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train.py
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train.py
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import argparse
import time
import numpy as np
import torch
from tqdm import tqdm
from models.RelMoE import RelMoE
from models.model import *
from models.modules import MIEstimator
from utils.data_loader import *
from utils.data_util import load_data
def parse_args():
config_args = {
'lr': 0.0005,
'dropout_gat': 0.3,
'dropout': 0.3,
'cuda': 0,
'epochs_gat': 3000,
'epochs': 2000,
'weight_decay_gat': 1e-5,
'weight_decay': 0,
'seed': 10010,
'model': 'RMoE',
'num-layers': 3,
'dim': 256,
'r_dim': 256,
'k_w': 10,
'k_h': 20,
'n_heads': 2,
'dataset': 'DB15K',
'pre_trained': 0,
'encoder': 0,
'image_features': 1,
'text_features': 1,
'patience': 5,
'eval_freq': 100,
'lr_reduce_freq': 500,
'gamma': 1.0,
'bias': 1,
'neg_num': 2,
'neg_num_gat': 2,
'alpha': 0.2,
'alpha_gat': 0.2,
'out_channels': 32,
'kernel_size': 3,
'batch_size': 1024,
'save': 1,
'n_exp': 3,
'mu': 0.0001,
'img_dim': 256,
'txt_dim': 256
}
parser = argparse.ArgumentParser()
for param, val in config_args.items():
parser.add_argument(f"--{param}", default=val, type=type(val))
args = parser.parse_args()
return args
args = parse_args()
print(args)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
args.device = 'cuda:' + str(args.cuda) if int(args.cuda) >= 0 else 'cpu'
print(f'Using: {args.device}')
torch.cuda.set_device(args.cuda)
for k, v in list(vars(args).items()):
print(str(k) + ':' + str(v))
entity2id, relation2id, img_features, text_features, train_data, val_data, test_data = load_data(args.dataset)
print("Training data {:04d}".format(len(train_data[0])))
corpus = ConvECorpus(args, train_data, val_data, test_data, entity2id, relation2id)
if args.image_features:
args.img = F.normalize(torch.Tensor(img_features), p=2, dim=1)
if args.text_features:
args.desp = F.normalize(torch.Tensor(text_features), p=2, dim=1)
args.entity2id = entity2id
args.relation2id = relation2id
model_name = {
'RMoE': RelMoE
}
time.sleep(5)
def train_decoder(args):
model = model_name[args.model](args)
args.img_dim = model.img_dim
args.txt_dim = model.txt_dim
estimator = MIEstimator(args)
print(str(model))
optimizer = torch.optim.Adam(params=model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, args.gamma)
optimizer_mi = torch.optim.Adam(params=estimator.parameters(), lr=args.lr, weight_decay=args.weight_decay)
tot_params = sum([np.prod(p.size()) for p in model.parameters()])
print(f'Total number of parameters: {tot_params}')
if args.cuda is not None and int(args.cuda) >= 0:
model = model.to(args.device)
estimator = estimator.to(args.device)
# Train Model
t_total = time.time()
counter = 0
best_val_metrics = model.init_metric_dict()
best_test_metrics = model.init_metric_dict()
corpus.batch_size = args.batch_size
corpus.neg_num = args.neg_num
training_range = tqdm(range(args.epochs))
for epoch in training_range:
model.train()
epoch_loss = []
epoch_mi_loss = []
t = time.time()
corpus.shuffle()
for batch_num in range(corpus.max_batch_num):
# Training the KGC model
estimator.eval()
optimizer.zero_grad()
train_indices, train_values = corpus.get_batch(batch_num)
train_indices = torch.LongTensor(train_indices)
if args.cuda is not None and int(args.cuda) >= 0:
train_indices = train_indices.to(args.device)
train_values = train_values.to(args.device)
output, embeddings = model.forward(train_indices)
loss = model.loss_func(output, train_values) + args.mu * estimator(embeddings)
loss.backward()
optimizer.step()
# Train the estimator
estimator.train()
optimizer_mi.zero_grad()
with torch.no_grad():
embeddings = model.get_batch_embeddings(train_indices)
estimator_loss = estimator.train_estimator(embeddings)
estimator_loss.backward()
optimizer_mi.step()
epoch_loss.append(loss.data.item())
epoch_mi_loss.append(0.0)
training_range.set_postfix(loss="main: {:.5} mi: {:.5}".format(sum(epoch_loss), sum(epoch_mi_loss)))
lr_scheduler.step()
if (epoch + 1) % args.eval_freq == 0:
print("Epoch {:04d} , average loss {:.4f} , epoch_time {:.4f}\n".format(
epoch + 1, sum(epoch_loss) / len(epoch_loss), time.time() - t))
model.eval()
with torch.no_grad():
val_metrics = corpus.get_validation_pred(model, 'test')
val_metrics_s = corpus.get_validation_pred_signle(model, 'test', 0)
val_metrics_i = corpus.get_validation_pred_signle(model, 'test', 1)
val_metrics_t = corpus.get_validation_pred_signle(model, 'test', 2)
val_metrics_mm = corpus.get_validation_pred_signle(model, 'test', 3)
if val_metrics['MRR'] > best_test_metrics['MRR']:
best_test_metrics['MRR'] = val_metrics['MRR']
if val_metrics['MR'] < best_test_metrics['MR']:
best_test_metrics['MR'] = val_metrics['MR']
if val_metrics['Hits@1'] > best_test_metrics['Hits@1']:
best_test_metrics['Hits@1'] = val_metrics['Hits@1']
if val_metrics['Hits@3'] > best_test_metrics['Hits@3']:
best_test_metrics['Hits@3'] = val_metrics['Hits@3']
if val_metrics['Hits@10'] > best_test_metrics['Hits@10']:
best_test_metrics['Hits@10'] = val_metrics['Hits@10']
if val_metrics['Hits@100'] > best_test_metrics['Hits@100']:
best_test_metrics['Hits@100'] = val_metrics['Hits@100']
print('\n'.join(['Epoch: {:04d}'.format(epoch + 1), model.format_metrics(val_metrics, 'test')]))
print('\n'.join(['Epoch: {:04d}, Structure: '.format(epoch + 1), model.format_metrics(val_metrics_s, 'test')]))
print('\n'.join(['Epoch: {:04d}, Image: '.format(epoch + 1), model.format_metrics(val_metrics_i, 'test')]))
print('\n'.join(['Epoch: {:04d}, Text: '.format(epoch + 1), model.format_metrics(val_metrics_t, 'test')]))
print('\n'.join(['Epoch: {:04d}, Multi-modal: '.format(epoch + 1), model.format_metrics(val_metrics_mm, 'test')]))
print("\n\n")
print('Total time elapsed: {:.4f}s'.format(time.time() - t_total))
if not best_test_metrics:
model.eval()
estimator.eval()
with torch.no_grad():
best_test_metrics = corpus.get_validation_pred(model, 'test')
print('\n'.join(['Val set results:', model.format_metrics(best_val_metrics, 'val')]))
print('\n'.join(['Test set results:', model.format_metrics(best_test_metrics, 'test')]))
print("\n\n\n\n\n\n")
if args.save:
torch.save(model.state_dict(), f'./checkpoint/{args.dataset}/{args.model}.pth')
print('Saved model!')
if __name__ == '__main__':
train_decoder(args)