simplified setup.py
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Jong Wook Kim
parent
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Example.ipynb
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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*.egg-info
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.pytest_cache
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# Ignore doctrees
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.doctrees/
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doctrees/
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docsrc/_build/*
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docs/*.ipynb
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docsrc/*.ipynb
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thumbs.db
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.DS_Store
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.idea
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@ -1,2 +0,0 @@
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include LICENSE
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include clip/bpe_simple_vocab_16e6.txt.gz
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11
README.md
11
README.md
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@ -14,14 +14,15 @@ CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a
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## Usage
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First, [install PyTorch 1.7.1](https://pytorch.org/get-started/locally/) and torchvision, as well as small additional dependencies. On a CUDA GPU machine, the following will do the trick:
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```bash
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$ pip install clip-by-openai
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```
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Or if you want cuda installation:
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```bash
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$ pip install clip-by-openai[cuda]
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$ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
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$ pip install ftfy regex tqdm
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```
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Replace `cudatoolkit=11.0` above with the appropriate CUDA version on your machine or `cpuonly` when installing on a machine without a GPU.
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```python
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import torch
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import clip
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@ -75,6 +75,8 @@ def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_a
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if not jit:
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model = build_model(model.state_dict()).to(device)
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if device == "cpu":
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model.float()
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return model, transform
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# patch the device names
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@ -0,0 +1,5 @@
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ftfy
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regex
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tqdm
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torch>=1.7.1,<1.7.2
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torchvision==0.8.2
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62
setup.py
62
setup.py
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@ -1,58 +1,24 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import os
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import pkg_resources
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from setuptools import setup, find_packages
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from pathlib import Path
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core_req = ['ftfy', 'regex', 'tqdm', 'torch==1.7.1', 'torchvision==0.8.2']
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extras_require={
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'cuda': ['cudatoolkit==11.0'],
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'dev': ['pytest']
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}
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package_data = [str(x) for x in list(Path('clip').rglob("*.gz"))]
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package_data.extend([str(x) for x in list(Path('clip').rglob("*.md"))])
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setup(
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name='clip_by_openai',
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version='0.1.1.3',
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name="clip",
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py_modules=["clip"],
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version="1.0",
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description="",
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author="OpenAI",
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author_email="",
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description="CLIP by OpenAI",
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long_description=open("README.md", "r", encoding="utf-8").read(),
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long_description_content_type="text/markdown",
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keywords="vector, embeddings, machinelearning, ai, artificialintelligence, nlp, pytorch, nearestneighbors, search, analytics, clustering, dimensionalityreduction",
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license="MIT",
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packages=find_packages(exclude=["tests*"]),
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package_data={'clip': package_data},
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include_package_data=True,
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python_requires=">=3",
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install_requires=core_req,
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extras_require=extras_require,
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classifiers=[
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"Development Status :: 5 - Production/Stable",
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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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"Intended Audience :: Information Technology",
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"Intended Audience :: Financial and Insurance Industry",
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"License :: OSI Approved :: Apache Software License",
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"Operating System :: OS Independent",
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"Programming Language :: Python",
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3.4",
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"Programming Language :: Python :: 3.5",
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"Programming Language :: Python :: 3.6",
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"Programming Language :: Python :: 3.7",
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"Programming Language :: Python :: Implementation :: PyPy",
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"Topic :: Database",
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"Topic :: Internet :: WWW/HTTP :: Indexing/Search",
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"Topic :: Multimedia :: Sound/Audio :: Conversion",
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"Topic :: Multimedia :: Video :: Conversion",
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"Topic :: Scientific/Engineering :: Artificial Intelligence",
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"Topic :: Scientific/Engineering :: Image Recognition",
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"Topic :: Scientific/Engineering :: Information Analysis",
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"Topic :: Scientific/Engineering :: Visualization",
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"Topic :: Software Development :: Libraries :: Application Frameworks",
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package_data={'clip': [str(x) for x in list(Path('clip').rglob("*.gz"))]},
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install_requires=[
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str(r)
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for r in pkg_resources.parse_requirements(
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open(os.path.join(os.path.dirname(__file__), "requirements.txt"))
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)
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],
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include_package_data=True,
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extras_require={'dev': ['pytest']},
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)
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@ -0,0 +1,25 @@
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import numpy as np
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import pytest
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import torch
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from PIL import Image
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import clip
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@pytest.mark.parametrize('model_name', clip.available_models())
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def test_consistency(model_name):
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device = "cpu"
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jit_model, transform = clip.load(model_name, device=device)
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py_model, _ = clip.load(model_name, device=device, jit=False)
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image = transform(Image.open("CLIP.png")).unsqueeze(0).to(device)
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text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
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with torch.no_grad():
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logits_per_image, _ = jit_model(image, text)
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jit_probs = logits_per_image.softmax(dim=-1).cpu().numpy()
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logits_per_image, _ = py_model(image, text)
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py_probs = logits_per_image.softmax(dim=-1).cpu().numpy()
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assert np.allclose(jit_probs, py_probs, atol=0.01, rtol=0.1)
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import clip
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import torch
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import torch
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from PIL import Image
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def test_smoke_simple_cpu():
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device = 'cpu'
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model, preprocess = clip.load("ViT-B/32", device=device)
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image = preprocess(Image.open('CLIP.png')).unsqueeze(0).to(device)
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text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
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with torch.no_grad():
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model.encode_image(image)
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model.encode_text(text)
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logits_per_image, logits_per_text = model(image, text)
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probs = logits_per_image.softmax(dim=-1).cpu().numpy()
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assert True
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