def forward()

in svoice/models/swave.py [0:0]

• 29 lines of code
• 6 McCabe index (conditional complexity)

    def forward(self, input):
        batch_size, _, d1, d2 = input.shape
        output = input
        output_all = []
        for i in range(self.num_layers):
            row_input = output.permute(0, 3, 2, 1).contiguous().view(
                batch_size * d2, d1, -1)
            row_output = self.rows_grnn[i](row_input)
            row_output = row_output.view(
                batch_size, d2, d1, -1).permute(0, 3, 2, 1).contiguous()
            row_output = self.rows_normalization[i](row_output)
            # apply a skip connection
            if self.training:
                output = output + row_output
            else:
                output += row_output

            col_input = output.permute(0, 2, 3, 1).contiguous().view(
                batch_size * d1, d2, -1)
            col_output = self.cols_grnn[i](col_input)
            col_output = col_output.view(
                batch_size, d1, d2, -1).permute(0, 3, 1, 2).contiguous()
            col_output = self.cols_normalization[i](col_output).contiguous()
            # apply a skip connection
            if self.training:
                output = output + col_output
            else:
                output += col_output

            output_i = self.output(output)
            if self.training or i == (self.num_layers - 1):
                output_all.append(output_i)
        return output_all