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dense_gsdnef.py
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import os.path as osp
import torch
import torch.nn.functional as F
from torch_geometric.datasets import Planetoid, PPI, Reddit, CoraFull
import torch_geometric.transforms as T
from src import DenseGSDNEFConv
import numpy as np
import scipy.sparse as sp
import argparse
import time
class Net(torch.nn.Module):
def __init__(self,
in_channels,
out_channels,
hidden_num,
alpha,
K):
super(Net, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.hidden_num = hidden_num
self.alpha = alpha
self.K = K
self.conv1 = DenseGSDNEFConv(self.in_channels, self.hidden_num, self.alpha, self.K)
self.conv2 = DenseGSDNEFConv(self.hidden_num, self.out_channels, self.alpha, self.K)
def forward(self, x, adj):
x = F.relu(self.conv1(x, adj))
x = F.dropout(x, training=self.training)
x = self.conv2(x, adj)
return F.log_softmax(x, dim=1)
def train(model, optimizer, data):
model.train()
optimizer.zero_grad()
F.nll_loss(model(data.x, data.adj)[data.train_mask], data.y[data.train_mask]).backward()
optimizer.step()
def test(model, data):
model.eval()
logits, accs = model(data.x, data.adj), []
for _, mask in data('train_mask', 'val_mask', 'test_mask'):
pred = logits[mask].max(1)[1]
acc = pred.eq(data.y[mask]).sum().item() / mask.sum().item()
accs.append(acc)
return accs
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--input',
type=str,
default='cora',
# default='citeseer',
# default='pubmed',
help='Input graph.')
parser.add_argument('--epochs',
type=int,
default=200,
help='Number of epochs to train.')
parser.add_argument('--lr',
type=float,
default=0.01,
help='Initial learning rate.')
parser.add_argument('--weight_decay',
type=float,
default=5e-4,
help='Weight decay (L2 loss on parameters).')
parser.add_argument('--hidden_num',
type=int,
default=16,
help='Number of hidden units.')
parser.add_argument('--alpha',
type=float,
default=0.6,
help='alpha.')
parser.add_argument('--K',
type=int,
default=4,
help='K.')
parser.add_argument('--dropout',
type=float,
default=0.5,
help='Dropout rate (1 - keep probability).')
args = parser.parse_args()
return args
def generate_split(data, num_classes, seed=2020, train_num_per_c=20, val_num_per_c=30):
torch.manual_seed(seed)
train_mask = torch.zeros(data.y.size(0), dtype=torch.bool)
val_mask = torch.zeros(data.y.size(0), dtype=torch.bool)
test_mask = torch.zeros(data.y.size(0), dtype=torch.bool)
for c in range(num_classes):
all_c_idx = (data.y == c).nonzero()
if all_c_idx.size(0) <= train_num_per_c + val_num_per_c:
test_mask[all_c_idx] = True
continue
perm = torch.randperm(all_c_idx.size(0))
c_train_idx = all_c_idx[perm[:train_num_per_c]]
train_mask[c_train_idx] = True
test_mask[c_train_idx] = True
c_val_idx = all_c_idx[perm[train_num_per_c : train_num_per_c + val_num_per_c]]
val_mask[c_val_idx] = True
test_mask[c_val_idx] = True
test_mask = ~test_mask
return train_mask, val_mask, test_mask
def load_dataset(dataset):
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', dataset)
if dataset in ['cora', 'citeseer', 'pubmed']:
dataset = Planetoid(path, dataset, transform=T.NormalizeFeatures())
num_features = dataset.num_features
num_classes = dataset.num_classes
data = dataset[0]
data.adj = torch.zeros((data.x.size(0), data.x.size(0)))
col, row = data.edge_index
data.adj[col, row] = 1
return data, num_features, num_classes
elif dataset == 'reddit':
dataset = Reddit(path)
elif dataset == 'corafull':
dataset = CoraFull(path)
num_features = dataset.num_features
num_classes = dataset.num_classes
data = dataset[0]
data.train_mask, data.val_mask, data.test_mask = generate_split(data, num_classes)
data.adj = torch.zeros((data.x.size[0], data.x.size(0)))
col, row = data.edge_index
data.adj[col, row] = 1
return data, num_features, num_classes
def main(args):
print('---------------dense gsdnef----------------')
## loading data
data, num_features, num_classes = load_dataset(args.input)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model, data = Net(in_channels=num_features,
out_channels=num_classes,
hidden_num=args.hidden_num,
alpha=args.alpha,
K=args.K).to(device), data.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
best_val_acc = test_acc = 0
t1 = time.time()
for epoch in range(1, args.epochs):
train(model, optimizer, data)
train_acc, val_acc, tmp_test_acc = test(model, data)
if val_acc > best_val_acc:
best_val_acc = val_acc
test_acc = tmp_test_acc
# log = 'Epoch: {:03d}, Train: {:.4f}, Val: {:.4f}, Test: {:.4f}'
# print(log.format(epoch, train_acc, best_val_acc, test_acc))
t2 = time.time()
print('{:.4f}'.format(test_acc))
# print('training time is: {}'.format(t2-t1))
if __name__ == '__main__':
main(get_args())