Method forward

complexnn.py:187–214

(self,inputs)

        nn.init.constant_(self.imag_conv.bias,0.)

    def forward(self,inputs):

        if isinstance(inputs, torch.Tensor):
            real,imag = torch.chunk(inputs, 2, self.complex_axis)
        elif isinstance(inputs, tuple) or isinstance(inputs, list):
            real = inputs[0]
            imag = inputs[1]
        if self.complex_axis == 0:
            real = self.real_conv(inputs)
            imag = self.imag_conv(inputs)
            real2real,imag2real = torch.chunk(real,2, self.complex_axis)
            real2imag,imag2imag = torch.chunk(imag,2, self.complex_axis)

        else:
            if isinstance(inputs, torch.Tensor):
                real,imag = torch.chunk(inputs, 2, self.complex_axis)

            real2real = self.real_conv(real,)
            imag2imag = self.imag_conv(imag,)

            real2imag = self.imag_conv(real)
            imag2real = self.real_conv(imag)

        real = real2real - imag2imag
        imag = real2imag + imag2real
        out = torch.cat([real, imag], self.complex_axis)

        return out

# Source: https://github.com/ChihebTrabelsi/deep_complex_networks/tree/pytorch 
# from https://github.com/IMLHF/SE_DCUNet/blob/f28bf1661121c8901ad38149ea827693f1830715/models/layers/complexnn.py#L55