research/object_detection/utils/config_util.py - master - githubmirror/models

research/object_detection/utils/config_util.py
1 271 строка50 KB
Yu-hui Chen
Updated the config_util to support the hyperparameter search
25 июн 2021, 00:00
25 июн 2021, 00:00b53e5dc
# 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.
# ==============================================================================
"""Functions for reading and updating configuration files."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

from google.protobuf import text_format
import tensorflow.compat.v1 as tf

from tensorflow.python.lib.io import file_io

from object_detection.protos import eval_pb2
from object_detection.protos import graph_rewriter_pb2
from object_detection.protos import input_reader_pb2
from object_detection.protos import model_pb2
from object_detection.protos import pipeline_pb2
from object_detection.protos import train_pb2


def get_image_resizer_config(model_config):
  """Returns the image resizer config from a model config.

  Args:
    model_config: A model_pb2.DetectionModel.

  Returns:
    An image_resizer_pb2.ImageResizer.

  Raises:
    ValueError: If the model type is not recognized.
  """
  meta_architecture = model_config.WhichOneof("model")
  meta_architecture_config = getattr(model_config, meta_architecture)

  if hasattr(meta_architecture_config, "image_resizer"):
    return getattr(meta_architecture_config, "image_resizer")
  else:
    raise ValueError("{} has no image_reszier_config".format(
        meta_architecture))


def get_spatial_image_size(image_resizer_config):
  """Returns expected spatial size of the output image from a given config.

  Args:
    image_resizer_config: An image_resizer_pb2.ImageResizer.

  Returns:
    A list of two integers of the form [height, width]. `height` and `width` are
    set  -1 if they cannot be determined during graph construction.

  Raises:
    ValueError: If the model type is not recognized.
  """
  if image_resizer_config.HasField("fixed_shape_resizer"):
    return [
        image_resizer_config.fixed_shape_resizer.height,
        image_resizer_config.fixed_shape_resizer.width
    ]
  if image_resizer_config.HasField("keep_aspect_ratio_resizer"):
    if image_resizer_config.keep_aspect_ratio_resizer.pad_to_max_dimension:
      return [image_resizer_config.keep_aspect_ratio_resizer.max_dimension] * 2
    else:
      return [-1, -1]
  if image_resizer_config.HasField(
      "identity_resizer") or image_resizer_config.HasField(
          "conditional_shape_resizer"):
    return [-1, -1]
  raise ValueError("Unknown image resizer type.")


def get_max_num_context_features(model_config):
  """Returns maximum number of context features from a given config.

  Args:
    model_config: A model config file.

  Returns:
    An integer specifying the max number of context features if the model
      config contains context_config, None otherwise

  """
  meta_architecture = model_config.WhichOneof("model")
  meta_architecture_config = getattr(model_config, meta_architecture)

  if hasattr(meta_architecture_config, "context_config"):
    return meta_architecture_config.context_config.max_num_context_features


def get_context_feature_length(model_config):
  """Returns context feature length from a given config.

  Args:
    model_config: A model config file.

  Returns:
    An integer specifying the fixed length of each feature in context_features.
  """
  meta_architecture = model_config.WhichOneof("model")
  meta_architecture_config = getattr(model_config, meta_architecture)

  if hasattr(meta_architecture_config, "context_config"):
    return meta_architecture_config.context_config.context_feature_length


def get_configs_from_pipeline_file(pipeline_config_path, config_override=None):
  """Reads config from a file containing pipeline_pb2.TrainEvalPipelineConfig.

  Args:
    pipeline_config_path: Path to pipeline_pb2.TrainEvalPipelineConfig text
      proto.
    config_override: A pipeline_pb2.TrainEvalPipelineConfig text proto to
      override pipeline_config_path.

  Returns:
    Dictionary of configuration objects. Keys are `model`, `train_config`,
      `train_input_config`, `eval_config`, `eval_input_config`. Value are the
      corresponding config objects.
  """
  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
  with tf.gfile.GFile(pipeline_config_path, "r") as f:
    proto_str = f.read()
    text_format.Merge(proto_str, pipeline_config)
  if config_override:
    text_format.Merge(config_override, pipeline_config)
  return create_configs_from_pipeline_proto(pipeline_config)


def clear_fine_tune_checkpoint(pipeline_config_path,
                               new_pipeline_config_path):
  """Clears fine_tune_checkpoint and writes a new pipeline config file."""
  configs = get_configs_from_pipeline_file(pipeline_config_path)
  configs["train_config"].fine_tune_checkpoint = ""
  configs["train_config"].load_all_detection_checkpoint_vars = False
  pipeline_proto = create_pipeline_proto_from_configs(configs)
  with tf.gfile.Open(new_pipeline_config_path, "wb") as f:
    f.write(text_format.MessageToString(pipeline_proto))


def update_fine_tune_checkpoint_type(train_config):
  """Set `fine_tune_checkpoint_type` using `from_detection_checkpoint`.

  `train_config.from_detection_checkpoint` field is deprecated. For backward
  compatibility, this function sets `train_config.fine_tune_checkpoint_type`
  based on `train_config.from_detection_checkpoint`.

  Args:
    train_config: train_pb2.TrainConfig proto object.

  """
  if not train_config.fine_tune_checkpoint_type:
    if train_config.from_detection_checkpoint:
      train_config.fine_tune_checkpoint_type = "detection"
    else:
      train_config.fine_tune_checkpoint_type = "classification"


def create_configs_from_pipeline_proto(pipeline_config):
  """Creates a configs dictionary from pipeline_pb2.TrainEvalPipelineConfig.

  Args:
    pipeline_config: pipeline_pb2.TrainEvalPipelineConfig proto object.

  Returns:
    Dictionary of configuration objects. Keys are `model`, `train_config`,
      `train_input_config`, `eval_config`, `eval_input_configs`. Value are
      the corresponding config objects or list of config objects (only for
      eval_input_configs).
  """
  configs = {}
  configs["model"] = pipeline_config.model
  configs["train_config"] = pipeline_config.train_config
  configs["train_input_config"] = pipeline_config.train_input_reader
  configs["eval_config"] = pipeline_config.eval_config
  configs["eval_input_configs"] = pipeline_config.eval_input_reader
  # Keeps eval_input_config only for backwards compatibility. All clients should
  # read eval_input_configs instead.
  if configs["eval_input_configs"]:
    configs["eval_input_config"] = configs["eval_input_configs"][0]
  if pipeline_config.HasField("graph_rewriter"):
    configs["graph_rewriter_config"] = pipeline_config.graph_rewriter

  return configs


def get_graph_rewriter_config_from_file(graph_rewriter_config_file):
  """Parses config for graph rewriter.

  Args:
    graph_rewriter_config_file: file path to the graph rewriter config.

  Returns:
    graph_rewriter_pb2.GraphRewriter proto
  """
  graph_rewriter_config = graph_rewriter_pb2.GraphRewriter()
  with tf.gfile.GFile(graph_rewriter_config_file, "r") as f:
    text_format.Merge(f.read(), graph_rewriter_config)
  return graph_rewriter_config


def create_pipeline_proto_from_configs(configs):
  """Creates a pipeline_pb2.TrainEvalPipelineConfig from configs dictionary.

  This function performs the inverse operation of
  create_configs_from_pipeline_proto().

  Args:
    configs: Dictionary of configs. See get_configs_from_pipeline_file().

  Returns:
    A fully populated pipeline_pb2.TrainEvalPipelineConfig.
  """
  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
  pipeline_config.model.CopyFrom(configs["model"])
  pipeline_config.train_config.CopyFrom(configs["train_config"])
  pipeline_config.train_input_reader.CopyFrom(configs["train_input_config"])
  pipeline_config.eval_config.CopyFrom(configs["eval_config"])
  pipeline_config.eval_input_reader.extend(configs["eval_input_configs"])
  if "graph_rewriter_config" in configs:
    pipeline_config.graph_rewriter.CopyFrom(configs["graph_rewriter_config"])
  return pipeline_config


def save_pipeline_config(pipeline_config, directory):
  """Saves a pipeline config text file to disk.

  Args:
    pipeline_config: A pipeline_pb2.TrainEvalPipelineConfig.
    directory: The model directory into which the pipeline config file will be
      saved.
  """
  if not file_io.file_exists(directory):
    file_io.recursive_create_dir(directory)
  pipeline_config_path = os.path.join(directory, "pipeline.config")
  config_text = text_format.MessageToString(pipeline_config)
  with tf.gfile.Open(pipeline_config_path, "wb") as f:
    tf.logging.info("Writing pipeline config file to %s",
                    pipeline_config_path)
    f.write(config_text)


def get_configs_from_multiple_files(model_config_path="",
                                    train_config_path="",
                                    train_input_config_path="",
                                    eval_config_path="",
                                    eval_input_config_path="",
                                    graph_rewriter_config_path=""):
  """Reads training configuration from multiple config files.

  Args:
    model_config_path: Path to model_pb2.DetectionModel.
    train_config_path: Path to train_pb2.TrainConfig.
    train_input_config_path: Path to input_reader_pb2.InputReader.
    eval_config_path: Path to eval_pb2.EvalConfig.
    eval_input_config_path: Path to input_reader_pb2.InputReader.
    graph_rewriter_config_path: Path to graph_rewriter_pb2.GraphRewriter.

  Returns:
    Dictionary of configuration objects. Keys are `model`, `train_config`,
      `train_input_config`, `eval_config`, `eval_input_config`. Key/Values are
        returned only for valid (non-empty) strings.
  """
  configs = {}
  if model_config_path:
    model_config = model_pb2.DetectionModel()
    with tf.gfile.GFile(model_config_path, "r") as f:
      text_format.Merge(f.read(), model_config)
      configs["model"] = model_config

  if train_config_path:
    train_config = train_pb2.TrainConfig()
    with tf.gfile.GFile(train_config_path, "r") as f:
      text_format.Merge(f.read(), train_config)
      configs["train_config"] = train_config

  if train_input_config_path:
    train_input_config = input_reader_pb2.InputReader()
    with tf.gfile.GFile(train_input_config_path, "r") as f:
      text_format.Merge(f.read(), train_input_config)
      configs["train_input_config"] = train_input_config

  if eval_config_path:
    eval_config = eval_pb2.EvalConfig()
    with tf.gfile.GFile(eval_config_path, "r") as f:
      text_format.Merge(f.read(), eval_config)
      configs["eval_config"] = eval_config

  if eval_input_config_path:
    eval_input_config = input_reader_pb2.InputReader()
    with tf.gfile.GFile(eval_input_config_path, "r") as f:
      text_format.Merge(f.read(), eval_input_config)
      configs["eval_input_configs"] = [eval_input_config]

  if graph_rewriter_config_path:
    configs["graph_rewriter_config"] = get_graph_rewriter_config_from_file(
        graph_rewriter_config_path)

  return configs


def get_number_of_classes(model_config):
  """Returns the number of classes for a detection model.

  Args:
    model_config: A model_pb2.DetectionModel.

  Returns:
    Number of classes.

  Raises:
    ValueError: If the model type is not recognized.
  """
  meta_architecture = model_config.WhichOneof("model")
  meta_architecture_config = getattr(model_config, meta_architecture)

  if hasattr(meta_architecture_config, "num_classes"):
    return meta_architecture_config.num_classes
  else:
    raise ValueError("{} does not have num_classes.".format(meta_architecture))


def get_optimizer_type(train_config):
  """Returns the optimizer type for training.

  Args:
    train_config: A train_pb2.TrainConfig.

  Returns:
    The type of the optimizer
  """
  return train_config.optimizer.WhichOneof("optimizer")


def get_learning_rate_type(optimizer_config):
  """Returns the learning rate type for training.

  Args:
    optimizer_config: An optimizer_pb2.Optimizer.

  Returns:
    The type of the learning rate.
  """
  return optimizer_config.learning_rate.WhichOneof("learning_rate")


def _is_generic_key(key):
  """Determines whether the key starts with a generic config dictionary key."""
  for prefix in [
      "graph_rewriter_config",
      "model",
      "train_input_config",
      "train_config",
      "eval_config"]:
    if key.startswith(prefix + "."):
      return True
  return False


def _check_and_convert_legacy_input_config_key(key):
  """Checks key and converts legacy input config update to specific update.

  Args:
    key: string indicates the target of update operation.

  Returns:
    is_valid_input_config_key: A boolean indicating whether the input key is to
      update input config(s).
    key_name: 'eval_input_configs' or 'train_input_config' string if
      is_valid_input_config_key is true. None if is_valid_input_config_key is
      false.
    input_name: always returns None since legacy input config key never
      specifies the target input config. Keeping this output only to match the
      output form defined for input config update.
    field_name: the field name in input config. `key` itself if
      is_valid_input_config_key is false.
  """
  key_name = None
  input_name = None
  field_name = key
  is_valid_input_config_key = True
  if field_name == "train_shuffle":
    key_name = "train_input_config"
    field_name = "shuffle"
  elif field_name == "eval_shuffle":
    key_name = "eval_input_configs"
    field_name = "shuffle"
  elif field_name == "train_input_path":
    key_name = "train_input_config"
    field_name = "input_path"
  elif field_name == "eval_input_path":
    key_name = "eval_input_configs"
    field_name = "input_path"
  elif field_name == "append_train_input_path":
    key_name = "train_input_config"
    field_name = "input_path"
  elif field_name == "append_eval_input_path":
    key_name = "eval_input_configs"
    field_name = "input_path"
  else:
    is_valid_input_config_key = False

  return is_valid_input_config_key, key_name, input_name, field_name


def check_and_parse_input_config_key(configs, key):
  """Checks key and returns specific fields if key is valid input config update.

  Args:
    configs: Dictionary of configuration objects. See outputs from
      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
    key: string indicates the target of update operation.

  Returns:
    is_valid_input_config_key: A boolean indicate whether the input key is to
      update input config(s).
    key_name: 'eval_input_configs' or 'train_input_config' string if
      is_valid_input_config_key is true. None if is_valid_input_config_key is
      false.
    input_name: the name of the input config to be updated. None if
      is_valid_input_config_key is false.
    field_name: the field name in input config. `key` itself if
      is_valid_input_config_key is false.

  Raises:
    ValueError: when the input key format doesn't match any known formats.
    ValueError: if key_name doesn't match 'eval_input_configs' or
      'train_input_config'.
    ValueError: if input_name doesn't match any name in train or eval input
      configs.
    ValueError: if field_name doesn't match any supported fields.
  """
  key_name = None
  input_name = None
  field_name = None
  fields = key.split(":")
  if len(fields) == 1:
    field_name = key
    return _check_and_convert_legacy_input_config_key(key)
  elif len(fields) == 3:
    key_name = fields[0]
    input_name = fields[1]
    field_name = fields[2]
  else:
    raise ValueError("Invalid key format when overriding configs.")

  # Checks if key_name is valid for specific update.
  if key_name not in ["eval_input_configs", "train_input_config"]:
    raise ValueError("Invalid key_name when overriding input config.")

  # Checks if input_name is valid for specific update. For train input config it
  # should match configs[key_name].name, for eval input configs it should match
  # the name field of one of the eval_input_configs.
  if isinstance(configs[key_name], input_reader_pb2.InputReader):
    is_valid_input_name = configs[key_name].name == input_name
  else:
    is_valid_input_name = input_name in [
        eval_input_config.name for eval_input_config in configs[key_name]
    ]
  if not is_valid_input_name:
    raise ValueError("Invalid input_name when overriding input config.")

  # Checks if field_name is valid for specific update.
  if field_name not in [
      "input_path", "label_map_path", "shuffle", "mask_type",
      "sample_1_of_n_examples"
  ]:
    raise ValueError("Invalid field_name when overriding input config.")

  return True, key_name, input_name, field_name


def merge_external_params_with_configs(configs, hparams=None, kwargs_dict=None):
  """Updates `configs` dictionary based on supplied parameters.

  This utility is for modifying specific fields in the object detection configs.
  Say that one would like to experiment with different learning rates, momentum
  values, or batch sizes. Rather than creating a new config text file for each
  experiment, one can use a single base config file, and update particular
  values.

  There are two types of field overrides:
  1. Strategy-based overrides, which update multiple relevant configuration
  options. For example, updating `learning_rate` will update both the warmup and
  final learning rates.
  In this case key can be one of the following formats:
      1. legacy update: single string that indicates the attribute to be
        updated. E.g. 'label_map_path', 'eval_input_path', 'shuffle'.
        Note that when updating fields (e.g. eval_input_path, eval_shuffle) in
        eval_input_configs, the override will only be applied when
        eval_input_configs has exactly 1 element.
      2. specific update: colon separated string that indicates which field in
        which input_config to update. It should have 3 fields:
        - key_name: Name of the input config we should update, either
          'train_input_config' or 'eval_input_configs'
        - input_name: a 'name' that can be used to identify elements, especially
          when configs[key_name] is a repeated field.
        - field_name: name of the field that you want to override.
        For example, given configs dict as below:
          configs = {
            'model': {...}
            'train_config': {...}
            'train_input_config': {...}
            'eval_config': {...}
            'eval_input_configs': [{ name:"eval_coco", ...},
                                   { name:"eval_voc", ... }]
          }
        Assume we want to update the input_path of the eval_input_config
        whose name is 'eval_coco'. The `key` would then be:
        'eval_input_configs:eval_coco:input_path'
  2. Generic key/value, which update a specific parameter based on namespaced
  configuration keys. For example,
  `model.ssd.loss.hard_example_miner.max_negatives_per_positive` will update the
  hard example miner configuration for an SSD model config. Generic overrides
  are automatically detected based on the namespaced keys.

  Args:
    configs: Dictionary of configuration objects. See outputs from
      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
    hparams: A `HParams`.
    kwargs_dict: Extra keyword arguments that are treated the same way as
      attribute/value pairs in `hparams`. Note that hyperparameters with the
      same names will override keyword arguments.

  Returns:
    `configs` dictionary.

  Raises:
    ValueError: when the key string doesn't match any of its allowed formats.
  """

  if kwargs_dict is None:
    kwargs_dict = {}
  if hparams:
    kwargs_dict.update(hparams.values())
  for key, value in kwargs_dict.items():
    tf.logging.info("Maybe overwriting %s: %s", key, value)
    # pylint: disable=g-explicit-bool-comparison
    if value == "" or value is None:
      continue
    # pylint: enable=g-explicit-bool-comparison
    elif _maybe_update_config_with_key_value(configs, key, value):
      continue
    elif _is_generic_key(key):
      _update_generic(configs, key, value)
    else:
      tf.logging.info("Ignoring config override key: %s", key)
  return configs


def _maybe_update_config_with_key_value(configs, key, value):
  """Checks key type and updates `configs` with the key value pair accordingly.

  Args:
    configs: Dictionary of configuration objects. See outputs from
      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
    key: String indicates the field(s) to be updated.
    value: Value used to override existing field value.

  Returns:
    A boolean value that indicates whether the override succeeds.

  Raises:
    ValueError: when the key string doesn't match any of the formats above.
  """
  is_valid_input_config_key, key_name, input_name, field_name = (
      check_and_parse_input_config_key(configs, key))
  if is_valid_input_config_key:
    update_input_reader_config(
        configs,
        key_name=key_name,
        input_name=input_name,
        field_name=field_name,
        value=value)
  elif field_name == "learning_rate":
    _update_initial_learning_rate(configs, value)
  elif field_name == "batch_size":
    _update_batch_size(configs, value)
  elif field_name == "momentum_optimizer_value":
    _update_momentum_optimizer_value(configs, value)
  elif field_name == "classification_localization_weight_ratio":
    # Localization weight is fixed to 1.0.
    _update_classification_localization_weight_ratio(configs, value)
  elif field_name == "focal_loss_gamma":
    _update_focal_loss_gamma(configs, value)
  elif field_name == "focal_loss_alpha":
    _update_focal_loss_alpha(configs, value)
  elif field_name == "train_steps":
    _update_train_steps(configs, value)
  elif field_name == "label_map_path":
    _update_label_map_path(configs, value)
  elif field_name == "mask_type":
    _update_mask_type(configs, value)
  elif field_name == "sample_1_of_n_eval_examples":
    _update_all_eval_input_configs(configs, "sample_1_of_n_examples", value)
  elif field_name == "eval_num_epochs":
    _update_all_eval_input_configs(configs, "num_epochs", value)
  elif field_name == "eval_with_moving_averages":
    _update_use_moving_averages(configs, value)
  elif field_name == "retain_original_images_in_eval":
    _update_retain_original_images(configs["eval_config"], value)
  elif field_name == "use_bfloat16":
    _update_use_bfloat16(configs, value)
  elif field_name == "retain_original_image_additional_channels_in_eval":
    _update_retain_original_image_additional_channels(configs["eval_config"],
                                                      value)
  elif field_name == "num_classes":
    _update_num_classes(configs["model"], value)
  elif field_name == "sample_from_datasets_weights":
    _update_sample_from_datasets_weights(configs["train_input_config"], value)
  elif field_name == "peak_max_pool_kernel_size":
    _update_peak_max_pool_kernel_size(configs["model"], value)
  elif field_name == "candidate_search_scale":
    _update_candidate_search_scale(configs["model"], value)
  elif field_name == "candidate_ranking_mode":
    _update_candidate_ranking_mode(configs["model"], value)
  elif field_name == "score_distance_offset":
    _update_score_distance_offset(configs["model"], value)
  elif field_name == "box_scale":
    _update_box_scale(configs["model"], value)
  elif field_name == "keypoint_candidate_score_threshold":
    _update_keypoint_candidate_score_threshold(configs["model"], value)
  elif field_name == "rescore_instances":
    _update_rescore_instances(configs["model"], value)
  elif field_name == "unmatched_keypoint_score":
    _update_unmatched_keypoint_score(configs["model"], value)
  elif field_name == "score_distance_multiplier":
    _update_score_distance_multiplier(configs["model"], value)
  elif field_name == "std_dev_multiplier":
    _update_std_dev_multiplier(configs["model"], value)
  elif field_name == "rescoring_threshold":
    _update_rescoring_threshold(configs["model"], value)
  else:
    return False
  return True


def _update_tf_record_input_path(input_config, input_path):
  """Updates input configuration to reflect a new input path.

  The input_config object is updated in place, and hence not returned.

  Args:
    input_config: A input_reader_pb2.InputReader.
    input_path: A path to data or list of paths.

  Raises:
    TypeError: if input reader type is not `tf_record_input_reader`.
  """
  input_reader_type = input_config.WhichOneof("input_reader")
  if input_reader_type == "tf_record_input_reader":
    input_config.tf_record_input_reader.ClearField("input_path")
    if isinstance(input_path, list):
      input_config.tf_record_input_reader.input_path.extend(input_path)
    else:
      input_config.tf_record_input_reader.input_path.append(input_path)
  else:
    raise TypeError("Input reader type must be `tf_record_input_reader`.")


def update_input_reader_config(configs,
                               key_name=None,
                               input_name=None,
                               field_name=None,
                               value=None,
                               path_updater=_update_tf_record_input_path):
  """Updates specified input reader config field.

  Args:
    configs: Dictionary of configuration objects. See outputs from
      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
    key_name: Name of the input config we should update, either
      'train_input_config' or 'eval_input_configs'
    input_name: String name used to identify input config to update with. Should
      be either None or value of the 'name' field in one of the input reader
      configs.
    field_name: Field name in input_reader_pb2.InputReader.
    value: Value used to override existing field value.
    path_updater: helper function used to update the input path. Only used when
      field_name is "input_path".

  Raises:
    ValueError: when input field_name is None.
    ValueError: when input_name is None and number of eval_input_readers does
      not equal to 1.
  """
  if isinstance(configs[key_name], input_reader_pb2.InputReader):
    # Updates singular input_config object.
    target_input_config = configs[key_name]
    if field_name == "input_path":
      path_updater(input_config=target_input_config, input_path=value)
    else:
      setattr(target_input_config, field_name, value)
  elif input_name is None and len(configs[key_name]) == 1:
    # Updates first (and the only) object of input_config list.
    target_input_config = configs[key_name][0]
    if field_name == "input_path":
      path_updater(input_config=target_input_config, input_path=value)
    else:
      setattr(target_input_config, field_name, value)
  elif input_name is not None and len(configs[key_name]):
    # Updates input_config whose name matches input_name.
    update_count = 0
    for input_config in configs[key_name]:
      if input_config.name == input_name:
        setattr(input_config, field_name, value)
        update_count = update_count + 1
    if not update_count:
      raise ValueError(
          "Input name {} not found when overriding.".format(input_name))
    elif update_count > 1:
      raise ValueError("Duplicate input name found when overriding.")
  else:
    key_name = "None" if key_name is None else key_name
    input_name = "None" if input_name is None else input_name
    field_name = "None" if field_name is None else field_name
    raise ValueError("Unknown input config overriding: "
                     "key_name:{}, input_name:{}, field_name:{}.".format(
                         key_name, input_name, field_name))


def _update_initial_learning_rate(configs, learning_rate):
  """Updates `configs` to reflect the new initial learning rate.

  This function updates the initial learning rate. For learning rate schedules,
  all other defined learning rates in the pipeline config are scaled to maintain
  their same ratio with the initial learning rate.
  The configs dictionary is updated in place, and hence not returned.

  Args:
    configs: Dictionary of configuration objects. See outputs from
      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
    learning_rate: Initial learning rate for optimizer.

  Raises:
    TypeError: if optimizer type is not supported, or if learning rate type is
      not supported.
  """

  optimizer_type = get_optimizer_type(configs["train_config"])
  if optimizer_type == "rms_prop_optimizer":
    optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
  elif optimizer_type == "momentum_optimizer":
    optimizer_config = configs["train_config"].optimizer.momentum_optimizer
  elif optimizer_type == "adam_optimizer":
    optimizer_config = configs["train_config"].optimizer.adam_optimizer
  else:
    raise TypeError("Optimizer %s is not supported." % optimizer_type)

  learning_rate_type = get_learning_rate_type(optimizer_config)
  if learning_rate_type == "constant_learning_rate":
    constant_lr = optimizer_config.learning_rate.constant_learning_rate
    constant_lr.learning_rate = learning_rate
  elif learning_rate_type == "exponential_decay_learning_rate":
    exponential_lr = (
        optimizer_config.learning_rate.exponential_decay_learning_rate)
    exponential_lr.initial_learning_rate = learning_rate
  elif learning_rate_type == "manual_step_learning_rate":
    manual_lr = optimizer_config.learning_rate.manual_step_learning_rate
    original_learning_rate = manual_lr.initial_learning_rate
    learning_rate_scaling = float(learning_rate) / original_learning_rate
    manual_lr.initial_learning_rate = learning_rate
    for schedule in manual_lr.schedule:
      schedule.learning_rate *= learning_rate_scaling
  elif learning_rate_type == "cosine_decay_learning_rate":
    cosine_lr = optimizer_config.learning_rate.cosine_decay_learning_rate
    learning_rate_base = cosine_lr.learning_rate_base
    warmup_learning_rate = cosine_lr.warmup_learning_rate
    warmup_scale_factor = warmup_learning_rate / learning_rate_base
    cosine_lr.learning_rate_base = learning_rate
    cosine_lr.warmup_learning_rate = warmup_scale_factor * learning_rate
  else:
    raise TypeError("Learning rate %s is not supported." % learning_rate_type)


def _update_batch_size(configs, batch_size):
  """Updates `configs` to reflect the new training batch size.

  The configs dictionary is updated in place, and hence not returned.

  Args:
    configs: Dictionary of configuration objects. See outputs from
      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
    batch_size: Batch size to use for training (Ideally a power of 2). Inputs
      are rounded, and capped to be 1 or greater.
  """
  configs["train_config"].batch_size = max(1, int(round(batch_size)))


def _validate_message_has_field(message, field):
  if not message.HasField(field):
    raise ValueError("Expecting message to have field %s" % field)


def _update_generic(configs, key, value):
  """Update a pipeline configuration parameter based on a generic key/value.

  Args:
    configs: Dictionary of pipeline configuration protos.
    key: A string key, dot-delimited to represent the argument key.
      e.g. "model.ssd.train_config.batch_size"
    value: A value to set the argument to. The type of the value must match the
      type for the protocol buffer. Note that setting the wrong type will
      result in a TypeError.
      e.g. 42

  Raises:
    ValueError if the message key does not match the existing proto fields.
    TypeError the value type doesn't match the protobuf field type.
  """
  fields = key.split(".")
  first_field = fields.pop(0)
  last_field = fields.pop()
  message = configs[first_field]
  for field in fields:
    _validate_message_has_field(message, field)
    message = getattr(message, field)
  _validate_message_has_field(message, last_field)
  setattr(message, last_field, value)


def _update_momentum_optimizer_value(configs, momentum):
  """Updates `configs` to reflect the new momentum value.

  Momentum is only supported for RMSPropOptimizer and MomentumOptimizer. For any
  other optimizer, no changes take place. The configs dictionary is updated in
  place, and hence not returned.

  Args:
    configs: Dictionary of configuration objects. See outputs from
      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
    momentum: New momentum value. Values are clipped at 0.0 and 1.0.

  Raises:
    TypeError: If the optimizer type is not `rms_prop_optimizer` or
    `momentum_optimizer`.
  """
  optimizer_type = get_optimizer_type(configs["train_config"])
  if optimizer_type == "rms_prop_optimizer":
    optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
  elif optimizer_type == "momentum_optimizer":
    optimizer_config = configs["train_config"].optimizer.momentum_optimizer
  else:
    raise TypeError("Optimizer type must be one of `rms_prop_optimizer` or "
                    "`momentum_optimizer`.")

  optimizer_config.momentum_optimizer_value = min(max(0.0, momentum), 1.0)


def _update_classification_localization_weight_ratio(configs, ratio):
  """Updates the classification/localization weight loss ratio.

  Detection models usually define a loss weight for both classification and
  objectness. This function updates the weights such that the ratio between
  classification weight to localization weight is the ratio provided.
  Arbitrarily, localization weight is set to 1.0.

  Note that in the case of Faster R-CNN, this same ratio is applied to the first
  stage objectness loss weight relative to localization loss weight.

  The configs dictionary is updated in place, and hence not returned.

  Args:
    configs: Dictionary of configuration objects. See outputs from
      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
    ratio: Desired ratio of classification (and/or objectness) loss weight to
      localization loss weight.
  """
  meta_architecture = configs["model"].WhichOneof("model")
  if meta_architecture == "faster_rcnn":
    model = configs["model"].faster_rcnn
    model.first_stage_localization_loss_weight = 1.0
    model.first_stage_objectness_loss_weight = ratio
    model.second_stage_localization_loss_weight = 1.0
    model.second_stage_classification_loss_weight = ratio
  if meta_architecture == "ssd":
    model = configs["model"].ssd
    model.loss.localization_weight = 1.0
    model.loss.classification_weight = ratio


def _get_classification_loss(model_config):
  """Returns the classification loss for a model."""
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "faster_rcnn":
    model = model_config.faster_rcnn
    classification_loss = model.second_stage_classification_loss
  elif meta_architecture == "ssd":
    model = model_config.ssd
    classification_loss = model.loss.classification_loss
  else:
    raise TypeError("Did not recognize the model architecture.")
  return classification_loss


def _update_focal_loss_gamma(configs, gamma):
  """Updates the gamma value for a sigmoid focal loss.

  The configs dictionary is updated in place, and hence not returned.

  Args:
    configs: Dictionary of configuration objects. See outputs from
      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
    gamma: Exponent term in focal loss.

  Raises:
    TypeError: If the classification loss is not `weighted_sigmoid_focal`.
  """
  classification_loss = _get_classification_loss(configs["model"])
  classification_loss_type = classification_loss.WhichOneof(
      "classification_loss")
  if classification_loss_type != "weighted_sigmoid_focal":
    raise TypeError("Classification loss must be `weighted_sigmoid_focal`.")
  classification_loss.weighted_sigmoid_focal.gamma = gamma


def _update_focal_loss_alpha(configs, alpha):
  """Updates the alpha value for a sigmoid focal loss.

  The configs dictionary is updated in place, and hence not returned.

  Args:
    configs: Dictionary of configuration objects. See outputs from
      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
    alpha: Class weight multiplier for sigmoid loss.

  Raises:
    TypeError: If the classification loss is not `weighted_sigmoid_focal`.
  """
  classification_loss = _get_classification_loss(configs["model"])
  classification_loss_type = classification_loss.WhichOneof(
      "classification_loss")
  if classification_loss_type != "weighted_sigmoid_focal":
    raise TypeError("Classification loss must be `weighted_sigmoid_focal`.")
  classification_loss.weighted_sigmoid_focal.alpha = alpha


def _update_train_steps(configs, train_steps):
  """Updates `configs` to reflect new number of training steps."""
  configs["train_config"].num_steps = int(train_steps)


def _update_all_eval_input_configs(configs, field, value):
  """Updates the content of `field` with `value` for all eval input configs."""
  for eval_input_config in configs["eval_input_configs"]:
    setattr(eval_input_config, field, value)


def _update_label_map_path(configs, label_map_path):
  """Updates the label map path for both train and eval input readers.

  The configs dictionary is updated in place, and hence not returned.

  Args:
    configs: Dictionary of configuration objects. See outputs from
      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
    label_map_path: New path to `StringIntLabelMap` pbtxt file.
  """
  configs["train_input_config"].label_map_path = label_map_path
  _update_all_eval_input_configs(configs, "label_map_path", label_map_path)




def _update_mask_type(configs, mask_type):
  """Updates the mask type for both train and eval input readers.

  The configs dictionary is updated in place, and hence not returned.

  Args:
    configs: Dictionary of configuration objects. See outputs from
      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
    mask_type: A string name representing a value of
      input_reader_pb2.InstanceMaskType
  """
  configs["train_input_config"].mask_type = mask_type
  _update_all_eval_input_configs(configs, "mask_type", mask_type)


def _update_use_moving_averages(configs, use_moving_averages):
  """Updates the eval config option to use or not use moving averages.

  The configs dictionary is updated in place, and hence not returned.

  Args:
    configs: Dictionary of configuration objects. See outputs from
      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
    use_moving_averages: Boolean indicating whether moving average variables
      should be loaded during evaluation.
  """
  configs["eval_config"].use_moving_averages = use_moving_averages


def _update_retain_original_images(eval_config, retain_original_images):
  """Updates eval config with option to retain original images.

  The eval_config object is updated in place, and hence not returned.

  Args:
    eval_config: A eval_pb2.EvalConfig.
    retain_original_images: Boolean indicating whether to retain original images
      in eval mode.
  """
  eval_config.retain_original_images = retain_original_images


def _update_use_bfloat16(configs, use_bfloat16):
  """Updates `configs` to reflect the new setup on whether to use bfloat16.

  The configs dictionary is updated in place, and hence not returned.

  Args:
    configs: Dictionary of configuration objects. See outputs from
      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
    use_bfloat16: A bool, indicating whether to use bfloat16 for training.
  """
  configs["train_config"].use_bfloat16 = use_bfloat16


def _update_retain_original_image_additional_channels(
    eval_config,
    retain_original_image_additional_channels):
  """Updates eval config to retain original image additional channels or not.

  The eval_config object is updated in place, and hence not returned.

  Args:
    eval_config: A eval_pb2.EvalConfig.
    retain_original_image_additional_channels: Boolean indicating whether to
      retain original image additional channels in eval mode.
  """
  eval_config.retain_original_image_additional_channels = (
      retain_original_image_additional_channels)


def remove_unnecessary_ema(variables_to_restore, no_ema_collection=None):
  """Remap and Remove EMA variable that are not created during training.

  ExponentialMovingAverage.variables_to_restore() returns a map of EMA names
  to tf variables to restore. E.g.:
  {
      conv/batchnorm/gamma/ExponentialMovingAverage: conv/batchnorm/gamma,
      conv_4/conv2d_params/ExponentialMovingAverage: conv_4/conv2d_params,
      global_step: global_step
  }
  This function takes care of the extra ExponentialMovingAverage variables
  that get created during eval but aren't available in the checkpoint, by
  remapping the key to the variable itself, and remove the entry of its EMA from
  the variables to restore. An example resulting dictionary would look like:
  {
      conv/batchnorm/gamma: conv/batchnorm/gamma,
      conv_4/conv2d_params: conv_4/conv2d_params,
      global_step: global_step
  }
  Args:
    variables_to_restore: A dictionary created by ExponentialMovingAverage.
      variables_to_restore().
    no_ema_collection: A list of namescope substrings to match the variables
      to eliminate EMA.

  Returns:
    A variables_to_restore dictionary excluding the collection of unwanted
    EMA mapping.
  """
  if no_ema_collection is None:
    return variables_to_restore

  restore_map = {}
  for key in variables_to_restore:
    if ("ExponentialMovingAverage" in key
        and any([name in key for name in no_ema_collection])):
      new_key = key.replace("/ExponentialMovingAverage", "")
    else:
      new_key = key
    restore_map[new_key] = variables_to_restore[key]
  return restore_map


def _update_num_classes(model_config, num_classes):
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "faster_rcnn":
    model_config.faster_rcnn.num_classes = num_classes
  if meta_architecture == "ssd":
    model_config.ssd.num_classes = num_classes


def _update_sample_from_datasets_weights(input_reader_config, weights):
  """Updated sample_from_datasets_weights with overrides."""
  if len(weights) != len(input_reader_config.sample_from_datasets_weights):
    raise ValueError(
        "sample_from_datasets_weights override has a different number of values"
        " ({}) than the configured dataset weights ({})."
        .format(
            len(input_reader_config.sample_from_datasets_weights),
            len(weights)))

  del input_reader_config.sample_from_datasets_weights[:]
  input_reader_config.sample_from_datasets_weights.extend(weights)


def _update_peak_max_pool_kernel_size(model_config, kernel_size):
  """Updates the max pool kernel size (NMS) for keypoints in CenterNet."""
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "center_net":
    if len(model_config.center_net.keypoint_estimation_task) == 1:
      kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0]
      kpt_estimation_task.peak_max_pool_kernel_size = kernel_size
    else:
      tf.logging.warning("Ignoring config override key for "
                         "peak_max_pool_kernel_size since there are multiple "
                         "keypoint estimation tasks")


def _update_candidate_search_scale(model_config, search_scale):
  """Updates the keypoint candidate search scale in CenterNet."""
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "center_net":
    if len(model_config.center_net.keypoint_estimation_task) == 1:
      kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0]
      kpt_estimation_task.candidate_search_scale = search_scale
    else:
      tf.logging.warning("Ignoring config override key for "
                         "candidate_search_scale since there are multiple "
                         "keypoint estimation tasks")


def _update_candidate_ranking_mode(model_config, mode):
  """Updates how keypoints are snapped to candidates in CenterNet."""
  if mode not in ("min_distance", "score_distance_ratio",
                  "score_scaled_distance_ratio", "gaussian_weighted"):
    raise ValueError("Attempting to set the keypoint candidate ranking mode "
                     "to {}, but the only options are 'min_distance', "
                     "'score_distance_ratio', 'score_scaled_distance_ratio', "
                     "'gaussian_weighted'.".format(mode))
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "center_net":
    if len(model_config.center_net.keypoint_estimation_task) == 1:
      kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0]
      kpt_estimation_task.candidate_ranking_mode = mode
    else:
      tf.logging.warning("Ignoring config override key for "
                         "candidate_ranking_mode since there are multiple "
                         "keypoint estimation tasks")


def _update_score_distance_offset(model_config, offset):
  """Updates the keypoint candidate selection metric. See CenterNet proto."""
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "center_net":
    if len(model_config.center_net.keypoint_estimation_task) == 1:
      kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0]
      kpt_estimation_task.score_distance_offset = offset
    else:
      tf.logging.warning("Ignoring config override key for "
                         "score_distance_offset since there are multiple "
                         "keypoint estimation tasks")


def _update_box_scale(model_config, box_scale):
  """Updates the keypoint candidate search region. See CenterNet proto."""
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "center_net":
    if len(model_config.center_net.keypoint_estimation_task) == 1:
      kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0]
      kpt_estimation_task.box_scale = box_scale
    else:
      tf.logging.warning("Ignoring config override key for box_scale since "
                         "there are multiple keypoint estimation tasks")


def _update_keypoint_candidate_score_threshold(model_config, threshold):
  """Updates the keypoint candidate score threshold. See CenterNet proto."""
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "center_net":
    if len(model_config.center_net.keypoint_estimation_task) == 1:
      kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0]
      kpt_estimation_task.keypoint_candidate_score_threshold = threshold
    else:
      tf.logging.warning("Ignoring config override key for "
                         "keypoint_candidate_score_threshold since there are "
                         "multiple keypoint estimation tasks")


def _update_rescore_instances(model_config, should_rescore):
  """Updates whether boxes should be rescored based on keypoint confidences."""
  if isinstance(should_rescore, str):
    should_rescore = True if should_rescore == "True" else False
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "center_net":
    if len(model_config.center_net.keypoint_estimation_task) == 1:
      kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0]
      kpt_estimation_task.rescore_instances = should_rescore
    else:
      tf.logging.warning("Ignoring config override key for "
                         "rescore_instances since there are multiple keypoint "
                         "estimation tasks")


def _update_unmatched_keypoint_score(model_config, score):
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "center_net":
    if len(model_config.center_net.keypoint_estimation_task) == 1:
      kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0]
      kpt_estimation_task.unmatched_keypoint_score = score
    else:
      tf.logging.warning("Ignoring config override key for "
                         "unmatched_keypoint_score since there are multiple "
                         "keypoint estimation tasks")


def _update_score_distance_multiplier(model_config, score_distance_multiplier):
  """Updates the keypoint candidate selection metric. See CenterNet proto."""
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "center_net":
    if len(model_config.center_net.keypoint_estimation_task) == 1:
      kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0]
      kpt_estimation_task.score_distance_multiplier = score_distance_multiplier
    else:
      tf.logging.warning("Ignoring config override key for "
                         "score_distance_multiplier since there are multiple "
                         "keypoint estimation tasks")
  else:
    raise ValueError(
        "Unsupported meta_architecture type: %s" % meta_architecture)


def _update_std_dev_multiplier(model_config, std_dev_multiplier):
  """Updates the keypoint candidate selection metric. See CenterNet proto."""
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "center_net":
    if len(model_config.center_net.keypoint_estimation_task) == 1:
      kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0]
      kpt_estimation_task.std_dev_multiplier = std_dev_multiplier
    else:
      tf.logging.warning("Ignoring config override key for "
                         "std_dev_multiplier since there are multiple "
                         "keypoint estimation tasks")
  else:
    raise ValueError(
        "Unsupported meta_architecture type: %s" % meta_architecture)


def _update_rescoring_threshold(model_config, rescoring_threshold):
  """Updates the keypoint candidate selection metric. See CenterNet proto."""
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "center_net":
    if len(model_config.center_net.keypoint_estimation_task) == 1:
      kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0]
      kpt_estimation_task.rescoring_threshold = rescoring_threshold
    else:
      tf.logging.warning("Ignoring config override key for "
                         "rescoring_threshold since there are multiple "
                         "keypoint estimation tasks")
  else:
    raise ValueError(
        "Unsupported meta_architecture type: %s" % meta_architecture)