sorted_unsorted.py

### pytorch version
def __rnn_pack_scan(self, sentence_embs, input_lengths, rnn, h0=None):
        '''
        sort the length and unsorted

        :param sentence_embs: batch x seq_len x emb_size
        :param input_lengths: batch
        :param rnn: RNN
        :param h0: (num_layers * num_directions, batch, hidden_size)
        :return:
        '''
        # sort the input
        len_sorted, indices = torch.sort(input_lengths, 0, descending=True)
        sentence_embs_sorted = torch.index_select(sentence_embs, 0, indices)
        # run
        packed = torch.nn.utils.rnn.pack_padded_sequence(sentence_embs_sorted, len_sorted.tolist(), batch_first=True)
        outputs, hidden = rnn(packed, h0) # hn = num_layers * num_directions, batch, hidden_size
                                            # output =  (batch, seq, feature)
        outputs, output_lengths = torch.nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True)
        # unsorted
        _, idx_unsorted = torch.sort(indices, 0)
        outputs_unsorted = torch.index_select(outputs,0,idx_unsorted)
        hidden_unsorted = torch.index_select(hidden,1,idx_unsorted)

        return outputs_unsorted, hidden_unsorted  # (max_sen_len+1) x b x 2h'''

sorted_unsorted_np.py

import numpy as np

a=np.random.random((4,6))
lens= np.array([1,3,4,2])

idx_sorted = np.argsort(-lens) # in descreasing order
idx_unsorted = np.argsort(idx_sorted) # in increasing order

print(a[idx_sorted][idx_unsorted]==a)