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main.py
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main.py
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import os
import time
import torch
import argparse
from model import SASRec
from utils import *
def str2bool(s):
if s not in {'false', 'true'}:
raise ValueError('Not a valid boolean string')
return s == 'true'
parser = argparse.ArgumentParser()
parser.add_argument("--data_dir", default="./data/Beauty/", type=str) # side info
parser.add_argument('--dataset', required=True)
parser.add_argument('--train_dir', required=True)
parser.add_argument('--batch_size', default=128, type=int)
parser.add_argument('--lr', default=0.001, type=float)
parser.add_argument('--maxlen', default=50, type=int)
parser.add_argument('--hidden_units', default=50, type=int)
parser.add_argument('--num_blocks', default=2, type=int)
parser.add_argument('--num_epochs', default=201, type=int)
parser.add_argument('--num_heads', default=1, type=int)
parser.add_argument('--dropout_rate', default=0.5, type=float)
parser.add_argument('--l2_emb', default=0.0, type=float)
parser.add_argument('--device', default='cpu', type=str)
parser.add_argument('--inference_only', default=False, type=str2bool)
parser.add_argument('--state_dict_path', default=None, type=str)
parser.add_argument('--sideinfo_type', default=False,type=bool) # side info type
args = parser.parse_args()
if not os.path.isdir('./result/' + args.dataset + '_' + args.train_dir):
os.makedirs('./result/' + args.dataset + '_' + args.train_dir)
with open(os.path.join('./result/' + args.dataset + '_' + args.train_dir, 'args.txt'), 'w') as f:
f.write('\n'.join([str(k) + ',' + str(v) for k, v in sorted(vars(args).items(), key=lambda x: x[0])]))
f.close()
if __name__ == '__main__':
# global dataset
dataset = data_partition(args.dataset)
[user_train, user_valid, user_test, usernum, itemnum] = dataset
if args.sideinfo_type==True:
data_dic = get_data_dic(args)
args.item_size = data_dic['n_items'] # 0 ~ max_item
args.feature_size = data_dic['feature_size']
args.items_feature = get_feats_vec(data_dic['items_feat'], data_dic)
import IPython; IPython.embed(colors="Linux"); exit(1)
num_batch = len(user_train) // args.batch_size # tail? + ((len(user_train) % args.batch_size) != 0)
cc = 0.0
for u in user_train:
cc += len(user_train[u])
print('average sequence length: %.2f' % (cc / len(user_train)))
f = open(os.path.join('./result/' + args.dataset + '_' + args.train_dir, 'log.txt'), 'w')
sampler = WarpSampler(user_train, usernum, itemnum, batch_size=args.batch_size, maxlen=args.maxlen, n_workers=3)
model = SASRec(usernum, itemnum, args).to(args.device) # no ReLU activation in original SASRec implementation?
for name, param in model.named_parameters():
try:
torch.nn.init.xavier_normal_(param.data)
except:
pass # just ignore those failed init layers
# this fails embedding init 'Embedding' object has no attribute 'dim'
# model.apply(torch.nn.init.xavier_uniform_)
model.train() # enable model training
epoch_start_idx = 1
if args.state_dict_path is not None:
try:
model.load_state_dict(torch.load(args.state_dict_path, map_location=torch.device(args.device)))
tail = args.state_dict_path[args.state_dict_path.find('epoch=') + 6:]
epoch_start_idx = int(tail[:tail.find('.')]) + 1
except: # in case your pytorch version is not 1.6 etc., pls debug by pdb if load weights failed
print('failed loading state_dicts, pls check file path: ', end="")
print(args.state_dict_path)
print('pdb enabled for your quick check, pls type exit() if you do not need it')
import pdb; pdb.set_trace()
if args.inference_only:
model.eval()
t_test = evaluate(model, dataset, args)
print('test (NDCG@10: %.4f, HR@10: %.4f)' % (t_test[0], t_test[1]))
# ce_criterion = torch.nn.CrossEntropyLoss()
# https://github.com/NVIDIA/pix2pixHD/issues/9 how could an old bug appear again...
bce_criterion = torch.nn.BCEWithLogitsLoss() # torch.nn.BCELoss()
adam_optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.98))
T = 0.0
t0 = time.time()
for epoch in range(epoch_start_idx, args.num_epochs + 1):
if args.inference_only: break # just to decrease identition
for step in range(num_batch): # tqdm(range(num_batch), total=num_batch, ncols=70, leave=False, unit='b'):
u, seq, pos, neg = sampler.next_batch() # tuples to ndarray
u, seq, pos, neg = np.array(u), np.array(seq), np.array(pos), np.array(neg)
pos_logits, neg_logits = model(u, seq, pos, neg)
pos_labels, neg_labels = torch.ones(pos_logits.shape, device=args.device), torch.zeros(neg_logits.shape, device=args.device)
# print("\neye ball check raw_logits:"); print(pos_logits); print(neg_logits) # check pos_logits > 0, neg_logits < 0
adam_optimizer.zero_grad()
indices = np.where(pos != 0)
loss = bce_criterion(pos_logits[indices], pos_labels[indices])
loss += bce_criterion(neg_logits[indices], neg_labels[indices])
print(loss)
for param in model.item_emb.parameters():
loss += args.l2_emb * torch.norm(param)
loss.backward()
adam_optimizer.step()
print("loss in epoch {} iteration {}: {}".format(epoch, step, loss.item())) # expected 0.4~0.6 after init few epochs
#if epoch % 20 == 0:
if epoch % 2 == 0:
model.eval()
t1 = time.time() - t0
T += t1
print('Evaluating', end='')
t_test = evaluate(model, dataset, args)
t_valid = evaluate_valid(model, dataset, args)
print('epoch:%d, time: %f(s), valid (NDCG@10: %.4f, HR@10: %.4f), test (NDCG@10: %.4f, HR@10: %.4f)'
% (epoch, T, t_valid[0], t_valid[1], t_test[0], t_test[1]))
f.write(str(t_valid) + ' ' + str(t_test) + '\n')
f.flush()
t0 = time.time()
model.train()
if epoch == args.num_epochs:
folder = os.path.join('./result/',args.dataset + '_' + args.train_dir)
#folder = args.dataset + '_' + args.train_dir
fname = 'SASRec.epoch={}.lr={}.layer={}.head={}.hidden={}.maxlen={}.pth'
fname = fname.format(args.num_epochs, args.lr, args.num_blocks, args.num_heads, args.hidden_units, args.maxlen)
model_path = os.path.join(folder, fname )
torch.save(model.state_dict(),model_path)
f.close()
sampler.close()
print("Done")