Upload ResidualStack.py

ResidualStack.py

@@ -0,0 +1,51 @@
+# Copyright 2019 Tomoki Hayashi
+# MIT License (https://opensource.org/licenses/MIT)
+# Adapted by Florian Lux 2021
+
+
+import torch
+
+
+class ResidualStack(torch.nn.Module):
+
+    def __init__(self, kernel_size=3, channels=32, dilation=1, bias=True, nonlinear_activation="LeakyReLU", nonlinear_activation_params={"negative_slope": 0.2},
+                 pad="ReflectionPad1d", pad_params={}, ):
+        """
+        Initialize ResidualStack module.
+
+        Args:
+            kernel_size (int): Kernel size of dilation convolution layer.
+            channels (int): Number of channels of convolution layers.
+            dilation (int): Dilation factor.
+            bias (bool): Whether to add bias parameter in convolution layers.
+            nonlinear_activation (str): Activation function module name.
+            nonlinear_activation_params (dict): Hyperparameters for activation function.
+            pad (str): Padding function module name before dilated convolution layer.
+            pad_params (dict): Hyperparameters for padding function.
+
+        """
+        super(ResidualStack, self).__init__()
+
+        # defile residual stack part
+        assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
+        self.stack = torch.nn.Sequential(getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
+                                         getattr(torch.nn, pad)((kernel_size - 1) // 2 * dilation, **pad_params),
+                                         torch.nn.Conv1d(channels, channels, kernel_size, dilation=dilation, bias=bias),
+                                         getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
+                                         torch.nn.Conv1d(channels, channels, 1, bias=bias), )
+
+        # defile extra layer for skip connection
+        self.skip_layer = torch.nn.Conv1d(channels, channels, 1, bias=bias)
+
+    def forward(self, c):
+        """
+        Calculate forward propagation.
+
+        Args:
+            c (Tensor): Input tensor (B, channels, T).
+
+        Returns:
+            Tensor: Output tensor (B, chennels, T).
+
+        """
+        return self.stack(c) + self.skip_layer(c)