text: """
  Graph Attention Networks in DGL using SPMV optimization.
  Multiple heads are also batched together for faster training.
  References
  ----------
  Paper: https://arxiv.org/abs/1710.10903
  Author's code: https://github.com/PetarV-/GAT
  Pytorch implementation: https://github.com/Diego999/pyGAT
  """
  
  import argparse
  import numpy as np
  import networkx as nx
  import time
  import torch
  import torch.nn.functional as F
  from dgl import DGLGraph
  from dgl.data import register_data_args, load_data
  from gat import GAT
  from utils import EarlyStopping
  
  
  def accuracy(logits, labels):
      _, indices = torch.max(logits, dim=1)
      correct = torch.sum(indices == labels)
      return correct.item() * 1.0 / len(labels)
  
  
  def evaluate(model, features, labels, mask):
      model.eval()
      with torch.no_grad():
          logits = model(features)
          logits = logits[mask]
          labels = labels[mask]
          return accuracy(logits, labels)
  
  
  def main(args):
      # load and preprocess dataset
      data = load_data(args)
      features = torch.FloatTensor(data.features)
      labels = torch.LongTensor(data.labels)
      np.save(f'{args.dataset}_labels', labels)
      if hasattr(torch, 'BoolTensor'):
          train_mask = torch.BoolTensor(data.train_mask)
          val_mask = torch.BoolTensor(data.val_mask)
          test_mask = torch.BoolTensor(data.test_mask)
      else:
          train_mask = torch.ByteTensor(data.train_mask)
          val_mask = torch.ByteTensor(data.val_mask)
          test_mask = torch.ByteTensor(data.test_mask)
      num_feats = features.shape[1]
      n_classes = data.num_labels
      n_edges = data.graph.number_of_edges()
      print("""----Data statistics------'
        #Edges %d
        #Classes %d 
        #Train samples %d
        #Val samples %d
        #Test samples %d""" %
            (n_edges, n_classes,
             train_mask.int().sum().item(),
             val_mask.int().sum().item(),
             test_mask.int().sum().item()))
  
      if args.gpu < 0:
          cuda = False
      else:
          cuda = True
          torch.cuda.set_device(args.gpu)
          features = features.cuda()
          labels = labels.cuda()
          train_mask = train_mask.cuda()
          val_mask = val_mask.cuda()
          test_mask = test_mask.cuda()
  
      g = data.graph
      # add self loop
      g.remove_edges_from(nx.selfloop_edges(g))
      g = DGLGraph(g)
      g.add_edges(g.nodes(), g.nodes())
      n_edges = g.number_of_edges()
      # create model
      heads = ([args.num_heads] * args.num_layers) + [args.num_out_heads]
      model = GAT(g,
                  args.num_layers,
                  num_feats,
                  args.num_hidden,
                  n_classes,
                  heads,
                  F.elu,
                  args.in_drop,
                  args.attn_drop,
                  args.negative_slope,
                  args.residual)
      print(model)
      if args.early_stop:
          stopper = EarlyStopping(patience=100)
      if cuda:
          model.cuda()
      loss_fcn = torch.nn.CrossEntropyLoss()
  
      # use optimizer
      optimizer = torch.optim.Adam(
          model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
  
      # initialize graph
      dur = []
      for epoch in range(args.epochs):
          model.train()
          if epoch >= 3:
              t0 = time.time()
          # forward
          logits = model(features)
          loss = loss_fcn(logits[train_mask], labels[train_mask])
  
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          if epoch >= 3:
              dur.append(time.time() - t0)
  
          train_acc = accuracy(logits[train_mask], labels[train_mask])
  
          if args.fastmode:
              val_acc = accuracy(logits[val_mask], labels[val_mask])
          else:
              val_acc = evaluate(model, features, labels, val_mask)
              if args.early_stop:
                  if stopper.step(val_acc, model):
                      break
  
          print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | TrainAcc {:.4f} |"
                " ValAcc {:.4f} | ETputs(KTEPS) {:.2f}".
                format(epoch, np.mean(dur), loss.item(), train_acc,
                       val_acc, n_edges / np.mean(dur) / 1000))
  
      print()
      if args.early_stop:
          model.load_state_dict(torch.load('es_checkpoint.pt'))
      acc = evaluate(model, features, labels, test_mask)
      print("Test Accuracy {:.4f}".format(acc))
  
  
  if __name__ == '__main__':
  
      parser = argparse.ArgumentParser(description='GAT')
      register_data_args(parser)
      parser.add_argument("--gpu", type=int, default=-1,
                          help="which GPU to use. Set -1 to use CPU.")
      parser.add_argument("--epochs", type=int, default=200,
                          help="number of training epochs")
      parser.add_argument("--num-heads", type=int, default=8,
                          help="number of hidden attention heads")
      parser.add_argument("--num-out-heads", type=int, default=1,
                          help="number of output attention heads")
      parser.add_argument("--num-layers", type=int, default=1,
                          help="number of hidden layers")
      parser.add_argument("--num-hidden", type=int, default=8,
                          help="number of hidden units")
      parser.add_argument("--residual", action="store_true", default=False,
                          help="use residual connection")
      parser.add_argument("--in-drop", type=float, default=.6,
                          help="input feature dropout")
      parser.add_argument("--attn-drop", type=float, default=.6,
                          help="attention dropout")
      parser.add_argument("--lr", type=float, default=0.005,
                          help="learning rate")
      parser.add_argument('--weight-decay', type=float, default=5e-4,
                          help="weight decay")
      parser.add_argument('--negative-slope', type=float, default=0.2,
                          help="the negative slope of leaky relu")
      parser.add_argument('--early-stop', action='store_true', default=False,
                          help="indicates whether to use early stop or not")
      parser.add_argument('--fastmode', action="store_true", default=False,
                          help="skip re-evaluate the validation set")
      args = parser.parse_args()
      print(args)
  
      main(args)
  