crack_segmenter

This is a toy system serving the crack segmentation model, as part of PV-Vision project. Before you start, you need to install the PV-Vision environment. The codes and models are used for demo only.

Download the model weights

curl https://datahub.duramat.org/dataset/a1417b84-3724-47bc-90b8-b34660e462bb/resource/45da3b55-fa96-471d-a231-07b98ec5dd8e/download/crack_segmentation.zip --output model_weights.zip

unzip model_weights.zip

Register the model

You can log the pretrained model and register it to the MLflow server using the following command:

python register_pretrained_model.py

Now you should be able to see the model in the MLflow UI. You can start the MLflow server by running the following command:

mlflow ui

Access http://127.0.0.1:5000 in your browser and you will see the registered model: Then assign an aliases (e.g., best_model) to this model:

serve the registered model

You can serve the model using the following command:

mlflow models serve -m models:/crack_seg_dev@best_model -p 1234 --env-manager=local

I set --env-manager=local to use the local environment. Note that my current local environment support pv-vision. You can also use --env-manager=conda to setup a new environment.

Now the registered model is serving at http://127.0.0.1:1234

Get the prediction from the server

Note that if you just want to run the prediction locally, there is no need to serve the model. You can directly utilize the modelhandler in pv-vision or load the model in mlflow and ust it. Here I just want to show how to get the prediction from the server if you are going to deploy the model on a remote server (e.g., AWS, GCP, etc.).

Run the following command to get the prediction from the server:

python segment_cracks.py -i img_for_prediction -o output --save_img --url http://127.0.0.1:1234/invocations

Replace img_for_prediction with your image folder and output with your output folder. If you want to save the predicted masks into images, add --save_img. The prediction will be saved in the output folder.

Remember to add invocations to the end of the url. This is the endpoint for the model serving.