Embedding image and sentence into fixed-length vectors via CLIP
Project description
Embedding image and sentence into fixed-length vectors via CLIP
CLIP-as-service is a low-latency high-scalability embedding service for images and texts. It can be easily integrated as a microservice into neural search solutions.
⚡ Fast: Serve CLIP models with ONNX runtime and PyTorch JIT with 800QPS[*]. Non-blocking duplex streaming on requests and responses, designed for large data and long-running tasks.
🫐 Elastic: Horizontally scale up and down multiple CLIP models on single GPU, with automatic load balancing.
🐥 Easy-to-use: No learning curve, minimalist design on client and server. Intuitive and consistent API for image and sentence embedding.
👒 Modern: Async client support. Easily switch between gRPC, HTTP, Websocket protocols with TLS and compressions.
🍱 Integration: Smoothly integrated with neural search ecosystem including Jina and DocArray. Build cross-modal and multi-modal solution in no time.
[*] with default config (single replica, PyTorch no JIT) on GeForce RTX 3090.
Documentation
Install
CLIP-as-service consists of two Python packages clip-server
and clip-client
that can be installed independently. Both require Python 3.7+.
Install server
pip install clip-server
To run CLIP model via ONNX (default is via PyTorch):
pip install "clip-server[onnx]"
Install client
pip install clip-client
Quick check
You can run a simple connectivity check after install.
C/S | Command | Expect output |
---|---|---|
Server |
python -m clip_server
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|
Client |
from clip_client import Client
c = Client('grpc://0.0.0.0:23456')
c.profile()
|
You can change 0.0.0.0
to the intranet or public IP address to test the connectivity over private and public network. If you encounter some errors, please find the solution here.
Get Started
Basic usage
- Start the server:
python -m clip_server
. Remember its address and port. - Create a client:
from clip_client import Client c = Client('grpc://87.191.159.105:51000')
- To get sentence embedding:
r = c.encode(['First do it', 'then do it right', 'then do it better']) print(r.shape) # [3, 512]
- To get image embedding:
r = c.encode(['apple.png', # local image 'https://clip-as-service.jina.ai/_static/favicon.png', # remote image 'data:image/gif;base64,R0lGODlhEAAQAMQAAORHHOVSKudfOulrSOp3WOyDZu6QdvCchPGolfO0o/XBs/fNwfjZ0frl3/zy7////wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACH5BAkAABAALAAAAAAQABAAAAVVICSOZGlCQAosJ6mu7fiyZeKqNKToQGDsM8hBADgUXoGAiqhSvp5QAnQKGIgUhwFUYLCVDFCrKUE1lBavAViFIDlTImbKC5Gm2hB0SlBCBMQiB0UjIQA7']) # in image URI print(r.shape) # [3, 512]
More comprehensive server & client configs can be found in the docs.
Text-to-image cross-modal search in 10 Lines
Let's build a text-to-image search using CLIP-as-service. Namely, user input a sentence and the program returns the matched images. We will use Totally Looks Like dataset and DocArray package. Note that DocArray is included within clip-client
as an upstream dependency, so you don't need to install it separately.
Load images
First we load images. You can simply pull it from Jina Cloud:
from docarray import DocumentArray
da = DocumentArray.pull('ttl-original', show_progress=True, local_cache=True)
or download TTL dataset, unzip, load manually
Alternatively, you can go to Totally Looks Like official website, unzip and load images as follows:
from docarray import DocumentArray
da = DocumentArray.from_files(['left/*.jpg', 'right/*.jpg'])
The dataset contains 12,032 images, hence it may take half minute to pull. Once done, you can visualize it and get the first taste of those images.
da.plot_image_sprites()
Encode images
Start the server with python -m clip_server
. Say it is at 87.191.159.105:51000
with GRPC
protocol (you will get this information after running the server).
Create a Python client script:
from clip_client import Client
c = Client(server='grpc://87.191.159.105:51000')
da = c.encode(da, show_progress=True)
Depending on your GPU and client-server network, it could take a while to embed 12K images. In my case, it takes ~2 minute.
Download the pre-encoded dataset
For people who are impatient or lack of GPU, waiting can be a hell. In this case, you can simply pull our pre-encoded image dataset.
from docarray import DocumentArray
da = DocumentArray.pull('ttl-embedding', show_progress=True, local_cache=True)
Search via sentence
Let's build a simple prompt to allow user to type sentence:
while True:
vec = c.encode([input('sentence> ')])
r = da.find(query=vec, limit=9)
r.plot_image_sprites()
Showcase
Now you can input arbitrary English sentences and view the top-9 matched images. Search is fast and instinct. Let's have some fun:
"a happy potato" | "a super evil AI" | "a guy enjoying his burger" |
---|---|---|
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"professor cat is very serious" | "an ego engineer lives with parent" | "there will be no tomorrow so lets eat unhealthy" |
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Let's save the embedding result for our next example:
da.save_binary('ttl-image')
Image-to-text cross-modal search in 10 Lines
We can also switch the input and output of the last program to achieve image-to-text search. Precisely, given a query image find the sentence that best describes the image.
Let's use all sentences from the book "Pride and Prejudice".
from docarray import Document, DocumentArray
d = Document(uri='https://www.gutenberg.org/files/1342/1342-0.txt').load_uri_to_text()
da = DocumentArray(
Document(text=s.strip()) for s in d.text.replace('\r\n', '').split('.') if s.strip()
)
Let's look at what we got:
da.summary()
Documents Summary
Length 6403
Homogenous Documents True
Common Attributes ('id', 'text')
Attributes Summary
Attribute Data type #Unique values Has empty value
──────────────────────────────────────────────────────────
id ('str',) 6403 False
text ('str',) 6030 False
Encode sentences
Now encode these 6403 sentences, it may take 10s or less depending on your GPU and network:
from clip_client import Client
c = Client('grpc://87.191.159.105:51000')
r = c.encode(da, show_progress=True)
Download the pre-encoded dataset
Again, for people who are impatient or lack of GPU, we have prepared a pre-encoded text dataset.
from docarray import DocumentArray
da = DocumentArray.pull('ttl-textual', show_progress=True, local_cache=True)
Search via image
Let's load our previously stored image embedding; randomly sample image Document from it, then find top-1 nearest neighbour of each.
from docarray import DocumentArray
img_da = DocumentArray.load_binary('ttl-image')
for d in img_da.sample(10):
print(da.find(d.embedding, limit=1)[0].text)
Showcase
Fun time! Note, unlike the previous example, here the input is an image, the sentence is the output. All sentences come from the book "Pride and Prejudice".
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Besides, there was truth in his looks | Gardiner smiled | what’s his name | By tea time, however, the dose had been enough, and Mr | You do not look well |
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“A gamester!” she cried | If you mention my name at the Bell, you will be attended to | Never mind Miss Lizzy’s hair | Elizabeth will soon be the wife of Mr | I saw them the night before last |
Intrigued? That's only scratching the surface of what CLIP-as-service is capable of. Read our docs to learn more.
Support
- Use Discussions to talk about your use cases, questions, and support queries.
- Join our Slack community and chat with other community members about ideas.
- Join our Engineering All Hands meet-up to discuss your use case and learn Jina's new features.
- When? The second Tuesday of every month
- Where? Zoom (see our public events calendar/.ical) and live stream on YouTube
- Subscribe to the latest video tutorials on our YouTube channel
Join Us
CLIP-as-service is backed by Jina AI and licensed under Apache-2.0. We are actively hiring AI engineers, solution engineers to build the next neural search ecosystem in open-source.
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