Dynamic Shape with Automatic Model Reloading


This guide explains how to configure a model to accept input data in different shapes. In this example, it is done by reloading the model with a new shape each time it receives the request with a shape different than the one which is currently set.

Enable dynamic shape via model reloading by setting the shape parameter to auto. To configure and use the dynamic batch size, take advantage of:

When using the face_detection_retail_0004 model with the face_detection.py script, images are reloaded and resized to the desired width and height. Then, the output is processed from the server, and the inference results are displayed with bounding boxes drawn around the predicted faces.


Clone OpenVINO Model Server GitHub repository and enter model_server directory.

git clone https://github.com/openvinotoolkit/model_server.git
cd model_server

Download the Pretrained Model

Download the model files and store them in the models directory

mkdir -p models/face_detection/1
curl https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/3/face-detection-retail-0004/FP32/face-detection-retail-0004.bin https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/3/face-detection-retail-0004/FP32/face-detection-retail-0004.xml -o models/face_detection/1/face-detection-retail-0004.bin -o models/face_detection/1/face-detection-retail-0004.xml

Pull the Latest Model Server Image

Pull the latest version of OpenVINO Model Server from Docker Hub:

docker pull openvino/model_server:latest

Start the Model Server Container with the Model and Dynamic Batch Size

Start the container using the image pulled in the previous step and mount the models directory:

docker run --rm -d -v $(pwd)/models:/models -p 9000:9000 openvino/model_server:latest --model_name face-detection --model_path /models/face_detection --shape auto --port 9000

Run the Client

cd demos/face_detection/python
virtualenv .venv
. .venv/bin/activate
pip install -r ../../common/python/requirements.txt
mkdir results_500x500

python face_detection.py --grpc_port 9000 --width 500 --height 500 --input_images_dir ../../common/static/images/people --output_dir results_500x500

mkdir results_600x400

python face_detection.py --grpc_port 9000 --width 600 --height 400 --input_images_dir ../../common/static/images/people --output_dir results_600x400

The results from running the client will be saved in the directory specified by --output_dir

> NOTE : reloading the model takes time and during each reload new requests are queued. Frequent model reloading may negatively affect overall performance.