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