urnn.py 文件源码

def _forward_rnn(cell, input_, length, hx):
        max_time = input_.size(0)
        output = []
        for time in range(max_time):
            h_next = cell(input_=input_[time], hx=hx)
            # mask = (time < length).float().unsqueeze(1).expand_as(h_next)
            # h_next = h_next*mask + hx*(1 - mask)
            output.append(h_next)
        output = torch.stack(output, 0)
        return output, h_next