Download data from Hub (#5)

- Move data to data dir (dec3021e71af4c43fed5f3b09e815f21b293ad99)
- Update script to download data from the Hub (db9ed1017f7752a08dbce2c5d01ad4982b63be11)
- Fix style (39fc043dd7eec650f180a7bec89278ab0273d146)


Co-authored-by: Albert Villanova <[email protected]>

AeroPath.py

@@ -1,29 +1,16 @@
-# 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."""
+"""AeroPath: An airway segmentation benchmark dataset with challenging pathology."""
 
 
-import csv
-import json
-import os
-
 import datasets
 
+_DESCRIPTION = """\
+AeroPath: An airway segmentation benchmark dataset with challenging pathology.
+"""
+
+_HOMEPAGE = "https://github.com/raidionics/AeroPath"
+
+_LICENSE = "MIT"
 
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
 @misc{støverud2023aeropath,
 title={AeroPath: An airway segmentation benchmark dataset with challenging pathology}, 
@@ -35,130 +22,49 @@ primaryClass={cs.CV}
 }
 """
 
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-AeroPath: An airway segmentation benchmark dataset with challenging pathology.
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://github.com/raidionics/AeroPath"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = "MIT"
-
-# 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": "https://huggingface.co/great-new-dataset-first_domain.zip",
-    #"second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
-    "zenodo": "https://zenodo.org/records/10069289/files/AeroPath.zip?download=1"
-}
+_URLS = [
+    {
+        "ct": f"data/{i}/{i}_CT_HR.nii.gz",
+        "airways": f"data/{i}/{i}_CT_HR_label_airways.nii.gz",
+        "lungs": f"data/{i}/{i}_CT_HR_label_lungs.nii.gz",
+    }
+    for i in range(1, 28)
+]
 
 
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
 class AeroPath(datasets.GeneratorBasedBuilder):
     """An airway segmentation benchmark dataset with challenging pathology."""
 
     VERSION = datasets.Version("1.0.0")
 
-    # 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"),
-        datasets.BuilderConfig(name="zenodo", version=VERSION, description="This includes all 27 CTs stored as a single zip on Zenodo"),
-    ]
-
-    DEFAULT_CONFIG_NAME = "zenodo"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self.DATA_DIR = None
-
-    def get_patient(self, patient_id):
-        if (patient_id < 1) or (patiend_id > 27):
-            raise ValueError("patient_id should be an integer in range [1, 27].")
-
     def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if self.config.name == "zenodo":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "ct": datasets.Value("string"),
-                    "airways": datasets.Value("string"),
-                    "lungs": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        else:  
-            raise ValueError("Only 'zenodo' is supported.")# This is an example to show how to have different features for "first_domain" and "second_domain"
-
+        features = datasets.Features(
+            {
+                "ct": datasets.Value("string"),
+                "airways": datasets.Value("string"),
+                "lungs": datasets.Value("string"),
+            }
+        )
         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
+            features=features,
             homepage=_HOMEPAGE,
-            # License for the dataset if available
             license=_LICENSE,
-            # Citation for the dataset
             citation=_CITATION,
         )
 
-    def get_data_dir(self):
-        return self.DATA_DIR
-    
     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]
-        self.DATA_DIR = dl_manager.download_and_extract(urls)
-
-        # append AeroPath
-        self.DATA_DIR = os.path.join(self.DATA_DIR, "AeroPath")
-
-        print("data is downloaded to:", self.DATA_DIR)
-
+        data_dirs = dl_manager.download(_URLS)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "split": "test",
+                    "data_dirs": data_dirs,
                 },
             ),
         ]
 
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, split):
-        # TODO: 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.
-        for patient_id in os.listdir(self.DATA_DIR):
-            curr_path = os.path.join(self.DATA_DIR, patient_id)
-            if patient_id in ["README.md", "license.md"]:
-                continue
-            yield patient_id, {
-                "ct": os.path.join(curr_path, patient_id + "_CT_HR.nii.gz"),
-                "airways": os.path.join(curr_path, patient_id + "_CT_HR_label_airways.nii.gz"),
-                "lungs": os.path.join(curr_path, patient_id + "_CT_HR_label_lungs.nii.gz"),
-            }
-
+    def _generate_examples(self, data_dirs):
+        for key, patient in enumerate(data_dirs):
+            yield key, patient