smallnet_mnist_cifar.py

from six.moves import xrange  # pylint: disable=redefined-builtin
from datetime import datetime
import math
import time

import tensorflow.python.platform
import tensorflow as tf

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_integer('batch_size', 128,
                            """Batch size.""")
tf.app.flags.DEFINE_integer('num_batches', 100,
                            """Number of batches to run.""")
tf.app.flags.DEFINE_boolean('forward_only', False,
                            """Only run the forward pass.""")
tf.app.flags.DEFINE_boolean('forward_backward_only', False,
                            """Only run the forward-forward pass.""")
tf.app.flags.DEFINE_string('data_format', 'NCHW',
                           """The data format for Convnet operations.
                           Can be either NHWC or NCHW.
                           """)
tf.app.flags.DEFINE_boolean('log_device_placement', False,
                            """Whether to log device placement.""")

parameters = []

conv_counter = 1
pool_counter = 1
affine_counter = 1

def _conv(inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.005, act=True):
    global conv_counter
    global parameters
    name = 'conv' + str(conv_counter)
    conv_counter += 1
    with tf.name_scope(name) as scope:
        kernel = tf.Variable(tf.truncated_normal([kH, kW, nIn, nOut],
                                                 dtype=tf.float32,
                                                 stddev=1e-1), name='weights')

        if wd is not None:
            weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss')
            tf.add_to_collection('losses', weight_decay)

        if FLAGS.data_format == 'NCHW':
          strides = [1, 1, dH, dW]
        else:
          strides = [1, dH, dW, 1]
        conv = tf.nn.conv2d(inpOp, kernel, strides, padding=padType,
                            data_format=FLAGS.data_format)
        biases = tf.Variable(tf.constant(0.0, shape=[nOut], dtype=tf.float32),
                             trainable=True, name='biases')
        bias = tf.reshape(tf.nn.bias_add(conv, biases,
                                         data_format=FLAGS.data_format),
                          conv.get_shape())

        conv1 = tf.nn.relu(bias, name=scope) if act else bias
        
        parameters += [kernel, biases]

        return conv1

def _affine(inpOp, nIn, nOut, wd=None, act=True):
    global affine_counter
    global parameters
    name = 'affine' + str(affine_counter)
    affine_counter += 1
    with tf.name_scope(name) as scope:
        kernel = tf.Variable(tf.truncated_normal([nIn, nOut],
                                                 dtype=tf.float32,
                                                 stddev=1e-1), name='weights')

        if wd is not None:
            weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss')
            tf.add_to_collection('losses', weight_decay)

        biases = tf.Variable(tf.constant(0.0, shape=[nOut], dtype=tf.float32),
                             trainable=True, name='biases')

        affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else tf.matmul(inpOp, kernel) + biases 

        parameters += [kernel, biases]

        return affine1

def _mpool(inpOp, kH, kW, dH, dW, padding):
    global pool_counter
    global parameters
    name = 'pool' + str(pool_counter)
    pool_counter += 1
    if FLAGS.data_format == 'NCHW':
      ksize = [1, 1, kH, kW]
      strides = [1, 1, dH, dW]
    else:
      ksize = [1, kH, kW, 1]
      strides = [1, dH, dW, 1]
    return tf.nn.max_pool(inpOp,
                          ksize=ksize,
                          strides=strides,
                          padding=padding,
                          data_format=FLAGS.data_format,
                          name=name)


def _apool(inpOp, kH, kW, dH, dW, padding):
    global pool_counter
    global parameters
    name = 'pool' + str(pool_counter)
    pool_counter += 1
    if FLAGS.data_format == 'NCHW':
      ksize = [1, 1, kH, kW]
      strides = [1, 1, dH, dW]
    else:
      ksize = [1, kH, kW, 1]
      strides = [1, dH, dW, 1]
    return tf.nn.avg_pool(inpOp,
                          ksize=ksize,
                          strides=strides,
                          padding=padding,
                          data_format=FLAGS.data_format,
                          name=name)

def _norm(name, l_input, lsize=4):
    return tf.nn.lrn(l_input, lsize, bias=1.0,
                     alpha=0.001 / 9.0,
                     beta=0.75, name=name)

def loss(logits, labels):
    batch_size = tf.size(labels)
    labels = tf.expand_dims(labels, 1)
    indices = tf.expand_dims(tf.range(0, batch_size, 1), 1)
    concated = tf.concat(1, [indices, labels])
    onehot_labels = tf.sparse_to_dense(
        concated, tf.pack([batch_size, 10]), 1.0, 0.0)
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits,
                                                            onehot_labels,
                                                            name='xentropy')
    loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
    return loss

def get_incoming_shape(incoming):
    """ Returns the incoming data shape """
    if isinstance(incoming, tf.Tensor):
        return incoming.get_shape().as_list()
    elif type(incoming) in [np.array, list, tuple]:
        return np.shape(incoming)
    else:
        raise Exception("Invalid incoming layer.")

def inference(images):
    conv1 = _conv (images, 3, 32, 5, 5, 1, 1, 'SAME')
    pool1 = _mpool(conv1,  3, 3, 2, 2, 'SAME')
    conv2 = _conv (pool1,  32, 32, 5, 5, 1, 1, 'SAME')
    pool2 = _apool(conv2,  3, 3, 2, 2, 'SAME')
    conv3 = _conv (pool2,  32, 64, 5, 5, 1, 1, 'SAME')
    pool3 = _apool(conv3,  3, 3, 2, 2, 'SAME')
    resh1 = tf.reshape(pool3, [-1, 64 * 4 * 4])
    affn1 = _affine(resh1, 64 * 4 * 4, 64)
    affn2 = _affine(affn1, 64, 10, act=False)

    print ('conv1:', get_incoming_shape(conv1))
    print ('pool1:', get_incoming_shape(pool1))
    print ('conv2:', get_incoming_shape(conv2))
    print ('pool2:', get_incoming_shape(pool2))
    print ('conv3:', get_incoming_shape(conv3))
    print ('pool3:', get_incoming_shape(pool3))
  
    return affn2


def time_tensorflow_run(session, target, info_string):
  num_steps_burn_in = 10
  total_duration = 0.0
  total_duration_squared = 0.0
  if not isinstance(target, list):
    target = [target]
  target_op = tf.group(*target)
  for i in xrange(FLAGS.num_batches + num_steps_burn_in):
    start_time = time.time()
    _ = session.run(target_op)
    duration = time.time() - start_time
    if i > num_steps_burn_in:
      if not i % 10:
        print ('%s: step %d, duration = %.3f' %
               (datetime.now(), i - num_steps_burn_in, duration))
      total_duration += duration
      total_duration_squared += duration * duration
  mn = total_duration / FLAGS.num_batches
  vr = total_duration_squared / FLAGS.num_batches - mn * mn
  sd = math.sqrt(vr)
  print ('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %
         (datetime.now(), info_string, FLAGS.num_batches, mn, sd))

def run_benchmark():
  global parameters
  with tf.Graph().as_default():
    # Generate some dummy images.
    image_size = 32 
    # Note that our padding definition is slightly different the cuda-convnet.
    # In order to force the model to start with the same activations sizes,
    # we add 3 to the image_size and employ VALID padding above.
    if FLAGS.data_format == 'NCHW':
      image_shape = [FLAGS.batch_size, 3, image_size, image_size]
    else:
      image_shape = [FLAGS.batch_size, image_size, image_size, 3]

    images = tf.get_variable('image', image_shape, 
                             initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32),
                             dtype=tf.float32,
                             trainable=False)

    labels = tf.get_variable('label', [FLAGS.batch_size],
                             initializer=tf.constant_initializer(1),
                             dtype=tf.int32,
                             trainable=False)

    # Build a Graph that computes the logits predictions from the
    # inference model.
    last_layer = inference(images)

    objective = loss(last_layer, labels)

    # Compute gradients.
    # opt = tf.train.GradientDescentOptimizer(0.001)
    opt = tf.train.MomentumOptimizer(0.001, 0.9)
    grads = opt.compute_gradients(objective) 
    global_step = tf.get_variable('global_step', [],
       initializer=tf.constant_initializer(0.0, dtype=tf.float32),
       trainable=False, dtype=tf.float32)
    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)

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


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

    # Start running operations on the Graph.
    sess = tf.Session(config=tf.ConfigProto(
      allow_soft_placement=True,
      log_device_placement=FLAGS.log_device_placement))
    sess.run(init)

    run_forward = True
    run_forward_backward = True
    if FLAGS.forward_only and FLAGS.forward_backward_only:
      raise ValueError("Cannot specify --forward_only and "
                       "--forward_backward_only at the same time.")
    if FLAGS.forward_only:
      run_forward_backward = False
    elif FLAGS.forward_backward_only:
      run_forward = False

    if run_forward:
      # Run the forward benchmark.
      time_tensorflow_run(sess, last_layer, "Forward")

    if run_forward_backward:
      with tf.control_dependencies([apply_gradient_op, variables_averages_op]):
          train_op = tf.no_op(name='train')
      time_tensorflow_run(sess, [train_op, objective], "Forward-backward")


def main(_):
  run_benchmark()


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