Awesome

About

Photofield AI is a companion to Photofield providing AI features. It's a separate REST API service both to keep the main app slim and because AI features are currently easier to implement in Python as opposed to Go. It is an API currently exposing the OpenAI CLIP image and text embedding functionality.

Features

Returns OpenAI CLIP images and text embeddings that you can then compare with Cosine similarity for use in semantic image search. Image embedding runs at up to ~20 requests/sec on an i7-5820K CPU and up to ~200 requests/sec using a GeForce GTX 1070 Ti. Resource utilization with a GPU is low, so I imagine there are some bottlenecks in some parts of the system, but 200 requests/sec seems plenty enough as is.

Limitations

The current REST API is tied pretty closely to Photofield. The machine learning model itself also has some limitations and bias, as was reported by OpenAI:

CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section.

See more on model use in the CLIP: Model Use section of the model card from OpenAI.

Built With

• Python
• FastAPI - REST API framework
• ONNX Runtime - machine learning inference
• CLIP Variants - CLIP converted to ONNX (by yours truly)
• + more Python libraries

Getting Started

Docker

docker run -it -p 8081:8081 ghcr.io/smilyorg/photofield-ai:latest

The clip-vit-base-patch32-(visual|textual)-float16 models are currently bundled for a good out-of-the-box experience.

The Docker image is currently CPU-only as I'm currently unable to test Docker GPU support (help wanted).

Connect it with photofield by adding the following snippet to its configuration.yaml:

ai:
  # photofield-ai API server URL
  host: http://localhost:8081

From Source

Prerequisites

1. Python
2. Poetry

Setup

1. Download the source or clone the Git repository
2. In the source directory you downloaded, run poetry install to install the required dependencies. You can also run poetry install --without gpu to skip installing GPU dependencies if you want to run it on CPU only (it is also a smaller install).
3. After Poetry installs all the required dependencies, the server should be ready to run.

Run

Run the server with poetry run python main.py. If you don't specify any model files, it should first download the default models and then start listening to requests.

❯ poetry run python main.py
Available providers: TensorrtExecutionProvider, CUDAExecutionProvider, CPUExecutionProvider
Using providers: TensorrtExecutionProvider, CUDAExecutionProvider, CPUExecutionProvider
Loading visual model: models/clip-vit-base-patch32-visual-float16.onnx
Loading textual model: models/clip-vit-base-patch32-textual-float16.onnx
2022-10-08 14:28:35.9706571 [W:onnxruntime:Default, tensorrt_execution_provider.h:60 onnxruntime::TensorrtLogger::log] [2022-10-08 13:28:35 WARNING] external\onnx-tensorrt\onnx2trt_utils.cpp:369: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.

Visual inference ready, input size 224, type tensor(float16)
Textual inference ready, input size 77, type tensor(int32)
Listening on 0.0.0.0:8081

If you are starting it with GPU support (default) it may take some time for it to start up. The WARNING above is to be expected for the TensorRT runtime, it seems to work fine regardless.

Usage

Some request/response examples are listed below. If you use the neat REST Client extension for VSCode you can even execute them directly if you open the README 😎. See examples.http for more.

{{api}} refers to the root URL of the API, the following defines it for the REST Client extension.

@api = http://localhost:8081

Embed Text

The /text-embeddings endpoint accepts a list of text strings that are converted to embeddings by the textual model.

Request

POST {{api}}/text-embeddings HTTP/1.1
Content-Type: application/json

{
    "texts": ["hawk"]
}

Response

{
  "texts": [
    {
      "text": "hawk",
      "embedding_f16_b64": "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",
      "embedding_inv_norm_f16_uint16": 11810
    }
  ]
}

• embedding_f16_b64 - embedding that comes out of machine learning model. It's a base64-encoded list of 512 (or more, depending on model) float16 2-byte floating point values. You can compare this embedding to any other text or image embedding via cosine similarity (normalized dot product) to get the semantic similarity between them.

• embedding_inv_norm_f16_uint16 - the Euclidean / L2 norm of the embedding (vector length). It is inverted, converted to float16 2-byte floating point and then written out as an integer uint16 value. Using this precomputed value comes in handy while computing the cosine similarity for semantic image search as you can skip computing it for each image embedding.

Embed Images

The /image-embeddings endpoint accepts multiple form multipart image uploads and computes the embedding for each using the visual model.

@image = heavy-industry.jpg

Request

POST {{api}}/image-embeddings
Content-Type: multipart/form-data; boundary=------------------------23f534be8db8eca0

--------------------------23f534be8db8eca0
Content-Disposition: form-data; name="image"; filename="heavy-industry.jpg"
Content-Type: image/jpeg

< {{image}}

--------------------------23f534be8db8eca0

Response

{
  "images": [
    {
      "field": "image",
      "filename": "heavy-industry.jpg",
      "embedding_f16_b64": "VykzsNCu7CQ2NLCqkjQjspWtlDeXrmm4z6htMZ6z7ayNO2M2xbVrqjU0gK3uN3c0hZiRMDY6jDTXm2GkUrZUrXarLzZINGonQ7ScuI64QTk+tH64LbhrNOU2BLU6tfYyjrT1t5QtYKgOMfi3b6lxty2437JWNA+x1TceMtQtNbBVqXegt6ttMMUtXLFaseo0H7BEt+U1bzF+tPGs0iAmsPAtCDFus/m0m64SNo8ujTDDJoIodi/vMY3Fa5meLMy3/LECOUWv/7QXIcOwO7o2K+03nLIev0A1YR1KNBkwnrbptuszlDWcrrkyOrP4Mdk7dzCGrk8tmTciMvWyti5iNGieNTMyMyWsJrmdOoAulyzGOHEvoLOYrE8nvy9TtQsym7HGJDS4MDVcsRsuyR2CN/wo7bIHODc4HDvPNWgiCy+kqkSkmrdtsuuyVDXhu+41r7FWLKc44a1rsFSx8bOJMnywRjLrNt4xNyYSpmgoLrSnsLA0KDUbq1gp7rSCLZkg+bExOIq01rAaqtuxM6yqtB4wELQ4sqC2UrYSrCe1WqzdtGOsEzbZqYE0oqBZMSo087VBNBO6UqorNuo0iLiktLO5gjYtNz+237LLomupHTgTMWkrQRyDPC80WjFQtCEwxjqaME23rytRr0E7Oy+SMma6+7B9ILwhaTjJM2Mwkjc+tKOwWT+SN5k2JKsAMu89LK4ttxqty7QVL7OuobUkolikITQ3trutbbHSGEK1cjQ5MwMszq5Itqq01LBzPFE0abfSJwe0XDB1t9M2orNytEg2QClbNW00Frmer9uz5qIVLPO0+CRxLUk1rTIOMBUwmzqrNlg3pjOGrz824bhutI40RT0stMS1yLK9LjKtwLAvuHevtzAlOioz/LEVuJa2CLgDMbY2PycplR00VLGPHvEws7gTMTMuxyh2NmI1srE6qFMse6yYtKm1QDUcs6Esz7jmsM2zUa8ltA4t/p0buGM2f6pHpw+127S7I4cwKa+1sMGhRbKiNWi06ytKtqCkh7Y7NoqxdCIJM+i3wLSYs+uvUTTPtFU1mSC4tE83UjMdMJitpzUZM7Qz8hn4tiMsbbJjK+m6BTDUsmc0+7MqLiI23DS2LO8mUCi+rB00zS1JrZKvKLDkM1S0mjCEOMqy3zeZKWk2mbD0uZWwDjXbpzU0/zoeMhS56qi5NIywXbilMi619TaCrrO3rawoqoOyOTHarBolarJFMGQySrf/sVWn5Tm0LXqvHLeCsssuVixJOF2uN5DMJi4vL7SSNGwqeLOEtHmtnThJOSowWjWsMEQ4bbiVK+qsmzNFuvwoM7amtjeqC7VWJh847rgvpIE0OToiuDSzE6R8M8U4wbQ1sA==",
      "embedding_inv_norm_f16_uint16": 11922
    }
  ]
}

Configuration

You can configure the app via environment variables.

Models

For PHOTOFIELD_AI_VISUAL_MODEL and PHOTOFIELD_AI_TEXTUAL_MODEL you can use any model from clip-variants models.

The bigger models are likely to be better, however it probably depends on your use-case. The different model types most likely won't be compatible with each other, however combining different data types might work fine.

Note that the qint8 models don't seem to work right now, so use quint8 ones instead.

Development Setup

Prerequisites

• Python
• Poetry - for dependency management
• just - to run common commands conveniently
• sh-like shell (e.g. sh, bash, busybox) - required by just

Scoop (Windows): scoop install busybox just

Installation

1. Clone the repo

   git clone https://github.com/smilyorg/photofield-ai.git
   

2. Install Python dependencies

   poetry install
   

Running

• poetry shell to enter the virtual environment and just watch the source files and auto-reload the server
• or just run the server

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgements

• OpenAI CLIP for the research and machine learning model weights used here
• Hugging Face for hosting the ONNX models
• CLIP-as-service by Jina as a big inspiration for this project
• openai-clip-js by josephrocca on how to convert CLIP to ONNX
• CLIP-ONNX by Lednik7 on more CLIP with ONNX example code
• Exporting a Model from PyTorch to ONNX and running it using ONNX Runtime - PyTorch
• imgbeddings by minimaxir for a similar image-focused CLIP ONNX implementation
• Best-README-Template
• readme.so