models/research/lstm_object_detection/train.py

# Copyright 2018 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
from absl import flags
import tensorflow.compat.v1 as tf
from lstm_object_detection import model_builder
from lstm_object_detection import trainer
from lstm_object_detection.inputs import seq_dataset_builder
from lstm_object_detection.utils import config_util
from object_detection.builders import preprocessor_builder

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']
  lstm_config = configs['lstm_model']
  train_config = configs['train_config']
  input_config = configs['train_input_config']

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

  def get_next(config, model_config, lstm_config, unroll_length):
    data_augmentation_options = [
        preprocessor_builder.build(step)
        for step in train_config.data_augmentation_options
    ]
    return seq_dataset_builder.build(
        config,
        model_config,
        lstm_config,
        unroll_length,
        data_augmentation_options,
        batch_size=train_config.batch_size)

  create_input_dict_fn = functools.partial(get_next, input_config, model_config,
                                           lstm_config,
                                           lstm_config.train_unroll_length)

  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

  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)


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