PhysAugNet 1.0.1
VQ-VQE powered augmentation for metal defect segmentation
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setup.py
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1from setuptools import setup, find_packages
2
4 name="physaugnet",
5 version="0.1.1",
6 author="Shantanusinh Parmar",
7 description="PhysAugNet: VQ-VAE and physics-inspired (thermal + grain) augmentation pipeline for metal defect segmentation",
8 packages=find_packages(),
9 install_requires=[
10 "absl-py==2.3.0",
11 "addict==2.4.0",
12 "basicsr==1.4.2",
13 "certifi==2025.7.9",
14 "charset-normalizer==3.4.2",
15 "contourpy==1.3.2",
16 "cycler==0.12.1",
17 "debugpy==1.8.13",
18 "facexlib==0.3.0",
19 "filelock==3.13.1",
20 "filterpy==1.4.5",
21 "fonttools==4.58.5",
22 "fsspec==2024.6.1",
23 "future==1.0.0",
24 "gfpgan==1.3.8",
25 "grpcio==1.73.1",
26 "idna==3.10",
27 "imageio==2.37.0",
28 "ipykernel==6.29.5",
29 "Jinja2==3.1.4",
30 "joblib==1.5.1",
31 "jupyter_client==8.6.3",
32 "kiwisolver==1.4.8",
33 "lazy_loader==0.4",
34 "llvmlite==0.44.0",
35 "lmdb==1.7.2",
36 "Markdown==3.8.2",
37 "MarkupSafe==2.1.5",
38 "matplotlib==3.10.3",
39 "mpmath==1.3.0",
40 "networkx==3.3",
41 "numba==0.61.2",
42 "numpy==1.24.4",
43 "opencv-python==4.11.0.86",
44 "packaging==25.0",
45 "pillow==11.0.0",
46 "platformdirs==4.3.8",
47 "protobuf==6.31.1",
48 "pyparsing==3.2.3",
49 "python-dateutil==2.9.0.post0",
50 "PyYAML==6.0.2",
51 "pyzmq==26.2.1",
52 "realesrgan==0.3.0",
53 "requests==2.32.4",
54 "scikit-image==0.25.2",
55 "scikit-learn==1.7.0",
56 "scipy==1.15.3",
57 "six==1.17.0",
58 "sympy==1.13.3",
59 "tb-nightly==2.20.0a20250711",
60 "tensorboard==2.19.0",
61 "tensorboard-data-server==0.7.2",
62 "threadpoolctl==3.6.0",
63 "tifffile==2025.5.10",
64 "tomli==2.2.1",
65 "torch==2.7.1+cu126",
66 "torchaudio==2.7.1+cu126",
67 "torchvision==0.22.1+cu126",
68 "tornado==6.4.2",
69 "tqdm==4.32.2",
70 "typing_extensions==4.12.2",
71 "urllib3==2.5.0",
72 "Werkzeug==3.1.3",
73 "yapf==0.43.0",
74 ],
75 python_requires='>=3.10',
76)
Definition setup.py:1