#### Imports ####
from torch_geometric.datasets import Planetoid
import torch
import torch.nn.functional as F
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import add_self_loops, degree


#### Loading the Dataset ####
dataset = Planetoid(root='/tmp/Cora', name='Cora')


#### The Graph Convolution Layer ####
class GraphConvolution(MessagePassing):
    def __init__(self, in_channels, out_channels,bias=True, **kwargs):
        super(GraphConvolution, self).__init__(aggr='add', **kwargs)
        self.lin = torch.nn.Linear(in_channels, out_channels,bias=bias)

    def forward(self, x, edge_index):
        edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))
        x = self.lin(x)
        return self.propagate(edge_index, size=(x.size(0), x.size(0)), x=x)

    def message(self, x_j, edge_index, size):
        row, col = edge_index
        deg = degree(row, size[0], dtype=x_j.dtype)
        deg_inv_sqrt = deg.pow(-0.5)
        norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]
        return norm.view(-1, 1) * x_j

    def update(self, aggr_out):
        return aggr_out


class Net(torch.nn.Module):
    def __init__(self,nfeat, nhid, nclass, dropout):
        super(Net, self).__init__()
        self.conv1 = GraphConvolution(nfeat, nhid)
        self.conv2 = GraphConvolution(nhid, nclass)
        self.dropout=dropout

    def forward(self, data):
        x, edge_index = data.x, data.edge_index

        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, self.dropout, training=self.training)
        x = self.conv2(x, edge_index)

        return F.log_softmax(x, dim=1)


nfeat=dataset.num_node_features
nhid=16
nclass=dataset.num_classes
dropout=0.5


#### Training ####
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net(nfeat, nhid, nclass, dropout).to(device)
data = dataset[0].to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)

model.train()
for epoch in range(200):
    optimizer.zero_grad()
    out = model(data)
    loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
    loss.backward()
    optimizer.step()


model.eval()
_, pred = model(data).max(dim=1)
correct = float (pred[data.test_mask].eq(data.y[data.test_mask]).sum().item())
acc = correct / data.test_mask.sum().item()
print('Accuracy: {:.4f}'.format(acc))