def main():
parser = argparse.ArgumentParser()
parser.add_argument("-vocab", type=str)
parser.add_argument("-model", type=str)
parser.add_argument("-config", type=str)
args = parser.parse_args()
config = utils.load_config(args.config)
use_cuda = False
if args.gpuid:
cuda.set_device(args.gpuid[0])
use_cuda = True
fields = IO.load_fields(torch.load(args.vocab))
# Build model.
seq2seq_model = create_seq2seq_tag_model(config, fields)
sampler_model = create_tag_sampler(config, fields)
model = create_seq2seq_rl_model(config, fields, sampler_model,
seq2seq_model)
print('Loading parameters ...')
if args.model:
model.load_checkpoint(args.model)
if use_cuda:
model = model.cuda()
def build_or_load_model(vocab, index_2_latent_sentence, device):
print('Building model...')
model = create_sampler_model(
vocab=vocab,
index_2_latent_sentence=index_2_latent_sentence,
device=device)
latest_ckpt = utils.latest_checkpoint(config.out_dir)
start_epoch_at = 0
start_epoch_at = model.load_checkpoint(latest_ckpt)
print('\n')
print(model)
print('\n')
return model
def build_or_load_model(vocab, device):
model = create_seq2seq_model(vocab=vocab, device=device)
latest_ckpt = utils.latest_checkpoint(config.out_dir)
ckpt = latest_ckpt
# latest_ckpt = nmt.misc_utils.latest_checkpoint(model_dir)
if ckpt:
print('Loding model from %s...'%(ckpt))
start_epoch_at = model.load_checkpoint(ckpt)
else:
print('Building model...')
print('\n')
print(model)
print('\n')
return model
def build_or_load_model(config, fields):
model = create_seq2seq_tag_model(config,fields)
latest_ckpt = utils.latest_checkpoint(config['Seq2Seq']['Trainer']['out_dir'])
start_epoch_at = 0
if config['Seq2Seq']['Trainer']['start_epoch_at'] is not None:
ckpt = 'checkpoint_epoch%d.pkl'%(config['Seq2Seq']['Trainer']['start_epoch_at'])
ckpt = os.path.join(config['Seq2Seq']['Trainer']['out_dir'],ckpt)
else:
ckpt = latest_ckpt
# latest_ckpt = nmt.misc_utils.latest_checkpoint(model_dir)
if ckpt:
print('Loding model from %s...'%(ckpt))
start_epoch_at = model.load_checkpoint(ckpt)
else:
print('Building model...')
print(model)
return model, start_epoch_at
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-test_data", type=str)
parser.add_argument("-test_out", type=str)
parser.add_argument("-vocab", type=str)
parser.add_argument("-model", type=str)
parser.add_argument("-config", type=str)
parser.add_argument("-dump_beam", default="", type=str)
parser.add_argument('-gpuid', default=[], nargs='+', type=int)
parser.add_argument("-beam_size", type=int)
parser.add_argument("-topk_tag", type=int)
parser.add_argument("-num_cluster", type=int)
parser.add_argument("-decode_max_length", type=int)
args = parser.parse_args()
config = utils.load_config(args.config)
use_cuda = False
if args.gpuid:
cuda.set_device(args.gpuid[0])
use_cuda = True
fields = IO.load_fields(
torch.load(args.vocab))
infer_dataset = InferDataset(
data_path=args.test_data,
fields=[('src', fields["src"])])
data_iter = IO.InferIterator(
dataset=infer_dataset, device=args.gpuid[0],
batch_size=1, train=False, sort=False,
sort_within_batch=True, shuffle=False)
# Build model.
seq2seq_model = create_seq2seq_tag_model(config, fields)
sampler_model = create_tag_sampler(config, fields)
model = create_seq2seq_rl_model(config, fields, sampler_model, seq2seq_model)
print('Loading parameters ...')
if args.model:
model.load_checkpoint(args.model)
if use_cuda:
model = model.cuda()
infer = Infer(model=model,
fields=fields,
beam_size=args.beam_size,
n_best=1,
max_length=args.decode_max_length,
global_scorer=None,
cuda=use_cuda,
beam_trace=True if args.dump_beam else False)
writer = None
if args.dump_beam:
writer = open(args.dump_beam,'w',encoding='utf8')
inference_file(infer, data_iter, args.test_out, fields, args.topk_tag, args.num_cluster, use_cuda, writer)
from dialog0.Seq2Seq.ModelHelper import create_seq2seq_tag_model
from dialog0.TagSampler.ModelHelper import create_tag_sampler
from dialog0.Seq2SeqWithRL.Dataset import RLDataset
from tensorboardX import SummaryWriter
parser = argparse.ArgumentParser()
parser.add_argument('-train_data', type=str)
parser.add_argument("-config", type=str)
parser.add_argument("-vocab", type=str)
parser.add_argument("-seq2seq", type=str)
parser.add_argument("-sampler", type=str)
parser.add_argument("-log_dir", type=str)
parser.add_argument('-gpuid', default=[], nargs='+', type=int)
args = parser.parse_args()
config = utils.load_config(args.config)
if args.gpuid:
cuda.set_device(args.gpuid[0])
summery_writer = SummaryWriter(args.log_dir)
def report_func(global_step, epoch, batch, num_batches,
start_time, lr, report_stats):
"""
This is the user-defined batch-level traing progress
report function.
Args:
epoch(int): current epoch count.
batch(int): current batch count.
num_batches(int): total number of batches.
start_time(float): last report time.