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Source code for eisen.datasets.msd

import os
import copy

from torch.utils.data import Dataset
from eisen.utils import read_json_from_file


[docs]class MSDDataset(Dataset): """ This object allows Medical Segmentation Decathlon data to be easily impoted in Eisen. More information about the data can be found here http://medicaldecathlon.com Through this module, users are able to make use of the challenge data by simply specifying the directory where the data is locally stored. Therefore it is necessary to first download the data, store or unpack it in a specific directory and then instantiate an object of type MSDDataset which will make use of the directory structure and the descriptive json file included in it and make the data available to Eisen. .. note:: This dataset will return data items with fields: 'image' and, optionally, 'label'. .. code-block:: python from eisen.datasets import MSDDataset dataset = MSDDataset( '/abs/path/to/data', '/path/to/dataset.json', 'training', transform, ) """
[docs] def __init__(self, data_dir, json_file, phase, transform=None): """ :param data_dir: the base directory where the data is located (dataset location after unzipping) :type data_dir: str :param json_file: the name of the json file containing for the MSD dataset :type json_file: str :param phase: training or test phase as per MSD dataset convention (look at MSD json file) :type phase: string :param transform: a transform object (can be the result of a composition of transforms) :type transform: callable .. code-block:: python from eisen.datasets import MSDDataset dataset = MSDDataset( data_dir='/abs/path/to/data', json_file='/path/to/dataset.json', phase='training', transform=transform, ) <json> [ {"name": "json_file", "type": "string", "value": ""}, {"name": "phase", "type": "string", "value": ["training", "test"]} ] </json> """ json_file = os.path.join(data_dir, json_file) msd_dataset = read_json_from_file(json_file) self.json_dataset = msd_dataset[phase] msd_dataset.pop("training", None) msd_dataset.pop("test", None) if phase == "test": # test images are stored as list of filenames instead of dictionaries. Need to convert that. dset = [] for elem in self.json_dataset: dset.append({"image": elem}) self.json_dataset = dset self.attributes = msd_dataset self.transform = transform
def __len__(self): return len(self.json_dataset) def __getitem__(self, idx): item = copy.deepcopy(self.json_dataset[idx]) if self.transform: item = self.transform(item) return item

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