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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Hyperparameters for the object detection model in TF.learn.
This file consolidates and documents the hyperparameters used by the model.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# pylint: disable=g-import-not-at-top
try:
from tensorflow.contrib import training as contrib_training
except ImportError:
# TF 2.0 doesn't ship with contrib.
pass
# pylint: enable=g-import-not-at-top
def create_hparams(hparams_overrides=None):
"""Returns hyperparameters, including any flag value overrides.
Args:
hparams_overrides: Optional hparams overrides, represented as a
string containing comma-separated hparam_name=value pairs.
Returns:
The hyperparameters as a tf.HParams object.
"""
hparams = contrib_training.HParams(
# Whether a fine tuning checkpoint (provided in the pipeline config)
# should be loaded for training.
load_pretrained=True)
# Override any of the preceding hyperparameter values.
if hparams_overrides:
hparams = hparams.parse(hparams_overrides)
return hparams
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