249 lines
9.0 KiB
Python
249 lines
9.0 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from collections import OrderedDict
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class DGCRM(nn.Module):
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def __init__(self, node_num, dim_in, dim_out, cheb_k, embed_dim, num_layers=1):
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super(DGCRM, self).__init__()
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assert num_layers >= 1, 'At least one DGCRM layer is required.'
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self.node_num = node_num
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self.input_dim = dim_in
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self.num_layers = num_layers
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# Initialize DGCRM cells
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self.DGCRM_cells = nn.ModuleList([
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DDGCRNCell(node_num, dim_in, dim_out, cheb_k, embed_dim)
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if i == 0 else
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DDGCRNCell(node_num, dim_out, dim_out, cheb_k, embed_dim)
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for i in range(num_layers)
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])
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def forward(self, x, init_state, node_embeddings):
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"""
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Forward pass of the DGCRM model.
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Parameters:
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- x: Input tensor of shape (B, T, N, D)
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- init_state: Initial hidden states of shape (num_layers, B, N, hidden_dim)
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- node_embeddings: Node embeddings
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"""
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assert x.shape[2] == self.node_num and x.shape[3] == self.input_dim
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seq_length = x.shape[1]
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current_inputs = x
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output_hidden = []
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for i in range(self.num_layers):
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state = init_state[i]
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inner_states = []
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for t in range(seq_length):
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state = self.DGCRM_cells[i](current_inputs[:, t, :, :], state,
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[node_embeddings[0][:, t, :, :], node_embeddings[1]])
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inner_states.append(state)
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output_hidden.append(state)
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current_inputs = torch.stack(inner_states, dim=1)
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return current_inputs, output_hidden
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def init_hidden(self, batch_size):
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"""
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Initialize hidden states for DGCRM layers.
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Parameters:
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- batch_size: Size of the batch
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Returns:
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- Initial hidden states tensor
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"""
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return torch.stack([
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self.DGCRM_cells[i].init_hidden_state(batch_size)
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for i in range(self.num_layers)
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], dim=0)
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class DDGCRN(nn.Module):
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def __init__(self, args):
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super(DDGCRN, self).__init__()
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self.num_node = args['num_nodes']
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self.input_dim = args['input_dim']
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self.hidden_dim = args['rnn_units']
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self.output_dim = args['output_dim']
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self.horizon = args['horizon']
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self.num_layers = args['num_layers']
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self.use_day = args['use_day']
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self.use_week = args['use_week']
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self.default_graph = args['default_graph']
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self.node_embeddings1 = nn.Parameter(torch.randn(self.num_node, args['embed_dim']), requires_grad=True)
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self.node_embeddings2 = nn.Parameter(torch.randn(self.num_node, args['embed_dim']), requires_grad=True)
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self.T_i_D_emb = nn.Parameter(torch.empty(288, args['embed_dim']))
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self.D_i_W_emb = nn.Parameter(torch.empty(7, args['embed_dim']))
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self.dropout1 = nn.Dropout(p=0.1)
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self.dropout2 = nn.Dropout(p=0.1)
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self.encoder1 = DGCRM(self.num_node, self.input_dim, self.hidden_dim, args['cheb_order'], args['embed_dim'],
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self.num_layers)
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self.encoder2 = DGCRM(self.num_node, self.input_dim, self.hidden_dim, args['cheb_order'], args['embed_dim'],
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self.num_layers)
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# Predictor
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self.end_conv1 = nn.Conv2d(1, self.horizon * self.output_dim, kernel_size=(1, self.hidden_dim), bias=True)
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self.end_conv2 = nn.Conv2d(1, self.horizon * self.output_dim, kernel_size=(1, self.hidden_dim), bias=True)
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self.end_conv3 = nn.Conv2d(1, self.horizon * self.output_dim, kernel_size=(1, self.hidden_dim), bias=True)
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def forward(self, source):
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"""
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Forward pass of the DDGCRN model.
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Parameters:
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- source: Input tensor of shape (B, T_1, N, D)
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- mode: Control mode for the forward pass
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Returns:
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- Output tensor
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"""
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node_embedding1 = self.node_embeddings1
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if self.use_day:
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t_i_d_data = source[..., 1]
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T_i_D_emb = self.T_i_D_emb[(t_i_d_data * 288).long()]
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node_embedding1 = node_embedding1 * T_i_D_emb
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if self.use_week:
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d_i_w_data = source[..., 2]
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D_i_W_emb = self.D_i_W_emb[d_i_w_data.long()]
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node_embedding1 = node_embedding1 * D_i_W_emb
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node_embeddings = [node_embedding1, self.node_embeddings1]
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source = source[..., 0].unsqueeze(-1)
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init_state1 = self.encoder1.init_hidden(source.shape[0])
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output, _ = self.encoder1(source, init_state1, node_embeddings)
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output = self.dropout1(output[:, -1:, :, :])
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output1 = self.end_conv1(output)
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source1 = self.end_conv2(output)
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source2 = source[:, -self.horizon:, ...] - source1
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init_state2 = self.encoder2.init_hidden(source2.shape[0])
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output2, _ = self.encoder2(source2, init_state2, node_embeddings)
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output2 = self.dropout2(output2[:, -1:, :, :])
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output2 = self.end_conv3(output2)
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return output1 + output2
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class DDGCRNCell(nn.Module):
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def __init__(self, node_num, dim_in, dim_out, cheb_k, embed_dim):
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super(DDGCRNCell, self).__init__()
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self.node_num = node_num
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self.hidden_dim = dim_out
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self.gate = DGCN(dim_in + self.hidden_dim, 2 * dim_out, cheb_k, embed_dim)
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self.update = DGCN(dim_in + self.hidden_dim, dim_out, cheb_k, embed_dim)
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def forward(self, x, state, node_embeddings):
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state = state.to(x.device)
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input_and_state = torch.cat((x, state), dim=-1)
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z_r = torch.sigmoid(self.gate(input_and_state, node_embeddings))
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z, r = torch.split(z_r, self.hidden_dim, dim=-1)
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candidate = torch.cat((x, z * state), dim=-1)
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hc = torch.tanh(self.update(candidate, node_embeddings))
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h = r * state + (1 - r) * hc
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return h
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def init_hidden_state(self, batch_size):
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return torch.zeros(batch_size, self.node_num, self.hidden_dim)
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class DGCN(nn.Module):
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def __init__(self, dim_in, dim_out, cheb_k, embed_dim):
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super(DGCN, self).__init__()
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self.cheb_k = cheb_k
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self.embed_dim = embed_dim
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# Initialize parameters
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self.weights_pool = nn.Parameter(torch.FloatTensor(embed_dim, cheb_k, dim_in, dim_out))
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self.weights = nn.Parameter(torch.FloatTensor(cheb_k, dim_in, dim_out))
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self.bias_pool = nn.Parameter(torch.FloatTensor(embed_dim, dim_out))
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self.bias = nn.Parameter(torch.FloatTensor(dim_out))
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# Hyperparameters
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self.hyperGNN_dim = 16
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self.middle_dim = 2
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# Fully connected layers
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self.fc = nn.Sequential(OrderedDict([
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('fc1', nn.Linear(dim_in, self.hyperGNN_dim)),
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('sigmoid1', nn.Sigmoid()),
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('fc2', nn.Linear(self.hyperGNN_dim, self.middle_dim)),
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('sigmoid2', nn.Sigmoid()),
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('fc3', nn.Linear(self.middle_dim, self.embed_dim))
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]))
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def forward(self, x, node_embeddings):
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"""
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Forward pass for the DGCN model.
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Parameters:
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- x: Input tensor of shape [B, N, C]
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- node_embeddings: Node embeddings tensor of shape [N, D]
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- connMtx: Connectivity matrix
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Returns:
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- x_gconv: Output tensor of shape [B, N, dim_out]
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"""
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node_num = node_embeddings[0].shape[1]
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supports1 = torch.eye(node_num).to(node_embeddings[0].device) # Identity matrix
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# Apply fully connected layers
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filter = self.fc(x)
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nodevec = torch.tanh(torch.mul(node_embeddings[0], filter)) # Element-wise multiplication
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# Compute Laplacian
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supports2 = self.get_laplacian(F.relu(torch.matmul(nodevec, nodevec.transpose(2, 1))), supports1)
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# Graph convolution
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x_g1 = torch.einsum("nm,bmc->bnc", supports1, x)
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x_g2 = torch.einsum("bnm,bmc->bnc", supports2, x)
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x_g = torch.stack([x_g1, x_g2], dim=1)
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# Apply graph convolution weights and biases
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weights = torch.einsum('nd,dkio->nkio', node_embeddings[1], self.weights_pool)
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bias = torch.matmul(node_embeddings[1], self.bias_pool)
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x_g = x_g.permute(0, 2, 1, 3) # Rearrange dimensions
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x_gconv = torch.einsum('bnki,nkio->bno', x_g, weights) + bias # Graph convolution operation
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return x_gconv
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@staticmethod
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def get_laplacian(graph, I, normalize=True):
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"""
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Compute the Laplacian of the graph.
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Parameters:
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- graph: Adjacency matrix of the graph, [N, N]
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- I: Identity matrix
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- normalize: Whether to use the normalized Laplacian
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Returns:
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- L: Graph Laplacian
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"""
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if normalize:
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D_inv_sqrt = torch.diag_embed(torch.sum(graph, dim=-1) ** (-1 / 2))
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L = torch.matmul(torch.matmul(D_inv_sqrt, graph), D_inv_sqrt)
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else:
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graph = graph + I
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D_inv_sqrt = torch.diag_embed(torch.sum(graph, dim=-1) ** (-1 / 2))
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L = torch.matmul(torch.matmul(D_inv_sqrt, graph), D_inv_sqrt)
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return L
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