import copy
import random
import numpy as np
import torch
import torch.nn.functional as F
from tqdm import tqdm
from cogdl import options
from cogdl.datasets import build_dataset
from cogdl.models import build_model
from . import BaseTask, register_task
[docs]def get_batches(train_nodes, train_labels, batch_size=64, shuffle=True):
if shuffle:
random.shuffle(train_nodes)
total = train_nodes.shape[0]
for i in range(0, total, batch_size):
if i + batch_size <= total:
cur_nodes = train_nodes[i: i+batch_size]
cur_labels = train_labels[cur_nodes]
yield cur_nodes, cur_labels
[docs]@register_task("node_classification_sampling")
class NodeClassificationSampling(BaseTask):
"""Node classification task with sampling."""
@staticmethod
[docs] def add_args(parser):
"""Add task-specific arguments to the parser."""
# fmt: off
parser.add_argument("--batch-size", type=int, default=20)
# fmt: on
def __init__(self, args, dataset=None, model=None):
super(NodeClassificationSampling, self).__init__(args)
self.device = torch.device('cpu' if args.cpu else 'cuda')
dataset = build_dataset(args) if dataset is None else dataset
self.data = dataset.data
self.data.apply(lambda x: x.to(self.device))
args.num_features = dataset.num_features
args.num_classes = dataset.num_classes
model = build_model(args) if model is None else model
self.num_nodes = self.data.x.shape[0]
self.model = model.to(self.device)
self.patience = args.patience
self.max_epoch = args.max_epoch
self.batch_size = args.batch_size
self.adj_list = self.data.edge_index.detach().cpu().numpy()
self.model.set_adj(self.adj_list, self.num_nodes)
self.optimizer = torch.optim.Adam(
self.model.parameters(), lr=args.lr, weight_decay=args.weight_decay
)
[docs] def train(self):
epoch_iter = tqdm(range(self.max_epoch))
patience = 0
best_score = 0
best_loss = np.inf
max_score = 0
min_loss = np.inf
for epoch in epoch_iter:
self._train_step()
train_acc, _ = self._test_step(split="train")
val_acc, val_loss = self._test_step(split="val")
epoch_iter.set_description(
f"Epoch: {epoch:03d}, Train: {train_acc:.4f}, Val: {val_acc:.4f}"
)
if val_loss <= min_loss or val_acc >= max_score:
if val_loss <= best_loss: # and val_acc >= best_score:
best_loss = val_loss
best_score = val_acc
best_model = copy.deepcopy(self.model)
min_loss = np.min((min_loss, val_loss))
max_score = np.max((max_score, val_acc))
patience = 0
else:
patience += 1
if patience == self.patience:
self.model = best_model
epoch_iter.close()
break
test_acc, _ = self._test_step(split="test")
print(f"Test accuracy = {test_acc}")
return dict(Acc=test_acc)
[docs] def _train_step(self):
self.model.train()
train_nodes = np.where(self.data.train_mask.detach().cpu().numpy())[0]
train_labels = self.data.y.detach().cpu().numpy()
for batch_nodes, batch_labels in get_batches(train_nodes, train_labels, batch_size=self.batch_size):
batch_nodes = torch.LongTensor(batch_nodes)
batch_labels = torch.LongTensor(batch_labels).to(self.device)
sampled_x, sampled_adj, var_loss = self.model.sampling(self.data.x, batch_nodes)
self.optimizer.zero_grad()
output = self.model(sampled_x, sampled_adj)
loss = F.nll_loss(output, batch_labels) + 0.5 * var_loss
loss.backward()
self.optimizer.step()
[docs] def _test_step(self, split="val"):
self.model.eval()
_, mask = list(self.data(f"{split}_mask"))[0]
test_nodes = np.where(mask.detach().cpu().numpy())[0]
test_labels = self.data.y.detach().cpu().numpy()
all_loss = []
all_acc = []
for batch_nodes, batch_labels in get_batches(test_nodes, test_labels, batch_size=self.batch_size):
batch_nodes = torch.LongTensor(batch_nodes)
batch_labels = torch.LongTensor(batch_labels).to(self.device)
sampled_x, sampled_adj, var_loss = self.model.sampling(self.data.x, batch_nodes)
with torch.no_grad():
logits = self.model(sampled_x, sampled_adj)
loss = F.nll_loss(logits, batch_labels)
pred = logits.max(1)[1]
acc = pred.eq(self.data.y[batch_nodes]).sum().item() / batch_nodes.shape[0]
all_loss.append(loss.item())
all_acc.append(acc)
return np.mean(all_acc), np.mean(all_loss)