train_object_detector.py

# Copyright 2016 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.
# ==============================================================================
"""Generic training script that trains a model using a given dataset."""

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

import tensorflow as tf

from tensorflow.python.ops import control_flow_ops
from datasets import dataset_factory
from deployment import model_deploy
from nets import nets_factory
from preprocessing import preprocessing_factory
from nets.mobilenetdet import encode_annos
from nets.mobilenetdet import losses

slim = tf.contrib.slim

tf.app.flags.DEFINE_string(
  'master', '', 'The address of the TensorFlow master to use.')

tf.app.flags.DEFINE_string(
  'train_dir', '/tmp/tfmodel/',
  'Directory where checkpoints and event logs are written to.')

tf.app.flags.DEFINE_integer('num_clones', 1,
                            'Number of model clones to deploy.')

tf.app.flags.DEFINE_boolean('clone_on_cpu', False,
                            'Use CPUs to deploy clones.')

tf.app.flags.DEFINE_integer('worker_replicas', 1, 'Number of worker replicas.')

tf.app.flags.DEFINE_integer(
  'num_ps_tasks', 0,
  'The number of parameter servers. If the value is 0, then the parameters '
  'are handled locally by the worker.')

tf.app.flags.DEFINE_integer(
  'num_readers', 4,
  'The number of parallel readers that read data from the dataset.')

tf.app.flags.DEFINE_integer(
  'num_preprocessing_threads', 4,
  'The number of threads used to create the batches.')

tf.app.flags.DEFINE_integer(
  'log_every_n_steps', 10,
  'The frequency with which logs are print.')

tf.app.flags.DEFINE_integer(
  'save_summaries_secs', 600,
  'The frequency with which summaries are saved, in seconds.')

tf.app.flags.DEFINE_integer(
  'save_interval_secs', 600,
  'The frequency with which the model is saved, in seconds.')

tf.app.flags.DEFINE_integer(
  'task', 0, 'Task id of the replica running the training.')

######################
# Optimization Flags #
######################

tf.app.flags.DEFINE_float(
  'weight_decay', 0.00004, 'The weight decay on the model weights.')

tf.app.flags.DEFINE_string(
  'optimizer', 'rmsprop',
  'The name of the optimizer, one of "adadelta", "adagrad", "adam",'
  '"ftrl", "momentum", "sgd" or "rmsprop".')

tf.app.flags.DEFINE_float(
  'adadelta_rho', 0.95,
  'The decay rate for adadelta.')

tf.app.flags.DEFINE_float(
  'adagrad_initial_accumulator_value', 0.1,
  'Starting value for the AdaGrad accumulators.')

tf.app.flags.DEFINE_float(
  'adam_beta1', 0.9,
  'The exponential decay rate for the 1st moment estimates.')

tf.app.flags.DEFINE_float(
  'adam_beta2', 0.999,
  'The exponential decay rate for the 2nd moment estimates.')

tf.app.flags.DEFINE_float('opt_epsilon', 1.0, 'Epsilon term for the optimizer.')

tf.app.flags.DEFINE_float('ftrl_learning_rate_power', -0.5,
                          'The learning rate power.')

tf.app.flags.DEFINE_float(
  'ftrl_initial_accumulator_value', 0.1,
  'Starting value for the FTRL accumulators.')

tf.app.flags.DEFINE_float(
  'ftrl_l1', 0.0, 'The FTRL l1 regularization strength.')

tf.app.flags.DEFINE_float(
  'ftrl_l2', 0.0, 'The FTRL l2 regularization strength.')

tf.app.flags.DEFINE_float(
  'momentum', 0.9,
  'The momentum for the MomentumOptimizer and RMSPropOptimizer.')

tf.app.flags.DEFINE_float('rmsprop_momentum', 0.9, 'Momentum.')

tf.app.flags.DEFINE_float('rmsprop_decay', 0.9, 'Decay term for RMSProp.')

#######################
# Learning Rate Flags #
#######################

tf.app.flags.DEFINE_string(
  'learning_rate_decay_type',
  'exponential',
  'Specifies how the learning rate is decayed. One of "fixed", "exponential",'
  ' or "polynomial"')

tf.app.flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')

tf.app.flags.DEFINE_float(
  'end_learning_rate', 0.0001,
  'The minimal end learning rate used by a polynomial decay learning rate.')

tf.app.flags.DEFINE_float(
  'label_smoothing', 0.0, 'The amount of label smoothing.')

tf.app.flags.DEFINE_float(
  'learning_rate_decay_factor', 0.94, 'Learning rate decay factor.')

tf.app.flags.DEFINE_float(
  'num_epochs_per_decay', 2.0,
  'Number of epochs after which learning rate decays.')

tf.app.flags.DEFINE_bool(
  'sync_replicas', False,
  'Whether or not to synchronize the replicas during training.')

tf.app.flags.DEFINE_integer(
  'replicas_to_aggregate', 1,
  'The Number of gradients to collect before updating params.')

tf.app.flags.DEFINE_float(
  'moving_average_decay', None,
  'The decay to use for the moving average.'
  'If left as None, then moving averages are not used.')

#######################
# Dataset Flags #
#######################

tf.app.flags.DEFINE_string(
  'dataset_name', 'imagenet', 'The name of the dataset to load.')

tf.app.flags.DEFINE_string(
  'dataset_split_name', 'train', 'The name of the train/test split.')

tf.app.flags.DEFINE_string(
  'dataset_dir', None, 'The directory where the dataset files are stored.')

tf.app.flags.DEFINE_integer(
  'labels_offset', 0,
  'An offset for the labels in the dataset. This flag is primarily used to '
  'evaluate the VGG and ResNet architectures which do not use a background '
  'class for the ImageNet dataset.')

tf.app.flags.DEFINE_string(
  'model_name', 'inception_v3', 'The name of the architecture to train.')

tf.app.flags.DEFINE_string(
  'preprocessing_name', None, 'The name of the preprocessing to use. If left '
                              'as `None`, then the model_name flag is used.')

tf.app.flags.DEFINE_integer(
  'batch_size', 32, 'The number of samples in each batch.')

tf.app.flags.DEFINE_integer(
  'train_image_size', None, 'Train image size')

tf.app.flags.DEFINE_integer('max_number_of_steps', None,
                            'The maximum number of training steps.')

tf.app.flags.DEFINE_float('width_multiplier', 1.0,
                          'Width Multiplier, for MobileNet only.')

#####################
# Fine-Tuning Flags #
#####################

tf.app.flags.DEFINE_string(
  'checkpoint_path', None,
  'The path to a checkpoint from which to fine-tune.')

tf.app.flags.DEFINE_string(
  'checkpoint_exclude_scopes', None,
  'Comma-separated list of scopes of variables to exclude when restoring '
  'from a checkpoint.')

tf.app.flags.DEFINE_string(
  'trainable_scopes', None,
  'Comma-separated list of scopes to filter the set of variables to train.'
  'By default, None would train all the variables.')

tf.app.flags.DEFINE_boolean(
  'ignore_missing_vars', False,
  'When restoring a checkpoint would ignore missing variables.')

FLAGS = tf.app.flags.FLAGS


def _configure_learning_rate(num_samples_per_epoch, global_step):
  """Configures the learning rate.

  Args:
    num_samples_per_epoch: The number of samples in each epoch of training.
    global_step: The global_step tensor.

  Returns:
    A `Tensor` representing the learning rate.

  Raises:
    ValueError: if
  """
  decay_steps = int(num_samples_per_epoch / FLAGS.batch_size *
                    FLAGS.num_epochs_per_decay)
  if FLAGS.sync_replicas:
    decay_steps /= FLAGS.replicas_to_aggregate

  if FLAGS.learning_rate_decay_type == 'exponential':
    return tf.train.exponential_decay(FLAGS.learning_rate,
                                      global_step,
                                      decay_steps,
                                      FLAGS.learning_rate_decay_factor,
                                      staircase=True,
                                      name='exponential_decay_learning_rate')
  elif FLAGS.learning_rate_decay_type == 'fixed':
    return tf.constant(FLAGS.learning_rate, name='fixed_learning_rate')
  elif FLAGS.learning_rate_decay_type == 'polynomial':
    return tf.train.polynomial_decay(FLAGS.learning_rate,
                                     global_step,
                                     decay_steps,
                                     FLAGS.end_learning_rate,
                                     power=1.0,
                                     cycle=False,
                                     name='polynomial_decay_learning_rate')
  else:
    raise ValueError('learning_rate_decay_type [%s] was not recognized',
                     FLAGS.learning_rate_decay_type)


def _configure_optimizer(learning_rate):
  """Configures the optimizer used for training.

  Args:
    learning_rate: A scalar or `Tensor` learning rate.

  Returns:
    An instance of an optimizer.

  Raises:
    ValueError: if FLAGS.optimizer is not recognized.
  """
  if FLAGS.optimizer == 'adadelta':
    optimizer = tf.train.AdadeltaOptimizer(
      learning_rate,
      rho=FLAGS.adadelta_rho,
      epsilon=FLAGS.opt_epsilon)
  elif FLAGS.optimizer == 'adagrad':
    optimizer = tf.train.AdagradOptimizer(
      learning_rate,
      initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value)
  elif FLAGS.optimizer == 'adam':
    optimizer = tf.train.AdamOptimizer(
      learning_rate,
      beta1=FLAGS.adam_beta1,
      beta2=FLAGS.adam_beta2,
      epsilon=FLAGS.opt_epsilon)
  elif FLAGS.optimizer == 'ftrl':
    optimizer = tf.train.FtrlOptimizer(
      learning_rate,
      learning_rate_power=FLAGS.ftrl_learning_rate_power,
      initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value,
      l1_regularization_strength=FLAGS.ftrl_l1,
      l2_regularization_strength=FLAGS.ftrl_l2)
  elif FLAGS.optimizer == 'momentum':
    optimizer = tf.train.MomentumOptimizer(
      learning_rate,
      momentum=FLAGS.momentum,
      name='Momentum')
  elif FLAGS.optimizer == 'rmsprop':
    optimizer = tf.train.RMSPropOptimizer(
      learning_rate,
      decay=FLAGS.rmsprop_decay,
      momentum=FLAGS.rmsprop_momentum,
      epsilon=FLAGS.opt_epsilon)
  elif FLAGS.optimizer == 'sgd':
    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
  else:
    raise ValueError('Optimizer [%s] was not recognized', FLAGS.optimizer)
  return optimizer


def _add_variables_summaries(learning_rate):
  summaries = []
  for variable in slim.get_model_variables():
    summaries.append(tf.summary.histogram(variable.op.name, variable))
  summaries.append(tf.summary.scalar('training/Learning Rate', learning_rate))
  return summaries


def _get_init_fn():
  """Returns a function run by the chief worker to warm-start the training.

  Note that the init_fn is only run when initializing the model during the very
  first global step.

  Returns:
    An init function run by the supervisor.
  """
  if FLAGS.checkpoint_path is None:
    return None

  # Warn the user if a checkpoint exists in the train_dir. Then we'll be
  # ignoring the checkpoint anyway.
  if tf.train.latest_checkpoint(FLAGS.train_dir):
    tf.logging.info(
      'Ignoring --checkpoint_path because a checkpoint already exists in %s'
      % FLAGS.train_dir)
    return None

  exclusions = []
  if FLAGS.checkpoint_exclude_scopes:
    exclusions = [scope.strip()
                  for scope in FLAGS.checkpoint_exclude_scopes.split(',')]

  # TODO(sguada) variables.filter_variables()
  variables_to_restore = []
  for var in slim.get_model_variables():
    excluded = False
    for exclusion in exclusions:
      if var.op.name.startswith(exclusion):
        excluded = True
        break
    if not excluded:
      variables_to_restore.append(var)

  if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
    checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
  else:
    checkpoint_path = FLAGS.checkpoint_path

  tf.logging.info('Fine-tuning from %s' % checkpoint_path)

  return slim.assign_from_checkpoint_fn(
    checkpoint_path,
    variables_to_restore,
    ignore_missing_vars=FLAGS.ignore_missing_vars)


def _get_variables_to_train():
  """Returns a list of variables to train.

  Returns:
    A list of variables to train by the optimizer.
  """
  if FLAGS.trainable_scopes is None:
    return tf.trainable_variables()
  else:
    scopes = [scope.strip() for scope in FLAGS.trainable_scopes.split(',')]

  variables_to_train = []
  for scope in scopes:
    variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)
    variables_to_train.extend(variables)
  return variables_to_train


def main(_):
  if not FLAGS.dataset_dir:
    raise ValueError('You must supply the dataset directory with --dataset_dir')

  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default():
    #######################
    # Config model_deploy #
    #######################
    deploy_config = model_deploy.DeploymentConfig(
      num_clones=FLAGS.num_clones,
      clone_on_cpu=FLAGS.clone_on_cpu,
      replica_id=FLAGS.task,
      num_replicas=FLAGS.worker_replicas,
      num_ps_tasks=FLAGS.num_ps_tasks)

    # Create global_step
    with tf.device(deploy_config.variables_device()):
      global_step = slim.create_global_step()

    ######################
    # Select the dataset #
    ######################
    dataset = dataset_factory.get_dataset(
      FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)

    ######################
    # Select the network #
    ######################
    network_fn = nets_factory.get_network_fn(
      FLAGS.model_name,
      num_classes=(dataset.num_classes - FLAGS.labels_offset),
      weight_decay=FLAGS.weight_decay,
      is_training=True,
      width_multiplier=FLAGS.width_multiplier)

    #####################################
    # Select the preprocessing function #
    #####################################
    preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
    image_preprocessing_fn = preprocessing_factory.get_preprocessing(
      preprocessing_name,
      is_training=True)

    ##############################################################
    # Create a dataset provider that loads data from the dataset #
    ##############################################################
    with tf.device(deploy_config.inputs_device()):
      provider = slim.dataset_data_provider.DatasetDataProvider(
        dataset,
        num_readers=FLAGS.num_readers,
        common_queue_capacity=20 * FLAGS.batch_size,
        common_queue_min=10 * FLAGS.batch_size)
      [image, img_shape, gt_labels, gt_bboxes] = provider.get(['image', 'shape',
                                                               'object/label',
                                                               'object/bbox'])

      train_image_size = FLAGS.train_image_size or network_fn.default_image_size

      # TODO(shizehao): add detection preprocessing fn, encode annotations

      # detection preprocesing
      # image, labels, bboxes = image_preprocessing_fn(image, train_image_size, train_image_size)

      # encode annotations for losses computation
      # input_mask, labels, ious, box_delta_input = encode_annos(image, labels, bboxes)

      # expand_dims
      image, gt_labels, gt_bboxes = tf.train.batch(
        [image, gt_labels, gt_bboxes],
        batch_size=FLAGS.batch_size,
        num_threads=FLAGS.num_preprocessing_threads,
        capacity=5 * FLAGS.batch_size)
      #labels = slim.one_hot_encoding(
        #labels, dataset.num_classes - FLAGS.labels_offset)
      batch_queue = slim.prefetch_queue.prefetch_queue(
        [image, gt_labels, gt_bboxes], capacity=2 * deploy_config.num_clones)

    ####################
    # Define the model #
    ####################
    def clone_fn(batch_queue):
      """Allows data parallelism by creating multiple clones of network_fn."""
      # TODO(shizehao): add detection model with loss
      image, gt_labels, gt_bboxes = batch_queue.dequeue()
      """
      logits, end_points = network_fn(images)

      #############################
      # Specify the loss function #
      #############################

      if 'AuxLogits' in end_points:
        tf.losses.softmax_cross_entropy(
          logits=end_points['AuxLogits'], onehot_labels=labels,
          label_smoothing=FLAGS.label_smoothing, weights=0.4, scope='aux_loss')
      tf.losses.softmax_cross_entropy(
        logits=logits, onehot_labels=labels,
        label_smoothing=FLAGS.label_smoothing, weights=1.0)
      return end_points"""
      return image, gt_labels, gt_bboxes  # test fetch data

    # Gather initial summaries.
    summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))

    clones = model_deploy.create_clones(deploy_config, clone_fn, [batch_queue])
    first_clone_scope = deploy_config.clone_scope(0)
    # Gather update_ops from the first clone. These contain, for example,
    # the updates for the batch_norm variables created by network_fn.
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone_scope)

    # Add summaries for end_points.
    end_points = clones[0].outputs
    for end_point in end_points:
      x = end_points[end_point]
      summaries.add(tf.summary.histogram('activations/' + end_point, x))
      summaries.add(tf.summary.scalar('sparsity/' + end_point,
                                      tf.nn.zero_fraction(x)))

    # Add summaries for losses.
    for loss in tf.get_collection(tf.GraphKeys.LOSSES, first_clone_scope):
      summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))

    # Add summaries for variables.
    for variable in slim.get_model_variables():
      summaries.add(tf.summary.histogram(variable.op.name, variable))

    #################################
    # Configure the moving averages #
    #################################
    if FLAGS.moving_average_decay:
      moving_average_variables = slim.get_model_variables()
      variable_averages = tf.train.ExponentialMovingAverage(
        FLAGS.moving_average_decay, global_step)
    else:
      moving_average_variables, variable_averages = None, None

    #########################################
    # Configure the optimization procedure. #
    #########################################
    with tf.device(deploy_config.optimizer_device()):
      learning_rate = _configure_learning_rate(dataset.num_samples, global_step)
      optimizer = _configure_optimizer(learning_rate)
      summaries.add(tf.summary.scalar('learning_rate', learning_rate))

    if FLAGS.sync_replicas:
      # If sync_replicas is enabled, the averaging will be done in the chief
      # queue runner.
      optimizer = tf.train.SyncReplicasOptimizer(
        opt=optimizer,
        replicas_to_aggregate=FLAGS.replicas_to_aggregate,
        variable_averages=variable_averages,
        variables_to_average=moving_average_variables,
        replica_id=tf.constant(FLAGS.task, tf.int32, shape=()),
        total_num_replicas=FLAGS.worker_replicas)
    elif FLAGS.moving_average_decay:
      # Update ops executed locally by trainer.
      update_ops.append(variable_averages.apply(moving_average_variables))

    # Variables to train.
    variables_to_train = _get_variables_to_train()

    #  and returns a train_tensor and summary_op
    total_loss, clones_gradients = model_deploy.optimize_clones(
      clones,
      optimizer,
      var_list=variables_to_train)
    # Add total_loss to summary.
    summaries.add(tf.summary.scalar('total_loss', total_loss))

    # Create gradient updates.
    grad_updates = optimizer.apply_gradients(clones_gradients,
                                             global_step=global_step)
    update_ops.append(grad_updates)

    update_op = tf.group(*update_ops)
    train_tensor = control_flow_ops.with_dependencies([update_op], total_loss,
                                                      name='train_op')

    # 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))

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

    ###########################
    # Kicks off the training. #
    ###########################
    slim.learning.train(
      train_tensor,
      logdir=FLAGS.train_dir,
      master=FLAGS.master,
      is_chief=(FLAGS.task == 0),
      init_fn=_get_init_fn(),
      summary_op=summary_op,
      number_of_steps=FLAGS.max_number_of_steps,
      log_every_n_steps=FLAGS.log_every_n_steps,
      save_summaries_secs=FLAGS.save_summaries_secs,
      save_interval_secs=FLAGS.save_interval_secs,
      sync_optimizer=optimizer if FLAGS.sync_replicas else None)


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