monailabel.interfaces.app module

class monailabel.interfaces.app.MONAILabelApp(app_dir, studies, conf, name='', description='', version='2.0', labels=None)

Bases: object

Default Pre-trained Path for downloading models

Base Class for Any MONAI Label App

Parameters

• app_dir (str) – path for your App directory

• studies (str) – path for studies/datalist

• conf (Dict[str, str]) – dictionary of key/value pairs provided by user while running the app

PRE_TRAINED_PATH: str = 'https://github.com/Project-MONAI/MONAILabel/releases/download/data'

__init__(app_dir, studies, conf, name='', description='', version='2.0', labels=None)

Base Class for Any MONAI Label App

Parameters

• app_dir (str) – path for your App directory

• studies (str) – path for studies/datalist

• conf (Dict[str, str]) – dictionary of key/value pairs provided by user while running the app

async_batch_infer(model, images, params=None)

async_scoring(method, params=None)

async_training(model, params=None, enqueue=False)

batch_infer(request, datastore=None)

Run batch inference for an existing pre-trained model.

Parameters

• request – JSON object which contains model, params and device

• datastore –

  Datastore object. If None then use default app level datastore to fetch the images

  For example:

  {
      "device": "cuda"
      "model": "segmentation_spleen",
      "images": "unlabeled",
      "label_tag": "original"
  }
  

Raises

MONAILabelException – When model is not found

Returns

JSON containing label and params

bundle_path(model)

cleanup_sessions()

datastore()

Return type

Datastore

infer(request, datastore=None)

Run Inference for an exiting pre-trained model.

Parameters

• request – JSON object which contains model, image, params and device

• datastore –

  Datastore object. If None then use default app level datastore to save labels if applicable

  For example:

  {
      "device": "cuda"
      "model": "segmentation_spleen",
      "image": "file://xyz",
      "save_label": "true/false",
      "label_tag": "original"
  }
  

Raises

MONAILabelException – When model is not found

Returns

JSON containing label and params

infer_wsi(request, datastore=None)

info()

Provide basic information about APP. This information is passed to client.

init_batch_infer()

Return type

Callable

init_datastore()

Return type

Datastore

init_infers()

Return type

Dict[str, InferTask]

init_remote_datastore()

Return type

Datastore

init_scoring_methods()

Return type

Dict[str, ScoringMethod]

init_strategies()

Return type

Dict[str, Strategy]

init_trainers()

Return type

Dict[str, TrainTask]

model_file(model, validate=True)

model_info(model)

next_sample(request)

Run Active Learning selection. User APP has to implement this method to provide next sample for labelling.

Parameters

request –

JSON object which contains active learning configs that are part APP info

For example:

{
    "strategy": "random"
}

Returns

JSON containing next image info that is selected for labeling

on_init_complete()

on_save_label(image_id, label_id)

Callback method when label is saved into datastore by a remote client

scoring(request, datastore=None)

Run scoring task over labels.

Parameters

• request – JSON object which contains model, params and device

• datastore –

  Datastore object. If None then use default app level datastore to fetch the images

  For example:

  {
      "device": "cuda"
      "method": "dice",
      "y": "final",
      "y_pred": "original",
  }
  

Raises

MONAILabelException – When method is not found

Returns

JSON containing result of scoring method

server_mode(mode)

sessions()

train(request)

Run Training. User APP has to implement this method to run training

Parameters

request –

JSON object which contains train configs that are part APP info

For example:

{
    "model": "mytrain",
    "device": "cuda"
    "max_epochs": 1,
}

Returns

JSON containing train stats

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