2103 Add deepcopy to PosNegCrop transform

monai/transforms/croppad/dictionary.py

@@ -664,6 +664,9 @@ class RandWeightedCropd(Randomizable, MapTransform):
             If its components have non-positive values, the corresponding size of `img` will be used.
         num_samples: number of samples (image patches) to take in the returned list.
         center_coord_key: if specified, the actual sampling location will be stored with the corresponding key.
+        meta_key_postfix: use `key_{postfix}` to to fetch the meta data according to the key data,
+            default is `meta_dict`, the meta data is a dictionary object.
+            used to add `patch_index` to the meta dict.
         allow_missing_keys: don't raise exception if key is missing.
 
     See Also:
@@ -677,13 +680,15 @@ def __init__(
         spatial_size: Union[Sequence[int], int],
         num_samples: int = 1,
         center_coord_key: Optional[str] = None,
+        meta_key_postfix: str = "meta_dict",
         allow_missing_keys: bool = False,
     ):
         MapTransform.__init__(self, keys, allow_missing_keys)
         self.spatial_size = ensure_tuple(spatial_size)
         self.w_key = w_key
         self.num_samples = int(num_samples)
         self.center_coord_key = center_coord_key
+        self.meta_key_postfix = meta_key_postfix
         self.centers: List[np.ndarray] = []
 
     def randomize(self, weight_map: np.ndarray) -> None:
@@ -710,9 +715,15 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> List[Dict[Hashable, n
                 if self.center_coord_key:
                     results[i][self.center_coord_key] = center
         # fill in the extra keys with unmodified data
-        for key in set(data.keys()).difference(set(self.keys)):
-            for i in range(self.num_samples):
-                results[i][key] = data[key]
+        for i in range(self.num_samples):
+            for key in set(data.keys()).difference(set(self.keys)):
+                results[i][key] = deepcopy(data[key])
+            # add `patch_index` to the meta data
+            for key in self.key_iterator(d):
+                meta_data_key = f"{key}_{self.meta_key_postfix}"
+                if meta_data_key not in results[i]:
+                    results[i][meta_data_key] = {}  # type: ignore
+                results[i][meta_data_key][Key.PATCH_INDEX] = i
 
         return results
 
@@ -829,7 +840,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> List[Dict[Hashable, n
                 results[i][key] = cropper(img)
             # fill in the extra keys with unmodified data
             for key in set(data.keys()).difference(set(self.keys)):
-                results[i][key] = data[key]
+                results[i][key] = deepcopy(data[key])
             # add `patch_index` to the meta data
             for key in self.key_iterator(d):
                 meta_data_key = f"{key}_{self.meta_key_postfix}"

tests/test_rand_crop_by_pos_neg_labeld.py

@@ -18,7 +18,7 @@
 
 TEST_CASE_0 = [
     {
-        "keys": ["image", "extral", "label"],
+        "keys": ["image", "extra", "label"],
         "label_key": "label",
         "spatial_size": [-1, 2, 2],
         "pos": 1,
@@ -29,18 +29,17 @@
     },
     {
         "image": np.random.randint(0, 2, size=[3, 3, 3, 3]),
-        "extral": np.random.randint(0, 2, size=[3, 3, 3, 3]),
+        "extra": np.random.randint(0, 2, size=[3, 3, 3, 3]),
         "label": np.random.randint(0, 2, size=[3, 3, 3, 3]),
-        "affine": np.eye(3),
-        "shape": "CHWD",
+        "image_meta_dict": {"affine": np.eye(3), "shape": "CHWD"},
     },
     list,
     (3, 3, 2, 2),
 ]
 
 TEST_CASE_1 = [
     {
-        "keys": ["image", "extral", "label"],
+        "keys": ["image", "extra", "label"],
         "label_key": "label",
         "spatial_size": [2, 2, 2],
         "pos": 1,
@@ -51,18 +50,17 @@
     },
     {
         "image": np.random.randint(0, 2, size=[3, 3, 3, 3]),
-        "extral": np.random.randint(0, 2, size=[3, 3, 3, 3]),
+        "extra": np.random.randint(0, 2, size=[3, 3, 3, 3]),
         "label": np.random.randint(0, 2, size=[3, 3, 3, 3]),
-        "affine": np.eye(3),
-        "shape": "CHWD",
+        "label_meta_dict": {"affine": np.eye(3), "shape": "CHWD"},
     },
     list,
     (3, 2, 2, 2),
 ]
 
 TEST_CASE_2 = [
     {
-        "keys": ["image", "extral", "label"],
+        "keys": ["image", "extra", "label"],
         "label_key": "label",
         "spatial_size": [2, 2, 2],
         "pos": 1,
@@ -73,10 +71,9 @@
     },
     {
         "image": np.zeros([3, 3, 3, 3]) - 1,
-        "extral": np.zeros([3, 3, 3, 3]),
+        "extra": np.zeros([3, 3, 3, 3]),
         "label": np.ones([3, 3, 3, 3]),
-        "affine": np.eye(3),
-        "shape": "CHWD",
+        "extra_meta_dict": {"affine": np.eye(3), "shape": "CHWD"},
     },
     list,
     (3, 2, 2, 2),
@@ -89,10 +86,12 @@ def test_type_shape(self, input_param, input_data, expected_type, expected_shape
         result = RandCropByPosNegLabeld(**input_param)(input_data)
         self.assertIsInstance(result, expected_type)
         self.assertTupleEqual(result[0]["image"].shape, expected_shape)
-        self.assertTupleEqual(result[0]["extral"].shape, expected_shape)
+        self.assertTupleEqual(result[0]["extra"].shape, expected_shape)
         self.assertTupleEqual(result[0]["label"].shape, expected_shape)
         for i, item in enumerate(result):
             self.assertEqual(item["image_meta_dict"]["patch_index"], i)
+            self.assertEqual(item["label_meta_dict"]["patch_index"], i)
+            self.assertEqual(item["extra_meta_dict"]["patch_index"], i)
 
 
 if __name__ == "__main__":

tests/test_rand_weighted_cropd.py

@@ -139,6 +139,24 @@ def test_rand_weighted_crop_bad_w(self):
         np.testing.assert_allclose(result[0]["seg"].shape, (1, 48, 64, 80))
         np.testing.assert_allclose(np.asarray(crop.centers), [[24, 32, 40], [24, 32, 40], [24, 32, 40]])
 
+    def test_rand_weighted_crop_patch_index(self):
+        img = self.imt[0]
+        n_samples = 3
+        crop = RandWeightedCropd(("img", "seg"), "w", (10, -1, -1), n_samples)
+        weight = np.zeros_like(img)
+        weight[0, 7, 17] = 1.1
+        weight[0, 13, 31] = 1.1
+        weight[0, 24, 21] = 1
+        crop.set_random_state(10)
+        result = crop({"img": img, "seg": self.segn[0], "w": weight, "img_meta_dict": {"affine": None}})
+        self.assertTrue(len(result) == n_samples)
+        np.testing.assert_allclose(np.asarray(crop.centers), [[14, 32, 40], [41, 32, 40], [20, 32, 40]])
+        for i in range(n_samples):
+            np.testing.assert_allclose(result[i]["img"].shape, (1, 10, 64, 80))
+            np.testing.assert_allclose(result[i]["seg"].shape, (1, 10, 64, 80))
+            np.testing.assert_allclose(result[i]["img_meta_dict"]["patch_index"], i)
+            np.testing.assert_allclose(result[i]["seg_meta_dict"]["patch_index"], i)
+
 
 if __name__ == "__main__":
     unittest.main()