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train.py
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train.py
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from datasets import DATASETS
from config import STATE_DICT_KEY
import argparse
import torch
from model import *
from dataloader import *
from trainer import *
from utils import *
def train(args, export_root=None, resume=False):
args.lr = 0.001
fix_random_seed_as(args.model_init_seed)
train_loader, val_loader, test_loader = dataloader_factory(args)
if args.model_code == 'bert':
model = BERT(args)
elif args.model_code == 'sas':
model = SASRec(args)
elif args.model_code == 'narm':
model = NARM(args)
if export_root == None:
export_root = 'experiments/' + args.model_code + '/' + args.dataset_code
if resume:
try:
model.load_state_dict(torch.load(os.path.join(export_root, 'models', 'best_acc_model.pth'), map_location='cpu').get(STATE_DICT_KEY))
except FileNotFoundError:
print('Failed to load old model, continue training new model...')
if args.model_code == 'bert':
trainer = BERTTrainer(args, model, train_loader, val_loader, test_loader, export_root)
if args.model_code == 'sas':
trainer = SASTrainer(args, model, train_loader, val_loader, test_loader, export_root)
elif args.model_code == 'narm':
args.num_epochs = 100
trainer = RNNTrainer(args, model, train_loader, val_loader, test_loader, export_root)
trainer.train()
trainer.test()
if __name__ == "__main__":
set_template(args)
# when use k-core beauty and k is not 5 (beauty-dense)
# args.min_uc = k
# args.min_sc = k
train(args, resume=True)