Pytorch+LSTM+Encoder+Decoder实现Seq2Seq模型

# !/usr/bin/env Python3
# -*- coding: utf-8 -*-
# @version: v1.0
# @Author   : Meng Li
# @contact: 925762221@qq.com
# @FILE     : torch_seq2seq.py
# @Time     : 2022/6/8 11:11
# @Software : PyCharm
# @site:
# @Description : 将Seq2Seq网络采用编码器和解码器两个类进行融合
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchsummary
from torch.utils.data import Dataset, DataLoader
import numpy as np
import osdevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')class my_dataset(Dataset):def __init__(self, enc_input, dec_input, dec_output):super().__init__()self.enc_input = enc_inputself.dec_input = dec_inputself.dec_output = dec_outputdef __getitem__(self, index):return self.enc_input[index], self.dec_input[index], self.dec_output[index]def __len__(self):return self.enc_input.size(0)class Encoder(nn.Module):def __init__(self, in_features, hidden_size):super().__init__()self.in_features = in_featuresself.hidden_size = hidden_sizeself.encoder = nn.LSTM(input_size=in_features, hidden_size=hidden_size, dropout=0.5, num_layers=1)  # encoderdef forward(self, enc_input):seq_len, batch_size, embedding_size = enc_input.size()h_0 = torch.rand(1, batch_size, self.hidden_size)c_0 = torch.rand(1, batch_size, self.hidden_size)# en_ht:[num_layers * num_directions,Batch_size,hidden_size]encode_output, (encode_ht, decode_ht) = self.encoder(enc_input, (h_0, c_0))return encode_output, (encode_ht, decode_ht)class Decoder(nn.Module):def __init__(self, in_features, hidden_size):super().__init__()self.in_features = in_featuresself.hidden_size = hidden_sizeself.crition = nn.CrossEntropyLoss()self.fc = nn.Linear(hidden_size, in_features)self.decoder = nn.LSTM(input_size=in_features, hidden_size=hidden_size, dropout=0.5, num_layers=1)  # encoderdef forward(self, enc_output, dec_input):(h0, c0) = enc_output# en_ht:[num_layers * num_directions,Batch_size,hidden_size]de_output, (_, _) = self.decoder(dec_input, (h0, c0))return de_outputclass Seq2seq(nn.Module):def __init__(self, encoder, decoder, in_features, hidden_size):super().__init__()self.encoder = encoderself.decoder = decoderself.in_features = in_featuresself.hidden_size = hidden_sizeself.fc = nn.Linear(hidden_size, in_features)self.crition = nn.CrossEntropyLoss()def forward(self, enc_input, dec_input, dec_output):enc_input = enc_input.permute(1, 0, 2)  # [seq_len,Batch_size,embedding_size]dec_input = dec_input.permute(1, 0, 2)  # [seq_len,Batch_size,embedding_size]# output:[seq_len,Batch_size,hidden_size]_, (ht, ct) = self.encoder(enc_input)  # en_ht:[num_layers * num_directions,Batch_size,hidden_size]de_output = self.decoder((ht, ct), dec_input)  # de_output:[seq_len,Batch_size,in_features]output = self.fc(de_output)output = output.permute(1, 0, 2)loss = 0for i in range(len(output)):  # 对seq的每一个输出进行二分类损失计算loss += self.crition(output[i], dec_output[i])return output, lossdef make_data(seq_data):enc_input_all, dec_input_all, dec_output_all = [], [], []vocab = [i for i in "SE?abcdefghijklmnopqrstuvwxyz上下人低国女孩王男白色高黑"]word2idx = {j: i for i, j in enumerate(vocab)}V = np.max([len(j) for i in seq_data for j in i])  # 求最长元素的长度for seq in seq_data:for i in range(2):seq[i] = seq[i] + '?' * (V - len(seq[i]))  # 'man??', 'women'enc_input = [word2idx[n] for n in (seq[0] + 'E')]dec_input = [word2idx[i] for i in [i for i in len(enc_input) * '?']]dec_output = [word2idx[n] for n in (seq[1] + 'E')]enc_input_all.append(np.eye(len(vocab))[enc_input])dec_input_all.append(np.eye(len(vocab))[dec_input])dec_output_all.append(dec_output)  # not one-hot# make tensorreturn torch.Tensor(enc_input_all), torch.Tensor(dec_input_all), torch.LongTensor(dec_output_all)def translate(word):vocab = [i for i in "SE?abcdefghijklmnopqrstuvwxyz上下人低国女孩王男白色高黑"]idx2word = {i: j for i, j in enumerate(vocab)}V = 5x, y, z = make_data([[word, "?" * V]])if not os.path.exists("translate.pt"):train()net = torch.load("translate.pt")pre, loss = net(x, y, z)pre = torch.argmax(pre, 2)[0]pre_word = [idx2word[i] for i in pre.numpy()]pre_word = "".join([i.replace("?", "") for i in pre_word])print(word, "->  ", pre_word[:pre_word.index('E')])def train():vocab = [i for i in "SE?abcdefghijklmnopqrstuvwxyz上下人低国女孩王男白色高黑"]word2idx = {j: i for i, j in enumerate(vocab)}idx2word = {i: j for i, j in enumerate(vocab)}seq_data = [['man', '男人'], ['black', '黑色'], ['king', '国王'], ['girl', '女孩'], ['up', '上'],['high', '高'], ['women', '女人'], ['white', '白色'], ['boy', '男孩'], ['down', '下'], ['low', '低'],['queen', '女王']]enc_input, dec_input, dec_output = make_data(seq_data)batch_size = 3in_features = len(vocab)hidden_size = 128train_data = my_dataset(enc_input, dec_input, dec_output)train_iter = DataLoader(train_data, batch_size, shuffle=True)encoder = Encoder(in_features, hidden_size)decoder = Decoder(in_features, hidden_size)net = Seq2seq(encoder, decoder, in_features, hidden_size)learning_rate = 0.001optimizer = optim.Adam(net.parameters(), lr=learning_rate)loss = 0for i in range(1000):for en_input, de_input, de_output in train_iter:output, loss = net(en_input, de_input, de_output)pre = torch.argmax(output, 2)optimizer.zero_grad()loss.backward()optimizer.step()if i % 100 == 0:print("step {0} loss {1}".format(i, loss))torch.save(net, "translate.pt")if __name__ == '__main__':before_test = ['man', 'black', 'king', 'girl', 'up', 'high', 'women', 'white', 'boy', 'down', 'low', 'queen','mman', 'woman'][translate(i) for i in before_test]# train()

仍然先上代码，接上一篇文章，这里将Seq2Seq模型个构建采用Encoder类和Decoder类融合起来

主要是为了后面的Attention作铺垫

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