openslide, pandas, einops, transformers, mlflow, clearml, matplotlib, tensorboardX, tifffile, imagecodecs, pyyaml, fire, jsonschema, ninja, pynrrd, pydicom, h5py, nni, optuna, onnx, onnxruntime, zarr, lpips, pynvml, huggingface_hub]
 ```
 
 which correspond to `nibabel`, `scikit-image`,`scipy`, `pillow`, `tensorboard`,
-`gdown`, `pytorch-ignite`, `torchvision`, `itk`, `tqdm`, `lmdb`, `psutil`, `cucim`, `openslide-python`, `pandas`, `einops`, `transformers`, `mlflow`, `clearml`, `matplotlib`, `tensorboardX`, `tifffile`, `imagecodecs`, `pyyaml`, `fire`, `jsonschema`, `ninja`, `pynrrd`, `pydicom`, `h5py`, `nni`, `optuna`, `onnx`, `onnxruntime`, `zarr`, `lpips`, `nvidia-ml-py`, `huggingface_hub`, `pyamg` and `segment-anything` respectively.
+`gdown`, `pytorch-ignite`, `torchvision`, `itk`, `tqdm`, `lmdb`, `psutil`, `cucim`, `openslide-python`, `pandas`, `einops`, `transformers`, `mlflow`, `clearml`, `matplotlib`, `tensorboardX`, `tifffile`, `imagecodecs`, `pyyaml`, `fire`, `jsonschema`, `ninja`, `pynrrd`, `pydicom`, `h5py`, `nni`, `optuna`, `onnx`, `onnxruntime`, `zarr`, `lpips`, `nvidia-ml-py`, `huggingface_hub` and `pyamg` respectively.
 
 - `pip install 'monai[all]'` installs all the optional dependencies.

docs/source/whatsnew.rst

@@ -6,6 +6,7 @@ What's New
 .. toctree::
    :maxdepth: 1
 
+   whatsnew_1_4.md
    whatsnew_1_3.md
    whatsnew_1_2.md
    whatsnew_1_1.md

docs/source/whatsnew_1_3.md

@@ -1,4 +1,4 @@
-# What's new in 1.3 🎉🎉
+# What's new in 1.3
 
 - Bundle usability enhancements
 - Integrating MONAI Generative into MONAI core