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101 lines (84 loc) · 2.03 KB
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[build-system]
requires = ["setuptools>=61.0", "wheel", "setuptools_scm"]
build-backend = "setuptools.build_meta"
[project]
name = "spectre-fm"
description = "SPECTRE: cross-modal self-supervised pretraining for CT representation extraction"
readme = { file = "README.md", content-type = "text/markdown" }
requires-python = ">=3.10"
dynamic = ["version"]
authors = [
{ name = "Cris Claessens", email = "c.h.b.claessens@tue.nl" },
{ name = "Christiaan Viviers", email = "c.g.a.viviers@tue.nl" }
]
license-files = ["LICENSE", "LICENSE_MODELS"]
classifiers = [
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Operating System :: OS Independent",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
]
keywords = ["medical", "ct", "self-supervised", "vision", "deep-learning"]
dependencies = [
"torch",
"numpy",
"timm",
"transformers",
"huggingface_hub",
"loralib",
]
[project.optional-dependencies]
# Everything needed to read CT scans off disk and run the `spectre` CLI. The model itself, and
# windowing an in-memory tensor, need nothing beyond the base dependencies.
inference = [
"nibabel",
"monai",
]
training = [
"wandb",
"monai",
"nibabel",
"SimpleITK",
"scipy",
"pandas",
"openpyxl",
"tqdm",
"accelerate",
"omegaconf",
]
eval = [
"monai",
"nibabel",
"SimpleITK",
"pillow",
"scipy",
"scikit-learn",
"pandas",
"openpyxl",
"tqdm",
"matplotlib",
"seaborn",
"umap-learn",
]
gds-cuda12 = [
"cupy-cuda12x",
"kvikio",
]
all = ["spectre-fm[training,eval,inference,gds-cuda12]"]
[project.scripts]
spectre = "spectre.cli:main"
[project.urls]
Homepage = "https://github.com/cclaess/SPECTRE"
Source = "https://github.com/cclaess/SPECTRE"
BugTracker = "https://github.com/cclaess/SPECTRE/issues"
[tool.setuptools]
include-package-data = true
[tool.setuptools.package-dir]
"" = "src"
[tool.setuptools.packages.find]
where = ["src"]
[tool.setuptools_scm]
version_scheme = "post-release"
local_scheme = "dirty-tag"
fallback_version = "0.1.0"