pack_ padded_ Sequence is to record the word of each sentence according to the principle of batch first, and change it into a tensor of indefinite length, which is convenient to calculate the loss function

pad_ packed_ Sequence is to add a pack_ padded_ The structure generated by sequence is transformed into the original structure, which is a constant length tensor

The content of test.txt

As they sat in a nice coffee shop, 
he was too nervous to say anything and she felt uncomfortable. 
Suddenly, he asked the waiter, 
"Could you please give me some salt?I'd like to put it in my coffee."

See the following code for details

import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import wordfreq

vocab = {}
token_id = 1
lengths = []

#Read files and generate dictionaries
with open('test.txt', 'r') as f:
    lines=f.readlines()
    for line in lines:
        tokens = wordfreq.tokenize(line.strip(), 'en')
        lengths.append(len(tokens))
        #Add each word to the vocab and save the corresponding index at the same time
        for word in tokens:
            if word not in vocab:
                vocab[word] = token_id
                token_id += 1

x = np.zeros((len(lengths), max(lengths)))
l_no = 0
#Converting words to numbers
with open('test.txt', 'r') as f:
    lines = f.readlines()
    for line in lines:
        tokens = wordfreq.tokenize(line.strip(), 'en')
        for i in range(len(tokens)):
            x[l_no, i] = vocab[tokens[i]]
        l_no += 1

x=torch.Tensor(x)
x = Variable(x)
print(x)
'''
tensor([[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  0.,  0.,  0.,  0.,  0.,  0.],
        [ 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,  0.,  0.,  0.],
        [20.,  9., 21., 22., 23.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
        [24., 25., 26., 27., 28., 29., 30., 31., 32., 13., 33., 34.,  4.,  7.]])
'''
lengths = torch.Tensor(lengths)
print(lengths)#tensor([ 8., 11.,  5., 14.])

_, idx_sort = torch.sort(torch.Tensor(lengths), dim=0, descending=True)
print(_) #tensor([14., 11.,  8.,  5.])
print(idx_sort)#tensor([3, 1, 0, 2])

lengths = list(lengths[idx_sort])#Fetch elements by subscript [tensor(14.), tensor(11.), tensor(8.), tensor(5.)]
t = x.index_select(0, idx_sort)#Fetch elements by subscript
print(t)
'''
tensor([[24., 25., 26., 27., 28., 29., 30., 31., 32., 13., 33., 34.,  4.,  7.],
        [ 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,  0.,  0.,  0.],
        [ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  0.,  0.,  0.,  0.,  0.,  0.],
        [20.,  9., 21., 22., 23.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])
'''
x_packed = nn.utils.rnn.pack_padded_sequence(input=t, lengths=lengths, batch_first=True)
print(x_packed)
'''
PackedSequence(data=tensor([24.,  9.,  1., 20., 25., 10.,  2.,  9., 26., 11.,  3., 21., 27., 12.,
         4., 22., 28., 13.,  5., 23., 29., 14.,  6., 30., 15.,  7., 31., 16.,
         8., 32., 17., 13., 18., 33., 19., 34.,  4.,  7.]), batch_sizes=tensor([4, 4, 4, 4, 4, 3, 3, 3, 2, 2, 2, 1, 1, 1]))
'''


x_padded = nn.utils.rnn.pad_packed_sequence(x_packed, batch_first=True)#x_padded是tuple
print(x_padded)
'''
(tensor([[24., 25., 26., 27., 28., 29., 30., 31., 32., 13., 33., 34.,  4.,  7.],
        [ 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,  0.,  0.,  0.],
        [ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  0.,  0.,  0.,  0.,  0.,  0.],
        [20.,  9., 21., 22., 23.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]]), tensor([14, 11,  8,  5]))
'''
_, idx_unsort = torch.sort(idx_sort)
output = x_padded[0].index_select(0, idx_unsort)
print(output)
'''
tensor([[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  0.,  0.,  0.,  0.,  0.,  0.],
        [ 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,  0.,  0.,  0.],
        [20.,  9., 21., 22., 23.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
        [24., 25., 26., 27., 28., 29., 30., 31., 32., 13., 33., 34.,  4.,  7.]])
'''