def test_appnp_conv():
ctx = F.ctx()
g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
appnp = nn.APPNPConv(10, 0.1)
feat = F.randn((100, 5))
appnp = appnp.to(ctx)
h = appnp(g, feat)
assert h.shape[-1] == 5
def test_appnp_conv(g, idtype):
ctx = F.ctx()
g = g.astype(idtype).to(ctx)
appnp = nn.APPNPConv(10, 0.1)
feat = F.randn((g.number_of_nodes(), 5))
appnp = appnp.to(ctx)
h = appnp(g, feat)
assert h.shape[-1] == 5
def test_appnp_conv_e_weight(g, idtype):
ctx = F.ctx()
g = g.astype(idtype).to(ctx)
appnp = nn.APPNPConv(10, 0.1)
feat = F.randn((g.number_of_nodes(), 5))
eweight = F.ones((g.num_edges(), ))
appnp = appnp.to(ctx)
h = appnp(g, feat, edge_weight=eweight)
assert h.shape[-1] == 5
def __init__(self,in_dim,hidden_dim,n_classes,hidden_layers,readout,
activation,feat_drop,edge_drop,alpha,K,grid,device):
super(Classifier, self).__init__()
self.device = device
self.readout = readout
self.layers = nn.ModuleList()
self.grid = grid
# input layer
self.layers.append(nn.Linear(in_dim, hidden_dim))
# hidden layers
for i in range(hidden_layers):
self.layers.append(nn.Linear(hidden_dim, hidden_dim))
self.activation = activation
if feat_drop:
self.feat_drop = nn.Dropout(feat_drop)
else:
self.feat_drop = lambda x: x
self.propagate = conv.APPNPConv(K, alpha, edge_drop)
# 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))
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*2),
nn.ReLU(inplace=True),
nn.Linear(2*hidden_dim, n_classes),
)
else:
var=hidden_dim
if self.readout == 'gap':
var*=2
self.classify = nn.Linear(var, n_classes)
self.reset_parameters()
