# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
#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.
from six.moves import xrange # pylint: disable=redefined-builtin
from datetime import datetime
import math
import re
import time
import tensorflow.python.platform
import tensorflow as tf
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('batch_size', 64, """Batch size.""")
tf.app.flags.DEFINE_integer('num_batches', 100, """Number of batches to run.""")
tf.app.flags.DEFINE_string('data_format', 'NCHW',
"""The data format for Convnet operations.
Can be either NHWC or NCHW.
""")
tf.app.flags.DEFINE_string('train_dir', '/train_model',
"""Directory where to write event logs """
"""and checkpoint.""")
tf.app.flags.DEFINE_integer('num_gpus', 4, """How many GPUs to use.""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
"""Whether to log device placement.""")
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
NUM_EPOCHS_PER_DECAY = 50
INITIAL_LEARNING_RATE = 0.1
LEARNING_RATE_DECAY_FACTOR = 0.1
TOWER_NAME = 'tower'
def _conv(name, inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.005):
with tf.name_scope(name) as scope:
kernel = tf.get_variable(
name + '_w', [kH, kW, nIn, nOut],
initializer=tf.truncated_normal_initializer(
stddev=0.01, dtype=tf.float32),
dtype=tf.float32)
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.get_variable(
name=name + '_b',
shape=[nOut],
initializer=tf.constant_initializer(
value=0.0, dtype=tf.float32),
dtype=tf.float32)
bias = tf.reshape(
tf.nn.bias_add(
conv, biases, data_format=FLAGS.data_format),
conv.get_shape())
conv1 = tf.nn.relu(bias, name=scope)
return conv1
def _affine(name, inpOp, nIn, nOut, wd=0.005, act=True):
with tf.name_scope(name) as scope:
kernel = tf.get_variable(
name + '_w', [nIn, nOut],
initializer=tf.truncated_normal_initializer(
stddev=0.01, dtype=tf.float32),
dtype=tf.float32)
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.get_variable(
name + '_b', [nOut],
initializer=tf.constant_initializer(
value=0.0, dtype=tf.float32),
dtype=tf.float32,
trainable=True)
affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \
tf.matmul(inpOp, kernel) + biases
return affine1
def _mpool(name, inpOp, kH, kW, dH, dW):
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='VALID',
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):
labels = tf.cast(labels, tf.int64)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits, labels, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
# The total loss is defined as the cross entropy loss plus all of the weight
# decay terms (L2 loss).
return tf.add_n(tf.get_collection('losses'), name='total_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('conv1', images, 3, 96, 11, 11, 4, 4, 'VALID')
pool1 = _mpool('pool1', conv1, 3, 3, 2, 2)
norm1 = _norm('norm1', pool1, lsize=5)
conv2 = _conv('conv2', norm1, 96, 256, 5, 5, 1, 1, 'SAME')
pool2 = _mpool('pool2', conv2, 3, 3, 2, 2)
norm2 = _norm('norm2', pool2, lsize=5)
conv3 = _conv('conv3', norm2, 256, 384, 3, 3, 1, 1, 'SAME')
conv4 = _conv('conv4', conv3, 384, 384, 3, 3, 1, 1, 'SAME')
conv5 = _conv('conv5', conv4, 384, 256, 3, 3, 1, 1, 'SAME')
pool5 = _mpool('pool5', conv5, 3, 3, 2, 2)
resh1 = tf.reshape(pool5, [-1, 256 * 6 * 6])
affn1 = _affine('fc6', resh1, 256 * 6 * 6, 4096)
affn2 = _affine('fc7', affn1, 4096, 4096)
affn3 = _affine('fc8', affn2, 4096, 1000, wd=None, act=False) # last fc
return affn3
def tower_loss(scope):
"""Calculate the total loss on a single tower running the model.
Args:
scope: unique prefix string identifying the tower, e.g. 'tower_0'
Returns:
Tensor of shape [] containing the total loss for a batch of data
"""
image_size = 224
if FLAGS.data_format == 'NCHW':
image_shape = [FLAGS.batch_size, 3, image_size + 3, image_size + 3]
else:
image_shape = [FLAGS.batch_size, image_size + 3, image_size + 3, 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)
# Build the portion of the Graph calculating the losses. Note that we will
# assemble the total_loss using a custom function below.
_ = loss(last_layer, 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]*/' % 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 time_tensorflow_run(session, target):
num_steps_burn_in = 50
total_duration = 0.0
total_duration_squared = 0.0
for i in xrange(FLAGS.num_batches + num_steps_burn_in):
start_time = time.time()
_, loss_value = session.run(target)
duration = time.time() - start_time
if i > num_steps_burn_in:
if not i % 10:
num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus
examples_per_sec = num_examples_per_step / duration
sec_per_batch = duration
format_str = (
'%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch batch_size = %d)')
print(format_str %
(datetime.now(), i - num_steps_burn_in, loss_value,
duration, sec_per_batch, num_examples_per_step))
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: FwdBwd across %d steps, %.3f +/- %.3f sec / batch' %
(datetime.now(), FLAGS.num_batches, mn, sd))
def run_benchmark():
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 = (NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /
FLAGS.batch_size)
decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)
# Decay the learning rate exponentially based on the number of steps.
lr = tf.train.exponential_decay(
INITIAL_LEARNING_RATE,
global_step,
decay_steps,
LEARNING_RATE_DECAY_FACTOR,
staircase=True)
# Create an optimizer that performs gradient descent.
opt = tf.train.MomentumOptimizer(lr, 0.9)
# 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' % (TOWER_NAME, i)) as scope:
# Calculate the loss for one tower of the model. This function
# constructs the entire 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 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)
# Apply the gradients to adjust the shared variables.
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Group all updates to into a single train op.
train_op = tf.group(apply_gradient_op)
# Build an initialization operation.
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)
time_tensorflow_run(sess, [train_op, loss])
def main(_):
run_benchmark()
if __name__ == '__main__':
tf.app.run()