score_fn=opt.score)
if opt.restore:
model.load_state_dict(checkpoints['model'])
if use_cuda:
model.cuda()
if len(opt.gpus) > 1:
model = nn.DataParallel(model, device_ids=opt.gpus, dim=1)
# optimizer
if opt.restore:
optim = checkpoints['optim']
else:
optim = Optim(config.optim,
config.learning_rate,
config.max_grad_norm,
lr_decay=config.learning_rate_decay,
start_decay_at=config.start_decay_at)
optim.set_parameters(model.parameters())
if config.schedule:
scheduler = L.CosineAnnealingLR(optim.optimizer, T_max=config.epoch)
# total number of parameters
param_count = 0
for param in model.parameters():
param_count += param.view(-1).size()[0]
if not os.path.exists(config.log):
os.mkdir(config.log)
if opt.log == '':
log_path = config.log + utils.format_time(time.localtime()) + '/'
def main():
# 设定种子
torch.manual_seed(args.seed)
if use_cuda:
torch.cuda.manual_seed(args.seed)
# checkpoint
if args.restore: # 存储已有模型的路径
print('loading checkpoint...\n')
checkpoints = torch.load(os.path.join(log_path, args.restore))
contentfile = os.path.join(config.data, "segged_content.txt")
# word2id, id2word, word2count = load_vocab(args.vocab_file, args.vocab_size)
vocab = Vocab(config.vocab, contentfile, config.vocab_size)
# Load data
start_time = time.time()
use_gnn = False
if args.graph_model == 'GNN':
use_gnn = True
dataloader = DataLoader(config, config.data, config.batch_size, vocab,
args.adj, use_gnn, args.model, args.notrain,
args.debug)
print("DATA loaded!")
torch.backends.cudnn.benchmark = True
# data
print('loading data...\n')
print('loading time cost: %.3f' % (time.time() - start_time))
# model
print('building model...\n')
# configure the model
# Model and optimizer
# 增加模型graph2gru, graoh2gru_noAtten
if args.model == 'graph2seq':
model = graph2seq(config, vocab, use_cuda, args.use_copy,
args.use_bert, args.word_level_model,
args.graph_model)
elif args.model == 'graph2gru':
model = graph2gru.graph2gru(config, vocab, use_cuda, args.use_copy,
args.use_bert, args.word_level_model,
args.graph_model)
elif args.model == 'graph2gru_noAtten':
model = graph2gru_noAtten.graph2gru_noAtten(config, vocab, use_cuda,
args.use_copy,
args.use_bert,
args.word_level_model,
args.graph_model)
elif args.model == 'seq2seq':
model = seq2seq(config, vocab, use_cuda, use_content=args.use_content)
elif args.model == 'bow2seq':
model = bow2seq(config, vocab, use_cuda)
elif args.model == 'h_attention':
model = hierarchical_attention(config, vocab, use_cuda)
if args.restore:
model.load_state_dict(checkpoints['model'])
if use_cuda:
model.cuda()
# lm_model.cuda()
if len(args.gpus) > 1: # 并行
model = nn.DataParallel(model, device_ids=args.gpus, dim=1)
logging(repr(model) + "\n\n") # 记录这个文件的框架
# total number of parameters
param_count = 0
for param in model.parameters():
param_count += param.view(-1).size()[0]
logging('total number of parameters: %d\n\n' % param_count)
# updates是已经进行了几个epoch, 防止中间出现程序中断的情况.
if args.restore:
updates = checkpoints['updates']
ori_updates = updates
else:
updates = 0
# optimizer
if args.restore:
optim = checkpoints['optim']
else:
optim = Optim(config.optim,
config.learning_rate,
config.max_grad_norm,
lr_decay=config.learning_rate_decay,
start_decay_at=config.start_decay_at)
# if opt.pretrain:
# pretrain_lm(lm_model, vocab)
optim.set_parameters(model.parameters())
if config.schedule:
scheduler = L.CosineAnnealingLR(optim.optimizer, T_max=config.epoch)
else:
scheduler = None
print("nana...")
if not args.notrain:
max_bleu = train(model, vocab, dataloader, scheduler, optim, updates)
logging("Best bleu score: %.2f\n" % (max_bleu))
else:
assert args.restore is not None
eval(model, vocab, dataloader, 0, updates, do_test=False)
print("nana```")
def main(vocab, dataloader):
# 设定种子
torch.manual_seed(args.seed)
if use_cuda:
torch.cuda.manual_seed(args.seed)
# checkpoint
if args.restore: # 存储已有模型的路径
print('loading checkpoint...\n')
checkpoints = torch.load(os.path.join(log_path, args.restore))
torch.backends.cudnn.benchmark = True
# model
print('building model...\n')
# configure the model
# Model and optimizer
model = GLSTM(config, vocab)
# model = hierarchical_attention(config, vocab)
# model = SLSTM(config, vocab)
# model = Transformer(config, vocab)
if args.restore:
model.load_state_dict(checkpoints['model'])
if use_cuda:
model.cuda()
if len(args.gpus) > 1: # 并行
model = nn.DataParallel(model, device_ids=args.gpus, dim=1)
logging(repr(model) + "\n\n") # 记录这个文件的框架
# total number of parameters
param_count = 0
for param in model.parameters():
param_count += param.view(-1).size()[0]
logging('total number of parameters: %d\n\n' % param_count)
# updates是已经进行了几个epoch, 防止中间出现程序中断的情况.
if args.restore:
updates = checkpoints['updates']
ori_updates = updates
else:
updates = 0
# optimizer
if args.restore:
optim = checkpoints['optim']
else:
optim = Optim(config.optim,
config.learning_rate,
config.max_grad_norm,
lr_decay=config.learning_rate_decay,
start_decay_at=config.start_decay_at)
optim.set_parameters(model.parameters())
if config.schedule:
scheduler = L.CosineAnnealingLR(optim.optimizer, T_max=config.epoch)
else:
scheduler = None
if not args.notrain:
max_acc, test_acc = train(model, dataloader, scheduler, optim, updates)
logging("Best accuracy: %.2f, test accuracy: %.2f\n" %
(max_acc * 100, test_acc * 100))
return test_acc
else:
assert args.restore is not None
eval(model, vocab, dataloader, 0, updates, do_test=True)
if use_cuda:
netG.cuda()
netD.cuda()
if len(opt.gpus) > 1:
netG = nn.DataParallel(netG, device_ids=opt.gpus, dim=1)
netD = nn.DataParallel(netD, device_ids=opt.gpus, dim=1)
# optimizer
if opt.restore:
optimG = checkpoints['optimG']
optimD = checkpoints['optimD']
else:
optimG = Optim(config.optim,
config.learning_rate,
config.max_grad_norm,
lr_decay=config.learning_rate_decay,
start_decay_at=config.start_decay_at)
optimD = Optim(config.optim,
config.learning_rate,
config.max_grad_norm,
lr_decay=config.learning_rate_decay,
start_decay_at=config.start_decay_at)
optimG.set_parameters(netG.parameters())
optimD.set_parameters(netD.parameters())
if config.schedule:
schedulerG = L.CosineAnnealingLR(optimG.optimizer, T_max=config.epoch)
schedulerD = L.CosineAnnealingLR(optimD.optimizer, T_max=config.epoch)
def main():
# 设定种子
torch.manual_seed(args.seed)
if use_cuda:
torch.cuda.manual_seed(args.seed)
# checkpoint
if args.restore: # 存储已有模型的路径
print('loading checkpoint...\n')
checkpoints = torch.load(os.path.join(log_path, args.restore))
# word2id, id2word, word2count = load_vocab(args.vocab_file, args.vocab_size)
vocab = Vocab(config.vocab, config.data, config.vocab_size)
# Load data
start_time = time.time()
dataloader = DataLoader(config, args.task, config.has_dev,
config.batch_size, vocab, args.model,
args.use_depparse, args.notrain, args.debug)
print("DATA loaded!")
torch.backends.cudnn.benchmark = True
# data
print('loading data...\n')
print('loading time cost: %.3f' % (time.time() - start_time))
# model
print('building model...\n')
# configure the model
# Model and optimizer
if args.model == 'h_attention':
model = hierarchical_attention(config, vocab, use_cuda)
elif args.model == 'slstm':
model = SLSTM(config, vocab, use_cuda)
elif args.model == 'glstm':
model = GLSTM(config, vocab, use_cuda)
elif args.model == 'hglstm':
model = HGLSTM(config, vocab, use_cuda)
if args.restore:
model.load_state_dict(checkpoints['model'])
if use_cuda:
model.cuda()
# lm_model.cuda()
if len(args.gpus) > 1: # 并行
model = nn.DataParallel(model, device_ids=args.gpus, dim=1)
logging(repr(model) + "\n\n") # 记录这个文件的框架
# total number of parameters
param_count = 0
for param in model.parameters():
param_count += param.view(-1).size()[0]
logging('total number of parameters: %d\n\n' % param_count)
# updates是已经进行了几个epoch, 防止中间出现程序中断的情况.
if args.restore:
updates = checkpoints['updates']
ori_updates = updates
else:
updates = 0
# optimizer
if args.restore:
optim = checkpoints['optim']
else:
optim = Optim(config.optim,
config.learning_rate,
config.max_grad_norm,
lr_decay=config.learning_rate_decay,
start_decay_at=config.start_decay_at)
# if opt.pretrain:
# pretrain_lm(lm_model, vocab)
optim.set_parameters(model.parameters())
if config.schedule:
scheduler = L.CosineAnnealingLR(optim.optimizer, T_max=config.epoch)
else:
scheduler = None
if not args.notrain:
max_acc, test_acc = train(model, vocab, dataloader, scheduler, optim,
updates)
logging("Best accuracy: %.2f, test accuracy: %.2f\n" %
(max_acc * 100, test_acc * 100))
else:
assert args.restore is not None
eval(model, vocab, dataloader, 0, updates, do_test=True)
# print('----')
#
# for i, p in enumerate(model.parameters()):
# print(i)
# print(p.shape)
if use_cuda:
model.cuda()
if len(opt.gpus) > 1:
model = nn.DataParallel(model, device_ids=opt.gpus, dim=1)
# optimizer
if opt.restore:
optim = checkpoints['optim']
else:
optim = Optim(config.optim, config.learning_rate, config.max_grad_norm,
lr_decay=config.learning_rate_decay, start_decay_at=config.start_decay_at)
optim.set_parameters(filter(lambda p: p.requires_grad,model.parameters()))
if config.schedule:
scheduler = L.CosineAnnealingLR(optim.optimizer, T_max=config.epoch)
# total number of parameters
param_count = 0
for param in model.parameters():
param_count += param.view(-1).size()[0]
logging_csv = utils.logging_csv(log_path+'record.csv')
for k, v in config.items():
logging("%s:\t%s\n" % (str(k), str(v)))
logging("\n")
def main():
# 设定种子
torch.manual_seed(args.seed)
if use_cuda:
torch.cuda.manual_seed(args.seed)
vocab = Vocab(config.vocab_file,
config.emb_size,
use_pre_emb=False,
vocab_size=config.vocab_size)
print('vocab clear')
torch.backends.cudnn.benchmark = True
# model
print('building model...\n')
# configure the model
# Model and optimizer
if args.model == 'seq2seq':
model_generate = seq2seq(config, vocab, use_cuda, pretrain=None)
elif args.model == 'transformer':
model_generate = Transformer(config, vocab, use_cuda, pretrain=None)
elif args.model == 'transformer_gcn':
model_generate = Transformer_gcn(config,
vocab,
use_cuda,
pretrain=None)
if args.restore:
print('loading checkpoint...\n')
checkpoints = torch.load(os.path.join(log_path, args.restore))
model_generate.load_state_dict(checkpoints['model_generate'])
if args.train_type == 'sample_rl':
emb = model_generate.embedding
model_sample = graph2seq_rl(config,
vocab,
use_cuda,
emb,
pretrain=None)
elif args.train_type == 'generate':
model_sample = graph2seq_rl(config,
vocab,
use_cuda,
0,
pretrain=None)
model_sample.load_state_dict(checkpoints['model_sample'])
else:
print('err')
else:
#model_sample = graph2seq_rl(config, vocab, use_cuda, model_generate.embedding, pretrain=None)
model_sample = graph2seq_rl(config, vocab, use_cuda, 0, pretrain=None)
'''if args.restore:
print('loading checkpoint...\n')
checkpoints = torch.load(os.path.join(log_path, args.restore))
model_sample.load_state_dict(checkpoints['model_sample'])
model_generate.load_state_dict(checkpoints['model_generate'])'''
if use_cuda:
model_sample.cuda()
model_generate.cuda()
# if len(args.gpus) > 1: # 并行
# model = nn.DataParallel(model, device_ids=args.gpus, dim=1)
logging(repr(model_sample) + "\n\n") # 记录这个文件的框架
logging(repr(model_generate) + "\n\n")
# total number of parameters
sample_param_count = 0
generate_param_count = 0
for param in model_sample.parameters():
sample_param_count += param.view(-1).size()[0]
for param in model_generate.parameters():
generate_param_count += param.view(-1).size()[0]
logging('total number of sample parameters: %d\n\n' % sample_param_count)
logging('total number of generate parameters: %d\n\n' %
generate_param_count)
print('# generator parameters:',
sum(param.numel() for param in model_generate.parameters()))
# updates是已经进行了几个epoch, 防止中间出现程序中断的情况.
if args.restore:
updates = checkpoints['updates']
ori_updates = updates
else:
updates = 0
# optimizer
'''if args.restore:
optim_sample = checkpoints['optim_sample']
optim_generate = checkpoints['optim_generate']
else:'''
#optimizer = optim.Adam(self.params, lr=self.lr)
optim_sample = Optim(config.optim,
config.learning_rate_sample,
config.max_grad_norm,
lr_decay=config.learning_rate_decay,
start_decay_at=config.start_decay_at)
optim_generate = Optim(config.optim,
config.learning_rate,
config.max_grad_norm,
lr_decay=config.learning_rate_decay,
start_decay_at=config.start_decay_at)
optim_sample.set_parameters(model_sample.parameters())
optim_generate.set_parameters(model_generate.parameters())
if config.schedule:
scheduler_sample = L.SetLR(optim_sample.optimizer)
scheduler_generate = L.SetLR(optim_generate.optimizer)
else:
scheduler_sample = None
scheduler_generate = None
if args.type == 'train':
start_time = time.time()
#dataloader_train = get_dataloader(vocab, split='train', train_type='qk')
dataloader_train = get_dataloader(vocab, split='train', train_type='k')
dataloader_dev = get_dataloader(vocab, split='dev', train_type='k')
dataloader_dev_qk = get_dataloader(vocab, split='dev', train_type='qk')
print('loading data...\n')
print('loading time cost: %.3f' % (time.time() - start_time))
max_bleu = train(model_sample, model_generate, vocab, dataloader_train,
dataloader_dev, dataloader_dev_qk, scheduler_sample,
scheduler_generate, optim_sample, optim_generate,
updates)
logging("Best bleu score: %.2f\n" % (max_bleu))
elif args.type == 'eval':
# Load data
start_time = time.time()
dataloader_test = get_dataloader(vocab, split='test', train_type='k')
dataloader_test_qk = get_dataloader(vocab,
split='test',
train_type='qk')
print('loading data...\n')
print('loading time cost: %.3f' % (time.time() - start_time))
assert args.restore is not None
eval(model_sample, model_generate, vocab, dataloader_test,
dataloader_test_qk, 0, updates)
else:
print('error')