2590 Enhance all the SpatialPad related

monai/data/utils.py

@@ -420,6 +420,7 @@ def pad_list_data_collate(
     batch: Sequence,
     method: Union[Method, str] = Method.SYMMETRIC,
     mode: Union[NumpyPadMode, str] = NumpyPadMode.CONSTANT,
+    **np_kwargs,
 ):
     """
     Function version of :py:class:`monai.transforms.croppad.batch.PadListDataCollate`.
@@ -437,10 +438,13 @@ def pad_list_data_collate(
         batch: batch of data to pad-collate
         method: padding method (see :py:class:`monai.transforms.SpatialPad`)
         mode: padding mode (see :py:class:`monai.transforms.SpatialPad`)
+        np_kwargs: other args for `np.pad` API, note that `np.pad` treats channel dimension as the first dimension.
+            more details: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
+
     """
     from monai.transforms.croppad.batch import PadListDataCollate  # needs to be here to avoid circular import
 
-    return PadListDataCollate(method, mode)(batch)
+    return PadListDataCollate(method=method, mode=mode, **np_kwargs)(batch)
 
 
 def no_collation(x):

monai/transforms/croppad/array.py

@@ -202,6 +202,7 @@ def __init__(
         self,
         k: Union[Sequence[int], int],
         mode: Union[NumpyPadMode, str] = NumpyPadMode.CONSTANT,
+        method: Union[Method, str] = Method.SYMMETRIC,
         **np_kwargs,
     ) -> None:
         """
@@ -213,13 +214,16 @@ def __init__(
                 ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
                 One of the listed string values or a user supplied function. Defaults to ``"constant"``.
                 See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
+            method: {``"symmetric"``, ``"end"``}
+                Pad image symmetrically on every side or only pad at the end sides. Defaults to ``"symmetric"``.
             np_kwargs: other args for `np.pad` API, note that `np.pad` treats channel dimension as the first dimension.
                 more details: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
 
         See also :py:class:`monai.transforms.SpatialPad`
         """
         self.k = k
         self.mode: NumpyPadMode = NumpyPadMode(mode)
+        self.method: Method = Method(method)
         self.np_kwargs = np_kwargs
 
     def __call__(self, img: np.ndarray, mode: Optional[Union[NumpyPadMode, str]] = None) -> np.ndarray:
@@ -235,7 +239,7 @@ def __call__(self, img: np.ndarray, mode: Optional[Union[NumpyPadMode, str]] = N
         new_size = compute_divisible_spatial_size(spatial_shape=img.shape[1:], k=self.k)
         spatial_pad = SpatialPad(
             spatial_size=new_size,
-            method=Method.SYMMETRIC,
+            method=self.method,
             mode=mode or self.mode,
             **self.np_kwargs,
         )

monai/transforms/croppad/batch.py

@@ -60,15 +60,20 @@ class PadListDataCollate(InvertibleTransform):
     Args:
         method: padding method (see :py:class:`monai.transforms.SpatialPad`)
         mode: padding mode (see :py:class:`monai.transforms.SpatialPad`)
+        np_kwargs: other args for `np.pad` API, note that `np.pad` treats channel dimension as the first dimension.
+            more details: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
+
     """
 
     def __init__(
         self,
         method: Union[Method, str] = Method.SYMMETRIC,
         mode: Union[NumpyPadMode, str] = NumpyPadMode.CONSTANT,
+        **np_kwargs,
     ) -> None:
         self.method = method
         self.mode = mode
+        self.np_kwargs = np_kwargs
 
     def __call__(self, batch: Any):
         """
@@ -99,7 +104,7 @@ def __call__(self, batch: Any):
             # Default params are central padding, padding with 0's
             # If input is dictionary, use the dictionary version so that the transformation is recorded
 
-            padder = SpatialPad(max_shape, self.method, self.mode)  # type: ignore
+            padder = SpatialPad(spatial_size=max_shape, method=self.method, mode=self.mode, **self.np_kwargs)
             transform = padder if not output_to_tensor else Compose([padder, ToTensor()])
 
             for idx, batch_i in enumerate(batch):

monai/transforms/croppad/dictionary.py

@@ -254,6 +254,7 @@ def __init__(
         keys: KeysCollection,
         k: Union[Sequence[int], int],
         mode: NumpyPadModeSequence = NumpyPadMode.CONSTANT,
+        method: Union[Method, str] = Method.SYMMETRIC,
         allow_missing_keys: bool = False,
         **np_kwargs,
     ) -> None:
@@ -269,6 +270,8 @@ def __init__(
                 One of the listed string values or a user supplied function. Defaults to ``"constant"``.
                 See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
                 It also can be a sequence of string, each element corresponds to a key in ``keys``.
+            method: {``"symmetric"``, ``"end"``}
+                Pad image symmetrically on every side or only pad at the end sides. Defaults to ``"symmetric"``.
             allow_missing_keys: don't raise exception if key is missing.
             np_kwargs: other args for `np.pad` API, note that `np.pad` treats channel dimension as the first dimension.
                 more details: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
@@ -278,7 +281,7 @@ def __init__(
         """
         super().__init__(keys, allow_missing_keys)
         self.mode = ensure_tuple_rep(mode, len(self.keys))
-        self.padder = DivisiblePad(k=k, **np_kwargs)
+        self.padder = DivisiblePad(k=k, method=method, **np_kwargs)
 
     def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:
         d = dict(data)

tests/test_divisible_pad.py

@@ -25,7 +25,7 @@
 
 # pad all dimensions to be divisible by 5
 TEST_CASE_2 = [
-    {"k": 5, "mode": "constant"},
+    {"k": 5, "mode": "constant", "method": "end"},
     np.zeros((3, 10, 5, 17)),
     np.zeros((3, 10, 5, 20)),
 ]

tests/test_divisible_padd.py

@@ -23,7 +23,7 @@
 ]
 
 TEST_CASE_2 = [
-    {"keys": ["img"], "k": 7, "mode": "constant"},
+    {"keys": ["img"], "k": 7, "mode": "constant", "method": "end"},
     {"img": np.zeros((3, 8, 7))},
     np.zeros((3, 14, 7)),
 ]

tests/test_pad_collation.py

@@ -34,7 +34,10 @@
 
 TESTS: List[Tuple] = []
 
-for pad_collate in [pad_list_data_collate, PadListDataCollate()]:
+for pad_collate in [
+    lambda x: pad_list_data_collate(batch=x, method="end", mode="constant", constant_values=1),
+    PadListDataCollate(method="end", mode="constant", constant_values=1),
+]:
     TESTS.append((dict, pad_collate, RandSpatialCropd("image", roi_size=[8, 7], random_size=True)))
     TESTS.append((dict, pad_collate, RandRotated("image", prob=1, range_x=np.pi, keep_size=False)))
     TESTS.append((dict, pad_collate, RandZoomd("image", prob=1, min_zoom=1.1, max_zoom=2.0, keep_size=False)))