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Python model_lib.continuous_eval方法代码示例

本文整理汇总了Python中object_detection.model_lib.continuous_eval方法的典型用法代码示例。如果您正苦于以下问题：Python model_lib.continuous_eval方法的具体用法？Python model_lib.continuous_eval怎么用？Python model_lib.continuous_eval使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类object_detection.model_lib的用法示例。


在下文中一共展示了model_lib.continuous_eval方法的8个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: main

# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import continuous_eval [as 别名]
def main(unused_argv):
  flags.mark_flag_as_required('model_dir')
  flags.mark_flag_as_required('pipeline_config_path')
  config = tf.estimator.RunConfig(model_dir=FLAGS.model_dir)

  train_and_eval_dict = model_lib.create_estimator_and_inputs(
      run_config=config,
      hparams=model_hparams.create_hparams(FLAGS.hparams_overrides),
      pipeline_config_path=FLAGS.pipeline_config_path,
      train_steps=FLAGS.num_train_steps,
      sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
      sample_1_of_n_eval_on_train_examples=(
          FLAGS.sample_1_of_n_eval_on_train_examples))
  estimator = train_and_eval_dict['estimator']
  train_input_fn = train_and_eval_dict['train_input_fn']
  eval_input_fns = train_and_eval_dict['eval_input_fns']
  eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
  predict_input_fn = train_and_eval_dict['predict_input_fn']
  train_steps = train_and_eval_dict['train_steps']

  if FLAGS.checkpoint_dir:
    if FLAGS.eval_training_data:
      name = 'training_data'
      input_fn = eval_on_train_input_fn
    else:
      name = 'validation_data'
      # The first eval input will be evaluated.
      input_fn = eval_input_fns[0]
    if FLAGS.run_once:
      estimator.evaluate(input_fn,
                         num_eval_steps=None,
                         checkpoint_path=tf.train.latest_checkpoint(
                             FLAGS.checkpoint_dir))
    else:
      model_lib.continuous_eval(estimator, FLAGS.checkpoint_dir, input_fn,
                                train_steps, name)
  else:
    train_spec, eval_specs = model_lib.create_train_and_eval_specs(
        train_input_fn,
        eval_input_fns,
        eval_on_train_input_fn,
        predict_input_fn,
        train_steps,
        eval_on_train_data=False)

    # Currently only a single Eval Spec is allowed.
    tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])
开发者ID:ahmetozlu，项目名称:vehicle_counting_tensorflow，代码行数:49，代码来源:model_main.py

示例2: main

# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import continuous_eval [as 别名]
def main(unused_argv):
  flags.mark_flag_as_required('model_dir')
  flags.mark_flag_as_required('pipeline_config_path')

  tpu_cluster_resolver = (
      tf.contrib.cluster_resolver.TPUClusterResolver(
          tpu=[FLAGS.tpu_name],
          zone=FLAGS.tpu_zone,
          project=FLAGS.gcp_project))
  tpu_grpc_url = tpu_cluster_resolver.get_master()

  config = tf.contrib.tpu.RunConfig(
      master=tpu_grpc_url,
      evaluation_master=tpu_grpc_url,
      model_dir=FLAGS.model_dir,
      tpu_config=tf.contrib.tpu.TPUConfig(
          iterations_per_loop=FLAGS.iterations_per_loop,
          num_shards=FLAGS.num_shards))

  kwargs = {}
  if FLAGS.train_batch_size:
    kwargs['batch_size'] = FLAGS.train_batch_size

  train_and_eval_dict = model_lib.create_estimator_and_inputs(
      run_config=config,
      hparams=model_hparams.create_hparams(FLAGS.hparams_overrides),
      pipeline_config_path=FLAGS.pipeline_config_path,
      train_steps=FLAGS.num_train_steps,
      sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
      sample_1_of_n_eval_on_train_examples=(
          FLAGS.sample_1_of_n_eval_on_train_examples),
      use_tpu_estimator=True,
      use_tpu=FLAGS.use_tpu,
      num_shards=FLAGS.num_shards,
      save_final_config=FLAGS.mode == 'train',
      **kwargs)
  estimator = train_and_eval_dict['estimator']
  train_input_fn = train_and_eval_dict['train_input_fn']
  eval_input_fns = train_and_eval_dict['eval_input_fns']
  eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
  train_steps = train_and_eval_dict['train_steps']

  if FLAGS.mode == 'train':
    estimator.train(input_fn=train_input_fn, max_steps=train_steps)

  # Continuously evaluating.
  if FLAGS.mode == 'eval':
    if FLAGS.eval_training_data:
      name = 'training_data'
      input_fn = eval_on_train_input_fn
    else:
      name = 'validation_data'
      # Currently only a single eval input is allowed.
      input_fn = eval_input_fns[0]
    model_lib.continuous_eval(estimator, FLAGS.model_dir, input_fn, train_steps,
                              name)
开发者ID:ahmetozlu，项目名称:vehicle_counting_tensorflow，代码行数:58，代码来源:model_tpu_main.py

示例3: main

# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import continuous_eval [as 别名]
def main(unused_argv):
  flags.mark_flag_as_required('model_dir')
  flags.mark_flag_as_required('pipeline_config_path')

  tpu_cluster_resolver = (
      tf.contrib.cluster_resolver.python.training.TPUClusterResolver(
          tpu_names=[FLAGS.tpu_name],
          zone=FLAGS.tpu_zone,
          project=FLAGS.gcp_project))
  tpu_grpc_url = tpu_cluster_resolver.get_master()

  config = tpu_config.RunConfig(
      master=tpu_grpc_url,
      evaluation_master=tpu_grpc_url,
      model_dir=FLAGS.model_dir,
      tpu_config=tpu_config.TPUConfig(
          iterations_per_loop=FLAGS.iterations_per_loop,
          num_shards=FLAGS.num_shards))

  kwargs = {}
  if FLAGS.train_batch_size:
    kwargs['batch_size'] = FLAGS.train_batch_size

  train_and_eval_dict = model_lib.create_estimator_and_inputs(
      run_config=config,
      hparams=model_hparams.create_hparams(FLAGS.hparams_overrides),
      pipeline_config_path=FLAGS.pipeline_config_path,
      train_steps=FLAGS.num_train_steps,
      eval_steps=FLAGS.num_eval_steps,
      use_tpu_estimator=True,
      use_tpu=FLAGS.use_tpu,
      num_shards=FLAGS.num_shards,
      **kwargs)
  estimator = train_and_eval_dict['estimator']
  train_input_fn = train_and_eval_dict['train_input_fn']
  eval_input_fn = train_and_eval_dict['eval_input_fn']
  eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
  train_steps = train_and_eval_dict['train_steps']
  eval_steps = train_and_eval_dict['eval_steps']

  if FLAGS.mode == 'train':
    estimator.train(input_fn=train_input_fn, max_steps=train_steps)

  # Continuously evaluating.
  if FLAGS.mode == 'eval':
    if FLAGS.eval_training_data:
      name = 'training_data'
      input_fn = eval_on_train_input_fn
    else:
      name = 'validation_data'
      input_fn = eval_input_fn
    model_lib.continuous_eval(estimator, FLAGS.model_dir, input_fn, eval_steps,
                              train_steps, name)
开发者ID:ambakick，项目名称:Person-Detection-and-Tracking，代码行数:55，代码来源:model_tpu_main.py

示例4: main

# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import continuous_eval [as 别名]
def main(unused_argv):
  flags.mark_flag_as_required('model_dir')
  flags.mark_flag_as_required('pipeline_config_path')
  config = tf.estimator.RunConfig(model_dir=FLAGS.model_dir)

  train_and_eval_dict = model_lib.create_estimator_and_inputs(
      run_config=config,
      hparams=model_hparams.create_hparams(FLAGS.hparams_overrides),
      pipeline_config_path=FLAGS.pipeline_config_path,
      train_steps=FLAGS.num_train_steps,
      eval_steps=FLAGS.num_eval_steps)
  estimator = train_and_eval_dict['estimator']
  train_input_fn = train_and_eval_dict['train_input_fn']
  eval_input_fn = train_and_eval_dict['eval_input_fn']
  eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
  predict_input_fn = train_and_eval_dict['predict_input_fn']
  train_steps = train_and_eval_dict['train_steps']
  eval_steps = train_and_eval_dict['eval_steps']

  if FLAGS.checkpoint_dir:
    if FLAGS.eval_training_data:
      name = 'training_data'
      input_fn = eval_on_train_input_fn
    else:
      name = 'validation_data'
      input_fn = eval_input_fn
    if FLAGS.run_once:
      estimator.evaluate(input_fn,
                         eval_steps,
                         checkpoint_path=tf.train.latest_checkpoint(
                             FLAGS.checkpoint_dir))
    else:
      model_lib.continuous_eval(estimator, FLAGS.model_dir, input_fn,
                                eval_steps, train_steps, name)
  else:
    train_spec, eval_specs = model_lib.create_train_and_eval_specs(
        train_input_fn,
        eval_input_fn,
        eval_on_train_input_fn,
        predict_input_fn,
        train_steps,
        eval_steps,
        eval_on_train_data=False)

    # Currently only a single Eval Spec is allowed.
    tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])
开发者ID:BMW-InnovationLab，项目名称:BMW-TensorFlow-Training-GUI，代码行数:48，代码来源:model_main.py

示例5: main

# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import continuous_eval [as 别名]
def main(unused_argv):
  flags.mark_flag_as_required('model_dir')
  flags.mark_flag_as_required('pipeline_config_path')

  tpu_cluster_resolver = (
      tf.contrib.cluster_resolver.TPUClusterResolver(
          tpu=[FLAGS.tpu_name],
          zone=FLAGS.tpu_zone,
          project=FLAGS.gcp_project))
  tpu_grpc_url = tpu_cluster_resolver.get_master()

  config = tf.contrib.tpu.RunConfig(
      master=tpu_grpc_url,
      evaluation_master=tpu_grpc_url,
      model_dir=FLAGS.model_dir,
      tpu_config=tf.contrib.tpu.TPUConfig(
          iterations_per_loop=FLAGS.iterations_per_loop,
          num_shards=FLAGS.num_shards))

  kwargs = {}
  if FLAGS.train_batch_size:
    kwargs['batch_size'] = FLAGS.train_batch_size

  train_and_eval_dict = model_lib.create_estimator_and_inputs(
      run_config=config,
      hparams=model_hparams.create_hparams(FLAGS.hparams_overrides),
      pipeline_config_path=FLAGS.pipeline_config_path,
      train_steps=FLAGS.num_train_steps,
      eval_steps=FLAGS.num_eval_steps,
      use_tpu_estimator=True,
      use_tpu=FLAGS.use_tpu,
      num_shards=FLAGS.num_shards,
      **kwargs)
  estimator = train_and_eval_dict['estimator']
  train_input_fn = train_and_eval_dict['train_input_fn']
  eval_input_fn = train_and_eval_dict['eval_input_fn']
  eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
  train_steps = train_and_eval_dict['train_steps']
  eval_steps = train_and_eval_dict['eval_steps']

  if FLAGS.mode == 'train':
    estimator.train(input_fn=train_input_fn, max_steps=train_steps)

  # Continuously evaluating.
  if FLAGS.mode == 'eval':
    if FLAGS.eval_training_data:
      name = 'training_data'
      input_fn = eval_on_train_input_fn
    else:
      name = 'validation_data'
      input_fn = eval_input_fn
    model_lib.continuous_eval(estimator, FLAGS.model_dir, input_fn, eval_steps,
                              train_steps, name)
开发者ID:BMW-InnovationLab，项目名称:BMW-TensorFlow-Training-GUI，代码行数:55，代码来源:model_tpu_main.py

示例6: main

# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import continuous_eval [as 别名]
def main(unused_argv):
  flags.mark_flag_as_required('model_dir')
  flags.mark_flag_as_required('pipeline_config_path')
  config = tf.estimator.RunConfig(model_dir=FLAGS.model_dir, log_step_count_steps=FLAGS.log_step_count_steps)

  train_and_eval_dict = model_lib.create_estimator_and_inputs(
      run_config=config,
      hparams=model_hparams.create_hparams(FLAGS.hparams_overrides),
      pipeline_config_path=FLAGS.pipeline_config_path,
      train_steps=FLAGS.num_train_steps,
      sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
      sample_1_of_n_eval_on_train_examples=(
          FLAGS.sample_1_of_n_eval_on_train_examples))
  estimator = train_and_eval_dict['estimator']
  train_input_fn = train_and_eval_dict['train_input_fn']
  eval_input_fns = train_and_eval_dict['eval_input_fns']
  eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
  predict_input_fn = train_and_eval_dict['predict_input_fn']
  train_steps = train_and_eval_dict['train_steps']

  if FLAGS.checkpoint_dir:
    if FLAGS.eval_training_data:
      name = 'training_data'
      input_fn = eval_on_train_input_fn
    else:
      name = 'validation_data'
      # The first eval input will be evaluated.
      input_fn = eval_input_fns[0]
    if FLAGS.run_once:
      estimator.evaluate(input_fn,
                         num_eval_steps=None,
                         checkpoint_path=tf.train.latest_checkpoint(
                             FLAGS.checkpoint_dir))
    else:
      model_lib.continuous_eval(estimator, FLAGS.checkpoint_dir, input_fn,
                                train_steps, name)
  else:
    train_spec, eval_specs = model_lib.create_train_and_eval_specs(
        train_input_fn,
        eval_input_fns,
        eval_on_train_input_fn,
        predict_input_fn,
        train_steps,
        eval_on_train_data=False)

    # Currently only a single Eval Spec is allowed.
    tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])
开发者ID:IBM，项目名称:MAX-Object-Detector，代码行数:49，代码来源:model_main.py

示例7: main

# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import continuous_eval [as 别名]
def main(unused_argv):
  flags.mark_flag_as_required('model_dir')
  flags.mark_flag_as_required('pipeline_config_path')
  config = tf.estimator.RunConfig(model_dir=FLAGS.model_dir)

  train_and_eval_dict = model_lib.create_estimator_and_inputs(
      run_config=config,
      hparams=model_hparams.create_hparams(FLAGS.hparams_overrides),
      pipeline_config_path=FLAGS.pipeline_config_path,
      train_steps=FLAGS.num_train_steps,
      sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
      sample_1_of_n_eval_on_train_examples=(
          FLAGS.sample_1_of_n_eval_on_train_examples))
  estimator = train_and_eval_dict['estimator']
  train_input_fn = train_and_eval_dict['train_input_fn']
  eval_input_fns = train_and_eval_dict['eval_input_fns']
  eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
  predict_input_fn = train_and_eval_dict['predict_input_fn']
  train_steps = train_and_eval_dict['train_steps']

  if FLAGS.checkpoint_dir:
    if FLAGS.eval_training_data:
      name = 'training_data'
      input_fn = eval_on_train_input_fn
    else:
      name = 'validation_data'
      # The first eval input will be evaluated.
      input_fn = eval_input_fns[0]
    if FLAGS.run_once:
      estimator.evaluate(input_fn,
                         steps=None,
                         checkpoint_path=tf.train.latest_checkpoint(
                             FLAGS.checkpoint_dir))
    else:
      model_lib.continuous_eval(estimator, FLAGS.checkpoint_dir, input_fn,
                                train_steps, name, FLAGS.max_eval_retries)
  else:
    train_spec, eval_specs = model_lib.create_train_and_eval_specs(
        train_input_fn,
        eval_input_fns,
        eval_on_train_input_fn,
        predict_input_fn,
        train_steps,
        eval_on_train_data=False)

    # Currently only a single Eval Spec is allowed.
    tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])
开发者ID:tensorflow，项目名称:models，代码行数:49，代码来源:model_main.py

示例8: main

# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import continuous_eval [as 别名]
def main(unused_argv):
  flags.mark_flag_as_required('model_dir')
  flags.mark_flag_as_required('pipeline_config_path')

  tpu_cluster_resolver = (
      contrib_cluster_resolver.TPUClusterResolver(
          tpu=[FLAGS.tpu_name], zone=FLAGS.tpu_zone, project=FLAGS.gcp_project))
  tpu_grpc_url = tpu_cluster_resolver.get_master()

  config = contrib_tpu.RunConfig(
      master=tpu_grpc_url,
      evaluation_master=tpu_grpc_url,
      model_dir=FLAGS.model_dir,
      tpu_config=contrib_tpu.TPUConfig(
          iterations_per_loop=FLAGS.iterations_per_loop,
          num_shards=FLAGS.num_shards))

  kwargs = {}
  if FLAGS.train_batch_size:
    kwargs['batch_size'] = FLAGS.train_batch_size

  train_and_eval_dict = model_lib.create_estimator_and_inputs(
      run_config=config,
      hparams=model_hparams.create_hparams(FLAGS.hparams_overrides),
      pipeline_config_path=FLAGS.pipeline_config_path,
      train_steps=FLAGS.num_train_steps,
      sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
      sample_1_of_n_eval_on_train_examples=(
          FLAGS.sample_1_of_n_eval_on_train_examples),
      use_tpu_estimator=True,
      use_tpu=FLAGS.use_tpu,
      num_shards=FLAGS.num_shards,
      save_final_config=FLAGS.mode == 'train',
      **kwargs)
  estimator = train_and_eval_dict['estimator']
  train_input_fn = train_and_eval_dict['train_input_fn']
  eval_input_fns = train_and_eval_dict['eval_input_fns']
  eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
  train_steps = train_and_eval_dict['train_steps']

  if FLAGS.mode == 'train':
    estimator.train(input_fn=train_input_fn, max_steps=train_steps)

  # Continuously evaluating.
  if FLAGS.mode == 'eval':
    if FLAGS.eval_training_data:
      name = 'training_data'
      input_fn = eval_on_train_input_fn
    else:
      name = 'validation_data'
      # Currently only a single eval input is allowed.
      input_fn = eval_input_fns[0]
    model_lib.continuous_eval(estimator, FLAGS.model_dir, input_fn, train_steps,
                              name, FLAGS.max_eval_retries)
开发者ID:tensorflow，项目名称:models，代码行数:56，代码来源:model_tpu_main.py


注：本文中的object_detection.model_lib.continuous_eval方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。