Informer时序模型(代码解析)-Toy模板网

这篇具有很好参考价值的文章主要介绍了Informer时序模型(代码解析)。希望对大家有所帮助。如果存在错误或未考虑完全的地方，请大家不吝赐教，您也可以点击"举报违法"按钮提交疑问。

代码解析

参考资料

建议大家在阅读前有一定Transformer模型基础，可以先看看Transformer论文，论文下载链接
阅读Informer时序模型论文，重点关注作者针对Transformer模型做了哪些改进，论文下载链接
Informer时序模型Github地址，数据没有包含在项目中，需要自行下载，这里提供下载地址 (包含代码文件和数据)

参数设定模块(main_informer)

值得注意的是'--model'、'--data'参数需要去掉required参数，否则运行代码可能会报'--model'、'--data'错误
修改完参数后运行该模块，保证代码运行不报错的情况下进行debug

参数含义

下面是各参数含义（注释）

# 选择模型(去掉required参数，选择informer模型)
parser.add_argument('--model', type=str, default='informer',help='model of experiment, options: [informer, informerstack, informerlight(TBD)]')

# 数据选择(去掉required参数)
parser.add_argument('--data', type=str, default='WTH', help='data')
# 数据上级目录
parser.add_argument('--root_path', type=str, default='./data/', help='root path of the data file')
# 数据名称
parser.add_argument('--data_path', type=str, default='WTH.csv', help='data file')
# 预测类型(多变量预测、单变量预测、多元预测单变量)
parser.add_argument('--features', type=str, default='M', help='forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate')
# 数据中要预测的标签列
parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task')
# 数据重采样(h:小时)
parser.add_argument('--freq', type=str, default='h', help='freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h')
# 模型保存位置
parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints')

# 输入序列长度
parser.add_argument('--seq_len', type=int, default=96, help='input sequence length of Informer encoder')
# 先验序列长度
parser.add_argument('--label_len', type=int, default=48, help='start token length of Informer decoder')
# 预测序列长度
parser.add_argument('--pred_len', type=int, default=24, help='prediction sequence length')
# Informer decoder input: concat[start token series(label_len), zero padding series(pred_len)]

# 编码器default参数为特征列数
parser.add_argument('--enc_in', type=int, default=7, help='encoder input size')
# 解码器default参数与编码器相同
parser.add_argument('--dec_in', type=int, default=7, help='decoder input size')
parser.add_argument('--c_out', type=int, default=7, help='output size')

# 模型宽度
parser.add_argument('--d_model', type=int, default=512, help='dimension of model')
# 多头注意力机制头数
parser.add_argument('--n_heads', type=int, default=8, help='num of heads')
# 模型中encoder层数
parser.add_argument('--e_layers', type=int, default=2, help='num of encoder layers')
# 模型中decoder层数
parser.add_argument('--d_layers', type=int, default=1, help='num of decoder layers')
# 网络架构循环次数
parser.add_argument('--s_layers', type=str, default='3,2,1', help='num of stack encoder layers')
# 全连接层神经元个数
parser.add_argument('--d_ff', type=int, default=2048, help='dimension of fcn')
# 采样因子数
parser.add_argument('--factor', type=int, default=5, help='probsparse attn factor')
# 1D卷积核
parser.add_argument('--padding', type=int, default=0, help='padding type')
# 是否需要序列长度衰减
parser.add_argument('--distil', action='store_false', help='whether to use distilling in encoder, using this argument means not using distilling', default=True)
# 神经网络正则化操作
parser.add_argument('--dropout', type=float, default=0.05, help='dropout')
# attention计算方式
parser.add_argument('--attn', type=str, default='prob', help='attention used in encoder, options:[prob, full]')
# 时间特征编码方式
parser.add_argument('--embed', type=str, default='timeF', help='time features encoding, options:[timeF, fixed, learned]')
# 激活函数
parser.add_argument('--activation', type=str, default='gelu',help='activation')
# 是否输出attention
parser.add_argument('--output_attention', action='store_true', help='whether to output attention in ecoder')
# 是否需要预测
parser.add_argument('--do_predict', action='store_true', help='whether to predict unseen future data')
parser.add_argument('--mix', action='store_false', help='use mix attention in generative decoder', default=True)
# 数据读取
parser.add_argument('--cols', type=str, nargs='+', help='certain cols from the data files as the input features')
# 多核训练(windows下选择0，否则容易报错)
parser.add_argument('--num_workers', type=int, default=0, help='data loader num workers')
# 训练轮数
parser.add_argument('--itr', type=int, default=2, help='experiments times')
# 训练迭代次数
parser.add_argument('--train_epochs', type=int, default=6, help='train epochs')
# mini-batch大小
parser.add_argument('--batch_size', type=int, default=32, help='batch size of train input data')
# 早停策略
parser.add_argument('--patience', type=int, default=3, help='early stopping patience')
# 学习率
parser.add_argument('--learning_rate', type=float, default=0.0001, help='optimizer learning rate')
parser.add_argument('--des', type=str, default='test',help='exp description')
# loss计算方式
parser.add_argument('--loss', type=str, default='mse',help='loss function')
# 学习率衰减参数
parser.add_argument('--lradj', type=str, default='type1',help='adjust learning rate')
# 是否使用自动混合精度训练
parser.add_argument('--use_amp', action='store_true', help='use automatic mixed precision training', default=False)
# 是否反转输出结果
parser.add_argument('--inverse', action='store_true', help='inverse output data', default=False)

# 是否使用GPU加速训练
parser.add_argument('--use_gpu', type=bool, default=True, help='use gpu')
parser.add_argument('--gpu', type=int, default=0, help='gpu')
# GPU分布式训练
parser.add_argument('--use_multi_gpu', action='store_true', help='use multiple gpus', default=False)
# 多GPU训练
parser.add_argument('--devices', type=str, default='0,1,2,3',help='device ids of multile gpus')

# 取参数值
args = parser.parse_args()
# 获取GPU
args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False

数据文件参数

因为用的是笔记本电脑，这里只能用最小的数据集进行试验，也就是下面的WTH数据集

# 数据参数
data_parser = {
    'ETTh1':{'data':'ETTh1.csv','T':'OT','M':[7,7,7],'S':[1,1,1],'MS':[7,7,1]},
    'ETTh2':{'data':'ETTh2.csv','T':'OT','M':[7,7,7],'S':[1,1,1],'MS':[7,7,1]},
    'ETTm1':{'data':'ETTm1.csv','T':'OT','M':[7,7,7],'S':[1,1,1],'MS':[7,7,1]},
    'ETTm2':{'data':'ETTm2.csv','T':'OT','M':[7,7,7],'S':[1,1,1],'MS':[7,7,1]},
    # data：数据文件名，T：标签列，M：预测变量数(如果要预测12个特征，则为[12,12,12]),
    'WTH':{'data':'WTH.csv','T':'WetBulbCelsius','M':[12,12,12],'S':[1,1,1],'MS':[12,12,1]},
    'ECL':{'data':'ECL.csv','T':'MT_320','M':[321,321,321],'S':[1,1,1],'MS':[321,321,1]},
    'Solar':{'data':'solar_AL.csv','T':'POWER_136','M':[137,137,137],'S':[1,1,1],'MS':[137,137,1]},
}

下面是模型训练函数，这里不进行注释了

数据处理模块（data_loader）

从main_informer.py文件中exp.train(setting)，train方法进入exp_informer.py文件，在_get_data中找到WTH数据处理方法

data_dict = {
            'ETTh1':Dataset_ETT_hour,
            'ETTh2':Dataset_ETT_hour,
            'ETTm1':Dataset_ETT_minute,
            'ETTm2':Dataset_ETT_minute,
            'WTH':Dataset_Custom,
            'ECL':Dataset_Custom,
            'Solar':Dataset_Custom,
            'custom':Dataset_Custom,}

可以看到WTH数据处理方法为Dataset_Custom，我们进入data_loader.py文件，找到Dataset_Custom类
__init__主要用于传各类参数，这里不过多赘述，主要对__read_data__进行说明

    def __read_data__(self):
        # 数据标准化
        self.scaler = StandardScaler()
        # 利用pandas将数据读入
        df_raw = pd.read_csv(os.path.join(self.root_path,
                                          self.data_path))
        # 如果指定了排除项
        if self.cols:
            cols=self.cols.copy()
            # 移除标签列
            cols.remove(self.target)
        else:
            # 提取数据列名；移除标签列；移除日期列
            cols = list(df_raw.columns); cols.remove(self.target); cols.remove('date')
        # 日期列+特征列+标签列(即：调整列顺序)
        df_raw = df_raw[['date']+cols+[self.target]]

        # 划分训练集
        num_train = int(len(df_raw)*0.7)
        # 划分测试集
        num_test = int(len(df_raw)*0.2)
        # 划分验证集
        num_vali = len(df_raw) - num_train - num_test
        # 计算数据起始点
        border1s = [0, num_train-self.seq_len, len(df_raw)-num_test-self.seq_len]
        border2s = [num_train, num_train+num_vali, len(df_raw)]
        border1 = border1s[self.set_type]
        border2 = border2s[self.set_type]

        # 若预测类型为M(多特征预测多特征)或MS(多特征预测单特征)
        if self.features=='M' or self.features=='MS':
            # 取除日期列的其他所有列
            cols_data = df_raw.columns[1:]
            df_data = df_raw[cols_data]
        # 若预测类型为S(单特征预测单特征)
        elif self.features=='S':
            # 取特征列
            df_data = df_raw[[self.target]]
        # 将数据进行归一化
        if self.scale:
            train_data = df_data[border1s[0]:border2s[0]]
            self.scaler.fit(train_data.values)
            data = self.scaler.transform(df_data.values)
        else:
            data = df_data.values
        # 取日期列
        df_stamp = df_raw[['date']][border1:border2]
        # 利用pandas将数据转换为日期格式
        df_stamp['date'] = pd.to_datetime(df_stamp.date)
        # 构建时间特征
        data_stamp = time_features(df_stamp, timeenc=self.timeenc, freq=self.freq)

        self.data_x = data[border1:border2]
        if self.inverse:
            self.data_y = df_data.values[border1:border2]
        else:
            # 取数据特征列
            self.data_y = data[border1:border2]
        self.data_stamp = data_stamp

需要注意的是time_features函数，用来提取日期特征，比如't':['month','day','weekday','hour','minute']，表示提取月，天，周，小时，分钟。可以打开timefeatures.py
文件进行查阅
同样的，对__getitem__进行说明

    def __getitem__(self, index):
        # 随机取得标签
        s_begin = index
        # 训练区间
        s_end = s_begin + self.seq_len
        # 有标签区间+无标签区间(预测时间步长)
        r_begin = s_end - self.label_len 
        r_end = r_begin + self.label_len + self.pred_len

        # 取训练数据
        seq_x = self.data_x[s_begin:s_end]
        if self.inverse:
            seq_y = np.concatenate([self.data_x[r_begin:r_begin+self.label_len], self.data_y[r_begin+self.label_len:r_end]], 0)
        else:
            # 取有标签区间+无标签区间(预测时间步长)数据
            seq_y = self.data_y[r_begin:r_end]
        # 取训练数据对应时间特征
        seq_x_mark = self.data_stamp[s_begin:s_end]
        # 取有标签区间+无标签区间(预测时间步长)对应时间特征
        seq_y_mark = self.data_stamp[r_begin:r_end]

        return seq_x, seq_y, seq_x_mark, seq_y_mark
    
    def __len__(self):
        # 返回数据长度
        return len(self.data_x) - self.seq_len- self.pred_len + 1

    def inverse_transform(self, data):
        return self.scaler.inverse_transform(data)

Informer模型架构（model）

这里贴上Informer模型论文中的结构图，方便大家对照理解。
K值选取原因与筛选方法
先进入exp_informer.py文件，train函数中包含有网络架构函数。

    def train(self, setting):
        # 数据加载器
        train_data, train_loader = self._get_data(flag = 'train')
        vali_data, vali_loader = self._get_data(flag = 'val')
        test_data, test_loader = self._get_data(flag = 'test')

        path = os.path.join(self.args.checkpoints, setting)
        if not os.path.exists(path):
            os.makedirs(path)

        # 记录时间
        time_now = time.time()
        # 训练steps
        train_steps = len(train_loader)
        # 早停策略
        early_stopping = EarlyStopping(patience=self.args.patience, verbose=True)

        # 优化器Adam
        model_optim = self._select_optimizer()
        # 损失函数(MSE)
        criterion =  self._select_criterion()

        # 分布式训练(windows一般不推荐)
        if self.args.use_amp:
            scaler = torch.cuda.amp.GradScaler()

        # 训练次数
        for epoch in range(self.args.train_epochs):
            iter_count = 0
            train_loss = []
            
            self.model.train()
            epoch_time = time.time()
            for i, (batch_x,batch_y,batch_x_mark,batch_y_mark) in enumerate(train_loader):
                iter_count += 1
                # 梯度归零
                model_optim.zero_grad()
                # 训练模型(网络架构)
                pred, true = self._process_one_batch(
                    train_data, batch_x, batch_y, batch_x_mark, batch_y_mark)
                # 计算损失
                loss = criterion(pred, true)
                # 加入数组
                train_loss.append(loss.item())

                # 输出信息
                if (i+1) % 100==0:
                    print("\titers: {0}, epoch: {1} | loss: {2:.7f}".format(i + 1, epoch + 1, loss.item()))
                    speed = (time.time()-time_now)/iter_count
                    left_time = speed*((self.args.train_epochs - epoch)*train_steps - i)
                    print('\tspeed: {:.4f}s/iter; left time: {:.4f}s'.format(speed, left_time))
                    iter_count = 0
                    time_now = time.time()
                
                if self.args.use_amp:
                    scaler.scale(loss).backward()
                    scaler.step(model_optim)
                    scaler.update()
                else:
                    # 反向传播
                    loss.backward()
                    # 更新梯度
                    model_optim.step()

            # 打印时间信息
            print("Epoch: {} cost time: {}".format(epoch+1, time.time()-epoch_time))
            train_loss = np.average(train_loss)
            vali_loss = self.vali(vali_data, vali_loader, criterion)
            test_loss = self.vali(test_data, test_loader, criterion)

            # 打印损失信息
            print("Epoch: {0}, Steps: {1} | Train Loss: {2:.7f} Vali Loss: {3:.7f} Test Loss: {4:.7f}".format(
                epoch + 1, train_steps, train_loss, vali_loss, test_loss))
            # 早停策略
            early_stopping(vali_loss, self.model, path)
            if early_stopping.early_stop:
                print("Early stopping")
                break

            adjust_learning_rate(model_optim, epoch+1, self.args)
        # 保存模型
        best_model_path = path+'/'+'checkpoint.pth'
        # 导入模型
        self.model.load_state_dict(torch.load(best_model_path))
        
        return self.model

注意模型训练那一块_process_one_batch，进入该方法

        def _process_one_batch(self, dataset_object, batch_x, batch_y, batch_x_mark, batch_y_mark):
        # 将数据集放入GPU中
        batch_x = batch_x.float().to(self.device)
        batch_y = batch_y.float()

        batch_x_mark = batch_x_mark.float().to(self.device)
        batch_y_mark = batch_y_mark.float().to(self.device)

        # decoder输入
        if self.args.padding==0:
            # 创建一个全0数组，维度为batch，预测序列长度，特征数，本例中为[32,24,12]
            dec_inp = torch.zeros([batch_y.shape[0], self.args.pred_len, batch_y.shape[-1]]).float()
        elif self.args.padding==1:
            dec_inp = torch.ones([batch_y.shape[0], self.args.pred_len, batch_y.shape[-1]]).float()
        # 维度变为[32,72,12](72 = 24 + 48)，48是预测中有标签的数据量
        dec_inp = torch.cat([batch_y[:,:self.args.label_len,:], dec_inp], dim=1).float().to(self.device)
        # encoder - decoder
        if self.args.use_amp:
            with torch.cuda.amp.autocast():
                if self.args.output_attention:
                    outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
                else:
                    outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
        else:
            if self.args.output_attention:
                outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
            else:
                # 运行到这一步，model中包含了网络架构
                # output维度[batch，预测序列长度，预测特征数]
                outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
        if self.args.inverse:
            outputs = dataset_object.inverse_transform(outputs)
        # 如果预测类型为多特征预测单特征(取结果最后一列)
        f_dim = -1 if self.args.features=='MS' else 0

        batch_y = batch_y[:,-self.args.pred_len:,f_dim:].to(self.device)

        return outputs, batch_y

可以看到outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)，model中包含Informer的核心架构（也是最重要的部分）
打开model.py文件，找到Informer类，直接看forward

        def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, 
                enc_self_mask=None, dec_self_mask=None, dec_enc_mask=None):
        # x_enc[batch,序列长度,特征列],x_mark_enc[batch,序列长度,时间特征列]
        # x_enc.shape:(32,96,12),x_mark_enc.shape:(32,96,4)
        enc_out = self.enc_embedding(x_enc, x_mark_enc)
        # enc_self_mask是数据中需要忽略的样本，本项目中为空
        enc_out, attns = self.encoder(enc_out, attn_mask=enc_self_mask)

        # 解码器embedding操作
        # x_dec维度[batch，有标签+无标签序列长度，特征列](32,72=48+24,12)
        dec_out = self.dec_embedding(x_dec, x_mark_dec)
        # 解码器decoder操作
        dec_out = self.decoder(dec_out, enc_out, x_mask=dec_self_mask, cross_mask=dec_enc_mask)
        # 利用全连接层输出结果512-->12
        dec_out = self.projection(dec_out)
        
        # dec_out = self.end_conv1(dec_out)
        # dec_out = self.end_conv2(dec_out.transpose(2,1)).transpose(1,2)
        if self.output_attention:
            return dec_out[:,-self.pred_len:,:], attns
        else:
            # 截断，只取后面24个需要预测的
            return dec_out[:,-self.pred_len:,:] # [B, L, D]

编码器Embedding操作

Embedding操作，在embed.py文件中

class DataEmbedding(nn.Module):
    def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
        super(DataEmbedding, self).__init__()

        self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
        self.position_embedding = PositionalEmbedding(d_model=d_model)
        self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type, freq=freq) if embed_type!='timeF' else TimeFeatureEmbedding(d_model=d_model, embed_type=embed_type, freq=freq)

        self.dropout = nn.Dropout(p=dropout)

    def forward(self, x, x_mark):
        # 12个特征列利用卷积层映射为512 + position_embedding + 4个时间特征利用全连接层映射为512
        x = self.value_embedding(x) + self.position_embedding(x) + self.temporal_embedding(x_mark)
        # 输出正则化后的embedding
        return self.dropout(x)

Encoder模块

Encoder模块，在encoder.py文件中

class Encoder(nn.Module):
    def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
        super(Encoder, self).__init__()
        self.attn_layers = nn.ModuleList(attn_layers)
        self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None
        self.norm = norm_layer

    def forward(self, x, attn_mask=None):
        # x [B, L, D]
        attns = []
        if self.conv_layers is not None:
            for attn_layer, conv_layer in zip(self.attn_layers, self.conv_layers):
                # 遍历注意力架构层
                x, attn = attn_layer(x, attn_mask=attn_mask)
                # 对x做maxpool1d操作，将512-->256
                # 也就是结构中的金字塔，为了加速模型训练提出
                x = conv_layer(x)
                attns.append(attn)
            # # 遍历注意力架构层
            x, attn = self.attn_layers[-1](x, attn_mask=attn_mask)
            attns.append(attn)
        else:
            for attn_layer in self.attn_layers:
                x, attn = attn_layer(x, attn_mask=attn_mask)
                attns.append(attn)

        if self.norm is not None:
            # 执行标准化操作
            x = self.norm(x)

        return x, attns

进入EncoderLayer类，找到注意力计算架构

class EncoderLayer(nn.Module):
    def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu"):
        super(EncoderLayer, self).__init__()
        d_ff = d_ff or 4*d_model
        self.attention = attention
        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)
        self.activation = F.relu if activation == "relu" else F.gelu

    def forward(self, x, attn_mask=None):
        # 传入3个x，分别用于计算Q、K、V
        new_x, attn = self.attention(
            x, x, x,
            attn_mask = attn_mask
        )
        # 残差连接
        x = x + self.dropout(new_x)

        y = x = self.norm1(x)
        y = self.dropout(self.activation(self.conv1(y.transpose(-1,1))))
        y = self.dropout(self.conv2(y).transpose(-1,1))

        return self.norm2(x+y), attn

注意代码中的new_x, attn = self.attention(x, x, x,attn_mask = attn_mask)

注意力层

注意力层在attn.py文件中，找到AttentionLayer类

class AttentionLayer(nn.Module):
    def __init__(self, attention, d_model, n_heads, 
                 d_keys=None, d_values=None, mix=False):
        super(AttentionLayer, self).__init__()

        d_keys = d_keys or (d_model//n_heads)
        d_values = d_values or (d_model//n_heads)

        self.inner_attention = attention
        self.query_projection = nn.Linear(d_model, d_keys * n_heads)
        self.key_projection = nn.Linear(d_model, d_keys * n_heads)
        self.value_projection = nn.Linear(d_model, d_values * n_heads)
        self.out_projection = nn.Linear(d_values * n_heads, d_model)
        self.n_heads = n_heads
        self.mix = mix

    def forward(self, queries, keys, values, attn_mask):
        # 取出batch，序列长度，特征数12(即B=32，L=96，_=12)
        B, L, _ = queries.shape
        # 同样的S=96
        _, S, _ = keys.shape
        # 多头注意力机制，这里为8
        H = self.n_heads

        # 通过全连接层将特征512-->512，映射到Q,K,V
        # 512是在进行Embedding后特征数量
        # 同时维度变为(batch，序列长度，多头注意力机制，自动计算)
        queries = self.query_projection(queries).view(B, L, H, -1)
        keys = self.key_projection(keys).view(B, S, H, -1)
        values = self.value_projection(values).view(B, S, H, -1)

        # 计算注意力
        out, attn = self.inner_attention(
            queries,
            keys,
            values,
            attn_mask
        )
        if self.mix:
            out = out.transpose(2,1).contiguous()
        # 维度batch，序列长度，自动计算值
        out = out.view(B, L, -1)
        # 连接全连接512-->512
        return self.out_projection(out), attn

注意代码中self.inner_attention，跳转到ProbAttention类
其中_prob_QK用于选取Q、K是非常模型核心，要认真读，贴一下公式：
$\overline{M}_{(q_i,k)} = \mathop{max} \limits_{j} \{\frac{q_ik_j^{T}}{\sqrt{d}}\}-\frac{1}{L_{k}}\sum^{L_k}_{j=1}\frac{q_ik_j^{T}}{\sqrt{d}}$
_get_initial_context计算初始V值，_update_context更新重要Q的V值

class ProbAttention(nn.Module):
    def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
        super(ProbAttention, self).__init__()
        self.factor = factor
        self.scale = scale
        self.mask_flag = mask_flag
        self.output_attention = output_attention
        self.dropout = nn.Dropout(attention_dropout)

    def _prob_QK(self, Q, K, sample_k, n_top): # n_top: c*ln(L_q)
        # 维度[batch，头数，序列长度，自动计算值]
        B, H, L_K, E = K.shape
        _, _, L_Q, _ = Q.shape

        # 添加一个维度，相当于复制维度，当前维度为[batch，头数，序列长度，序列长度，自动计算值]
        K_expand = K.unsqueeze(-3).expand(B, H, L_Q, L_K, E)
        # 随机取样，取值范围0~96，取样维度为[序列长度，25]
        index_sample = torch.randint(L_K, (L_Q, sample_k)) # real U = U_part(factor*ln(L_k))*L_q
        # 96个Q与25个K做计算，维度为[batch，头数，Q个数，K个数，自动计算值]
        K_sample = K_expand[:, :, torch.arange(L_Q).unsqueeze(1), index_sample, :]
        # 矩阵重组，维度为[batch，头数，Q个数，K个数]
        Q_K_sample = torch.matmul(Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze(-2)

        # 分别取到96个Q中每一个Q跟K关系最大的值
        M = Q_K_sample.max(-1)[0] - torch.div(Q_K_sample.sum(-1), L_K)
        # 在96个Q中选出前25个
        M_top = M.topk(n_top, sorted=False)[1]

        # 取出Q特征，维度为[batch，头数，Q个数，自动计算值]
        Q_reduce = Q[torch.arange(B)[:, None, None],
                     torch.arange(H)[None, :, None],
                     M_top, :] # factor*ln(L_q)
        Q_K = torch.matmul(Q_reduce, K.transpose(-2, -1)) # factor*ln(L_q)*L_k

        return Q_K, M_top

    # 计算V值
    def _get_initial_context(self, V, L_Q):
        # 取出batch，头数，序列长度，自动计算值
        B, H, L_V, D = V.shape
        if not self.mask_flag:
            # 对25个Q以外其他Q的V值，使用平均值(让其继续平庸下去)
            V_sum = V.mean(dim=-2)
            # 先把96个V全部使用平均值代替
            contex = V_sum.unsqueeze(-2).expand(B, H, L_Q, V_sum.shape[-1]).clone()
        else: # use mask
            assert(L_Q == L_V) # requires that L_Q == L_V, i.e. for self-attention only
            contex = V.cumsum(dim=-2)
        return contex

    # 更新25个V值
    def _update_context(self, context_in, V, scores, index, L_Q, attn_mask):
        B, H, L_V, D = V.shape

        if self.mask_flag:
            attn_mask = ProbMask(B, H, L_Q, index, scores, device=V.device)
            scores.masked_fill_(attn_mask.mask, -np.inf)

        # 计算softmax值
        attn = torch.softmax(scores, dim=-1)

        # 对25个Q更新V，其他仍然为平均值
        context_in[torch.arange(B)[:, None, None],
                   torch.arange(H)[None, :, None],
                   index, :] = torch.matmul(attn, V).type_as(context_in)
        if self.output_attention:
            attns = (torch.ones([B, H, L_V, L_V])/L_V).type_as(attn).to(attn.device)
            attns[torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], index, :] = attn
            return (context_in, attns)
        else:
            return (context_in, None)

    def forward(self, queries, keys, values, attn_mask):
        # 取出batch，序列长度，头数，自动计算值
        B, L_Q, H, D = queries.shape
        # 取出序列长度(相当于96个Q，96个K)
        _, L_K, _, _ = keys.shape

        # 维度转置操作，维度变为(batch，头数，序列长度，自动计算值)
        queries = queries.transpose(2,1)
        keys = keys.transpose(2,1)
        values = values.transpose(2,1)

        # 选取K的个数，模型核心，用于加速
        # factor为常数5，可以自行修改，其值越大，计算成本越高
        U_part = self.factor * np.ceil(np.log(L_K)).astype('int').item() # c*ln(L_k)
        u = self.factor * np.ceil(np.log(L_Q)).astype('int').item() # c*ln(L_q) 

        U_part = U_part if U_part<L_K else L_K
        u = u if u<L_Q else L_Q

        # Q、K选择标准
        scores_top, index = self._prob_QK(queries, keys, sample_k=U_part, n_top=u) 

        # 削弱维度对结果的影响
        scale = self.scale or 1./sqrt(D)
        if scale is not None:
            scores_top = scores_top * scale
        # 初始化V值
        context = self._get_initial_context(values, L_Q)
        # 更新25个Q的V值
        context, attn = self._update_context(context, values, scores_top, index, L_Q, attn_mask)
        
        return context.transpose(2,1).contiguous(), attn

解码器Embedding操作

解码器的Embedding操作与编码器Embedding操作完全一致，只不过需要注意传入数组维度x_dec维度[batch，有标签+无标签序列长度，特征列](32,72=48+24,12)

Decoder模块

在decoder.py文件中找到Decoder类

class Decoder(nn.Module):
    def __init__(self, layers, norm_layer=None):
        super(Decoder, self).__init__()
        self.layers = nn.ModuleList(layers)
        self.norm = norm_layer

    def forward(self, x, cross, x_mask=None, cross_mask=None):
        for layer in self.layers:
            # 遍历层，需要注意的是该处计算自注意力，也就是self-attention
            # 72个Q，72个K，重复编码器中的decoder操作
            x = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask)

        if self.norm is not None:
            x = self.norm(x)

        return x

代码中的layer层定义在该文件中，找到DecoderLayer类

class DecoderLayer(nn.Module):
    def __init__(self, self_attention, cross_attention, d_model, d_ff=None,
                 dropout=0.1, activation="relu"):
        super(DecoderLayer, self).__init__()
        d_ff = d_ff or 4*d_model
        self.self_attention = self_attention
        self.cross_attention = cross_attention
        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)
        self.activation = F.relu if activation == "relu" else F.gelu

    def forward(self, x, cross, x_mask=None, cross_mask=None):
        x = x + self.dropout(self.self_attention(
            # Decoder(序列长度为72)中的Q,K,V
            x, x, x,
            attn_mask=x_mask
        )[0])
        x = self.norm1(x)

        # cross_attention，在Encoder与Decoder间计算attention
        # 结构图中Encoder与Decoder连接线部分
        x = x + self.dropout(self.cross_attention(
            # x为Q，cross是Encoder中的K，ross是Encoder中的V
            x, cross, cross,
            attn_mask=cross_mask
        )[0])

        y = x = self.norm2(x)
        y = self.dropout(self.activation(self.conv1(y.transpose(-1,1))))
        y = self.dropout(self.conv2(y).transpose(-1,1))

        return self.norm3(x+y)

这里需要注意，在Decoder板块中有两个和Encoder不一样的操作，即self-attention和corss-attention。
self-attention是自注意力机制，比如在本例中带标签长度+预测长度为72，那么会在72个Q与72个K中进行与在Decoder中同样的筛选、更新操作
cross-attention是交叉注意力机制，选值分别为Decoder中的Q，Encoder中的K，Encoder中的V进行与在Decoder中同样的筛选、更新操作
到这里model.py中的模型板块结束，回到exp_informer.py文件中的_process_one_batch，通过output变量得到预测值
回到exp_informer.py文件中的train函数，得到预测值与真实值，继续接下来的梯度、学习率更新，计算损失函数

结果展示

我用自己笔记本电脑跑的，因为没有GPU，所以耗费大概7小时（注：模型文件我放在上面的下载链接中了，包括带注释的代码文件）

train 24425
val 3485
test 6989
	iters: 100, epoch: 1 | loss: 0.4753647
	speed: 5.8926s/iter; left time: 26393.0550s
	iters: 200, epoch: 1 | loss: 0.3887450
	speed: 5.6093s/iter; left time: 24563.0934s
	iters: 300, epoch: 1 | loss: 0.3397639
	speed: 5.6881s/iter; left time: 24339.4008s
	iters: 400, epoch: 1 | loss: 0.3773919
	speed: 5.5947s/iter; left time: 23380.1260s
	iters: 500, epoch: 1 | loss: 0.3424160
	speed: 5.8912s/iter; left time: 24030.1962s
	iters: 600, epoch: 1 | loss: 0.3589063
	speed: 6.0372s/iter; left time: 24021.9204s
	iters: 700, epoch: 1 | loss: 0.3522923
	speed: 5.2896s/iter; left time: 20518.3927s
Epoch: 1 cost time: 4319.718204259872
Epoch: 1, Steps: 763 | Train Loss: 0.3825711 Vali Loss: 0.4002144 Test Loss: 0.3138740
Validation loss decreased (inf --> 0.400214).  Saving model ...
Updating learning rate to 0.0001
	iters: 100, epoch: 2 | loss: 0.3452260
	speed: 12.8896s/iter; left time: 47897.7932s
	iters: 200, epoch: 2 | loss: 0.2782844
	speed: 4.7867s/iter; left time: 17308.6180s
	iters: 300, epoch: 2 | loss: 0.2653053
	speed: 4.7938s/iter; left time: 16855.0160s
	iters: 400, epoch: 2 | loss: 0.3157508
	speed: 4.7083s/iter; left time: 16083.5403s
	iters: 500, epoch: 2 | loss: 0.3046930
	speed: 4.7699s/iter; left time: 15816.8855s
	iters: 600, epoch: 2 | loss: 0.2360453
	speed: 4.8311s/iter; left time: 15536.9307s
	iters: 700, epoch: 2 | loss: 0.2668953
	speed: 4.7713s/iter; left time: 14867.4169s
Epoch: 2 cost time: 3644.3840498924255
Epoch: 2, Steps: 763 | Train Loss: 0.2945577 Vali Loss: 0.3963071 Test Loss: 0.3274192
Validation loss decreased (0.400214 --> 0.396307).  Saving model ...
Updating learning rate to 5e-05
	iters: 100, epoch: 3 | loss: 0.2556470
	speed: 12.6569s/iter; left time: 37375.7115s
	iters: 200, epoch: 3 | loss: 0.2456252
	speed: 4.7655s/iter; left time: 13596.0810s
	iters: 300, epoch: 3 | loss: 0.2562804
	speed: 4.7336s/iter; left time: 13031.4940s
	iters: 400, epoch: 3 | loss: 0.2049552
	speed: 4.7622s/iter; left time: 12634.1883s
	iters: 500, epoch: 3 | loss: 0.2604980
	speed: 4.7524s/iter; left time: 12132.7789s
	iters: 600, epoch: 3 | loss: 0.2539216
	speed: 4.7413s/iter; left time: 11630.3915s
	iters: 700, epoch: 3 | loss: 0.2098076
	speed: 4.7394s/iter; left time: 11151.7416s
Epoch: 3 cost time: 3628.159082174301
Epoch: 3, Steps: 763 | Train Loss: 0.2486252 Vali Loss: 0.4155475 Test Loss: 0.3301197
EarlyStopping counter: 1 out of 3
Updating learning rate to 2.5e-05
	iters: 100, epoch: 4 | loss: 0.2175551
	speed: 12.6253s/iter; left time: 27649.4546s
	iters: 200, epoch: 4 | loss: 0.2459734
	speed: 4.7335s/iter; left time: 9892.9213s
	iters: 300, epoch: 4 | loss: 0.2354426
	speed: 4.7546s/iter; left time: 9461.6300s
	iters: 400, epoch: 4 | loss: 0.2267139
	speed: 4.7719s/iter; left time: 9018.9749s
	iters: 500, epoch: 4 | loss: 0.2379844
	speed: 4.8038s/iter; left time: 8598.7446s
	iters: 600, epoch: 4 | loss: 0.2434178
	speed: 4.7608s/iter; left time: 8045.7994s
	iters: 700, epoch: 4 | loss: 0.2231207
	speed: 4.7765s/iter; left time: 7594.6586s
Epoch: 4 cost time: 3649.547614812851
Epoch: 4, Steps: 763 | Train Loss: 0.2224283 Vali Loss: 0.4230270 Test Loss: 0.3334258
EarlyStopping counter: 2 out of 3
Updating learning rate to 1.25e-05
	iters: 100, epoch: 5 | loss: 0.1837259
	speed: 12.7564s/iter; left time: 18203.3974s
	iters: 200, epoch: 5 | loss: 0.1708880
	speed: 4.7804s/iter; left time: 6343.6200s
	iters: 300, epoch: 5 | loss: 0.2529005
	speed: 4.7426s/iter; left time: 5819.1675s
	iters: 400, epoch: 5 | loss: 0.2434390
	speed: 4.7388s/iter; left time: 5340.6568s
	iters: 500, epoch: 5 | loss: 0.2078404
	speed: 4.7515s/iter; left time: 4879.7921s
	iters: 600, epoch: 5 | loss: 0.2372987
	speed: 4.7986s/iter; left time: 4448.2748s
	iters: 700, epoch: 5 | loss: 0.2022571
	speed: 4.7718s/iter; left time: 3946.2739s
Epoch: 5 cost time: 3636.7107157707214
Epoch: 5, Steps: 763 | Train Loss: 0.2088229 Vali Loss: 0.4305894 Test Loss: 0.3341273
EarlyStopping counter: 3 out of 3
Early stopping
>>>>>>>testing : informer_WTH_ftM_sl96_ll48_pl24_dm512_nh8_el2_dl1_df2048_atprob_fc5_ebtimeF_dtTrue_mxTrue_test_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
test 6989
test shape: (218, 32, 24, 12) (218, 32, 24, 12)
test shape: (6976, 24, 12) (6976, 24, 12)
mse:0.3277873396873474, mae:0.3727897107601166
Use CPU
>>>>>>>start training : informer_WTH_ftM_sl96_ll48_pl24_dm512_nh8_el2_dl1_df2048_atprob_fc5_ebtimeF_dtTrue_mxTrue_test_1>>>>>>>>>>>>>>>>>>>>>>>>>>
train 24425
val 3485
test 6989
	iters: 100, epoch: 1 | loss: 0.4508476
	speed: 4.7396s/iter; left time: 21228.7904s
	iters: 200, epoch: 1 | loss: 0.3859568
	speed: 4.7742s/iter; left time: 20906.0895s
	iters: 300, epoch: 1 | loss: 0.3749838
	speed: 4.7690s/iter; left time: 20406.5500s
	iters: 400, epoch: 1 | loss: 0.3673764
	speed: 4.8070s/iter; left time: 20088.4627s
	iters: 500, epoch: 1 | loss: 0.3068828
	speed: 4.7643s/iter; left time: 19433.6961s
	iters: 600, epoch: 1 | loss: 0.4173551
	speed: 4.7621s/iter; left time: 18948.4516s
	iters: 700, epoch: 1 | loss: 0.2720438
	speed: 4.7609s/iter; left time: 18467.4719s
Epoch: 1 cost time: 3639.997560977936
Epoch: 1, Steps: 763 | Train Loss: 0.3788956 Vali Loss: 0.3947107 Test Loss: 0.3116618
Validation loss decreased (inf --> 0.394711).  Saving model ...
Updating learning rate to 0.0001
	iters: 100, epoch: 2 | loss: 0.3547252
	speed: 12.6113s/iter; left time: 46863.7093s
	iters: 200, epoch: 2 | loss: 0.3236437
	speed: 4.7504s/iter; left time: 17177.4475s
	iters: 300, epoch: 2 | loss: 0.2898968
	speed: 4.7720s/iter; left time: 16778.2666s
	iters: 400, epoch: 2 | loss: 0.3107039
	speed: 4.7412s/iter; left time: 16195.8892s
	iters: 500, epoch: 2 | loss: 0.2816701
	speed: 4.7244s/iter; left time: 15666.2476s
	iters: 600, epoch: 2 | loss: 0.2226012
	speed: 4.7348s/iter; left time: 15227.0618s
	iters: 700, epoch: 2 | loss: 0.2239729
	speed: 4.8806s/iter; left time: 15208.0025s
Epoch: 2 cost time: 3635.6160113811493
Epoch: 2, Steps: 763 | Train Loss: 0.2962583 Vali Loss: 0.4018708 Test Loss: 0.3213752
EarlyStopping counter: 1 out of 3
Updating learning rate to 5e-05
	iters: 100, epoch: 3 | loss: 0.2407307
	speed: 12.5584s/iter; left time: 37084.8281s
	iters: 200, epoch: 3 | loss: 0.2294409
	speed: 5.1105s/iter; left time: 14580.3263s
	iters: 300, epoch: 3 | loss: 0.3180184
	speed: 5.9484s/iter; left time: 16376.0364s
	iters: 400, epoch: 3 | loss: 0.2101320
	speed: 5.7987s/iter; left time: 15384.0189s
	iters: 500, epoch: 3 | loss: 0.2701742
	speed: 5.5463s/iter; left time: 14159.6749s
	iters: 600, epoch: 3 | loss: 0.2391748
	speed: 4.8338s/iter; left time: 11857.4335s
	iters: 700, epoch: 3 | loss: 0.2280931
	speed: 4.7718s/iter; left time: 11228.1147s
Epoch: 3 cost time: 3975.2745430469513
Epoch: 3, Steps: 763 | Train Loss: 0.2494072 Vali Loss: 0.4189631 Test Loss: 0.3308771
EarlyStopping counter: 2 out of 3
Updating learning rate to 2.5e-05
	iters: 100, epoch: 4 | loss: 0.2260314
	speed: 12.7037s/iter; left time: 27821.0994s
	iters: 200, epoch: 4 | loss: 0.2191769
	speed: 4.7906s/iter; left time: 10012.3575s
	iters: 300, epoch: 4 | loss: 0.2044496
	speed: 4.7498s/iter; left time: 9452.0362s
	iters: 400, epoch: 4 | loss: 0.2167130
	speed: 4.7545s/iter; left time: 8985.9758s
	iters: 500, epoch: 4 | loss: 0.2340788
	speed: 4.7329s/iter; left time: 8471.8863s
	iters: 600, epoch: 4 | loss: 0.2137127
	speed: 4.7037s/iter; left time: 7949.1748s
	iters: 700, epoch: 4 | loss: 0.1899967
	speed: 4.7049s/iter; left time: 7480.8388s
Epoch: 4 cost time: 3624.2080821990967
Epoch: 4, Steps: 763 | Train Loss: 0.2222918 Vali Loss: 0.4390603 Test Loss: 0.3350959
EarlyStopping counter: 3 out of 3
Early stopping
>>>>>>>testing : informer_WTH_ftM_sl96_ll48_pl24_dm512_nh8_el2_dl1_df2048_atprob_fc5_ebtimeF_dtTrue_mxTrue_test_1<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
test 6989
test shape: (218, 32, 24, 12) (218, 32, 24, 12)
test shape: (6976, 24, 12) (6976, 24, 12)
mse:0.3116863965988159, mae:0.36840054392814636

跑完以后项目文件中会生成两个文件夹，checkpoints文件夹中存放模型文件，后缀名为.pht；results文件夹中有3个文件，pred.npy为预测值，true.npy为真实值
作者在GitHub上留下了关于预测的具体方法，这里因为篇幅原因就不继续写了，可以看后续Informer时序模型(自定义项目)

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