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train.py
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train.py
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import numpy as np
import torch
from torch import optim
from tensorboardX import SummaryWriter
torch.manual_seed(0)
import models
import utils
import data
import os
import sys
class ModelTrainer:
def __init__(self, args):
self._args = args
self._init()
self.writer = SummaryWriter(log_dir="runs/BGRL_dataset({})".format(args.name))
def _init(self):
args = self._args
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.device)
self._device = f'cuda:{args.device}' if torch.cuda.is_available() else "cpu"
self._dataset = data.Dataset(root=args.root, name=args.name)[0]
print(f"Data: {self._dataset}")
hidden_layers = [int(l) for l in args.layers]
layers = [self._dataset.x.shape[1]] + hidden_layers
self._model = models.BGRL(layer_config=layers, pred_hid=args.pred_hid, dropout=args.dropout, epochs=args.epochs).to(self._device)
print(self._model)
self._optimizer = optim.AdamW(params=self._model.parameters(), lr=args.lr, weight_decay= 1e-5)
# learning rate
scheduler = lambda epoch: epoch / 1000 if epoch < 1000 \
else ( 1 + np.cos((epoch-1000) * np.pi / (self._args.epochs - 1000))) * 0.5
self._scheduler = optim.lr_scheduler.LambdaLR(self._optimizer, lr_lambda = scheduler)
def train(self):
# get initial test results
print("start training!")
print("Initial Evaluation...")
self.infer_embeddings()
dev_best, dev_std_best, test_best, test_std_best = self.evaluate()
self.writer.add_scalar("accs/val_acc", dev_best, 0)
self.writer.add_scalar("accs/test_acc", test_best, 0)
print("validation: {:.4f}, test: {:.4f}".format(dev_best, test_best))
# start training
self._model.train()
for epoch in range(self._args.epochs):
self._dataset.to(self._device)
augmentation = utils.Augmentation(float(self._args.aug_params[0]),float(self._args.aug_params[1]),float(self._args.aug_params[2]),float(self._args.aug_params[3]))
view1, view2 = augmentation._feature_masking(self._dataset, self._device)
v1_output, v2_output, loss = self._model(
x1=view1.x, x2=view2.x, edge_index_v1=view1.edge_index, edge_index_v2=view2.edge_index,
edge_weight_v1=view1.edge_attr, edge_weight_v2=view2.edge_attr)
self._optimizer.zero_grad()
loss.backward()
self._optimizer.step()
self._scheduler.step()
self._model.update_moving_average()
sys.stdout.write('\rEpoch {}/{}, loss {:.4f}, lr {}'.format(epoch + 1, self._args.epochs, loss.data, self._optimizer.param_groups[0]['lr']))
sys.stdout.flush()
if (epoch + 1) % self._args.cache_step == 0:
print("")
print("\nEvaluating {}th epoch..".format(epoch + 1))
self.infer_embeddings()
dev_acc, dev_std, test_acc, test_std = self.evaluate()
if dev_best < dev_acc:
dev_best = dev_acc
dev_std_best = dev_std
test_best = test_acc
test_std_best = test_std
self.writer.add_scalar("stats/learning_rate", self._optimizer.param_groups[0]["lr"] , epoch + 1)
self.writer.add_scalar("accs/val_acc", dev_acc, epoch + 1)
self.writer.add_scalar("accs/test_acc", test_acc, epoch + 1)
print("validation: {:.4f}, test: {:.4f} \n".format(dev_acc, test_acc))
f = open("BGRL_dataset({})_node.txt".format(self._args.name), "a")
f.write("best valid acc : {} best valid std : {} best test acc : {} best test std : {} \n".format(dev_best, dev_std_best, test_best, test_std_best))
f.close()
print()
print("Training Done!")
def infer_embeddings(self):
self._model.train(False)
self._embeddings = self._labels = None
self._dataset.to(self._device)
v1_output, v2_output, _ = self._model(
x1=self._dataset.x, x2=self._dataset.x,
edge_index_v1=self._dataset.edge_index,
edge_index_v2=self._dataset.edge_index,
edge_weight_v1=self._dataset.edge_attr,
edge_weight_v2=self._dataset.edge_attr)
emb = v1_output.detach()
y = self._dataset.y.detach()
if self._embeddings is None:
self._embeddings, self._labels = emb, y
else:
self._embeddings = torch.cat([self._embeddings, emb])
self._labels = torch.cat([self._labels, y])
def evaluate(self):
"""
Used for producing the results of Experiment 3.2 in the BGRL paper.
"""
emb_dim, num_class = self._embeddings.shape[1], self._labels.unique().shape[0]
dev_accs, test_accs = [], []
for i in range(20):
self._train_mask = self._dataset.train_mask[i]
self._dev_mask = self._dataset.val_mask[i]
if self._args.name == "WikiCS":
self._test_mask = self._dataset.test_mask
else :
self._test_mask = self._dataset.test_mask[i]
classifier = models.LogisticRegression(emb_dim, num_class).to(self._device)
optimizer = torch.optim.Adam(classifier.parameters(), lr=0.01, weight_decay=0.0)
for epoch in range(100):
classifier.train()
logits, loss = classifier(self._embeddings[self._train_mask], self._labels[self._train_mask])
optimizer.zero_grad()
loss.backward()
optimizer.step()
dev_logits, _ = classifier(self._embeddings[self._dev_mask], self._labels[self._dev_mask])
test_logits, _ = classifier(self._embeddings[self._test_mask], self._labels[self._test_mask])
dev_preds = torch.argmax(dev_logits, dim=1)
test_preds = torch.argmax(test_logits, dim=1)
dev_acc = (torch.sum(dev_preds == self._labels[self._dev_mask]).float() / self._labels[self._dev_mask].shape[0]).detach().cpu().numpy()
test_acc = (torch.sum(test_preds == self._labels[self._test_mask]).float() / self._labels[self._test_mask].shape[0]).detach().cpu().numpy()
dev_accs.append(dev_acc * 100)
test_accs.append(test_acc * 100)
dev_accs = np.stack(dev_accs)
test_accs = np.stack(test_accs)
dev_acc, dev_std = dev_accs.mean(), dev_accs.std()
test_acc, test_std = test_accs.mean(), test_accs.std()
return dev_acc, dev_std, test_acc, test_std
def train_eval(args):
trainer = ModelTrainer(args)
trainer.train()
trainer.writer.close()
def main():
args = utils.parse_args()
train_eval(args)
if __name__ == "__main__":
main()