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

from datetime import datetime
import os.path
import re
import time

import numpy as np
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf
import cifar10

parser = cifar10.parser

parser.add_argument('--train_dir', type=str, default='/tmp/cifar10_train',
                    help='Directory where to write event logs and checkpoint.')

parser.add_argument('--max_steps', type=int, default=1000000,
                    help='Number of batches to run.')

parser.add_argument('--num_gpus', type=int, default=1,
                    help='How many GPUs to use.')

parser.add_argument('--log_device_placement', type=bool, default=False,
                    help='Whether to log device placement.')

def tower_loss(scope, images, labels):
  """Calculate the total loss on a single tower running the CIFAR model.

  Args:
    scope: unique prefix string identifying the CIFAR tower, e.g. 'tower_0'
    images: Images. 4D tensor of shape [batch_size, height, width, 3].
    labels: Labels. 1D tensor of shape [batch_size].

  Returns:
     Tensor of shape [] containing the total loss for a batch of data
  """

  # Build inference Graph.
  logits = cifar10.inference(images)

  # Build the portion of the Graph calculating the losses. Note that we will
  # assemble the total_loss using a custom function below.
  _ = cifar10.loss(logits, labels)

  # Assemble all of the losses for the current tower only.
  losses = tf.get_collection('losses', scope)

  # Calculate the total loss for the current tower.
  total_loss = tf.add_n(losses, name='total_loss')

  # Attach a scalar summary to all individual losses and the total loss; do the
  # same for the averaged version of the losses.
  for l in losses + [total_loss]:
    # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
    # session. This helps the clarity of presentation on tensorboard.
    loss_name = re.sub('%s_[0-9]*/' % cifar10.TOWER_NAME, '', l.op.name)
    tf.summary.scalar(loss_name, l)

  return total_loss

def train():
  """Train CIFAR-10 for a number of steps."""
  with tf.Graph().as_default(), tf.device('/cpu:0'):
    # Create a variable to count the number of train() calls. This equals the
    # number of batches processed * FLAGS.num_gpus.
    global_step = tf.get_variable(
        'global_step', [],
        initializer=tf.constant_initializer(0), trainable=False)

    # Calculate the learning rate schedule.
    num_batches_per_epoch = (cifar10.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /
                             FLAGS.batch_size)
    decay_steps = int(num_batches_per_epoch * cifar10.NUM_EPOCHS_PER_DECAY)

    # Decay the learning rate exponentially based on the number of steps.
    lr = tf.train.exponential_decay(cifar10.INITIAL_LEARNING_RATE,
                                    global_step,
                                    decay_steps,
                                    cifar10.LEARNING_RATE_DECAY_FACTOR,
                                    staircase=True)

    # Create an optimizer that performs gradient descent.
    opt = tf.train.GradientDescentOptimizer(lr)

    # Get images and labels for CIFAR-10.
    images, labels = cifar10.distorted_inputs()
    batch_queue = tf.contrib.slim.prefetch_queue.prefetch_queue(
          [images, labels], capacity=2 * FLAGS.num_gpus)
    # Calculate the gradients for each model tower.
    tower_grads = []
    with tf.variable_scope(tf.get_variable_scope()):
      for i in xrange(FLAGS.num_gpus):
        with tf.device('/gpu:%d' % i):
          with tf.name_scope('%s_%d' % (cifar10.TOWER_NAME, i)) as scope:
            # Dequeues one batch for the GPU
            image_batch, label_batch = batch_queue.dequeue()
            # Calculate the loss for one tower of the CIFAR model. This function
            # constructs the entire CIFAR model but shares the variables across
            # all towers.
            loss = tower_loss(scope, image_batch, label_batch)

            # Reuse variables for the next tower.
            tf.get_variable_scope().reuse_variables()

            # Retain the summaries from the final tower.
            summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)

            # Calculate the gradients for the batch of data on this CIFAR tower.
            grads = opt.compute_gradients(loss)

            # Keep track of the gradients across all towers.
            tower_grads.append(grads)

    # We must calculate the mean of each gradient. Note that this is the
    # synchronization point across all towers.
    grads = average_gradients(tower_grads)

    # Add a summary to track the learning rate.
    summaries.append(tf.summary.scalar('learning_rate', lr))

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

    # Apply the gradients to adjust the shared variables.
    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)

    # Add histograms for trainable variables.
    for var in tf.trainable_variables():
      summaries.append(tf.summary.histogram(var.op.name, var))

    # Track the moving averages of all trainable variables.
    variable_averages = tf.train.ExponentialMovingAverage(
        cifar10.MOVING_AVERAGE_DECAY, global_step)
    variables_averages_op = variable_averages.apply(tf.trainable_variables())

    # Group all updates to into a single train op.
    train_op = tf.group(apply_gradient_op, variables_averages_op)

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

    # Build the summary operation from the last tower summaries.
    summary_op = tf.summary.merge(summaries)

    # Build an initialization operation to run below.
    init = tf.global_variables_initializer()

    # Start running operations on the Graph. allow_soft_placement must be set to
    # True to build towers on GPU, as some of the ops do not have GPU
    # implementations.
    sess = tf.Session(config=tf.ConfigProto(
        allow_soft_placement=True,
        log_device_placement=FLAGS.log_device_placement))
    sess.run(init)

    # Start the queue runners.
    tf.train.start_queue_runners(sess=sess)

    summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)

    for step in xrange(FLAGS.max_steps):
      start_time = time.time()
      _, loss_value = sess.run([train_op, loss])
      duration = time.time() - start_time

      assert not np.isnan(loss_value), 'Model diverged with loss = NaN'

      if step % 10 == 0:
        num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus
        examples_per_sec = num_examples_per_step / duration
        sec_per_batch = duration / FLAGS.num_gpus

        format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
                      'sec/batch)')
        print (format_str % (datetime.now(), step, loss_value,
                             examples_per_sec, sec_per_batch))

      if step % 100 == 0:
        summary_str = sess.run(summary_op)
        summary_writer.add_summary(summary_str, step)

      # Save the model checkpoint periodically.
      if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
        checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
        saver.save(sess, checkpoint_path, global_step=step)

def main(argv=None):  # pylint: disable=unused-argument
  cifar10.maybe_download_and_extract()
  if tf.gfile.Exists(FLAGS.train_dir):
    tf.gfile.DeleteRecursively(FLAGS.train_dir)
  tf.gfile.MakeDirs(FLAGS.train_dir)
  train()

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