cuda_functional.py 文件源码

def forward(self, input, c0=None, return_hidden=True):
        assert input.dim() == 3 # (len, batch, n_in)
        dir_ = 2 if self.bidirectional else 1
        if c0 is None:
            zeros = Variable(input.data.new(
                input.size(1), self.n_out*dir_
            ).zero_())
            c0 = [ zeros for i in range(self.depth) ]
        else:
            assert c0.dim() == 3    # (depth, batch, n_out*dir_)
            c0 = [ x.squeeze(0) for x in c0.chunk(self.depth, 0) ]

        prevx = input
        lstc = []
        for i, rnn in enumerate(self.rnn_lst):
            h, c = rnn(prevx, c0[i])
            prevx = h
            lstc.append(c)

        if return_hidden:
            return prevx, torch.stack(lstc)
        else:
            return prevx