Project-MONAI · VenkateswarluNagineni · Jul 16, 2026 · Jul 16, 2026 · Jul 16, 2026 · Jul 21, 2026
@@ -343,6 +343,11 @@ Layers
 
 .. currentmodule:: monai.networks.layers
 
+`LayerNormNd`
+~~~~~~~~~~~~~
+.. autoclass:: LayerNormNd
+    :members:
+
 `ChannelPad`
 ~~~~~~~~~~~~
 .. autoclass:: ChannelPad
@@ -467,6 +472,31 @@ Nets
 .. autoclass:: AHNet
   :members:
 
+`ConvNeXt`
+~~~~~~~~~~
+.. autoclass:: ConvNeXt
+  :members:
+
+`ConvNeXtTiny`
+~~~~~~~~~~~~~~
+.. autoclass:: ConvNeXtTiny
+
+`ConvNeXtSmall`
+~~~~~~~~~~~~~~~
+.. autoclass:: ConvNeXtSmall
+
+`ConvNeXtBase`
+~~~~~~~~~~~~~~
+.. autoclass:: ConvNeXtBase
+
+`ConvNeXtLarge`
+~~~~~~~~~~~~~~~
+.. autoclass:: ConvNeXtLarge
+
+`ConvNeXtXLarge`
+~~~~~~~~~~~~~~~~
+.. autoclass:: ConvNeXtXLarge
+
 `DenseNet`
 ~~~~~~~~~~
 .. autoclass:: DenseNet

@@ -17,6 +17,7 @@
 from .factories import Act, Conv, Dropout, LayerFactory, Norm, Pad, Pool, RelPosEmbedding, split_args
 from .filtering import BilateralFilter, PHLFilter, TrainableBilateralFilter, TrainableJointBilateralFilter
 from .gmm import GaussianMixtureModel
+from .layer_norm_nd import LayerNormNd
 from .simplelayers import (
     LLTM,
     ApplyFilter,

@@ -0,0 +1,46 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 __future__ import annotations
+
+import torch
+import torch.nn as nn
+
+
+class LayerNormNd(nn.Module):
+    """
+    Layer normalization over the channel dimension of a channels-first tensor.
+
+    `torch.nn.LayerNorm` normalizes over the trailing dimensions, so it expects a channels-last layout
+    such as ``(batch, *spatial, channel)``. Convolutional feature maps in MONAI are channels-first,
+    ``(batch, channel, *spatial)``, and this module normalizes those over the channel dimension only,
+    for any number of spatial dimensions.
+
+    Args:
+        num_channels: number of channels of the input, i.e. the size of dimension 1.
+        spatial_dims: number of spatial dimensions of the input image.
+        eps: value added to the denominator for numerical stability.
+    """
+
+    def __init__(self, num_channels: int, spatial_dims: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        # broadcast the affine parameters against (batch, channel, *spatial); precomputed so that the
+        # module is scriptable without inspecting the rank of the input at runtime.
+        self.param_shape = [1, num_channels] + [1] * spatial_dims
-    def __init__(self, num_channels: int, spatial_dims: int, eps: float = 1e-6) -> None:
-        super().__init__()
-        self.eps = eps
-        self.weight = nn.Parameter(torch.ones(num_channels))
-        self.bias = nn.Parameter(torch.zeros(num_channels))
-        # broadcast the affine parameters against (batch, channel, *spatial); precomputed so that the
-        # module is scriptable without inspecting the rank of the input at runtime.
-        self.param_shape = [1, num_channels] + [1] * spatial_dims
+    def __init__(self, num_channels: int, spatial_dims: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        if num_channels <= 0:
+            raise ValueError("num_channels must be positive.")
+        if spatial_dims < 0:
+            raise ValueError("spatial_dims must be non-negative.")
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        # broadcast the affine parameters against (batch, channel, *spatial); precomputed so that the
+        # module is scriptable without inspecting the rank of the input at runtime.
+        self.param_shape = [1, num_channels] + [1] * spatial_dims
-    def __init__(self, num_channels: int, spatial_dims: int, eps: float = 1e-6) -> None:
-        super().__init__()
-        self.eps = eps
-        self.weight = nn.Parameter(torch.ones(num_channels))
-        self.bias = nn.Parameter(torch.zeros(num_channels))
-        # broadcast the affine parameters against (batch, channel, *spatial); precomputed so that the
-        # module is scriptable without inspecting the rank of the input at runtime.
-        self.param_shape = [1, num_channels] + [1] * spatial_dims
+    def __init__(self, num_channels: int, spatial_dims: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        if num_channels <= 0:
+            raise ValueError("num_channels must be positive.")
+        if spatial_dims < 0:
+            raise ValueError("spatial_dims must be non-negative.")
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        # broadcast the affine parameters against (batch, channel, *spatial); precomputed so that the
+        # module is scriptable without inspecting the rank of the input at runtime.
+        self.param_shape = [1, num_channels] + [1] * spatial_dims
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        mean = x.mean(dim=1, keepdim=True)
+        var = (x - mean).pow(2).mean(dim=1, keepdim=True)
+        x = (x - mean) / torch.sqrt(var + self.eps)
+        return self.weight.view(self.param_shape) * x + self.bias.view(self.param_shape)
@@ -19,6 +19,25 @@
 from .basic_unetplusplus import BasicUNetPlusPlus, BasicUnetPlusPlus, BasicunetPlusPlus, basicunetplusplus
 from .classifier import Classifier, Critic, Discriminator
 from .controlnet import ControlNet
+from .convnext import (
+    ConvNeXt,
+    Convnext,
+    Convnext_base,
+    Convnext_large,
+    Convnext_small,
+    Convnext_tiny,
+    Convnext_xlarge,
+    ConvNeXtBase,
+    ConvNeXtLarge,
+    ConvNeXtSmall,
+    ConvNeXtTiny,
+    ConvNeXtXLarge,
+    convnext_base,
+    convnext_large,
+    convnext_small,
+    convnext_tiny,
+    convnext_xlarge,
+)
 from .daf3d import DAF3D
 from .densenet import (
     DenseNet,