from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import tensorflow.contrib.slim as slim
def fire_module(inputs,
squeeze_depth,
expand_depth,
reuse=None,
scope=None,
outputs_collections=None):
with tf.variable_scope(scope, 'fire', [inputs], reuse=reuse):
with slim.arg_scope([slim.conv2d, slim.max_pool2d],
outputs_collections=None):
net = squeeze(inputs, squeeze_depth)
outputs = expand(net, expand_depth)
return outputs
def squeeze(inputs, num_outputs):
return slim.conv2d(inputs, num_outputs, [1, 1], stride=1, scope='squeeze')
def expand(inputs, num_outputs):
with tf.variable_scope('expand'):
e1x1 = slim.conv2d(inputs, num_outputs, [1, 1], stride=1, scope='1x1')
e3x3 = slim.conv2d(inputs, num_outputs, [3, 3], scope='3x3')
return tf.concat([e1x1, e3x3], 3)
def inference(images, keep_probability, phase_train=True, bottleneck_layer_size=128, weight_decay=0.0, reuse=None):
batch_norm_params = {
# Decay for the moving averages.
'decay': 0.995,
# epsilon to prevent 0s in variance.
'epsilon': 0.001,
# force in-place updates of mean and variance estimates
'updates_collections': None,
# Moving averages ends up in the trainable variables collection
'variables_collections': [ tf.GraphKeys.TRAINABLE_VARIABLES ],
}
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=slim.xavier_initializer_conv2d(uniform=True),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params):
with tf.variable_scope('squeezenet', [images], reuse=reuse):
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=phase_train):
net = slim.conv2d(images, 96, [7, 7], stride=2, scope='conv1')
net = slim.max_pool2d(net, [3, 3], stride=2, scope='maxpool1')
net = fire_module(net, 16, 64, scope='fire2')
net = fire_module(net, 16, 64, scope='fire3')
net = fire_module(net, 32, 128, scope='fire4')
net = slim.max_pool2d(net, [2, 2], stride=2, scope='maxpool4')
net = fire_module(net, 32, 128, scope='fire5')
net = fire_module(net, 48, 192, scope='fire6')
net = fire_module(net, 48, 192, scope='fire7')
net = fire_module(net, 64, 256, scope='fire8')
net = slim.max_pool2d(net, [3, 3], stride=2, scope='maxpool8')
net = fire_module(net, 64, 256, scope='fire9')
net = slim.dropout(net, keep_probability)
net = slim.conv2d(net, 1000, [1, 1], activation_fn=None, normalizer_fn=None, scope='conv10')
net = slim.avg_pool2d(net, net.get_shape()[1:3], scope='avgpool10')
net = tf.squeeze(net, [1, 2], name='logits')
net = slim.fully_connected(net, bottleneck_layer_size, activation_fn=None,
scope='Bottleneck', reuse=False)
return net, None
