第三十四節，目標檢測之谷歌Object Detection API源碼解析

我們在第三十二節，使用谷歌Object Detection API進行目標檢測、訓練新的模型(使用VOC 2012數據集)那一節我們介紹了如何使用谷歌Object Detection API進行目標檢測，以及如何使用谷歌提供的目標檢測模型訓練自己的數據。在訓練自己的數據集時，主要包括以下幾步：

• 制作自己的數據集，注意這里數據集在進行標注時，需要按照一定的格式。然后調object_detection\dataset_tools下對應的腳本生成tfrecord文件。如下圖，如果我們想調用create_pascal_tf_record.py文件生成tfrecord文件，那么我們的數據集要和voc 2012數據集的標注方式一樣。你也可以通過解讀create_pascal_tf_record.py文件了解我們的數據集的標注方式。

• 下載我們所要使用的目標檢測模型，進行預訓練，不然從頭開始訓練時間成本會很高。
• 在object_detection/samples/configs文件夾下有一些配置文件，選擇與我們所要使用的目標檢測模型相對應的配置文件，並進行一些修改。
• 使用object_detection/train.py文件進行訓練。
• 使用export_inference_graph.py腳本導出訓練好的模型，並進行目標檢測。

在這里我主要解析一下train.py文件的工作流程。

一 train.py文件解析

先附上源碼：

# 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.
# ==============================================================================

r"""Training executable for detection models.

This executable is used to train DetectionModels. There are two ways of
configuring the training job:

1) A single pipeline_pb2.TrainEvalPipelineConfig configuration file
can be specified by --pipeline_config_path.

Example usage:
    ./train \
        --logtostderr \
        --train_dir=path/to/train_dir \
        --pipeline_config_path=pipeline_config.pbtxt

2) Three configuration files can be provided: a model_pb2.DetectionModel
configuration file to define what type of DetectionModel is being trained, an
input_reader_pb2.InputReader file to specify what training data will be used and
a train_pb2.TrainConfig file to configure training parameters.

Example usage:
    ./train \
        --logtostderr \
        --train_dir=path/to/train_dir \
        --model_config_path=model_config.pbtxt \
        --train_config_path=train_config.pbtxt \
        --input_config_path=train_input_config.pbtxt
"""

import functools
import json
import os
import tensorflow as tf

from object_detection import trainer
from object_detection.builders import dataset_builder
from object_detection.builders import graph_rewriter_builder
from object_detection.builders import model_builder
from object_detection.utils import config_util
from object_detection.utils import dataset_util

tf.logging.set_verbosity(tf.logging.INFO)

flags = tf.app.flags
flags.DEFINE_string('master', '', 'Name of the TensorFlow master to use.')
flags.DEFINE_integer('task', 0, 'task id')
flags.DEFINE_integer('num_clones', 1, 'Number of clones to deploy per worker.')
flags.DEFINE_boolean('clone_on_cpu', False,
                     'Force clones to be deployed on CPU.  Note that even if '
                     'set to False (allowing ops to run on gpu), some ops may '
                     'still be run on the CPU if they have no GPU kernel.')
flags.DEFINE_integer('worker_replicas', 1, 'Number of worker+trainer '
                     'replicas.')
flags.DEFINE_integer('ps_tasks', 0,
                     'Number of parameter server tasks. If None, does not use '
                     'a parameter server.')
flags.DEFINE_string('train_dir', '',
                    'Directory to save the checkpoints and training summaries.')

flags.DEFINE_string('pipeline_config_path', '',
                    'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
                    'file. If provided, other configs are ignored')

flags.DEFINE_string('train_config_path', '',
                    'Path to a train_pb2.TrainConfig config file.')
flags.DEFINE_string('input_config_path', '',
                    'Path to an input_reader_pb2.InputReader config file.')
flags.DEFINE_string('model_config_path', '',
                    'Path to a model_pb2.DetectionModel config file.')

FLAGS = flags.FLAGS


def main(_):
  assert FLAGS.train_dir, '`train_dir` is missing.'
  if FLAGS.task == 0: tf.gfile.MakeDirs(FLAGS.train_dir)
  if FLAGS.pipeline_config_path:
    configs = config_util.get_configs_from_pipeline_file(
        FLAGS.pipeline_config_path)
    if FLAGS.task == 0:
      tf.gfile.Copy(FLAGS.pipeline_config_path,
                    os.path.join(FLAGS.train_dir, 'pipeline.config'),
                    overwrite=True)
  else:
    configs = config_util.get_configs_from_multiple_files(
        model_config_path=FLAGS.model_config_path,
        train_config_path=FLAGS.train_config_path,
        train_input_config_path=FLAGS.input_config_path)
    if FLAGS.task == 0:
      for name, config in [('model.config', FLAGS.model_config_path),
                           ('train.config', FLAGS.train_config_path),
                           ('input.config', FLAGS.input_config_path)]:
        tf.gfile.Copy(config, os.path.join(FLAGS.train_dir, name),
                      overwrite=True)

  model_config = configs['model']
  train_config = configs['train_config']
  input_config = configs['train_input_config']

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=True)

  def get_next(config):
    return dataset_util.make_initializable_iterator(
        dataset_builder.build(config)).get_next()

  create_input_dict_fn = functools.partial(get_next, input_config)

  env = json.loads(os.environ.get('TF_CONFIG', '{}'))
  cluster_data = env.get('cluster', None)
  cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else None
  task_data = env.get('task', None) or {'type': 'master', 'index': 0}
  task_info = type('TaskSpec', (object,), task_data)

  # Parameters for a single worker.
  ps_tasks = 0
  worker_replicas = 1
  worker_job_name = 'lonely_worker'
  task = 0
  is_chief = True
  master = ''

  if cluster_data and 'worker' in cluster_data:
    # Number of total worker replicas include "worker"s and the "master".
    worker_replicas = len(cluster_data['worker']) + 1
  if cluster_data and 'ps' in cluster_data:
    ps_tasks = len(cluster_data['ps'])

  if worker_replicas > 1 and ps_tasks < 1:
    raise ValueError('At least 1 ps task is needed for distributed training.')

  if worker_replicas >= 1 and ps_tasks > 0:
    # Set up distributed training.
    server = tf.train.Server(tf.train.ClusterSpec(cluster), protocol='grpc',
                             job_name=task_info.type,
                             task_index=task_info.index)
    if task_info.type == 'ps':
      server.join()
      return

    worker_job_name = '%s/task:%d' % (task_info.type, task_info.index)
    task = task_info.index
    is_chief = (task_info.type == 'master')
    master = server.target

  graph_rewriter_fn = None
  if 'graph_rewriter_config' in configs:
    graph_rewriter_fn = graph_rewriter_builder.build(
        configs['graph_rewriter_config'], is_training=True)

  trainer.train(
      create_input_dict_fn,
      model_fn,
      train_config,
      master,
      task,
      FLAGS.num_clones,
      worker_replicas,
      FLAGS.clone_on_cpu,
      ps_tasks,
      worker_job_name,
      is_chief,
      FLAGS.train_dir,
      graph_hook_fn=graph_rewriter_fn)


if __name__ == '__main__':
  tf.app.run()

1、先定義了tf.app.flags，用於支持接受命令行傳遞參數，相當於接受argv。

flags = tf.app.flags
flags.DEFINE_string('master', '', 'Name of the TensorFlow master to use.')
flags.DEFINE_integer('task', 0, 'task id')
flags.DEFINE_integer('num_clones', 1, 'Number of clones to deploy per worker.')
flags.DEFINE_boolean('clone_on_cpu', False,
                     'Force clones to be deployed on CPU.  Note that even if '
                     'set to False (allowing ops to run on gpu), some ops may '
                     'still be run on the CPU if they have no GPU kernel.')
flags.DEFINE_integer('worker_replicas', 1, 'Number of worker+trainer '
                     'replicas.')
flags.DEFINE_integer('ps_tasks', 0,
                     'Number of parameter server tasks. If None, does not use '
                     'a parameter server.')
flags.DEFINE_string('train_dir', '',
                    'Directory to save the checkpoints and training summaries.')

flags.DEFINE_string('pipeline_config_path', '',
                    'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
                    'file. If provided, other configs are ignored')

flags.DEFINE_string('train_config_path', '',
                    'Path to a train_pb2.TrainConfig config file.')
flags.DEFINE_string('input_config_path', '',
                    'Path to an input_reader_pb2.InputReader config file.')
flags.DEFINE_string('model_config_path', '',
                    'Path to a model_pb2.DetectionModel config file.')

FLAGS = flags.FLAGS

這里面有幾個比較重要的參數，train_dir目錄用於保存訓練的模型和日志文件，pipeline_config_path用於指定pipeline_pb2.TrainEvalPipelineConfig配置文件的全路徑(如果不指定指定這個參數，需要指定train_config_path，input_config_path，model_config_path配置文件，其實這三個文件就是把pipeline_pb2.TrainEvalPipelineConfig配置文件分成了三部分)。

2、再來看一下main函數，我們把它分成幾部分來解讀。

假設我們在控制台下的命令如下：

python train.py --train_dir voc/train_dir/ --pipeline_config_path voc/faster_rcnn_inception_resnet_v2_atrous_voc.config

3、第一部分

  assert FLAGS.train_dir, '`train_dir` is missing.'
  if FLAGS.task == 0: tf.gfile.MakeDirs(FLAGS.train_dir)
  if FLAGS.pipeline_config_path:
    configs = config_util.get_configs_from_pipeline_file(
        FLAGS.pipeline_config_path)
    if FLAGS.task == 0:
      tf.gfile.Copy(FLAGS.pipeline_config_path,
                    os.path.join(FLAGS.train_dir, 'pipeline.config'),
                    overwrite=True)
  else:
    configs = config_util.get_configs_from_multiple_files(
        model_config_path=FLAGS.model_config_path,
        train_config_path=FLAGS.train_config_path,
        train_input_config_path=FLAGS.input_config_path)
    if FLAGS.task == 0:
      for name, config in [('model.config', FLAGS.model_config_path),
                           ('train.config', FLAGS.train_config_path),
                           ('input.config', FLAGS.input_config_path)]:
        tf.gfile.Copy(config, os.path.join(FLAGS.train_dir, name),
                      overwrite=True)

因為我們傳入了train_dir,pipeline_config_path參數，程序執行時會：

• 讀取pipeline_config_path配置文件，返回一個dict，保存配置文件中`model`, `train_config`,  `train_input_config`, `eval_config`, `eval_input_config`信息。
• 把pipeline_config_path配置文件復制到train_dir目錄下，命名為pipeline.config

4、第二部分

  model_config = configs['model']
  train_config = configs['train_config']
  input_config = configs['train_input_config']

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=True)

  def get_next(config):
    return dataset_util.make_initializable_iterator(
        dataset_builder.build(config)).get_next()

  create_input_dict_fn = functools.partial(get_next, input_config)

•  變量model_config，train_config，input_config初始化
• model_builder.build函數，指定兩個固定參數model_config，is_training並返回一個新的函數model_fn 。這個函數很重要，包括對目標檢測模型的實現，后面會詳細介紹。
• get_next函數，指定固定參數input_config。這個函數主要實現了tfrecord數據的讀取，我們也放在后面介紹。

5、第三部分

  env = json.loads(os.environ.get('TF_CONFIG', '{}'))
  cluster_data = env.get('cluster', None)
  cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else None
  task_data = env.get('task', None) or {'type': 'master', 'index': 0}
  task_info = type('TaskSpec', (object,), task_data)

  # Parameters for a single worker.
  ps_tasks = 0
  worker_replicas = 1
  worker_job_name = 'lonely_worker'
  task = 0
  is_chief = True
  master = ''

  if cluster_data and 'worker' in cluster_data:
    # Number of total worker replicas include "worker"s and the "master".
    worker_replicas = len(cluster_data['worker']) + 1
  if cluster_data and 'ps' in cluster_data:
    ps_tasks = len(cluster_data['ps'])

  if worker_replicas > 1 and ps_tasks < 1:
    raise ValueError('At least 1 ps task is needed for distributed training.')

  if worker_replicas >= 1 and ps_tasks > 0:
    # Set up distributed training.
    server = tf.train.Server(tf.train.ClusterSpec(cluster), protocol='grpc',
                             job_name=task_info.type,
                             task_index=task_info.index)
    if task_info.type == 'ps':
      server.join()
      return

    worker_job_name = '%s/task:%d' % (task_info.type, task_info.index)
    task = task_info.index
    is_chief = (task_info.type == 'master')
    master = server.target

• 這部分代碼主要是用來實現分布式部署訓練的。如果想了解的請點擊這里第八節，配置分布式TensorFlow。

6、第四部分

  graph_rewriter_fn = None
  if 'graph_rewriter_config' in configs:
    graph_rewriter_fn = graph_rewriter_builder.build(
        configs['graph_rewriter_config'], is_training=True)

  trainer.train(
      create_input_dict_fn,
      model_fn,
      train_config,
      master,
      task,
      FLAGS.num_clones,
      worker_replicas,
      FLAGS.clone_on_cpu,
      ps_tasks,
      worker_job_name,
      is_chief,
      FLAGS.train_dir,
      graph_hook_fn=graph_rewriter_fn)

• 由於沒有定義graph_rewriter_config，所以會直接執行trainer.train，開始讀取數據，進行預處理后訓練。

 二  dataset_builder.build函數

先附上代碼：

def build(input_reader_config, transform_input_data_fn=None,
          batch_size=None, max_num_boxes=None, num_classes=None,
          spatial_image_shape=None):
  """Builds a tf.data.Dataset.

  Builds a tf.data.Dataset by applying the `transform_input_data_fn` on all
  records. Applies a padded batch to the resulting dataset.

  Args:
    input_reader_config: A input_reader_pb2.InputReader object.
    transform_input_data_fn: Function to apply to all records, or None if
      no extra decoding is required.
    batch_size: Batch size. If None, batching is not performed.
    max_num_boxes: Max number of groundtruth boxes needed to compute shapes for
      padding. If None, will use a dynamic shape.
    num_classes: Number of classes in the dataset needed to compute shapes for
      padding. If None, will use a dynamic shape.
    spatial_image_shape: A list of two integers of the form [height, width]
      containing expected spatial shape of the image after applying
      transform_input_data_fn. If None, will use dynamic shapes.

  Returns:
    A tf.data.Dataset based on the input_reader_config.

  Raises:
    ValueError: On invalid input reader proto.
    ValueError: If no input paths are specified.
  """
  if not isinstance(input_reader_config, input_reader_pb2.InputReader):
    raise ValueError('input_reader_config not of type '
                     'input_reader_pb2.InputReader.')

  if input_reader_config.WhichOneof('input_reader') == 'tf_record_input_reader':
    config = input_reader_config.tf_record_input_reader
    if not config.input_path:
      raise ValueError('At least one input path must be specified in '
                       '`input_reader_config`.')

    label_map_proto_file = None
    if input_reader_config.HasField('label_map_path'):
      label_map_proto_file = input_reader_config.label_map_path

　　 #初始化需要解碼的字段，以及解碼對應字段的 handler
    decoder = tf_example_decoder.TfExampleDecoder(
        load_instance_masks=input_reader_config.load_instance_masks,
        instance_mask_type=input_reader_config.mask_type,
        label_map_proto_file=label_map_proto_file)

    def process_fn(value):
      processed = decoder.decode(value)
      if transform_input_data_fn is not None:
        return transform_input_data_fn(processed)
      return processed

　　 # 調用 tf.data.TFRecordDataset 從 config.input_path 讀數據，調用 process_fn 對讀取的數據解碼數，預提取 input_reader_config.prefetch_size 條數據
    dataset = dataset_util.read_dataset(
        functools.partial(tf.data.TFRecordDataset, buffer_size=8 * 1000 * 1000),
        process_fn, config.input_path[:], input_reader_config)

    if batch_size:
      padding_shapes = _get_padding_shapes(dataset, max_num_boxes, num_classes,
                                           spatial_image_shape)
      dataset = dataset.apply(
          tf.contrib.data.padded_batch_and_drop_remainder(batch_size,
                                                          padding_shapes))
    return dataset

  raise ValueError('Unsupported input_reader_config.')

整個流程

• 獲取訓練集tfrecord文件路徑，label_map_path文件路徑，input_reader_config設置參數如下：

train_input_reader: {
  tf_record_input_reader {
    input_path: "voc/pascal_train.record"
  }
  label_map_path: "voc/pascal_label_map.pbtxt"
}

• 初始化需要解碼的字段，以及解碼對應字段的 handler
• 調用 tf.data.TFRecordDataset 從 config.input_path 讀數據，調用 process_fn (定義了數據的解碼格式)對讀取的數據解碼，預提取 input_reader_config.prefetch_size 條數據
• 對數據集應用 tf.contrib.data.padded_batch_and_drop_remainder，如果不夠一個 batch_size 就丟棄該部分數據
• 返回一個迭代器

三 model_builder.build函數

代碼如下：

def build(model_config, is_training, add_summaries=True,
          add_background_class=True):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.
    add_summaries: Whether to add tensorflow summaries in the model graph.
    add_background_class: Whether to add an implicit background class to one-hot
      encodings of groundtruth labels. Set to false if using groundtruth labels
      with an explicit background class or using multiclass scores instead of
      truth in the case of distillation. Ignored in the case of faster_rcnn.
  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training, add_summaries,
                            add_background_class)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training,
                                    add_summaries)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))

先獲取我們使用的目標檢測模型，由於我們使用的是faster_rcnn_inception_resnet_v2，因此會調用_build_faster_rcnn_model函數，並且傳入參數faster_rcnn，is_training,add_summaries。其中faster_rcnn的內容如下：

model {
  faster_rcnn {
    num_classes: 20
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 600
        max_dimension: 1024
      }
    }
    feature_extractor {
      type: 'faster_rcnn_inception_resnet_v2'
      first_stage_features_stride: 8
    }
    first_stage_anchor_generator {
      grid_anchor_generator {
        scales: [0.25, 0.5, 1.0, 2.0]
        aspect_ratios: [0.5, 1.0, 2.0]
        height_stride: 8
        width_stride: 8
      }
    }
    first_stage_atrous_rate: 2
    first_stage_box_predictor_conv_hyperparams {
      op: CONV
      regularizer {
        l2_regularizer {
          weight: 0.0
        }
      }
      initializer {
        truncated_normal_initializer {
          stddev: 0.01
        }
      }
    }
    first_stage_nms_score_threshold: 0.0
    first_stage_nms_iou_threshold: 0.7
    first_stage_max_proposals: 300
    first_stage_localization_loss_weight: 2.0
    first_stage_objectness_loss_weight: 1.0
    initial_crop_size: 17
    maxpool_kernel_size: 1
    maxpool_stride: 1
    second_stage_box_predictor {
      mask_rcnn_box_predictor {
        use_dropout: false
        dropout_keep_probability: 1.0
        fc_hyperparams {
          op: FC
          regularizer {
            l2_regularizer {
              weight: 0.0
            }
          }
          initializer {
            variance_scaling_initializer {
              factor: 1.0
              uniform: true
              mode: FAN_AVG
            }
          }
        }
      }
    }
    second_stage_post_processing {
      batch_non_max_suppression {
        score_threshold: 0.0
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SOFTMAX
    }
    second_stage_localization_loss_weight: 2.0
    second_stage_classification_loss_weight: 1.0
  }
}

我們再來看一下_build_faster_rcnn_model的源碼：

def _build_faster_rcnn_model(frcnn_config, is_training, add_summaries):
  """Builds a Faster R-CNN or R-FCN detection model based on the model config.

  Builds R-FCN model if the second_stage_box_predictor in the config is of type
  `rfcn_box_predictor` else builds a Faster R-CNN model.

  Args:
    frcnn_config: A faster_rcnn.proto object containing the config for the
      desired FasterRCNNMetaArch or RFCNMetaArch.
    is_training: True if this model is being built for training purposes.
    add_summaries: Whether to add tf summaries in the model.

  Returns:
    FasterRCNNMetaArch based on the config.

  Raises:
    ValueError: If frcnn_config.type is not recognized (i.e. not registered in
      model_class_map).
  """
  num_classes = frcnn_config.num_classes
  image_resizer_fn = image_resizer_builder.build(frcnn_config.image_resizer)

  feature_extractor = _build_faster_rcnn_feature_extractor(
      frcnn_config.feature_extractor, is_training,
      frcnn_config.inplace_batchnorm_update)

  number_of_stages = frcnn_config.number_of_stages
  first_stage_anchor_generator = anchor_generator_builder.build(
      frcnn_config.first_stage_anchor_generator)

  first_stage_atrous_rate = frcnn_config.first_stage_atrous_rate
  first_stage_box_predictor_arg_scope_fn = hyperparams_builder.build(
      frcnn_config.first_stage_box_predictor_conv_hyperparams, is_training)
  first_stage_box_predictor_kernel_size = (
      frcnn_config.first_stage_box_predictor_kernel_size)
  first_stage_box_predictor_depth = frcnn_config.first_stage_box_predictor_depth
  first_stage_minibatch_size = frcnn_config.first_stage_minibatch_size
  first_stage_positive_balance_fraction = (
      frcnn_config.first_stage_positive_balance_fraction)
  first_stage_nms_score_threshold = frcnn_config.first_stage_nms_score_threshold
  first_stage_nms_iou_threshold = frcnn_config.first_stage_nms_iou_threshold
  first_stage_max_proposals = frcnn_config.first_stage_max_proposals
  first_stage_loc_loss_weight = (
      frcnn_config.first_stage_localization_loss_weight)
  first_stage_obj_loss_weight = frcnn_config.first_stage_objectness_loss_weight

  initial_crop_size = frcnn_config.initial_crop_size
  maxpool_kernel_size = frcnn_config.maxpool_kernel_size
  maxpool_stride = frcnn_config.maxpool_stride

  second_stage_box_predictor = box_predictor_builder.build(
      hyperparams_builder.build,
      frcnn_config.second_stage_box_predictor,
      is_training=is_training,
      num_classes=num_classes)
  second_stage_batch_size = frcnn_config.second_stage_batch_size
  second_stage_balance_fraction = frcnn_config.second_stage_balance_fraction
  (second_stage_non_max_suppression_fn, second_stage_score_conversion_fn
  ) = post_processing_builder.build(frcnn_config.second_stage_post_processing)
  second_stage_localization_loss_weight = (
      frcnn_config.second_stage_localization_loss_weight)
  second_stage_classification_loss = (
      losses_builder.build_faster_rcnn_classification_loss(
          frcnn_config.second_stage_classification_loss))
  second_stage_classification_loss_weight = (
      frcnn_config.second_stage_classification_loss_weight)
  second_stage_mask_prediction_loss_weight = (
      frcnn_config.second_stage_mask_prediction_loss_weight)

  hard_example_miner = None
  if frcnn_config.HasField('hard_example_miner'):
    hard_example_miner = losses_builder.build_hard_example_miner(
        frcnn_config.hard_example_miner,
        second_stage_classification_loss_weight,
        second_stage_localization_loss_weight)

  common_kwargs = {
      'is_training': is_training,
      'num_classes': num_classes,
      'image_resizer_fn': image_resizer_fn,
      'feature_extractor': feature_extractor,
      'number_of_stages': number_of_stages,
      'first_stage_anchor_generator': first_stage_anchor_generator,
      'first_stage_atrous_rate': first_stage_atrous_rate,
      'first_stage_box_predictor_arg_scope_fn':
      first_stage_box_predictor_arg_scope_fn,
      'first_stage_box_predictor_kernel_size':
      first_stage_box_predictor_kernel_size,
      'first_stage_box_predictor_depth': first_stage_box_predictor_depth,
      'first_stage_minibatch_size': first_stage_minibatch_size,
      'first_stage_positive_balance_fraction':
      first_stage_positive_balance_fraction,
      'first_stage_nms_score_threshold': first_stage_nms_score_threshold,
      'first_stage_nms_iou_threshold': first_stage_nms_iou_threshold,
      'first_stage_max_proposals': first_stage_max_proposals,
      'first_stage_localization_loss_weight': first_stage_loc_loss_weight,
      'first_stage_objectness_loss_weight': first_stage_obj_loss_weight,
      'second_stage_batch_size': second_stage_batch_size,
      'second_stage_balance_fraction': second_stage_balance_fraction,
      'second_stage_non_max_suppression_fn':
      second_stage_non_max_suppression_fn,
      'second_stage_score_conversion_fn': second_stage_score_conversion_fn,
      'second_stage_localization_loss_weight':
      second_stage_localization_loss_weight,
      'second_stage_classification_loss':
      second_stage_classification_loss,
      'second_stage_classification_loss_weight':
      second_stage_classification_loss_weight,
      'hard_example_miner': hard_example_miner,
      'add_summaries': add_summaries}

  if isinstance(second_stage_box_predictor, box_predictor.RfcnBoxPredictor):
    return rfcn_meta_arch.RFCNMetaArch(
        second_stage_rfcn_box_predictor=second_stage_box_predictor,
        **common_kwargs)
  else:
    return faster_rcnn_meta_arch.FasterRCNNMetaArch(
        initial_crop_size=initial_crop_size,
        maxpool_kernel_size=maxpool_kernel_size,
        maxpool_stride=maxpool_stride,
        second_stage_mask_rcnn_box_predictor=second_stage_box_predictor,
        second_stage_mask_prediction_loss_weight=(
            second_stage_mask_prediction_loss_weight),
        **common_kwargs)

•  代碼的前半部分是加載Faster  R-CNN模型的參數，如num_classes，第一階段RPN網絡的參數，第二階段Fast R-CNN網絡的參數。
• 由於配置文件中指定了網絡結構是faster_rcnn_inception_resnet_v2，因此會調用inception_resnet_v2網絡進行特征提取(位於object_detection\object_detection\models文件夾下)，特征提取分為兩部分，利用inception_resnet_v2的前半部分得到供RPN網絡輸入的特征圖，網絡繼續前向傳播至特有卷積層，產生更高維特征圖，用於Fast R-CNN網絡網絡的特征提取，詳情參考第三十一節，目標檢測算法之 Faster R-CNN算法詳解。
• 代碼后半部分用來構建目標檢測模型以及損失函數。

四 trainer.train函數

代碼如下：

def train(create_tensor_dict_fn,
          create_model_fn,
          train_config,
          master,
          task,
          num_clones,
          worker_replicas,
          clone_on_cpu,
          ps_tasks,
          worker_job_name,
          is_chief,
          train_dir,
          graph_hook_fn=None):
  """Training function for detection models.

  Args:
    create_tensor_dict_fn: a function to create a tensor input dictionary.
    create_model_fn: a function that creates a DetectionModel and generates
                     losses.
    train_config: a train_pb2.TrainConfig protobuf.
    master: BNS name of the TensorFlow master to use.
    task: The task id of this training instance.
    num_clones: The number of clones to run per machine.
    worker_replicas: The number of work replicas to train with.
    clone_on_cpu: True if clones should be forced to run on CPU.
    ps_tasks: Number of parameter server tasks.
    worker_job_name: Name of the worker job.
    is_chief: Whether this replica is the chief replica.
    train_dir: Directory to write checkpoints and training summaries to.
    graph_hook_fn: Optional function that is called after the inference graph is
      built (before optimization). This is helpful to perform additional changes
      to the training graph such as adding FakeQuant ops. The function should
      modify the default graph.
  """

  detection_model = create_model_fn()
  data_augmentation_options = [
      preprocessor_builder.build(step)
      for step in train_config.data_augmentation_options]

  with tf.Graph().as_default():
    # Build a configuration specifying multi-GPU and multi-replicas.
    deploy_config = model_deploy.DeploymentConfig(
        num_clones=num_clones,
        clone_on_cpu=clone_on_cpu,
        replica_id=task,
        num_replicas=worker_replicas,
        num_ps_tasks=ps_tasks,
        worker_job_name=worker_job_name)

    # Place the global step on the device storing the variables.
    with tf.device(deploy_config.variables_device()):
      global_step = slim.create_global_step()

    with tf.device(deploy_config.inputs_device()):
      input_queue = create_input_queue(
          train_config.batch_size // num_clones, create_tensor_dict_fn,
          train_config.batch_queue_capacity,
          train_config.num_batch_queue_threads,
          train_config.prefetch_queue_capacity, data_augmentation_options)

    # Gather initial summaries.
    # TODO(rathodv): See if summaries can be added/extracted from global tf
    # collections so that they don't have to be passed around.
    summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
    global_summaries = set([])

    model_fn = functools.partial(_create_losses,
                                 create_model_fn=create_model_fn,
                                 train_config=train_config)
    clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue])
    first_clone_scope = clones[0].scope

    if graph_hook_fn:
      with tf.device(deploy_config.variables_device()):
        graph_hook_fn()

    # Gather update_ops from the first clone. These contain, for example,
    # the updates for the batch_norm variables created by model_fn.
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone_scope)

    with tf.device(deploy_config.optimizer_device()):
      training_optimizer, optimizer_summary_vars = optimizer_builder.build(
          train_config.optimizer)
      for var in optimizer_summary_vars:
        tf.summary.scalar(var.op.name, var, family='LearningRate')

    sync_optimizer = None
    if train_config.sync_replicas:
      training_optimizer = tf.train.SyncReplicasOptimizer(
          training_optimizer,
          replicas_to_aggregate=train_config.replicas_to_aggregate,
          total_num_replicas=worker_replicas)
      sync_optimizer = training_optimizer

    with tf.device(deploy_config.optimizer_device()):
      regularization_losses = (None if train_config.add_regularization_loss
                               else [])
      total_loss, grads_and_vars = model_deploy.optimize_clones(
          clones, training_optimizer,
          regularization_losses=regularization_losses)
      total_loss = tf.check_numerics(total_loss, 'LossTensor is inf or nan.')

      # Optionally multiply bias gradients by train_config.bias_grad_multiplier.
      if train_config.bias_grad_multiplier:
        biases_regex_list = ['.*/biases']
        grads_and_vars = variables_helper.multiply_gradients_matching_regex(
            grads_and_vars,
            biases_regex_list,
            multiplier=train_config.bias_grad_multiplier)

      # Optionally freeze some layers by setting their gradients to be zero.
      if train_config.freeze_variables:
        grads_and_vars = variables_helper.freeze_gradients_matching_regex(
            grads_and_vars, train_config.freeze_variables)

      # Optionally clip gradients
      if train_config.gradient_clipping_by_norm > 0:
        with tf.name_scope('clip_grads'):
          grads_and_vars = slim.learning.clip_gradient_norms(
              grads_and_vars, train_config.gradient_clipping_by_norm)

      # Create gradient updates.
      grad_updates = training_optimizer.apply_gradients(grads_and_vars,
                                                        global_step=global_step)
      update_ops.append(grad_updates)
      update_op = tf.group(*update_ops, name='update_barrier')
      with tf.control_dependencies([update_op]):
        train_tensor = tf.identity(total_loss, name='train_op')

    # Add summaries.
    for model_var in slim.get_model_variables():
      global_summaries.add(tf.summary.histogram('ModelVars/' +
                                                model_var.op.name, model_var))
    for loss_tensor in tf.losses.get_losses():
      global_summaries.add(tf.summary.scalar('Losses/' + loss_tensor.op.name,
                                             loss_tensor))
    global_summaries.add(
        tf.summary.scalar('Losses/TotalLoss', tf.losses.get_total_loss()))

    # Add the summaries from the first clone. These contain the summaries
    # created by model_fn and either optimize_clones() or _gather_clone_loss().
    summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES,
                                       first_clone_scope))
    summaries |= global_summaries

    # Merge all summaries together.
    summary_op = tf.summary.merge(list(summaries), name='summary_op')

    # Soft placement allows placing on CPU ops without GPU implementation.
    session_config = tf.ConfigProto(allow_soft_placement=True,
                                    log_device_placement=False)

    # Save checkpoints regularly.
    keep_checkpoint_every_n_hours = train_config.keep_checkpoint_every_n_hours
    saver = tf.train.Saver(
        keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)

    # Create ops required to initialize the model from a given checkpoint.
    init_fn = None
    if train_config.fine_tune_checkpoint:
      if not train_config.fine_tune_checkpoint_type:
        # train_config.from_detection_checkpoint field is deprecated. For
        # backward compatibility, fine_tune_checkpoint_type is set based on
        # from_detection_checkpoint.
        if train_config.from_detection_checkpoint:
          train_config.fine_tune_checkpoint_type = 'detection'
        else:
          train_config.fine_tune_checkpoint_type = 'classification'
      var_map = detection_model.restore_map(
          fine_tune_checkpoint_type=train_config.fine_tune_checkpoint_type,
          load_all_detection_checkpoint_vars=(
              train_config.load_all_detection_checkpoint_vars))
      available_var_map = (variables_helper.
                           get_variables_available_in_checkpoint(
                               var_map, train_config.fine_tune_checkpoint))
      init_saver = tf.train.Saver(available_var_map)
      def initializer_fn(sess):
        init_saver.restore(sess, train_config.fine_tune_checkpoint)
      init_fn = initializer_fn

    slim.learning.train(
        train_tensor,
        logdir=train_dir,
        master=master,
        is_chief=is_chief,
        session_config=session_config,
        startup_delay_steps=train_config.startup_delay_steps,
        init_fn=init_fn,
        summary_op=summary_op,
        number_of_steps=(
            train_config.num_steps if train_config.num_steps else None),
        save_summaries_secs=120,
        sync_optimizer=sync_optimizer,
        saver=saver)

• 批量讀數據前，通過 data_augmentation_options (數據增強)類指定預處理操作

• 批量數據讀取：創建兩個隊列
  隊列1 ： 開啟 N 個線程，每個線程從數據集依次讀一條數據，寫入隊列 1。一個線程從隊列 1 每次讀 batch_size 條數據
  隊列2：將隊列 1 出隊列的數據寫入隊列 2， 當調用 dequeue 的事實，從隊列 2 讀取 batch_size 的數據。
• 定義損失函數，優化器，Saver對象。
• 批量讀數據后，通過模型的預處理函數進行預處理 detection_model.preprocess 之后，喂給模型，開始訓練。

五 總結

一個訓練流程下來，我們會發現谷歌源代碼封裝的很嚴重，不易於我們之后的改進，因此自己實現一個目標檢測模型顯得尤為重要，后面我會簡單的實現一個目標檢測算法。

 參考文章

[1]Tensorflow 物體識別(object detection) 之如何優雅地讀數據

[2]Tensorflow學習筆記-通過slim讀取TFRecord文件

[3]Tensorflow 物體識別(object detection) 之如何優雅地預處理數據