# Copyright 2016 Google Inc. 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.
# ==============================================================================
"""A library to train Inception using multiple replicas with synchronous update.

Please see accompanying README.md for details and instructions.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from datetime import datetime
import os.path
import time

import numpy as np
import tensorflow as tf

import image_processing
import inception_model as inception
from camelyon16.inception.slim import slim

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string('job_name', '', 'One of "ps", "worker"')
tf.app.flags.DEFINE_string('ps_hosts', '',
                           """Comma-separated list of hostname:port for the """
                           """parameter server jobs. e.g. """
                           """'machine1:2222,machine2:1111,machine2:2222'""")
tf.app.flags.DEFINE_string('worker_hosts', '',
                           """Comma-separated list of hostname:port for the """
                           """worker jobs. e.g. """
                           """'machine1:2222,machine2:1111,machine2:2222'""")

tf.app.flags.DEFINE_string('train_dir', '/tmp/imagenet_train',
                           """Directory where to write event logs """
                           """and checkpoint.""")
tf.app.flags.DEFINE_integer('max_steps', 1000000, 'Number of batches to run.')
tf.app.flags.DEFINE_string('subset', 'train', 'Either "train" or "validation".')
tf.app.flags.DEFINE_boolean('log_device_placement', False,
                            'Whether to log device placement.')

# Task ID is used to select the chief and also to access the local_step for
# each replica to check staleness of the gradients in sync_replicas_optimizer.
tf.app.flags.DEFINE_integer(
    'task_id', 0, 'Task ID of the worker/replica running the training.')

# More details can be found in the sync_replicas_optimizer class:
# tensorflow/python/training/sync_replicas_optimizer.py
tf.app.flags.DEFINE_integer('num_replicas_to_aggregate', -1,
                            """Number of gradients to collect before """
                            """updating the parameters.""")
tf.app.flags.DEFINE_integer('save_interval_secs', 10 * 60,
                            'Save interval seconds.')
tf.app.flags.DEFINE_integer('save_summaries_secs', 180,
                            'Save summaries interval seconds.')

# **IMPORTANT**
# Please note that this learning rate schedule is heavily dependent on the
# hardware architecture, batch size and any changes to the model architecture
# specification. Selecting a finely tuned learning rate schedule is an
# empirical process that requires some experimentation. Please see README.md
# more guidance and discussion.
#
# Learning rate decay factor selected from https://arxiv.org/abs/1604.00981
tf.app.flags.DEFINE_float('initial_learning_rate', 0.045,
                          'Initial learning rate.')
tf.app.flags.DEFINE_float('num_epochs_per_decay', 2.0,
                          'Epochs after which learning rate decays.')
tf.app.flags.DEFINE_float('learning_rate_decay_factor', 0.94,
                          'Learning rate decay factor.')

# Constants dictating the learning rate schedule.
RMSPROP_DECAY = 0.9  # Decay term for RMSProp.
RMSPROP_MOMENTUM = 0.9  # Momentum in RMSProp.
RMSPROP_EPSILON = 1.0  # Epsilon term for RMSProp.


def train(target, dataset, cluster_spec):
    """Train Inception on a dataset for a number of steps."""
    # Number of workers and parameter servers are infered from the workers and ps
    # hosts string.
    num_workers = len(cluster_spec.as_dict()['worker'])
    num_parameter_servers = len(cluster_spec.as_dict()['ps'])
    # If no value is given, num_replicas_to_aggregate defaults to be the number of
    # workers.
    if FLAGS.num_replicas_to_aggregate == -1:
        num_replicas_to_aggregate = num_workers
    else:
        num_replicas_to_aggregate = FLAGS.num_replicas_to_aggregate

    # Both should be greater than 0 in a distributed training.
    assert num_workers > 0 and num_parameter_servers > 0, (' num_workers and '
                                                           'num_parameter_servers'
                                                           ' must be > 0.')

    # Choose worker 0 as the chief. Note that any worker could be the chief
    # but there should be only one chief.
    is_chief = (FLAGS.task_id == 0)

    # Ops are assigned to worker by default.
    with tf.device('/job:worker/task:%d' % FLAGS.task_id):
        # Variables and its related init/assign ops are assigned to ps.
        with slim.scopes.arg_scope(
                [slim.variables.variable, slim.variables.global_step],
                device=slim.variables.VariableDeviceChooser(num_parameter_servers)):
            # Create a variable to count the number of train() calls. This equals the
            # number of updates applied to the variables.
            global_step = slim.variables.global_step()

            # Calculate the learning rate schedule.
            num_batches_per_epoch = (dataset.num_examples_per_epoch() /
                                     FLAGS.batch_size)
            # Decay steps need to be divided by the number of replicas to aggregate.
            decay_steps = int(num_batches_per_epoch * FLAGS.num_epochs_per_decay /
                              num_replicas_to_aggregate)

            # Decay the learning rate exponentially based on the number of steps.
            lr = tf.train.exponential_decay(FLAGS.initial_learning_rate,
                                            global_step,
                                            decay_steps,
                                            FLAGS.learning_rate_decay_factor,
                                            staircase=True)
            # Add a summary to track the learning rate.
            tf.summary.scalar('learning_rate', lr)

            # Create an optimizer that performs gradient descent.
            opt = tf.train.RMSPropOptimizer(lr,
                                            RMSPROP_DECAY,
                                            momentum=RMSPROP_MOMENTUM,
                                            epsilon=RMSPROP_EPSILON)

            images, labels = image_processing.distorted_inputs(
                dataset,
                batch_size=FLAGS.batch_size,
                num_preprocess_threads=FLAGS.num_preprocess_threads)

            # Number of classes in the Dataset label set plus 1.
            # Label 0 is reserved for an (unused) background class.
            num_classes = dataset.num_classes() + 1
            logits = inception.inference(images, num_classes, for_training=True)
            # Add classification loss.
            inception.loss(logits, labels)

            # Gather all of the losses including regularization losses.
            losses = tf.get_collection(slim.losses.LOSSES_COLLECTION)
            losses += tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)

            total_loss = tf.add_n(losses, name='total_loss')

            if is_chief:
                # Compute the moving average of all individual losses and the
                # total loss.
                loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
                loss_averages_op = loss_averages.apply(losses + [total_loss])

                # Attach a scalar summmary to all individual losses and the total loss;
                # do the same for the averaged version of the losses.
                for l in losses + [total_loss]:
                    loss_name = l.op.name
                    # Name each loss as '(raw)' and name the moving average version of the
                    # loss as the original loss name.
                    tf.summary.scalar(loss_name + ' (raw)', l)
                    tf.summary.scalar(loss_name, loss_averages.average(l))

                # Add dependency to compute loss_averages.
                with tf.control_dependencies([loss_averages_op]):
                    total_loss = tf.identity(total_loss)

            # Track the moving averages of all trainable variables.
            # Note that we maintain a 'double-average' of the BatchNormalization
            # global statistics.
            # This is not needed when the number of replicas are small but important
            # for synchronous distributed training with tens of workers/replicas.
            exp_moving_averager = tf.train.ExponentialMovingAverage(
                inception.MOVING_AVERAGE_DECAY, global_step)

            variables_to_average = (
                tf.trainable_variables() + tf.moving_average_variables())

            # Add histograms for model variables.
            for var in variables_to_average:
                tf.summary.histogram(var.op.name, var)

            # Create synchronous replica optimizer.
            opt = tf.train.SyncReplicasOptimizer(
                opt,
                replicas_to_aggregate=num_replicas_to_aggregate,
                replica_id=FLAGS.task_id,
                total_num_replicas=num_workers,
                variable_averages=exp_moving_averager,
                variables_to_average=variables_to_average)

            batchnorm_updates = tf.get_collection(slim.ops.UPDATE_OPS_COLLECTION)
            assert batchnorm_updates, 'Batchnorm updates are missing'
            batchnorm_updates_op = tf.group(*batchnorm_updates)
            # Add dependency to compute batchnorm_updates.
            with tf.control_dependencies([batchnorm_updates_op]):
                total_loss = tf.identity(total_loss)

            # Compute gradients with respect to the loss.
            grads = opt.compute_gradients(total_loss)

            # Add histograms for gradients.
            for grad, var in grads:
                if grad is not None:
                    tf.summary.histogram(var.op.name + '/gradients', grad)

            apply_gradients_op = opt.apply_gradients(grads, global_step=global_step)

            with tf.control_dependencies([apply_gradients_op]):
                train_op = tf.identity(total_loss, name='train_op')

            # Get chief queue_runners, init_tokens and clean_up_op, which is used to
            # synchronize replicas.
            # More details can be found in sync_replicas_optimizer.
            chief_queue_runners = [opt.get_chief_queue_runner()]
            init_tokens_op = opt.get_init_tokens_op()
            clean_up_op = opt.get_clean_up_op()

            # Create a saver.
            saver = tf.train.Saver()

            # Build the summary operation based on the TF collection of Summaries.
            summary_op = tf.merge_all_summaries()

            # Build an initialization operation to run below.
            init_op = tf.initialize_all_variables()

            # We run the summaries in the same thread as the training operations by
            # passing in None for summary_op to avoid a summary_thread being started.
            # Running summaries and training operations in parallel could run out of
            # GPU memory.
            sv = tf.train.Supervisor(is_chief=is_chief,
                                     logdir=FLAGS.train_dir,
                                     init_op=init_op,
                                     summary_op=None,
                                     global_step=global_step,
                                     saver=saver,
                                     save_model_secs=FLAGS.save_interval_secs)

            tf.logging.info('%s Supervisor' % datetime.now())

            sess_config = tf.ConfigProto(
                allow_soft_placement=True,
                log_device_placement=FLAGS.log_device_placement)

            # Get a session.
            sess = sv.prepare_or_wait_for_session(target, config=sess_config)

            # Start the queue runners.
            queue_runners = tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS)
            sv.start_queue_runners(sess, queue_runners)
            tf.logging.info('Started %d queues for processing input data.',
                            len(queue_runners))

            if is_chief:
                sv.start_queue_runners(sess, chief_queue_runners)
                sess.run(init_tokens_op)

            # Train, checking for Nans. Concurrently run the summary operation at a
            # specified interval. Note that the summary_op and train_op never run
            # simultaneously in order to prevent running out of GPU memory.
            next_summary_time = time.time() + FLAGS.save_summaries_secs
            while not sv.should_stop():
                try:
                    start_time = time.time()
                    loss_value, step = sess.run([train_op, global_step])
                    assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
                    if step > FLAGS.max_steps:
                        break
                    duration = time.time() - start_time

                    if step % 30 == 0:
                        examples_per_sec = FLAGS.batch_size / float(duration)
                        format_str = ('Worker %d: %s: step %d, loss = %.2f'
                                      '(%.1f examples/sec; %.3f  sec/batch)')
                        tf.logging.info(format_str %
                                        (FLAGS.task_id, datetime.now(), step, loss_value,
                                         examples_per_sec, duration))

                    # Determine if the summary_op should be run on the chief worker.
                    if is_chief and next_summary_time < time.time():
                        tf.logging.info('Running Summary operation on the chief.')
                        summary_str = sess.run(summary_op)
                        sv.summary_computed(sess, summary_str)
                        tf.logging.info('Finished running Summary operation.')

                        # Determine the next time for running the summary.
                        next_summary_time += FLAGS.save_summaries_secs
                except:
                    if is_chief:
                        tf.logging.info('About to execute sync_clean_up_op!')
                        sess.run(clean_up_op)
                    raise

            # Stop the supervisor.  This also waits for service threads to finish.
            sv.stop()

            # Save after the training ends.
            if is_chief:
                saver.save(sess,
                           os.path.join(FLAGS.train_dir, 'model.ckpt'),
                           global_step=global_step)
