2901 ndim -> spatial_dims

monai/apps/deepgrow/transforms.py

@@ -19,7 +19,7 @@
 from monai.transforms import Resize, SpatialCrop
 from monai.transforms.transform import MapTransform, Randomizable, Transform
 from monai.transforms.utils import generate_spatial_bounding_box
-from monai.utils import InterpolateMode, ensure_tuple, ensure_tuple_rep, min_version, optional_import
+from monai.utils import InterpolateMode, deprecated_arg, ensure_tuple, ensure_tuple_rep, min_version, optional_import
 
 measure, _ = optional_import("skimage.measure", "0.14.2", min_version)
 distance_transform_cdt, _ = optional_import("scipy.ndimage.morphology", name="distance_transform_cdt")
@@ -476,7 +476,7 @@ class AddGuidanceFromPointsd(Transform):
         background: key that represents user background (-ve) clicks.
         axis: axis that represents slices in 3D volume. (axis to Depth)
         depth_first: if depth (slices) is positioned at first dimension.
-        dimensions: dimensions based on model used for deepgrow (2D vs 3D).
+        spatial_dims: dimensions based on model used for deepgrow (2D vs 3D).
         slice_key: key that represents applicable slice to add guidance.
         meta_keys: explicitly indicate the key of the meta data dictionary of `ref_image`.
             for example, for data with key `image`, the metadata by default is in `image_meta_dict`.
@@ -486,8 +486,13 @@ class AddGuidanceFromPointsd(Transform):
             to the key data, default is `meta_dict`, the meta data is a dictionary object.
             For example, to handle key `image`,  read/write affine matrices from the
             metadata `image_meta_dict` dictionary's `affine` field.
+
+    .. deprecated:: 0.6.0
+        ``dimensions`` is deprecated, use ``spatial_dims`` instead.
+
     """
 
+    @deprecated_arg(name="dimensions", since="0.6", msg_suffix="Please use `spatial_dims` instead.")
     def __init__(
         self,
         ref_image,
@@ -496,18 +501,19 @@ def __init__(
         background: str = "background",
         axis: int = 0,
         depth_first: bool = True,
-        dimensions: int = 2,
+        spatial_dims: int = 2,
         slice_key: str = "slice",
         meta_keys: Optional[str] = None,
         meta_key_postfix: str = "meta_dict",
+        dimensions: Optional[int] = None,
     ):
         self.ref_image = ref_image
         self.guidance = guidance
         self.foreground = foreground
         self.background = background
         self.axis = axis
         self.depth_first = depth_first
-        self.dimensions = dimensions
+        self.dimensions = spatial_dims if dimensions is None else dimensions
         self.slice = slice_key
         self.meta_keys = meta_keys
         self.meta_key_postfix = meta_key_postfix

monai/losses/image_dissimilarity.py

@@ -8,14 +8,14 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Tuple, Union
+from typing import Optional, Tuple, Union
 
 import torch
 from torch.nn import functional as F
 from torch.nn.modules.loss import _Loss
 
 from monai.networks.layers import gaussian_1d, separable_filtering
-from monai.utils import LossReduction
+from monai.utils import LossReduction, deprecated_arg
 
 
 def make_rectangular_kernel(kernel_size: int) -> torch.Tensor:
@@ -59,18 +59,20 @@ class LocalNormalizedCrossCorrelationLoss(_Loss):
         DeepReg (https://github.com/DeepRegNet/DeepReg)
     """
 
+    @deprecated_arg(name="ndim", since="0.6", msg_suffix="Please use `spatial_dims` instead.")
     def __init__(
         self,
-        ndim: int = 3,
+        spatial_dims: int = 3,
         kernel_size: int = 3,
         kernel_type: str = "rectangular",
         reduction: Union[LossReduction, str] = LossReduction.MEAN,
         smooth_nr: float = 1e-5,
         smooth_dr: float = 1e-5,
+        ndim: Optional[int] = None,
     ) -> None:
         """
         Args:
-            ndim: number of spatial ndimensions, {``1``, ``2``, ``3``}. Defaults to 3.
+            spatial_dims: number of spatial ndimensions, {``1``, ``2``, ``3``}. Defaults to 3.
             kernel_size: kernel spatial size, must be odd.
             kernel_type: {``"rectangular"``, ``"triangular"``, ``"gaussian"``}. Defaults to ``"rectangular"``.
             reduction: {``"none"``, ``"mean"``, ``"sum"``}
@@ -81,10 +83,15 @@ def __init__(
                 - ``"sum"``: the output will be summed.
             smooth_nr: a small constant added to the numerator to avoid nan.
             smooth_dr: a small constant added to the denominator to avoid nan.
+
+        .. deprecated:: 0.6.0
+            ``ndim`` is deprecated, use ``spatial_dims``.
         """
         super(LocalNormalizedCrossCorrelationLoss, self).__init__(reduction=LossReduction(reduction).value)
 
-        self.ndim = ndim
+        if ndim is not None:
+            spatial_dims = ndim
+        self.ndim = spatial_dims
         if self.ndim not in [1, 2, 3]:
             raise ValueError(f"Unsupported ndim: {self.ndim}-d, only 1-d, 2-d, and 3-d inputs are supported")
 

monai/networks/blocks/activation.py

@@ -48,8 +48,7 @@ class Swish(nn.Module):
 
 
     Shape:
-        - Input: :math:`(N, *)` where `*` means, any number of additional
-          dimensions
+        - Input: :math:`(N, *)` where `*` means, any number of additional dimensions
         - Output: :math:`(N, *)`, same shape as the input
 
 
@@ -143,8 +142,7 @@ class Mish(nn.Module):
     this class will utilize `torch.nn.functional.mish` to do the calculation if meets the version.
 
     Shape:
-        - Input: :math:`(N, *)` where `*` means, any number of additional
-          dimensions
+        - Input: :math:`(N, *)` where `*` means, any number of additional dimensions
         - Output: :math:`(N, *)`, same shape as the input
 
 

monai/networks/blocks/aspp.py

@@ -86,7 +86,7 @@ def __init__(
 
         out_channels = conv_out_channels * len(pads)  # final conv. output channels
         self.conv_k1 = Convolution(
-            dimensions=spatial_dims,
+            spatial_dims=spatial_dims,
             in_channels=out_channels,
             out_channels=out_channels,
             kernel_size=1,

monai/networks/blocks/convolutions.py

@@ -18,6 +18,7 @@
 from monai.networks.blocks import ADN
 from monai.networks.layers.convutils import same_padding, stride_minus_kernel_padding
 from monai.networks.layers.factories import Conv
+from monai.utils.deprecated import deprecated_arg
 
 
 class Convolution(nn.Sequential):
@@ -59,7 +60,7 @@ class Convolution(nn.Sequential):
         )
 
     Args:
-        dimensions: number of spatial dimensions.
+        spatial_dims: number of spatial dimensions.
         in_channels: number of input channels.
         out_channels: number of output channels.
         strides: convolution stride. Defaults to 1.
@@ -69,13 +70,13 @@ class Convolution(nn.Sequential):
         act: activation type and arguments. Defaults to PReLU.
         norm: feature normalization type and arguments. Defaults to instance norm.
         dropout: dropout ratio. Defaults to no dropout.
-        dropout_dim: determine the dimensions of dropout. Defaults to 1.
+        dropout_dim: determine the spatial dimensions of dropout. Defaults to 1.
 
             - When dropout_dim = 1, randomly zeroes some of the elements for each channel.
             - When dropout_dim = 2, Randomly zeroes out entire channels (a channel is a 2D feature map).
             - When dropout_dim = 3, Randomly zeroes out entire channels (a channel is a 3D feature map).
 
-            The value of dropout_dim should be no no larger than the value of `dimensions`.
+            The value of dropout_dim should be no no larger than the value of `spatial_dims`.
         dilation: dilation rate. Defaults to 1.
         groups: controls the connections between inputs and outputs. Defaults to 1.
         bias: whether to have a bias term. Defaults to True.
@@ -86,16 +87,22 @@ class Convolution(nn.Sequential):
         output_padding: controls the additional size added to one side of the output shape.
             Defaults to None.
 
+    .. deprecated:: 0.6.0
+        ``dimensions`` is deprecated, use ``spatial_dims`` instead.
+
     See also:
 
         :py:class:`monai.networks.layers.Conv`
         :py:class:`monai.networks.blocks.ADN`
 
     """
 
+    @deprecated_arg(
+        name="dimensions", new_name="spatial_dims", since="0.6", msg_suffix="Please use `spatial_dims` instead."
+    )
     def __init__(
         self,
-        dimensions: int,
+        spatial_dims: int,
         in_channels: int,
         out_channels: int,
         strides: Union[Sequence[int], int] = 1,
@@ -112,15 +119,16 @@ def __init__(
         is_transposed: bool = False,
         padding: Optional[Union[Sequence[int], int]] = None,
         output_padding: Optional[Union[Sequence[int], int]] = None,
+        dimensions: Optional[int] = None,
     ) -> None:
         super().__init__()
-        self.dimensions = dimensions
+        self.dimensions = spatial_dims if dimensions is None else dimensions
         self.in_channels = in_channels
         self.out_channels = out_channels
         self.is_transposed = is_transposed
         if padding is None:
             padding = same_padding(kernel_size, dilation)
-        conv_type = Conv[Conv.CONVTRANS if is_transposed else Conv.CONV, dimensions]
+        conv_type = Conv[Conv.CONVTRANS if is_transposed else Conv.CONV, self.dimensions]
 
         conv: nn.Module
         if is_transposed:
@@ -159,7 +167,7 @@ def __init__(
                     in_channels=out_channels,
                     act=act,
                     norm=norm,
-                    norm_dim=dimensions,
+                    norm_dim=self.dimensions,
                     dropout=dropout,
                     dropout_dim=dropout_dim,
                 ),
@@ -177,7 +185,7 @@ class ResidualUnit(nn.Module):
         from monai.networks.blocks import ResidualUnit
 
         convs = ResidualUnit(
-            dimensions=3,
+            spatial_dims=3,
             in_channels=1,
             out_channels=1,
             adn_ordering="AN",
@@ -209,7 +217,7 @@ class ResidualUnit(nn.Module):
         )
 
     Args:
-        dimensions: number of spatial dimensions.
+        spatial_dims: number of spatial dimensions.
         in_channels: number of input channels.
         out_channels: number of output channels.
         strides: convolution stride. Defaults to 1.
@@ -234,15 +242,19 @@ class ResidualUnit(nn.Module):
         padding: controls the amount of implicit zero-paddings on both sides for padding number of points
             for each dimension. Defaults to None.
 
+    .. deprecated:: 0.6.0
+        ``dimensions`` is deprecated, use ``spatial_dims`` instead.
+
     See also:
 
         :py:class:`monai.networks.blocks.Convolution`
 
     """
 
+    @deprecated_arg(name="dimensions", since="0.6", msg_suffix="Please use `spatial_dims` instead.")
     def __init__(
         self,
-        dimensions: int,
+        spatial_dims: int,
         in_channels: int,
         out_channels: int,
         strides: Union[Sequence[int], int] = 1,
@@ -257,9 +269,10 @@ def __init__(
         bias: bool = True,
         last_conv_only: bool = False,
         padding: Optional[Union[Sequence[int], int]] = None,
+        dimensions: Optional[int] = None,
     ) -> None:
         super().__init__()
-        self.dimensions = dimensions
+        self.dimensions = spatial_dims if dimensions is None else dimensions
         self.in_channels = in_channels
         self.out_channels = out_channels
         self.conv = nn.Sequential()
@@ -273,7 +286,7 @@ def __init__(
         for su in range(subunits):
             conv_only = last_conv_only and su == (subunits - 1)
             unit = Convolution(
-                dimensions,
+                self.dimensions,
                 schannels,
                 out_channels,
                 strides=sstrides,
@@ -304,7 +317,7 @@ def __init__(
                 rkernel_size = 1
                 rpadding = 0
 
-            conv_type = Conv[Conv.CONV, dimensions]
+            conv_type = Conv[Conv.CONV, self.dimensions]
             self.residual = conv_type(in_channels, out_channels, rkernel_size, strides, rpadding, bias=bias)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:

monai/networks/blocks/fcn.py

@@ -155,7 +155,7 @@ def __init__(
 
         if self.upsample_mode == "transpose":
             self.up_conv = UpSample(
-                dimensions=2,
+                spatial_dims=2,
                 in_channels=self.out_channels,
                 scale_factor=2,
                 mode="deconv",
@@ -236,7 +236,7 @@ def __init__(
         )
 
         self.init_proj = Convolution(
-            dimensions=2,
+            spatial_dims=2,
             in_channels=in_channels,
             out_channels=3,
             kernel_size=1,

monai/networks/blocks/localnet_block.py

@@ -29,7 +29,7 @@ def get_conv_block(
     norm: Optional[Union[Tuple, str]] = "BATCH",
 ) -> nn.Module:
     padding = same_padding(kernel_size)
-    return Convolution(
+    mod: nn.Module = Convolution(
         spatial_dims,
         in_channels,
         out_channels,
@@ -40,6 +40,7 @@ def get_conv_block(
         conv_only=False,
         padding=padding,
     )
+    return mod
 
 
 def get_conv_layer(
@@ -49,7 +50,7 @@ def get_conv_layer(
     kernel_size: Union[Sequence[int], int] = 3,
 ) -> nn.Module:
     padding = same_padding(kernel_size)
-    return Convolution(
+    mod: nn.Module = Convolution(
         spatial_dims,
         in_channels,
         out_channels,
@@ -58,15 +59,16 @@ def get_conv_layer(
         conv_only=True,
         padding=padding,
     )
+    return mod
 
 
 def get_deconv_block(
     spatial_dims: int,
     in_channels: int,
     out_channels: int,
 ) -> nn.Module:
-    return Convolution(
-        dimensions=spatial_dims,
+    mod: nn.Module = Convolution(
+        spatial_dims=spatial_dims,
         in_channels=in_channels,
         out_channels=out_channels,
         strides=2,
@@ -77,6 +79,7 @@ def get_deconv_block(
         padding=1,
         output_padding=1,
     )
+    return mod
 
 
 class ResidualBlock(nn.Module):