app_integrate_bundle

Easy Integrate Bundle Tutorial

A MONAI bundle contains the stored weights of a model, training, inference, post-processing transform sequences and other information. This tutorial aims to demonstrate users how to quickly integrate the bundle into their own application. The tutorial create a straightforward ensemble application and instruct users on how to use the existing bundle.

The example training dataset is Task09_Spleen.tar from http://medicaldecathlon.com/.

Requirements

The script is tested with:

• Ubuntu 20.04 | Python 3.8.13 | CUDA 11.7 | Pytorch 1.11.0

• the default pipeline requires at least 8GB memory per gpu

• it is tested on 24gb single-gpu machine

Dependencies and installation

MONAI

You can conda environments to install the dependencies.

pip install scikit-learn==0.24.2

or you can just use MONAI docker.

docker pull projectmonai/monai:latest

For more information please check out the installation guide.

Examples

Check all possible options

python ./ensemble.py -h

Get started

1. Prepare your bundle.

   First download a bundle to somewhere as your bundle_root_path:

   python -m monai.bundle download --name spleen_ct_segmentation --bundle_dir "./"

2. Prepare your data.

   Put your data in data_root_path. We prefer you organize your dataset as MSD datasets structure. Then, split your dataset into train and test subsets, and generate a json file named dataset.json under the data_root_path, like:

    {
       "training": [
           {
               "image": "./image1.nii.gz"
               "label": "./label1.nii.gz"
           },
           {
               "image": "./image2.nii.gz",
               "label": "./label2.nii.gz"
           },
           ...
       ],
       "test": [
           {
               "image": "./image.nii.gz"
           },
           ...
       ]
   }
   

   The data in training will random split into n_splits which you can specify with --n_splits xx

3. Run the script. Make sure bundle_root_path and data_root_path is correct.

python ensemble.py --bundle_root bundle_root_path --dataset_dir data_root_path
--ensemble Mean

How to integrate Bundle in your own application

Get component from bundle

Check all supported properties in https://github.com/Project-MONAI/MONAI/blob/dev/monai/bundle/properties.py.

from monai.bundle import create_workflow

train_workflow = create_workflow(config_file=bundle_config_path, workflow_type="train")

# get train postprocessing
postprocessing = train_workflow.train_postprocessing

# get meta information
version = train_workflow.version
description = train_workflow.description

Use component in your pipeline

# Notice that the `postprocessing` got from `train_workflow` is instantiated.

evaluator = SupervisedEvaluator(
            device=device,
            val_data_loader=your_dataloader,
            network=your_networks,
            inferer=SimpleInferer(),
            postprocessing=postprocessing,
        )

Update component with your own args

• If the component you want to replace is listed here, you can replace it directly as below:

# update `max_epochs` in workflow
train_workflow.max_epochs = max_epochs

# must execute 'initialize' again after changing the content
train_workflow.initialize()
print(train_workflow.max_epochs)

• Otherwise, you can override the components when you create the workflow.

override = {
            "network": "$@network_def.to(@device)",
            "dataset#_target_": "Dataset",
            "dataset#data": [{"image": filename}],
            "postprocessing#transforms#2#output_postfix": "seg",
        }
train_workflow = create_workflow(config_file=bundle_config_path, workflow_type="train", **override)

Questions and bugs

• For questions relating to the use of MONAI, please use our Discussions tab on the main repository of MONAI.
• For bugs relating to MONAI functionality, please create an issue on the main repository.
• For bugs relating to the running of a tutorial, please create an issue in this repository.