train_ssd_network.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 SSD model using a given dataset."""
from pprint import pprint

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

slim = tf.contrib.slim


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(
    '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.')

# =========================================================================== #
# 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_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_integer(
    'num_classes', 8, 'Number of classes to use in the dataset.')
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.')

# =========================================================================== #
# 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.
    """
    decay_steps = int(num_samples_per_epoch / FLAGS.batch_size *
                      FLAGS.num_epochs_per_decay)

    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.
    """
    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 _reshape_list(l, shape=None):
    """Reshape list of (list): 1D to 2D and the other way.
    """
    r = []
    if shape is None:
        # Flatten everything.
        for a in l:
            if isinstance(a, (list, tuple)):
                r = r + list(a)
            else:
                r.append(a)
    else:
        # Reshape to list of list.
        i = 0
        for s in shape:
            if s == 1:
                r.append(l[i])
            else:
                r.append(l[i:i+s])
            i += s
    return r


# =========================================================================== #
# Main training routine.
# =========================================================================== #
def main(_):
    if not FLAGS.dataset_dir:
        raise ValueError('You must supply the dataset directory with --dataset_dir')
    print('Training FLAGS:')
    pprint(FLAGS.__flags)

    tf.logging.set_verbosity(tf.logging.DEBUG)
    with tf.Graph().as_default():
        # Config model_deploy. Keep TF Slim Models structure.
        # Useful if want to need multiple GPUs and/or servers in the future.
        deploy_config = model_deploy.DeploymentConfig(
            num_clones=FLAGS.num_clones,
            clone_on_cpu=FLAGS.clone_on_cpu,
            replica_id=0,
            num_replicas=1,
            num_ps_tasks=0)
        # 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)

        # Get the SSD network and its anchors.
        ssd_class = nets_factory.get_network(FLAGS.model_name)
        ssd_params = ssd_class.default_params._replace(num_classes=FLAGS.num_classes)
        ssd_net = ssd_class(ssd_params)
        ssd_shape = ssd_net.params.img_shape
        ssd_anchors = ssd_net.anchors(ssd_shape)
        pprint(dict(ssd_params._asdict()))

        # 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 and batches.
        # =================================================================== #
        with tf.device(deploy_config.inputs_device()):
            with tf.name_scope(FLAGS.dataset_name + '_data_provider'):
                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,
                    shuffle=True)
            # Get for SSD network: image, labels, bboxes.
            [image, shape, glabels, gbboxes] = provider.get(['image', 'shape',
                                                             'object/label',
                                                             'object/bbox'])
            # Pre-processing image, labels and bboxes.
            image, glabels, gbboxes = \
                image_preprocessing_fn(image, glabels, gbboxes, ssd_shape)
            # Encode groundtruth labels and bboxes.
            gclasses, glocalisations, gscores = \
                ssd_net.bboxes_encode(glabels, gbboxes, ssd_anchors)
            batch_shape = [1] + [len(ssd_anchors)] * 3

            # Training batches and queue.
            r = tf.train.batch(
                _reshape_list([image, gclasses, glocalisations, gscores]),
                batch_size=FLAGS.batch_size,
                num_threads=FLAGS.num_preprocessing_threads,
                capacity=5 * FLAGS.batch_size)
            b_image, b_gclasses, b_glocalisations, b_gscores = \
                _reshape_list(r, batch_shape)

            # Intermediate queueing: unique batch computation pipeline for all
            # GPUs running the training.
            batch_queue = slim.prefetch_queue.prefetch_queue(
                _reshape_list([b_image, b_gclasses, b_glocalisations, b_gscores]),
                capacity=2 * deploy_config.num_clones)

        # =================================================================== #
        # Define the model running on every GPU.
        # =================================================================== #
        def clone_fn(batch_queue):
            """Allows data parallelism by creating multiple
            clones of network_fn."""
            # Dequeue batch.
            b_image, b_gclasses, b_glocalisations, b_gscores = \
                _reshape_list(batch_queue.dequeue(), batch_shape)

            # Construct SSD network.
            arg_scope = ssd_net.arg_scope(weight_decay=FLAGS.weight_decay)
            with slim.arg_scope(arg_scope):
                predictions, localisations, logits, end_points = \
                    ssd_net.net(b_image, is_training=True)

            # Add loss functions.
            ssd_net.losses(logits, localisations,
                           b_gclasses, b_glocalisations, b_gscores,
                           label_smoothing=FLAGS.label_smoothing)
            return end_points

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

        # =================================================================== #
        # Add summaries from first clone.
        # =================================================================== #
        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(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.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
        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.
        # =================================================================== #
        config = tf.ConfigProto(log_device_placement=False)
        saver = tf.train.Saver(max_to_keep=5,
                               keep_checkpoint_every_n_hours=1.0,
                               write_version=2,
                               pad_step_number=False)
        slim.learning.train(
            train_tensor,
            logdir=FLAGS.train_dir,
            master='',
            is_chief=True,
            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,
            saver=saver,
            save_interval_secs=FLAGS.save_interval_secs,
            sync_optimizer=None,
            session_config=config)


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