Datawhale AI 夏令营之基于术语词典干预的机器翻译挑战赛

导入必须的库

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.nn.utils import clip_grad_norm_
from torchtext.data.metrics import bleu_score
from torch.utils.data import Dataset, DataLoader
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from typing import List, Tuple
import jieba
import random
from torch.nn.utils.rnn import pad_sequence
import sacrebleu
import time
import math

数据预处理

# 定义tokenizer
en_tokenizer = get_tokenizer('spacy', language='en_core_web_trf')
zh_tokenizer = lambda x: list(jieba.cut(x))  # 使用jieba分词
# 读取数据函数
def read_data(file_path: str) -> List[str]:
    with open(file_path, 'r', encoding='utf-8') as f:
        return [line.strip() for line in f]
 
# 数据预处理函数
def preprocess_data(en_data: List[str], zh_data: List[str]) -> List[Tuple[List[str], List[str]]]:
    processed_data = []
    for en, zh in zip(en_data, zh_data):
        en_tokens = en_tokenizer(en.lower())[:MAX_LENGTH]
        zh_tokens = zh_tokenizer(zh)[:MAX_LENGTH]
        if en_tokens and zh_tokens:  # 确保两个序列都不为空
            processed_data.append((en_tokens, zh_tokens))
    return processed_data
 
# 构建词汇表
def build_vocab(data: List[Tuple[List[str], List[str]]]):
    en_vocab = build_vocab_from_iterator(
        (en for en, _ in data),
        specials=['<unk>', '<pad>', '<bos>', '<eos>']
    )
    zh_vocab = build_vocab_from_iterator(
        (zh for _, zh in data),
        specials=['<unk>', '<pad>', '<bos>', '<eos>']
    )
    en_vocab.set_default_index(en_vocab['<unk>'])
    zh_vocab.set_default_index(zh_vocab['<unk>'])
    return en_vocab, zh_vocab
 
 
class TranslationDataset(Dataset):
    def __init__(self, data: List[Tuple[List[str], List[str]]], en_vocab, zh_vocab):
        self.data = data
        self.en_vocab = en_vocab
        self.zh_vocab = zh_vocab
 
    def __len__(self):
        return len(self.data)
 
    def __getitem__(self, idx):
        en, zh = self.data[idx]
        en_indices = [self.en_vocab['<bos>']] + [self.en_vocab[token] for token in en] + [self.en_vocab['<eos>']]
        zh_indices = [self.zh_vocab['<bos>']] + [self.zh_vocab[token] for token in zh] + [self.zh_vocab['<eos>']]
        return en_indices, zh_indices
def collate_fn(batch):
    en_batch, zh_batch = [], []
    for en_item, zh_item in batch:
        if en_item and zh_item:  # 确保两个序列都不为空
            # print("都不为空")
            en_batch.append(torch.tensor(en_item))
            zh_batch.append(torch.tensor(zh_item))
        else:
            print("存在为空")
    if not en_batch or not zh_batch:  # 如果整个批次为空，返回空张量
        return torch.tensor([]), torch.tensor([])
    
    # src_sequences = [item[0] for item in batch]
    # trg_sequences = [item[1] for item in batch]
    
    en_batch = nn.utils.rnn.pad_sequence(en_batch, batch_first=True, padding_value=en_vocab['<pad>'])
    zh_batch = nn.utils.rnn.pad_sequence(zh_batch, batch_first=True, padding_value=zh_vocab['<pad>'])
 
    # en_batch = pad_sequence(en_batch, batch_first=True, padding_value=en_vocab['<pad>'])
    # zh_batch = pad_sequence(zh_batch, batch_first=True, padding_value=zh_vocab['<pad>'])
    
    return en_batch, zh_batch
# 数据加载函数
def load_data(train_path: str, dev_en_path: str, dev_zh_path: str, test_en_path: str):
    # 读取训练数据
    train_data = read_data(train_path)
    train_en, train_zh = zip(*(line.split('\t') for line in train_data))
    
    # 读取开发集和测试集
    dev_en = read_data(dev_en_path)
    dev_zh = read_data(dev_zh_path)
    test_en = read_data(test_en_path)
 
    # 预处理数据
    train_processed = preprocess_data(train_en, train_zh)
    dev_processed = preprocess_data(dev_en, dev_zh)
    test_processed = [(en_tokenizer(en.lower())[:MAX_LENGTH], []) for en in test_en if en.strip()]
 
    # 构建词汇表
    global en_vocab, zh_vocab
    en_vocab, zh_vocab = build_vocab(train_processed)
 
    # 创建数据集
    train_dataset = TranslationDataset(train_processed, en_vocab, zh_vocab)
    dev_dataset = TranslationDataset(dev_processed, en_vocab, zh_vocab)
    test_dataset = TranslationDataset(test_processed, en_vocab, zh_vocab)
    
    from torch.utils.data import Subset
 
    # 假设你有10000个样本，你只想用前1000个样本进行测试
    indices = list(range(N))
    train_dataset = Subset(train_dataset, indices)
 
    # 创建数据加载器
    train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn, drop_last=True)
    dev_loader = DataLoader(dev_dataset, batch_size=BATCH_SIZE, collate_fn=collate_fn, drop_last=True)
    test_loader = DataLoader(test_dataset, batch_size=1, collate_fn=collate_fn, drop_last=True)
 
    return train_loader, dev_loader, test_loader, en_vocab, zh_vocab

模型构建

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)
 
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)
 
    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)
 
class TransformerModel(nn.Module):
    def __init__(self, src_vocab, tgt_vocab, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout):
        super(TransformerModel, self).__init__()
        self.transformer = nn.Transformer(d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout)
        self.src_embedding = nn.Embedding(len(src_vocab), d_model)
        self.tgt_embedding = nn.Embedding(len(tgt_vocab), d_model)
        self.positional_encoding = PositionalEncoding(d_model, dropout)
        self.fc_out = nn.Linear(d_model, len(tgt_vocab))
        self.src_vocab = src_vocab
        self.tgt_vocab = tgt_vocab
        self.d_model = d_model
 
    def forward(self, src, tgt):
        # 调整src和tgt的维度
        src = src.transpose(0, 1)  # (seq_len, batch_size)
        tgt = tgt.transpose(0, 1)  # (seq_len, batch_size)
 
        src_mask = self.transformer.generate_square_subsequent_mask(src.size(0)).to(src.device)
        tgt_mask = self.transformer.generate_square_subsequent_mask(tgt.size(0)).to(tgt.device)
 
        src_padding_mask = (src == self.src_vocab['<pad>']).transpose(0, 1)
        tgt_padding_mask = (tgt == self.tgt_vocab['<pad>']).transpose(0, 1)
 
        src_embedded = self.positional_encoding(self.src_embedding(src) * math.sqrt(self.d_model))
        tgt_embedded = self.positional_encoding(self.tgt_embedding(tgt) * math.sqrt(self.d_model))
 
        output = self.transformer(src_embedded, tgt_embedded,
                                  src_mask, tgt_mask, None, src_padding_mask, tgt_padding_mask, src_padding_mask)
        return self.fc_out(output).transpose(0, 1)
 
def initialize_model(src_vocab, tgt_vocab, d_model=512, nhead=8, num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=2048, dropout=0.1):
    model = TransformerModel(src_vocab, tgt_vocab, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout)
    return model

开始训练

# 定义优化器
def initialize_optimizer(model, learning_rate=0.001):
    return optim.Adam(model.parameters(), lr=learning_rate)
 
# 运行时间
def epoch_time(start_time, end_time):
    elapsed_time = end_time - start_time
    elapsed_mins = int(elapsed_time / 60)
    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
    return elapsed_mins, elapsed_secs
 
def train(model, iterator, optimizer, criterion, clip):
    model.train()
    epoch_loss = 0
    
    for i, batch in enumerate(iterator):
        src, tgt = batch
        if src.numel() == 0 or tgt.numel() == 0:
            continue
        
        src, tgt = src.to(DEVICE), tgt.to(DEVICE)
        
        optimizer.zero_grad()
        output = model(src, tgt[:, :-1])
        
        output_dim = output.shape[-1]
        output = output.contiguous().view(-1, output_dim)
        tgt = tgt[:, 1:].contiguous().view(-1)
        
        loss = criterion(output, tgt)
        loss.backward()
        
        clip_grad_norm_(model.parameters(), clip)
        optimizer.step()
        
        epoch_loss += loss.item()
 
    return epoch_loss / len(iterator)
 
def evaluate(model, iterator, criterion):
    model.eval()
    epoch_loss = 0
    with torch.no_grad():
        for i, batch in enumerate(iterator):
            src, tgt = batch
            if src.numel() == 0 or tgt.numel() == 0:
                continue
            
            src, tgt = src.to(DEVICE), tgt.to(DEVICE)
            
            output = model(src, tgt[:, :-1])
            
            output_dim = output.shape[-1]
            output = output.contiguous().view(-1, output_dim)
            tgt = tgt[:, 1:].contiguous().view(-1)
            
            loss = criterion(output, tgt)
            epoch_loss += loss.item()
        
    return epoch_loss / len(iterator)
 
def translate_sentence(src_indexes, src_vocab, tgt_vocab, model, device, max_length=50):
    model.eval()
    
    src_tensor = src_indexes.unsqueeze(0).to(device)  # 添加批次维度
    
    with torch.no_grad():
        encoder_outputs = model.transformer.encoder(model.positional_encoding(model.src_embedding(src_tensor) * math.sqrt(model.d_model)))
 
    trg_indexes = [tgt_vocab['<bos>']]
    for i in range(max_length):
        trg_tensor = torch.LongTensor(trg_indexes).unsqueeze(0).to(device)
        
        with torch.no_grad():
            output = model(src_tensor, trg_tensor)
        
        pred_token = output.argmax(2)[:, -1].item()
        trg_indexes.append(pred_token)
        
        if pred_token == tgt_vocab['<eos>']:
            break
    
    trg_tokens = [tgt_vocab.get_itos()[i] for i in trg_indexes]
    return trg_tokens[1:-1]  # 移除<bos>和<eos>标记
 
def calculate_bleu(dev_loader, src_vocab, tgt_vocab, model, device):
    model.eval()
    translations = []
    references = []
    
    with torch.no_grad():
        for src, tgt in dev_loader:
            src = src.to(device)
            for sentence in src:
                translated = translate_sentence(sentence, src_vocab, tgt_vocab, model, device)
                translations.append(' '.join(translated))
            
            for reference in tgt:
                ref_tokens = [tgt_vocab.get_itos()[idx] for idx in reference if idx not in [tgt_vocab['<bos>'], tgt_vocab['<eos>'], tgt_vocab['<pad>']]]
                references.append([' '.join(ref_tokens)])
    
    bleu = sacrebleu.corpus_bleu(translations, references)
    return bleu.score
 
# 主训练循环
def train_model(model, train_iterator, valid_iterator, optimizer, criterion, N_EPOCHS=10, CLIP=1, save_path = '../model/best-model_transformer.pt'):
    best_valid_loss = float('inf')
    
    for epoch in range(N_EPOCHS):
        start_time = time.time()
        
        #print(f"Starting Epoch {epoch + 1}")
        train_loss = train(model, train_iterator, optimizer, criterion, CLIP)
        valid_loss = evaluate(model, valid_iterator, criterion)
        
        end_time = time.time()
        epoch_mins, epoch_secs = epoch_time(start_time, end_time)
        
        if valid_loss < best_valid_loss:
            best_valid_loss = valid_loss
            torch.save(model.state_dict(), save_path)
        
        print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')
        print(f'\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')
        print(f'\t Val. Loss: {valid_loss:.3f} |  Val. PPL: {math.exp(valid_loss):7.3f}')
    
 
 
# 定义常量
MAX_LENGTH = 100    # 最大句子长度
BATCH_SIZE = 32
DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
N = 148363  # 采样训练集的数量，最多148363  
 
train_path = '../dataset/train.txt'
dev_en_path = '../dataset/dev_en.txt'
dev_zh_path = '../dataset/dev_zh.txt'
test_en_path = '../dataset/test_en.txt'
 
train_loader, dev_loader, test_loader, en_vocab, zh_vocab = load_data(
    train_path, dev_en_path, dev_zh_path, test_en_path
)
 
 
print(f"英语词汇表大小: {len(en_vocab)}")
print(f"中文词汇表大小: {len(zh_vocab)}")
print(f"训练集大小: {len(train_loader.dataset)}")
print(f"开发集大小: {len(dev_loader.dataset)}")
print(f"测试集大小: {len(test_loader.dataset)}")
 
 
# 主函数    
if __name__ == '__main__':
    
    # 模型参数
    D_MODEL = 256
    NHEAD = 8
    NUM_ENCODER_LAYERS = 3
    NUM_DECODER_LAYERS = 3
    DIM_FEEDFORWARD = 512
    DROPOUT = 0.1
    
    N_EPOCHS = 5
    CLIP = 1
 
    # 初始化模型
    model = initialize_model(en_vocab, zh_vocab, D_MODEL, NHEAD, NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, DIM_FEEDFORWARD, DROPOUT).to(DEVICE)
    print(f'The model has {sum(p.numel() for p in model.parameters() if p.requires_grad):,} trainable parameters')
 
    # 定义损失函数
    criterion = nn.CrossEntropyLoss(ignore_index=zh_vocab['<pad>'])
    # 初始化优化器
    optimizer = optim.Adam(model.parameters(), lr=0.0001, betas=(0.9, 0.98), eps=1e-9)
 
    # 训练模型
    save_path = '../model/best-model_transformer.pt'
    train_model(model, train_loader, dev_loader, optimizer, criterion, N_EPOCHS, CLIP, save_path=save_path)
 
    print(f"训练完成！模型已保存到：{save_path}")
 
 
 

在测试集运行模型
 

# 加载最佳模型
model.load_state_dict(torch.load('../model/best-model_transformer.pt'))
 
save_dir = '../results/submit_task3.txt'
with open(save_dir, 'w') as f:
    translated_sentences = []
    for batch in test_loader:  # 遍历所有数据
        src, _ = batch
        src = src.to(DEVICE)
        translated = translate_sentence(src[0], en_vocab, zh_vocab, model, DEVICE)  #翻译结果
        results = "".join(translated)
        f.write(results + '\n')  # 将结果写入文件
    print(f"翻译完成，结果已保存到{save_dir}")