https://github.com/bendangnuksung/mrcnn_serving_ready

🛠 Mask R-CNN Keras to Tensorflow and TFX models + Serving models using TFX GRPC & RESTAPI
https://github.com/bendangnuksung/mrcnn_serving_ready

grpc mask-rcnn rest-api tensorflow-keras tensorflow-model tensorflow-model-server

Last synced: 2 months ago
JSON representation

🛠 Mask R-CNN Keras to Tensorflow and TFX models + Serving models using TFX GRPC & RESTAPI

• Host: GitHub
• URL: https://github.com/bendangnuksung/mrcnn_serving_ready
• Owner: bendangnuksung
• License: mit
• Created: 2019-04-11T10:24:33.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-10-16T17:47:58.000Z (about 4 years ago)
• Last Synced: 2023-03-02T05:05:28.775Z (almost 2 years ago)
• Topics: grpc, mask-rcnn, rest-api, tensorflow-keras, tensorflow-model, tensorflow-model-server
• Language: Python
• Homepage: https://github.com/matterport/Mask_RCNN
• Size: 158 KB
• Stars: 100
• Watchers: 2
• Forks: 35
• Open Issues: 17
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
### MRCNN Model conversion

Script to convert [MatterPort Mask_RCNN](https://github.com/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

    ```bash

    python3 main.py

    ```

    

#### For Custom Config class

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

```python

# 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:

    ```bash

    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:

    ```bash

    # 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](https://github.com/rahulgullan) for RESTAPI client code.