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setup.py
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setup.py
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#!/usr/bin/env python
"""The setup script."""
import sys, re, os
from setuptools import setup, find_packages
try:
with open("README.md") as readme_file:
readme = readme_file.read()
except Exception as error:
readme = "No README information found."
sys.stderr.write("Warning: Could not open '%s' due %s\n" % ("README.md", error))
try:
filepath = "GANDLF/version.py"
version_file = open(filepath)
(__version__,) = re.findall('__version__ = "(.*)"', version_file.read())
except Exception as error:
__version__ = "0.0.1"
sys.stderr.write("Warning: Could not open '%s' due %s\n" % (filepath, error))
# Handle cases where specific files need to be bundled into the final package as installed via PyPI
dockerfiles = [
item
for item in os.listdir(os.path.dirname(os.path.abspath(__file__)))
if (os.path.isfile(item) and item.startswith("Dockerfile-"))
]
# Any extra files should be located at `GANDLF` module folder (not in repo root)
extra_files = ["logging_config.yaml"]
toplevel_package_excludes = ["testing*"]
# specifying version for `black` separately because it is also used to [check for lint](https://github.com/mlcommons/GaNDLF/blob/master/.github/workflows/black.yml)
black_version = "23.11.0"
requirements = [
"torch==2.3.1",
f"black=={black_version}",
"numpy==1.25.0",
"scipy",
"SimpleITK!=2.0.*",
"SimpleITK!=2.2.1", # https://github.com/mlcommons/GaNDLF/issues/536
"torchvision",
"tqdm",
"torchio==0.19.6",
"pandas>=2.0.0",
"scikit-learn>=0.23.2",
"scikit-image>=0.19.1",
"setuptools",
"seaborn",
"pyyaml==6.0.1",
"tiffslide",
"matplotlib",
"gdown==5.1.0",
"pytest",
"coverage",
"pytest-cov",
"psutil",
"medcam",
"opencv-python",
"torchmetrics==1.1.2",
"zarr==2.10.3",
"pydicom",
"onnx",
"torchinfo==1.7.0",
"segmentation-models-pytorch==0.3.3",
"ACSConv==0.1.1",
# https://github.com/docker/docker-py/issues/3256
"requests>=2.32.2",
"docker",
"dicom-anonymizer==1.0.12",
"twine",
"zarr",
"keyring",
"monai==1.3.0",
"click>=8.0.0",
"deprecated",
"packaging==24.0",
"typer==0.9.0",
"colorlog",
"opacus==1.5.2",
"huggingface-hub==0.25.1",
]
if __name__ == "__main__":
setup(
name="GANDLF",
version=__version__,
author="MLCommons",
author_email="[email protected]",
python_requires=">3.8, <3.12",
packages=find_packages(
where=os.path.dirname(os.path.abspath(__file__)),
exclude=toplevel_package_excludes,
),
entry_points={
"console_scripts": [
"gandlf=GANDLF.entrypoints.cli_tool:gandlf",
# old entrypoints
"gandlf_run=GANDLF.entrypoints.run:old_way",
"gandlf_constructCSV=GANDLF.entrypoints.construct_csv:old_way",
"gandlf_collectStats=GANDLF.entrypoints.collect_stats:old_way",
"gandlf_patchMiner=GANDLF.entrypoints.patch_miner:old_way",
"gandlf_preprocess=GANDLF.entrypoints.preprocess:old_way",
"gandlf_anonymizer=GANDLF.entrypoints.anonymizer:old_way",
"gandlf_configGenerator=GANDLF.entrypoints.config_generator:old_way",
"gandlf_verifyInstall=GANDLF.entrypoints.verify_install:old_way",
"gandlf_recoverConfig=GANDLF.entrypoints.recover_config:old_way",
"gandlf_deploy=GANDLF.entrypoints.deploy:old_way",
"gandlf_optimizeModel=GANDLF.entrypoints.optimize_model:old_way",
"gandlf_generateMetrics=GANDLF.entrypoints.generate_metrics:old_way",
"gandlf_debugInfo=GANDLF.entrypoints.debug_info:old_way",
"gandlf_splitCSV=GANDLF.entrypoints.split_csv:old_way",
]
},
classifiers=[
"Development Status :: 3 - Alpha",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: Apache Software License",
"Natural Language :: English",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Topic :: Scientific/Engineering :: Medical Science Apps.",
],
description=(
"PyTorch-based framework that handles segmentation/regression/classification using various DL architectures for medical imaging."
),
install_requires=requirements,
license="Apache-2.0",
long_description=readme,
long_description_content_type="text/markdown",
include_package_data=True,
package_data={"GANDLF": extra_files},
keywords="semantic, segmentation, regression, classification, data-augmentation, medical-imaging, clinical-workflows, deep-learning, pytorch",
zip_safe=False,
)