Remove usage of deprecated AsChannelFirst, AddChannel , and SplitChannel

monai/transforms/transform.py

@@ -275,8 +275,7 @@ def __call__(self, data: Any):
 
           #. string data without shape, `LoadImage` transform expects file paths,
           #. most of the pre-/post-processing transforms expect: ``(num_channels, spatial_dim_1[, spatial_dim_2, ...])``,
-             except for example: `AddChannel` expects (spatial_dim_1[, spatial_dim_2, ...]) and
-             `AsChannelFirst` expects (spatial_dim_1[, spatial_dim_2, ...], num_channels),
+             except for example: `AddChannel` expects (spatial_dim_1[, spatial_dim_2, ...])
 
         - the channel dimension is often not omitted even if number of channels is one.
 
@@ -441,8 +440,7 @@ def __call__(self, data):
 
           #. string data without shape, `LoadImaged` transform expects file paths,
           #. most of the pre-/post-processing transforms expect: ``(num_channels, spatial_dim_1[, spatial_dim_2, ...])``,
-             except for example: `AddChanneld` expects (spatial_dim_1[, spatial_dim_2, ...]) and
-             `AsChannelFirstd` expects (spatial_dim_1[, spatial_dim_2, ...], num_channels)
+             except for example: `AddChanneld` expects (spatial_dim_1[, spatial_dim_2, ...])
 
         - the channel dimension is often not omitted even if number of channels is one.
 

monai/transforms/utility/array.py

@@ -142,38 +142,6 @@ def __call__(self, data: Any) -> Any:
         return data
 
 
-@deprecated(since="0.8", msg_suffix="please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.")
-class AsChannelFirst(Transform):
-    """
-    Change the channel dimension of the image to the first dimension.
-
-    Most of the image transformations in ``monai.transforms``
-    assume the input image is in the channel-first format, which has the shape
-    (num_channels, spatial_dim_1[, spatial_dim_2, ...]).
-
-    This transform could be used to convert, for example, a channel-last image array in shape
-    (spatial_dim_1[, spatial_dim_2, ...], num_channels) into the channel-first format,
-    so that the multidimensional image array can be correctly interpreted by the other transforms.
-
-    Args:
-        channel_dim: which dimension of input image is the channel, default is the last dimension.
-    """
-
-    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
-
-    def __init__(self, channel_dim: int = -1) -> None:
-        if not (isinstance(channel_dim, int) and channel_dim >= -1):
-            raise ValueError(f"invalid channel dimension ({channel_dim}).")
-        self.channel_dim = channel_dim
-
-    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
-        """
-        Apply the transform to `img`.
-        """
-        out: NdarrayOrTensor = convert_to_tensor(moveaxis(img, self.channel_dim, 0), track_meta=get_track_meta())
-        return out
-
-
 class AsChannelLast(Transform):
     """
     Change the channel dimension of the image to the last dimension.
@@ -204,31 +172,6 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
         return out
 
 
-@deprecated(since="0.8", msg_suffix="please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.")
-class AddChannel(Transform):
-    """
-    Adds a 1-length channel dimension to the input image.
-
-    Most of the image transformations in ``monai.transforms``
-    assumes the input image is in the channel-first format, which has the shape
-    (num_channels, spatial_dim_1[, spatial_dim_2, ...]).
-
-    This transform could be used, for example, to convert a (spatial_dim_1[, spatial_dim_2, ...])
-    spatial image into the channel-first format so that the
-    multidimensional image array can be correctly interpreted by the other
-    transforms.
-    """
-
-    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
-
-    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
-        """
-        Apply the transform to `img`.
-        """
-        out: NdarrayOrTensor = convert_to_tensor(img[None], track_meta=get_track_meta())
-        return out
-
-
 class EnsureChannelFirst(Transform):
     """
     Adjust or add the channel dimension of input data to ensure `channel_first` shape.
@@ -291,6 +234,54 @@ def __call__(self, img: torch.Tensor, meta_dict: Mapping | None = None) -> torch
         return convert_to_tensor(result, track_meta=get_track_meta())  # type: ignore
 
 
+@deprecated(
+    since="0.8",
+    removed="1.3",
+    msg_suffix="please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.",
+)
+class AsChannelFirst(EnsureChannelFirst):
+    """
+    Change the channel dimension of the image to the first dimension.
+    Most of the image transformations in ``monai.transforms``
+    assume the input image is in the channel-first format, which has the shape
+    (num_channels, spatial_dim_1[, spatial_dim_2, ...]).
+    This transform could be used to convert, for example, a channel-last image array in shape
+    (spatial_dim_1[, spatial_dim_2, ...], num_channels) into the channel-first format,
+    so that the multidimensional image array can be correctly interpreted by the other transforms.
+    Args:
+        channel_dim: which dimension of input image is the channel, default is the last dimension.
+    """
+
+    def __init__(self, channel_dim: int = -1) -> None:
+        super().__init__(channel_dim=channel_dim)
+
+
+@deprecated(
+    since="0.8",
+    removed="1.3",
+    msg_suffix="please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead"
+    " with `channel_dim='no_channel'`.",
+)
+class AddChannel(EnsureChannelFirst):
+    """
+    Adds a 1-length channel dimension to the input image.
+
+    Most of the image transformations in ``monai.transforms``
+    assumes the input image is in the channel-first format, which has the shape
+    (num_channels, spatial_dim_1[, spatial_dim_2, ...]).
+
+    This transform could be used, for example, to convert a (spatial_dim_1[, spatial_dim_2, ...])
+    spatial image into the channel-first format so that the
+    multidimensional image array can be correctly interpreted by the other
+    transforms.
+    """
+
+    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
+
+    def __init__(self) -> None:
+        super().__init__(channel_dim="no_channel")
+
+
 class RepeatChannel(Transform):
     """
     Repeat channel data to construct expected input shape for models.
@@ -391,7 +382,7 @@ def __call__(self, img: torch.Tensor) -> list[torch.Tensor]:
         return outputs
 
 
-@deprecated(since="0.8", msg_suffix="please use `SplitDim` instead.")
+@deprecated(since="0.8", removed="1.3", msg_suffix="please use `SplitDim` instead.")
 class SplitChannel(SplitDim):
     """
     Split Numpy array or PyTorch Tensor data according to the channel dim.

monai/transforms/utility/dictionary.py

@@ -33,10 +33,8 @@
 from monai.transforms.traits import MultiSampleTrait, RandomizableTrait
 from monai.transforms.transform import MapTransform, Randomizable, RandomizableTransform
 from monai.transforms.utility.array import (
-    AddChannel,
     AddCoordinateChannels,
     AddExtremePointsChannel,
-    AsChannelFirst,
     AsChannelLast,
     CastToType,
     ClassesToIndices,
@@ -221,31 +219,6 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, N
         return d
 
 
-class AsChannelFirstd(MapTransform):
-    """
-    Dictionary-based wrapper of :py:class:`monai.transforms.AsChannelFirst`.
-    """
-
-    backend = AsChannelFirst.backend
-
-    def __init__(self, keys: KeysCollection, channel_dim: int = -1, allow_missing_keys: bool = False) -> None:
-        """
-        Args:
-            keys: keys of the corresponding items to be transformed.
-                See also: :py:class:`monai.transforms.compose.MapTransform`
-            channel_dim: which dimension of input image is the channel, default is the last dimension.
-            allow_missing_keys: don't raise exception if key is missing.
-        """
-        super().__init__(keys, allow_missing_keys)
-        self.converter = AsChannelFirst(channel_dim=channel_dim)
-
-    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, NdarrayOrTensor]:
-        d = dict(data)
-        for key in self.key_iterator(d):
-            d[key] = self.converter(d[key])
-        return d
-
-
 class AsChannelLastd(MapTransform):
     """
     Dictionary-based wrapper of :py:class:`monai.transforms.AsChannelLast`.
@@ -271,30 +244,6 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, N
         return d
 
 
-class AddChanneld(MapTransform):
-    """
-    Dictionary-based wrapper of :py:class:`monai.transforms.AddChannel`.
-    """
-
-    backend = AddChannel.backend
-
-    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False) -> None:
-        """
-        Args:
-            keys: keys of the corresponding items to be transformed.
-                See also: :py:class:`monai.transforms.compose.MapTransform`
-            allow_missing_keys: don't raise exception if key is missing.
-        """
-        super().__init__(keys, allow_missing_keys)
-        self.adder = AddChannel()
-
-    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, NdarrayOrTensor]:
-        d = dict(data)
-        for key in self.key_iterator(d):
-            d[key] = self.adder(d[key])
-        return d
-
-
 class EnsureChannelFirstd(MapTransform):
     """
     Dictionary-based wrapper of :py:class:`monai.transforms.EnsureChannelFirst`.
@@ -336,6 +285,50 @@ def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> dict[Hashable, torc
         return d
 
 
+@deprecated(
+    since="0.8",
+    removed="1.3",
+    msg_suffix="please use MetaTensor data type and monai.transforms.EnsureChannelFirstd instead.",
+)
+class AsChannelFirstd(EnsureChannelFirstd):
+    """
+    Dictionary-based wrapper of :py:class:`monai.transforms.AsChannelFirst`.
+    """
+
+    def __init__(self, keys: KeysCollection, channel_dim: int = -1, allow_missing_keys: bool = False) -> None:
+        """
+        Args:
+            keys: keys of the corresponding items to be transformed.
+                See also: :py:class:`monai.transforms.compose.MapTransform`
+            channel_dim: which dimension of input image is the channel, default is the last dimension.
+            allow_missing_keys: don't raise exception if key is missing.
+        """
+        super().__init__(keys=keys, channel_dim=channel_dim, allow_missing_keys=allow_missing_keys)
+
+
+@deprecated(
+    since="0.8",
+    removed="1.3",
+    msg_suffix="please use MetaTensor data type and monai.transforms.EnsureChannelFirstd instead"
+    " with `channel_dim='no_channel'`.",
+)
+class AddChanneld(EnsureChannelFirstd):
+    """
+    Dictionary-based wrapper of :py:class:`monai.transforms.AddChannel`.
+    """
+
+    backend = EnsureChannelFirstd.backend
+
+    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False) -> None:
+        """
+        Args:
+            keys: keys of the corresponding items to be transformed.
+                See also: :py:class:`monai.transforms.compose.MapTransform`
+            allow_missing_keys: don't raise exception if key is missing.
+        """
+        super().__init__(keys, allow_missing_keys, channel_dim="no_channel")
+
+
 class RepeatChanneld(MapTransform):
     """
     Dictionary-based wrapper of :py:class:`monai.transforms.RepeatChannel`.
@@ -452,7 +445,7 @@ def __call__(
         return d
 
 
-@deprecated(since="0.8", msg_suffix="please use `SplitDimd` instead.")
+@deprecated(since="0.8", removed="1.3", msg_suffix="please use `SplitDimd` instead.")
 class SplitChanneld(SplitDimd):
     """
     Dictionary-based wrapper of :py:class:`monai.transforms.SplitChannel`.

tests/test_arraydataset.py

@@ -22,18 +22,18 @@
 from torch.utils.data import DataLoader
 
 from monai.data import ArrayDataset
-from monai.transforms import AddChannel, Compose, LoadImage, RandAdjustContrast, RandGaussianNoise, Spacing
+from monai.transforms import Compose, EnsureChannelFirst, LoadImage, RandAdjustContrast, RandGaussianNoise, Spacing
 
 TEST_CASE_1 = [
-    Compose([LoadImage(image_only=True), AddChannel(), RandGaussianNoise(prob=1.0)]),
-    Compose([LoadImage(image_only=True), AddChannel(), RandGaussianNoise(prob=1.0)]),
+    Compose([LoadImage(image_only=True), EnsureChannelFirst(channel_dim="no_channel"), RandGaussianNoise(prob=1.0)]),
+    Compose([LoadImage(image_only=True), EnsureChannelFirst(channel_dim="no_channel"), RandGaussianNoise(prob=1.0)]),
     (0, 1),
     (1, 128, 128, 128),
 ]
 
 TEST_CASE_2 = [
-    Compose([LoadImage(image_only=True), AddChannel(), RandAdjustContrast(prob=1.0)]),
-    Compose([LoadImage(image_only=True), AddChannel(), RandAdjustContrast(prob=1.0)]),
+    Compose([LoadImage(image_only=True), EnsureChannelFirst(channel_dim="no_channel"), RandAdjustContrast(prob=1.0)]),
+    Compose([LoadImage(image_only=True), EnsureChannelFirst(channel_dim="no_channel"), RandAdjustContrast(prob=1.0)]),
     (0, 1),
     (1, 128, 128, 128),
 ]
@@ -50,13 +50,30 @@ def __call__(self, input_):
 
 
 TEST_CASE_3 = [
-    TestCompose([LoadImage(image_only=True), AddChannel(), Spacing(pixdim=(2, 2, 4)), RandAdjustContrast(prob=1.0)]),
-    TestCompose([LoadImage(image_only=True), AddChannel(), Spacing(pixdim=(2, 2, 4)), RandAdjustContrast(prob=1.0)]),
+    TestCompose(
+        [
+            LoadImage(image_only=True),
+            EnsureChannelFirst(channel_dim="no_channel"),
+            Spacing(pixdim=(2, 2, 4)),
+            RandAdjustContrast(prob=1.0),
+        ]
+    ),
+    TestCompose(
+        [
+            LoadImage(image_only=True),
+            EnsureChannelFirst(channel_dim="no_channel"),
+            Spacing(pixdim=(2, 2, 4)),
+            RandAdjustContrast(prob=1.0),
+        ]
+    ),
     (0, 2),
     (1, 64, 64, 33),
 ]
 
-TEST_CASE_4 = [Compose([LoadImage(image_only=True), AddChannel(), RandGaussianNoise(prob=1.0)]), (1, 128, 128, 128)]
+TEST_CASE_4 = [
+    Compose([LoadImage(image_only=True), EnsureChannelFirst(channel_dim="no_channel"), RandGaussianNoise(prob=1.0)]),
+    (1, 128, 128, 128),
+]
 
 
 class TestArrayDataset(unittest.TestCase):

tests/test_compose.py

@@ -194,8 +194,8 @@ def __call__(self, data):
             c.randomize()
 
     def test_err_msg(self):
-        transforms = mt.Compose([abs, mt.AddChannel(), round])
-        with self.assertRaisesRegex(Exception, "AddChannel"):
+        transforms = mt.Compose([abs, mt.EnsureChannelFirst(), round])
+        with self.assertRaisesRegex(Exception, "EnsureChannelFirst"):
             transforms(42.1)
 
     def test_data_loader(self):
@@ -244,7 +244,7 @@ def test_data_loader_2(self):
         set_determinism(None)
 
     def test_flatten_and_len(self):
-        x = mt.AddChannel()
+        x = mt.EnsureChannelFirst(channel_dim="no_channel")
         t1 = mt.Compose([x, x, x, x, mt.Compose([mt.Compose([x, x]), x, x])])
 
         t2 = t1.flatten()

tests/test_cross_validation.py

@@ -16,7 +16,7 @@
 
 from monai.apps import CrossValidation, DecathlonDataset
 from monai.data import MetaTensor
-from monai.transforms import AddChanneld, Compose, LoadImaged, ScaleIntensityd
+from monai.transforms import Compose, EnsureChannelFirstd, LoadImaged, ScaleIntensityd
 from tests.utils import skip_if_downloading_fails, skip_if_quick
 
 
@@ -25,7 +25,11 @@ class TestCrossValidation(unittest.TestCase):
     def test_values(self):
         testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data")
         train_transform = Compose(
-            [LoadImaged(keys=["image", "label"]), AddChanneld(keys=["image", "label"]), ScaleIntensityd(keys="image")]
+            [
+                LoadImaged(keys=["image", "label"]),
+                EnsureChannelFirstd(keys=["image", "label"], channel_dim="no_channel"),
+                ScaleIntensityd(keys="image"),
+            ]
         )
         val_transform = LoadImaged(keys=["image", "label"])