添加STIDGCN

# Conflicts:
#	model/model_selector.py

config/STIDGCN/PEMSD3.yaml

@@ -0,0 +1,48 @@
+data:
+  num_nodes: 358
+  lag: 12
+  horizon: 12
+  val_ratio: 0.2
+  test_ratio: 0.2
+  tod: False
+  normalizer: std
+  column_wise: False
+  default_graph: True
+  add_time_in_day: True
+  add_day_in_week: True
+  steps_per_day: 288
+  days_per_week: 7
+
+model:
+  input_dim: 3
+  output_dim: 1
+  history: 12
+  horizon: 12
+  granularity: 288
+  dropout: 0.1
+  channels: 32
+
+
+train:
+  loss_func: mae
+  seed: 10
+  batch_size: 64
+  epochs: 300
+  lr_init: 0.003
+  weight_decay: 0
+  lr_decay: False
+  lr_decay_rate: 0.3
+  lr_decay_step: "5,20,40,70"
+  early_stop: True
+  early_stop_patience: 15
+  grad_norm: False
+  max_grad_norm: 5
+  real_value: True
+
+test:
+  mae_thresh: null
+  mape_thresh: 0.0
+
+log:
+  log_step: 200
+  plot: False

config/STIDGCN/PEMSD4.yaml

@@ -0,0 +1,48 @@
+data:
+  num_nodes: 307
+  lag: 12
+  horizon: 12
+  val_ratio: 0.2
+  test_ratio: 0.2
+  tod: False
+  normalizer: std
+  column_wise: False
+  default_graph: True
+  add_time_in_day: True
+  add_day_in_week: True
+  steps_per_day: 288
+  days_per_week: 7
+
+model:
+  input_dim: 3
+  output_dim: 1
+  history: 12
+  horizon: 12
+  granularity: 288
+  dropout: 0.1
+  channels: 64
+
+
+train:
+  loss_func: mae
+  seed: 10
+  batch_size: 64
+  epochs: 300
+  lr_init: 0.003
+  weight_decay: 0
+  lr_decay: False
+  lr_decay_rate: 0.3
+  lr_decay_step: "5,20,40,70"
+  early_stop: True
+  early_stop_patience: 15
+  grad_norm: False
+  max_grad_norm: 5
+  real_value: True
+
+test:
+  mae_thresh: null
+  mape_thresh: 0.0
+
+log:
+  log_step: 200
+  plot: False

config/STIDGCN/PEMSD7.yaml

@@ -0,0 +1,48 @@
+data:
+  num_nodes: 883
+  lag: 12
+  horizon: 12
+  val_ratio: 0.2
+  test_ratio: 0.2
+  tod: False
+  normalizer: std
+  column_wise: False
+  default_graph: True
+  add_time_in_day: True
+  add_day_in_week: True
+  steps_per_day: 288
+  days_per_week: 7
+
+model:
+  input_dim: 3
+  output_dim: 1
+  history: 12
+  horizon: 12
+  granularity: 288
+  dropout: 0.1
+  channels: 128
+
+
+train:
+  loss_func: mae
+  seed: 10
+  batch_size: 16
+  epochs: 300
+  lr_init: 0.003
+  weight_decay: 0
+  lr_decay: False
+  lr_decay_rate: 0.3
+  lr_decay_step: "5,20,40,70"
+  early_stop: True
+  early_stop_patience: 15
+  grad_norm: False
+  max_grad_norm: 5
+  real_value: True
+
+test:
+  mae_thresh: null
+  mape_thresh: 0.0
+
+log:
+  log_step: 200
+  plot: False

config/STIDGCN/PEMSD8.yaml

@@ -0,0 +1,48 @@
+data:
+  num_nodes: 170
+  lag: 12
+  horizon: 12
+  val_ratio: 0.2
+  test_ratio: 0.2
+  tod: False
+  normalizer: std
+  column_wise: False
+  default_graph: True
+  add_time_in_day: True
+  add_day_in_week: True
+  steps_per_day: 288
+  days_per_week: 7
+
+model:
+  input_dim: 3
+  output_dim: 1
+  history: 12
+  horizon: 12
+  granularity: 288
+  dropout: 0.1
+  channels: 96
+
+
+train:
+  loss_func: mae
+  seed: 10
+  batch_size: 64
+  epochs: 300
+  lr_init: 0.003
+  weight_decay: 0
+  lr_decay: False
+  lr_decay_rate: 0.3
+  lr_decay_step: "5,20,40,70"
+  early_stop: True
+  early_stop_patience: 15
+  grad_norm: False
+  max_grad_norm: 5
+  real_value: True
+
+test:
+  mae_thresh: null
+  mape_thresh: 0.0
+
+log:
+  log_step: 200
+  plot: False

model/STIDGCN/STIDGCN.py

@@ -0,0 +1,368 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+
+class GLU(nn.Module):
+    def __init__(self, features, dropout=0.1):
+        super(GLU, self).__init__()
+        self.conv1 = nn.Conv2d(features, features, (1, 1))
+        self.conv2 = nn.Conv2d(features, features, (1, 1))
+        self.conv3 = nn.Conv2d(features, features, (1, 1))
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        x1 = self.conv1(x)
+        x2 = self.conv2(x)
+        out = x1 * torch.sigmoid(x2)
+        out = self.dropout(out)
+        out = self.conv3(out)
+        return out
+
+
+# class TemporalEmbedding(nn.Module):
+#     def __init__(self, time, features):
+#         super(TemporalEmbedding, self).__init__()
+#
+#         self.time = time
+#         # self.time_day = nn.Parameter(torch.empty(time, features))
+#         # nn.init.xavier_uniform_(self.time_day)
+#         #
+#         # self.time_week = nn.Parameter(torch.empty(7, features))
+#         # nn.init.xavier_uniform_(self.time_week)
+#         self.time_day = nn.Embedding(time, features)
+#         self.time_week = nn.Embedding(7, features)
+#
+#     def forward(self, x):
+#         day_emb = x[..., 1]
+#         # time_day = self.time_day[(day_emb[:, :, :] * self.time).type(torch.LongTensor)]
+#         # time_day = time_day.transpose(1, 2).contiguous()
+#
+#         week_emb = x[..., 2]
+#         # time_week = self.time_week[(week_emb[:, :, :]).type(torch.LongTensor)]
+#         # time_week = time_week.transpose(1, 2).contiguous()
+#
+#         t_idx = (day_emb[:, -1, :, ] * (self.time - 1)).long()  # (B, N)
+#         d_idx = week_emb[:, -1, :, ].long()  # (B, N)
+#         # time_emb = self.time_embedding(t_idx)  # (B, N, hidden_dim)
+#         # day_emb = self.day_embedding(d_idx)  # (B, N, hidden_dim)
+#
+#         tem_emb = t_idx + d_idx
+#
+#         # tem_emb = tem_emb.permute(0, 3, 1, 2)
+#
+#         return tem_emb
+class TemporalEmbedding(nn.Module):
+    def __init__(self, time, features):
+        super(TemporalEmbedding, self).__init__()
+
+        self.time = time
+        self.time_day = nn.Parameter(torch.empty(time, features))
+        nn.init.xavier_uniform_(self.time_day)
+
+        self.time_week = nn.Parameter(torch.empty(7, features))
+        nn.init.xavier_uniform_(self.time_week)
+
+    def forward(self, x):
+        day_emb = x[..., 1]
+        time_day = self.time_day[(day_emb[:, :, :] * self.time).type(torch.LongTensor)]
+        time_day = time_day.transpose(1, 2).contiguous()
+
+        week_emb = x[..., 2]
+        time_week = self.time_week[(week_emb[:, :, :]).type(torch.LongTensor)]
+        time_week = time_week.transpose(1, 2).contiguous()
+
+        tem_emb = time_day + time_week
+
+        tem_emb = tem_emb.permute(0,3,1,2)
+
+        return tem_emb
+
+class Diffusion_GCN(nn.Module):
+    def __init__(self, channels=128, diffusion_step=1, dropout=0.1):
+        super().__init__()
+        self.diffusion_step = diffusion_step
+        self.conv = nn.Conv2d(diffusion_step * channels, channels, (1, 1))
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x, adj):
+        out = []
+        for i in range(0, self.diffusion_step):
+            if adj.dim() == 3:
+                x = torch.einsum("bcnt,bnm->bcmt", x, adj).contiguous()
+                out.append(x)
+            elif adj.dim() == 2:
+                x = torch.einsum("bcnt,nm->bcmt", x, adj).contiguous()
+                out.append(x)
+        x = torch.cat(out, dim=1)
+        x = self.conv(x)
+        output = self.dropout(x)
+        return output
+
+
+class Graph_Generator(nn.Module):
+    def __init__(self, channels=128, num_nodes=170, diffusion_step=1, dropout=0.1):
+        super().__init__()
+        self.memory = nn.Parameter(torch.randn(channels, num_nodes))
+        nn.init.xavier_uniform_(self.memory)
+        self.fc = nn.Linear(2, 1)
+
+    def forward(self, x):
+        adj_dyn_1 = torch.softmax(
+            F.relu(
+                torch.einsum("bcnt, cm->bnm", x, self.memory).contiguous()
+                / math.sqrt(x.shape[1])
+            ),
+            -1,
+        )
+        adj_dyn_2 = torch.softmax(
+            F.relu(
+                torch.einsum("bcn, bcm->bnm", x.sum(-1), x.sum(-1)).contiguous()
+                / math.sqrt(x.shape[1])
+            ),
+            -1,
+        )
+        # adj_dyn = (adj_dyn_1 + adj_dyn_2 + adj)/2
+        adj_f = torch.cat([(adj_dyn_1).unsqueeze(-1)] + [(adj_dyn_2).unsqueeze(-1)], dim=-1)
+        adj_f = torch.softmax(self.fc(adj_f).squeeze(), -1)
+
+        topk_values, topk_indices = torch.topk(adj_f, k=int(adj_f.shape[1] * 0.8), dim=-1)
+        mask = torch.zeros_like(adj_f)
+        mask.scatter_(-1, topk_indices, 1)
+        adj_f = adj_f * mask
+
+        return adj_f
+
+
+class DGCN(nn.Module):
+    def __init__(self, channels=128, num_nodes=170, diffusion_step=1, dropout=0.1, emb=None):
+        super().__init__()
+        self.conv = nn.Conv2d(channels, channels, (1, 1))
+        self.generator = Graph_Generator(channels, num_nodes, diffusion_step, dropout)
+        self.gcn = Diffusion_GCN(channels, diffusion_step, dropout)
+        self.emb = emb
+
+    def forward(self, x):
+        skip = x
+        x = self.conv(x)
+        adj_dyn = self.generator(x)
+        x = self.gcn(x, adj_dyn)
+        x = x * self.emb + skip
+        return x
+
+
+class Splitting(nn.Module):
+    def __init__(self):
+        super(Splitting, self).__init__()
+
+    def even(self, x):
+        return x[:, :, :, ::2]
+
+    def odd(self, x):
+        return x[:, :, :, 1::2]
+
+    def forward(self, x):
+        return (self.even(x), self.odd(x))
+
+
+class IDGCN(nn.Module):
+    def __init__(
+            self,
+            device,
+            channels=64,
+            diffusion_step=1,
+            splitting=True,
+            num_nodes=170,
+            dropout=0.2, emb=None
+    ):
+        super(IDGCN, self).__init__()
+
+        device = device
+        self.dropout = dropout
+        self.num_nodes = num_nodes
+        self.splitting = splitting
+        self.split = Splitting()
+
+        Conv1 = []
+        Conv2 = []
+        Conv3 = []
+        Conv4 = []
+        pad_l = 3
+        pad_r = 3
+
+        k1 = 5
+        k2 = 3
+        Conv1 += [
+            nn.ReplicationPad2d((pad_l, pad_r, 0, 0)),
+            nn.Conv2d(channels, channels, kernel_size=(1, k1)),
+            nn.LeakyReLU(negative_slope=0.01, inplace=True),
+            nn.Dropout(self.dropout),
+            nn.Conv2d(channels, channels, kernel_size=(1, k2)),
+            nn.Tanh(),
+        ]
+        Conv2 += [
+            nn.ReplicationPad2d((pad_l, pad_r, 0, 0)),
+            nn.Conv2d(channels, channels, kernel_size=(1, k1)),
+            nn.LeakyReLU(negative_slope=0.01, inplace=True),
+            nn.Dropout(self.dropout),
+            nn.Conv2d(channels, channels, kernel_size=(1, k2)),
+            nn.Tanh(),
+        ]
+        Conv4 += [
+            nn.ReplicationPad2d((pad_l, pad_r, 0, 0)),
+            nn.Conv2d(channels, channels, kernel_size=(1, k1)),
+            nn.LeakyReLU(negative_slope=0.01, inplace=True),
+            nn.Dropout(self.dropout),
+            nn.Conv2d(channels, channels, kernel_size=(1, k2)),
+            nn.Tanh(),
+        ]
+        Conv3 += [
+            nn.ReplicationPad2d((pad_l, pad_r, 0, 0)),
+            nn.Conv2d(channels, channels, kernel_size=(1, k1)),
+            nn.LeakyReLU(negative_slope=0.01, inplace=True),
+            nn.Dropout(self.dropout),
+            nn.Conv2d(channels, channels, kernel_size=(1, k2)),
+            nn.Tanh(),
+        ]
+
+        self.conv1 = nn.Sequential(*Conv1)
+        self.conv2 = nn.Sequential(*Conv2)
+        self.conv3 = nn.Sequential(*Conv3)
+        self.conv4 = nn.Sequential(*Conv4)
+
+        self.dgcn = DGCN(channels, num_nodes, diffusion_step, dropout, emb)
+
+    def forward(self, x):
+        if self.splitting:
+            (x_even, x_odd) = self.split(x)
+        else:
+            (x_even, x_odd) = x
+
+        x1 = self.conv1(x_even)
+        x1 = self.dgcn(x1)
+        d = x_odd.mul(torch.tanh(x1))
+
+        x2 = self.conv2(x_odd)
+        x2 = self.dgcn(x2)
+        c = x_even.mul(torch.tanh(x2))
+
+        x3 = self.conv3(c)
+        x3 = self.dgcn(x3)
+        x_odd_update = d + x3
+
+        x4 = self.conv4(d)
+        x4 = self.dgcn(x4)
+        x_even_update = c + x4
+
+        return (x_even_update, x_odd_update)
+
+
+class IDGCN_Tree(nn.Module):
+    def __init__(
+            self, device, channels=64, diffusion_step=1, num_nodes=170, dropout=0.1
+    ):
+        super().__init__()
+
+        self.memory1 = nn.Parameter(torch.randn(channels, num_nodes, 6))
+        self.memory2 = nn.Parameter(torch.randn(channels, num_nodes, 3))
+        self.memory3 = nn.Parameter(torch.randn(channels, num_nodes, 3))
+
+        self.IDGCN1 = IDGCN(
+            device=device,
+            splitting=True,
+            channels=channels,
+            diffusion_step=diffusion_step,
+            num_nodes=num_nodes,
+            dropout=dropout, emb=self.memory1
+        )
+        self.IDGCN2 = IDGCN(
+            device=device,
+            splitting=True,
+            channels=channels,
+            diffusion_step=diffusion_step,
+            num_nodes=num_nodes,
+            dropout=dropout, emb=self.memory2
+        )
+        self.IDGCN3 = IDGCN(
+            device=device,
+            splitting=True,
+            channels=channels,
+            diffusion_step=diffusion_step,
+            num_nodes=num_nodes,
+            dropout=dropout, emb=self.memory2
+        )
+
+    def concat(self, even, odd):
+        even = even.permute(3, 1, 2, 0)
+        odd = odd.permute(3, 1, 2, 0)
+        len = even.shape[0]
+        _ = []
+        for i in range(len):
+            _.append(even[i].unsqueeze(0))
+            _.append(odd[i].unsqueeze(0))
+        return torch.cat(_, 0).permute(3, 1, 2, 0)
+
+    def forward(self, x):
+        x_even_update1, x_odd_update1 = self.IDGCN1(x)
+        x_even_update2, x_odd_update2 = self.IDGCN2(x_even_update1)
+        x_even_update3, x_odd_update3 = self.IDGCN3(x_odd_update1)
+        concat1 = self.concat(x_even_update2, x_odd_update2)
+        concat2 = self.concat(x_even_update3, x_odd_update3)
+        concat0 = self.concat(concat1, concat2)
+        output = concat0 + x
+        return output
+
+
+class STIDGCN(nn.Module):
+    def __init__(self, args):
+        """
+        device, input_dim, num_nodes, channels, granularity, dropout=0.1
+        """
+        super().__init__()
+
+        device = args['device']
+        input_dim = args['input_dim']
+        self.num_nodes = args['num_nodes']
+        self.output_len = 12
+        channels = args['channels']
+        granularity = args['granularity']
+        dropout = args['dropout']
+        diffusion_step = 1
+
+        self.Temb = TemporalEmbedding(granularity, channels)
+
+        self.start_conv = nn.Conv2d(
+            in_channels=input_dim, out_channels=channels, kernel_size=(1, 1)
+        )
+
+        self.tree = IDGCN_Tree(
+            device=device,
+            channels=channels * 2,
+            diffusion_step=diffusion_step,
+            num_nodes=self.num_nodes,
+            dropout=dropout,
+        )
+
+        self.glu = GLU(channels * 2, dropout)
+
+        self.regression_layer = nn.Conv2d(
+            channels * 2, self.output_len, kernel_size=(1, self.output_len)
+        )
+
+    def param_num(self):
+        return sum([param.nelement() for param in self.parameters()])
+
+    def forward(self, input):
+        input = input.transpose(1, 3)
+        x = input
+        # Encoder
+        # Data Embedding
+        time_emb = self.Temb(input.permute(0, 3, 2, 1))
+        x = torch.cat([self.start_conv(x)] + [time_emb], dim=1)
+        # IDGCN_Tree
+        x = self.tree(x)
+        # Decoder
+        gcn = self.glu(x) + x
+        prediction = self.regression_layer(F.relu(gcn))
+        return prediction

model/model_selector.py

@@ -14,6 +14,7 @@ from model.STSGCN.STSGCN import STSGCN
 from model.STGODE.STGODE import ODEGCN
 from model.PDG2SEQ.PDG2Seq import PDG2Seq
 from model.STMLP.STMLP import STMLP
+from model.STIDGCN.STIDGCN import STIDGCN
 
 def model_selector(model):
     match model['type']:
@@ -33,4 +34,5 @@ def model_selector(model):
         case 'STGODE': return ODEGCN(model)
         case 'PDG2SEQ': return PDG2Seq(model)
         case 'STMLP': return STMLP(model)
+        case 'STIDGCN': return STIDGCN(model)