Dataset trust remote tweaks

timm/data/dataset.py

@@ -103,6 +103,7 @@ def __init__(
             transform=None,
             target_transform=None,
             max_steps=None,
+            **kwargs,
     ):
         assert reader is not None
         if isinstance(reader, str):
@@ -121,6 +122,7 @@ def __init__(
                 input_key=input_key,
                 target_key=target_key,
                 max_steps=max_steps,
+                **kwargs,
             )
         else:
             self.reader = reader

timm/data/dataset_factory.py

@@ -74,34 +74,37 @@ def create_dataset(
         seed: int = 42,
         repeats: int = 0,
         input_img_mode: str = 'RGB',
+        trust_remote_code: bool = False,
         **kwargs,
 ):
     """ Dataset factory method
 
     In parentheses after each arg are the type of dataset supported for each arg, one of:
-      * folder - default, timm folder (or tar) based ImageDataset
-      * torch - torchvision based datasets
+      * Folder - default, timm folder (or tar) based ImageDataset
+      * Torch - torchvision based datasets
       * HFDS - Hugging Face Datasets
+      * HFIDS - Hugging Face Datasets Iterable (streaming mode, with IterableDataset)
       * TFDS - Tensorflow-datasets wrapper in IterabeDataset interface via IterableImageDataset
       * WDS - Webdataset
-      * all - any of the above
+      * All - any of the above
 
     Args:
-        name: dataset name, empty is okay for folder based datasets
-        root: root folder of dataset (all)
-        split: dataset split (all)
-        search_split: search for split specific child fold from root so one can specify
-            `imagenet/` instead of `/imagenet/val`, etc on cmd line / config. (folder, torch/folder)
-        class_map: specify class -> index mapping via text file or dict (folder)
-        load_bytes: load data, return images as undecoded bytes (folder)
-        download: download dataset if not present and supported (HFDS, TFDS, torch)
-        is_training: create dataset in train mode, this is different from the split.
-            For Iterable / TDFS it enables shuffle, ignored for other datasets. (TFDS, WDS)
-        batch_size: batch size hint for (TFDS, WDS)
-        seed: seed for iterable datasets (TFDS, WDS)
-        repeats: dataset repeats per iteration i.e. epoch (TFDS, WDS)
-        input_img_mode: Input image color conversion mode e.g. 'RGB', 'L' (folder, TFDS, WDS, HFDS)
-        **kwargs: other args to pass to dataset
+        name: Dataset name, empty is okay for folder based datasets
+        root: Root folder of dataset (All)
+        split: Dataset split (All)
+        search_split: Search for split specific child fold from root so one can specify
+            `imagenet/` instead of `/imagenet/val`, etc on cmd line / config. (Folder, Torch)
+        class_map: Specify class -> index mapping via text file or dict (Folder)
+        load_bytes: Load data, return images as undecoded bytes (Folder)
+        download: Download dataset if not present and supported (HFIDS, TFDS, Torch)
+        is_training: Create dataset in train mode, this is different from the split.
+            For Iterable / TDFS it enables shuffle, ignored for other datasets. (TFDS, WDS, HFIDS)
+        batch_size: Batch size hint for iterable datasets (TFDS, WDS, HFIDS)
+        seed: Seed for iterable datasets (TFDS, WDS, HFIDS)
+        repeats: Dataset repeats per iteration i.e. epoch (TFDS, WDS, HFIDS)
+        input_img_mode: Input image color conversion mode e.g. 'RGB', 'L' (folder, TFDS, WDS, HFDS, HFIDS)
+        trust_remote_code: Trust remote code in Hugging Face Datasets if True (HFDS, HFIDS)
+        **kwargs: Other args to pass through to underlying Dataset and/or Reader classes
 
     Returns:
         Dataset object
@@ -162,6 +165,7 @@ def create_dataset(
             split=split,
             class_map=class_map,
             input_img_mode=input_img_mode,
+            trust_remote_code=trust_remote_code,
             **kwargs,
         )
     elif name.startswith('hfids/'):
@@ -177,7 +181,8 @@ def create_dataset(
             repeats=repeats,
             seed=seed,
             input_img_mode=input_img_mode,
-            **kwargs
+            trust_remote_code=trust_remote_code,
+            **kwargs,
         )
     elif name.startswith('tfds/'):
         ds = IterableImageDataset(

timm/data/readers/reader_hfds.py

@@ -48,7 +48,7 @@ def __init__(
         self.dataset = datasets.load_dataset(
             name,  # 'name' maps to path arg in hf datasets
             split=split,
-            cache_dir=self.root,  # timm doesn't expect hidden cache dir for datasets, specify a path
+            cache_dir=self.root,  # timm doesn't expect hidden cache dir for datasets, specify a path if root set
             trust_remote_code=trust_remote_code
         )
         # leave decode for caller, plus we want easy access to original path names...

timm/data/readers/reader_hfids.py

@@ -44,6 +44,7 @@ def __init__(
             target_img_mode: str = '',
             shuffle_size: Optional[int] = None,
             num_samples: Optional[int] = None,
+            trust_remote_code: bool = False
     ):
         super().__init__()
         self.root = root
@@ -60,7 +61,11 @@ def __init__(
         self.target_key = target_key
         self.target_img_mode = target_img_mode
 
-        self.builder = datasets.load_dataset_builder(name, cache_dir=root)
+        self.builder = datasets.load_dataset_builder(
+            name,
+            cache_dir=root,
+            trust_remote_code=trust_remote_code,
+        )
         if download:
             self.builder.download_and_prepare()
 

train.py

@@ -103,6 +103,8 @@
                    help='Dataset key for input images.')
 group.add_argument('--target-key', default=None, type=str,
                    help='Dataset key for target labels.')
+group.add_argument('--dataset-trust-remote-code', action='store_true', default=False,
+                   help='Allow huggingface dataset import to execute code downloaded from the dataset\'s repo.')
 
 # Model parameters
 group = parser.add_argument_group('Model parameters')
@@ -653,6 +655,7 @@ def main():
         input_key=args.input_key,
         target_key=args.target_key,
         num_samples=args.train_num_samples,
+        trust_remote_code=args.dataset_trust_remote_code,
     )
 
     if args.val_split:
@@ -668,6 +671,7 @@ def main():
             input_key=args.input_key,
             target_key=args.target_key,
             num_samples=args.val_num_samples,
+            trust_remote_code=args.dataset_trust_remote_code,
         )
 
     # setup mixup / cutmix

validate.py

@@ -66,6 +66,8 @@
                    help='Dataset image conversion mode for input images.')
 parser.add_argument('--target-key', default=None, type=str,
                    help='Dataset key for target labels.')
+parser.add_argument('--dataset-trust-remote-code', action='store_true', default=False,
+                   help='Allow huggingface dataset import to execute code downloaded from the dataset\'s repo.')
 
 parser.add_argument('--model', '-m', metavar='NAME', default='dpn92',
                     help='model architecture (default: dpn92)')
@@ -268,6 +270,7 @@ def validate(args):
         input_key=args.input_key,
         input_img_mode=input_img_mode,
         target_key=args.target_key,
+        trust_remote_code=args.dataset_trust_remote_code,
     )
 
     if args.valid_labels: