1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
|
"""Timing benchmark for AlexNet inference.
To run, use:
bazel run -c opt --config=cuda \
third_party/tensorflow/models/image/alexnet:alexnet_benchmark
Across 100 steps on batch size = 128.
Forward pass:
Run on Tesla K40c: 145 +/- 1.5 ms / batch
Run on Titan X: 70 +/- 0.1 ms / batch
Forward-backward pass:
Run on Tesla K40c: 480 +/- 48 ms / batch
Run on Titan X: 244 +/- 30 ms / batch
"""
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.""")
def print_activations(t):
print t.op.name, ' ', t.get_shape().as_list()
def inference(images):
"""Build the AlexNet model.
Args:
images: Images Tensor
Returns:
pool5: the last Tensor in the convolutional component of AlexNet.
parameters: a list of Tensors corresponding to the weights and biases of the
AlexNet model.
"""
parameters = []
# conv1
with tf.name_scope('conv1') as scope:
kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 64], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(images, kernel, [1, 4, 4, 1], padding='VALID')
biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
conv1 = tf.nn.relu(bias, name=scope)
print_activations(conv1)
parameters += [kernel, biases]
# lrn1
# TODO(shlens, jiayq): Add a GPU version of local response normalization.
# pool1
pool1 = tf.nn.max_pool(conv1,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID',
name='pool1')
print_activations(pool1)
# conv2
with tf.name_scope('conv2') as scope:
kernel = tf.Variable(tf.truncated_normal([5, 5, 64, 192], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[192], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
conv2 = tf.nn.relu(bias, name=scope)
parameters += [kernel, biases]
print_activations(conv2)
# pool2
pool2 = tf.nn.max_pool(conv2,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID',
name='pool2')
print_activations(pool2)
# conv3
with tf.name_scope('conv3') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 192, 384],
dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[384], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
conv3 = tf.nn.relu(bias, name=scope)
parameters += [kernel, biases]
print_activations(conv3)
# conv4
with tf.name_scope('conv4') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 256],
dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
conv4 = tf.nn.relu(bias, name=scope)
parameters += [kernel, biases]
print_activations(conv4)
# conv5
with tf.name_scope('conv5') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256],
dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
conv5 = tf.nn.relu(bias, name=scope)
parameters += [kernel, biases]
print_activations(conv5)
# pool5
pool5 = tf.nn.max_pool(conv5,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID',
name='pool5')
print_activations(pool5)
return pool5, parameters
def time_tensorflow_run(session, target, info_string):
"""Run the computation to obtain the target tensor and print timing stats.
Args:
session: the TensorFlow session to run the computation under.
target: the targe Tensor that is passed to the session's run() function.
info_string: a string summarizing this run, to be printed with the stats.
Returns:
None
"""
num_steps_burn_in = 10
total_duration = 0.0
total_duration_squared = 0.0
for i in xrange(FLAGS.num_batches + num_steps_burn_in):
start_time = time.time()
_ = session.run(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 across %d steps, %.3f +/- %.3f sec / batch' %
(datetime.now(), info_string, FLAGS.num_batches, mn, sd))
def run_benchmark():
"""Run the benchmark on AlexNet."""
with tf.Graph().as_default():
# Generate some dummy images.
image_size = 224
# 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.
images = tf.Variable(tf.random_normal([FLAGS.batch_size,
image_size + 3,
image_size + 3, 3],
dtype=tf.float32,
stddev=1e-1))
# Build a Graph that computes the logits predictions from the
# inference model.
pool5, parameters = inference(images)
# Build an initialization operation.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
sess = tf.Session('')
sess.run(init)
# Run the forward benchmark.
time_tensorflow_run(sess, pool5, "Forward")
# Add a simple objective so we can calculate the backward pass.
objective = tf.nn.l2_loss(pool5)
# Compute the gradient with respect to all the parameters.
grad = tf.gradients(objective, parameters)
# Run the backward benchmark.
time_tensorflow_run(sess, grad, "Forward-backward")
def main(_):
run_benchmark()
if __name__ == '__main__':
tf.app.run()
|
