89 lines
3 KiB
Python
89 lines
3 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#============================================================================
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"""Utils to handle parameters IO."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import six
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import yaml
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import tensorflow as tf
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def save_hparams_to_yaml(hparams, file_path):
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with tf.io.gfile.GFile(file_path, 'w') as f:
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try:
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hparams_val = hparams.values()
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except AttributeError:
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hparams_val = hparams.__dict__
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yaml.dump(hparams_val, f)
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def override_hparams(hparams, dict_or_string_or_yaml_file):
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"""Override a given hparams using a dict or a string or a JSON file.
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Args:
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hparams: a HParams object to be overridden.
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dict_or_string_or_yaml_file: a Python dict, or a comma-separated string,
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or a path to a YAML file specifying the parameters to be overridden.
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Returns:
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hparams: the overridden HParams object.
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Raises:
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ValueError: if failed to override the parameters.
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"""
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if not dict_or_string_or_yaml_file:
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return hparams
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if isinstance(dict_or_string_or_yaml_file, dict):
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for key, val in dict_or_string_or_yaml_file.items():
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if key not in hparams:
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try: # TF 1.x
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hparams.add_hparam(key, val)
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except AttributeError: # TF 2.x
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try: # Dict
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hparams[key] = val
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except TypeError: # Namespace
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setattr(hparams, key, val)
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else:
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raise ValueError("Parameter `%s` is already defined" % key)
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# hparams.override_from_dict(dict_or_string_or_yaml_file)
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elif isinstance(dict_or_string_or_yaml_file, six.string_types):
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try:
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hparams.parse(dict_or_string_or_yaml_file)
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except ValueError as parse_error:
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try:
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with tf.io.gfile.GFile(dict_or_string_or_yaml_file) as f:
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hparams.override_from_dict(yaml.load(f))
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except Exception as read_error:
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parse_message = ('Failed to parse config string: %s\n' % parse_error.message)
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read_message = ('Failed to parse yaml file provided. %s' % read_error.message)
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raise ValueError(parse_message + read_message)
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else:
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raise ValueError('Unknown input type to parse.')
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return hparams
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