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user_models.py
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user_models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class NConv(nn.Module):
def __init__(self):
super(NConv, self).__init__()
def forward(self, x, A):
x = torch.einsum('ncvl,vw->ncwl', (x, A))
return x.contiguous()
class Linear(nn.Module):
def __init__(self, c_in, c_out):
super(Linear, self).__init__()
self.mlp = torch.nn.Conv2d(c_in, c_out, kernel_size=(1, 1), padding=(0, 0), stride=(1, 1), bias=True)
def forward(self, x):
return self.mlp(x)
class GCN(nn.Module):
def __init__(self, c_in, c_out, dropout, support_len=3, order=2):
super(GCN, self).__init__()
self.nconv = NConv()
c_in = (order * support_len + 1) * c_in
self.mlp = Linear(c_in, c_out)
self.dropout = dropout
self.order = order
def forward(self, x, support):
out = [x]
for a in support:
x1 = self.nconv(x, a)
out.append(x1)
for k in range(2, self.order + 1):
x2 = self.nconv(x1, a)
out.append(x2)
x1 = x2
h = torch.cat(out, dim=1)
h = self.mlp(h)
h = F.dropout(h, self.dropout, training=self.training)
return h
class GraphWaveNet(nn.Module):
def __init__(self, device, node_cnt, dropout=0.3, supports=None, gcn_bool=True, adapt_adj=True, adj_init=None,
in_dim=1, out_dim=12, residual_channels=32, dilation_channels=32, skip_channels=256, end_channels=512,
kernel_size=2, blocks=4, layers=2):
super(GraphWaveNet, self).__init__()
self.dropout = dropout
self.blocks = blocks
self.layers = layers
self.gcn_bool = gcn_bool
self.adapt_adj = adapt_adj
self.filter_convs = nn.ModuleList()
self.gate_convs = nn.ModuleList()
self.residual_convs = nn.ModuleList()
self.skip_convs = nn.ModuleList()
self.bn = nn.ModuleList()
self.gconv = nn.ModuleList()
self.start_conv = nn.Conv2d(in_channels=in_dim,
out_channels=residual_channels,
kernel_size=(1, 1))
self.supports = supports
self.final_adj = None
receptive_field = 1
self.supports_len = 0
if supports is not None:
self.supports_len += len(supports)
if gcn_bool and adapt_adj:
if adj_init is None:
if supports is None:
self.supports = []
self.nodevec1 = nn.Parameter(torch.randn(node_cnt, 10).to(device), requires_grad=True).to(device)
self.nodevec2 = nn.Parameter(torch.randn(10, node_cnt).to(device), requires_grad=True).to(device)
self.supports_len += 1
else:
if supports is None:
self.supports = []
m, p, n = torch.svd(adj_init)
initemb1 = torch.mm(m[:, :10], torch.diag(p[:10] ** 0.5))
initemb2 = torch.mm(torch.diag(p[:10] ** 0.5), n[:, :10].t())
self.nodevec1 = nn.Parameter(initemb1, requires_grad=True).to(device)
self.nodevec2 = nn.Parameter(initemb2, requires_grad=True).to(device)
self.supports_len += 1
for b in range(blocks):
additional_scope = kernel_size - 1
new_dilation = 1
for i in range(layers):
self.filter_convs.append(nn.Conv2d(in_channels=residual_channels,
out_channels=dilation_channels,
kernel_size=(1, kernel_size), dilation=new_dilation))
self.gate_convs.append(nn.Conv1d(in_channels=residual_channels,
out_channels=dilation_channels,
kernel_size=(1, kernel_size), dilation=new_dilation))
self.residual_convs.append(nn.Conv1d(in_channels=dilation_channels,
out_channels=residual_channels,
kernel_size=(1, 1)))
self.skip_convs.append(nn.Conv1d(in_channels=dilation_channels,
out_channels=skip_channels,
kernel_size=(1, 1)))
self.bn.append(nn.BatchNorm2d(residual_channels))
new_dilation *= 2
receptive_field += additional_scope
additional_scope *= 2
if self.gcn_bool:
self.gconv.append(GCN(dilation_channels, residual_channels, dropout, support_len=self.supports_len))
self.end_conv_1 = nn.Conv2d(in_channels=skip_channels,
out_channels=end_channels,
kernel_size=(1, 1),
bias=True)
self.end_conv_2 = nn.Conv2d(in_channels=end_channels,
out_channels=out_dim,
kernel_size=(1, 1),
bias=True)
self.receptive_field = receptive_field
def forward(self, inputs):
in_len = inputs.size(3)
if in_len < self.receptive_field:
x = nn.functional.pad(inputs, (self.receptive_field - in_len, 0, 0, 0))
else:
x = inputs
x = self.start_conv(x)
skip = 0
new_supports = None
if self.gcn_bool and self.adapt_adj:
adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)), dim=1)
if self.supports is not None:
new_supports = self.supports + [adp]
else:
new_supports = [adp]
self.final_adj = new_supports
# WaveNet layers
for i in range(self.blocks * self.layers):
residual = x
filter_ = self.filter_convs[i](residual)
filter_ = torch.tanh(filter_)
gate = self.gate_convs[i](residual)
gate = torch.sigmoid(gate)
x = filter_ * gate
s = x
s = self.skip_convs[i](s)
try:
skip = skip[:, :, :, -s.size(3):]
except BaseException:
skip = 0
skip = s + skip
if self.gcn_bool and self.supports is not None:
if self.adapt_adj:
x = self.gconv[i](x, new_supports)
else:
x = self.gconv[i](x, self.supports)
else:
x = self.residual_convs[i](x)
x = x + residual[:, :, :, -x.size(3):]
x = self.bn[i](x)
x = F.relu(skip)
x = F.relu(self.end_conv_1(x))
x = self.end_conv_2(x)
return x