npow/mrcnn_serving_ready

🛠 Converting Mask R-CNN Keras model to Tensorflow model and Serving model

MRCNN Model conversion

Script to convert MatterPort Mask_RCNN Keras model to Tensorflow Frozen Graph and Tensorflow Serving Model.
Plus inferencing with GRPC or RESTAPI using Tensorflow Model Server.

How to Run

1. Modify the path variables in 'user_config.py'
2. Run main.py

   python3 main.py

For Custom Config class

If you have a different config class you can replace the existing config in 'main.py'

# main.py
# Current config load
config = get_config()

# replace it with your config class
config = your_custom_config_class

Inferencing

Follow once you finish converting it to a saved_model using the above code

Tensorflow Model Server with GRPC and RESTAPI

1. First run your saved_model.pb in Tensorflow Model Server, using:

   tensorflow_model_server --port=8500 --rest_api_port=8501 --model_name=mask --model_base_path=/path/to/saved_model/

2. Modify the variables and add your Config Class if needed in inferencing/saved_model_config.py. No need to change if the saved_model is the default COCO model.
3. Then run the inferencing/saved_model_inference.py with the image path:

   # Set Python Path
   export PYTHONPATH=$PYTHONPATH:$pwd
   
   # Run Inference with GRPC
   python3 inferencing/saved_model_inference.py -t grpc -p test_image/monalisa.jpg
   
   # Run Inference with RESTAPI
   python3 inferencing/saved_model_inference.py -t restapi -p test_image/monalisa.jpg

Acknowledgement

Thanks to @rahulgullan for RESTAPI client code.