Update utils/download_swim.py

utils/download_swim.py

@@ -1,23 +1,17 @@
 """
 download_swim.py
-
 Streams and downloads the full paired dataset (images + label txt files) from a Hugging Face Hub repository.
 It recursively processes all available chunk subfolders (e.g., '000', '001', ...) under given parent paths.
-
 Features:
 - Recursively discovers subdirs (chunks) using HfFileSystem
 - Optionally flattens the directory structure by removing the deepest chunk level
 - Saves each .png image with its corresponding .txt label
-
 Use this script if you want to download the complete dataset for model training or offline access.
-
 Usage:
     # Download all chunks (flattened)
     python utils/download_swim.py --output-dir ./SWiM --flatten
-
     # Download specific chunks
     python utils/download_swim.py --chunks 000 001 002 --flatten False
-
 Arguments:
     --repo-id          Hugging Face dataset repository ID
     --images-parent    Parent directory for image chunks (e.g., Baseline/images/train)
@@ -56,7 +50,7 @@ def sample_dataset(
     labels_parent: str,
     output_dir: str,
 #    max_files: int = 500,
-    flatten: bool = True,
+    flatten: bool,
     chunks: list = None
 ):
     total_downloaded = 0
@@ -126,7 +120,7 @@ def parse_args():
     parser.add_argument("--labels-parent", default="Baseline/labels", help="Parent directory for label chunks.")
     parser.add_argument("--output-dir", default="./SWiM", help="Where to save sampled data.")
     #parser.add_argument("--count", type=int, default=500, help="How many samples to download in total.")
-    parser.add_argument("--flatten", default=True, type=bool, help="Save all samples in a single folder without subdirectories.")
+    parser.add_argument("--flatten",  action='store_true', help="Save all samples in a single folder without subdirectories.")
     parser.add_argument("--chunks", nargs="*", default=None, help="Specific chunk names to sample (e.g. 000 001). Leave empty to process all.")
     return parser.parse_args()