Inference Docker Build

After pipeline training, copy all branch checkpoints to ckpts folder, then modify line 144-149 of predict.py to select correct branch for each modality. The default settings of varaible model_dict is as follows:

    model_dict = {
        'bf': 'general.pt',  # brightfield branch
        'gs': 'grayscale.pt', # grayscale branch
        'fl': 'fl.pt',  # flourescence branch
        'omni': 'omnipose.pt' # omnipose model
    }

Then you can predict masks with the following command:

python predict.py -i "path_to_inputs"  -o "path_to_outputs"

If the program runs correctly, build and save docker image with following commands:

docker build -t name:tag .

docker save name:tag -o name.tar.gz