CIFAR10 多 GPU 版本例程源码分析

 # Copyright 2015 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 binary to train CIFAR-10 using multiple GPU's with synchronous updates.

 Accuracy:
 cifar10_multi_gpu_train.py achieves ~86% accuracy after 100K steps (256
 epochs of data) as judged by cifar10_eval.py.

 Speed: With batch_size 128.

 System        | Step Time (sec/batch)  |     Accuracy
 --------------------------------------------------------------------
 1 Tesla K20m  | 0.35-0.60              | ~86% at 60K steps  (5 hours)
 1 Tesla K40m  | 0.25-0.35              | ~86% at 100K steps (4 hours)
 2 Tesla K20m  | 0.13-0.20              | ~84% at 30K steps  (2.5 hours)
 3 Tesla K20m  | 0.13-0.18              | ~84% at 30K steps
 4 Tesla K20m  | ~0.10                  | ~84% at 30K steps

 Usage:
 Please see the tutorial and website for how to download the CIFAR-10
 data set, compile the program and train the model.

 http://tensorflow.org/tutorials/deep_cnn/
 """
 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
 from tensorflow.models.image.cifar10 import cifar10

 FLAGS = tf.app.flags.FLAGS

 tf.app.flags.DEFINE_string('train_dir', '/tmp/cifar10_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_integer('num_gpus', 1,
                             """How many GPUs to use.""")
 tf.app.flags.DEFINE_boolean('log_device_placement', False,
                             """Whether to log device placement.""")


 def tower_loss(scope):
   """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'

   Returns:
      Tensor of shape [] containing the total loss for a batch of data
   """
   # Get images and labels for CIFAR-10.
   images, labels = cifar10.distorted_inputs()

   # 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')

   # 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 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)
     # Name each loss as '(raw)' and name the moving average version of the loss
     # as the original loss name.
     tf.scalar_summary(loss_name +' (raw)', l)
     tf.scalar_summary(loss_name, loss_averages.average(l))

   with tf.control_dependencies([loss_averages_op]):
     total_loss = tf.identity(total_loss)
   return total_loss


 def average_gradients(tower_grads):
   """Calculate the average gradient for each shared variable across all towers.

   Note that this function provides a synchronization point across all towers.

   Args:
     tower_grads: List of lists of (gradient, variable) tuples. The outer list
       is over individual gradients. The inner list is over the gradient
       calculation for each tower.
   Returns:
      List of pairs of (gradient, variable) where the gradient has been averaged
      across all towers.
   """
   average_grads = []
   for grad_and_vars in zip(*tower_grads):
     # Note that each grad_and_vars looks like the following:
     #   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
     grads = []
     for g, _ in grad_and_vars:
       # Add 0 dimension to the gradients to represent the tower.
       expanded_g = tf.expand_dims(g, 0)

       # Append on a 'tower' dimension which we will average over below.
       grads.append(expanded_g)

     # Average over the 'tower' dimension.
     grad = tf.concat(0, grads)
     grad = tf.reduce_mean(grad, 0)

     # Keep in mind that the Variables are redundant because they are shared
     # across towers. So .. we will just return the first tower's pointer to
     # the Variable.
     v = grad_and_vars[0][1]
     grad_and_var = (grad, v)
     average_grads.append(grad_and_var)
   return average_grads


 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)

     # Calculate the gradients for each model tower.
     tower_grads = []
     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:
           # 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)

           # 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.scalar_summary('learning_rate', lr))

     # Add histograms for gradients.
     for grad, var in grads:
       if grad is not None:
         summaries.append(
             tf.histogram_summary(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.histogram_summary(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.all_variables())

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

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

     # 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.train.SummaryWriter(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__':
   tf.app.run()