def __init__(self, in_dim, hidden_dim, n_classes,hidden_layers,n_steps,readout,
activation_func,dropout,grid,device):
super(Classifier, self).__init__()
self.device = device
self.readout = readout
self.layers = nn.ModuleList()
self.batch_norms = nn.ModuleList()
self.grid = grid
# input layer
self.layers.append(conv.GatedGraphConv(in_dim,hidden_dim,n_steps,1))
self.batch_norms.append(nn.BatchNorm1d(hidden_dim))
# hidden layers
for k in range(0,hidden_layers):
self.layers.append(conv.GatedGraphConv(hidden_dim,hidden_dim,n_steps,1))
self.batch_norms.append(nn.BatchNorm1d(hidden_dim))
# dropout layer
self.dropout=nn.Dropout(p=dropout)
# last layer
if self.readout == 'max':
self.readout_fcn = conv.MaxPooling()
elif self.readout == 'mean':
self.readout_fcn = conv.AvgPooling()
elif self.readout == 'sum':
self.readout_fcn = conv.SumPooling()
elif self.readout == 'gap':
self.readout_fcn = conv.GlobalAttentionPooling(nn.Linear(hidden_dim,1),nn.Linear(hidden_dim,hidden_dim*2))
elif self.readout == 'sort':
self.readout_fcn = conv.SortPooling(100)
elif self.readout == 'set':
self.readout_fcn = conv.Set2Set(hidden_dim,2,2)
else:
self.readout_fcn = SppPooling(hidden_dim,self.grid)
if self.readout == 'spp':
self.classify = nn.Sequential(
nn.Dropout(),
nn.Linear(hidden_dim * self.grid * self.grid, hidden_dim),
nn.ReLU(inplace=True),
nn.Linear(hidden_dim, n_classes),
)
elif self.readout == 'sort':
self.classify = nn.Sequential(
nn.Dropout(),
nn.Linear(hidden_dim*100, n_classes),
)
else:
var=hidden_dim
if self.readout == 'gap' or self.readout == 'set':
var*=2
self.classify = nn.Linear(var, n_classes)
def test_gated_graph_conv():
ctx = F.ctx()
g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
ggconv = nn.GatedGraphConv(5, 10, 5, 3)
etypes = th.arange(g.number_of_edges()) % 3
feat = F.randn((100, 5))
ggconv = ggconv.to(ctx)
etypes = etypes.to(ctx)
h = ggconv(g, feat, etypes)
# current we only do shape check
assert h.shape[-1] == 10
def test_gated_graph_conv(g, idtype):
ctx = F.ctx()
g = g.astype(idtype).to(ctx)
ggconv = nn.GatedGraphConv(5, 10, 5, 3)
etypes = th.arange(g.number_of_edges()) % 3
feat = F.randn((g.number_of_nodes(), 5))
ggconv = ggconv.to(ctx)
etypes = etypes.to(ctx)
h = ggconv(g, feat, etypes)
# current we only do shape check
assert h.shape[-1] == 10
def test_gated_graph_conv_one_etype(g, idtype):
ctx = F.ctx()
g = g.astype(idtype).to(ctx)
ggconv = nn.GatedGraphConv(5, 10, 5, 1)
etypes = th.zeros(g.number_of_edges())
feat = F.randn((g.number_of_nodes(), 5))
ggconv = ggconv.to(ctx)
etypes = etypes.to(ctx)
h = ggconv(g, feat, etypes)
h2 = ggconv(g, feat)
# current we only do shape check
assert F.allclose(h, h2)
assert h.shape[-1] == 10
