extending the mlp module

monai/networks/blocks/mlp.py

@@ -9,34 +9,61 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import Tuple, Union
+
 import torch.nn as nn
 
+from monai.networks.layers import get_act_layer
+from monai.utils import look_up_option
+
+SUPPORTED_DROPOUT_MODE = {"vit", "swin"}
+
 
 class MLPBlock(nn.Module):
     """
     A multi-layer perceptron block, based on: "Dosovitskiy et al.,
     An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>"
     """
 
-    def __init__(self, hidden_size: int, mlp_dim: int, dropout_rate: float = 0.0) -> None:
+    def __init__(
+        self,
+        hidden_size: int,
+        mlp_dim: int,
+        dropout_rate: float = 0.0,
+        act: Union[Tuple, str] = "GELU",
+        dropout_mode="vit",
+    ) -> None:
         """
         Args:
             hidden_size: dimension of hidden layer.
-            mlp_dim: dimension of feedforward layer.
+            mlp_dim: dimension of feedforward layer. If 0, `hidden_size` will be used.
             dropout_rate: faction of the input units to drop.
+            act: activation type and arguments. Defaults to GELU.
+            dropout_mode: dropout mode, can be "vit" or "swin".
+                "vit" mode uses two dropout instances as implemented in
+                https://github.com/google-research/vision_transformer/blob/main/vit_jax/models.py#L87
+                "swin" corresponds to one instance as implemented in
+                https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_mlp.py#L23
+
 
         """
 
         super().__init__()
 
         if not (0 <= dropout_rate <= 1):
             raise ValueError("dropout_rate should be between 0 and 1.")
-
+        mlp_dim = mlp_dim or hidden_size
         self.linear1 = nn.Linear(hidden_size, mlp_dim)
         self.linear2 = nn.Linear(mlp_dim, hidden_size)
-        self.fn = nn.GELU()
+        self.fn = get_act_layer(act)
         self.drop1 = nn.Dropout(dropout_rate)
-        self.drop2 = nn.Dropout(dropout_rate)
+        dropout_opt = look_up_option(dropout_mode, SUPPORTED_DROPOUT_MODE)
+        if dropout_opt == "vit":
+            self.drop2 = nn.Dropout(dropout_rate)
+        elif dropout_opt == "swin":
+            self.drop2 = self.drop1
+        else:
+            raise ValueError(f"dropout_mode should be one of {SUPPORTED_DROPOUT_MODE}")
 
     def forward(self, x):
         x = self.fn(self.linear1(x))

tests/test_mlp.py

@@ -21,7 +21,7 @@
 TEST_CASE_MLP = []
 for dropout_rate in np.linspace(0, 1, 4):
     for hidden_size in [128, 256, 512, 768]:
-        for mlp_dim in [512, 1028, 2048, 3072]:
+        for mlp_dim in [0, 1028, 2048, 3072]:
 
             test_case = [
                 {"hidden_size": hidden_size, "mlp_dim": mlp_dim, "dropout_rate": dropout_rate},