diff --git a/brats/README.md b/brats/README.md new file mode 100644 index 0000000..d4bd8e2 --- /dev/null +++ b/brats/README.md @@ -0,0 +1,170 @@ +# MedPerf - MLCube - BraTs Challange Integration + +## Project setup + +```bash +# Create Python environment and install MLCube Docker runner +virtualenv -p python3 ./env && source ./env/bin/activate && pip install mlcube-docker + +# Fetch the boston housing example from GitHub +git clone https://github.com/mlcommons/mlcube_examples && cd ./mlcube_examples +git fetch origin pull/39/head:feature/brats && git checkout feature/brats +cd ./brats/metrics/mlcube +``` + +## Execute docker-based MLCubes with Singularity runner + +```bash +virtualenv -p python3 env && source ./env/bin/activate + +git clone https://github.com/mlcommons/mlcube && cd ./mlcube + +git fetch origin pull/223/head:feature/singularity_with_docker_images && git checkout feature/singularity_with_docker_images + +pip install semver spython && pip install ./mlcube + +pip install --no-deps --force-reinstall ./runners/mlcube_singularity +``` + +## MedPerf API Server + +To run locally, clone this repo: + +```Bash +git clone https://github.com/mlcommons/medperf.git +``` + +Go to the `server` folder + +```Bash +cd server +``` + +Install all dependencies + +```Bash +pip install -r requirements.txt +``` + +Create .env file with your environment settings + +Sample .env.example is added to root. Rename `.env.example` to `.env` and modify with your env vars. + +```Bash +cp .env.example .env +``` + +Create tables and existing models + +```Bash +python manage.py migrate +``` + +Start the server + +```Bash +python manage.py runserver +``` + +API Server is running at `http://127.0.0.1:8000/` by default. You can view and experiment Medperf API at `http://127.0.0.1:8000/swagger` + +## Medperf CLI + +The Medperf CLI is a command-line-interface that provides tools for preparing datasets and executing benchmarks on such datasets. + +To install, clone this repo (If you already did skip this step): + +```Bash +git clone https://github.com/mlcommons/medperf.git +``` + +Go to the `cli` folder + +```Bash +cd cli +``` + +Install using pip + +```Bash +pip install -e . +``` + +## How to run + +The MedPerf CLI provides the following commands: + +- `login`: authenticates the CLI with the medperf backend server + +```Bash +medperf login +``` + +- `dataset ls`: Lists all registered datasets by the user + +```Bash +medperf dataset ls +``` + +- `dataset create`: Prepares a raw dataset for a specific benchmark + +```Bash +medperf dataset create -b -d -l +``` + +- `dataset submit`: Submits a prepared local dataset to the platform. + +```Bash +medperf dataset submit -d +``` + +- `dataset associate`: Associates a prepared dataset with a specific benchmark + +```Bash +medperf associate -b -d +``` + +- `run`: Alias for `result create`. Runs a specific model from a benchmark with a specified prepared dataset + +```Bash +medperf run -b -d -m +``` + +- `result ls`: Displays all results created by the user + +```Bash +medperf result ls +``` + + +- `result create`: Runs a specific model from a benchmark with a specified prepared dataset + +```Bash +medperf result create -b -d -m +``` + +- `result submit`: Submits already obtained results to the platform + +```Bash +medperf result submit -b -d -m +``` + +- `mlcube ls`: Lists all mlcubes created by the user. Lists all mlcubes if `--all` is passed + +```Bash +medperf mlcube ls [--all] +``` + +- `mlcube submit`: Submits a new mlcube to the platform + +```Bash +medperf mlcube submit +``` + +- `mlcube associate`: Associates an MLCube to a benchmark + +```Bash +medperf mlcube associate -b -m +``` + +The CLI runs MLCubes behind the scene. This cubes require a container engine like docker, and so that engine must be running before running commands like `prepare` and `execute` diff --git a/brats/data/tmp.tar.gz b/brats/data/tmp.tar.gz new file mode 100644 index 0000000..a3dd006 Binary files /dev/null and b/brats/data/tmp.tar.gz differ diff --git a/brats/metrics/.gitignore b/brats/metrics/.gitignore new file mode 100644 index 0000000..19b45fa --- /dev/null +++ b/brats/metrics/.gitignore @@ -0,0 +1,2 @@ +__pycache__/ +mlcube/workspace/results.yaml \ No newline at end of file diff --git a/brats/metrics/README.md b/brats/metrics/README.md new file mode 100644 index 0000000..8524032 --- /dev/null +++ b/brats/metrics/README.md @@ -0,0 +1,104 @@ +# BraTS Challenge - MLCube integration - Metrics + +Original implementation: ["BraTS Instructions Repo"](https://github.com/BraTS/Instructions) + +## Dataset + +Please refer to the [BraTS challenge page](http://braintumorsegmentation.org/) and follow the instructions in the data section. + +## Project setup + +```bash +# Create Python environment and install MLCube Docker runner +virtualenv -p python3 ./env && source ./env/bin/activate && pip install mlcube-docker + +# Fetch the boston housing example from GitHub +git clone https://github.com/mlcommons/mlcube_examples && cd ./mlcube_examples +git fetch origin pull/39/head:feature/brats && git checkout feature/brats +cd ./brats/metrics/mlcube +``` + +## Important files + +These are the most important files on this project: + +```bash + +├── mlcube +│ ├── mlcube.yaml # MLCube configuration file, it defines the project, author, platform, docker and tasks. +│ └── workspace +│ ├── data +│ │ ├── ground_truth +│ │ │ └── BraTS_example_seg.nii.gz # Ground truth example file +│ │ └── predictions +│ │ └── BraTS_example_seg.nii.gz # Prediction example file +│ ├── parameters.yaml +│ └── results.yaml # Final output file containing result metrics. +└── project + ├── Dockerfile # Docker file with instructions to create the image for the project. + ├── metrics.py # Python file that contains the main logic of the project. + ├── mlcube.py # Python entrypoint used by MLCube, contains the logic for MLCube tasks. + └── requirements.txt # Python requirements needed to run the project inside Docker. +``` + +## How to modify this project + +You can change each file described above in order to add your own implementation. + +### Requirements file + +In this file (`requirements.txt`) you can add all the python dependencies needed for running your implementation, these dependencies will be installed during the creation of the docker image, this happens when you run the ```mlcube run ...``` command. + +### Dockerfile + +You can use both, CPU or GPU version for the dockerfile (`Dockerfile_CPU`, `Dockerfile_GPU`), also, you can add or modify any steps inside the file, this comes handy when you need to install some OS dependencies or even when you want to change the base docker image, inside the file you can find some information about the existing steps. + +### Parameters file + +This is a yaml file (`parameters.yaml`)that contains all extra parameters that aren't files or directories, for example, here you can place all the hyperparameters that you will use for training a model. This file will be passed as an **input parameter** in the MLCube tasks and then it will be read inside the MLCube container. + +### MLCube yaml file + +In this file (`mlcube.yaml`) you can find the instructions about the docker image and platform that will be used, information about the project (name, description, authors), and also the tasks defined for the project. + +In the existing implementation you will find 1 task: + +* evaluate: + + This task takes the following parameters: + + * Input parameters: + * predictions: Folder path containing predictions + * ground_truth: Folder path containing ground truth data + * parameters_file: Extra parameters + * Output parameters: + * output_path: File path where output metrics will be stored + + This task takes the input predictions and ground truth data, perform the evaluation and then save the output result in the output_path. + +### MLCube python file + +The `mlcube.py` file is the handler file and entrypoint described in the dockerfile, here you can find all the logic related to how to process each MLCube task. If you want to add a new task first you must define it inside the `mlcube.yaml` file with its input and output parameters and then you need to add the logic to handle this new task inside the `mlcube.py` file. + +### Metrics file + +The `metrics.py` file contains the main logic of the project, you can modify this file and write your implementation here to calculate different metrics, this metrics file is called from the `mlcube.py` file and there are other ways to link your implementation and shown in the [MLCube examples repo](https://github.com/mlcommons/mlcube_examples). + +## Tasks execution + +```bash +# Run evaluate task. +mlcube run --mlcube=mlcube.yaml --task=evaluate +``` + +To use Singularity runner add the flag `--platform=singularity`, example: + +```bash +mlcube run --mlcube=mlcube.yaml --task=evaluate --platform=singularity +``` + +We are targeting pull-type installation, so MLCube images should be available on Docker Hub. If not, try this: + +```Bash +mlcube run ... -Pdocker.build_strategy=always +``` diff --git a/brats/metrics/mlcube/mlcube.yaml b/brats/metrics/mlcube/mlcube.yaml new file mode 100644 index 0000000..e6112af --- /dev/null +++ b/brats/metrics/mlcube/mlcube.yaml @@ -0,0 +1,26 @@ +name: MLCommons Brats metrics +description: MLCommons Brats integration for metrics +authors: + - {name: "MLCommons Best Practices Working Group"} + +platform: + accelerator_count: 0 + +docker: + # Image name. + image: mlcommons/brats_metrics:0.0.1 + # Docker build context relative to $MLCUBE_ROOT. Default is `build`. + build_context: "../project" + # Docker file name within docker build context, default is `Dockerfile`. + build_file: "Dockerfile" + +tasks: + evaluate: + # Executes a number of metrics specified by the params file + parameters: + inputs: { + predictions: data/predictions/, + labels: data/ground_truth/, + parameters_file: {type: file, default: parameters.yaml} + } + outputs: {output_path: {type: "file", default: "results.yaml"}} diff --git a/brats/metrics/mlcube/workspace/data/ground_truth/BraTS_example_seg.nii.gz b/brats/metrics/mlcube/workspace/data/ground_truth/BraTS_example_seg.nii.gz new file mode 100644 index 0000000..40c2c46 Binary files /dev/null and b/brats/metrics/mlcube/workspace/data/ground_truth/BraTS_example_seg.nii.gz differ diff --git a/brats/metrics/mlcube/workspace/data/predictions/BraTS_example_seg.nii.gz b/brats/metrics/mlcube/workspace/data/predictions/BraTS_example_seg.nii.gz new file mode 100644 index 0000000..40c2c46 Binary files /dev/null and b/brats/metrics/mlcube/workspace/data/predictions/BraTS_example_seg.nii.gz differ diff --git a/brats/metrics/mlcube/workspace/parameters.yaml b/brats/metrics/mlcube/workspace/parameters.yaml new file mode 100644 index 0000000..13309f0 --- /dev/null +++ b/brats/metrics/mlcube/workspace/parameters.yaml @@ -0,0 +1,2 @@ +treshold: 0.5 +eps: 0 \ No newline at end of file diff --git a/brats/metrics/project/Dockerfile b/brats/metrics/project/Dockerfile new file mode 100644 index 0000000..83eecbb --- /dev/null +++ b/brats/metrics/project/Dockerfile @@ -0,0 +1,22 @@ +# for a CPU app use this Dockerfile. +FROM python:3.8-buster + +# fill in your info here +LABEL author="chuck@norris.org" +LABEL application="your application name" +LABEL maintainer="chuck@norris.org" +LABEL version="0.0.1" +LABEL status="beta" + +# basic +RUN apt-get -y update && apt -y full-upgrade && apt-get -y install apt-utils wget git tar build-essential curl nano + +# install all python requirements +WORKDIR /workspace +COPY ./requirements.txt ./requirements.txt +RUN pip3 install -r requirements.txt + +# copy all files +COPY ./ /workspace + +ENTRYPOINT [ "python3", "/workspace/mlcube.py"] diff --git a/brats/metrics/project/metrics.py b/brats/metrics/project/metrics.py new file mode 100644 index 0000000..e0b6e3b --- /dev/null +++ b/brats/metrics/project/metrics.py @@ -0,0 +1,182 @@ +"""Metrics file""" +import argparse +import glob +import yaml +import nibabel as nib +import numpy as np + + +def dice_coef_metric( + predictions: np.ndarray, truth: np.ndarray, treshold: float = 0.5, eps: float = 0 +) -> np.ndarray: + """ + Calculate Dice score for data batch. + Params: + predictions: model outputs after activation function. + truth: truth values. + threshold: threshold for predictions. + eps: additive to refine the estimate. + Returns: dice score aka f1. + """ + + scores = [] + num = predictions.shape[0] + predictions = predictions >= treshold + assert predictions.shape == truth.shape + for i in range(num): + prediction = predictions[i] + truth_ = truth[i] + intersection = 2.0 * (truth_ * prediction).sum() + union = truth_.sum() + prediction.sum() + if truth_.sum() == 0 and prediction.sum() == 0: + scores.append(1.0) + else: + scores.append((intersection + eps) / union) + return np.mean(scores) + + +def jaccard_coef_metric( + predictions: np.ndarray, truth: np.ndarray, treshold: float = 0.5, eps: float = 0 +) -> np.ndarray: + """ + Calculate Jaccard index for data batch. + Params: + predictions: model outputs after activation function. + truth: truth values. + threshold: threshold for predictions. + eps: additive to refine the estimate. + Returns: jaccard score aka iou." + """ + + scores = [] + num = predictions.shape[0] + predictions = predictions >= treshold + assert predictions.shape == truth.shape + + for i in range(num): + prediction = predictions[i] + truth_ = truth[i] + intersection = (prediction * truth_).sum() + union = (prediction.sum() + truth_.sum()) - intersection + eps + if truth_.sum() == 0 and prediction.sum() == 0: + scores.append(1.0) + else: + scores.append((intersection + eps) / union) + return np.mean(scores) + + +def preprocess_mask_labels(mask: np.ndarray): + """Preprocess the mask labels from a numpy array""" + + mask_WT = mask.copy() + mask_WT[mask_WT == 1] = 1 + mask_WT[mask_WT == 2] = 1 + mask_WT[mask_WT == 4] = 1 + + mask_TC = mask.copy() + mask_TC[mask_TC == 1] = 1 + mask_TC[mask_TC == 2] = 0 + mask_TC[mask_TC == 4] = 1 + + mask_ET = mask.copy() + mask_ET[mask_ET == 1] = 0 + mask_ET[mask_ET == 2] = 0 + mask_ET[mask_ET == 4] = 1 + + mask = np.stack([mask_WT, mask_TC, mask_ET]) + mask = np.moveaxis(mask, (0, 1, 2, 3), (0, 3, 2, 1)) + + return mask + + +def load_img(file_path): + """Reads segmentations image as a numpy array""" + + data = nib.load(file_path) + data = np.asarray(data.dataobj) + return data + + +def get_data_arr(predictions_path, ground_truth_path): + """Reads the content for the predictions and ground truth folders + and then returns the data in numpy array format""" + + predictions = glob.glob(predictions_path + "/*") + ground_truth = glob.glob(ground_truth_path + "/*") + if not len(predictions) == len(ground_truth): + raise ValueError( + "Number of predictions should be the same of ground truth labels" + ) + gt_arr, prediction_arr = [], [] + for gt_path, prediction_path in zip(ground_truth, predictions): + gt = load_img(gt_path) + gt = preprocess_mask_labels(gt) + prediction = load_img(prediction_path) + prediction = preprocess_mask_labels(prediction) + gt_arr.append(gt) + prediction_arr.append(prediction) + gt_arr = np.concatenate(gt_arr) + prediction_arr = np.concatenate(prediction_arr) + return gt_arr, prediction_arr + + +def create_metrics_file(output_file, results): + """Writes metrics to an output yaml file""" + with open(output_file, "w") as f: + yaml.dump(results, f) + + +def main(): + """Main function that recieves input parameters and calculate metrics""" + + parser = argparse.ArgumentParser() + parser.add_argument( + "--ground_truth", + type=str, + required=True, + help="Directory containing the ground truth data", + ) + parser.add_argument( + "--predictions", + type=str, + required=True, + help="Directory containing the predictions", + ) + parser.add_argument( + "--output_file", + "--output-file", + type=str, + required=True, + help="file to store metrics results as YAML", + ) + parser.add_argument( + "--parameters_file", + "--parameters-file", + type=str, + required=True, + help="File containing parameters for evaluation", + ) + args = parser.parse_args() + + with open(args.parameters_file, "r") as f: + params = yaml.full_load(f) + + gt_arr, pred_arr = get_data_arr(args.predictions, args.ground_truth) + + treshold = float(params["treshold"]) + eps = float(params["eps"]) + + dice_coef = dice_coef_metric(pred_arr, gt_arr, treshold, eps) + jaccard_coef = jaccard_coef_metric(pred_arr, gt_arr, treshold, eps) + + results = { + "dice_coef": str(dice_coef), + "jaccard_coef": str(jaccard_coef), + } + + print(results) + create_metrics_file(args.output_file, results) + + +if __name__ == "__main__": + main() diff --git a/brats/metrics/project/mlcube.py b/brats/metrics/project/mlcube.py new file mode 100644 index 0000000..d49dee6 --- /dev/null +++ b/brats/metrics/project/mlcube.py @@ -0,0 +1,41 @@ +"""MLCube handler file""" +import os +import yaml +import typer +import subprocess + + +app = typer.Typer() + + +class EvaluateTask: + """Runs evaluation metrics given the predictions and ground truth files""" + + @staticmethod + def run( + ground_truth: str, predictions: str, parameters_file: str, output_file: str + ) -> None: + cmd = f"python3 /workspace/metrics.py --ground_truth={ground_truth} --predictions={predictions} --parameters_file={parameters_file} --output_file={output_file}" + splitted_cmd = cmd.split() + + process = subprocess.Popen(splitted_cmd, cwd=".") + process.wait() + + +@app.command("evaluate") +def evaluate( + labels: str = typer.Option(..., "--labels"), + predictions: str = typer.Option(..., "--predictions"), + parameters_file: str = typer.Option(..., "--parameters_file"), + output_path: str = typer.Option(..., "--output_path"), +): + EvaluateTask.run(labels, predictions, parameters_file, output_path) + + +@app.command("test") +def test(): + pass + + +if __name__ == "__main__": + app() diff --git a/brats/metrics/project/requirements.txt b/brats/metrics/project/requirements.txt new file mode 100644 index 0000000..0f4b422 --- /dev/null +++ b/brats/metrics/project/requirements.txt @@ -0,0 +1,4 @@ +PyYAML +typer +numpy +nibabel \ No newline at end of file diff --git a/brats/model/.gitignore b/brats/model/.gitignore new file mode 100644 index 0000000..ba0430d --- /dev/null +++ b/brats/model/.gitignore @@ -0,0 +1 @@ +__pycache__/ \ No newline at end of file diff --git a/brats/model/README.md b/brats/model/README.md new file mode 100644 index 0000000..54c57c2 --- /dev/null +++ b/brats/model/README.md @@ -0,0 +1,115 @@ +# BraTS Challenge - MLCube integration - Model + +Original implementation: ["BraTS Instructions Repo"](https://github.com/BraTS/Instructions) + +## Dataset + +Please refer to the [BraTS challenge page](http://braintumorsegmentation.org/) and follow the instructions in the data section. + +## Project setup + +```bash +# Create Python environment and install MLCube Docker runner +virtualenv -p python3 ./env && source ./env/bin/activate && pip install mlcube-docker + +# Fetch the boston housing example from GitHub +git clone https://github.com/mlcommons/mlcube_examples && cd ./mlcube_examples +git fetch origin pull/39/head:feature/brats && git checkout feature/brats +cd ./brats/model/mlcube +``` + +## Important files + +These are the most important files on this project: + +```bash +├── mlcube +│ ├── mlcube.yaml # MLCube configuration, defines the project, author, platform, docker and tasks. +│ └── workspace +│ └── parameters.yaml # File containing all extra parameters. +└── project + ├── Dockerfile # Docker file with instructions to create the image. + ├── mlcube.py # Python entrypoint used by MLCube, contains the logic for MLCube tasks. + ├── requirements.txt # Python requirements needed to run the project inside Docker. + └── src + ├── my_logic.py # Python file that contains the main logic of the project. + └── utils + └── utilities.py # Python utilities file that stores useful functions. +``` + +## Project workflow + +![MLCube workflow](https://i.imgur.com/qXRp3Tb.png) + +## How to modify this project + +You can change each file described above in order to add your own implementation. + +### Requirements file + +In this file (`requirements.txt`) you can add all the python dependencies needed for running your implementation, these dependencies will be installed during the creation of the docker image, this happens when you run the ```mlcube run ...``` command. + +### Dockerfile + +In this file users can define the image for CPU or GPU version, also, users add or modify any steps inside the file, this comes handy when you need to install some OS dependencies or even when you want to change the base docker image, inside the file you can find some information about the existing steps. + +### Parameters file + +This is a yaml file (`parameters.yaml`)that contains all extra parameters that aren't files or directories, for example, here you can place all the hyperparameters that you will use for training a model. This file will be passed as an **input parameter** in the MLCube tasks and then it will be read inside the MLCube container. + +### MLCube yaml file + +In this file you can find the instructions about the docker image and platform that will be used, information about the project (name, description, authors), and also the tasks defined for the project. + +In the existing implementation you will find 2 tasks: + +* example: + + It only takes one input parameter: parameters file. + This task reads one specific parameter from the parameters file () and then prints the value of the parameter. + +* run: + + This task takes the following parameters: + + * Input parameters: + * input_folder: folder path containing input data + * parameters_file: Extra parameters + * Output parameters: + * output_folder: folder path where output data will be stored + + This task takes the input data, "process it" and then save the output result in the output_folder, it also prints some information from the extra parameters. + +### MLCube python file + +The `mlcube.py` file is the handler file and entrypoint described in the dockerfile, here you can find all the logic related to how to process each MLCube task. If you want to add a new task first you must define it inside the `mlcube.yaml` file with its input and output parameters and then you need to add the logic to handle this new task inside the `mlcube.py` file. + +### Main logic file + +The `my_logic.py` file contains the main logic of the project, you can modify this file and write your implementation here, this logic file is called from the `mlcube.py` file and there are other ways to link your implementation and shown in the [MLCube examples repo](https://github.com/mlcommons/mlcube_examples). + +### Utilities file + +In the `utilities.py` file you can add some functions that will be useful for your main implementation, in this case, the functions from the utilities file are used inside the main logic file. + +## Tasks execution + +```bash +# Run example task. +mlcube run --mlcube=mlcube.yaml --task=example + +# Run main task. +mlcube run --mlcube=mlcube.yaml --task=infer +``` + +To use Singularity runner add the flag `--platform=singularity`, example: + +```bash +mlcube run --mlcube=mlcube.yaml --task=example --platform=singularity +``` + +We are targeting pull-type installation, so MLCube images should be available on Docker Hub. If not, try this: + +```Bash +mlcube run ... -Pdocker.build_strategy=always +``` diff --git a/brats/model/mlcube/mlcube.yaml b/brats/model/mlcube/mlcube.yaml new file mode 100644 index 0000000..1569075 --- /dev/null +++ b/brats/model/mlcube/mlcube.yaml @@ -0,0 +1,27 @@ +name: MLCommons Brats +description: MLCommons Brats integration +authors: + - {name: "MLCommons Best Practices Working Group"} + +platform: + accelerator_count: 0 + +docker: + # Image name. + image: mlcommons/brats_model:0.0.1 + # Docker build context relative to $MLCUBE_ROOT. Default is `build`. + build_context: "../project" + # Docker file name within docker build context, default is `Dockerfile`. + build_file: "Dockerfile" + +tasks: + example: + # Run implementation + parameters: + inputs: {parameters_file: {type: file, default: parameters.yaml}} + + infer: + # Run implementation + parameters: + inputs: {data_path: data/, parameters_file: {type: file, default: parameters.yaml}} + outputs: {output_path: output/} diff --git a/brats/model/mlcube/workspace/data/BraTS_example_seg.nii.gz b/brats/model/mlcube/workspace/data/BraTS_example_seg.nii.gz new file mode 100644 index 0000000..40c2c46 Binary files /dev/null and b/brats/model/mlcube/workspace/data/BraTS_example_seg.nii.gz differ diff --git a/brats/model/mlcube/workspace/output/.dockerignore b/brats/model/mlcube/workspace/output/.dockerignore new file mode 100644 index 0000000..4462c1c --- /dev/null +++ b/brats/model/mlcube/workspace/output/.dockerignore @@ -0,0 +1 @@ +*.nii.gz diff --git a/brats/model/mlcube/workspace/output/.gitignore b/brats/model/mlcube/workspace/output/.gitignore new file mode 100644 index 0000000..331cf24 --- /dev/null +++ b/brats/model/mlcube/workspace/output/.gitignore @@ -0,0 +1,3 @@ +* +!.gitignore +!.dockerignore diff --git a/brats/model/mlcube/workspace/parameters.yaml b/brats/model/mlcube/workspace/parameters.yaml new file mode 100644 index 0000000..211fd56 --- /dev/null +++ b/brats/model/mlcube/workspace/parameters.yaml @@ -0,0 +1,9 @@ +# Here you can define new parameters +MY_NEW_PARAMETER_EXAMPLE: "*Example*" +# author info +AUTHOR_NAME: "Chuck Norris" +AUTHOR_EMAIL: "chuck@norris.org" +# put your app name here +APPLICATION_NAME: "AMAZING APPLICATION" +# specify version here, if possible use semantic versioning +APPLICATION_VERSION: "0.0.1" \ No newline at end of file diff --git a/brats/model/project/Dockerfile b/brats/model/project/Dockerfile new file mode 100644 index 0000000..83eecbb --- /dev/null +++ b/brats/model/project/Dockerfile @@ -0,0 +1,22 @@ +# for a CPU app use this Dockerfile. +FROM python:3.8-buster + +# fill in your info here +LABEL author="chuck@norris.org" +LABEL application="your application name" +LABEL maintainer="chuck@norris.org" +LABEL version="0.0.1" +LABEL status="beta" + +# basic +RUN apt-get -y update && apt -y full-upgrade && apt-get -y install apt-utils wget git tar build-essential curl nano + +# install all python requirements +WORKDIR /workspace +COPY ./requirements.txt ./requirements.txt +RUN pip3 install -r requirements.txt + +# copy all files +COPY ./ /workspace + +ENTRYPOINT [ "python3", "/workspace/mlcube.py"] diff --git a/brats/model/project/LICENSE b/brats/model/project/LICENSE new file mode 100644 index 0000000..29ebfa5 --- /dev/null +++ b/brats/model/project/LICENSE @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +our General Public Licenses are intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. + + When we speak of free software, we are referring to freedom, not +price. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. \ No newline at end of file diff --git a/brats/model/project/mlcube.py b/brats/model/project/mlcube.py new file mode 100644 index 0000000..51648df --- /dev/null +++ b/brats/model/project/mlcube.py @@ -0,0 +1,60 @@ +"""MLCube handler file""" +import typer +import yaml +from src.my_logic import run_code + +app = typer.Typer() + + +class ExampleTask(object): + """Example task Class + It reads the content of the parameters file and then + prints "MY_NEW_PARAMETER_EXAMPLE".""" + + @staticmethod + def run_example(parameters_file: str) -> None: + + # Load parameters from the paramters file + with open(parameters_file, "r") as stream: + parameters = yaml.safe_load(stream) + + print("This is my new parameter example:") + print(parameters["MY_NEW_PARAMETER_EXAMPLE"]) + + +class InferTask(object): + """Run task Class + It defines the environment variables: + data_path: Directory path to dataset + output_path: Directory path to final results + All other parameters are defined in parameters_file + Then executes the run_code method inside my_logic script""" + + @staticmethod + def run(data_path: str, output_path: str, parameters_file: str) -> None: + + # Load parameters from the paramters file + with open(parameters_file, "r") as stream: + parameters = yaml.safe_load(stream) + + application_name = parameters["APPLICATION_NAME"] + application_version = parameters["APPLICATION_VERSION"] + run_code(data_path, output_path, application_name, application_version) + + +@app.command("example") +def example(parameters_file: str = typer.Option(..., "--parameters_file")): + ExampleTask.run_example(parameters_file) + + +@app.command("infer") +def infer( + data_path: str = typer.Option(..., "--data_path"), + output_path: str = typer.Option(..., "--output_path"), + parameters_file: str = typer.Option(..., "--parameters_file") +): + InferTask.run(data_path, output_path, parameters_file) + + +if __name__ == "__main__": + app() diff --git a/brats/model/project/requirements.txt b/brats/model/project/requirements.txt new file mode 100644 index 0000000..8d2d45a --- /dev/null +++ b/brats/model/project/requirements.txt @@ -0,0 +1,2 @@ +PyYAML +typer \ No newline at end of file diff --git a/brats/model/project/src/__init__.py b/brats/model/project/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/brats/model/project/src/my_logic.py b/brats/model/project/src/my_logic.py new file mode 100644 index 0000000..cfc8d3b --- /dev/null +++ b/brats/model/project/src/my_logic.py @@ -0,0 +1,32 @@ +"""Logic file""" +import os +from shutil import copyfile +from src.utils.utilities import helper + + +def logic_wrapper(input_folder, output_folder): + """Edit your logic here""" + input_file = os.path.normpath(input_folder + "/BraTS_example_seg.nii.gz") + output_file = os.path.normpath(output_folder + "/BraTS_example_seg.nii.gz") + + # copy paste your logic here + print("wrapper: Here you can place your own logic") + + # example logic + copyfile(input_file, output_file) + helper() + + +def run_code(input_folder, output_folder, application_name, application_version): + """Main function""" + print( + "*** code execution started:", application_name, + "version:", application_version, "! ***", + ) + + logic_wrapper(input_folder, output_folder) + + print( + "*** code execution finished:", application_name, + "version:", application_version, "! ***", + ) diff --git a/brats/model/project/src/utils/__init__.py b/brats/model/project/src/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/brats/model/project/src/utils/utilities.py b/brats/model/project/src/utils/utilities.py new file mode 100644 index 0000000..da685e7 --- /dev/null +++ b/brats/model/project/src/utils/utilities.py @@ -0,0 +1,4 @@ +"""utility functions here""" +def helper(): + """helper function""" + print("helper: Here you can store all your utility functions") diff --git a/brats/preprocessing/.gitignore b/brats/preprocessing/.gitignore new file mode 100644 index 0000000..b5ca5c9 --- /dev/null +++ b/brats/preprocessing/.gitignore @@ -0,0 +1,2 @@ +__pycache__/ +mlcube/workspace/results \ No newline at end of file diff --git a/brats/preprocessing/README.md b/brats/preprocessing/README.md new file mode 100644 index 0000000..cbc80dd --- /dev/null +++ b/brats/preprocessing/README.md @@ -0,0 +1,107 @@ +# BraTS Challenge - MLCube integration - preprocess + +Original implementation: ["BraTS Instructions Repo"](https://github.com/BraTS/Instructions) + +## Dataset + +Please refer to the [BraTS challenge page](http://braintumorsegmentation.org/) and follow the instructions in the data section. + +## Project setup + +```bash +# Create Python environment and install MLCube Docker runner +virtualenv -p python3 ./env && source ./env/bin/activate && pip install mlcube-docker + +# Fetch the boston housing example from GitHub +git clone https://github.com/mlcommons/mlcube_examples && cd ./mlcube_examples +git fetch origin pull/39/head:feature/brats && git checkout feature/brats +cd ./brats/preprocessing/mlcube +``` + +## Important files + +These are the most important files on this project: + +```bash + +├── mlcube +│ ├── mlcube.yaml # MLCube configuration file, it defines the project, author, platform, docker and tasks. +│ └── workspace +│ ├── data +│ │ └── BraTS_example_seg.nii.gz # Input data +│ ├── results +│ │ └── output.npy # Output processed data +│ ├── parameters.yaml +└── project + ├── Dockerfile # Docker file with instructions to create the image for the project. + ├── preprocess.py # Python file that contains the main logic of the project. + ├── mlcube.py # Python entrypoint used by MLCube, contains the logic for MLCube tasks. + └── requirements.txt # Python requirements needed to run the project inside Docker. + └── run.sh # Bash file containing logic to call preprocess.py script. +``` + +## How to modify this project + +You can change each file described above in order to add your own implementation. + +### Requirements file + +In this file (`requirements.txt`) you can add all the python dependencies needed for running your implementation, these dependencies will be installed during the creation of the docker image, this happens when you run the ```mlcube run ...``` command. + +### Dockerfile + +You can use both, CPU or GPU version for the dockerfile (`Dockerfile_CPU`, `Dockerfile_GPU`), also, you can add or modify any steps inside the file, this comes handy when you need to install some OS dependencies or even when you want to change the base docker image, inside the file you can find some information about the existing steps. + +### Parameters file + +This is a yaml file (`parameters.yaml`)that contains all extra parameters that aren't files or directories, for example, here you can place all the hyperparameters that you will use for training a model. This file will be passed as an **input parameter** in the MLCube tasks and then it will be read inside the MLCube container. + +### MLCube yaml file + +In this file (`mlcube.yaml`) you can find the instructions about the docker image and platform that will be used, information about the project (name, description, authors), and also the tasks defined for the project. + +In the existing implementation you will find 1 task: + +* evaluate: + + This task takes the following parameters: + + * Input parameters: + * predictions: Folder path containing predictions + * ground_truth: Folder path containing ground truth data + * parameters_file: Extra parameters + * Output parameters: + * output_path: File path where output preprocess will be stored + + This task takes the input predictions and ground truth data, perform the evaluation and then save the output result in the output_path. + +### MLCube python file + +The `mlcube.py` file is the handler file and entrypoint described in the dockerfile, here you can find all the logic related to how to process each MLCube task. If you want to add a new task first you must define it inside the `mlcube.yaml` file with its input and output parameters and then you need to add the logic to handle this new task inside the `mlcube.py` file. + +### Preprocess file + +The `preprocess.py` file contains the main logic of the project, you can modify this file and write your implementation here to perform the different preprocessing steps, this preprocess file is called from the `run.sh` file and there are other ways to link your implementation and shown in the [MLCube examples repo](https://github.com/mlcommons/mlcube_examples). + +### Run bash file + +The `run.sh` file is called from `mlcube.py` and it receives the arguments, here we can perform different steps to then call the `preprocess.py` script. + +## Tasks execution + +```bash +# Run preprocess task. +mlcube run --mlcube=mlcube.yaml --task=preprocess +``` + +To use Singularity runner add the flag `--platform=singularity`, example: + +```bash +mlcube run --mlcube=mlcube.yaml --task=preprocess --platform=singularity +``` + +We are targeting pull-type installation, so MLCube images should be available on Docker Hub. If not, try this: + +```Bash +mlcube run ... -Pdocker.build_strategy=always +``` diff --git a/brats/preprocessing/mlcube/mlcube.yaml b/brats/preprocessing/mlcube/mlcube.yaml new file mode 100644 index 0000000..f29de99 --- /dev/null +++ b/brats/preprocessing/mlcube/mlcube.yaml @@ -0,0 +1,31 @@ +name: MLCommons Brats preprocessing +description: MLCommons Brats integration for preprocessing +authors: + - {name: "MLCommons Best Practices Working Group"} + +platform: + accelerator_count: 0 + +docker: + # Image name. + image: mlcommons/brats_preprocessing:0.0.1 + # Docker build context relative to $MLCUBE_ROOT. Default is `build`. + build_context: "../project" + # Docker file name within docker build context, default is `Dockerfile`. + build_file: "Dockerfile" + +tasks: + prepare: + # Run preprocessing + parameters: + inputs: {data_path: data/, labels_path: {type: file, default: labels.csv}, parameters_file: {type: file, default: parameters.yaml}} + outputs: {output_path: results/} + + sanity_check: + parameters: + inputs: {data_path: data/, parameters_file: {type: file, default: parameters.yaml}} + + statistics: + parameters: + inputs: {data_path: data/, parameters_file: {type: file, default: parameters.yaml}} + outputs: {output_path: {type: file, default: statistics.yaml}} diff --git a/brats/preprocessing/mlcube/workspace/data/BraTS_example_seg.nii.gz b/brats/preprocessing/mlcube/workspace/data/BraTS_example_seg.nii.gz new file mode 100644 index 0000000..40c2c46 Binary files /dev/null and b/brats/preprocessing/mlcube/workspace/data/BraTS_example_seg.nii.gz differ diff --git a/brats/preprocessing/mlcube/workspace/parameters.yaml b/brats/preprocessing/mlcube/workspace/parameters.yaml new file mode 100644 index 0000000..df19873 --- /dev/null +++ b/brats/preprocessing/mlcube/workspace/parameters.yaml @@ -0,0 +1 @@ +output_filename: "output.npy" \ No newline at end of file diff --git a/brats/preprocessing/project/Dockerfile b/brats/preprocessing/project/Dockerfile new file mode 100644 index 0000000..25aa40f --- /dev/null +++ b/brats/preprocessing/project/Dockerfile @@ -0,0 +1,24 @@ +# for a CPU app use this Dockerfile. +FROM python:3.8-buster + +# fill in your info here +LABEL author="chuck@norris.org" +LABEL application="your application name" +LABEL maintainer="chuck@norris.org" +LABEL version="0.0.1" +LABEL status="beta" + +# basic +RUN apt-get -y update && apt -y full-upgrade && apt-get -y install apt-utils wget git tar build-essential curl nano + +# install all python requirements +WORKDIR /workspace +COPY ./requirements.txt ./requirements.txt +RUN pip3 install -r requirements.txt + +# copy all files +COPY ./ /workspace + +RUN chmod +x /workspace/run.sh + +ENTRYPOINT [ "python3", "/workspace/mlcube.py"] diff --git a/brats/preprocessing/project/mlcube.py b/brats/preprocessing/project/mlcube.py new file mode 100644 index 0000000..d5d80dd --- /dev/null +++ b/brats/preprocessing/project/mlcube.py @@ -0,0 +1,83 @@ +"""MLCube handler file""" +import os +import typer +import yaml +import subprocess + + +app = typer.Typer() + + +class PreprocessTask: + """Run preprocessing given the input data path""" + + @staticmethod + def run( + data_path: str, parameters_file: str, output_path: str + ) -> None: + + cmd = f"python3 /workspace/preprocess.py --data_path={data_path} \ + --parameters_file {parameters_file} --output_path {output_path}" + splitted_cmd = cmd.split() + + process = subprocess.Popen(splitted_cmd, cwd=".") + process.wait() + +class SanityCheckTask: + """Run sanity check""" + + @staticmethod + def run( + data_path: str, parameters_file: str + ) -> None: + + cmd = f"python3 sanity_check.py --data_path={data_path}" + splitted_cmd = cmd.split() + + process = subprocess.Popen(splitted_cmd, cwd=".") + process.wait() + + +class StatisticsTask: + """Run statistics""" + + @staticmethod + def run( + data_path: str, parameters_file: str, output_path: str + ) -> None: + + cmd = f"python3 statistics.py --data_path={data_path} --out_file={output_path}" + splitted_cmd = cmd.split() + + process = subprocess.Popen(splitted_cmd, cwd=".") + process.wait() + + +@app.command("prepare") +def prepare( + data_path: str = typer.Option(..., "--data_path"), + labels_path: str = typer.Option(..., "--labels_path"), + parameters_file: str = typer.Option(..., "--parameters_file"), + output_path: str = typer.Option(..., "--output_path"), +): + PreprocessTask.run(data_path, parameters_file, output_path) + + +@app.command("sanity_check") +def sanity_check( + data_path: str = typer.Option(..., "--data_path"), + parameters_file: str = typer.Option(..., "--parameters_file"), +): + SanityCheckTask.run(data_path, parameters_file) + +@app.command("statistics") +def statistics( + data_path: str = typer.Option(..., "--data_path"), + parameters_file: str = typer.Option(..., "--parameters_file"), + output_path: str = typer.Option(..., "--output_path") +): + StatisticsTask.run(data_path, parameters_file, output_path) + + +if __name__ == "__main__": + app() diff --git a/brats/preprocessing/project/preprocess.py b/brats/preprocessing/project/preprocess.py new file mode 100644 index 0000000..56d5bf3 --- /dev/null +++ b/brats/preprocessing/project/preprocess.py @@ -0,0 +1,104 @@ +"""Metrics file""" +import os +import argparse +import glob +import yaml +import numpy as np +import nibabel as nib +from shutil import copyfile +from tqdm import tqdm + + +def preprocess(image: np.ndarray): + """Preprocess the image labels from a numpy array""" + + image_WT = image.copy() + image_WT[image_WT == 1] = 1 + image_WT[image_WT == 2] = 1 + image_WT[image_WT == 4] = 1 + + image_TC = image.copy() + image_TC[image_TC == 1] = 1 + image_TC[image_TC == 2] = 0 + image_TC[image_TC == 4] = 1 + + image_ET = image.copy() + image_ET[image_ET == 1] = 0 + image_ET[image_ET == 2] = 0 + image_ET[image_ET == 4] = 1 + + image = np.stack([image_WT, image_TC, image_ET]) + image = np.moveaxis(image, (0, 1, 2, 3), (0, 3, 2, 1)) + + return image + + +def load_img(file_path): + """Reads segmentations image as a numpy array""" + + data = nib.load(file_path) + data = np.asarray(data.dataobj) + return data + + +def get_data_arr(data_path): + """Reads the content for the data path folder + and then returns the data in numpy array format""" + + image_path_list = glob.glob(data_path + "/*") + images_arr = [] + for image_path in image_path_list: + image = load_img(image_path) + image = preprocess(image) + images_arr.append(image) + images_arr = np.concatenate(images_arr) + return images_arr + + +def save_processed_data(output_path, output_filename, images_arr): + """Writes processed images to the target output path""" + output_file_path = os.path.join(output_path, output_filename) + with open(output_file_path, 'wb') as f: + np.save(f, images_arr) + print("File correctly saved!") + + +def main(): + """Main function that recieves input data and preprocess it""" + + parser = argparse.ArgumentParser() + parser.add_argument( + "--data_path", + "--data-path", + type=str, + required=True, + help="Directory containing input data", + ) + parser.add_argument( + "--output_path", + "--output-path", + type=str, + required=True, + help="Path where output data will be stored", + ) + parser.add_argument( + "--parameters_file", + "--parameters-file", + type=str, + required=True, + help="File containing parameters for processing", + ) + args = parser.parse_args() + + with open(args.parameters_file, "r") as f: + params = yaml.full_load(f) + + images_arr = get_data_arr(args.data_path) + save_processed_data(args.output_path, params["output_filename"], images_arr) + + input_file = os.path.normpath(args.data_path + "/BraTS_example_seg.nii.gz") + output_file = os.path.normpath(args.output_path + "/BraTS_example_seg.nii.gz") + copyfile(input_file, output_file) + +if __name__ == "__main__": + main() diff --git a/brats/preprocessing/project/requirements.txt b/brats/preprocessing/project/requirements.txt new file mode 100644 index 0000000..2f512cf --- /dev/null +++ b/brats/preprocessing/project/requirements.txt @@ -0,0 +1,5 @@ +PyYAML +typer +numpy +nibabel +tqdm \ No newline at end of file diff --git a/brats/preprocessing/project/run.sh b/brats/preprocessing/project/run.sh new file mode 100644 index 0000000..8647fe1 --- /dev/null +++ b/brats/preprocessing/project/run.sh @@ -0,0 +1,15 @@ +#!/bin/bash + +set -e + +: ${data_path:=${1:-}} +: ${parameters_file:=${2:-}} +: ${output_path:=${2:-}} + +ARGS="--data_path=$data_path" +ARGS+=" --parameters_file $parameters_file" +ARGS+=" --output_path $output_path" + +# Execute command and time it +echo Processing data. This may take a while... +time python3 preprocess.py ${ARGS} \ No newline at end of file diff --git a/brats/preprocessing/project/sanity_check.py b/brats/preprocessing/project/sanity_check.py new file mode 100644 index 0000000..4766663 --- /dev/null +++ b/brats/preprocessing/project/sanity_check.py @@ -0,0 +1,28 @@ +"""Sanity check logic""" +import os +import argparse + + +def sanity_check(data): + """Runs a few checks to ensure data quality and integrity + Args: + names_df (pd.DataFrame): DataFrame containing transformed data. + """ + # Here you must add all the checks you consider important regarding the + # state of the data + assert len(data) > 0 + + +if __name__ == "__main__": + parser = argparse.ArgumentParser("Medperf Model Sanity Check Example") + parser.add_argument( + "--data_path", + dest="data", + type=str, + help="directory containing the prepared data", + ) + + args = parser.parse_args() + + data = os.listdir(args.data) + sanity_check(data) diff --git a/brats/preprocessing/project/statistics.py b/brats/preprocessing/project/statistics.py new file mode 100644 index 0000000..daaf7e2 --- /dev/null +++ b/brats/preprocessing/project/statistics.py @@ -0,0 +1,39 @@ +import os +import yaml +import argparse + + +def get_statistics(data_path): + """Computes statistics about the data. This statistics are uploaded + to the Medperf platform under the data owner's approval. Include + every statistic you consider useful for determining the nature of the + data, but keep in mind that we want to keep the data as private as + possible. + """ + + len_data = len(os.listdir(data_path)) + + stats = { + "data length": len_data + } + + return stats + + +if __name__ == "__main__": + parser = argparse.ArgumentParser("MedPerf Statistics Example") + parser.add_argument( + "--data_path", + type=str, + help="directory containing the prepared data", + ) + parser.add_argument( + "--out_file", dest="out_file", type=str, help="file to store statistics" + ) + + args = parser.parse_args() + + stats = get_statistics(args.data_path) + + with open(args.out_file, "w") as f: + yaml.dump(stats, f)