# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # TODO: Address all TODOs and remove all explanatory comments """TODO: Add a description here.""" # Import packages import csv import json import os from typing import Dict, List, Optional, Set, Tuple import numpy as np import datasets import SimpleITK as sitk # Define functions def import_csv_data(filepath: str) -> List[Dict[str, str]]: """Import all rows of CSV file.""" results = [] with open(filepath, encoding='utf-8') as f: reader = csv.DictReader(f) for line in reader: results.append(line) return results # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @InProceedings{huggingface:dataset, title = {A great new dataset}, author={huggingface, Inc. }, year={2020} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ This new dataset is designed to solve this great NLP task and is crafted with a lot of care. """ _HOMEPAGE = "https://zenodo.org/records/10159290" _LICENSE = """Creative Commons Attribution 4.0 International License \ (https://creativecommons.org/licenses/by/4.0/legalcode)""" # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { "first_domain": { "images":"https://zenodo.org/records/10159290/files/images.zip", "masks":"https://zenodo.org/records/10159290/files/masks.zip", "overview":"https://zenodo.org/records/10159290/files/overview.csv", "gradings":"https://zenodo.org/records/10159290/files/radiological_gradings.csv", } } class SPIDER(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("1.1.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'first_domain') # data = datasets.load_dataset('my_dataset', 'second_domain') BUILDER_CONFIGS = [ datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"), datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"), ] DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset if self.config.name == "first_domain": # This is the name of the configuration selected in BUILDER_CONFIGS above features = datasets.Features( { "sentence": datasets.Value("string"), "option1": datasets.Value("string"), "answer": datasets.Value("string") # These are the features of your dataset like images, labels ... } ) else: # This is an example to show how to have different features for "first_domain" and "second_domain" features = datasets.Features( { "sentence": datasets.Value("string"), "option2": datasets.Value("string"), "second_domain_answer": datasets.Value("string") # These are the features of your dataset like images, labels ... } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive urls = _URLS[self.config.name] paths_dict = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "paths_dict": paths_dict, "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "paths_dict": paths_dict, "split": "dev", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "paths_dict": paths_dict, "split": "test" }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples( self, paths_dict: Dict[str, str], scan_types: List[str] = ['t1', 't2', 't2_SPACE'], split: str = 'train', validate_share: float = 0.3, test_share: float = 0.2, raw_image: bool = True, numeric_array: bool = True, metadata: bool = True, rad_gradings: bool = True, random_seed: int = 9999, ) -> Tuple[str, Dict]: """ This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. Args paths_dict scan_types: split: validate_share test_share raw_image numeric_array metadata rad_gradings Yields Tuple (unique patient-scan ID, dict of """ # Set constants N_PATIENTS = 257 train_share = (1.0 - validate_share - test_share) np.random.seed(int(random_seed)) # Validate params for item in scan_types: if item not in ['t1', 't2', 't2_SPACE']: raise ValueError( 'Scan type "{item}" not recognized as valid scan type.\ Verify scan type argument.' ) if split not in ['train', 'validate', 'test']: raise ValueError( f'Split argument "{split}" is not recognized. \ Please enter one of ["train", "validate", "test"]' ) if train_share <= 0.0: raise ValueError( f'Training share is calculated as (1 - validate_share - test_share) \ and must be greater than 0. Current calculated value is \ {round(train_share, 3)}. Adjust validate_share and/or \ test_share parameters.' ) if validate_share > 1.0 or validate_share < 0.0: raise ValueError( f'Validation share must be between (0, 1). Current value is \ {validate_share}.' ) if test_share > 1.0 or test_share < 0.0: raise ValueError( f'Testing share must be between (0, 1). Current value is \ {test_share}.' ) # Generate train/validate/test partitions of patient IDs partition = np.random.choice( ['train', 'dev', 'test'], p=[train_share, validate_share, test_share], size=N_PATIENTS, ) patient_ids = (np.arange(N_PATIENTS) + 1) train_ids = set(patient_ids[partition == 'train']) validate_ids = set(patient_ids[partition == 'dev']) test_ids = set(patient_ids[partition == 'test']) assert len(train_ids.union(validate_ids, test_ids)) == N_PATIENTS # Import patient/scanner data and radiological gradings data overview_data = import_csv_data(paths_dict['overview']) grades_data = import_csv_data(paths_dict['gradings']) # Convert overview data list of dicts to dict of dicts overview_dict = {} for item in overview_data: key = item['new_file_name'] overview_dict[key] = item # Merge patient records for radiological gradings data grades_dict = {} for patient_id in patient_ids: patient_grades = [ x for x in grades_data if x['Patient'] == str(patient_id) ] if patient_grades: grades_dict[str(patient_id)] = patient_grades # Import image and mask data image_files = [ file for file in os.listdir(os.path.join(paths_dict['images'], 'images')) if file.endswith('.mha') ] assert len(image_files) > 0, "No image files found--check directory path." mask_files = [ file for file in os.listdir(os.path.join(paths_dict['masks'], 'masks')) if file.endswith('.mha') ] assert len(mask_files) > 0, "No mask files found--check directory path." # Filter image and mask data based on scan types image_files = [ file for file in image_files if any(scan_type in file for scan_type in scan_types) ] mask_files = [ file for file in mask_files if any(scan_type in file for scan_type in scan_types) ] # Subset train/validation/test partition images and mask files if split == 'train': subset_ids = train_ids elif split == 'validate': subset_ids = validate_ids elif split == 'test': subset_ids = test_ids image_files = [ file for file in image_files if any(str(patient_id) in file.split('_')[0] for patient_id in subset_ids) ] mask_files = [ file for file in mask_files if any(str(patient_id) in file.split('_')[0] for patient_id in subset_ids) ] assert len(image_files) == len(mask_files), "The number of image files\ does not match the number of mask files--verify subsetting operation." # Shuffle order of patient scans # (note that only images need to be shuffled since masks and metadata # will be linked to the selected image) np.random.shuffle(image_files) ## Generate next example # ---------------------- for example in image_files: # Extract linking data scan_id = example.replace('.mha', '') patient_id = scan_id.split('_')[0] scan_type = '_'.join(scan_id.split('_')[1:]) # Load .mha file image_path = os.path.join(paths_dict['images'], 'images', example) image = sitk.ReadImage(image_path) # Convert .mha image to numeric array image_array = sitk.GetArrayFromImage(image) # Extract overview data corresponding to image image_overview = overview_dict[scan_id] # Extract patient radiological gradings corresponding to patient patient_grades_dict = {} for item in grades_dict[patient_id]: key = f'IVD{item["IVD label"]}' value = { k:v for k,v in item.items() if k not in ['Patient', 'IVD label'] } patient_grades_dict[key] = value # Prepare example return dict return_dict = {'patient_id':patient_id, 'scan_type':scan_type} if raw_image: return_dict['raw_image'] = image if numeric_array: return_dict['numeric_array'] = image_array if metadata: return_dict['metadata'] = image_overview if rad_gradings: return_dict['rad_gradings'] = patient_grades_dict # Yield example yield (scan_id, return_dict)