-
Notifications
You must be signed in to change notification settings - Fork 2
Expand file tree
/
Copy pathsetup.py
More file actions
77 lines (72 loc) · 2.17 KB
/
setup.py
File metadata and controls
77 lines (72 loc) · 2.17 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
from distutils.extension import Extension
from setuptools import find_packages
from setuptools import setup
from setuptools import dist
# Allows np to be downloaded before import
dist.Distribution().fetch_build_eggs(["numpy>=1.19"])
import numpy as np
hidalgo = Extension("gibbs", sources=["vae_ld/ext/gibbs.c"], include_dirs=[np.get_include()])
extras = {
'doc': ['sphinx==5.0.2', 'sphinx-rtd-theme']
}
setup(
name="vae_ld",
version="0.1",
description="Library for research on VAE learning dynamics",
author="Lisa Bonheme",
author_email="lb732@kent.ac.uk",
url="https://github.com/bonheml/VAE_learning_dynamics",
license="Apache 2.0",
ext_modules=[hidalgo],
packages=find_packages(),
include_package_data=True,
scripts=[
"bin/train",
"bin/save_activations",
"bin/visualise_similarity",
"bin/visualise_images",
"bin/visualise_images_transfer",
"bin/get_layers_estimate",
"bin/hidalgo",
"bin/compute_similarity",
"bin/compute_ph",
"bin/test_dataset",
"bin/filter_variables",
"bin/stitch_train",
"bin/fondue",
"bin/visualise_ides",
"bin/latent_traversal",
"bin/evaluate_downstream_task",
"bin/ivae_latent_histograms",
"bin/vae_latent_histograms",
"bin/transfer",
"bin/save_activations",
"bin/compute_similarity_from_sa",
],
install_requires=[
"pillow>=7.2.0",
"pandas>=1.0.5",
"tensorflow_hub~=0.12",
"tensorflow~=2.6.0",
"tensorflow-datasets",
"imageio",
"hydra-core",
"scikit-learn",
"pandas",
"simplejson",
"six",
"requests",
"matplotlib",
"seaborn",
"tensorflow_probability",
"scipy",
"gudhi",
],
extras_require=extras,
classifiers=[
"Intended Audience :: Science/Research",
"License :: OSI Approved :: Apache Software License",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
],
keywords="tensorflow2, machine learning, variational autoencoders, deep learning, representational similarity",
)