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benchmark_googlenet.py
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benchmark_googlenet.py
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from builtins import range
from collections import namedtuple
from datetime import datetime
import os
import math
import time
import tensorflow.python.platform
import tensorflow as tf
import tools.storeincsv as incsv
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_float('gpu_fraction', 0.9,
"""GPU Memory fraction to use 0..1. Default is 0.9.""")
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_string('csv_file', '',
"""File to output timing information to in csv
format. If not file is passed in, csv file will
not be cteated.
""")
parameters = []
conv_counter = 1
pool_counter = 1
affine_counter = 1
ParamHeader = ['Timestamp', 'Script', 'Info', 'Batch_size', 'Num_batches', 'Data_format', 'TotalTime', 'MeanPerBatch', 'StDev']
ParamEntry = namedtuple('ParamEntry', ParamHeader)
def _conv(inpOp, nIn, nOut, kH, kW, dH, dW, padType):
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 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)
parameters += [kernel, biases]
return conv1
def _affine(inpOp, nIn, nOut):
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')
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)
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 _inception(inp, inSize, o1s, o2s1, o2s2, o3s1, o3s2, o4s1, o4s2):
conv1 = _conv(inp, inSize, o1s, 1, 1, 1, 1, 'SAME')
conv3_ = _conv(inp, inSize, o2s1, 1, 1, 1, 1, 'SAME')
conv3 = _conv(conv3_, o2s1, o2s2, 3, 3, 1, 1, 'SAME')
conv5_ = _conv(inp, inSize, o3s1, 1, 1, 1, 1, 'SAME')
conv5 = _conv(conv5_, o3s1, o3s2, 5, 5, 1, 1, 'SAME')
pool_ = _mpool(inp, o4s1, o4s1, 1, 1, 'SAME')
pool = _conv(pool_, inSize, o4s2, 1, 1, 1, 1, 'SAME')
if FLAGS.data_format == 'NCHW':
channel_dim = 1
else:
channel_dim = 3
incept = tf.concat([conv1, conv3, conv5, pool], channel_dim)
return incept
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([indices, labels], 1)
onehot_labels = tf.sparse_to_dense(
concated, tf.stack([batch_size, 1000]), 1.0, 0.0)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=onehot_labels, name='xentropy')
loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
return loss
def inference(images):
conv1 = _conv (images, 3, 64, 7, 7, 2, 2, 'SAME')
pool1 = _mpool(conv1, 3, 3, 2, 2, 'SAME')
conv2 = _conv (pool1, 64, 64, 1, 1, 1, 1, 'SAME')
conv3 = _conv (conv2, 64, 192, 3, 3, 1, 1, 'SAME')
pool3 = _mpool(conv3, 3, 3, 2, 2, 'SAME')
incept3a = _inception(pool3, 192, 64, 96, 128, 16, 32, 3, 32)
incept3b = _inception(incept3a, 256, 128, 128, 192, 32, 96, 3, 64)
pool4 = _mpool(incept3b, 3, 3, 2, 2, 'SAME')
incept4a = _inception(pool4, 480, 192, 96, 208, 16, 48, 3, 64)
incept4b = _inception(incept4a, 512, 160, 112, 224, 24, 64, 3, 64)
incept4c = _inception(incept4b, 512, 128, 128, 256, 24, 64, 3, 64)
incept4d = _inception(incept4c, 512, 112, 144, 288, 32, 64, 3, 64)
incept4e = _inception(incept4d, 528, 256, 160, 320, 32, 128, 3, 128)
pool5 = _mpool(incept4e, 3, 3, 2, 2, 'SAME')
incept5a = _inception(pool5, 832, 256, 160, 320, 32, 128, 3, 128)
incept5b = _inception(incept5a, 832, 384, 192, 384, 48, 128, 3, 128)
pool6 = _apool(incept5b, 7, 7, 1, 1, 'VALID')
resh1 = tf.reshape(pool6, [-1, 1024])
affn1 = _affine(resh1, 1024, 1000)
return affn1
def time_tensorflow_run(session, target, info_string):
num_steps_burn_in = 10
total_duration = 0.0
total_duration_squared = 0.0
for i in range(FLAGS.num_batches + num_steps_burn_in):
start_time = time.time()
_ = session.run(target) #target_op = tf.group(*target)
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 (batch size: %d) across %d steps, %.3f +/- %.3f sec / batch' %
(datetime.now(), info_string, FLAGS.batch_size, FLAGS.num_batches, mn, sd))
return ParamEntry(datetime.now(), os.path.basename(__file__), info_string,
FLAGS.batch_size, FLAGS.num_batches, FLAGS.data_format,
total_duration, mn, sd)
def run_benchmark():
global parameters
param_entries = []
param_entries.append(ParamHeader)
with tf.Graph().as_default():
# Generate some dummy images.
image_size = 224
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.Variable(tf.random_normal(image_shape,
dtype=tf.float32,
stddev=1e-1))
labels = tf.Variable(tf.ones([FLAGS.batch_size],
dtype=tf.int32))
# Build a Graph that computes the logits predictions from the
# inference model.
last_layer = inference(images)
# Build an initialization operation.
init = tf.global_variables_initializer()
# Start running operations on the Graph.
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = FLAGS.gpu_fraction
sess = tf.Session(config=config)
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.
param_entries.append(time_tensorflow_run(sess, last_layer, "Forward"))
if run_forward_backward:
# Add a simple objective so we can calculate the backward pass.
objective = loss(last_layer, labels)
# Compute the gradient with respect to all the parameters.
grad = tf.gradients(objective, parameters)
# Run the backward benchmark.
param_entries.append(time_tensorflow_run(sess, grad, "Forward-backward"))
if FLAGS.csv_file:
incsv.store_data_in_csv(FLAGS.csv_file, param_entries)
def main(_):
run_benchmark()
if __name__ == '__main__':
tf.app.run()