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import os
import datasets
from datasets import Features, Value
from huggingface_hub import snapshot_download
import glob
import yaml
class PathoBenchConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
# Extract task_in_dataset and dataset_to_download from kwargs
self.task_in_dataset = kwargs.pop("task_in_dataset", None)
self.dataset_to_download = kwargs.pop("dataset_to_download", None)
self.force_download = kwargs.pop("force_download", True)
# Set default values for task_in_dataset and dataset_to_download
if self.dataset_to_download is None and self.task_in_dataset is None:
# If neither are provided, default both to '*'
self.dataset_to_download = '*'
self.task_in_dataset = '*'
elif self.dataset_to_download is None and self.task_in_dataset is not None:
# If task_in_dataset is provided but dataset_to_download is not, raise an error
raise AssertionError("Dataset needs to be defined for the task_in_dataset provided.")
elif self.dataset_to_download is not None and self.task_in_dataset is None:
# If dataset_to_download is provided but task_in_dataset is not, default task_in_dataset to '*'
self.task_in_dataset = '*'
super().__init__(**kwargs)
class PathoBenchDataset(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
PathoBenchConfig(name="custom_config", version="1.0.0", description="PathoBench config")
]
BUILDER_CONFIG_CLASS = PathoBenchConfig
def _info(self):
return datasets.DatasetInfo(
description="PathoBench: collection of canonical computational pathology tasks",
homepage="https://github.com/mahmoodlab/patho-bench",
license="CC BY-NC-SA 4.0 Deed",
features=Features({
'path': Value('string')
})
)
def _split_generators(self, dl_manager):
repo_id = self.repo_id
dataset_to_download = self.config.dataset_to_download
local_dir = self._cache_dir_root
force_download = self.config.force_download
task_in_dataset = self.config.task_in_dataset
# Ensure the base local directory exists
os.makedirs(local_dir, exist_ok=True)
# download available_splits.yaml if not yet downloaded
snapshot_download(
repo_id=repo_id,
allow_patterns=["available_splits.yaml"],
repo_type="dataset",
local_dir=local_dir,
force_download=force_download,
)
# open yaml and get a list of datasets implemented
with open(os.path.join(local_dir, "available_splits.yaml"), 'r') as file:
available_splits = yaml.safe_load(file)
# ensure dataset_to_download is in implemented_datasets
if dataset_to_download != "*":
assert dataset_to_download in available_splits, f"{dataset_to_download} was not found. Available splits: ({available_splits})"
if task_in_dataset != "*":
assert task_in_dataset in available_splits[dataset_to_download], f"{task_in_dataset} was not found in {dataset_to_download}. Available tasks: ({available_splits[dataset_to_download]})"
# Determine parent folder based on dataset naming
os.makedirs(local_dir, exist_ok=True)
# Determine the download pattern
if dataset_to_download == "*":
allow_patterns = [f"*/*"]
else:
task_path = "*" if task_in_dataset == '*' else f"{task_in_dataset}/*"
allow_patterns = [f"{dataset_to_download}/{task_path}"]
# Download the required datasets
snapshot_download(
repo_id=repo_id,
allow_patterns=allow_patterns,
repo_type="dataset",
local_dir=local_dir,
force_download=force_download,
)
# Locate all .tsv files
search_pattern = os.path.join(local_dir, '**', '*.tsv')
all_tsv_splits = glob.glob(search_pattern, recursive=True)
return [
datasets.SplitGenerator(
name="full",
gen_kwargs={"filepath": all_tsv_splits},
)
]
def _generate_examples(self, filepath):
idx = 0
for file in filepath:
yield idx, {
'path': file
}
idx += 1 |