7994-enhance-mlpblock

.github/workflows/pythonapp.yml

@@ -99,6 +99,7 @@ jobs:
       name: Install itk pre-release (Linux only)
       run: |
         python -m pip install --pre -U itk
+        find  /opt/hostedtoolcache/* -maxdepth 0 ! -name 'Python' -exec rm -rf {} \;
     - name: Install the dependencies
       run: |
         python -m pip install --user --upgrade pip wheel

monai/networks/blocks/mlp.py

@@ -11,12 +11,15 @@
 
 from __future__ import annotations
 
+from typing import Union
+
 import torch.nn as nn
 
 from monai.networks.layers import get_act_layer
+from monai.networks.layers.factories import split_args
 from monai.utils import look_up_option
 
-SUPPORTED_DROPOUT_MODE = {"vit", "swin"}
+SUPPORTED_DROPOUT_MODE = {"vit", "swin", "vista3d"}
 
 
 class MLPBlock(nn.Module):
@@ -39,7 +42,7 @@ def __init__(
                 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
-
+                "vista3d" mode does not use dropout.
 
         """
 
@@ -48,15 +51,24 @@ def __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) if act != "GEGLU" else nn.Linear(hidden_size, mlp_dim * 2)
+        act_name, _ = split_args(act)
+        self.linear1 = nn.Linear(hidden_size, mlp_dim) if act_name != "GEGLU" else nn.Linear(hidden_size, mlp_dim * 2)
         self.linear2 = nn.Linear(mlp_dim, hidden_size)
         self.fn = get_act_layer(act)
-        self.drop1 = nn.Dropout(dropout_rate)
+        # Use Union[nn.Dropout, nn.Identity] for type annotations
+        self.drop1: Union[nn.Dropout, nn.Identity]
+        self.drop2: Union[nn.Dropout, nn.Identity]
+
         dropout_opt = look_up_option(dropout_mode, SUPPORTED_DROPOUT_MODE)
         if dropout_opt == "vit":
+            self.drop1 = nn.Dropout(dropout_rate)
             self.drop2 = nn.Dropout(dropout_rate)
         elif dropout_opt == "swin":
+            self.drop1 = nn.Dropout(dropout_rate)
             self.drop2 = self.drop1
+        elif dropout_opt == "vista3d":
+            self.drop1 = nn.Identity()
+            self.drop2 = nn.Identity()
         else:
             raise ValueError(f"dropout_mode should be one of {SUPPORTED_DROPOUT_MODE}")
 

tests/test_mlp.py

@@ -15,10 +15,12 @@
 
 import numpy as np
 import torch
+import torch.nn as nn
 from parameterized import parameterized
 
 from monai.networks import eval_mode
 from monai.networks.blocks.mlp import MLPBlock
+from monai.networks.layers.factories import split_args
 
 TEST_CASE_MLP = []
 for dropout_rate in np.linspace(0, 1, 4):
@@ -31,6 +33,14 @@
             ]
             TEST_CASE_MLP.append(test_case)
 
+# test different activation layers
+TEST_CASE_ACT = []
+for act in ["GELU", "GEGLU", ("GEGLU", {})]:  # type: ignore
+    TEST_CASE_ACT.append([{"hidden_size": 128, "mlp_dim": 0, "act": act}, (2, 512, 128), (2, 512, 128)])
+
+# test different dropout modes
+TEST_CASE_DROP = [["vit", nn.Dropout], ["swin", nn.Dropout], ["vista3d", nn.Identity]]
+
 
 class TestMLPBlock(unittest.TestCase):
 
@@ -45,6 +55,24 @@ def test_ill_arg(self):
         with self.assertRaises(ValueError):
             MLPBlock(hidden_size=128, mlp_dim=512, dropout_rate=5.0)
 
+    @parameterized.expand(TEST_CASE_ACT)
+    def test_act(self, input_param, input_shape, expected_shape):
+        net = MLPBlock(**input_param)
+        with eval_mode(net):
+            result = net(torch.randn(input_shape))
+            self.assertEqual(result.shape, expected_shape)
+        act_name, _ = split_args(input_param["act"])
+        if act_name == "GEGLU":
+            self.assertEqual(net.linear1.in_features, net.linear1.out_features // 2)
+        else:
+            self.assertEqual(net.linear1.in_features, net.linear1.out_features)
+
+    @parameterized.expand(TEST_CASE_DROP)
+    def test_dropout_mode(self, dropout_mode, dropout_layer):
+        net = MLPBlock(hidden_size=128, mlp_dim=512, dropout_rate=0.1, dropout_mode=dropout_mode)
+        self.assertTrue(isinstance(net.drop1, dropout_layer))
+        self.assertTrue(isinstance(net.drop2, dropout_layer))
+
 
 if __name__ == "__main__":
     unittest.main()