зеркало из https://github.com/microsoft/MMdnn.git
100 строки
3.6 KiB
Python
100 строки
3.6 KiB
Python
from __future__ import absolute_import
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from setuptools import setup, find_packages
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from io import open
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# Get the long description from the README file
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with open('README.md', encoding='utf-8') as f:
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long_description = f.read()
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setup(
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name='mmdnn',
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# Versions should comply with PEP440. For a discussion on single-sourcing
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# the version across setup.py and the project code, see
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# https://packaging.python.org/en/latest/single_source_version.html
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version='0.3.0',
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description='Deep learning model converter, visualization and editor.',
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long_description=long_description,
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long_description_content_type='text/markdown',
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# The project's main homepage.
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url='https://github.com/Microsoft/MMdnn',
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# Author details
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author = 'System Research Group, Microsoft Research Asia',
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author_email='mmdnn_feedback@microsoft.com',
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# Choose your license
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license='MIT',
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# See https://pypi.python.org/pypi?%3Aaction=list_classifiers
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classifiers=[
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# How mature is this project? Common values are
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# 3 - Alpha
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# 4 - Beta
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# 5 - Production/Stable
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'Development Status :: 3 - Alpha',
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# Indicate who your project is intended for
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'Intended Audience :: Developers',
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'Intended Audience :: Education',
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'Intended Audience :: Science/Research',
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'Topic :: Scientific/Engineering :: Mathematics',
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'Topic :: Software Development :: Libraries :: Python Modules',
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'Topic :: Software Development :: Libraries',
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# Pick your license as you wish (should match "license" above)
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'License :: OSI Approved :: MIT License',
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# Specify the Python versions you support here. In particular, ensure
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# that you indicate whether you support Python 2, Python 3 or both.
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'Programming Language :: Python :: 2',
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'Programming Language :: Python :: 3'
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],
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# What does your project relate to?
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keywords='deep learning model converter visualization',
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# You can just specify the packages manually here if your project is
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# simple. Or you can use find_packages().
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packages=find_packages(),
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package_data={
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'mmdnn':['visualization/public/*',
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'visualization/*.json',
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'visualization/*.js',
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'visualization/*.html',
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'visualization/*.css']
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},
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# Alternatively, if you want to distribute just a my_module.py, uncomment
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# this:
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# py_modules=["my_module"],
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# List run-time dependencies here. These will be installed by pip when
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# your project is installed. For an analysis of "install_requires" vs pip's
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# requirements files see:
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# https://packaging.python.org/en/latest/requirements.html
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install_requires=[
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'numpy >= 1.15.0',
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'protobuf >= 3.6.0',
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'six >= 1.10.0',
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'pillow >= 6.2.1',
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],
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# To provide executable scripts, use entry points in preference to the
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# "scripts" keyword. Entry points provide cross-platform support and allow
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# pip to create the appropriate form of executable for the target platform.
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entry_points={
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'console_scripts': [
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'mmconvert = mmdnn.conversion._script.convert:_main',
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'mmdownload = mmdnn.conversion._script.extractModel:_main',
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'mmvismeta = mmdnn.conversion.examples.tensorflow.vis_meta:_main',
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'mmtoir = mmdnn.conversion._script.convertToIR:_main',
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'mmtocode = mmdnn.conversion._script.IRToCode:_main',
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'mmtomodel = mmdnn.conversion._script.dump_code:_main',
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],
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},
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)
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