config/STIDGCN/PEMSD3.yaml

@@ -1,48 +0,0 @@
-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

@@ -1,48 +0,0 @@
-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

@@ -1,48 +0,0 @@
-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

@@ -1,48 +0,0 @@
-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

@@ -1,368 +0,0 @@
-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

@@ -14,7 +14,6 @@ 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']:
@@ -34,5 +33,4 @@ def model_selector(model):
         case 'STGODE': return ODEGCN(model)
         case 'PDG2SEQ': return PDG2Seq(model)
         case 'STMLP': return STMLP(model)
-        case 'STIDGCN': return STIDGCN(model)