To start dev containers and run framework type:

cd /project/path
./run_env.sh

To stop and delete dev containers type:

cd /project/path
./down_env.sh

This framework consist of 4 services named deployment, inference, mmdetection and tensorflow(tf) api.

Deployment and inference apis are designed for endusers whereas mmdetection api and tensorflow api are used in application internally. mmdetection service is responsible from inference with mmdetection models similarly tensorflow service is responsible from inference with tensorflow models. Deployment api lets us deploy our models and we can make inference with deployed model using inference api.

Endusers utilizes deployment api to deploy their models. Deployment api has one endpoint which is deploy. Users make POST request to localhost:5003/deploy with request body shown below.

{
    "type": "tf" # or "mmdetection",
    "checkpoint_url": "https://www..." # the model checkpoint address,
    "deployment_name": "my_object_detection_model_1" # this is unique name and will be used in inference
    "config": "/faster_rcnn/faster_rcnn_1x_rpn.py" # if model is mmdetection, you need to add config path from original mmdetection repo(https://github.com/open-mmlab/mmdetection)
}

If everything is OK, the backend starts deployment asynchronously(download checkpoint, save informations in mongodb) and returns,

{
    "result": "success"
}

Endusers utilizes inference api to use their models. Inference api has one endpoint which is infer. Users make GET request to localhost:5004/infer with request body shown below.

{
    "deployment_name": "my_object_detection_model_1" # this is unique name it was set at deployment time
    "image": "5aWDj..." # The base64 image that we apply inference on",
}

If everything is OK, the backend starts inference and returns,

{
    "objects":[
        {
            "class": "dog",
            "bbox": [123, 235, 432, 45],
            "confidence_score": 0.8
        },
        {
            "class": "cat",
            "bbox": [53, 35, 49, 25],
            "confidence_score": 0.9
        }
        ...
    ],
}