Merge branch 'dev' into warp-doc-differentiability-warning

Author: ericspod

monai/data/dataset.py

@@ -230,6 +230,8 @@ def __init__(
         pickle_protocol: int = DEFAULT_PROTOCOL,
         hash_transform: Callable[..., bytes] | None = None,
         reset_ops_id: bool = True,
+        track_meta: bool = False,
+        weights_only: bool = True,
     ) -> None:
         """
         Args:
@@ -264,7 +266,17 @@ def __init__(
                 When this is enabled, the traced transform instance IDs will be removed from the cached MetaTensors.
                 This is useful for skipping the transform instance checks when inverting applied operations
                 using the cached content and with re-created transform instances.
-
+            track_meta: whether to track the meta information, if `True`, will convert to `MetaTensor`.
+                default to `False`. Cannot be used with `weights_only=True`.
+            weights_only: keyword argument passed to `torch.load` when reading cached files.
+                default to `True`. When set to `True`, `torch.load` restricts loading to tensors and
+                other safe objects. Setting this to `False` is required for loading `MetaTensor`
+                objects saved with `track_meta=True`, however this creates the possibility of remote
+                code execution through `torch.load` so be aware of the security implications of doing so.
+
+        Raises:
+            ValueError: When both `track_meta=True` and `weights_only=True`, since this combination
+                prevents cached MetaTensors from being reloaded and causes perpetual cache regeneration.
         """
         super().__init__(data=data, transform=transform)
         self.cache_dir = Path(cache_dir) if cache_dir is not None else None
@@ -280,6 +292,13 @@ def __init__(
         if hash_transform is not None:
             self.set_transform_hash(hash_transform)
         self.reset_ops_id = reset_ops_id
+        if track_meta and weights_only:
+            raise ValueError(
+                "Invalid argument combination: `track_meta=True` cannot be used with `weights_only=True`. "
+                "To cache and reload MetaTensors, set `track_meta=True` and `weights_only=False`."
+            )
+        self.track_meta = track_meta
+        self.weights_only = weights_only
 
     def set_transform_hash(self, hash_xform_func: Callable[..., bytes]):
         """Get hashable transforms, and then hash them. Hashable transforms
@@ -377,7 +396,7 @@ def _cachecheck(self, item_transformed):
 
         if hashfile is not None and hashfile.is_file():  # cache hit
             try:
-                return torch.load(hashfile, weights_only=True)
+                return torch.load(hashfile, weights_only=self.weights_only)
             except PermissionError as e:
                 if sys.platform != "win32":
                     raise e
@@ -398,7 +417,7 @@ def _cachecheck(self, item_transformed):
             with tempfile.TemporaryDirectory() as tmpdirname:
                 temp_hash_file = Path(tmpdirname) / hashfile.name
                 torch.save(
-                    obj=convert_to_tensor(_item_transformed, convert_numeric=False),
+                    obj=convert_to_tensor(_item_transformed, convert_numeric=False, track_meta=self.track_meta),
                     f=temp_hash_file,
                     pickle_module=look_up_option(self.pickle_module, SUPPORTED_PICKLE_MOD),
                     pickle_protocol=self.pickle_protocol,

monai/transforms/croppad/functional.py

@@ -144,7 +144,7 @@ def crop_or_pad_nd(img: torch.Tensor, translation_mat, spatial_size: tuple[int,
         _mode = _convert_pt_pad_mode(mode)
         img = pad_nd(img, to_pad, mode=_mode, **kwargs)
     if do_crop:
-        img = img[to_crop]
+        img = img[tuple(to_crop)]
     return img
 
 

tests/data/test_persistentdataset.py

@@ -11,6 +11,7 @@
 
 from __future__ import annotations
 
+import contextlib
 import os
 import tempfile
 import unittest
@@ -20,7 +21,7 @@
 import torch
 from parameterized import parameterized
 
-from monai.data import PersistentDataset, json_hashing
+from monai.data import MetaTensor, PersistentDataset, json_hashing
 from monai.transforms import Compose, Flip, Identity, LoadImaged, SimulateDelayd, Transform
 
 TEST_CASE_1 = [
@@ -43,9 +44,16 @@
 
 TEST_CASE_3 = [None, (128, 128, 128)]
 
+TEST_CASE_4 = [True, False, False, MetaTensor]
+
+TEST_CASE_5 = [True, True, True, None]
+
+TEST_CASE_6 = [False, False, False, torch.Tensor]
+
+TEST_CASE_7 = [False, True, False, torch.Tensor]
 
-class _InplaceXform(Transform):
 
+class _InplaceXform(Transform):
     def __call__(self, data):
         if data:
             data[0] = data[0] + np.pi
@@ -55,7 +63,6 @@ def __call__(self, data):
 
 
 class TestDataset(unittest.TestCase):
-
     def test_cache(self):
         """testing no inplace change to the hashed item"""
         items = [[list(range(i))] for i in range(5)]
@@ -168,6 +175,31 @@ def test_different_transforms(self):
             l2 = ((im1 - im2) ** 2).sum() ** 0.5
             self.assertGreater(l2, 1)
 
+    @parameterized.expand([TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7])
+    def test_track_meta_and_weights_only(self, track_meta, weights_only, expected_error, expected_type):
+        """
+        Ensure expected behavior for all combinations of `track_meta` and `weights_only`.
+        """
+        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]).astype(float), np.eye(4))
+        with tempfile.TemporaryDirectory() as tempdir:
+            nib.save(test_image, os.path.join(tempdir, "test_image.nii.gz"))
+            test_data = [{"image": os.path.join(tempdir, "test_image.nii.gz")}]
+            transform = Compose([LoadImaged(keys=["image"])])
+            cache_dir = os.path.join(os.path.join(tempdir, "cache"), "data")
+
+            cm = self.assertRaises(ValueError) if expected_error else contextlib.nullcontext()
+            with cm:
+                test_dataset = PersistentDataset(
+                    data=test_data,
+                    transform=transform,
+                    cache_dir=cache_dir,
+                    track_meta=track_meta,
+                    weights_only=weights_only,
+                )
+
+                im = test_dataset[0]["image"]
+                self.assertIsInstance(im, expected_type)
+
 
 if __name__ == "__main__":
     unittest.main()