model.py 文件源码

def encode(self, x):
        x = x.unsqueeze(1)
        x = self.conv(x)

        # At this point x should have shape
        # (batch, channels, time, freq)
        x = torch.transpose(x, 1, 2).contiguous()

        # Reshape x to be (batch, time, freq * channels)
        # for the RNN
        b, t, f, c = x.size()
        x = x.view((b, t, f * c))

        x, h = self.rnn(x)

        if self.rnn.bidirectional:
            half = x.size()[-1] // 2
            x = x[:, :, :half] + x[:, :, half:]

        return x