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GroundingDINO Service

Why do this

GroundingDINO is an open-set object detector, which can detect objects that are not in the training set, it can be used to automatic annotation or other applications. However, it is not a service, and it is not easy to use. This project is to provide a service for GroundingDINO, so that it can be used easily with http requests.

Basic Background Knowledge

We use TorchServe to serve GroundingDINO model. TorchServe is a flexible and easy to use tool for serving PyTorch models. TorchServe also provides a flexible serialization format and example libraries for TorchServe to serve PyTorch models. TorchServe is a great tool for serving PyTorch models in production and it is easy to use. We can also easily deploy our other model with TorchServe instead of write a new service for each model. For more information about TorchServe, please refer to TorchServe.

How to use

1. clone our repo and cd into it

2. Download GroundingDINO and Bert model

Download GroundingDINO model:

wget -q https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth

suppose the model is in the weights folder.
Download Bert model from huggingface:
https://huggingface.co/bert-base-uncased/tree/main , in this project, we need:

config.json
pytorch_model.bin
tokenizer_config.json
tokenizer.json
vocab.txt

suppose these files are in the bert-base-uncased folder.

3. Build docker image or install dependencies locally

docker build -t torchserve:groundingdino .

or you can use the image I have built:

docker pull haoliuhust/torchserve:groundingdino

4. convert model to torchserve format

docker run --rm -it -v $(pwd):/data -w /data torchserve:groundingdino bash -c "torch-model-archiver --model-name groundingdino --version 1.0 --serialized-file weights/groundingdino_swint_ogc.pth --handler grounding_dino_handler.py --extra-files GroundingDINO_SwinT_OGC.py,bert-base-uncased/*"

after it done, you will get a file named groundingdino.mar in the current folder.
make a folder named model_store, and put the model in it.

5. start torchserve

modify torchserve configurations in config.properties, for more information, please refer to https://github.com/pytorch/serve/blob/master/docs/configuration.md , then start torchserve(change the port as you set in config.properties)

docker run -d --name groundingdino -v $(pwd)/model_store:/model_store -p 8080:8080 -p 8081:8081 -p 8082:8082 torchserve:groundingdino bash -c "torchserve --start --foreground --model-store /model_store --models groundingdino=groundingdino.mar"

6. test and use

import requests
import base64
import time
# URL for the web service
url = "http://ip:8080/predictions/groundingdino"
headers = {"Content-Type": "application/json"}

# Input data
with open("test.jpg", "rb") as f:
    image = f.read()

data = {
        "image": base64.b64encode(image).decode("utf-8"), # base64 encoded image or BytesIO
        "caption": "steel pipe", # text prompt, split by "." for multiple phrases
        "box_threshold": 0.25, # threshold for object detection
        "caption_threshold": 0.25 # threshold for text similarity
        }

# Make the request and display the response

resp = requests.post(url=url, headers=headers, json=data)
outputs = resp.json()
'''
the outputs will be like:
    {
        "boxes": [[0.0, 0.0, 1.0, 1.0]], # list of bounding boxes in xyxy format
        "scores": [0.9999998807907104], # list of object detection scores
        "phrases": ["steel pipe"] # list of text phrases
    }

'''

License

The code is licensed under the Apache 2.0 license.

Reference

[1] GroundingDINO
[2] TorchServe
[3] segment-anything-services