[model] refactor tcn and ds_tcn share the same base class (#35)

kws/model/tcn.py

@@ -20,15 +20,43 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 
-class CnnBlock(nn.Module):
+class Block(nn.Module):
     def __init__(self,
                  channel: int,
                  kernel_size: int,
                  dilation: int,
                  dropout: float = 0.1):
         super().__init__()
-        # The CNN used here is causal convolution
         self.padding = (kernel_size - 1) * dilation
+
+    def forward(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
+        """
+        Args:
+            x(torch.Tensor): Input tensor (B, D, T)
+        Returns:
+            torch.Tensor(B, D, T)
+        """
+        # The CNN used here is causal convolution
+        if cache is None:
+            y = F.pad(x, (self.padding, 0), value=0.0)
+        else:
+            y = torch.cat((cache, x), dim=2)
+        assert y.size(2) > self.padding
+        new_cache = y[:, :, -self.padding:]
+
+        # self.cnn is defined in the subclass of Block
+        y = self.cnn(y)
+        y = y + x  # residual connection
+        return y, new_cache
+
+
+class CnnBlock(Block):
+    def __init__(self,
+                 channel: int,
+                 kernel_size: int,
+                 dilation: int,
+                 dropout: float = 0.1):
+        super().__init__(channel, kernel_size, dilation, dropout)
         self.cnn = nn.Sequential(
             nn.Conv1d(channel,
                       channel,
@@ -40,26 +68,8 @@ class CnnBlock(nn.Module):
             nn.Dropout(dropout),
         )
 
-    def forward(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
-        """
-        Args:
-            x(torch.Tensor): Input tensor (B, D, T)
-        Returns:
-            torch.Tensor(B, D, T)
-        """
-        if cache is None:
-            y = F.pad(x, (self.padding, 0), value=0.0)
-        else:
-            y = torch.cat((cache, x), dim=2)
-        assert y.size(2) > self.padding
-        new_cache = y[:, :, -self.padding:]
 
-        y = self.cnn(y)
+class DsCnnBlock(Block):
-        y = y + x  # residual connection
-        return y, new_cache
-
-
-class DsCnnBlock(nn.Module):
     """ Depthwise Separable Convolution
     """
     def __init__(self,
@@ -67,9 +77,7 @@ class DsCnnBlock(nn.Module):
                  kernel_size: int,
                  dilation: int,
                  dropout: float = 0.1):
-        super().__init__()
+        super().__init__(channel, kernel_size, dilation, dropout)
-        # The CNN used here is causal convolution
-        self.padding = (kernel_size - 1) * dilation
         self.cnn = nn.Sequential(
             nn.Conv1d(channel,
                       channel,
@@ -85,24 +93,6 @@ class DsCnnBlock(nn.Module):
             nn.Dropout(dropout),
         )
 
-    def forward(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
-        """
-        Args:
-            x(torch.Tensor): Input tensor (B, D, T)
-        Returns:
-            torch.Tensor(B, D, T)
-        """
-        if cache is None:
-            y = F.pad(x, (self.padding, 0), value=0.0)
-        else:
-            y = torch.cat((cache, x), dim=2)
-        assert y.size(2) > self.padding
-        new_cache = y[:, :, -self.padding:]
-
-        y = self.cnn(y)
-        y = y + x  # residual connection
-        return y, new_cache
-
 
 class TCN(nn.Module):
     def __init__(self,