linearized_convolution.py 文件源码

def incremental_forward(self, input):
        """Forward convolution one time step at a time.

        This function maintains an internal state to buffer signal and accepts
        a single frame as input. If the input order changes between time steps,
        call reorder_incremental_state. To apply to fresh inputs, call
        clear_incremental_state.
        """
        # reshape weight
        weight = self._get_linearized_weight()
        kw = self.kernel_size[0]

        bsz = input.size(0)  # input: bsz x len x dim
        if kw > 1:
            input = input.data
            if self.input_buffer is None:
                self.input_buffer = input.new(bsz, kw, input.size(2))
                self.input_buffer.zero_()
            else:
                # shift buffer
                self.input_buffer[:, :-1, :] = self.input_buffer[:, 1:, :].clone()
            # append next input
            self.input_buffer[:, -1, :] = input[:, -1, :]
            input = torch.autograd.Variable(self.input_buffer, volatile=True)
        output = F.linear(input.view(bsz, -1), weight, self.bias)
        return output.view(bsz, 1, -1)