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embedder.py
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embedder.py
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import numpy as np
import torch
import torch.nn as nn
from argument import printConfig, config2string
from tensorboardX import SummaryWriter
from models import LogisticRegression
from torch_geometric.nn import GCNConv
# To fix the random seed
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
import random
random.seed(0)
class embedder:
def __init__(self, args):
self.args = args
self.hidden_layers = args.layers
printConfig(args)
self.config_str = config2string(args)
print("\n[Config] {}\n".format(self.config_str))
self.writer = SummaryWriter(log_dir="runs/{}".format(self.config_str))
# Model Checkpoint Path
CHECKPOINT_PATH = "RGRL_checkpoints/"
self.check_dir = CHECKPOINT_PATH + self.config_str + ".pt"
def infer_embeddings(self):
self._model.train(False)
self._embeddings = self._labels = None
self._dataset.data.to(self._device)
_, _, _, _, embeddings = self._model(
x1=self._dataset.data.x, x2=self._dataset.data.x,
edge_index1=self._dataset.data.edge_index,
edge_index2=self._dataset.data.edge_index,
edge_weight1=self._dataset.data.edge_attr,
edge_weight2=self._dataset.data.edge_attr)
emb = embeddings.detach()
y = self._dataset.data.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, epoch):
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.data.train_mask[i]
self._dev_mask = self._dataset.data.val_mask[i]
if self._args.dataset == "wikics":
self._test_mask = self._dataset.data.test_mask
else:
self._test_mask = self._dataset.data.test_mask[i]
classifier = LogisticRegression(emb_dim, num_class).to(self._device)
optimizer = torch.optim.Adam(classifier.parameters(), lr=0.01)
for _ 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()
print('** [{}] [Epoch: {}] Val: {:.4f} ({:.4f}) | Test: {:.4f} ({:.4f}) **'.format(self.args.embedder, epoch, dev_acc, dev_std, test_acc, test_std))
if dev_acc > self.best_dev_acc:
self.best_dev_acc = dev_acc
self.best_test_acc = test_acc
self.best_dev_std = dev_std
self.best_test_std = test_std
self.best_epoch = epoch
checkpoint = {'epoch': epoch, 'embeddings': self._embeddings.detach().cpu().numpy()}
torch.save(checkpoint, self.check_dir)
self.writer.add_scalar("acc/val_accuracy", dev_acc, epoch+1)
self.writer.add_scalar("acc/best_val_accuracy", self.best_dev_acc, epoch+1)
self.writer.add_scalar("acc/test_accuracy", test_acc, epoch+1)
self.writer.add_scalar("acc/best_test_accuracy", self.best_test_acc, epoch+1)
self.best_dev_accs.append(self.best_dev_acc)
self.st_best = '** [Best epoch: {}] Best val | Best test: {:.4f} ({:.4f}) / {:.4f} ({:.4f})**\n'.format(
self.best_epoch, self.best_dev_acc, self.best_dev_std, self.best_test_acc, self.best_test_std)
print(self.st_best)
class Encoder(nn.Module):
def __init__(self, layer_config, dropout=None, project=False, **kwargs):
super().__init__()
self.conv1 = GCNConv(layer_config[0], layer_config[1])
self.bn1 = nn.BatchNorm1d(layer_config[1], momentum = 0.01)
self.prelu1 = nn.PReLU()
self.conv2 = GCNConv(layer_config[1],layer_config[2])
self.bn2 = nn.BatchNorm1d(layer_config[2], momentum = 0.01)
self.prelu2 = nn.PReLU()
def forward(self, x, edge_index, edge_weight=None):
x = self.conv1(x, edge_index, edge_weight=edge_weight)
x = self.prelu1(self.bn1(x))
x = self.conv2(x, edge_index, edge_weight=edge_weight)
x = self.prelu2(self.bn2(x))
return x