model.py 文件源码

def forward(self, input, hidden, ctx, ctx_mask=None):
        """Propogate input through the layer."""
        h_0, c_0 = hidden
        h_1, c_1 = [], []
        for i, layer in enumerate(self.layers):
            if ctx_mask is not None:
                ctx_mask = torch.ByteTensor(
                    ctx_mask.data.cpu().numpy().astype(np.int32).tolist()
                ).cuda()
            output, (h_1_i, c_1_i) = layer(input, (h_0, c_0), ctx, ctx_mask)

            input = output

            if i != len(self.layers):
                input = self.dropout(input)

            h_1 += [h_1_i]
            c_1 += [c_1_i]

        h_1 = torch.stack(h_1)
        c_1 = torch.stack(c_1)

        return input, (h_1, c_1)