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train.py
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train.py
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from __future__ import division
from __future__ import print_function
import time
import argparse
import numpy as np
import logging
from pathlib import Path
import torch
import torch.nn.functional as F
import torch.optim as optim
from pygcn.utils import accuracy, masked_loss, masked_acc
from pygcn.models import GCN
from pygcn.data import load_data, preprocess_features, preprocess_adj, load_cora_new
# Training settings
parser = argparse.ArgumentParser()
parser.add_argument('--no-cuda', action='store_true', default=False,
help='Disables CUDA training.')
parser.add_argument('--fastmode', action='store_true', default=False,
help='Validate during training pass.')
parser.add_argument('--seed', type=int, default=42, help='Random seed.')
parser.add_argument('--epochs', type=int, default=200,
help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=0.01,
help='Initial learning rate.')
parser.add_argument('--weight_decay', type=float, default=5e-4,
help='Weight decay (L2 loss on parameters).')
parser.add_argument('--hidden', type=int, default=16,
help='Number of hidden units.')
parser.add_argument('--dropout', type=float, default=0.5,
help='Dropout rate (1 - keep probability).')
parser.add_argument('--dataset', type=str, default='cora',
help='Dataset string')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
### set up logger
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
Path("log/").mkdir(parents=True, exist_ok=True)
fh = logging.FileHandler('log/{}.log'.format(str(time.time())))
fh.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.WARN)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
logger.info(args)
# Load data
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = load_data(dataset_str=args.dataset)
# features = load_cora_new()
logger.info('adj: {}'.format(adj.shape))
logger.info('features: {}'.format(features.shape))
logger.info('y tr{} val{} te{} '.format(y_train.shape, y_val.shape, y_test.shape))
logger.info('mask tr{} val{} te{}'.format(train_mask.shape, val_mask.shape, test_mask.shape))
features = preprocess_features(features) # [49216, 2], [49216], [2708, 1433]
supports = preprocess_adj(adj)
device = torch.device('cuda')
train_label = torch.from_numpy(y_train).long().to(device)
num_classes = train_label.shape[1]
train_label = train_label.argmax(dim=1)
train_mask = torch.from_numpy(train_mask.astype(np.int)).float().to(device)
val_label = torch.from_numpy(y_val).long().to(device)
val_label = val_label.argmax(dim=1)
val_mask = torch.from_numpy(val_mask.astype(np.int)).to(device)
test_label = torch.from_numpy(y_test).long().to(device)
test_label = test_label.argmax(dim=1)
test_mask = torch.from_numpy(test_mask.astype(np.int)).to(device)
i = torch.from_numpy(features[0]).long().to(device)
v = torch.from_numpy(features[1]).to(device)
feature = torch.sparse.FloatTensor(i.t(), v, features[2]).float().to(device)
i = torch.from_numpy(supports[0]).long().to(device)
v = torch.from_numpy(supports[1]).to(device)
support = torch.sparse.FloatTensor(i.t(), v, supports[2]).float().to(device)
logger.info('x :{}'.format(feature.shape))
logger.info('sp: {}'.format(support.shape))
num_features_nonzero = feature._nnz()
feat_dim = feature.shape[1]
# Model and optimizer
model = GCN(nfeat=feat_dim,
nhid=args.hidden,
nclass=num_classes,
dropout=args.dropout)
model.to(device)
optimizer = optim.Adam(model.parameters(),
lr=args.lr, weight_decay=args.weight_decay)
model.train()
def train(epoch):
t = time.time()
model.train()
optimizer.zero_grad()
out = model(feature, support)
logger.info ("out: {}".format(out.shape))
logger.info ("train_label {}".format(train_label.shape))
logger.info ("train_mask {}".format(train_mask.shape))
# out = output[0]
loss_train = masked_loss(out, train_label, train_mask)
loss_train += args.weight_decay * model.l2_loss()
acc_train = masked_acc(out, train_label, train_mask)
loss_train.backward()
optimizer.step()
loss_val = masked_loss(out, val_label, val_mask)
acc_val = masked_acc(out, val_label, val_mask)
logger.info('Epoch: {:04d}, loss_train: {:.4f}, acc_train: {:.4f}, loss_val: {:.4f}, acc_val: {:.4f}, time: {:.4f}s\
'.format(epoch+1, loss_train.item(), acc_train.item(), loss_val.item(), acc_val.item(), time.time() - t))
def test():
model.eval()
out = model(feature, support)
# out = out[0]
loss_test = masked_loss(out, test_label, test_mask)
acc_test = masked_acc(out, test_label, test_mask)
logger.info("Test set results: \
loss= {:.4f}, accuracy= {:.4f}".format(loss_test.item(), acc_test.item()))
# Train model
t_total = time.time()
for epoch in range(args.epochs):
train(epoch)
logger.info("Optimization Finished!")
logger.info("Total time elapsed: {:.4f}s".format(time.time() - t_total))
# Testing
test()