NLP-Pytorch-项目流程-案例（一）：文本摘要【Seq2Seq（BiLSTM-LSTM）+ Attention】【预测算法：GreedySearch、BeamSearch】

- 日理万妓 2023-10-03 15:17 5阅读 0赞

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5b-N6ICF44Gu5Lmx5aSq6YOO_size_12_color_FFFFFF_t_70_g_se_x_16]

## 一、数据预处理 ##

### 1、原始数据 ###

/data/raw\_data/服饰\_50k.json（共50000条数据）

{
    	"1": {
    		"title": "巴拉巴 拉 旗下 梦 多多 童装 男童 毛衫 冬季 中大童 毛衫 黑色",
    		"kb": {
    			"适用季节": "冬季",
    			"厚度": "适中",
    			"领型": "高领",
    			"适用年龄": "9-12岁",
    			"材质成分": "锦纶",
    			"图案": "其它",
    			"上市时间": "2018冬季",
    			"面料": "其它",
    			"风格": "休闲风",
    			"衣门襟": "套头",
    			"适用性别": "男",
    			"安全等级": "B类",
    			"毛线粗细": "普通毛线"
    		},
    		"ocr": "中国蓝，深土黄，健康安全，A门襟，黑色，衣袖，面料展，产品信息，领口，可水洗，细节展示，不宜暴晒，不可漂白，短拉链设计，简洁实用，吊牌价:239.00，适合季节:秋冬季，半开领设计，舒适亲肤，]面料构成，田属性说明，不可源自，合格证，不可干流",
    		"reference": "三合一混纺纱线制成，柔软亲肤，贴身穿也没有扎感。半开领的立领设计，在较凉的天气，保护脖颈，穿脱也更为方便。侧袖的拼接撞色设计，凸现个性，宝宝穿上更帅气。"
    	},
    	"2": {
    		"title": "土拨鼠 男款 户外运动 休闲 抓绒 开衫 黑色",
    		"kb": {
    			"适用人群": "男士",
    			"功能": "超轻",
    			"尺码": "XS",
    			"分类": "抓绒衣",
    			"适用场景": "徒步",
    			"品牌": "土拨鼠（Marmot）"
    		},
    		"ocr": "立领设计，拉链口袋，细节展示，产品展示，土拨鼠吊，前胸口袋，洗涤方式，能迅速吸收运动过程产生的汗水，快速干燥，舒适保暖，衣身两侧拉链口袋，立领设计商务休闲，舒适保暖，拉链采用YKK拉链制作，土拨鼠洗唛标展示，美观大，保护收纳物品，产品特，不易丢失，(以吊牌为准)，徒步、登山、旅行、露营、跑步、日常穿着、骑行，基本信息，收纳物品，前胸拉链口袋方便，大方，产地:越南，4759青鬼蓝，颜色:，展示，2975深海军蓝，深海军蓝，面料:聚苯，黑色",
    		"reference": "时尚的小高领设计，可以有效锁住勃颈处的温度，让寒风也无法侵袭。前胸处的口袋搭配上时尚的品牌logo，美观还实用，让你的穿搭造型更显品味和档次。采用100wt抓绒，穿起来更暖和。"
    	}
    }

### 2、数据预处理（process.py） ###

将文件“服饰\_50k.json”中的数据保存成以下格式数据：

短外套 女 春夏 新款 女装 复古 百搭 牛仔 外套 女 宽松 韩版 连帽 长袖 上衣 女 图色 图案 纯色 袖型 常规袖 风格 休闲风 衣门襟 拉链 适用年龄 25-29周岁 衣领材质 其它 类型 牛仔外套 流行元素 带帽 品牌 xzoo 材质 聚酯纤维 厚度 常规 版型 宽松型 衣长 常规款 袖长 长袖 组合形式 单件 领型 连帽 上市时间 2019年夏季 潮流 时尚 ， 3D 立体 裁剪 ， 肌理 舒适 面料 ， 无 牛仔 不 时尚 ， 打造 修身 S 曲线 ， 修身 显瘦 版型 ， 舒适 挺括 ， 精美 口袋 设计 ， L 精美 口袋 设计 ， L 精致 袖口 设计 ， 细节 展示 ， 时尚 翻领 设计 ， 细节 解析 ， 吸湿 透气 ， 分割线 裁剪 ， 时尚 大方 ， 肌理 时尚 ， 不 起球 ， 舒适 面料 ， 轻微 弹力 ， 修身 版型 设计 ， 舒适 自 在 ， 多条 ， 精细 剪裁 ， 抗皱 免烫 ， 直筒 样式 长袖 袖口 ， 时尚 大气 ， ， 时尚 百搭 ， 优雅 显瘦 ， 时尚 翻领 设计 ， 恰到好处 ， 时 ， 提升 了 外套 的 设计 感 和 立体感 的 亮点 ， 整体 的 曲线 ， 勾勒 纤细 线条 ， 显瘦 大方 ， 两侧 口袋 的 设计 ， 方便使用 的 同 ， 两侧 微 开叉 弧形 下摆 设计 ， 修饰<sep>气质 的 牛仔 外套 ， 修身 的 版型 设计 ， 勾勒 出 少女 娇美 曼妙 的 身姿 ， 经典 的 连帽 设计 ， 修饰 脸型 ， 清纯 的 蓝色 ， 显白 衬肤 ， 打造 知性 优雅 的 淑女风格 。

*  <sep>符号前面的内容是原始文件（是json文件中的title、kb、ocr字段合并后的文本）；
 *  <sep>符号后面的内容是Summary参考答案（json文件中的reference字段内容）；

# -*- coding: utf-8 -*-
    
    import sys
    import os
    import pathlib
    import json
    import jieba
    
    abs_path = pathlib.Path(__file__).parent.absolute()
    sys.path.append(sys.path.append(abs_path))
    
    
    def write_samples(lines, file_path, opt='w'):
        with open(file_path, opt, encoding='utf8') as file:
            for line in lines:
                file.write(line)
                file.write('\n')
    
    
    if __name__ == '__main__':
        samples = set()
        json_path = os.path.join(abs_path, './raw_data/服饰_50k.json')  # 原始数据地址
    
        with open(json_path, 'r', encoding='utf8') as file:
            json_objs = json.load(file)
    
        for json_obj in json_objs.values():
            # ----------------------- 处理样本文本信息(x) -----------------------
            title = json_obj['title'] + ' '  # 样本标题
            kb = dict(json_obj['kb']).items()  # 样本所有属性
            kb_merged = ''  # 将当前样本的所有属性合并为一段文本，用空格分隔
            for key, val in kb:
                kb_merged += key + ' ' + val + ' '
            ocr = ' '.join(list(jieba.cut(json_obj['ocr'])))  # 样本图谱OCR后的文本
            source_text = title + kb_merged + ocr  # 合并所有样本多模型信息成一个source
            # ----------------------- 处理样本标签信息(y) -----------------------
            reference = ' '.join(list(jieba.cut(json_obj['reference'])))
            # ----------------------- 合并样本文本信息(x)、样本标签信息(y)【二者用<sep>分开】 -----------------------
            sample = source_text + '<sep>' + reference
            samples.add(sample)
            print('len(samples) = ', len(samples))
    
        # ----------------------- 将处理后的所有样本写入 ./processed_data/samples.txt 文件 -----------------------
        write_samples(samples, os.path.join(abs_path, './processed_data/samples.txt'))
    
        # ----------------------- 将所有样本samples分隔成：训练集、验证集、测试集 -----------------------
        train, dev, test = [], [], []
        count = 0
        for sample in samples:
            count += 1
            if count <= 1000:  # Test set size.
                test.append(sample)
            elif count <= 6000:  # Dev set size.
                dev.append(sample)
            else:
                train.append(sample)
    
        write_samples(train, os.path.join(abs_path, './processed_data/train.txt'))
        write_samples(dev, os.path.join(abs_path, './processed_data/dev.txt'))
        write_samples(test, os.path.join(abs_path, './processed_data/test.txt'))

*  测试集（test.txt）分配1000个样本；
 *  验证集（dev.txt）分配5000个样本；
 *  剩下的分配给训练集（train.txt）；

### 3、文本对提取（pairs\_of\_src\_tgt.py） ###

从train.txt数据集(每行数据的格式为src++tgt)中提取训练数据集的(src, tgt)文本数据对

# -*- coding: utf-8 -*-
    '''
    @Description: 用于提取(source、target)文本对
    '''
    
    import sys
    import pathlib
    from typing import Callable
    from utils import simple_tokenizer
    
    abs_path = pathlib.Path(__file__).parent.absolute()
    sys.path.append(sys.path.append(abs_path))
    
    
    class PairsOfSrcTgt(object):
        """
        The class represents source-reference pairs.
        """
    
        def __init__(self, filename, tokenize: Callable = simple_tokenizer, max_src_len: int = None, max_tgt_len: int = None, truncate_src: bool = False, truncate_tgt: bool = False):
            print("Reading dataset %s..." % filename, end=' ', flush=True)
            self.filename = filename
            self.pairs = []
    
            with open(filename, 'rt', encoding='utf-8') as f:
                for i, line in enumerate(f):
                    # Split the source and reference by the <sep> tag.
                    pair = line.strip().split('<sep>')
                    if len(pair) != 2:
                        print("Line %d of %s is malformed." % (i, filename))
                        print(line)
                        continue
                    src = tokenize(pair[0])
                    if max_src_len and len(src) > max_src_len:
                        if truncate_src:
                            src = src[:max_src_len]
                        else:
                            continue
                    tgt = tokenize(pair[1])
                    if max_tgt_len and len(tgt) > max_tgt_len:
                        if truncate_tgt:
                            tgt = tgt[:max_tgt_len]
                        else:
                            continue
                    self.pairs.append((src, tgt))
            print("%d pairs." % len(self.pairs))

### 4、构建字典（build\_vocab.py） ###

# -*- coding: utf-8 -*-
    '''
    @Description: 构建词典(vocb)
    '''
    
    from collections import Counter
    from utils import count_words
    import config
    import numpy as np
    
    
    class BuildVocab(object):
        def __init__(self, pairs: list = [], embed_file: str = None):
            """
            Build the vocabulary for the data set.
    
            Args:
                pairs：[(src01,tgr01),(src02,tgr02)...]
                embed_file (str, optional): The file path of the pre-trained embedding word vector. Defaults to None.
            Returns:
                vocab00000.Vocab: The vocab object.
            """
            self.vocab = Vocab()
            word_counts = Counter()  # word frequency
            count_words(word_counts, [src + tgr for src, tgr in pairs])
            # Filter the vocabulary by keeping only the top k tokens in terms of word frequncy in the data set, where k is the maximum vocab size set in "config.py".
            for word, count in word_counts.most_common(config.max_vocab_size):
                self.vocab.add_words([word])
            if embed_file is not None:
                count = self.vocab.load_embeddings(embed_file)
                print("%d pre-trained embeddings loaded." % count)
    
    
    class Vocab(object):
        PAD = 0
        SOS = 1
        EOS = 2
        UNK = 3
    
        def __init__(self):
            '''
            @Description: Define the vocabulary object.
            '''
            self.word2index = {
       
        '<PAD>': 0, '<SOS>': 1, '<EOS>': 2, '<UNK>': 3}
            self.index2word = {
       
        val: key for key, val in self.word2index.items()}
            self.word_count = Counter()
            self.embeddings = None
    
        def add_words(self, words):
            """Add a new token to the vocab and do mapping between word and index.
            Args:
                words (list): The list of tokens to be added.
            """
            for word in words:
                if word not in self.word2index:
                    self.word2index[word] = len(self.word2index)
                    self.index2word[len(self.word2index) - 1] = word
            self.word_count.update(words)  # 根据新添加的元素更新Counter()
    
        def __getitem__(self, item):
            if type(item) is int:
                return self.index2word.get(item)  # 根据id取对应的token
            else:
                return self.word2index.get(item, self.UNK)  # 根据token取对应的id，如果该token不在字典里，则默认为self.UNK
    
        def __len__(self):
            return len(self.index2word)
    
        def size(self):
            """Returns the total size of the vocabulary"""
            return len(self.index2word)
    
        def load_embeddings(self, embed_file_path: str, dtype=np.float32) -> int:
            """
            Load embedding word vector.
            Args:
                embed_file_path (str): The file path of word vector to load.
                dtype (numpy dtype, optional): Defaults to np.float32.
            Returns:
                int: Number of embedded tokens.
            """
            num_embeddings = 0
            vocab_size = len(self)
            with open(embed_file_path, 'rb') as f:
                for line in f:
                    line = line.split()
                    word = line[0].decode('utf-8')
                    idx = self.word2index.get(word)
                    if idx is not None:
                        vec = np.array(line[1:], dtype=dtype)
                        if self.embeddings is None:
                            n_dims = len(vec)
                            self.embeddings = np.random.normal(np.zeros((vocab_size, n_dims))).astype(dtype)
                            self.embeddings[self.PAD] = np.zeros(n_dims)
                        self.embeddings[idx] = vec
                        num_embeddings += 1
            return num_embeddings

### 5、数据批次化加载（sample\_dataset.py） ###

# -*- coding: utf-8 -*-
    '''
    @Description: Define the format of data used in the model.
    '''
    
    import sys
    import pathlib
    import torch
    from torch.utils.data import Dataset
    from utils import sort_batch_by_len, source2ids
    
    abs_path = pathlib.Path(__file__).parent.absolute()
    sys.path.append(sys.path.append(abs_path))
    
    
    class SampleDataset(Dataset):
        """
        The class represents a sample set for training.
        """
    
        def __init__(self, data_pairs, vocab):
            self.src_texts = [data_pair[0] for data_pair in data_pairs]
            # print("self.src_texts[:2]", self.src_texts[:2])
            self.tgt_texts = [data_pair[1] for data_pair in data_pairs]
            # print("self.tgt_texts[:2]", self.tgt_texts[:2])
            self.vocab = vocab
            self._len = len(data_pairs)  # Keep track of how many data points.
    
        def __getitem__(self, index):
            # print("self.src_texts[{0}] = {1}".format(index, self.src_texts[index]))
            src_ids, oovs = source2ids(self.src_texts[index], self.vocab)  # 将当前文本self.src_texts[index]转为ids，oovs为超出词典范围的词汇文本
            return {
       
        
                'x': [self.vocab.SOS] + src_ids + [self.vocab.EOS],
                'y': [self.vocab.SOS] + [self.vocab[i] for i in self.tgt_texts[index]] + [self.vocab.EOS],
                'x_len': len(self.src_texts[index]),
                'y_len': len(self.tgt_texts[index]),
                'oovs': oovs,
                'len_oovs': len(oovs)
            }
    
        def __len__(self):
            return self._len
    
    
    def collate_fn(batch):
        """Split data set into batches and do padding for each batch.
    
        Args:
            x_padded (Tensor): Padded source sequences.
            y_padded (Tensor): Padded reference sequences.
            x_len (int): Sequence length of the sources.
            y_len (int): Sequence length of the references.
            oovs (dict): Out-of-vocabulary tokens.
            len_oovs (int): Number of OOV tokens.
        """
    
        def padding(indice, max_length, pad_idx=0):
            pad_indice = [item + [pad_idx] * max(0, max_length - len(item)) for item in indice]
            return torch.tensor(pad_indice)
    
        data_batch = sort_batch_by_len(batch)
    
        x = data_batch["x"]
        x_max_length = max([len(t) for t in x])
        y = data_batch["y"]
        y_max_length = max([len(t) for t in y])
    
        oovs = data_batch["oovs"]
        len_oovs = torch.tensor(data_batch["len_oovs"])
    
        x_padded = padding(x, x_max_length)
        y_padded = padding(y, y_max_length)
    
        x_len = torch.tensor(data_batch["x_len"])
        y_len = torch.tensor(data_batch["y_len"])
    
        return x_padded, y_padded, x_len, y_len, oovs, len_oovs

## 二、模型构建 ##

# -*- coding: utf-8 -*-
    '''
    @Description: Define the model.
    '''
    
    import os
    import sys
    import pathlib
    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    import config
    
    abs_path = pathlib.Path(__file__).parent.absolute()
    sys.path.append(sys.path.append(abs_path))
    
    
    class Encoder(nn.Module):
        def __init__(self, vocab_size, embed_size, hidden_size, rnn_drop: float = 0):
            super(Encoder, self).__init__()
            self.embedding = nn.Embedding(vocab_size, embed_size)  # Embedding层【输入维度：vocab_size、输出维度：embed_size】
            self.hidden_size = hidden_size
            self.lstm = nn.LSTM(embed_size, hidden_size, bidirectional=True, dropout=rnn_drop, batch_first=True)  # LSTM层
    
        def forward(self, x):
            """
            Define forward propagation for the encoder.
            Args:
                x (Tensor): The input samples as shape (batch_size, seq_len).
            Returns:
                output (Tensor): The output of lstm with shape(batch_size, seq_len, 2 * hidden_size).
                hidden (tuple):  The hidden states of lstm (h_n, c_n). Each with shape (2, batch_size, hidden_size)
            """
            embedded = self.embedding(x)
            output, hidden = self.lstm(embedded)
            return output, hidden
    
    
    class Attention(nn.Module):
        def __init__(self, hidden_size):
            super(Attention, self).__init__()
            # Define feed-forward layers.
            self.Wh_Linear = nn.Linear(2 * hidden_size, 2 * hidden_size, bias=False)
            self.Ws_Linear = nn.Linear(2 * hidden_size, 2 * hidden_size)
            self.v_Linear = nn.Linear(2 * hidden_size, 1, bias=False)
    
        def forward(self, decoder_hidden_states, encoder_output, x_padding_masks):
            """
            Define forward propagation for the attention network.《论文：Get To The Point: Summarization with Pointer-Generator Networks》
            Args:
                decoder_hidden_states (tuple): The hidden states from lstm (h_n, c_n) in the decoder, each with shape (1, batch_size, hidden_size)
                encoder_output (Tensor): The output from the lstm in the decoder with shape (batch_size, seq_len, hidden_size).
                x_padding_masks (Tensor): The padding masks for the input sequences with shape (batch_size, seq_len).
            Returns:
                context_vector (Tensor): Dot products of attention weights and encoder hidden states. The shape is (batch_size, 2*hidden_size).
                attention_weights (Tensor): The shape is (batch_size, seq_length).
            """
            # ----------------------- 获取 Decoder端的 hidden state（Concatenate h and c to get s_t and expand the dim of s_t.） -----------------------
            h_dec, c_dec = decoder_hidden_states  # Decoder端是UniLSTM，合并 hidden、cell使其维度与Encoder端(BiLSTM)的hidden维度一致，都为 2*hidden_size
            s_t = torch.cat([h_dec, c_dec], dim=2)  # (1, batch_size, 2*hidden_size)
            s_t = s_t.transpose(0, 1)  # (batch_size, 1, 2*hidden_size)
            s_t = s_t.expand_as(encoder_output).contiguous()  # (batch_size, seq_length, 2*hidden_size)
            # ----------------------- 计算 Attention scores 【论文公式(1)：score = v×tanh(W_h×h_i + W_s×s_t)；其中v、W_h、W_s都是参数】-----------------------
            encoder_features = self.Wh_Linear(encoder_output.contiguous())  # W_h×h_i (batch_size, seq_length, 2*hidden_size)
            decoder_features = self.Ws_Linear(s_t)  # W_s×s_t (batch_size, seq_length, 2*hidden_size)
            att_inputs = encoder_features + decoder_features  # W_h×h_i + W_s×s_t (batch_size, seq_length, 2*hidden_size)
            score = self.v_Linear(torch.tanh(att_inputs))  # (batch_size, seq_length, 1)
            # ----------------------- 论文公式(2)：对 Attention scores 进行softmax操作，得到 Attention Weight-----------------------
            attention_weights = F.softmax(score, dim=1).squeeze(2)  # (batch_size, seq_length)
            attention_weights = attention_weights * x_padding_masks  # 删除Mask部分的权重
            # 排除Mask部分的权重后重新对Attention weights进行归一化操作
            normalization_factor = attention_weights.sum(1, keepdim=True)
            attention_weights = attention_weights / normalization_factor
            # ----------------------- 论文公式(3)：计算 Context vector-----------------------
            context_vector = torch.bmm(attention_weights.unsqueeze(1), encoder_output)  # (batch_size, 1, 2*hidden_size)
            context_vector = context_vector.squeeze(1)  # (batch_size, 2*hidden_size)
    
            return context_vector, attention_weights
    
    
    class Decoder(nn.Module):
        def __init__(self, vocab_size, embed_size, hidden_size, enc_hidden_size=None, is_cuda=False):
            super(Decoder, self).__init__()
            self.DEVICE = torch.device('cuda') if is_cuda else torch.device('cpu')
            self.embedding = nn.Embedding(vocab_size, embed_size)
            self.vocab_size = vocab_size
            self.hidden_size = hidden_size
    
            self.lstm = nn.LSTM(embed_size, hidden_size, batch_first=True)
    
            self.W1_Linear = nn.Linear(self.hidden_size * 3, self.hidden_size)
            self.W2_Linear = nn.Linear(self.hidden_size, vocab_size)
    
        def forward(self, decoder_input, decoder_hidden_states, encoder_output, context_vector):
            """Define forward propagation for the decoder.
    
            Args:
                decoder_input (Tensor): The input of the decoder x_t of shape (batch_size, 1).
                decoder_hidden_states (tuple): The hidden states(h_n, c_n) of the decoder from last time step. The shapes are (1, batch_size, hidden_size) for each.
                encoder_output (Tensor): The output from the encoder of shape (batch_size, seq_length, 2*hidden_size).
                context_vector (Tensor): The context vector from the attention network of shape (batch_size,2*hidden_size).
    
            Returns:
                p_vocab (Tensor): The vocabulary distribution of shape (batch_size, vocab_size).
                docoder_states (tuple):
                    The lstm states in the decoder.
                    The shapes are (1, batch_size, hidden_size) for each.
            """
    
            decoder_emb = self.embedding(decoder_input)
            decoder_output, decoder_hidden_states = self.lstm(decoder_emb, decoder_hidden_states)
    
            # concatenate context vector and decoder state (batch_size, 3*hidden_size)
            decoder_output = decoder_output.view(-1, config.hidden_size)
    
            # ----------------------- 论文公式(4)：P_vocab = softmax(V'×(V×[s_t,h^*]+b)+b')-----------------------
            concat_vector = torch.cat([decoder_output, context_vector], dim=-1)
            # calculate vocabulary distribution (batch_size, hidden_size)
            FF1_out = self.W1_Linear(concat_vector)
            # (batch_size, vocab_size)
            FF2_out = self.W2_Linear(FF1_out)
            # (batch_size, vocab_size)
            P_vocab = F.softmax(FF2_out, dim=1)
    
            return P_vocab, decoder_hidden_states
    
    
    class ReduceState(nn.Module):
        """
        Since the encoder has a bidirectional LSTM layer while the decoder has a unidirectional LSTM layer,
        we add this module to reduce the hidden states output by the encoder (merge two directions) before input the hidden states into the decoder.
        """
    
        def __init__(self):
            super(ReduceState, self).__init__()
    
        def forward(self, hidden):
            """
            The forward propagation of reduce state module.
            Args: hidden (tuple): Hidden states of encoder, each with shape (2, batch_size, hidden_size).
            Returns:
                tuple:
                    Reduced hidden states,
                    each with shape (1, batch_size, hidden_size).
            """
            h, c = hidden
            h_reduced = torch.sum(h, dim=0, keepdim=True)
            c_reduced = torch.sum(c, dim=0, keepdim=True)
            hidden = (h_reduced, c_reduced)
            return hidden
    
    
    class Seq2seq(nn.Module):
        def __init__(self, vocab):
            super(Seq2seq, self).__init__()
            self.DEVICE = torch.device("cuda" if config.is_cuda else "cpu")
            self.vocab = vocab  # 初始化 词典
            self.attention = Attention(config.hidden_size)  # 初始化 Attention
            self.encoder = Encoder(len(vocab), config.embed_size, config.hidden_size)  # 初始化 Encoder
            self.decoder = Decoder(len(vocab), config.embed_size, config.hidden_size)  # 初始化 Decoder
            self.reduce_state = ReduceState()  # 初始化 降维组件
    
        def load_model(self):
            if os.path.exists(config.encoder_saved_name):
                self.encoder = torch.load(config.encoder_saved_name, map_location='gpu' if config.is_cuda else 'cpu')
                self.decoder = torch.load(config.decoder_saved_name, map_location='gpu' if config.is_cuda else 'cpu')
                self.attention = torch.load(config.attention_saved_name, map_location='gpu' if config.is_cuda else 'cpu')
                self.reduce_state = torch.load(config.reduce_state_saved_name, map_location='gpu' if config.is_cuda else 'cpu')
    
        def forward(self, x, x_len, y, y_len, len_oovs, epoch_idx, batch_idx, is_train):  # x：序列化后的输入文本；y：序列化后的输出文本；oovs：超出自定义词典的词汇文本列表【各含有batch_size个样本】
            """
            Define the forward propagation for the seq2seq model.
    
            Args:
                x (Tensor): Input sequences as source with shape (batch_size, seq_len)
                x_len ([int): Sequence length of the current batch.
                y (Tensor): Input sequences as reference with shape (bacth_size, y_len)
                len_oovs (int): The number of out-of-vocabulary words in this sample.
                batch (int): The number of the current batch.
    
            Returns:
                batch_loss (Tensor): The average loss of the current batch.
            """
            print('\n************************************* epoch_idx = {0}；batch_idx = {1}： 向model喂入的数据 *************************************\n'.format(epoch_idx, batch_idx))
            print('batch_idx = ', batch_idx)
            print('x_len = ', x_len)
            print('x = ', x)
            print('y_len = ', y_len)  # y_len是在sample_dataset中手工计算的y的真实长度
            print('y.shape = ', y.shape, '; y.shape[1] = ', y.shape[1])  # 每一个batch的所有y.shape的每一个y的shape都相同，且等于该batch中长度最长的文本的长度。
            print('y = ', y)
            # ----------------------- 对输入的序列化样本进行处理，并生成本条样本的 padding_mask -----------------------
            print('\n************************************* 对输入的序列化样本进行处理，并生成本条样本的 padding_mask *************************************\n')
            oov_token = torch.full(x.shape, self.vocab.UNK).long().to(self.DEVICE)  # torch.full(size, fill_value)【Creates a tensor of size filled with fill_value.】
            x_copy = torch.where(x > len(self.vocab) - 1, oov_token, x)  # torch.where(condition, x, y)【对x中的每个元素进行三目运算，符合condition则取x里的值，否则取y里的值】
            x_padding_masks = torch.ne(x_copy, 0).byte().float()  # torch.ne(input, other) 【按元素判断input与other是否相等，如果不相等则为True,如果相等则为False（The second argument can be a number or a tensor whose shape is broadcastable with the first argument.）】
            print('\nx_padding_masks.shape = ', x_padding_masks.shape)
            print('x_padding_masks = \n', x_padding_masks)
            # ----------------------- 将序列化后的本条样本的x_copy作为输入喂给Encoder -----------------------
            encoder_output, encoder_hidden_states = self.encoder(x_copy)
            print('encoder_output.shape = ', encoder_output.shape)
    
            # ----------------------- 将Encoder的隐层向量encoder_states进行降维, 将降维后的结果作为Decoder的初始化隐层向量（因为Encoder用的是BiLSTM, Decoder用的是UniLSTM） -----------------------
            decoder_hidden_states = self.reduce_state(encoder_hidden_states)
    
            # ----------------------- 计算预测当前样本的目标summary中的每一个词汇的loss -----------------------
            step_losses = []  # 用于存放当前batch所有样本的目标summary的每一个词汇的loss
            for i in range(y.shape[1] - 1):  # for i in range(y_len - 1)，此y_len是每一个样本目标summary的真实长度
                print('\n--------------------------------- is_train = {0}; epoch_idx = {1}; batch_idx = {2}, 当前预测时间步 i = {3} ---------------------------------\n'.format(is_train, epoch_idx, batch_idx, i))
                decoder_input_i = y[:, i]  # x_i：特征值【将每个样本的第i个id作为第i+1个时间步的输入】
                decoder_target_i = y[:, i + 1]  # y_i：目标值【每个样本的第i+1个id作为目标值】
                print('decoder_input_i.shape = ', decoder_input_i.shape, '; decoder_input_i = ', decoder_input_i, '; decoder_input_i.unsqueeze(1).shape = ', decoder_input_i.unsqueeze(1).shape, '\ndecoder_input_i.unsqueeze(1) = \n', decoder_input_i.unsqueeze(1))
                print('decoder_target_i.shape = ', decoder_target_i.shape, '; decoder_target_i = ', decoder_input_i, '; decoder_target_i.unsqueeze(1).shape = ', decoder_target_i.unsqueeze(1).shape, '\ndecoder_target_i.unsqueeze(1) = \n', decoder_target_i.unsqueeze(1))
                # ----------------------- 通过Attention机制计算得出Context Vector以及Attention Weight -----------------------
                context_vector, attention_weights = self.attention(decoder_hidden_states, encoder_output, x_padding_masks)
                # Get vocab distribution and hidden states from the decoder.
                # ----------------------- 论文公式(4)：通过Decoder计算得出第i+1个的预测值的概率分布-----------------------
                p_vocab, decoder_hidden_states = self.decoder(decoder_input_i.unsqueeze(1), decoder_hidden_states, encoder_output, context_vector)  # p_vocab.shape =  torch.Size([3, 20004]) ----decoder_hidden_states[0].shape =  torch.Size([1, 4, 512]) ----decoder_hidden_states[1].shape =  torch.Size([1, 4, 512])
                print('\np_vocab.shape = ', p_vocab.shape, '----decoder_hidden_states[0].shape = ', decoder_hidden_states[0].shape, '----decoder_hidden_states[1].shape = ', decoder_hidden_states[1].shape)
                # ----------------------- 论文公式(5)：概率分布 p_vocab([batch_size, vocab_size]) 表示的是当前样本在词汇表中所有词汇的概率，根据真实目标值的索引值decoder_target_i获取该索引值所获取的概率分布值 -----------------------
                target_probs = torch.gather(p_vocab, 1, decoder_target_i.unsqueeze(1))  # torch.Size([3, 1])；torch.gather(input, dim, index) → Tensor【在input的dim维度，提取索引为index的值】
                print('target_probs.shape = ', target_probs.shape, '; \ntarget_probs = ', target_probs)
                target_probs = target_probs.squeeze(1)  # torch.Size([3]) 表示本batch中所有3个样本在本epoch预测中真实值所得到的概率预测值
                print('squeeze之后：target_probs.shape = ', target_probs.shape, '; target_probs = ', target_probs)
                # ----------------------- 论文公式(6)：loss_t = -logP(w^*_t)【config.eps的作用是为了方式-log(0)】-----------------------
                loss = -torch.log(target_probs + config.eps)
                print('\nepoch({0})-batch_idx({1})：预测y序列文本中第({2})个时间步的token的损失值: loss.shape = {3}; loss = {4} '.format(epoch_idx, batch_idx, i, loss.shape, loss))
                # ----------------------- 通过mask消除padding处的影响(如果for循环用的是各个样本真实的长度参数y_len,则无需用mask来消除padding的影响) -----------------------
                mask = torch.ne(decoder_target_i, 0).byte().float()  # torch.ne(input, other) 【按元素判断input与other是否相等，如果不相等则为True,如果相等则为False
                print('\nmask.shape = ', mask.shape, '; mask = ', mask)
                loss = loss * mask  # 元素对应相乘
                print('经过mask后：loss.shape = ', loss.shape, '; loss = ', loss)
                step_losses.append(loss)
    
            print('\nlen(step_losses) =', len(step_losses), '; step_losses[0].shape = ', step_losses[0].shape)  # len(step_losses) = 54;  step_losses[0].shape =  torch.Size([3])
            stack_losses = torch.stack(step_losses, 1)  # torch.stack(tensors, dim)-->torch.Size([3, 54])：对序列数据内部的张量进行扩维拼接，指定维度由程序员选择、大小是生成后数据的维度区间。
            print('stack_losses.shape = ', stack_losses.shape, '; \nstack_losses = \n', stack_losses)
            sum_losses = torch.sum(stack_losses, 1)
            print('sum_losses.shape = ', sum_losses.shape, '; sum_losses = ', sum_losses)  # sum_losses.shape =  torch.Size([3]) ; sum_losses =  tensor([514.7693, 534.7229, 455.4084], grad_fn=<SumBackward1>)代表当前batch里的3个样本各自的loss
            # get the non-padded length of each sequence in the batch
            seq_len_mask = torch.ne(y, 0).byte().float()
            print('seq_len_mask.shape = ', seq_len_mask.shape)
            print('seq_len_mask = \n', seq_len_mask)
            batch_seq_len = torch.sum(seq_len_mask, dim=1)  # 将每个样本的所有mask值(1/0)加和得到的数值即为该样本的真实长度【即：模型输入值y_len】
            print('batch_seq_len.shape = ', batch_seq_len.shape, '; batch_seq_len = ', batch_seq_len)
            batch_losses = sum_losses / batch_seq_len  # 将当前batch中的每个样本的总loss除以该样本的长度，得到每个样本的平均loss
            batch_loss = torch.mean(batch_losses)  # 计算当前batch所有样本的平均loss
            print('batch_loss = ', batch_loss)
    
            return batch_loss

## 三、模型训练 ##

# -*- coding: utf-8 -*-
    '''
    @Description: Train the model.
    '''
    
    import os
    import sys
    import pathlib
    import numpy as np
    import pickle
    from torch.utils.data import DataLoader
    from torch import optim
    from torch.nn.utils import clip_grad_norm_
    from dataset import PairsOfSrcTgt, BuildVocab, SampleDataset, collate_fn
    import torch
    import config
    from tqdm import tqdm, trange
    from model import Seq2seq
    from tensorboardX import SummaryWriter
    
    abs_path = pathlib.Path(__file__).parent.absolute()
    sys.path.append(sys.path.append(abs_path))
    
    
    def train(pairs_train, pairs_val, vocab, start_epoch=0):
        """Train the model, evaluate it and store it.
    
        Args:
            pairs_train (dataset.PairsOfSrcTgt): The training dataset.
            pairs_val (dataset.PairDataset): The evaluation dataset.
            vocab (vocab.Vocab): The vocabulary built from the training dataset.
            start_epoch (int, optional): The starting epoch number. Defaults to 0.
        """
        print('\n\n\n ****************************** loading model ******************************\n')
        DEVICE = torch.device("cuda" if config.is_cuda else "cpu")
    
        # 加载并批次化训练数据
        dataset_train = SampleDataset(pairs_train, vocab)  # 训练数据集大小：len(dataset_train) = 43996
        dataset_valid = SampleDataset(pairs_val, vocab)  # 验证数据集大小：len(dataset_valid) = 5000
        print('len(dataset_train) = {0}----dataset_train = {1}'.format(len(dataset_train), dataset_train))
        print('len(dataset_valid) = {0}----dataset_valid = {1}'.format(len(dataset_valid), dataset_valid))
    
        dataloader_train = DataLoader(dataset=dataset_train, batch_size=config.batch_size, shuffle=True, collate_fn=collate_fn)
        dataloader_valid = DataLoader(dataset=dataset_valid, batch_size=config.batch_size, shuffle=True, pin_memory=True, drop_last=True, collate_fn=collate_fn)
        print('\nlen(dataloader_train) = {0}----dataloader_train = {1}'.format(len(dataloader_train), dataloader_train))
        print('len(dataloader_valid) = {0}----dataloader_valid = {1}'.format(len(dataloader_valid), dataloader_valid))
    
        # ----------------------- 初始化 -----------------------
        model = Seq2seq(vocab)  # 初始化model
        optimizer = optim.Adam(model.parameters(), lr=config.learning_rate)  # 初始化优化器
        prev_val_loss = np.inf  # 初始化验证集loss
    
        # ----------------------- 加载上次未训练完的模型参数以及验证集loss，然后继续训练 -----------------------
        model.load_model()
        if os.path.exists(config.prev_val_loss_path):
            with open(config.prev_val_loss_path, 'rb') as f:
                prev_val_loss = pickle.load(f)
    
        model.to(DEVICE)
    
        # SummaryWriter: Log writer used for TensorboardX visualization.【开启Tensorboard命令行：tensorboard --logdir ./runs/baseline】
        writer_batch = SummaryWriter(config.log_path_batch_train)  # 将每一个epoch中的每100个batch的平均loss保存在文件里供可视化使用。
        writer_epoch_train = SummaryWriter(config.log_path_epoch_train)  # 将当前epoch的train的平均loss保存在文件里供可视化使用。
        writer_epoch_valid = SummaryWriter(config.log_path_epoch_valid)  # 将当前epoch的val的平均loss保存在文件里供可视化使用。
    
        # ----------------------- 开始训练 -----------------------
        print('\n ***************************************** 开始训练 *****************************************\n')
        epoch_progress_bar = tqdm(range(start_epoch, config.epochs))
        epoch_progress_bar.set_description(f'Epoch Loss: ')
        for epoch_idx in epoch_progress_bar:
            print('\n ***************************************** epoch_idx = {0} *****************************************\n'.format(epoch_idx))
            batch_losses = []  # 存放各个batch计算的loss
            # tqdm进度条
            batch_progress_bar = tqdm(dataloader_train)
            batch_progress_bar.set_description(f'Epoch_idx = {
         
          epoch_idx},')
            for batch_idx, data in enumerate(batch_progress_bar):
                # print('\n************************* epoch_idx = {0}; batch_idx = {1} *************************\n'.format(epoch_idx, batch_idx))
                x, y, x_len, y_len, oovs, len_oovs = data  # x：序列化后的输入文本；y：序列化后的输出文本；oovs：超出自定义词典的词汇文本列表【各含有batch_size个样本】
                assert not np.any(np.isnan(x.numpy()))
                if config.is_cuda:  # Training with GPUs.
                    x = x.to(DEVICE)
                    y = y.to(DEVICE)
                    x_len = x_len.to(DEVICE)
                    len_oovs = len_oovs.to(DEVICE)
                model.train()  # 设置model进入训练模式
    
                # ----------------------- 梯度置零(进入每一个batch后都要先梯度置零) -----------------------
                optimizer.zero_grad()
    
                # ----------------------- 计算当前batch的平均loss -----------------------
                loss = model(x=x, x_len=x_len, y=y, y_len=y_len, len_oovs=len_oovs, epoch_idx=epoch_idx, batch_idx=batch_idx, is_train=True)  # 每次将batch_size个样本喂给model
                batch_losses.append(loss.item())
    
                # ----------------------- loss 反向传播 -----------------------
                loss.backward()  # 反向传播
    
                # 进行梯度裁剪，防止梯度爆炸
                clip_grad_norm_(model.encoder.parameters(), config.max_grad_norm)
                clip_grad_norm_(model.decoder.parameters(), config.max_grad_norm)
                clip_grad_norm_(model.attention.parameters(), config.max_grad_norm)
    
                # ----------------------- 更新model的所有可训练参数 -----------------------
                optimizer.step()
    
                # ----------------------- 更新一次 tqdm bar进度条 -----------------------
                batch_progress_bar.set_postfix(Batch_idx=batch_idx, Loss_Of_This_Batch=loss.item())
    
                if batch_idx % 5 == 0:
                    # 将当前epoch里每100个batch计算的平均loss数据保存在文件里面供可视化使用。【这里是Scalar类型，所以使用writer.add_scalar()】
                    writer_batch.add_scalar(f'Average loss for epoch {
         
          epoch_idx}', np.mean(batch_losses), global_step=batch_idx)  # 第一个参数可以简单理解为保存图的名称，第二个参数是可以理解为Y轴数据，第三个参数可以理解为X轴数据。
    
            # ----------------------- 计算当前epoch中所有样本平均后的Loss -----------------------
            curr_epoch_train_loss = np.mean(batch_losses)
            # ----------------------- 更新 以epoch为单位的 tqdm bar进度条 -----------------------
            epoch_progress_bar.set_postfix(Loss_Of_This_Epoch=curr_epoch_train_loss)
    
            # ----------------------- 计算模型在验证集上的loss值(在验证数据集上验证本epoch训练后的效果) -----------------------
            val_loss = []
            with torch.no_grad():
                print('\n************************************************************ 开始进入验证 ************************************************************')
                for batch_idx, data in enumerate(tqdm(dataloader_valid)):
                    x, y, x_len, y_len, oovs, len_oovs = data  # x：序列化后的输入文本；y：序列化后的输出文本；oovs：超出自定义词典的词汇文本列表【各含有batch_size个样本】
                    if config.is_cuda:
                        x = x.to(DEVICE)
                        y = y.to(DEVICE)
                        x_len = x_len.to(DEVICE)
                        len_oovs = len_oovs.to(DEVICE)
                    loss = model(x=x, x_len=x_len, y=y, y_len=y_len, len_oovs=len_oovs, epoch_idx=epoch_idx, batch_idx=batch_idx, is_train=False)  # 每次将batch_size个样本喂给model
                    val_loss.append(loss.item())
            curr_epoch_valid_loss = np.mean(val_loss)
    
            writer_epoch_train.add_scalar(f'Loss Of Train for All epoch', curr_epoch_train_loss, global_step=epoch_idx)
            writer_epoch_valid.add_scalar(f'Loss Of Val for All epoch', curr_epoch_valid_loss, global_step=epoch_idx)
    
            print('\n\n☆☆☆☆☆☆☆☆  第{0}个epoch: curr_epoch_train_loss = {1}; curr_epoch_valid_loss = {2}'.format(epoch_idx, curr_epoch_train_loss, curr_epoch_valid_loss))
    
            # 当验证数据集上的loss比上一个epoch的loss减少时，保存模型参数
            if curr_epoch_valid_loss < prev_val_loss:
                torch.save(model.encoder, config.encoder_saved_name)
                torch.save(model.decoder, config.decoder_saved_name)
                torch.save(model.attention, config.attention_saved_name)
                torch.save(model.reduce_state, config.reduce_state_saved_name)
                prev_val_loss = curr_epoch_valid_loss
            with open(config.prev_val_loss_path, 'wb') as f:
                pickle.dump(prev_val_loss, f)
    
        writer_batch.close()
        writer_epoch_train.close()
        writer_epoch_valid.close()
    
    
    if __name__ == "__main__":
        # 声明训练设备
        DEVICE = torch.device('cuda') if config.is_cuda else torch.device('cpu')
        # 从数据集(每行数据的格式为src+<sep>+tgt)中提取训练数据集的(src, tgt)文本数据对
        pairs_train = PairsOfSrcTgt(config.data_train_path, max_src_len=config.max_src_len, max_tgt_len=config.max_tgt_len, truncate_src=config.truncate_src, truncate_tgt=config.truncate_tgt).pairs
        print('len(pairs_train) = ', len(pairs_train))
        # 根据训练数据集的(src, tgt)文本数据对构建词典
        vocab = BuildVocab(pairs_train, embed_file=config.embed_file).vocab
        print('len(vocab) = ', len(vocab))
        print('vocab.vocab.__getitem__(3) = ', vocab.__getitem__(3))
        # 从预训练数据集(每行数据的格式为src+<sep>+tgt)中提取验证数据集的(src, tgt)文本数据对
        pairs_val = PairsOfSrcTgt(config.data_val_path, max_src_len=config.max_src_len, max_tgt_len=config.max_tgt_len, truncate_src=config.truncate_src, truncate_tgt=config.truncate_tgt).pairs
        print('len(pairs_val) = ', len(pairs_val))
    
        train(pairs_train, pairs_val, vocab, start_epoch=0)

打印结果：

Reading dataset data/processed_data/train.txt... 43996 pairs.
    len(pairs_train) =  43996
    len(vocab) =  20004
    vocab.vocab.__getitem__(3) =  <UNK>
    Reading dataset ./data/processed_data/dev.txt... 5000 pairs.
    len(pairs_val) =  5000
     ****************************** loading model ******************************
    len(dataset_train) = 43996----dataset_train = <sample_dataset.SampleDataset object at 0x00000218344F8438>
    len(dataset_valid) = 5000----dataset_valid = <sample_dataset.SampleDataset object at 0x00000218382238D0>
    len(dataloader_train) = 5500----dataloader_train = <torch.utils.data.dataloader.DataLoader object at 0x000002186F2FF208>
    len(dataloader_valid) = 625----dataloader_valid = <torch.utils.data.dataloader.DataLoader object at 0x000002187B137978>
     ***************************************** 开始训练 *****************************************
     ***************************************** epoch_idx = 0 *****************************************
    
    ************************************* epoch_idx = 0；batch_idx = 0： 向model喂入的数据 *************************************
    batch_idx =  0
    x_len =  tensor([197, 121,  85])
    x =  tensor([[  1, 403, 404,  77, 174,  56, 405, 406,  81, 407,   9, 408, 174,  79,
             175, 409,  25,  45, 152,  25, 410, 176,  35, 411,  72, 412, 413, 414,
             162, 415,   6, 177,  30,  82, 416, 117, 163, 417,  20,  29,  26, 418,
              81,   4, 419, 178,   4,   9,   4,  73,   6,   4, 179, 420,  41,   4,
              14, 421,   4,  14, 153,   4,  17,   4,  40,   8,   4,  45,   4, 422,
               5, 180,   4,  44, 423, 424,   4, 425, 426, 427, 179,   4, 181,   4,
              70,  27,  28,   4,  80,   6,   4,  83, 428,   4,  67,   9,  42,  82,
             182,   4,  14,  35,  28, 181,  10,  45,  10, 429,  10, 430,  10, 431,
               4,  54,  27,  28,   4, 432, 433, 183,   4, 434, 435, 436,   4,  31,
              27,   4, 437,  27,  28,   4, 168, 178,  61,   4, 438,  42, 184,   5,
             439,   4, 142, 440,  89,   4, 441, 442,   4, 443, 183,   5, 444,   4,
              83,  73,   4,  45,   4, 185,  59,   4, 185,  22,   4, 445,   4, 446,
             447,   5,  82,   4, 448,   4, 101,   4, 449,   4,  65,   4, 450,   4,
             166,   4, 451,   4, 452,   4, 176,   4, 453,   4, 454, 180,   4, 455,
               2],
            [  1,  89, 193,  90,  91,  48,  92, 194, 195,  93,  94,  95,  49,  50,
              16,  29, 196,  17, 197, 198,  51,  52,   6,  18,  30, 199,  19,  18,
              96, 200,  97, 201,  53,  95,  20,  29,  54,  98,  31, 202,  99,  94,
              21,  93, 203,  48,   4, 204, 205,   9,   6,   4,  55, 206,   4, 207,
             208, 209,   4,  90,   4, 100,   4, 100,   4,  56,   4, 210,   4, 101,
               4, 102,   6, 103,  57, 211, 104, 212,   4,  58, 213,  21, 214,   5,
               6,   4,  32, 105,  10, 106, 215,  10, 216,  10, 217,  10, 218, 107,
             219,   4, 220, 221,  13,   5,   4, 222, 223, 224,   5, 108,   4, 225,
               4, 226,   4,  53,   4,  22,   4,  59,   2,   0,   0,   0,   0,   0,
               0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
               0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
               0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
               0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
               0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
               0],
            [  1, 323, 114, 144, 145, 146,  25, 147, 324, 148,  49, 149, 145, 150,
             151,  54,  98,  31, 325,  53, 144,  51, 326,  96, 327, 152,  25,  20,
              29,  99, 328, 329,  18,  97, 330,   6,  18,  72, 331,  19, 102,  30,
             332,  24,   8,   4,  40,   8,   4,   6, 153,   4, 333, 334,   4,  71,
             335,   4, 336,  36,   4, 337,  73,   4, 131, 338,   4,  51, 339,   4,
             340, 154,   4, 341, 342,   4,  74, 343,   4,  74, 344,   4,  74, 345,
               2,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
               0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
               0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
               0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
               0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
               0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
               0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
               0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
               0]])
    y_len =  tensor([38, 48, 54])
    y.shape =  torch.Size([3, 54]) ; y.shape[1] =  54
    y =  tensor([[  1, 186,  13, 456, 457,  79, 175,  11,   4, 458,  61,   4, 459, 460,
             461, 462,   7,  68, 177,   6,   4, 172, 463,   4,  84, 464,   4, 465,
             466, 467,  83,   4, 173,   9,  41, 109,   7,   2,   0,   0,   0,   0,
               0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
            [  1, 227, 228, 109,   5, 229,  48, 230, 231,   4, 232, 233, 234,   5,
             108,   4,  60,  23, 110, 235, 236, 237, 238,   4, 239, 240,   5,  92,
             241, 242,   7,  13, 243,   5,  52,  11,   4,   9,  61, 111,   4, 244,
              33,   9,   5, 112,   7,   2,   0,   0,   0,   0,   0,   0],
            [  1, 346,  15,  41, 112,   5, 146, 148,   4, 149, 150,   5,  31,  11,
               4, 155, 347,   4, 348, 349,  49, 350,   5, 351, 352, 353,   7, 354,
             126,   5, 355,   4, 356, 357,   4,  67, 156, 358, 359, 157, 158, 360,
               7, 361, 151, 362, 363, 147,  21,   4, 159,  16,   7,   2]])
    ************************************* 对输入的序列化样本进行处理，并生成本条样本的 padding_mask *************************************
    x_padding_masks.shape =  torch.Size([3, 197])
    x_padding_masks = 
     tensor([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
            [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0.,
             0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
             0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
             0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
             0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
            [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
             1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0.,
             0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
             0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
             0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
             0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
             0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
             0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
    
    encoder_output.shape =  torch.Size([3, 197, 1024])
    
    --------------------------------- is_train = True; epoch_idx = 0; batch_idx = 0, 当前预测时间步 i = 0 ---------------------------------
    
    decoder_input_i.shape =  torch.Size([3]) ; decoder_input_i =  tensor([1, 1, 1]) ; decoder_input_i.unsqueeze(1).shape =  torch.Size([3, 1]) 
    decoder_input_i.unsqueeze(1) = 
     tensor([[1],
            [1],
            [1]])
    decoder_target_i.shape =  torch.Size([3]) ; decoder_target_i =  tensor([1, 1, 1]) ; decoder_target_i.unsqueeze(1).shape =  torch.Size([3, 1]) 
    decoder_target_i.unsqueeze(1) = 
     tensor([[186],
            [227],
            [346]])
    p_vocab.shape =  torch.Size([3, 1003]) ----decoder_states[0].shape =  torch.Size([1, 3, 512]) ----decoder_states[1].shape =  torch.Size([1, 3, 512])
    target_probs.shape =  torch.Size([3, 1]) ; 
    target_probs =  tensor([[5.2464e-05],
            [5.7170e-05],
            [2.6298e-05]], grad_fn=<GatherBackward>)
    squeeze之后：target_probs.shape =  torch.Size([3]) ; target_probs =  tensor([5.2464e-05, 5.7170e-05, 2.6298e-05], grad_fn=<SqueezeBackward1>)
    epoch(0)-batch_idx(0)：预测y序列文本中第(0)个时间步的token的损失值: loss.shape = torch.Size([3]); loss = tensor([ 9.8554,  9.7695, 10.5460], grad_fn=<NegBackward>) 
    mask.shape =  torch.Size([3]) ; mask =  tensor([1., 1., 1.])
    经过mask后：loss.shape =  torch.Size([3]) ; loss =  tensor([ 9.8554,  9.7695, 10.5460], grad_fn=<MulBackward0>)
    
    --------------------------------- is_train = True; epoch_idx = 0; batch_idx = 0, 当前预测时间步 i = 1 ---------------------------------
    
    decoder_input_i.shape =  torch.Size([3]) ; decoder_input_i =  tensor([186, 227, 346]) ; decoder_input_i.unsqueeze(1).shape =  torch.Size([3, 1]) 
    decoder_input_i.unsqueeze(1) = 
     tensor([[186],
            [227],
            [346]])
    decoder_target_i.shape =  torch.Size([3]) ; decoder_target_i =  tensor([186, 227, 346]) ; decoder_target_i.unsqueeze(1).shape =  torch.Size([3, 1]) 
    decoder_target_i.unsqueeze(1) = 
     tensor([[ 13],
            [228],
            [ 15]])
    p_vocab.shape =  torch.Size([3, 1003]) ----decoder_states[0].shape =  torch.Size([1, 3, 512]) ----decoder_states[1].shape =  torch.Size([1, 3, 512])
    target_probs.shape =  torch.Size([3, 1]) ; 
    target_probs =  tensor([[2.9507e-05],
            [2.9505e-05],
            [1.0280e-03]], grad_fn=<GatherBackward>)
    squeeze之后：target_probs.shape =  torch.Size([3]) ; target_probs =  tensor([2.9507e-05, 2.9505e-05, 1.0280e-03], grad_fn=<SqueezeBackward1>)
    epoch(0)-batch_idx(0)：预测y序列文本中第(1)个时间步的token的损失值: loss.shape = torch.Size([3]); loss = tensor([10.4309, 10.4309,  6.8801], grad_fn=<NegBackward>) 
    mask.shape =  torch.Size([3]) ; mask =  tensor([1., 1., 1.])
    经过mask后：loss.shape =  torch.Size([3]) ; loss =  tensor([10.4309, 10.4309,  6.8801], grad_fn=<MulBackward0>)
    
    ......
    
    --------------------------------- is_train = True; epoch_idx = 0; batch_idx = 0, 当前预测时间步 i = 36 ---------------------------------
    decoder_input_i.shape =  torch.Size([3]) ; decoder_input_i =  tensor([  7,   4, 156]) ; decoder_input_i.unsqueeze(1).shape =  torch.Size([3, 1]) 
    decoder_input_i.unsqueeze(1) = 
     tensor([[  7],
            [  4],
            [156]])
    decoder_target_i.shape =  torch.Size([3]) ; decoder_target_i =  tensor([  7,   4, 156]) ; decoder_target_i.unsqueeze(1).shape =  torch.Size([3, 1]) 
    decoder_target_i.unsqueeze(1) = 
     tensor([[  2],
            [  9],
            [358]])
    p_vocab.shape =  torch.Size([3, 1003]) ----decoder_states[0].shape =  torch.Size([1, 3, 512]) ----decoder_states[1].shape =  torch.Size([1, 3, 512])
    target_probs.shape =  torch.Size([3, 1]) ; 
    target_probs =  tensor([[3.0631e-03],
            [1.1222e-03],
            [1.7396e-05]], grad_fn=<GatherBackward>)
    squeeze之后：target_probs.shape =  torch.Size([3]) ; target_probs =  tensor([3.0631e-03, 1.1222e-03, 1.7396e-05], grad_fn=<SqueezeBackward1>)
    epoch(0)-batch_idx(0)：预测y序列文本中第(36)个时间步的token的损失值: loss.shape = torch.Size([3]); loss = tensor([ 5.7883,  6.7925, 10.9593], grad_fn=<NegBackward>) 
    mask.shape =  torch.Size([3

[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5b-N6ICF44Gu5Lmx5aSq6YOO_size_12_color_FFFFFF_t_70_g_se_x_16]: https://img-blog.csdnimg.cn/4be6a508636a4f9581a90e070be860a6.png?x-oss-process=image/watermark,type_ZHJvaWRzYW5zZmFsbGJhY2s,shadow_50,text_Q1NETiBA5b-N6ICF44Gu5Lmx5aSq6YOO,size_12,color_FFFFFF,t_70,g_se,x_16