def _get_parser():
parser = ArgumentParser(description='train.py')
opts.config_opts(parser)
opts.model_opts(parser)
opts.train_opts(parser)
return parser
def _get_parser():
parser = ArgumentParser(description='build_copy_transformer.py')
opts.config_opts(parser)
opts.model_opts(parser)
opts.train_opts(parser)
return parser
def _get_parser():
parser = ArgumentParser(description='train.py')
opts.config_opts(parser)
opts.model_opts(parser)
opts.train_opts(parser)
parser.add('--data', '-data', required=False,
default='F:/Project/Python/selfProject/translate_NMT/data/demo',
help='Path prefix to the ".train.pt" and '
'".valid.pt" file path from preprocess.py')
parser.add('--save_model', '-save_model', required=False,
default='F:/Project/Python/selfProject/translate_NMT/data',
help="Model filename (the model will be saved as "
"<save_model>_N.pt where N is the number "
"of steps")
parser.add('--save_checkpoint_steps', '-save_checkpoint_steps',
type=int, default=500,
help="""Save a checkpoint every X steps""")
parser.add('--train_from', '-train_from',
# default='F:/Project/Python/selfProject/translate_NMT/data/demo-model_step_150.pt',
default='',
type=str,
help="If training from a checkpoint then this is the "
"path to the pretrained model's state_dict.")
# default = 100000,
parser.add('--train_steps', '-train_steps', type=int, default=100000,
help='训练多少步')
return parser
def _get_parser():
parser = ArgumentParser(description='train.py')
parser.add_argument('--teacher_model_path',
action='store',
dest='teacher_model_path',
help='the path direct to the teacher model path')
parser.add_argument("--word_sampling",
action="store",
default=False,
help="optional arg")
opts.config_opts(parser)
opts.model_opts(parser)
opts.train_opts(parser)
return parser
def parse_args():
parser = configargparse.ArgumentParser(
description='train.py',
config_file_parser_class=configargparse.YAMLConfigFileParser,
formatter_class=configargparse.ArgumentDefaultsHelpFormatter)
opts.general_opts(parser)
opts.config_opts(parser)
opts.add_md_help_argument(parser)
opts.model_opts(parser)
opts.train_opts(parser)
opt = parser.parse_args()
return opt
def _get_parser():
parser = ArgumentParser(description='train.py')
opts.config_opts(parser)
opts.model_opts(parser)
opts.train_opts(parser)
'''extended opts for pretrained language models'''
group = parser.add_argument_group("extended opts")
group.add('--pretrained_encoder',
'-pretrained_encoder',
default="bert",
type=str,
choices=["bert", "roberta", "xlnet"],
help="choose a pretrained language model as encoder")
return parser
'opt': self.model_opt,
'optim': self.optim,
}
logger.info("Saving checkpoint %s_step_%d.pt" % (self.base_path, step))
checkpoint_path = '%s_step_%d.pt' % (self.base_path, step)
torch.save(checkpoint, checkpoint_path)
return checkpoint, checkpoint_path
def _rm_checkpoint(self, name):
"""
Remove a checkpoint
Args:
name(str): name that indentifies the checkpoint
(it may be a filepath)
"""
os.remove(name)
if __name__ == "__main__":
parser = configargparse.ArgumentParser(
description='train.py',
formatter_class=configargparse.ArgumentDefaultsHelpFormatter)
opts.model_opts(parser)
opts.train_opts(parser)
opt = parser.parse_args()
main(opt)
import onmt.opts as opts
from train_multi import main as multi_main
from train_single import main as single_main
def main(opt):
if opt.rnn_type == "SRU" and not opt.gpuid:
raise AssertionError("Using SRU requires -gpuid set.")
if torch.cuda.is_available() and not opt.gpuid:
print("WARNING: You have a CUDA device, should run with -gpuid 0")
if len(opt.gpuid) > 1:
multi_main(opt)
else:
single_main(opt)
if __name__ == "__main__":
PARSER = argparse.ArgumentParser(
description='train.py',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
opts.add_md_help_argument(PARSER)
opts.model_opts(PARSER)
opts.train_opts(PARSER)
OPT = PARSER.parse_args()
main(OPT)
def _get_parser():
parser = ArgumentParser(description='train.py')
train_opts(parser)
return parser
os.path.join(temp, "data", "train_source.txt"), "-train_tgt",
os.path.join(temp, "data", "train_target.txt"), "-valid_src",
os.path.join(temp, "data", "dev_target.txt"), "-valid_tgt",
os.path.join(temp, "data", "dev_target.txt"), "-save_data",
os.path.join(temp, "data", "out")
])
preproc_args.shuffle = 0
preproc_args.src_seq_length = source_max
preproc_args.tgt_seq_length = target_max
train_parser = argparse.ArgumentParser(
description='vivisect example',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
opts.add_md_help_argument(train_parser)
opts.model_opts(train_parser)
opts.train_opts(train_parser)
train_args = train_parser.parse_args([
"-data",
os.path.join(temp, "data/out"),
"-train_steps",
str(args.epochs - 1),
"-save_model",
os.path.join(temp, "model"),
"-enc_layers",
"3",
"-dec_layers",
"3",
"-rnn_size",
"50",
"-src_word_vec_size",
"25",
