def test_gcn():
print("test_gcn begin")
np.random.seed(SEED)
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
save_graphs=True)
config = ConfigGCN()
adj, feature, label = get_adj_features_labels(DATA_DIR)
nodes_num = label.shape[0]
train_mask = get_mask(nodes_num, 0, TRAIN_NODE_NUM)
eval_mask = get_mask(nodes_num, TRAIN_NODE_NUM,
TRAIN_NODE_NUM + EVAL_NODE_NUM)
test_mask = get_mask(nodes_num, nodes_num - TEST_NODE_NUM, nodes_num)
class_num = label.shape[1]
gcn_net = GCN(config, adj, feature, class_num)
gcn_net.add_flags_recursive(fp16=True)
eval_net = LossAccuracyWrapper(gcn_net, label, eval_mask,
config.weight_decay)
test_net = LossAccuracyWrapper(gcn_net, label, test_mask,
config.weight_decay)
train_net = TrainNetWrapper(gcn_net, label, train_mask, config)
loss_list = []
for epoch in range(config.epochs):
t = time.time()
train_net.set_train()
train_result = train_net()
train_loss = train_result[0].asnumpy()
train_accuracy = train_result[1].asnumpy()
eval_net.set_train(False)
eval_result = eval_net()
eval_loss = eval_result[0].asnumpy()
eval_accuracy = eval_result[1].asnumpy()
loss_list.append(eval_loss)
print("Epoch:", '%04d' % (epoch + 1), "train_loss=",
"{:.5f}".format(train_loss), "train_acc=",
"{:.5f}".format(train_accuracy), "val_loss=",
"{:.5f}".format(eval_loss), "val_acc=",
"{:.5f}".format(eval_accuracy), "time=",
"{:.5f}".format(time.time() - t))
if epoch > config.early_stopping and loss_list[-1] > np.mean(
loss_list[-(config.early_stopping + 1):-1]):
print("Early stopping...")
break
test_net.set_train(False)
test_result = test_net()
test_loss = test_result[0].asnumpy()
test_accuracy = test_result[1].asnumpy()
print("Test set results:", "loss=", "{:.5f}".format(test_loss),
"accuracy=", "{:.5f}".format(test_accuracy))
assert test_accuracy > 0.812
def train():
"""Train model."""
parser = argparse.ArgumentParser(description='GCN')
parser.add_argument('--data_dir', type=str, default='./data/cora/cora_mr', help='Dataset directory')
parser.add_argument('--seed', type=int, default=123, help='Random seed')
parser.add_argument('--train_nodes_num', type=int, default=140, help='Nodes numbers for training')
parser.add_argument('--eval_nodes_num', type=int, default=500, help='Nodes numbers for evaluation')
parser.add_argument('--test_nodes_num', type=int, default=1000, help='Nodes numbers for test')
args_opt = parser.parse_args()
np.random.seed(args_opt.seed)
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend", save_graphs=False)
config = ConfigGCN()
adj, feature, label = get_adj_features_labels(args_opt.data_dir)
nodes_num = label.shape[0]
train_mask = get_mask(nodes_num, 0, args_opt.train_nodes_num)
eval_mask = get_mask(nodes_num, args_opt.train_nodes_num, args_opt.train_nodes_num + args_opt.eval_nodes_num)
test_mask = get_mask(nodes_num, nodes_num - args_opt.test_nodes_num, nodes_num)
class_num = label.shape[1]
gcn_net = GCN(config, adj, feature, class_num)
gcn_net.add_flags_recursive(fp16=True)
eval_net = LossAccuracyWrapper(gcn_net, label, eval_mask, config.weight_decay)
test_net = LossAccuracyWrapper(gcn_net, label, test_mask, config.weight_decay)
train_net = TrainNetWrapper(gcn_net, label, train_mask, config)
loss_list = []
for epoch in range(config.epochs):
t = time.time()
train_net.set_train()
train_result = train_net()
train_loss = train_result[0].asnumpy()
train_accuracy = train_result[1].asnumpy()
eval_net.set_train(False)
eval_result = eval_net()
eval_loss = eval_result[0].asnumpy()
eval_accuracy = eval_result[1].asnumpy()
loss_list.append(eval_loss)
print("Epoch:", '%04d' % (epoch + 1), "train_loss=", "{:.5f}".format(train_loss),
"train_acc=", "{:.5f}".format(train_accuracy), "val_loss=", "{:.5f}".format(eval_loss),
"val_acc=", "{:.5f}".format(eval_accuracy), "time=", "{:.5f}".format(time.time() - t))
if epoch > config.early_stopping and loss_list[-1] > np.mean(loss_list[-(config.early_stopping+1):-1]):
print("Early stopping...")
break
t_test = time.time()
test_net.set_train(False)
test_result = test_net()
test_loss = test_result[0].asnumpy()
test_accuracy = test_result[1].asnumpy()
print("Test set results:", "loss=", "{:.5f}".format(test_loss),
"accuracy=", "{:.5f}".format(test_accuracy), "time=", "{:.5f}".format(time.time() - t_test))
def train(args_opt):
"""Train model."""
np.random.seed(args_opt.seed)
config = ConfigGCN()
adj, feature, label = get_adj_features_labels(args_opt.data_dir)
nodes_num = label.shape[0]
train_mask = get_mask(nodes_num, 0, args_opt.train_nodes_num)
eval_mask = get_mask(nodes_num, args_opt.train_nodes_num,
args_opt.train_nodes_num + args_opt.eval_nodes_num)
test_mask = get_mask(nodes_num, nodes_num - args_opt.test_nodes_num,
nodes_num)
class_num = label.shape[1]
gcn_net = GCN(config, adj, feature, class_num)
gcn_net.add_flags_recursive(fp16=True)
eval_net = LossAccuracyWrapper(gcn_net, label, eval_mask,
config.weight_decay)
test_net = LossAccuracyWrapper(gcn_net, label, test_mask,
config.weight_decay)
train_net = TrainNetWrapper(gcn_net, label, train_mask, config)
loss_list = []
for epoch in range(config.epochs):
t = time.time()
train_net.set_train()
train_result = train_net()
train_loss = train_result[0].asnumpy()
train_accuracy = train_result[1].asnumpy()
eval_net.set_train(False)
eval_result = eval_net()
eval_loss = eval_result[0].asnumpy()
eval_accuracy = eval_result[1].asnumpy()
loss_list.append(eval_loss)
if epoch % 10 == 0:
print("Epoch:", '%04d' % (epoch), "train_loss=",
"{:.5f}".format(train_loss), "train_acc=",
"{:.5f}".format(train_accuracy), "val_loss=",
"{:.5f}".format(eval_loss), "val_acc=",
"{:.5f}".format(eval_accuracy), "time=",
"{:.5f}".format(time.time() - t))
if epoch > config.early_stopping and loss_list[-1] > np.mean(
loss_list[-(config.early_stopping + 1):-1]):
print("Early stopping...")
break
t_test = time.time()
test_net.set_train(False)
test_result = test_net()
test_loss = test_result[0].asnumpy()
test_accuracy = test_result[1].asnumpy()
print("Test set results:", "loss=", "{:.5f}".format(test_loss),
"accuracy=", "{:.5f}".format(test_accuracy), "time=",
"{:.5f}".format(time.time() - t_test))
def train():
"""Train model."""
parser = argparse.ArgumentParser(description='GCN')
parser.add_argument('--data_dir', type=str, default='./data/cora/cora_mr', help='Dataset directory')
parser.add_argument('--seed', type=int, default=0, help='Random seed')
parser.add_argument('--train_nodes_num', type=int, default=140, help='Nodes numbers for training')
parser.add_argument('--eval_nodes_num', type=int, default=500, help='Nodes numbers for evaluation')
parser.add_argument('--test_nodes_num', type=int, default=1000, help='Nodes numbers for test')
parser.add_argument('--save_TSNE', type=ast.literal_eval, default=False, help='Whether to save t-SNE graph')
args_opt = parser.parse_args()
if not os.path.exists("ckpts"):
os.mkdir("ckpts")
set_seed(args_opt.seed)
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend", save_graphs=False)
config = ConfigGCN()
adj, feature, label_onehot, label = get_adj_features_labels(args_opt.data_dir)
nodes_num = label_onehot.shape[0]
train_mask = get_mask(nodes_num, 0, args_opt.train_nodes_num)
eval_mask = get_mask(nodes_num, args_opt.train_nodes_num, args_opt.train_nodes_num + args_opt.eval_nodes_num)
test_mask = get_mask(nodes_num, nodes_num - args_opt.test_nodes_num, nodes_num)
class_num = label_onehot.shape[1]
gcn_net = GCN(config, adj, feature, class_num)
gcn_net.add_flags_recursive(fp16=True)
eval_net = LossAccuracyWrapper(gcn_net, label_onehot, eval_mask, config.weight_decay)
train_net = TrainNetWrapper(gcn_net, label_onehot, train_mask, config)
loss_list = []
if args_opt.save_TSNE:
out_feature = gcn_net()
tsne_result = t_SNE(out_feature.asnumpy(), 2)
graph_data = []
graph_data.append(tsne_result)
fig = plt.figure()
scat = plt.scatter(tsne_result[:, 0], tsne_result[:, 1], s=2, c=label, cmap='rainbow')
plt.title('t-SNE visualization of Epoch:0', fontsize='large', fontweight='bold', verticalalignment='center')
for epoch in range(config.epochs):
t = time.time()
train_net.set_train()
train_result = train_net()
train_loss = train_result[0].asnumpy()
train_accuracy = train_result[1].asnumpy()
eval_net.set_train(False)
eval_result = eval_net()
eval_loss = eval_result[0].asnumpy()
eval_accuracy = eval_result[1].asnumpy()
loss_list.append(eval_loss)
print("Epoch:", '%04d' % (epoch + 1), "train_loss=", "{:.5f}".format(train_loss),
"train_acc=", "{:.5f}".format(train_accuracy), "val_loss=", "{:.5f}".format(eval_loss),
"val_acc=", "{:.5f}".format(eval_accuracy), "time=", "{:.5f}".format(time.time() - t))
if args_opt.save_TSNE:
out_feature = gcn_net()
tsne_result = t_SNE(out_feature.asnumpy(), 2)
graph_data.append(tsne_result)
if epoch > config.early_stopping and loss_list[-1] > np.mean(loss_list[-(config.early_stopping+1):-1]):
print("Early stopping...")
break
save_checkpoint(gcn_net, "ckpts/gcn.ckpt")
gcn_net_test = GCN(config, adj, feature, class_num)
load_checkpoint("ckpts/gcn.ckpt", net=gcn_net_test)
gcn_net_test.add_flags_recursive(fp16=True)
test_net = LossAccuracyWrapper(gcn_net_test, label_onehot, test_mask, config.weight_decay)
t_test = time.time()
test_net.set_train(False)
test_result = test_net()
test_loss = test_result[0].asnumpy()
test_accuracy = test_result[1].asnumpy()
print("Test set results:", "loss=", "{:.5f}".format(test_loss),
"accuracy=", "{:.5f}".format(test_accuracy), "time=", "{:.5f}".format(time.time() - t_test))
if args_opt.save_TSNE:
ani = animation.FuncAnimation(fig, update_graph, frames=range(config.epochs + 1), fargs=(graph_data, scat, plt))
ani.save('t-SNE_visualization.gif', writer='imagemagick')
choices=["AIR", "ONNX", "MINDIR"],
default="AIR",
help="file format")
parser.add_argument("--device_target",
type=str,
default="Ascend",
choices=["Ascend", "GPU", "CPU"],
help="device target (default: Ascend)")
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE,
device_target=args.device_target,
device_id=args.device_id)
if __name__ == "__main__":
config = ConfigGCN()
if args.dataset == "cora":
input_dim = 1433
class_num = 7
adj = Tensor(np.zeros((2708, 2708), np.float64))
feature = Tensor(np.zeros((2708, 1433), np.float32))
else:
input_dim = 3703
class_num = 6
adj = Tensor(np.zeros((3312, 3312), np.float64))
feature = Tensor(np.zeros((3312, 3703), np.float32))
gcn_net = GCN(config, input_dim, class_num)
gcn_net.set_train(False)