diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index d1fd917..73ba72f 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -66,10 +66,10 @@ please specify your docker version, You can find it by running: docker --version ``` -### Version of !PACKAGE_NAME! ? -please specify your version of !PACKAGE_NAME! (please make sure you run the latest version): +### Version of !stroke_segmentor! ? +please specify your version of !stroke_segmentor! (please make sure you run the latest version): ```sh -pip freeze | grep !PACKAGE_NAME! +pip freeze | grep !stroke_segmentor! ``` **Additional context** diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 7724ad0..910814e 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -16,7 +16,7 @@ jobs: fail-fast: false matrix: os: [ubuntu-latest] #, windows-latest, macos-latest] # Extend to your needs - python-version: ["3.10", "3.11", "3.12"] # Extend to your needs + python-version: ["3.9","3.10", "3.11", "3.12", "3.13"] # Extend to your needs steps: - uses: actions/checkout@v4 @@ -39,7 +39,7 @@ jobs: poetry run flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Test with pytest run: | - poetry run pytest --cov=package_name --cov-report=xml --cov-report=term + poetry run pytest --cov=stroke_segmentor --cov-report=xml --cov-report=term # Add the following block to upload coverage to Codecov, requires a CODECOV_TOKEN secret to be set in the repository # - name: Upload results to Codecov # # Only upload to Codecov after a merge to the main branch diff --git a/.gitignore b/.gitignore index 68bc17f..0694516 100644 --- a/.gitignore +++ b/.gitignore @@ -158,3 +158,11 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ + +testdata +NVAUTO +*.nii.gz +*.ipynb +.vscode + +stroke_segmentor/weights \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..d0c3cbf --- /dev/null +++ b/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/README.md b/README.md index 5e232f2..054fa2b 100644 --- a/README.md +++ b/README.md @@ -1,22 +1,21 @@ -# PACKAGE_NAME +# Stroke Segmentor -[![Python Versions](https://img.shields.io/pypi/pyversions/PACKAGE_NAME)](https://pypi.org/project/PACKAGE_NAME/) -[![Stable Version](https://img.shields.io/pypi/v/PACKAGE_NAME?label=stable)](https://pypi.python.org/pypi/PACKAGE_NAME/) -[![Documentation Status](https://readthedocs.org/projects/PACKAGE_NAME/badge/?version=latest)](http://PACKAGE_NAME.readthedocs.io/?badge=latest) -[![tests](https://github.com/BrainLesion/PACKAGE_NAME/actions/workflows/tests.yml/badge.svg)](https://github.com/BrainLesion/PACKAGE_NAME/actions/workflows/tests.yml) -[![codecov](https://codecov.io/gh/BrainLesion/PACKAGE_NAME/graph/badge.svg?token=A7FWUKO9Y4)](https://codecov.io/gh/BrainLesion/PACKAGE_NAME) +[![Python Versions](https://img.shields.io/pypi/pyversions/stroke_segmentor)](https://pypi.org/project/stroke_segmentor/) +[![Stable Version](https://img.shields.io/pypi/v/stroke_segmentor?label=stable)](https://pypi.python.org/pypi/stroke_segmentor/) +[![Documentation Status](https://readthedocs.org/projects/stroke_segmentor/badge/?version=latest)](http://stroke_segmentor.readthedocs.io/?badge=latest) +[![tests](https://github.com/BrainLesion/stroke_segmentor/actions/workflows/tests.yml/badge.svg)](https://github.com/BrainLesion/stroke_segmentor/actions/workflows/tests.yml) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) + -Description -## Features +State-of-the-art ischemic stroke lesion segmentation in MRI ## Installation -With a Python 3.8+ environment, you can install `PACKAGE_NAME` directly from [PyPI](https://pypi.org/project/PACKAGE_NAME/): +With a Python 3.8+ environment, you can install `stroke_segmentor` directly from [PyPI](https://pypi.org/project/stroke_segmentor/): ```bash -pip install PACKAGE_NAME +pip install stroke_segmentor ``` @@ -25,19 +24,25 @@ pip install PACKAGE_NAME A minimal example to create a segmentation could look like this: ```python - # example +from stroke_segmentor.inferer import Inferer + +inferer = Inferer() +pred = inferer.infer( + dwi_path="path/to/dwi.nii.gz", + adc_path="path/to/adc.nii.gz", + segmentation_path="seg.nii.gz", # optional. the numpy array is always returned for direct usage +) ``` - - - ## Citation -If you use PACKAGE_NAME in your research, please cite it to support the development! +> [!IMPORTANT] +> `stroke_segmentor` is part of the [BrainLesion](https://github.com/BrainLesion) suite. +> Please cite it support the development! +> https://github.com/BrainLesion#-citing-brainlesion-suite -``` -TODO: citation will be added asap -``` + This project is based on [DeepISLES](https://github.com/ezequieldlrosa/DeepIsles) and offers its NVAUTO algorithm. + Please cite the [original manuscript](https://github.com/ezequieldlrosa/DeepIsles?tab=readme-ov-file#citations). ## Contributing @@ -45,7 +50,7 @@ We welcome all kinds of contributions from the community! ### Reporting Bugs, Feature Requests and Questions -Please open a new issue [here](https://github.com/BrainLesion/PACKAGE_NAME/issues). +Please open a new issue [here](https://github.com/BrainLesion/stroke_segmentor/issues). ### Code contributions diff --git a/docs/README_preprocessed.md b/docs/README_preprocessed.md new file mode 100644 index 0000000..590ea1d --- /dev/null +++ b/docs/README_preprocessed.md @@ -0,0 +1,57 @@ +# Stroke Segmentor + +[![Python Versions](https://img.shields.io/pypi/pyversions/stroke_segmentor)](https://pypi.org/project/stroke_segmentor/) +[![Stable Version](https://img.shields.io/pypi/v/stroke_segmentor?label=stable)](https://pypi.python.org/pypi/stroke_segmentor/) +[![Documentation Status](https://readthedocs.org/projects/stroke_segmentor/badge/?version=latest)](http://stroke_segmentor.readthedocs.io/?badge=latest) +[![tests](https://github.com/BrainLesion/stroke_segmentor/actions/workflows/tests.yml/badge.svg)](https://github.com/BrainLesion/stroke_segmentor/actions/workflows/tests.yml) +[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) + + +State-of-the-art ischemic stroke lesion segmentation in MRI + + +## Installation + +With a Python 3.8+ environment, you can install `stroke_segmentor` directly from [PyPI](https://pypi.org/project/stroke_segmentor/): + +```bash +pip install stroke_segmentor +``` + + +## Use Cases and Tutorials + +A minimal example to create a segmentation could look like this: + +```python +from stroke_segmentor.inferer import Inferer + +inferer = Inferer() +pred = inferer.infer( + dwi_path="path/to/dwi.nii.gz", + adc_path="path/to/adc.nii.gz", + segmentation_path="seg.nii.gz", # optional. the numpy array is always returned for direct usage +) +``` + +## Citation + +```{IMPORTANT} +`stroke_segmentor` is part of the [BrainLesion](https://github.com/BrainLesion) suite. +Please cite it support the development! +https://github.com/BrainLesion#-citing-brainlesion-suite +``` + This project is based on [DeepISLES](https://github.com/ezequieldlrosa/DeepIsles) and offers its NVAUTO algorithm. + Please cite the [original manuscript](https://github.com/ezequieldlrosa/DeepIsles?tab=readme-ov-file#citations). + +## Contributing + +We welcome all kinds of contributions from the community! + +### Reporting Bugs, Feature Requests and Questions + +Please open a new issue [here](https://github.com/BrainLesion/stroke_segmentor/issues). + +### Code contributions + +Nice to have you on board! Please have a look at our [CONTRIBUTING.md](CONTRIBUTING.md) file. diff --git a/docs/source/conf.py b/docs/source/conf.py index 7bc126f..9240b8f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -16,8 +16,8 @@ # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information -project = "PACKAGE_NAME" -copyright = "2024, Marcel Rosier et al." +project = "stroke_segmentor" +copyright = "2025, Marcel Rosier et al." author = "Marcel Rosier, Florian Kofler" # -- General configuration --------------------------------------------------- diff --git a/docs/source/index.rst b/docs/source/index.rst index ee56e5b..93b29e3 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,4 +1,4 @@ -PACKAGE_NAME documentation +stroke_segmentor documentation =================== .. include:: ../README_preprocessed.md @@ -20,3 +20,6 @@ Sections :maxdepth: 2 :caption: API Reference: + inferer + model_handler + diff --git a/docs/source/inferer.rst b/docs/source/inferer.rst new file mode 100644 index 0000000..0755b21 --- /dev/null +++ b/docs/source/inferer.rst @@ -0,0 +1,5 @@ +Inferer +=================== + +.. automodule:: stroke_segmentor.inferer + :members: diff --git a/docs/source/model_handler.rst b/docs/source/model_handler.rst new file mode 100644 index 0000000..c632a04 --- /dev/null +++ b/docs/source/model_handler.rst @@ -0,0 +1,5 @@ +Model Handler +=================== + +.. automodule:: stroke_segmentor.model_handler + :members: diff --git a/make.bat b/make.bat new file mode 100644 index 0000000..747ffb7 --- /dev/null +++ b/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/package_name/__init__.py b/package_name/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/poetry.lock b/poetry.lock index 66a899d..dc1d0e1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,23 @@ -# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. + +[[package]] +name = "accessible-pygments" +version = "0.0.5" +description = "A collection of accessible pygments styles" +optional = false +python-versions = ">=3.9" +groups = ["docs"] +files = [ + {file = "accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7"}, + {file = "accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872"}, +] + +[package.dependencies] +pygments = ">=1.5" + +[package.extras] +dev = ["pillow", "pkginfo (>=1.10)", "playwright", "pre-commit", "setuptools", "twine (>=5.0)"] +tests = ["hypothesis", "pytest"] [[package]] name = "alabaster" @@ -14,33 +33,34 @@ files = [ [[package]] name = "babel" -version = "2.16.0" +version = "2.17.0" description = "Internationalization utilities" optional = false python-versions = ">=3.8" groups = ["docs"] files = [ - {file = "babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b"}, - {file = "babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"}, + {file = "babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2"}, + {file = "babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d"}, ] [package.extras] -dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"] +dev = ["backports.zoneinfo ; python_version < \"3.9\"", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata ; sys_platform == \"win32\""] [[package]] name = "beautifulsoup4" -version = "4.12.3" +version = "4.13.4" description = "Screen-scraping library" optional = false -python-versions = ">=3.6.0" +python-versions = ">=3.7.0" groups = ["docs"] files = [ - {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"}, - {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"}, + {file = "beautifulsoup4-4.13.4-py3-none-any.whl", hash = "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b"}, + {file = "beautifulsoup4-4.13.4.tar.gz", hash = "sha256:dbb3c4e1ceae6aefebdaf2423247260cd062430a410e38c66f2baa50a8437195"}, ] [package.dependencies] soupsieve = ">1.2" +typing-extensions = ">=4.0.0" [package.extras] cchardet = ["cchardet"] @@ -51,114 +71,103 @@ lxml = ["lxml"] [[package]] name = "certifi" -version = "2024.8.30" +version = "2025.8.3" description = "Python package for providing Mozilla's CA Bundle." optional = false -python-versions = ">=3.6" -groups = ["docs"] +python-versions = ">=3.7" +groups = ["main", "docs"] files = [ - {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"}, - {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"}, + {file = "certifi-2025.8.3-py3-none-any.whl", hash = "sha256:f6c12493cfb1b06ba2ff328595af9350c65d6644968e5d3a2ffd78699af217a5"}, + {file = "certifi-2025.8.3.tar.gz", hash = "sha256:e564105f78ded564e3ae7c923924435e1daa7463faeab5bb932bc53ffae63407"}, ] [[package]] name = "charset-normalizer" -version = "3.3.2" +version = "3.4.3" description = "The Real First Universal Charset Detector. 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import NDArray + +from stroke_segmentor.model_handler import ModelHandler + + +class Inferer: + """ + Handles inference for stroke segmentation using DWI and ADC images. + This class wraps a model handler to perform inference on given images and can optionally + save the resulting segmentation mask to disk. + + Basic usage: + ``` + .. code-block:: python + + inferer = Inferer() + prediction = inferer.infer(adc_path, dwi_path, segmentation_path) + ``` + """ + + def __init__( + self, + force_cpu: bool = False, + ): + self.model_handler = ModelHandler(force_cpu=force_cpu) + + def _save( + self, + dwi_path: Union[str, Path], + prediction: NDArray, + segmentation_path: Union[str, Path], + ) -> None: + """Save the prediction as a SimpleITK image. + + Args: + dwi_path (str | Path): Path to the DWI image. + prediction (NDArray): The predicted segmentation mask. + segmentation_path (str | Path): Path to save the segmentation mask. + + Returns: + None + """ + dwi_image = SimpleITK.ReadImage(str(dwi_path)) + + # Get origin, spacing and direction from the DWI image. + origin, spacing, direction = ( + dwi_image.GetOrigin(), + dwi_image.GetSpacing(), + dwi_image.GetDirection(), + ) + + # Build the itk object. + output_image = SimpleITK.GetImageFromArray(prediction.astype(np.uint8)) + output_image.SetOrigin(origin) + output_image.SetSpacing(spacing) + output_image.SetDirection(direction) + + Path(segmentation_path).parent.mkdir(parents=True, exist_ok=True) + SimpleITK.WriteImage(output_image, str(segmentation_path)) + + def infer( + self, + adc_path: Union[str, Path], + dwi_path: Union[str, Path], + segmentation_path: Optional[Union[str, Path]] = None, + ) -> NDArray: + """Run inference on the provided ADC and DWI images. + + Args: + adc_path (str | Path): Path to the ADC image. + dwi_path (str | Path): Path to the DWI image. + segmentation_path (Optional[str | Path], optional): Path to save the segmentation mask. Defaults to None. + + Returns: + NDArray: The predicted segmentation mask. + """ + + prediction = self.model_handler.infer( + adc_path=adc_path, + dwi_path=dwi_path, + ) + if segmentation_path: + self._save( + dwi_path=dwi_path, + prediction=prediction, + segmentation_path=segmentation_path, + ) + return prediction diff --git a/stroke_segmentor/model_handler.py b/stroke_segmentor/model_handler.py new file mode 100644 index 0000000..f7e99cd --- /dev/null +++ b/stroke_segmentor/model_handler.py @@ -0,0 +1,167 @@ +from __future__ import annotations + +from contextlib import nullcontext +from pathlib import Path +from typing import Any, Dict, List, Union + +import numpy as np +import torch +from monai.data.dataloader import DataLoader +from monai.data.dataset import Dataset +from monai.data.utils import decollate_batch +from monai.inferers.inferer import SlidingWindowInferer +from monai.transforms.compose import Compose +from monai.transforms.intensity.dictionary import NormalizeIntensityd +from monai.transforms.io.dictionary import LoadImaged +from monai.transforms.post.dictionary import Invertd +from monai.transforms.spatial.dictionary import Spacingd +from monai.transforms.utility.dictionary import ( + CastToTyped, + EnsureChannelFirstd, + EnsureTyped, +) +from numpy.typing import NDArray +from torch.amp.autocast_mode import autocast + +from stroke_segmentor.zenodo import fetch_weights + + +class ModelHandler: + """Class for model loading, inference and post processing""" + + def __init__(self, force_cpu: bool = False): + """Initialize the ModelHandler class. + + Args: + force_cpu (bool): Whether to force the use of CPU for inference. + + Returns: + ModelHandler: ModelHandler instance. + """ + + self.device = torch.device( + "cuda" if torch.cuda.is_available() and not force_cpu else "cpu" + ) + # get location of model weights + self.model_weights_folder = fetch_weights() + self.checkpoints = [ + self.model_weights_folder / f"model{i}.ts" for i in range(15) + ] + + self.transforms = self._get_transforms() + + def _get_transforms(self) -> Compose: + """Get the transforms to be applied to the input data. + + Returns: + Compose: Composed transforms. + """ + load_keys = ["image"] + + return Compose( + [ + LoadImaged(keys=load_keys), + EnsureChannelFirstd(keys=load_keys), + CastToTyped(keys=["image"], dtype=np.float32), + EnsureTyped(keys=load_keys, data_type="tensor"), + Spacingd(keys=["image"], pixdim=[1, 1, 1], mode=["bilinear"]), + NormalizeIntensityd(keys="image", nonzero=True, channel_wise=True), + ] + ) + + def _get_data_loader( + self, + adc_path: Union[str, Path], + dwi_path: Union[str, Path], + ) -> DataLoader: + """Get the data loader for the input data. + + Args: + adc_path (str | Path): Path to the ADC image. + dwi_path (str | Path): Path to the DWI image. + + Returns: + DataLoader: DataLoader for the input data. + """ + + files = [{"image": [adc_path, dwi_path]}] + + ds = Dataset( + data=files, + transform=self.transforms, + ) + return DataLoader( + ds, + batch_size=1, + shuffle=False, + num_workers=0, + sampler=None, + ) + + def infer( + self, + adc_path: Union[str, Path], + dwi_path: Union[str, Path], + ) -> NDArray: + """Run inference on the provided ADC and DWI images. + + Args: + adc_path (str | Path): Path to the ADC image. + dwi_path (str | Path): Path to the DWI image. + + Returns: + NDArray: The predicted segmentation mask. + """ + + dataloader = self._get_data_loader(adc_path, dwi_path) + model_inferer = SlidingWindowInferer( + roi_size=[192, 192, 128], + overlap=0.625, + mode="gaussian", + cache_roi_weight_map=True, + sw_batch_size=2, + ) + + with torch.no_grad(): + batch_data: Dict[str, Any] = next(iter(dataloader)) + image = batch_data["image"].to(self.device) + + all_probs = [] + for checkpoint in self.checkpoints: + + model = torch.jit.load(checkpoint).to(self.device) + model.eval() + + with ( + autocast("cuda", enabled=True) + if self.device.type == "cuda" + else nullcontext() + ): + logits = model_inferer(inputs=image, network=model) + + probs = torch.softmax(logits.float(), dim=1) + + batch_data["pred"] = probs + inverter = Invertd( + keys="pred", + transform=self.transforms, + orig_keys="image", + meta_keys="pred_meta_dict", + nearest_interp=False, + to_tensor=True, + ) + probs = [ + inverter(x)["pred"] for x in decollate_batch(batch_data) + ] # invert resampling if any + probs = torch.stack(probs, dim=0) + + all_probs.append(probs.cpu()) + + avg_probs = torch.mean(torch.stack(all_probs), dim=0) + labels = torch.argmax(avg_probs, dim=1).cpu().numpy() + + prediction = labels[0].copy() + + prediction = prediction.transpose((2, 1, 0)) + + return prediction.astype(np.int8) diff --git a/stroke_segmentor/zenodo.py b/stroke_segmentor/zenodo.py new file mode 100644 index 0000000..ec7397f --- /dev/null +++ b/stroke_segmentor/zenodo.py @@ -0,0 +1,191 @@ +from __future__ import annotations + +import shutil +import zipfile +from io import BytesIO +from pathlib import Path +from typing import Dict, List, Tuple + +import requests +from loguru import logger +from rich.progress import Progress, SpinnerColumn, TextColumn + +WEIGHTS_FOLDER = Path(__file__).parent / "weights" +WEIGHTS_RECORD_ID = "16920681" + + +def fetch_weights() -> Path: + """ + Ensure that the required weights are available locally, downloading them if necessary. + + Returns: + Path: The path to the folder containing the model weights. + """ + record = ZenodoRecord( + record_id=WEIGHTS_RECORD_ID, + target_dir=WEIGHTS_FOLDER, + label="weights", + ) + return record.fetch() + + +class ZenodoException(Exception): + """Raised when Zenodo cannot be reached or the request fails.""" + + pass + + +class ZenodoRecord: + BASE_URL = "https://zenodo.org/api/records" + + def __init__( + self, + record_id: str, + target_dir: Path, + label: str = "asset", + ): + self.record_id = record_id + self.target_dir = target_dir + self.label = label + + def fetch(self) -> Path: + """Fetch the latest version of the record from Zenodo or from local storage.""" + zenodo_response = self._get_metadata_and_archive_url() + + pattern = self._glob_pattern() + matching_folders = list(self.target_dir.glob(pattern)) + latest_local = self._get_latest_version_folder_name(matching_folders) + + if not latest_local: + if not zenodo_response: + msg = f"{self.label.title()} not found locally and Zenodo could not be reached." + logger.error(msg) + raise ZenodoException(msg) + + logger.info(f"{self.label.title()} not found locally.") + metadata, archive_url = zenodo_response + return self._download(metadata, archive_url) + + logger.info(f"Found local {self.label}: {latest_local}") + + if not zenodo_response: + logger.warning(f"Zenodo unreachable. Using latest downloaded {self.label}.") + return self.target_dir / latest_local + + metadata, archive_url = zenodo_response + remote_version = metadata["version"] + local_version = latest_local.split("_v")[1] + + if remote_version == local_version: + logger.info(f"Latest {self.label} ({remote_version}) already present.") + return self.target_dir / latest_local + + logger.info( + f"New version of {self.label} available on Zenodo ({remote_version}). Replacing local copy..." + ) + shutil.rmtree( + self.target_dir / latest_local, + onerror=lambda func, path, excinfo: logger.warning( + f"Failed to delete {path}: {excinfo}" + ), + ) + return self._download(metadata, archive_url) + + def _glob_pattern(self) -> str: + return f"{self.record_id}_v*.*.*" + + def _build_folder_path( + self, + version: str, + ) -> Path: + return self.target_dir / f"{self.record_id}_v{version}" + + def _get_latest_version_folder_name( + self, + folders: List[Path], + ) -> str | None: + if not folders: + return None + latest = sorted( + folders, + reverse=True, + key=lambda x: tuple(map(int, str(x.name).split("_v")[1].split("."))), + )[0] + if not list(latest.glob("*")): + return None + return latest.name + + def _get_metadata_and_archive_url(self) -> Tuple[Dict, str] | None: + try: + response = requests.get(f"{self.BASE_URL}/{self.record_id}") + if response.status_code != 200: + error_msg = ( + f"Cannot find record '{self.record_id}' on Zenodo " + f"({response.status_code=})." + ) + logger.error(error_msg) + raise ZenodoException(error_msg) + + data = response.json() + return data["metadata"], data["links"]["archive"] + + except requests.exceptions.RequestException as e: + logger.warning(f"Failed to fetch metadata from Zenodo: {e}") + return None + + def _download( + self, + metadata: Dict, + archive_url: str, + ) -> Path: + folder = self._build_folder_path(metadata["version"]) + folder.mkdir(parents=True, exist_ok=True) + + logger.info(f"Downloading {self.label} from Zenodo. This may take a while...") + + response = requests.get(archive_url, stream=True) + if response.status_code != 200: + msg = ( + f"Failed to download {self.label}. Status code: {response.status_code}" + ) + logger.error(msg) + raise ZenodoException(msg) + + self._extract_archive(response, folder) + logger.info(f"{self.label.title()} extracted to {folder}") + return folder + + def _extract_archive( + self, + response: requests.Response, + folder: Path, + ): + chunk_size = 1024 # 1KB + buffer = BytesIO() + + with Progress( + SpinnerColumn(), + TextColumn(f"[cyan]Downloading {self.label}..."), + TextColumn("[cyan]{task.completed:.2f} MB"), + transient=True, + ) as progress: + task = progress.add_task("", total=None) + for chunk in response.iter_content(chunk_size=chunk_size): + buffer.write(chunk) + progress.update(task, advance=len(chunk) / (chunk_size**2)) + + with zipfile.ZipFile(buffer) as z: + z.extractall(folder) + + for file in folder.iterdir(): + if file.is_file() and file.suffix == ".zip": + with zipfile.ZipFile(file) as inner_zip: + files = inner_zip.namelist() + with Progress(transient=True) as progress: + task = progress.add_task( + f"[cyan]Extracting inner zip...", total=len(files) + ) + for i, f in enumerate(files): + inner_zip.extract(f, folder) + progress.update(task, completed=i + 1) + file.unlink() # Remove inner zip after extraction diff --git a/tests/test_dummy.py b/tests/test_dummy.py deleted file mode 100644 index f4f5361..0000000 --- a/tests/test_dummy.py +++ /dev/null @@ -1,2 +0,0 @@ -def test_dummy(): - assert True diff --git a/tests/test_inferer.py b/tests/test_inferer.py new file mode 100644 index 0000000..4fe996f --- /dev/null +++ b/tests/test_inferer.py @@ -0,0 +1,60 @@ +import numpy as np +import pytest + +from stroke_segmentor.inferer import Inferer + + +@pytest.fixture +def mock_model_handler(mocker): + """Fixture to mock ModelHandler inside Inferer.""" + mock = mocker.patch("stroke_segmentor.inferer.ModelHandler") + instance = mock.return_value + instance.infer.return_value = np.array([[1, 0], [0, 1]], dtype=np.uint8) + return instance + + +def test_infer_without_save(mock_model_handler): + inferer = Inferer(force_cpu=True) + + # Run inference + adc = "fake_adc.nii.gz" + dwi = "fake_dwi.nii.gz" + result = inferer.infer(adc_path=adc, dwi_path=dwi) + + # Assertions + assert isinstance(result, np.ndarray) + np.testing.assert_array_equal(result, np.array([[1, 0], [0, 1]], dtype=np.uint8)) + mock_model_handler.infer.assert_called_once_with(adc_path=adc, dwi_path=dwi) + + +def test_infer_with_save(tmp_path, mocker, mock_model_handler): + inferer = Inferer(force_cpu=False) + + adc = "adc_fake.nii.gz" + dwi = "dwi_fake.nii.gz" + seg_out = tmp_path / "segmentation.nii.gz" + + # Mock SimpleITK functions + mock_image = mocker.Mock() + mock_image.GetOrigin.return_value = (0, 0, 0) + mock_image.GetSpacing.return_value = (1, 1, 1) + mock_image.GetDirection.return_value = (1, 0, 0, 0, 1, 0, 0, 0, 1) + + mocker.patch( + "stroke_segmentor.inferer.SimpleITK.ReadImage", return_value=mock_image + ) + mocker.patch( + "stroke_segmentor.inferer.SimpleITK.GetImageFromArray", + return_value=mocker.Mock(), + ) + write_mock = mocker.patch("stroke_segmentor.inferer.SimpleITK.WriteImage") + + # Run inference with save path + result = inferer.infer(adc_path=adc, dwi_path=dwi, segmentation_path=seg_out) + + # Assertions + assert isinstance(result, np.ndarray) + write_mock.assert_called_once() + # Check if file got written to correct path + args, kwargs = write_mock.call_args + assert str(seg_out) in args or kwargs.values() diff --git a/tests/test_model_handler.py b/tests/test_model_handler.py new file mode 100644 index 0000000..739b4e0 --- /dev/null +++ b/tests/test_model_handler.py @@ -0,0 +1,19 @@ +from pathlib import Path + +import numpy as np +import torch + +from stroke_segmentor.model_handler import ModelHandler + + +def test_modelhandler_init_cpu(mocker): + """Check that ModelHandler initializes on CPU if force_cpu=True.""" + mocker.patch( + "stroke_segmentor.model_handler.fetch_weights", + return_value=Path("/fake/weights"), + ) + handler = ModelHandler(force_cpu=True) + + assert handler.device.type == "cpu" + assert len(handler.checkpoints) == 15 + assert all(str(c).startswith("/fake/weights/model") for c in handler.checkpoints) diff --git a/tests/test_zenodo.py b/tests/test_zenodo.py new file mode 100644 index 0000000..fab7080 --- /dev/null +++ b/tests/test_zenodo.py @@ -0,0 +1,173 @@ +from pathlib import Path +from unittest.mock import MagicMock, patch + +import pytest +import requests + +from stroke_segmentor.zenodo import ( + ZenodoException, + ZenodoRecord, + fetch_weights, +) + +# ---- Fixtures ---- + + +@pytest.fixture +def dummy_metadata(): + return { + "version": "1.2.3", + "title": "Test Record", + } + + +@pytest.fixture +def dummy_archive_url(): + return "https://zenodo.org/record/dummy/archive.zip" + + +@pytest.fixture +def dummy_zenodo_response(dummy_metadata, dummy_archive_url): + return dummy_metadata, dummy_archive_url + + +# ---- Tests for _get_metadata_and_archive_url ---- + + +def test_get_metadata_and_archive_url_success( + monkeypatch, dummy_metadata, dummy_archive_url +): + response_mock = MagicMock() + response_mock.status_code = 200 + response_mock.json.return_value = { + "metadata": dummy_metadata, + "links": {"archive": dummy_archive_url}, + } + + monkeypatch.setattr("requests.get", lambda *args, **kwargs: response_mock) + record = ZenodoRecord("123", Path("/tmp"), "test") + + metadata, url = record._get_metadata_and_archive_url() + + assert metadata == dummy_metadata + assert url == dummy_archive_url + + +def test_get_metadata_and_archive_url_failure(monkeypatch): + response_mock = MagicMock() + response_mock.status_code = 404 + + monkeypatch.setattr("requests.get", lambda *args, **kwargs: response_mock) + record = ZenodoRecord("invalid", Path("/tmp"), "test") + + with pytest.raises(ZenodoException): + record._get_metadata_and_archive_url() + + +@patch( + "requests.get", side_effect=requests.exceptions.RequestException("Connection error") +) +def test_get_metadata_and_archive_url_connection_error(mock_get): + record = ZenodoRecord("123", Path("/tmp"), "test") + + assert record._get_metadata_and_archive_url() is None + + +# ---- Tests for _get_latest_version_folder_name ---- + + +def test_get_latest_version_folder_name(tmp_path): + folder = tmp_path / "123_v1.2.3" + folder.mkdir() + (folder / "dummy.txt").touch() + + record = ZenodoRecord("123", tmp_path, "test") + result = record._get_latest_version_folder_name(list(tmp_path.glob("*"))) + + assert result == "123_v1.2.3" + + +def test_get_latest_version_folder_name_empty(tmp_path): + record = ZenodoRecord("123", tmp_path, "test") + assert record._get_latest_version_folder_name([]) is None + + +def test_get_latest_version_folder_name_ignores_empty_folder(tmp_path): + folder = tmp_path / "123_v1.2.3" + folder.mkdir() + record = ZenodoRecord("123", tmp_path, "test") + assert record._get_latest_version_folder_name([folder]) is None + + +# ---- Tests for fetch() method ---- + + +@patch.object(ZenodoRecord, "_get_metadata_and_archive_url") +@patch.object(ZenodoRecord, "_download") +def test_fetch_downloads_new_if_no_local( + mock_download, mock_metadata, tmp_path, dummy_zenodo_response +): + mock_metadata.return_value = dummy_zenodo_response + mock_download.return_value = tmp_path / "123_v1.2.3" + + record = ZenodoRecord("123", tmp_path, "test") + result = record.fetch() + + assert result.name == "123_v1.2.3" + mock_download.assert_called_once() + + +@patch.object(ZenodoRecord, "_get_metadata_and_archive_url", return_value=None) +def test_fetch_zenodo_unreachable_raises(mock_metadata, tmp_path): + record = ZenodoRecord("123", tmp_path, "test") + + with pytest.raises(ZenodoException): + record.fetch() + + +@patch.object(ZenodoRecord, "_get_metadata_and_archive_url") +@patch.object(ZenodoRecord, "_download") +def test_fetch_skips_if_latest_present( + mock_download, mock_metadata, tmp_path, dummy_zenodo_response +): + local_folder = tmp_path / "123_v1.2.3" + local_folder.mkdir() + (local_folder / "dummy.txt").touch() + + mock_metadata.return_value = dummy_zenodo_response + record = ZenodoRecord("123", tmp_path, "test") + + result = record.fetch() + assert result.name == "123_v1.2.3" + mock_download.assert_not_called() + + +@patch.object(ZenodoRecord, "_get_metadata_and_archive_url") +@patch.object(ZenodoRecord, "_download") +def test_fetch_replaces_old_version( + mock_download, mock_metadata, tmp_path, dummy_zenodo_response +): + old_folder = tmp_path / "123_v1.0.0" + old_folder.mkdir() + (old_folder / "dummy.txt").touch() + + mock_metadata.return_value = dummy_zenodo_response + mock_download.return_value = tmp_path / "123_v1.2.3" + + record = ZenodoRecord("123", tmp_path, "test") + + result = record.fetch() + assert result.name == "123_v1.2.3" + assert not old_folder.exists() + mock_download.assert_called_once() + + +# ---- fetch_weights ---- + + +@patch("stroke_segmentor.zenodo.ZenodoRecord.fetch") +def test_fetch_weights_calls_fetch(mock_fetch): + mock_fetch.return_value = Path("/fake/path") + result = fetch_weights() + assert result == Path("/fake/path") + mock_fetch.assert_called_once()