def main(opt, device_id):
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
# Load default opts values then overwrite it with opts from
# the checkpoint. It's usefull in order to re-train a model
# after adding a new option (not set in checkpoint)
dummy_parser = configargparse.ArgumentParser()
opts.model_opts(dummy_parser)
default_opt = dummy_parser.parse_known_args([])[0]
model_opt = default_opt
model_opt.__dict__.update(checkpoint['opt'].__dict__)
else:
checkpoint = None
model_opt = opt
# Load fields generated from preprocess phase.
fields = load_fields(opt, checkpoint)
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = build_optim(model, opt, checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt, device_id, model, fields,
optim, model_saver=model_saver)
def train_iter_fct():
return build_dataset_iter(
load_dataset("train", opt), fields, opt)
def valid_iter_fct():
return build_dataset_iter(
load_dataset("valid", opt), fields, opt, is_train=False)
# Do training.
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def main(opt):
if opt.gpu == 0:
device_id = 0
else:
device_id = -1
# dummy_parser = configargparse.ArgumentParser(description='reinforce.py')
# opts.model_opts(dummy_parser)
# dummy_opt = dummy_parser.parse_known_args([])[0]
# # build the model and get the checkpoint and field
# fields, model = nmt_model.load_reinforce_model(opt, dummy_opt.__dict__)
opt = training_opt_reinforcing(opt, device_id)
init_logger(opt.log_file)
logger.info("Input args: %r", opt)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
# Load default opts values then overwrite it with opts from
# the checkpoint. It's usefull in order to re-train a model
# after adding a new option (not set in checkpoint)
dummy_parser = configargparse.ArgumentParser()
opts.model_opts(dummy_parser)
default_opt = dummy_parser.parse_known_args([])[0]
model_opt = default_opt
model_opt.__dict__.update(checkpoint['opt'].__dict__)
else:
checkpoint = None
model_opt = opt
# Load fields generated from preprocess phase.
fields = load_fields(opt, checkpoint)
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
optim = build_optim(model, opt, checkpoint)
optim.learning_rate = 1e-5
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
reinforcor = build_reinforcor(model,
fields,
opt,
model_saver=model_saver,
optim=optim)
# out_file = codecs.open(opt.output, 'w+', 'utf-8')
# X_train, X_valid, X_test, y_train, y_valid, y_test = data_loader.test_mosei_emotion_data()
# src_path=X_train
# src_iter = make_text_iterator_from_file(src_path)#(opt.src)
# tgt_path=y_train
# tgt_iter=make_text_iterator_from_file(tgt_path)
def train_iter_fct():
return build_dataset_iter(load_dataset("train", opt), fields, opt)
# if opt.tgt is not None:
# tgt_iter = make_text_iterator_from_file(opt.tgt)
# else:
# tgt_iter = None
# reinforcor.reinforce(src_data_iter=src_iter,
# tgt_data_iter=tgt_iter,
# batch_size=opt.batch_size,
# out_file=out_file)
reinforcor.reinforce(train_iter_fct, opt.rein_steps)
def main(opt, device_id):
# device_id = -1
# 初始化gpu
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
# Load all tensors onto the CPU
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
# Load default opts values then overwrite it with opts from
# the checkpoint. It's usefull in order to re-train a model
# after adding a new option (not set in checkpoint)
dummy_parser = configargparse.ArgumentParser()
opts.model_opts(dummy_parser)
# 返回值为两个，第一个与parse_args()返回值类型相同
default_opt = dummy_parser.parse_known_args([])[0]
model_opt = default_opt
# 把opt中原有的选项也加入新的参数列表中
# 也就是说选项只可以增加而不可以删除或者修改,
# 如果是这样，那么后文就不需要opt了?
model_opt.__dict__.update(checkpoint['opt'].__dict__)
else:
# 第一次载入
checkpoint = None
model_opt = opt
# Load fields generated from preprocess phase.
# {"src": Field, "tgt": Field, "indices": Field}
# Field中最重要的是vocab属性，其中包含freqs、itos、stoi
# freqs是词频，不包含特殊字符
# src : stoi中含有<unk>、<blank>, 不含<s>与</s>
# tgt : stoi含有<unk>、<blank>、<s>、</s>
# <unk> = 0, <blank>(pad) = 1
fields = load_fields(opt, checkpoint)
# Build model.
# 第一次应该不需要opt参数，可用model_opt代替
model = build_model(model_opt, opt, fields, checkpoint)
# for name, param in model.named_parameters():
# if param.requires_grad:
# print(name)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
# 没有模型保存目录则创建该目录
_check_save_model_path(opt)
# Build optimizer.
optim = build_optim(model, opt, checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt, device_id, model, fields,
optim, model_saver=model_saver)
# 打印模型所有参数
# for name, param in model.named_parameters():
# if param.requires_grad:
# print(param)
def train_iter_fct():
return build_dataset_iter(
load_dataset("train", opt), fields, opt)
def valid_iter_fct():
return build_dataset_iter(
load_dataset("valid", opt), fields, opt, is_train=False)
# Do training.
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
