Merge branch 'master' into 'main'HEADmain

readme

See merge request zyq/time_series_anomaly_detection!1

5 files changed, 1419 insertions, 0 deletions

diff --git a/method/AnomalyTransformer.py b/method/AnomalyTransformer.py
new file mode 100644
index 0000000..9dba21a
--- /dev/null
+++ b/method/AnomalyTransformer.py
@@ -0,0 +1,305 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import math
+from math import sqrt
+import torch.utils.data as tud
+
+
+class PositionalEmbedding(nn.Module):
+    def __init__(self, d_model, max_len=5000):
+        super(PositionalEmbedding, self).__init__()
+        # Compute the positional encodings once in log space.
+        pe = torch.zeros(max_len, d_model).float()
+        pe.require_grad = False
+
+        position = torch.arange(0, max_len).float().unsqueeze(1)
+        div_term = (torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)).exp()
+
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+
+        pe = pe.unsqueeze(0)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        return self.pe[:, :x.size(1)]
+
+
+class TokenEmbedding(nn.Module):
+    def __init__(self, c_in, d_model):
+        super(TokenEmbedding, self).__init__()
+        padding = 1 if torch.__version__ >= '1.5.0' else 2
+        self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
+                                   kernel_size=3, padding=padding, padding_mode='circular', bias=False)
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='leaky_relu')
+
+    def forward(self, x):
+        x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2)
+        return x
+
+
+class DataEmbedding(nn.Module):
+    def __init__(self, c_in, d_model, dropout=0.0):
+        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.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, x):
+        x = self.value_embedding(x) + self.position_embedding(x)
+        return self.dropout(x)
+
+
+class TriangularCausalMask():
+    def __init__(self, B, L, device="cpu"):
+        mask_shape = [B, 1, L, L]
+        with torch.no_grad():
+            self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device)
+
+    @property
+    def mask(self):
+        return self._mask
+
+
+class AnomalyAttention(nn.Module):
+    def __init__(self, win_size, mask_flag=True, scale=None, attention_dropout=0.0, output_attention=False):
+        super(AnomalyAttention, self).__init__()
+        self.scale = scale
+        self.mask_flag = mask_flag
+        self.output_attention = output_attention
+        self.dropout = nn.Dropout(attention_dropout)
+        window_size = win_size
+        self.distances = torch.zeros((window_size, window_size)).cuda()
+        for i in range(window_size):
+            for j in range(window_size):
+                self.distances[i][j] = abs(i - j)
+
+    def forward(self, queries, keys, values, sigma, attn_mask):
+        B, L, H, E = queries.shape
+        _, S, _, D = values.shape
+        scale = self.scale or 1. / sqrt(E)
+
+        scores = torch.einsum("blhe,bshe->bhls", queries, keys)
+        if self.mask_flag:
+            if attn_mask is None:
+                attn_mask = TriangularCausalMask(B, L, device=queries.device)
+            scores.masked_fill_(attn_mask.mask, -np.inf)
+        attn = scale * scores
+
+        sigma = sigma.transpose(1, 2)  # B L H ->  B H L
+        window_size = attn.shape[-1]
+        sigma = torch.sigmoid(sigma * 5) + 1e-5
+        sigma = torch.pow(3, sigma) - 1
+        sigma = sigma.unsqueeze(-1).repeat(1, 1, 1, window_size)  # B H L L
+        prior = self.distances.unsqueeze(0).unsqueeze(0).repeat(sigma.shape[0], sigma.shape[1], 1, 1).cuda()
+        prior = 1.0 / (math.sqrt(2 * math.pi) * sigma) * torch.exp(-prior ** 2 / 2 / (sigma ** 2))
+
+        series = self.dropout(torch.softmax(attn, dim=-1))
+        V = torch.einsum("bhls,bshd->blhd", series, values)
+
+        if self.output_attention:
+            return (V.contiguous(), series, prior, sigma)
+        else:
+            return (V.contiguous(), None)
+
+
+class AttentionLayer(nn.Module):
+    def __init__(self, attention, d_model, n_heads, d_keys=None,
+                 d_values=None):
+        super(AttentionLayer, self).__init__()
+
+        d_keys = d_keys or (d_model // n_heads)
+        d_values = d_values or (d_model // n_heads)
+        self.norm = nn.LayerNorm(d_model)
+        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.sigma_projection = nn.Linear(d_model,
+                                          n_heads)
+        self.out_projection = nn.Linear(d_values * n_heads, d_model)
+
+        self.n_heads = n_heads
+
+    def forward(self, queries, keys, values, attn_mask):
+        B, L, _ = queries.shape
+        _, S, _ = keys.shape
+        H = self.n_heads
+        x = queries
+        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)
+        sigma = self.sigma_projection(x).view(B, L, H)
+
+        out, series, prior, sigma = self.inner_attention(
+            queries,
+            keys,
+            values,
+            sigma,
+            attn_mask
+        )
+        out = out.view(B, L, -1)
+
+        return self.out_projection(out), series, prior, sigma
+
+
+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):
+        new_x, attn, mask, sigma = self.attention(
+            x, x, x,
+            attn_mask=attn_mask
+        )
+        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, mask, sigma
+
+
+class Encoder(nn.Module):
+    def __init__(self, attn_layers, norm_layer=None):
+        super(Encoder, self).__init__()
+        self.attn_layers = nn.ModuleList(attn_layers)
+        self.norm = norm_layer
+
+    def forward(self, x, attn_mask=None):
+        # x [B, L, D]
+        series_list = []
+        prior_list = []
+        sigma_list = []
+        for attn_layer in self.attn_layers:
+            x, series, prior, sigma = attn_layer(x, attn_mask=attn_mask)
+            series_list.append(series)
+            prior_list.append(prior)
+            sigma_list.append(sigma)
+
+        if self.norm is not None:
+            x = self.norm(x)
+
+        return x, series_list, prior_list, sigma_list
+
+
+class Model(nn.Module):
+    def __init__(self, customs: {}, dataloader: tud.DataLoader):
+        super(Model, self).__init__()
+        win_size = int(customs["input_size"])
+        enc_in = c_out = dataloader.dataset.train_inputs.shape[-1]
+        d_model = 512
+        n_heads = 8
+        e_layers = 3
+        d_ff = 512
+        dropout = 0.0
+        activation = 'gelu'
+        output_attention = True
+        self.k = 3
+        self.win_size = win_size
+
+        self.name = "AnomalyTransformer"
+        # Encoding
+        self.embedding = DataEmbedding(enc_in, d_model, dropout)
+
+        # Encoder
+        self.encoder = Encoder(
+            [
+                EncoderLayer(
+                    AttentionLayer(
+                        AnomalyAttention(win_size, False, attention_dropout=dropout, output_attention=output_attention),
+                        d_model, n_heads),
+                    d_model,
+                    d_ff,
+                    dropout=dropout,
+                    activation=activation
+                ) for l in range(e_layers)
+            ],
+            norm_layer=torch.nn.LayerNorm(d_model)
+        )
+
+        self.projection = nn.Linear(d_model, c_out, bias=True)
+
+    def forward(self, x):
+        enc_out = self.embedding(x)
+        enc_out, series, prior, sigmas = self.encoder(enc_out)
+        enc_out = self.projection(enc_out)
+        return enc_out, series, prior, sigmas
+
+    @staticmethod
+    def my_kl_loss(p, q):
+        res = p * (torch.log(p + 0.0001) - torch.log(q + 0.0001))
+        return torch.mean(torch.sum(res, dim=-1), dim=1)
+
+    def loss(self, x, y_true, epoch: int = None, device: str = "cpu"):
+        output, series, prior, _ = self.forward(x)
+        series_loss = 0.0
+        prior_loss = 0.0
+        for u in range(len(prior)):
+            series_loss += (torch.mean(self.my_kl_loss(series[u], (prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1, self.win_size)).detach())) +
+                            torch.mean(self.my_kl_loss((prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1, self.win_size)).detach(), series[u])))
+
+            prior_loss += (torch.mean(self.my_kl_loss((prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1, self.win_size)), series[u].detach())) +
+                           torch.mean(self.my_kl_loss(series[u].detach(), (prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1, self.win_size)))))
+        series_loss = series_loss / len(prior)
+        prior_loss = prior_loss / len(prior)
+        rec_loss = nn.MSELoss()(output, x)
+
+        loss1 = rec_loss - self.k * series_loss
+        loss2 = rec_loss + self.k * prior_loss
+
+        # Minimax strategy
+        loss1.backward(retain_graph=True)
+        loss2.backward()
+
+        return loss1.item()
+
+    def detection(self, x, y_true, epoch: int = None, device: str = "cpu"):
+        temperature = 50
+        output, series, prior, _ = self.forward(x)
+
+        loss = torch.mean(nn.MSELoss()(x, output), dim=-1)
+
+        series_loss = 0.0
+        prior_loss = 0.0
+        for u in range(len(prior)):
+            if u == 0:
+                series_loss = self.my_kl_loss(series[u], (
+                        prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1,
+                                                                                               self.win_size)).detach()) * temperature
+                prior_loss = self.my_kl_loss(
+                    (prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1,
+                                                                                            self.win_size)),
+                    series[u].detach()) * temperature
+            else:
+                series_loss += self.my_kl_loss(series[u], (
+                        prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1,
+                                                                                               self.win_size)).detach()) * temperature
+                prior_loss += self.my_kl_loss(
+                    (prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1,
+                                                                                            self.win_size)),
+                    series[u].detach()) * temperature
+        metric = torch.softmax((-series_loss - prior_loss), dim=-1)
+
+        cri = metric * loss
+        cri = cri.mean(dim=-1)
+        return cri, None
+
+
diff --git a/method/MtadGat.py b/method/MtadGat.py
new file mode 100644
index 0000000..1ed679f
--- /dev/null
+++ b/method/MtadGat.py
@@ -0,0 +1,414 @@
+"""
+github找的第三方实现的Mtad-Gat源码
+由于源码使用了torch.empty，经常导致loss为nan，因此替换为了torch.zeros
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.data as tud
+
+
+class ConvLayer(nn.Module):
+    """1-D Convolution layer to extract high-level features of each time-series input
+    :param n_features: Number of input features/nodes
+    :param window_size: length of the input sequence
+    :param kernel_size: size of kernel to use in the convolution operation
+    """
+
+    def __init__(self, n_features, kernel_size=7):
+        super(ConvLayer, self).__init__()
+        self.padding = nn.ConstantPad1d((kernel_size - 1) // 2, 0.0)
+        self.conv = nn.Conv1d(in_channels=n_features, out_channels=n_features, kernel_size=kernel_size)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = x.permute(0, 2, 1)
+        x = self.padding(x)
+        x = self.relu(self.conv(x))
+        return x.permute(0, 2, 1)  # Permute back
+
+
+class FeatureAttentionLayer(nn.Module):
+    """Single Graph Feature/Spatial Attention Layer
+    :param n_features: Number of input features/nodes
+    :param window_size: length of the input sequence
+    :param dropout: percentage of nodes to dropout
+    :param alpha: negative slope used in the leaky rely activation function
+    :param embed_dim: embedding dimension (output dimension of linear transformation)
+    :param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT
+    :param use_bias: whether to include a bias term in the attention layer
+    """
+
+    def __init__(self, n_features, window_size, dropout, alpha, embed_dim=None, use_gatv2=True, use_bias=True):
+        super(FeatureAttentionLayer, self).__init__()
+        self.n_features = n_features
+        self.window_size = window_size
+        self.dropout = dropout
+        self.embed_dim = embed_dim if embed_dim is not None else window_size
+        self.use_gatv2 = use_gatv2
+        self.num_nodes = n_features
+        self.use_bias = use_bias
+
+        # Because linear transformation is done after concatenation in GATv2
+        if self.use_gatv2:
+            self.embed_dim *= 2
+            lin_input_dim = 2 * window_size
+            a_input_dim = self.embed_dim
+        else:
+            lin_input_dim = window_size
+            a_input_dim = 2 * self.embed_dim
+
+        self.lin = nn.Linear(lin_input_dim, self.embed_dim)
+        # self.a = nn.Parameter(torch.empty((a_input_dim, 1)))
+        self.a = nn.Parameter(torch.zeros((a_input_dim, 1)))
+        nn.init.xavier_uniform_(self.a.data, gain=1.414)
+
+        if self.use_bias:
+            # self.bias = nn.Parameter(torch.empty(n_features, n_features))
+            self.bias = nn.Parameter(torch.zeros(n_features, n_features))
+
+        self.leakyrelu = nn.LeakyReLU(alpha)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        # x shape (b, n, k): b - batch size, n - window size, k - number of features
+        # For feature attention we represent a node as the values of a particular feature across all timestamps
+
+        x = x.permute(0, 2, 1)
+
+        # 'Dynamic' GAT attention
+        # Proposed by Brody et. al., 2021 (https://arxiv.org/pdf/2105.14491.pdf)
+        # Linear transformation applied after concatenation and attention layer applied after leakyrelu
+        if self.use_gatv2:
+            a_input = self._make_attention_input(x)                 # (b, k, k, 2*window_size)
+            a_input = self.leakyrelu(self.lin(a_input))             # (b, k, k, embed_dim)
+            e = torch.matmul(a_input, self.a).squeeze(3)            # (b, k, k, 1)
+
+        # Original GAT attention
+        else:
+            Wx = self.lin(x)                                                  # (b, k, k, embed_dim)
+            a_input = self._make_attention_input(Wx)                          # (b, k, k, 2*embed_dim)
+            e = self.leakyrelu(torch.matmul(a_input, self.a)).squeeze(3)      # (b, k, k, 1)
+
+        if self.use_bias:
+            e += self.bias
+
+        # Attention weights
+        attention = torch.softmax(e, dim=2)
+        attention = torch.dropout(attention, self.dropout, train=self.training)
+
+        # Computing new node features using the attention
+        h = self.sigmoid(torch.matmul(attention, x))
+
+        return h.permute(0, 2, 1)
+
+    def _make_attention_input(self, v):
+        """Preparing the feature attention mechanism.
+        Creating matrix with all possible combinations of concatenations of node.
+        Each node consists of all values of that node within the window
+            v1 || v1,
+            ...
+            v1 || vK,
+            v2 || v1,
+            ...
+            v2 || vK,
+            ...
+            ...
+            vK || v1,
+            ...
+            vK || vK,
+        """
+
+        K = self.num_nodes
+        blocks_repeating = v.repeat_interleave(K, dim=1)  # Left-side of the matrix
+        blocks_alternating = v.repeat(1, K, 1)  # Right-side of the matrix
+        combined = torch.cat((blocks_repeating, blocks_alternating), dim=2)  # (b, K*K, 2*window_size)
+
+        if self.use_gatv2:
+            return combined.view(v.size(0), K, K, 2 * self.window_size)
+        else:
+            return combined.view(v.size(0), K, K, 2 * self.embed_dim)
+
+
+class TemporalAttentionLayer(nn.Module):
+    """Single Graph Temporal Attention Layer
+    :param n_features: number of input features/nodes
+    :param window_size: length of the input sequence
+    :param dropout: percentage of nodes to dropout
+    :param alpha: negative slope used in the leaky rely activation function
+    :param embed_dim: embedding dimension (output dimension of linear transformation)
+    :param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT
+    :param use_bias: whether to include a bias term in the attention layer
+
+    """
+
+    def __init__(self, n_features, window_size, dropout, alpha, embed_dim=None, use_gatv2=True, use_bias=True):
+        super(TemporalAttentionLayer, self).__init__()
+        self.n_features = n_features
+        self.window_size = window_size
+        self.dropout = dropout
+        self.use_gatv2 = use_gatv2
+        self.embed_dim = embed_dim if embed_dim is not None else n_features
+        self.num_nodes = window_size
+        self.use_bias = use_bias
+
+        # Because linear transformation is performed after concatenation in GATv2
+        if self.use_gatv2:
+            self.embed_dim *= 2
+            lin_input_dim = 2 * n_features
+            a_input_dim = self.embed_dim
+        else:
+            lin_input_dim = n_features
+            a_input_dim = 2 * self.embed_dim
+
+        self.lin = nn.Linear(lin_input_dim, self.embed_dim)
+        # self.a = nn.Parameter(torch.empty((a_input_dim, 1)))
+        self.a = nn.Parameter(torch.zeros((a_input_dim, 1)))
+        nn.init.xavier_uniform_(self.a.data, gain=1.414)
+
+        if self.use_bias:
+            # self.bias = nn.Parameter(torch.empty(window_size, window_size))
+            self.bias = nn.Parameter(torch.zeros(window_size, window_size))
+        self.leakyrelu = nn.LeakyReLU(alpha)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        # x shape (b, n, k): b - batch size, n - window size, k - number of features
+        # For temporal attention a node is represented as all feature values at a specific timestamp
+
+        # 'Dynamic' GAT attention
+        # Proposed by Brody et. al., 2021 (https://arxiv.org/pdf/2105.14491.pdf)
+        # Linear transformation applied after concatenation and attention layer applied after leakyrelu
+        if self.use_gatv2:
+            a_input = self._make_attention_input(x)              # (b, n, n, 2*n_features)
+            a_input = self.leakyrelu(self.lin(a_input))          # (b, n, n, embed_dim)
+            e = torch.matmul(a_input, self.a).squeeze(3)         # (b, n, n, 1)
+
+        # Original GAT attention
+        else:
+            Wx = self.lin(x)                                                  # (b, n, n, embed_dim)
+            a_input = self._make_attention_input(Wx)                          # (b, n, n, 2*embed_dim)
+            e = self.leakyrelu(torch.matmul(a_input, self.a)).squeeze(3)      # (b, n, n, 1)
+
+        if self.use_bias:
+            e += self.bias  # (b, n, n, 1)
+
+        # Attention weights
+        attention = torch.softmax(e, dim=2)
+        attention = torch.dropout(attention, self.dropout, train=self.training)
+
+        h = self.sigmoid(torch.matmul(attention, x))    # (b, n, k)
+
+        return h
+
+    def _make_attention_input(self, v):
+        """Preparing the temporal attention mechanism.
+        Creating matrix with all possible combinations of concatenations of node values:
+            (v1, v2..)_t1 || (v1, v2..)_t1
+            (v1, v2..)_t1 || (v1, v2..)_t2
+
+            ...
+            ...
+
+            (v1, v2..)_tn || (v1, v2..)_t1
+            (v1, v2..)_tn || (v1, v2..)_t2
+
+        """
+
+        K = self.num_nodes
+        blocks_repeating = v.repeat_interleave(K, dim=1)  # Left-side of the matrix
+        blocks_alternating = v.repeat(1, K, 1)  # Right-side of the matrix
+        combined = torch.cat((blocks_repeating, blocks_alternating), dim=2)
+
+        if self.use_gatv2:
+            return combined.view(v.size(0), K, K, 2 * self.n_features)
+        else:
+            return combined.view(v.size(0), K, K, 2 * self.embed_dim)
+
+
+class GRULayer(nn.Module):
+    """Gated Recurrent Unit (GRU) Layer
+    :param in_dim: number of input features
+    :param hid_dim: hidden size of the GRU
+    :param n_layers: number of layers in GRU
+    :param dropout: dropout rate
+    """
+
+    def __init__(self, in_dim, hid_dim, n_layers, dropout):
+        super(GRULayer, self).__init__()
+        self.hid_dim = hid_dim
+        self.n_layers = n_layers
+        self.dropout = 0.0 if n_layers == 1 else dropout
+        self.gru = nn.GRU(in_dim, hid_dim, num_layers=n_layers, batch_first=True, dropout=self.dropout)
+
+    def forward(self, x):
+        out, h = self.gru(x)
+        out, h = out[-1, :, :], h[-1, :, :]  # Extracting from last layer
+        return out, h
+
+
+class RNNDecoder(nn.Module):
+    """GRU-based Decoder network that converts latent vector into output
+    :param in_dim: number of input features
+    :param n_layers: number of layers in RNN
+    :param hid_dim: hidden size of the RNN
+    :param dropout: dropout rate
+    """
+
+    def __init__(self, in_dim, hid_dim, n_layers, dropout):
+        super(RNNDecoder, self).__init__()
+        self.in_dim = in_dim
+        self.dropout = 0.0 if n_layers == 1 else dropout
+        self.rnn = nn.GRU(in_dim, hid_dim, n_layers, batch_first=True, dropout=self.dropout)
+
+    def forward(self, x):
+        decoder_out, _ = self.rnn(x)
+        return decoder_out
+
+
+class ReconstructionModel(nn.Module):
+    """Reconstruction Model
+    :param window_size: length of the input sequence
+    :param in_dim: number of input features
+    :param n_layers: number of layers in RNN
+    :param hid_dim: hidden size of the RNN
+    :param in_dim: number of output features
+    :param dropout: dropout rate
+    """
+
+    def __init__(self, window_size, in_dim, hid_dim, out_dim, n_layers, dropout):
+        super(ReconstructionModel, self).__init__()
+        self.window_size = window_size
+        self.decoder = RNNDecoder(in_dim, hid_dim, n_layers, dropout)
+        self.fc = nn.Linear(hid_dim, out_dim)
+
+    def forward(self, x):
+        # x will be last hidden state of the GRU layer
+        h_end = x
+        h_end_rep = h_end.repeat_interleave(self.window_size, dim=1).view(x.size(0), self.window_size, -1)
+
+        decoder_out = self.decoder(h_end_rep)
+        out = self.fc(decoder_out)
+        return out
+
+
+class Forecasting_Model(nn.Module):
+    """Forecasting model (fully-connected network)
+    :param in_dim: number of input features
+    :param hid_dim: hidden size of the FC network
+    :param out_dim: number of output features
+    :param n_layers: number of FC layers
+    :param dropout: dropout rate
+    """
+
+    def __init__(self, in_dim, hid_dim, out_dim, n_layers, dropout):
+        super(Forecasting_Model, self).__init__()
+        layers = [nn.Linear(in_dim, hid_dim)]
+        for _ in range(n_layers - 1):
+            layers.append(nn.Linear(hid_dim, hid_dim))
+
+        layers.append(nn.Linear(hid_dim, out_dim))
+
+        self.layers = nn.ModuleList(layers)
+        self.dropout = nn.Dropout(dropout)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        for i in range(len(self.layers) - 1):
+            x = self.relu(self.layers[i](x))
+            x = self.dropout(x)
+        return self.layers[-1](x)
+
+
+class Model(nn.Module):
+    """ MTAD_GAT model class.
+
+    :param n_features: Number of input features
+    :param window_size: Length of the input sequence
+    :param out_dim: Number of features to output
+    :param kernel_size: size of kernel to use in the 1-D convolution
+    :param feat_gat_embed_dim: embedding dimension (output dimension of linear transformation)
+           in feat-oriented GAT layer
+    :param time_gat_embed_dim: embedding dimension (output dimension of linear transformation)
+           in time-oriented GAT layer
+    :param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT
+    :param gru_n_layers: number of layers in the GRU layer
+    :param gru_hid_dim: hidden dimension in the GRU layer
+    :param forecast_n_layers: number of layers in the FC-based Forecasting Model
+    :param forecast_hid_dim: hidden dimension in the FC-based Forecasting Model
+    :param recon_n_layers: number of layers in the GRU-based Reconstruction Model
+    :param recon_hid_dim: hidden dimension in the GRU-based Reconstruction Model
+    :param dropout: dropout rate
+    :param alpha: negative slope used in the leaky rely activation function
+
+    """
+
+    def __init__(self, customs: {}, dataloader: tud.DataLoader):
+        super(Model, self).__init__()
+        n_features = dataloader.dataset.train_inputs.shape[-1]
+        window_size = int(customs["input_size"])
+        out_dim = n_features
+        kernel_size = 7
+        feat_gat_embed_dim = None
+        time_gat_embed_dim = None
+        use_gatv2 = True
+        gru_n_layers = 1
+        gru_hid_dim = 150
+        forecast_n_layers = 1
+        forecast_hid_dim = 150
+        recon_n_layers = 1
+        recon_hid_dim = 150
+        dropout = 0.2
+        alpha = 0.2
+
+        self.name = "MtadGat"
+        self.conv = ConvLayer(n_features, kernel_size)
+        self.feature_gat = FeatureAttentionLayer(
+            n_features, window_size, dropout, alpha, feat_gat_embed_dim, use_gatv2)
+        self.temporal_gat = TemporalAttentionLayer(n_features, window_size, dropout, alpha, time_gat_embed_dim,
+                                                   use_gatv2)
+        self.gru = GRULayer(3 * n_features, gru_hid_dim, gru_n_layers, dropout)
+        self.forecasting_model = Forecasting_Model(
+            gru_hid_dim, forecast_hid_dim, out_dim, forecast_n_layers, dropout)
+        self.recon_model = ReconstructionModel(window_size, gru_hid_dim, recon_hid_dim, out_dim, recon_n_layers,
+                                               dropout)
+
+    def forward(self, x):
+        # x shape (b, n, k): b - batch size, n - window size, k - number of features
+
+        x = self.conv(x)
+        h_feat = self.feature_gat(x)
+        h_temp = self.temporal_gat(x)
+
+        h_cat = torch.cat([x, h_feat, h_temp], dim=2)  # (b, n, 3k)
+
+        _, h_end = self.gru(h_cat)
+        h_end = h_end.view(x.shape[0], -1)  # Hidden state for last timestamp
+
+        predictions = self.forecasting_model(h_end)
+        recons = self.recon_model(h_end)
+
+        return predictions, recons
+
+    def loss(self, x, y_true, epoch: int = None, device: str = "cpu"):
+        preds, recons = self.forward(x)
+        if preds.ndim == 3:
+            preds = preds.squeeze(1)
+        if y_true.ndim == 3:
+            y_true = y_true.squeeze(1)
+        forecast_criterion = nn.MSELoss()
+        recon_criterion = nn.MSELoss()
+        forecast_loss = torch.sqrt(forecast_criterion(y_true, preds))
+        recon_loss = torch.sqrt(recon_criterion(x, recons))
+        loss = forecast_loss + recon_loss
+        loss.backward()
+        return loss.item()
+
+    def detection(self, x, y_true, epoch: int = None, device: str = "cpu"):
+        preds, recons = self.forward(x)
+        score = F.pairwise_distance(recons.reshape(recons.size(0), -1), x.reshape(x.size(0), -1)) + \
+                F.pairwise_distance(y_true.reshape(y_true.size(0), -1), preds.reshape(preds.size(0), -1))
+        return score, None
+
+
diff --git a/method/MtadGatAtt.py b/method/MtadGatAtt.py
new file mode 100644
index 0000000..7ee50d5
--- /dev/null
+++ b/method/MtadGatAtt.py
@@ -0,0 +1,647 @@
+import torch
+import torch.nn as nn
+from math import sqrt
+import torch.nn.functional as F
+import numpy as np
+import torch.utils.data as tud
+
+
+class ConvLayer(nn.Module):
+    """1-D Convolution layer to extract high-level features of each time-series input
+    :param n_features: Number of input features/nodes
+    :param window_size: length of the input sequence
+    :param kernel_size: size of kernel to use in the convolution operation
+    """
+
+    def __init__(self, n_features, kernel_size=7):
+        super(ConvLayer, self).__init__()
+        self.padding = nn.ConstantPad1d((kernel_size - 1) // 2, 0.0)
+        self.conv = nn.Conv1d(in_channels=n_features, out_channels=n_features, kernel_size=kernel_size)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = x.permute(0, 2, 1)
+        x = self.padding(x)
+        x = self.relu(self.conv(x))
+        return x.permute(0, 2, 1)  # Permute back
+
+
+class FeatureAttentionLayer(nn.Module):
+    """Single Graph Feature/Spatial Attention Layer
+    :param n_features: Number of input features/nodes
+    :param window_size: length of the input sequence
+    :param dropout: percentage of nodes to dropout
+    :param alpha: negative slope used in the leaky rely activation function
+    :param embed_dim: embedding dimension (output dimension of linear transformation)
+    :param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT
+    :param use_bias: whether to include a bias term in the attention layer
+    """
+
+    def __init__(self, n_features, window_size, dropout, alpha, embed_dim=None, use_gatv2=True, use_bias=True,
+                 use_softmax=True):
+        super(FeatureAttentionLayer, self).__init__()
+        self.n_features = n_features
+        self.window_size = window_size
+        self.dropout = dropout
+        self.embed_dim = embed_dim if embed_dim is not None else window_size
+        self.use_gatv2 = use_gatv2
+        self.num_nodes = n_features
+        self.use_bias = use_bias
+        self.use_softmax = use_softmax
+
+        # Because linear transformation is done after concatenation in GATv2
+        if self.use_gatv2:
+            self.embed_dim *= 2
+            lin_input_dim = 2 * window_size
+            a_input_dim = self.embed_dim
+        else:
+            lin_input_dim = window_size
+            a_input_dim = 2 * self.embed_dim
+
+        self.lin = nn.Linear(lin_input_dim, self.embed_dim)
+        self.a = nn.Parameter(torch.empty((a_input_dim, 1)))
+        nn.init.xavier_uniform_(self.a.data, gain=1.414)
+
+        if self.use_bias:
+            self.bias = nn.Parameter(torch.ones(n_features, n_features))
+
+        self.leakyrelu = nn.LeakyReLU(alpha)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        # x shape (b, n, k): b - batch size, n - window size, k - number of features
+        # For feature attention we represent a node as the values of a particular feature across all timestamps
+
+        x = x.permute(0, 2, 1)
+
+        # 'Dynamic' GAT attention
+        # Proposed by Brody et. al., 2021 (https://arxiv.org/pdf/2105.14491.pdf)
+        # Linear transformation applied after concatenation and attention layer applied after leakyrelu
+        if self.use_gatv2:
+            a_input = self._make_attention_input(x)                 # (b, k, k, 2*window_size)
+            a_input = self.leakyrelu(self.lin(a_input))             # (b, k, k, embed_dim)
+            e = torch.matmul(a_input, self.a).squeeze(3)            # (b, k, k, 1)
+
+        # Original GAT attention
+        else:
+            Wx = self.lin(x)                                                  # (b, k, k, embed_dim)
+            a_input = self._make_attention_input(Wx)                          # (b, k, k, 2*embed_dim)
+            e = self.leakyrelu(torch.matmul(a_input, self.a)).squeeze(3)      # (b, k, k, 1)
+
+        if self.use_bias:
+            e += self.bias
+
+        # Attention weights
+        if self.use_softmax:
+            e = torch.softmax(e, dim=2)
+        attention = torch.dropout(e, self.dropout, train=self.training)
+
+        # Computing new node features using the attention
+        h = self.sigmoid(torch.matmul(attention, x))
+
+        return h.permute(0, 2, 1)
+
+    def _make_attention_input(self, v):
+        """Preparing the feature attention mechanism.
+        Creating matrix with all possible combinations of concatenations of node.
+        Each node consists of all values of that node within the window
+            v1 || v1,
+            ...
+            v1 || vK,
+            v2 || v1,
+            ...
+            v2 || vK,
+            ...
+            ...
+            vK || v1,
+            ...
+            vK || vK,
+        """
+
+        K = self.num_nodes
+        blocks_repeating = v.repeat_interleave(K, dim=1)  # Left-side of the matrix
+        blocks_alternating = v.repeat(1, K, 1)  # Right-side of the matrix
+        combined = torch.cat((blocks_repeating, blocks_alternating), dim=2)  # (b, K*K, 2*window_size)
+
+        if self.use_gatv2:
+            return combined.view(v.size(0), K, K, 2 * self.window_size)
+        else:
+            return combined.view(v.size(0), K, K, 2 * self.embed_dim)
+
+
+class TemporalAttentionLayer(nn.Module):
+    """Single Graph Temporal Attention Layer
+    :param n_features: number of input features/nodes
+    :param window_size: length of the input sequence
+    :param dropout: percentage of nodes to dropout
+    :param alpha: negative slope used in the leaky rely activation function
+    :param embed_dim: embedding dimension (output dimension of linear transformation)
+    :param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT
+    :param use_bias: whether to include a bias term in the attention layer
+
+    """
+
+    def __init__(self, n_features, window_size, dropout, alpha, embed_dim=None, use_gatv2=True, use_bias=True,
+                 use_softmax=True):
+        super(TemporalAttentionLayer, self).__init__()
+        self.n_features = n_features
+        self.window_size = window_size
+        self.dropout = dropout
+        self.use_gatv2 = use_gatv2
+        self.embed_dim = embed_dim if embed_dim is not None else n_features
+        self.num_nodes = window_size
+        self.use_bias = use_bias
+        self.use_softmax = use_softmax
+
+        # Because linear transformation is performed after concatenation in GATv2
+        if self.use_gatv2:
+            self.embed_dim *= 2
+            lin_input_dim = 2 * n_features
+            a_input_dim = self.embed_dim
+        else:
+            lin_input_dim = n_features
+            a_input_dim = 2 * self.embed_dim
+
+        self.lin = nn.Linear(lin_input_dim, self.embed_dim)
+        self.a = nn.Parameter(torch.empty((a_input_dim, 1)))
+        nn.init.xavier_uniform_(self.a.data, gain=1.414)
+
+        if self.use_bias:
+            self.bias = nn.Parameter(torch.ones(window_size, window_size))
+
+        self.leakyrelu = nn.LeakyReLU(alpha)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        # x shape (b, n, k): b - batch size, n - window size, k - number of features
+        # For temporal attention a node is represented as all feature values at a specific timestamp
+
+        # 'Dynamic' GAT attention
+        # Proposed by Brody et. al., 2021 (https://arxiv.org/pdf/2105.14491.pdf)
+        # Linear transformation applied after concatenation and attention layer applied after leakyrelu
+        if self.use_gatv2:
+            a_input = self._make_attention_input(x)              # (b, n, n, 2*n_features)
+            a_input = self.leakyrelu(self.lin(a_input))          # (b, n, n, embed_dim)
+            e = torch.matmul(a_input, self.a).squeeze(3)         # (b, n, n, 1)
+
+        # Original GAT attention
+        else:
+            Wx = self.lin(x)                                                  # (b, n, n, embed_dim)
+            a_input = self._make_attention_input(Wx)                          # (b, n, n, 2*embed_dim)
+            e = self.leakyrelu(torch.matmul(a_input, self.a)).squeeze(3)      # (b, n, n, 1)
+
+        if self.use_bias:
+            e += self.bias  # (b, n, n, 1)
+
+        # Attention weights
+        if self.use_softmax:
+            e = torch.softmax(e, dim=2)
+        attention = torch.dropout(e, self.dropout, train=self.training)
+
+        h = self.sigmoid(torch.matmul(attention, x))    # (b, n, k)
+
+        return h
+
+    def _make_attention_input(self, v):
+        """Preparing the temporal attention mechanism.
+        Creating matrix with all possible combinations of concatenations of node values:
+            (v1, v2..)_t1 || (v1, v2..)_t1
+            (v1, v2..)_t1 || (v1, v2..)_t2
+
+            ...
+            ...
+
+            (v1, v2..)_tn || (v1, v2..)_t1
+            (v1, v2..)_tn || (v1, v2..)_t2
+
+        """
+
+        K = self.num_nodes
+        blocks_repeating = v.repeat_interleave(K, dim=1)  # Left-side of the matrix
+        blocks_alternating = v.repeat(1, K, 1)  # Right-side of the matrix
+        combined = torch.cat((blocks_repeating, blocks_alternating), dim=2)
+
+        if self.use_gatv2:
+            return combined.view(v.size(0), K, K, 2 * self.n_features)
+        else:
+            return combined.view(v.size(0), K, K, 2 * self.embed_dim)
+
+
+class FullAttention(nn.Module):
+    def __init__(self, mask_flag=True, scale=None, attention_dropout=0.1, output_attention=False):
+        super(FullAttention, self).__init__()
+        self.scale = scale
+        self.mask_flag = mask_flag
+        self.output_attention = output_attention
+        self.dropout = nn.Dropout(attention_dropout)
+        self.relu_q = nn.ReLU()
+        self.relu_k = nn.ReLU()
+
+    @staticmethod
+    def TriangularCausalMask(B, L, S, device='cpu'):
+        mask_shape = [B, 1, L, S]
+        with torch.no_grad():
+            mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1)
+            return mask.to(device)
+
+    def forward(self, queries, keys, values, attn_mask):
+        B, L, H, E = queries.shape
+        _, S, _, D = values.shape
+        scale = self.scale or 1. / sqrt(E) # scale相对于取多少比例，取前1/根号n
+
+        scores = torch.einsum("blhe,bshe->bhls", queries, keys)
+        if self.mask_flag:
+            if attn_mask is None:
+                attn_mask = self.TriangularCausalMask(B, L, S, device=queries.device)
+
+            scores.masked_fill_(attn_mask, 0)
+
+        A = self.dropout(torch.softmax(scale * scores, dim=-1))
+        V = torch.einsum("bhls,bshd->blhd", A, values)
+
+        # queries = self.relu_q(queries)
+        # keys = self.relu_k(keys)
+        # KV = torch.einsum("blhe,bshe->bhls", keys, values)
+        # A = self.dropout(scale * KV)
+        # V = torch.einsum("bshd,bhls->blhd", queries, A)
+
+        if self.output_attention:
+            return (V.contiguous(), A)
+        else:
+            return (V.contiguous(), None)
+
+
+class ProbAttention(nn.Module):
+    def __init__(self, mask_flag=True, factor=2, 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
+
+    @staticmethod
+    def ProbMask(B, H, D, index, scores, device='cpu'):
+        _mask = torch.ones(D, scores.shape[-2], dtype=torch.bool).triu(1)
+        _mask_ex = _mask[None, None, :].expand(B, H, D, scores.shape[-2])
+        indicator = _mask_ex.transpose(-2, -1)[torch.arange(B)[:, None, None],
+                    torch.arange(H)[None, :, None],
+                    index, :].transpose(-2, -1)
+        mask = indicator.view(scores.shape)
+        return mask.to(device)
+
+    def _prob_KV(self, K, V, sample_v, n_top):  # n_top: c*ln(L_q)
+        # Q [B, H, L, D]
+        B, H, L, E_V = V.shape
+        _, _, _, E_K = K.shape
+
+        # calculate the sampled K_V
+
+        V_expand = V.transpose(-2, -1).unsqueeze(-2).expand(B, H, E_V, E_K, L)
+        index_sample = torch.randint(E_V, (E_K, sample_v))  # real U = U_part(factor*ln(L_k))*L_q
+        V_sample = V_expand[:, :, torch.arange(E_V).unsqueeze(1), index_sample, :]
+        K_V_sample = torch.matmul(K.transpose(-2, -1).unsqueeze(-2), V_sample.transpose(-2, -1)).squeeze()
+
+        # find the Top_k query with sparisty measurement
+        M = K_V_sample.max(-1)[0] - torch.div(K_V_sample.sum(-1), E_V)
+        M_top = M.topk(n_top, sorted=False)[1]
+
+        # use the reduced Q to calculate Q_K
+        V_reduce = V.transpose(-2, -1)[torch.arange(B)[:, None, None],
+                   torch.arange(H)[None, :, None],
+                   M_top, :].transpose(-2, -1)  # factor*ln(L_q)
+        K_V = torch.matmul(K.transpose(-2, -1), V_reduce)  # factor*ln(L_q)*L_k
+        #
+        return K_V, M_top
+
+    def _get_initial_context(self, V, L_Q):
+        B, H, L_V, D = V.shape
+        if not self.mask_flag:
+            # V_sum = V.sum(dim=-2)
+            V_sum = V.mean(dim=-2)
+            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
+
+    def _update_context(self, context_in, Q, scores, index, D_K, attn_mask):
+        B, H, L, D_Q = Q.shape
+
+        if self.mask_flag:
+            attn_mask = self.ProbMask(B, H, D_K, index, scores, device=Q.device)
+            scores.masked_fill_(attn_mask, -np.inf)
+
+        attn = torch.softmax(scores, dim=-1)  # nn.Softmax(dim=-1)(scores)
+
+        context_in.transpose(-2, -1)[torch.arange(B)[:, None, None],
+        torch.arange(H)[None, :, None],
+        index, :] = torch.matmul(Q, attn).type_as(context_in).transpose(-2, -1)
+        if self.output_attention:
+            attns = (torch.ones([B, H, D_K, D_K]) / D_K).type_as(attn)
+            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):
+        # B, L_Q, H, D = queries.shape
+        # _, L_K, _, _ = keys.shape
+
+        B, L, H, D_K = keys.shape
+        _, _, _, D_V = values.shape
+
+        queries = queries.transpose(2, 1)
+        keys = keys.transpose(2, 1)
+        values = values.transpose(2, 1)
+
+        U_part = self.factor * np.ceil(np.log(D_V)).astype('int').item()  # c*ln(L_k)
+        u = self.factor * np.ceil(np.log(D_K)).astype('int').item()  # c*ln(L_q)
+
+        U_part = U_part if U_part < D_V else D_V
+        u = u if u < D_K else D_K
+
+        scores_top, index = self._prob_KV(keys, values, sample_v=U_part, n_top=u)
+
+        # add scale factor
+        scale = self.scale or 1. / sqrt(D_K)
+        if scale is not None:
+            scores_top = scores_top * scale
+        # get the context
+        context = self._get_initial_context(queries, L)
+        # update the context with selected top_k queries
+        context, attn = self._update_context(context, queries, scores_top, index, D_K, attn_mask)
+
+        return context.contiguous(), attn
+
+
+class AttentionBlock(nn.Module):
+    def __init__(self, d_model, n_model, n_heads=8, d_keys=None, d_values=None):
+        super(AttentionBlock, self).__init__()
+
+        d_keys = d_keys or (d_model // n_heads)
+        d_values = d_values or (d_model // n_heads)
+        self.inner_attention = FullAttention()
+        # self.inner_attention = ProbAttention(device=device)
+        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.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
+
+    def forward(self, queries, keys, values, attn_mask):
+        '''
+        Q: [batch_size, len_q, d_k]
+        K: [batch_size, len_k, d_k]
+        V: [batch_size, len_v(=len_k), d_v]
+        attn_mask: [batch_size, seq_len, seq_len]
+        '''
+        batch_size, len_q, _ = queries.shape
+        _, len_k, _ = keys.shape
+
+        queries = self.query_projection(queries).view(batch_size, len_q, self.n_heads, -1)
+        keys = self.key_projection(keys).view(batch_size, len_k, self.n_heads, -1)
+        values = self.value_projection(values).view(batch_size, len_k, self.n_heads, -1)
+
+        out, attn = self.inner_attention(
+            queries,
+            keys,
+            values,
+            attn_mask
+        )
+        out = out.view(batch_size, len_q, -1)
+        out = self.out_projection(out)
+        out = self.layer_norm(out)
+        return out, attn
+
+
+class GRULayer(nn.Module):
+    """Gated Recurrent Unit (GRU) Layer
+    :param in_dim: number of input features
+    :param hid_dim: hidden size of the GRU
+    :param n_layers: number of layers in GRU
+    :param dropout: dropout rate
+    """
+
+    def __init__(self, in_dim, hid_dim, n_layers, dropout):
+        super(GRULayer, self).__init__()
+        self.hid_dim = hid_dim
+        self.n_layers = n_layers
+        self.dropout = 0.0 if n_layers == 1 else dropout
+        self.gru = nn.GRU(in_dim, hid_dim, num_layers=n_layers, batch_first=True, dropout=self.dropout)
+
+    def forward(self, x):
+        out, h = self.gru(x)
+        out, h = out[-1, :, :], h[-1, :, :]  # Extracting from last layer
+        return out, h
+
+
+class RNNDecoder(nn.Module):
+    """GRU-based Decoder network that converts latent vector into output
+    :param in_dim: number of input features
+    :param n_layers: number of layers in RNN
+    :param hid_dim: hidden size of the RNN
+    :param dropout: dropout rate
+    """
+
+    def __init__(self, in_dim, hid_dim, n_layers, dropout):
+        super(RNNDecoder, self).__init__()
+        self.in_dim = in_dim
+        self.dropout = 0.0 if n_layers == 1 else dropout
+        self.rnn = nn.GRU(in_dim, hid_dim, n_layers, batch_first=True, dropout=self.dropout)
+
+    def forward(self, x):
+        decoder_out, _ = self.rnn(x)
+        return decoder_out
+
+
+class ReconstructionModel(nn.Module):
+    """Reconstruction Model
+    :param window_size: length of the input sequence
+    :param in_dim: number of input features
+    :param n_layers: number of layers in RNN
+    :param hid_dim: hidden size of the RNN
+    :param in_dim: number of output features
+    :param dropout: dropout rate
+    """
+
+    def __init__(self, window_size, in_dim, hid_dim, out_dim, n_layers, dropout):
+        super(ReconstructionModel, self).__init__()
+        self.window_size = window_size
+        self.decoder = RNNDecoder(in_dim, hid_dim, n_layers, dropout)
+        self.fc = nn.Linear(hid_dim, out_dim)
+
+    def forward(self, x):
+        # x will be last hidden state of the GRU layer
+        h_end = x
+        h_end_rep = h_end.repeat_interleave(self.window_size, dim=1).view(x.size(0), self.window_size, -1)
+
+        decoder_out = self.decoder(h_end_rep)
+        out = self.fc(decoder_out)
+        return out
+
+
+class Forecasting_Model(nn.Module):
+    """Forecasting model (fully-connected network)
+    :param in_dim: number of input features
+    :param hid_dim: hidden size of the FC network
+    :param out_dim: number of output features
+    :param n_layers: number of FC layers
+    :param dropout: dropout rate
+    """
+
+    def __init__(self, in_dim, hid_dim, out_dim, n_layers, dropout):
+        super(Forecasting_Model, self).__init__()
+        layers = [nn.Linear(in_dim, hid_dim)]
+        for _ in range(n_layers - 1):
+            layers.append(nn.Linear(hid_dim, hid_dim))
+
+        layers.append(nn.Linear(hid_dim, out_dim))
+
+        self.layers = nn.ModuleList(layers)
+        self.dropout = nn.Dropout(dropout)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        for i in range(len(self.layers) - 1):
+            x = self.relu(self.layers[i](x))
+            x = self.dropout(x)
+        return self.layers[-1](x)
+
+
+class Model(nn.Module):
+    """ MTAD_GAT model class.
+
+    :param n_features: Number of input features
+    :param window_size: Length of the input sequence
+    :param out_dim: Number of features to output
+    :param kernel_size: size of kernel to use in the 1-D convolution
+    :param feat_gat_embed_dim: embedding dimension (output dimension of linear transformation)
+           in feat-oriented GAT layer
+    :param time_gat_embed_dim: embedding dimension (output dimension of linear transformation)
+           in time-oriented GAT layer
+    :param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT
+    :param gru_n_layers: number of layers in the GRU layer
+    :param gru_hid_dim: hidden dimension in the GRU layer
+    :param forecast_n_layers: number of layers in the FC-based Forecasting Model
+    :param forecast_hid_dim: hidden dimension in the FC-based Forecasting Model
+    :param recon_n_layers: number of layers in the GRU-based Reconstruction Model
+    :param recon_hid_dim: hidden dimension in the GRU-based Reconstruction Model
+    :param dropout: dropout rate
+    :param alpha: negative slope used in the leaky rely activation function
+
+    """
+
+    def __init__(self, customs: dict, dataloader: tud.DataLoader = None):
+        super(Model, self).__init__()
+        n_features = dataloader.dataset.train_inputs.shape[-1]
+        window_size = int(customs["input_size"])
+        out_dim = n_features
+        kernel_size = 7
+        feat_gat_embed_dim = None
+        time_gat_embed_dim = None
+        use_gatv2 = True
+        gru_n_layers = 1
+        gru_hid_dim = 150
+        forecast_n_layers = 1
+        forecast_hid_dim = 150
+        recon_n_layers = 1
+        recon_hid_dim = 150
+        dropout = 0.2
+        alpha = 0.2
+        optimize = True
+
+        self.name = "MtadGatAtt"
+        self.optimize = optimize
+        use_softmax = not optimize
+
+        self.conv = ConvLayer(n_features, kernel_size)
+        self.feature_gat = FeatureAttentionLayer(
+            n_features, window_size, dropout, alpha, feat_gat_embed_dim, use_gatv2, use_softmax=use_softmax)
+        self.temporal_gat = TemporalAttentionLayer(n_features, window_size, dropout, alpha, time_gat_embed_dim,
+                                                   use_gatv2, use_softmax=use_softmax)
+        self.forecasting_model = Forecasting_Model(
+            gru_hid_dim, forecast_hid_dim, out_dim, forecast_n_layers, dropout)
+        if optimize:
+            self.encode = AttentionBlock(3 * n_features, window_size)
+            self.encode_feature = nn.Linear(3 * n_features * window_size, gru_hid_dim)
+            self.decode_feature = nn.Linear(gru_hid_dim, n_features * window_size)
+            self.decode = AttentionBlock(n_features, window_size)
+        else:
+            self.gru = GRULayer(3 * n_features, gru_hid_dim, gru_n_layers, dropout)
+            self.recon_model = ReconstructionModel(window_size, gru_hid_dim, recon_hid_dim, out_dim, recon_n_layers,
+                                                   dropout)
+
+    def forward(self, x):
+        x = self.conv(x)
+        h_feat = self.feature_gat(x)
+        h_temp = self.temporal_gat(x)
+        h_cat = torch.cat([x, h_feat, h_temp], dim=2)  # (b, n, 3k)
+
+        if self.optimize:
+            h_end, _ = self.encode(h_cat, h_cat, h_cat, None)
+            h_end = self.encode_feature(h_end.reshape(h_end.size(0), -1))
+        else:
+            _, h_end = self.gru(h_cat)
+            h_end = h_end.view(x.shape[0], -1)  # Hidden state for last timestamp
+
+        predictions = self.forecasting_model(h_end)
+
+        if self.optimize:
+            h_end = self.decode_feature(h_end)
+            h_end = h_end.reshape(x.shape[0], x.shape[1], x.shape[2])
+            recons, _ = self.decode(h_end, h_end, h_end, None)
+        else:
+            recons = self.recon_model(h_end)
+
+        return predictions, recons
+
+    def loss(self, x, y_true, epoch: int = None, device: str = "cpu"):
+        preds, recons = self.forward(x)
+
+        if preds.ndim == 3:
+            preds = preds.squeeze(1)
+        if y_true.ndim == 3:
+            y_true = y_true.squeeze(1)
+        forecast_criterion = nn.MSELoss()
+        recon_criterion = nn.MSELoss()
+
+        forecast_loss = torch.sqrt(forecast_criterion(y_true, preds))
+        recon_loss = torch.sqrt(recon_criterion(x, recons))
+
+        loss = forecast_loss + recon_loss
+        loss.backward()
+        return loss.item()
+
+    def detection(self, x, y_true, epoch: int = None, device: str = "cpu"):
+        preds, recons = self.forward(x)
+        score = F.pairwise_distance(recons.reshape(recons.size(0), -1), x.reshape(x.size(0), -1)) + F.pairwise_distance(y_true.reshape(y_true.size(0), -1), preds.reshape(preds.size(0), -1))
+        return score, None
+
+
+if __name__ == "__main__":
+    from tqdm import tqdm
+    import time
+    epoch = 10000
+    batch_size = 1
+    # device = 'cuda:1' if torch.cuda.is_available() else 'cpu'
+    device = 'cpu'
+    input_len_list = [30, 60, 90, 120, 150, 180, 210, 240, 270, 300]
+    for input_len in input_len_list:
+        model = Model(52, input_len, 52, optimize=False, device=device).to(device)
+        a = torch.Tensor(torch.ones((batch_size, input_len, 52))).to(device)
+        start = time.time()
+        for i in tqdm(range(epoch)):
+            model(a)
+        end = time.time()
+        speed1 = batch_size * epoch / (end - start)
+
+        model = Model(52, input_len, 52, optimize=True, device=device).to(device)
+        a = torch.Tensor(torch.ones((batch_size, input_len, 52))).to(device)
+        start = time.time()
+        for i in tqdm(range(epoch)):
+            model(a)
+        end = time.time()
+        speed2 = batch_size * epoch / (end - start)
+        print(input_len, (speed2 - speed1)/speed1, speed1, speed2)
+
diff --git a/method/__init__.py b/method/__init__.py
new file mode 100644
index 0000000..39fe90b
--- /dev/null
+++ b/method/__init__.py
@@ -0,0 +1,3 @@
+from .MtadGat import Model
+from .MtadGatAtt import Model
+from .AnomalyTransformer import Model
\ No newline at end of file
diff --git a/method/template.py b/method/template.py
new file mode 100644
index 0000000..a608627
--- /dev/null
+++ b/method/template.py
@@ -0,0 +1,50 @@
+import torch.nn as nn
+import torch.utils.data as tud
+import torch
+
+
+class Model(nn.Module):
+    def __init__(self, customs: dict, dataloader: tud.DataLoader = None):
+        """
+        :param customs: 自定义参数，内容取自于config.ini文件的[CustomParameters]部分。
+        :param dataloader: 数据集初始化完成的dataloader。在自定义的预处理方法文件中，可以增加内部变量或者方法，提供给模型。
+                           例如：模型初始化需要数据的维度数量，可通过n_features = dataloader.dataset.train_inputs.shape[-1]获取
+                                或在预处理方法的MyDataset类中，定义self.n_features = self.train_inputs.shape[-1]，
+                                通过n_features = dataloader.dataset.n_features获取
+        """
+        super(Model, self).__init__()
+
+    def forward(self, x):
+        """
+        :param x: 模型的输入，在本工具中为MyDataset类中__getitem__方法返回的三个变量中的第一个变量。
+        :return: 模型的输出，可以自定义
+        """
+        return None
+
+    def loss(self, x, y_true, epoch: int = None, device: str = "cpu"):
+        """
+        计算loss。注意，计算loss时如采用torch之外的库计算会造成梯度截断，请全部使用torch的方法
+        :param x: 输入数据
+        :param y_true: 真实输出数据
+        :param epoch: 当前是第几个epoch
+        :param device: 设备，cpu或者cuda
+        :return: loss值
+        """
+        y_pred = self.forward(x)  # 模型的输出
+        loss = torch.Tensor([1])  # 示例，请修改
+        loss.backward()
+        return loss.item()
+
+    def detection(self, x, y_true, epoch: int = None, device: str = "cpu"):
+        """
+        检测方法，可以输出异常的分数，也可以输出具体的标签。
+        如输出异常分数，则后续会根据异常分数自动划分阈值，高于阈值的为异常，自动赋予标签；如输出标签，则直接进行评估。
+        :param x: 输入数据
+        :param y_true: 真实输出数据
+        :param epoch: 当前是第几个epoch
+        :param device: 设备，cpu或者cuda
+        :return: score，label。如选择输出异常的分数，则输出score，label为None；如选择输出标签，则输出label，score为None。
+                 score的格式为torch的Tensor格式，尺寸为[batch_size]；label的格式为torch的IntTensor格式，尺寸为[batch_size]
+        """
+        y_pred = self.forward(x)  # 模型的输出
+        return None, None
\ No newline at end of file