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]@register_task("node_classification")
class NodeClassification(BaseTask):
"""Node classification task."""
@staticmethod
"""Add task-specific arguments to the parser."""
# fmt: off
# parser.add_argument("--num-features", type=int)
# fmt: on
def __init__(self, args, dataset=None, model=None):
super(NodeClassification, self).__init__(args)
self.device = torch.device('cpu' if args.cpu else 'cuda')
if dataset is None:
dataset = build_dataset(args)
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
if model is None:
model = build_model(args)
self.model = model.to(self.device)
self.patience = args.patience
self.max_epoch = args.max_epoch
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()
self.optimizer.zero_grad()
self.model.loss(self.data).backward()
self.optimizer.step()
[docs] def _test_step(self, split="val"):
self.model.eval()
logits = self.model.predict(self.data)
if split == "train":
mask = self.data.train_mask
elif split == "val":
mask = self.data.val_mask
else:
mask = self.data.test_mask
loss = F.nll_loss(logits[mask], self.data.y[mask]).item()
pred = logits[mask].max(1)[1]
acc = pred.eq(self.data.y[mask]).sum().item() / mask.sum().item()
return acc, loss