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egs/wsj/s5/steps/nnet3/xconfig_to_config.py
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#!/usr/bin/env python3
# Copyright 2016-2018 Johns Hopkins University (Dan Povey)
# 2016 Vijayaditya Peddinti
# 2017 Google Inc. (vpeddinti@google.com)
# Apache 2.0.
# This is like xconfig_to_configs.py but with a simpler interface; it writes
# to a single named file.
import argparse
import os
import sys
from collections import defaultdict
sys.path.insert(0, 'steps/')
# the following is in case we weren't running this from the normal directory.
sys.path.insert(0, os.path.realpath(os.path.dirname(sys.argv[0])) + '/')
import libs.nnet3.xconfig.parser as xparser
import libs.common as common_lib
def get_args():
# we add compulsory arguments as named arguments for readability
parser = argparse.ArgumentParser(
description="Reads an xconfig file and creates config files "
"for neural net creation and training",
epilog='Search egs/*/*/local/{nnet3,chain}/*sh for examples')
parser.add_argument('--xconfig-file', required=True,
help='Filename of input xconfig file')
parser.add_argument('--existing-model',
help='Filename of previously trained neural net '
'(e.g. final.mdl) which is useful in case of '
'using nodes from list of component-nodes in '
'already trained model '
'to generate new config file for new model.'
'The context info is also generated using '
'a model generated by adding final.config '
'to the existing model.'
'e.g. In Transfer learning: generate new model using '
'component nodes in existing model.')
parser.add_argument('--config-file-out', required=True,
help='Filename to write nnet config file.');
parser.add_argument('--nnet-edits', type=str, default=None,
action=common_lib.NullstrToNoneAction,
help="""This option is useful in case the network you
are creating does not have an output node called
'output' (e.g. for multilingual setups). You can set
this to an edit-string like: 'rename-node old-name=xxx
new-name=output' if node xxx plays the role of the
output node in this network. This is only used for
computing the left/right context.""")
print(' '.join(sys.argv), file=sys.stderr)
args = parser.parse_args()
return args
def write_config_file(config_file_out, all_layers):
# config_basename_to_lines is map from the basename of the
# config, as a string (i.e. 'ref', 'all', 'init') to a list of
# strings representing lines to put in the config file.
config_basename_to_lines = defaultdict(list)
for layer in all_layers:
try:
pairs = layer.get_full_config()
for config_basename, line in pairs:
config_basename_to_lines[config_basename].append(line)
except Exception as e:
print("{0}: error producing config lines from xconfig "
"line '{1}': error was: {2}".format(sys.argv[0],
str(layer), repr(e)),
file=sys.stderr)
# we use raise rather than raise(e) as using a blank raise
# preserves the backtrace
raise
with open(config_file_out, 'w') as f:
print('# This file was created by the command:
'
'# {0} '.format(sys.argv), file=f)
lines = config_basename_to_lines['final']
for line in lines:
print(line, file=f)
def main():
args = get_args()
existing_layers = []
if args.existing_model is not None:
existing_layers = xparser.get_model_component_info(args.existing_model)
all_layers = xparser.read_xconfig_file(args.xconfig_file, existing_layers)
write_config_file(args.config_file_out, all_layers)
if __name__ == '__main__':
main()
# test:
# (echo 'input dim=40 name=input'; echo 'output name=output input=Append(-1,0,1)') >xconfig; steps/nnet3/xconfig_to_config.py --xconfig-file=xconfig --config-file-out=foo
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