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from __future__ import absolute_import
from setuptools import setup, find_packages
from io import open
# Get the long description from the README file
with open('README.md', encoding='utf-8') as f:
long_description = f.read()
setup(
name='mmdnn',
# Versions should comply with PEP440. For a discussion on single-sourcing
# the version across setup.py and the project code, see
# https://packaging.python.org/en/latest/single_source_version.html
version='0.2.4',
description='Deep learning model converter, visualization and editor.',
long_description=long_description,
long_description_content_type='text/markdown',
# The project's main homepage.
url='https://github.com/Microsoft/MMdnn',
# Author details
author = 'System Research Group, Microsoft Research Asia',
author_email='mmdnn_feedback@microsoft.com',
# Choose your license
license='MIT',
# See https://pypi.python.org/pypi?%3Aaction=list_classifiers
classifiers=[
# How mature is this project? Common values are
# 3 - Alpha
# 4 - Beta
# 5 - Production/Stable
'Development Status :: 3 - Alpha',
# Indicate who your project is intended for
'Intended Audience :: Developers',
'Intended Audience :: Education',
'Intended Audience :: Science/Research',
'Topic :: Scientific/Engineering :: Mathematics',
'Topic :: Software Development :: Libraries :: Python Modules',
'Topic :: Software Development :: Libraries',
# Pick your license as you wish (should match "license" above)
'License :: OSI Approved :: MIT License',
# Specify the Python versions you support here. In particular, ensure
# that you indicate whether you support Python 2, Python 3 or both.
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 3'
],
# What does your project relate to?
keywords='deep learning model converter visualization',
# You can just specify the packages manually here if your project is
# simple. Or you can use find_packages().
packages=find_packages(),
package_data={
'mmdnn':['visualization/public/*',
'visualization/*.json',
'visualization/*.js',
'visualization/*.html',
'visualization/*.css']
},
# Alternatively, if you want to distribute just a my_module.py, uncomment
# this:
# py_modules=["my_module"],
# List run-time dependencies here. These will be installed by pip when
# your project is installed. For an analysis of "install_requires" vs pip's
# requirements files see:
# https://packaging.python.org/en/latest/requirements.html
install_requires=[
'numpy >= 1.15.0',
'protobuf >= 3.6.0',
'six >= 1.10.0',
'pillow >= 3.1.0',
],
# To provide executable scripts, use entry points in preference to the
# "scripts" keyword. Entry points provide cross-platform support and allow
# pip to create the appropriate form of executable for the target platform.
entry_points={
'console_scripts': [
'mmconvert = mmdnn.conversion._script.convert:_main',
'mmdownload = mmdnn.conversion._script.extractModel:_main',
'mmvismeta = mmdnn.conversion.examples.tensorflow.vis_meta:_main',
'mmtoir = mmdnn.conversion._script.convertToIR:_main',
'mmtocode = mmdnn.conversion._script.IRToCode:_main',
'mmtomodel = mmdnn.conversion._script.dump_code:_main',
],
},
)
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