# -*- coding: utf-8 -*-
"""
Created on Thu Apr 26 08:52:10 2018
@author: yanghe
"""
from datetime import datetime
import numpy as np
import time
import tensorflow as tf
slim = tf.contrib.slim
trunc_normal = lambda stddev : tf.truncated_normal_initializer(stddev=stddev)
def inception_v3_arg_scope(weight_decay=0.00004,
stddev=0.1,
batch_norm_var_collection='moving_vars'):
batch_norm_params = {
'decay':0.9997,
'epsilon':0.001,
'updates_collections':tf.GraphKeys.UPDATE_OPS,
'variables_collections':{
'beta':None,
'gama':None,
'moving_mean':[batch_norm_var_collection],
'moving_variance':[batch_norm_var_collection],
}
}
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_regularizer=slim.l2_regularizer(weight_decay)):
with slim.arg_scope([slim.conv2d],
weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params) as sc:
return sc
def inception_v3_base(inputs, scope=None):
end_points = {}
with tf.variable_scope(scope,'InceptionV3',[inputs]):
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='VALID'):
net = slim.conv2d(inputs, 32,[3,3], stride=2, scope='Conv2d_1a_3x3')
net = slim.conv2d(net, 32, [3,3], scope='Conv2d_2a_3x3')
net = slim.conv2d(net, 64, [3,3], padding='SAME',scope='Conv2d_2b_3x3')
net = slim.max_pool2d(net, [3,3], stride=2, scope='MaxPool_3a_3x3')
net = slim.conv2d(net, 80, [1,1], scope='Conv2d_3b_1x1')
net = slim.conv2d(net, 192, [3,3], scope='Conv2d_4a_3x3')
net = slim.max_pool2d(net, [3,3], stride=2, scope='MaxPool_5a_3x3')
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='SAME'):
with tf.variable_scope('Mixed_5b'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 64, [1,1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 48, [1,1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1,64, [5,5], scope='Conv2d_0b_5x5')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 64, [1,1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 96, [3,3], scope='Conv2d_0b_3x3')
branch_2 = slim.conv2d(branch_2, 96, [3,3], scope='Conv2d_0c_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3,3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3,32, [1,1], scope='Conv2d_0b_1x1')
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
with tf.variable_scope('Mixed_5c'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 64, [1,1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 48, [1,1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1,64, [5,5], scope='Conv2d_0b_5x5')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 64, [1,1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 96, [3,3], scope='Conv2d_0b_3x3')
branch_2 = slim.conv2d(branch_2, 96, [3,3], scope='Conv2d_0c_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3,3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3,64, [1,1], scope='Conv2d_0b_1x1')
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
with tf.variable_scope('Mixed_5d'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 64, [1,1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 48, [1,1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1,64, [5,5], scope='Conv2d_0b_5x5')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 64, [1,1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 96, [3,3], scope='Conv2d_0b_3x3')
branch_2 = slim.conv2d(branch_2, 96, [3,3], scope='Conv2d_0c_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3,3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3,64, [1,1], scope='Conv2d_0b_1x1')
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
with tf.variable_scope('Mixed_6a'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 384, [3,3], stride=2, padding='VALID',
scope='Conv2d_1a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 64, [1,1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 96, [3,3], scope='Conv2d_0b_3x3')
branch_1 = slim.conv2d(branch_1, 64, [3,3], stride=2, padding='VALID',
scope='Conv2d_0c_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.max_pool2d(net, [3,3], stride=2, padding='VALID',
scope='MaxPool_1a_3x3')
net = tf.concat([branch_0, branch_1, branch_2], 3)
with tf.variable_scope('Mixed_6b'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 192, [1,1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 128, [1,1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 128, [1,7], scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, 192, [7,1], scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 128, [1,1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 128, [7,1], scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, 128, [1,7], scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, 128, [7,1], scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, 192, [1,7], scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3,3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3,192, [1,1], scope='Conv2d_0b_1x1')
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
with tf.variable_scope('Mixed_6c'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 192, [1,1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 160, [1,1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 160, [1,7], scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, 192, [7,1], scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 128, [1,1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 160, [7,1], scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, 160, [1,7], scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, 160, [7,1], scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, 192, [1,7], scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3,3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3,192, [1,1], scope='Conv2d_0b_1x1')
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
with tf.variable_scope('Mixed_6d'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 192, [1,1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 160, [1,1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 160, [1,7], scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, 192, [7,1], scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 128, [1,1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 160, [7,1], scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, 160, [1,7], scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, 160, [7,1], scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, 192, [1,7], scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3,3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3,192, [1,1], scope='Conv2d_0b_1x1')
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
with tf.variable_scope('Mixed_6e'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 192, [1,1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 160, [1,1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 160, [1,7], scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, 192, [7,1], scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 128, [1,1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 160, [7,1], scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, 160, [1,7], scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, 160, [7,1], scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, 192, [1,7], scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3,3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3,192, [1,1], scope='Conv2d_0b_1x1')
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
end_points['Mixed_6e'] = net
with tf.variable_scope('Mixed_7a'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 192, [1,1], scope='Conv2d_0a_1x1')
branch_0 = slim.conv2d(branch_0, 320, [3,3], stride=2, padding='VALID',
scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 192, [1,1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 192, [1,7], scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, 192, [7,1], scope='Conv2d_0c_7x1')
branch_1 = slim.conv2d(branch_1, 192, [3,3], stride=2, padding='VALID',
scope='Conv2d_0d_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.max_pool2d(net, [3,3], stride=2, padding='VALID',
scope='MaxPool_1a_3x3')
net = tf.concat([branch_0, branch_1, branch_2], 3)
with tf.variable_scope('Mixed_7b'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 320, [1,1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 384, [1,1], scope='Conv2d_0a_1x1')
branch_1 = tf.concat([slim.conv2d(branch_1, 384, [1,3], scope='Conv2d_0b_1x3'),
slim.conv2d(branch_1, 384, [3,1], scope='Conv2d_0c_3x1')], 3)
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 448, [1,1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 384, [3,3], scope='Conv2d_0b_3x3')
branch_2 = tf.concat([slim.conv2d(branch_2, 384, [1,3], scope='Conv2d_0c_1x3'),
slim.conv2d(branch_2, 384, [3,1], scope='Conv2d_0d_3x1')], 3)
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3,3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3,192, [1,1], scope='Conv2d_0b_1x1')
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
with tf.variable_scope('Mixed_7c'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 320, [1,1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 384, [1,1], scope='Conv2d_0a_1x1')
branch_1 = tf.concat([slim.conv2d(branch_1, 384, [1,3], scope='Conv2d_0b_1x3'),
slim.conv2d(branch_1, 384, [3,1], scope='Conv2d_0c_3x1')], 3)
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 448, [1,1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 384, [3,3], scope='Conv2d_0b_3x3')
branch_2 = tf.concat([slim.conv2d(branch_2, 384, [1,3], scope='Conv2d_0c_1x3'),
slim.conv2d(branch_2, 384, [3,1], scope='Conv2d_0d_3x1')], 3)
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3,3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3,192, [1,1], scope='Conv2d_0b_1x1')
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
return net , end_points
def inception_v3(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.8,
prediction_fn=slim.softmax,
spatial_squeeze=True,
resue=None,
scope='InceptionV3'):
with tf.variable_scope(scope, 'InceptionV3', [inputs, num_classes],
reuse=resue) as scope:
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training):
net, end_points = inception_v3_base(inputs, scope=scope)
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='SAME'):
aux_logits = end_points['Mixed_6e']
with tf.variable_scope('AuxLogits'):
aux_logits = slim.avg_pool2d(aux_logits, [5,5], stride=3, padding='VALID',
scope='AvgPool_1a_5x5')
aux_logits = slim.conv2d(aux_logits, 128, [1,1] ,scope='Conv2d_1b_1x1')
aux_logits = slim.conv2d(aux_logits, 768, [5,5] ,
weights_initializer=trunc_normal(0.01),
padding='VALID',
scope='Conv2d_1c_5x5')
aux_logits = slim.conv2d(aux_logits,num_classes, [1,1] ,activation_fn=None,
normalizer_fn=None,
weights_initializer=trunc_normal(0.001),
scope='Conv2d_1d_1x1')
if spatial_squeeze :
aux_logits = tf.squeeze(aux_logits, [1,2],
name='SpatialSqueeze')
end_points['AuxLogits'] = aux_logits
with tf.variable_scope('Logits'):
net = slim.avg_pool2d(net,[8,8], padding='VALID',
scope='Avgpool_1a_8x8')
net = slim.dropout(net, keep_prob=dropout_keep_prob,
scope='Dropout_1b')
end_points['PreLogits'] = net
logits = slim.conv2d(net, num_classes, [1,1],activation_fn=None,
normalizer_fn=None,scope='Conv2d_1c_1x1')
if spatial_squeeze :
logits = tf.squeeze(logits, [1,2],
name='SpatialSqueeze')
end_points['logits'] = logits
end_points['predictions'] = prediction_fn(logits, scope='Predictions')
return logits , end_points
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(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 / num_batches
vr = total_duration_squared / num_batches - mn * mn
sd = np.sqrt(np.abs( vr) )
print ('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %
(datetime.now(), info_string, num_batches, mn, sd))
def run_benchmark(scope):
with tf.Graph().as_default():
image_size = 299
images = tf.Variable(tf.random_normal([batch_size,
image_size,
image_size, 3],
dtype=tf.float32,
stddev=1e-1))
with slim.arg_scope(scope()):
logits , end_points = inception_v3(images, is_training=False)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
time_tensorflow_run(sess, logits, "Forward")
batch_size = 32
num_batches = 10
run_benchmark(inception_v3_arg_scope)
