fakedynamic.py 文件源码

def forward(self, x, lengths):
        batch_size = x.size(0)
        max_len = max(lengths)

        emb = Variable(torch.from_numpy(
            self.initial_embeddings.take(x.numpy(), 0)),
            volatile=not self.training)

        for t in range(max_len):
            indices = []
            for i, l in enumerate(lengths):
                if l >= max(lengths) - t:
                    indices.append(i)

            # Build batch.
            dynamic_batch_size = len(indices)
            inp = Variable(torch.FloatTensor(dynamic_batch_size, self.word_embedding_dim), volatile=not self.training)
            h = Variable(torch.FloatTensor(dynamic_batch_size, self.model_dim), volatile=not self.training)
            output = self.rnn(inp, h)

        hn = output
        h = F.relu(self.l0(F.dropout(hn.squeeze(), 0.5, self.training)))
        h = F.relu(self.l1(F.dropout(h, 0.5, self.training)))
        y = F.log_softmax(h)
        return y