ultralytics 8.2.70 Segment Anything Model 2 (SAM 2) (#14813)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/nn/modules/transformer.py

@@ -174,18 +174,20 @@ class MLPBlock(nn.Module):
 class MLP(nn.Module):
     """Implements a simple multi-layer perceptron (also called FFN)."""
 
-    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+    def __init__(self, input_dim, hidden_dim, output_dim, num_layers, act=nn.ReLU, sigmoid=False):
         """Initialize the MLP with specified input, hidden, output dimensions and number of layers."""
         super().__init__()
         self.num_layers = num_layers
         h = [hidden_dim] * (num_layers - 1)
         self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
+        self.sigmoid = sigmoid
+        self.act = act()
 
     def forward(self, x):
         """Forward pass for the entire MLP."""
         for i, layer in enumerate(self.layers):
-            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
-        return x
+            x = self.act(layer(x)) if i < self.num_layers - 1 else layer(x)
+        return x.sigmoid() if self.sigmoid else x
 
 
 class LayerNorm2d(nn.Module):