import argparse
import copy
from typing import Optional
import numpy as np
import torch
from tqdm import tqdm
from cogdl.datasets import build_dataset
from cogdl.models import build_model
from cogdl.models.supervised_model import SupervisedHomogeneousNodeClassificationModel
from . import BaseTask, register_task
[docs]@register_task("node_classification")
class NodeClassification(BaseTask):
"""Node classification task."""
[docs] @staticmethod
def add_args(parser: argparse.ArgumentParser):
"""Add task-specific arguments to the parser."""
# fmt: off
parser.add_argument("--missing-rate", type=int, default=0, help="missing rate, from 0 to 100")
# fmt: on
def __init__(
self,
args,
dataset=None,
model: Optional[SupervisedHomogeneousNodeClassificationModel] = None,
):
super(NodeClassification, self).__init__(args)
self.args = args
self.model_name = args.model
self.device = "cpu" if not torch.cuda.is_available() or args.cpu else args.device_id[0]
dataset = build_dataset(args) if dataset is None else dataset
self.dataset = dataset
self.data = dataset[0]
args.num_features = dataset.num_features
args.num_classes = dataset.num_classes
args.num_nodes = dataset.data.x.shape[0]
self.model: SupervisedHomogeneousNodeClassificationModel = build_model(args) if model is None else model
self.model.set_device(self.device)
self.set_loss_fn(dataset)
self.set_evaluator(dataset)
self.trainer = self.get_trainer(self.model, self.args)
if not self.trainer:
self.optimizer = (
torch.optim.Adam(self.model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
if not hasattr(self.model, "get_optimizer")
else self.model.get_optimizer(args)
)
self.data.apply(lambda x: x.to(self.device))
self.model: SupervisedHomogeneousNodeClassificationModel = self.model.to(self.device)
self.patience = args.patience
self.max_epoch = args.max_epoch
[docs] def train(self):
if self.trainer:
result = self.trainer.fit(self.model, self.dataset)
if issubclass(type(result), torch.nn.Module):
self.model = result
self.model.to(self.data.x.device)
else:
return result
else:
epoch_iter = tqdm(range(self.max_epoch))
patience = 0
best_score = 0
best_loss = np.inf
max_score = 0
min_loss = np.inf
best_model = copy.deepcopy(self.model)
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.cpu()))
max_score = np.max((max_score, val_acc))
patience = 0
else:
patience += 1
if patience == self.patience:
epoch_iter.close()
break
print(f"Valid accurracy = {best_score: .4f}")
self.model = best_model
test_acc, _ = self._test_step(split="test")
val_acc, _ = self._test_step(split="val")
print(f"Test accuracy = {test_acc:.4f}")
return dict(Acc=test_acc, ValAcc=val_acc)
def _train_step(self):
self.model.train()
self.optimizer.zero_grad()
self.model.node_classification_loss(self.data).backward()
self.optimizer.step()
def _test_step(self, split="val", logits=None):
self.model.eval()
with torch.no_grad():
logits = logits if logits else 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 = self.loss_fn(logits[mask], self.data.y[mask])
metric = self.evaluator(logits[mask], self.data.y[mask])
return metric, loss