fix duplication of key_transforms in RandSpatialCropSamplesd

monai/transforms/croppad/dictionary.py

@@ -543,8 +543,11 @@ def randomize(self, data: Optional[Any] = None) -> None:
 
     def __call__(self, data: Mapping[Hashable, np.ndarray]) -> List[Dict[Hashable, np.ndarray]]:
         ret = []
-        d = dict(data)
         for i in range(self.num_samples):
+            d = dict(data)
+            # deep copy all the unmodified data
+            for key in set(data.keys()).difference(set(self.keys)):
+                d[key] = deepcopy(data[key])
             cropped = self.cropper(d)
             # add `patch_index` to the meta data
             for key in self.key_iterator(d):

tests/test_rand_spatial_crop_samplesd.py

@@ -14,7 +14,7 @@
 import numpy as np
 from parameterized import parameterized
 
-from monai.transforms import RandSpatialCropSamplesd
+from monai.transforms import Compose, RandSpatialCropSamplesd, ToTensord
 
 TEST_CASE_1 = [
     {"keys": ["img", "seg"], "num_samples": 4, "roi_size": [2, 2, 2], "random_center": True},
@@ -76,6 +76,22 @@ def test_shape(self, input_param, input_data, expected_shape, expected_last):
         np.testing.assert_allclose(item["img"], expected_last["img"])
         np.testing.assert_allclose(item["seg"], expected_last["seg"])
 
+    def test_deep_copy(self):
+        data = {"img": np.ones((1, 10, 11, 12))}
+        num_samples = 3
+        sampler = RandSpatialCropSamplesd(
+            keys=["img"],
+            roi_size=(3, 3, 3),
+            num_samples=num_samples,
+            random_center=True,
+            random_size=False,
+        )
+        transform = Compose([ToTensord(keys="img"), sampler])
+        samples = transform(data)
+        self.assertEqual(len(samples), num_samples)
+        for sample in samples:
+            self.assertEqual(len(sample["img_transforms"]), len(transform))
+
 
 if __name__ == "__main__":
     unittest.main()