def forward()

in src/models.py [0:0]

• 18 lines of code
• 3 McCabe index (conditional complexity)

    def forward(self, mono, view):
        '''
        :param mono: the input signal as a B x 1 x T tensor
        :param view: the receiver/transmitter position as a B x 7 x T tensor
        :return: out: the binaural output produced by the network
                 intermediate: a two-channel audio signal obtained from the output of each intermediate layer
                               as a list of B x 2 x T tensors
        '''
        warped = self.warper(mono, view)
        x = self.input(warped)
        _, skips = self.hyperconv_wavenet(x, view)
        # collect output and skips after each layer
        x = []
        for k in range(len(skips), 0, -1):
            y = th.mean(th.stack(skips[:k], dim=0), dim=0)
            y = self.output_net[k-1](y)
            x += [y]
        x += [warped]
        return {"output": x[0], "intermediate": x[1:]}