def main():
parser = get_parser_with_args()
args = parser.parse_args()
if args.output_file == "":
print("No action taken. Need output_file to be specified.")
parser.print_help()
return
checkpoint_filenames = args.path.split(CHECKPOINT_PATHS_DELIMITER)
beam_search = BeamSearch.build_from_checkpoints(
checkpoint_filenames=checkpoint_filenames,
src_dict_filename=args.src_dict,
dst_dict_filename=args.dst_dict,
beam_size=args.beam_size,
word_reward=args.word_reward,
unk_reward=args.unk_reward,
)
beam_search.save_to_pytorch(output_path=args.output_file)
if args.output_graph_file:
with open(args.output_graph_file.path, "w") as f:
f.write(str(beam_search.graph))
def main():
parser = argparse.ArgumentParser(
description=(
'Export PyTorch-trained FBTranslate models to caffe2'
),
)
parser.add_argument(
'--checkpoint',
action='append',
nargs='+',
help='PyTorch checkpoint file (at least one required)',
)
parser.add_argument(
'--output_file',
default='',
help='File name to which to save beam search network',
)
parser.add_argument(
'--src_dict',
required=True,
help='File encoding PyTorch dictionary for source language',
)
parser.add_argument(
'--dst_dict',
required=True,
help='File encoding PyTorch dictionary for source language',
)
parser.add_argument(
'--beam_size',
type=int,
default=6,
help='Number of top candidates returned by each decoder step',
)
parser.add_argument(
'--word_penalty',
type=float,
default=0.0,
help='Value to add for each word (besides EOS)',
)
parser.add_argument(
'--unk_penalty',
type=float,
default=0.0,
help='Value to add for each word UNK token',
)
args = parser.parse_args()
if args.output_file == '':
print('No action taken. Need output_file to be specified.')
parser.print_help()
return
checkpoint_filenames = [arg[0] for arg in args.checkpoint]
beam_search = BeamSearch.build_from_checkpoints(
checkpoint_filenames=checkpoint_filenames,
src_dict_filename=args.src_dict,
dst_dict_filename=args.dst_dict,
beam_size=args.beam_size,
word_penalty=args.word_penalty,
unk_penalty=args.unk_penalty,
)
beam_search.save_to_db(
args.output_file,
)
def main():
parser = argparse.ArgumentParser(
description=("Export PyTorch-trained FBTranslate models to caffe2"))
parser.add_argument(
"--checkpoint",
action="append",
nargs="+",
help="PyTorch checkpoint file (at least one required)",
)
parser.add_argument(
"--output_file",
default="",
help="File name to which to save beam search network",
)
parser.add_argument(
"--src_dict",
required=True,
help="File encoding PyTorch dictionary for source language",
)
parser.add_argument(
"--dst_dict",
required=True,
help="File encoding PyTorch dictionary for source language",
)
parser.add_argument(
"--beam_size",
type=int,
default=6,
help="Number of top candidates returned by each decoder step",
)
parser.add_argument(
"--word_reward",
type=float,
default=0.0,
help="Value to add for each word (besides EOS)",
)
parser.add_argument(
"--unk_reward",
type=float,
default=0.0,
help="Value to add for each word UNK token",
)
args = parser.parse_args()
if args.output_file == "":
print("No action taken. Need output_file to be specified.")
parser.print_help()
return
checkpoint_filenames = [arg[0] for arg in args.checkpoint]
beam_search = BeamSearch.build_from_checkpoints(
checkpoint_filenames=checkpoint_filenames,
src_dict_filename=args.src_dict,
dst_dict_filename=args.dst_dict,
beam_size=args.beam_size,
word_reward=args.word_reward,
unk_reward=args.unk_reward,
)
beam_search.save_to_db(args.output_file)
def main():
parser = argparse.ArgumentParser(
description=("Export PyTorch-trained FBTranslate models"))
parser.add_argument(
"--path",
"--checkpoint",
metavar="FILE",
help="path(s) to model file(s), colon separated",
)
parser.add_argument(
"--output-file",
default="",
help="File name to which to save beam search network",
)
parser.add_argument(
"--output-graph-file",
default="",
help="File name to which to save the beam search graph for debugging",
)
parser.add_argument(
"--src-dict",
required=True,
help="File encoding PyTorch dictionary for source language",
)
parser.add_argument(
"--dst-dict",
required=True,
help="File encoding PyTorch dictionary for source language",
)
parser.add_argument(
"--beam-size",
type=int,
default=6,
help="Number of top candidates returned by each decoder step",
)
parser.add_argument(
"--word-reward",
type=float,
default=0.0,
help="Value to add for each word (besides EOS)",
)
parser.add_argument(
"--unk-reward",
type=float,
default=0.0,
help="Value to add for each word UNK token",
)
args = parser.parse_args()
if args.output_file == "":
print("No action taken. Need output_file to be specified.")
parser.print_help()
return
checkpoint_filenames = args.path.split(":")
beam_search = BeamSearch.build_from_checkpoints(
checkpoint_filenames=checkpoint_filenames,
src_dict_filename=args.src_dict,
dst_dict_filename=args.dst_dict,
beam_size=args.beam_size,
word_reward=args.word_reward,
unk_reward=args.unk_reward,
)
beam_search.save_to_pytorch(output_path=args.output_file)
if args.output_graph_file:
with open(args.output_graph_file.path, "w") as f:
f.write(str(beam_search.graph))