Class ResidualBlock

modules/parallel_wavegan/layers/residual_block.py:39–129

Residual block module in WaveNet.

class ResidualBlock(torch.nn.Module):
    """Residual block module in WaveNet."""

    def __init__(self,
                 kernel_size=3,
                 residual_channels=64,
                 gate_channels=128,
                 skip_channels=64,
                 aux_channels=80,
                 dropout=0.0,
                 dilation=1,
                 bias=True,
                 use_causal_conv=False
                 ):
        """Initialize ResidualBlock module.

        Args:
            kernel_size (int): Kernel size of dilation convolution layer.
            residual_channels (int): Number of channels for residual connection.
            skip_channels (int): Number of channels for skip connection.
            aux_channels (int): Local conditioning channels i.e. auxiliary input dimension.
            dropout (float): Dropout probability.
            dilation (int): Dilation factor.
            bias (bool): Whether to add bias parameter in convolution layers.
            use_causal_conv (bool): Whether to use use_causal_conv or non-use_causal_conv convolution.

        """
        super(ResidualBlock, self).__init__()
        self.dropout = dropout
        # no future time stamps available
        if use_causal_conv:
            padding = (kernel_size - 1) * dilation
        else:
            assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
            padding = (kernel_size - 1) // 2 * dilation
        self.use_causal_conv = use_causal_conv

        # dilation conv
        self.conv = Conv1d(residual_channels, gate_channels, kernel_size,
                           padding=padding, dilation=dilation, bias=bias)

        # local conditioning
        if aux_channels > 0:
            self.conv1x1_aux = Conv1d1x1(aux_channels, gate_channels, bias=False)
        else:
            self.conv1x1_aux = None

        # conv output is split into two groups
        gate_out_channels = gate_channels // 2
        self.conv1x1_out = Conv1d1x1(gate_out_channels, residual_channels, bias=bias)
        self.conv1x1_skip = Conv1d1x1(gate_out_channels, skip_channels, bias=bias)

    def forward(self, x, c):
        """Calculate forward propagation.

        Args:
            x (Tensor): Input tensor (B, residual_channels, T).
            c (Tensor): Local conditioning auxiliary tensor (B, aux_channels, T).