5902 allow for missing filename_or_obj key

monai/data/folder_layout.py

@@ -21,7 +21,11 @@
 def default_name_formatter(metadict, saver):
     """Returns a kwargs dict for :py:meth:`FolderLayout.filename`,
     according to the input metadata and SaveImage transform."""
-    subject = metadict[monai.utils.ImageMetaKey.FILENAME_OR_OBJ] if metadict else getattr(saver, "_data_index", 0)
+    subject = (
+        metadict.get(monai.utils.ImageMetaKey.FILENAME_OR_OBJ, getattr(saver, "_data_index", 0))
+        if metadict
+        else getattr(saver, "_data_index", 0)
+    )
     patch_index = metadict.get(monai.utils.ImageMetaKey.PATCH_INDEX, None) if metadict else None
     return {"subject": f"{subject}", "idx": patch_index}
 

monai/transforms/spatial/array.py

@@ -346,7 +346,7 @@ def update_meta(self, img: torch.Tensor, dst_affine=None, img_dst=None):
         if dst_affine is not None:
             super().update_meta(img, dst_affine)
         if isinstance(img_dst, MetaTensor) and isinstance(img, MetaTensor):
-            original_fname = img.meta[Key.FILENAME_OR_OBJ]
+            original_fname = img.meta.get(Key.FILENAME_OR_OBJ, "resample_to_match_source")
             img.meta = deepcopy(img_dst.meta)
             img.meta[Key.FILENAME_OR_OBJ] = original_fname  # keep the original name, the others are overwritten
 

tests/test_resample_to_match.py

@@ -21,11 +21,13 @@
 
 import nibabel as nib
 import numpy as np
+import torch
 from parameterized import parameterized
 
+from monai.data import MetaTensor
 from monai.data.image_reader import ITKReader, NibabelReader
 from monai.data.image_writer import ITKWriter
-from monai.transforms import Compose, EnsureChannelFirstd, LoadImaged, ResampleToMatch, SaveImaged
+from monai.transforms import Compose, EnsureChannelFirstd, LoadImaged, ResampleToMatch, SaveImage, SaveImaged
 from monai.utils import optional_import
 from tests.utils import assert_allclose, download_url_or_skip_test, testing_data_config
 
@@ -90,6 +92,13 @@ def test_inverse(self):
         self.assertLess(((im_mod2.affine - data["im2"].affine) ** 2).sum() ** 0.5, 1e-2)
         self.assertEqual(im_mod2.applied_operations, [])
 
+    def test_no_name(self):
+        img_1 = MetaTensor(torch.zeros(1, 2, 2, 2))
+        img_2 = MetaTensor(torch.zeros(1, 3, 3, 3))
+        im_mod = ResampleToMatch()(img_1, img_2)
+        self.assertEqual(im_mod.meta["filename_or_obj"], "resample_to_match_source")
+        SaveImage(output_dir=self.tmpdir, output_postfix="", separate_folder=False, resample=False)(im_mod)
+
 
 if __name__ == "__main__":
     unittest.main()