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src/model/mlp.py

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+import torch
+from torch import nn
+
+
+class MultiLayerPerceptron(nn.Module):
+    """Multi-Layer Perceptron with residual links."""
+    def __init__(self, input_dim, hidden_dim) -> None:
+        super().__init__()
+        self.fc1 = nn.Conv2d(
+            in_channels=input_dim,  out_channels=hidden_dim, kernel_size=(1, 1), bias=True)
+        self.fc2 = nn.Conv2d(
+            in_channels=hidden_dim, out_channels=hidden_dim, kernel_size=(1, 1), bias=True)
+        self.act = nn.ReLU()
+        self.drop = nn.Dropout(p=0.15)
+
+    def forward(self, input_data: torch.Tensor) -> torch.Tensor:
+        """Feed forward of MLP.
+        Args:
+            input_data (torch.Tensor): input data with shape [B, D, N]
+        Returns:
+            torch.Tensor: latent repr
+        """
+        hidden = self.fc2(self.drop(self.act(self.fc1(input_data))))      # MLP
+        hidden = hidden + input_data                           # residual
+        return hidden