downloader script

Patho-Bench.py

@@ -0,0 +1,115 @@
+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