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第三十四节，目标检测之谷歌Object Detection API源码解析

我们在第三十二节，使用谷歌Object Detection API进行目标检测、训练新的模型(使用VOC 2012数据集)那一节我们介绍了如何使用谷歌Object Detection API进行目标检测，以及如何使用谷歌提供的目标检测模型训练自己的数据。在训练自己的数据集时，主要包括以下几步：

  • 制作自己的数据集，注意这里数据集在进行标注时，需要按照一定的格式。然后调object_detection\dataset_tools下对应的脚本生成tfrecord文件。如下图，如果我们想调用create_pascal_tf_record.py文件生成tfrecord文件，那么我们的数据集要和voc 2012数据集的标注方式一样。你也可以通过解读create_pascal_tf_record.py文件了解我们的数据集的标注方式。

  • 下载我们所要使用的目标检测模型，进行预训练，不然从头开始训练时间成本会很高。
  • 在object_detection/samples/configs文件夹下有一些配置文件，选择与我们所要使用的目标检测模型相对应的配置文件，并进行一些修改。
  • 使用object_detection/train.py文件进行训练。
  • 使用export_inference_graph.py脚本导出训练好的模型，并进行目标检测。

在这里我主要解析一下train.py文件的工作流程。

一 train.py文件解析

先附上源码：

复制代码
# 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()
View Code
复制代码

1、先定义了tf.app.flags，用于支持接受命令行传递参数，相当于接受argv。

复制代码
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
复制代码

这里面有几个比较重要的参数，train_dir目录用于保存训练的模型和日志文件，pipeline_config_path用于指定pipeline_pb2.TrainEvalPipelineConfig配置文件的全路径(如果不指定指定这个参数，需要指定train_config_path，input_config_path，model_config_path配置文件，其实这三个文件就是把pipeline_pb2.TrainEvalPipelineConfig配置文件分成了三部分)。

2、再来看一下main函数，我们把它分成几部分来解读。

假设我们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

复制代码
  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)
复制代码

因为我们传入了train_dir,pipeline_config_path参数，程序执行时会：

  • 读取pipeline_config_path配置文件，返回一个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配置文件复制到train_dir目录下，命名为pipeline.config

4、第二部分

复制代码
  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)
复制代码
  •  变量model_config，train_config，input_config初始化
  • model_builder.build函数，指定两个固定参数model_config，is_training并返回一个新的函数model_fn 。这个函数很重要，包括对目标检测模型的实现，后面会详细介绍。
  • get_next函数，指定固定参数input_config。这个函数主要实现了tfrecord数据的读取，我们也放在后面介绍。

5、第三部分

复制代码
  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
复制代码

6、第四部分

复制代码
  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)
复制代码
  • 由于没有定义graph_rewriter_config，所以会直接执行trainer.train，开始读取数据，进行预处理后训练。

 二  dataset_builder.build函数

先附上代码：

复制代码
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

　　 #初始化需要解码的字段，以及解码对应字段的 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

　　 # 调用 tf.data.TFRecordDataset 从 config.input_path 读数据，调用 process_fn 对读取的数据解码数，预提取 input_reader_config.prefetch_size 条数据
    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.')
复制代码

整个流程

  • 获取训练集tfrecord文件路径，label_map_path文件路径，input_reader_config设置参数如下：
train_input_reader: {
  tf_record_input_reader {
    input_path: "voc/pascal_train.record"
  }
  label_map_path: "voc/pascal_label_map.pbtxt"
}
  • 初始化需要解码的字段，以及解码对应字段的 handler
  • 调用 tf.data.TFRecordDataset 从 config.input_path 读数据，调用 process_fn (定义了数据的解码格式)对读取的数据解码，预提取 input_reader_config.prefetch_size 条数据
  • 对数据集应用 tf.contrib.data.padded_batch_and_drop_remainder，如果不够一个 batch_size 就丢弃该部分数据
  • 返回一个迭代器

三 model_builder.build函数

代码如下：

亲爱的读者和支持者们，自动博客加入了打赏功能，陆陆续续收到了各位老铁的打赏。在此，我想由衷地感谢每一位对我们博客的支持和打赏。你们的慷慨与支持，是我们前行的动力与源泉。

日期姓名金额
2023-09-06*源19
2023-09-11*朝科88
2023-09-21*号5
2023-09-16*真60
2023-10-26*通9.9
2023-11-04*慎0.66
2023-11-24*恩0.01
2023-12-30I*B1
2024-01-28*兴20
2024-02-01QYing20
2024-02-11*督6
2024-02-18一*x1
2024-02-20c*l18.88
2024-01-01*I5
2024-04-08*程150
2024-04-18*超20
2024-04-26.*V30
2024-05-08D*W5
2024-05-29*辉20
2024-05-30*雄10
2024-06-08*:10
2024-06-23小狮子666
2024-06-28*s6.66
2024-06-29*炼1
2024-06-30*!1
2024-07-08*方20
2024-07-18A*16.66
2024-07-31*北12
2024-08-13*基1
2024-08-23n*s2
2024-09-02*源50
2024-09-04*J2
2024-09-06*强8.8
2024-09-09*波1
2024-09-10*口1
2024-09-10*波1
2024-09-12*波10
2024-09-18*明1.68
2024-09-26B*h10
2024-09-30岁10
2024-10-02M*i1
2024-10-14*朋10
2024-10-22*海10
2024-10-23*南10
2024-10-26*节6.66
2024-10-27*o5
2024-10-28W*F6.66
2024-10-29R*n6.66
2024-11-02*球6
2024-11-021*鑫6.66
2024-11-25*沙5
2024-11-29C*n2.88
posted @   大奥特曼打小怪兽  阅读(8950)  评论(9)  编辑  收藏  举报
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