c13a558767cbc19b612968eb4d01a1f26d5df688,onmt/Models.py,NMTModel,_fix_enc_hidden,#NMTModel#Any#,132

Before Change

        //  the encoder hidden is  (layers*directions) x batch x dim
        //  we need to convert it to layers x batch x (directions*dim)
        if self.encoder.num_directions == 2:
            return h.view(h.size(0) // 2, 2, h.size(1), h.size(2)) \
                    .transpose(1, 2).contiguous() \
                    .view(h.size(0) // 2, h.size(1), h.size(2) * 2)
        else:
            return h

    def forward(self, input):

After Change

        //  the encoder hidden is  (layers*directions) x batch x dim
        //  we need to convert it to layers x batch x (directions*dim)
        if self.encoder.num_directions == 2:
            h = torch.cat([h[0:h.size(0):2], h[1:h.size(0):2]], 2)
        return h

    def forward(self, input):
        src = input[0]

Instances